title stringlengths 1 185 | diff stringlengths 0 32.2M | body stringlengths 0 123k ⌀ | url stringlengths 57 58 | created_at stringlengths 20 20 | closed_at stringlengths 20 20 | merged_at stringlengths 20 20 ⌀ | updated_at stringlengths 20 20 |
|---|---|---|---|---|---|---|---|
CI: Pin matplotlib < 3.8 | diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index 682708d811878..2522605cf5c38 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 d09d4e5dea648..dc67122c6e72e 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 f458546fc3a1b..c9faaa2146235 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 4ee8dc0c6d3fc..f51a8e04fbc7e 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 c520fa17551e0..d1a9e336eaeac 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 5caa57ef37ee8..db6138c34f37c 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 b8e7e376378c6..98339f45a5052 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
| - [ ] 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/55210 | 2023-09-20T01:55:39Z | 2023-09-20T10:53:16Z | 2023-09-20T10:53:16Z | 2023-11-17T23:12:03Z |
Backport PR #54952 on branch 2.1.x (REGR: Arrow backed objects not propagating exceptions) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 6d5da7cdff3b3..13de1d5e2ea52 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -26,6 +26,7 @@ Fixed regressions
- Fixed regression in :meth:`Series.drop_duplicates` for PyArrow strings (:issue:`54904`)
- Fixed regression in :meth:`Series.interpolate` raising when ``fill_value`` was given (:issue:`54920`)
- Fixed regression in :meth:`Series.value_counts` raising for numeric data if ``bins`` was specified (:issue:`54857`)
+- Fixed regression in comparison operations for PyArrow backed columns not propagating exceptions correctly (:issue:`54944`)
- Fixed regression when comparing a :class:`Series` with ``datetime64`` dtype with ``None`` (:issue:`54870`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 09c7c6c7706ad..49305128267be 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -627,20 +627,22 @@ def __setstate__(self, state) -> None:
def _cmp_method(self, other, op):
pc_func = ARROW_CMP_FUNCS[op.__name__]
- try:
+ if isinstance(other, (ArrowExtensionArray, np.ndarray, list, BaseMaskedArray)):
result = pc_func(self._pa_array, self._box_pa(other))
- except (pa.lib.ArrowNotImplementedError, pa.lib.ArrowInvalid):
- if is_scalar(other):
+ elif is_scalar(other):
+ try:
+ result = pc_func(self._pa_array, self._box_pa(other))
+ except (pa.lib.ArrowNotImplementedError, pa.lib.ArrowInvalid):
mask = isna(self) | isna(other)
valid = ~mask
result = np.zeros(len(self), dtype="bool")
result[valid] = op(np.array(self)[valid], other)
result = pa.array(result, type=pa.bool_())
result = pc.if_else(valid, result, None)
- else:
- raise NotImplementedError(
- f"{op.__name__} not implemented for {type(other)}"
- )
+ else:
+ raise NotImplementedError(
+ f"{op.__name__} not implemented for {type(other)}"
+ )
return ArrowExtensionArray(result)
def _evaluate_op_method(self, other, op, arrow_funcs):
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 5017e827c3761..8702701f161ae 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -3072,6 +3072,14 @@ def test_duration_fillna_numpy(pa_type):
tm.assert_series_equal(result, expected)
+def test_comparison_not_propagating_arrow_error():
+ # GH#54944
+ a = pd.Series([1 << 63], dtype="uint64[pyarrow]")
+ b = pd.Series([None], dtype="int64[pyarrow]")
+ with pytest.raises(pa.lib.ArrowInvalid, match="Integer value"):
+ a < b
+
+
def test_factorize_chunked_dictionary():
# GH 54844
pa_array = pa.chunked_array(
| Backport PR #54952: REGR: Arrow backed objects not propagating exceptions | https://api.github.com/repos/pandas-dev/pandas/pulls/55209 | 2023-09-20T01:50:12Z | 2023-09-20T13:38:23Z | 2023-09-20T13:38:23Z | 2023-09-20T13:38:23Z |
aca va tu mensaje de commit | diff --git a/prueba.py b/prueba.py
new file mode 100644
index 0000000000000..e69de29bb2d1d
| - [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [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/55208 | 2023-09-20T01:40:24Z | 2023-09-20T13:06:14Z | null | 2023-09-20T13:06:14Z |
DOC: Add whatsnew for 2.1.2 | diff --git a/doc/source/whatsnew/index.rst b/doc/source/whatsnew/index.rst
index 1f12859e4081e..dbc7800196007 100644
--- a/doc/source/whatsnew/index.rst
+++ b/doc/source/whatsnew/index.rst
@@ -24,6 +24,7 @@ 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 18054e0b01191..d422fb1be9fdf 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -891,4 +891,4 @@ 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 0dc113bd9ab7f..9d9a7dd363cac 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
+~~~~~~~~~~~~
| - [ ] 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/55207 | 2023-09-19T23:57:31Z | 2023-09-20T13:52:35Z | 2023-09-20T13:52:35Z | 2023-09-20T13:52:36Z |
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',
| - [ ] closes #54888 (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/55206 | 2023-09-19T23:12:27Z | 2023-09-20T16:59:56Z | 2023-09-20T16:59:56Z | 2023-09-20T17:35:16Z |
Backport PR #55189 on branch 2.1.x (Revert "DEPR: Deprecate returning a DataFrame in SeriesApply.apply_standard) | diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 66ee571d6b5a5..b7a59516ae671 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -794,12 +794,12 @@ Apply
index=["I", "II", "III"],
)
- def make_df(ser):
- new_vals = [pd.Series(value, name=name) for name, value in ser.items()]
- return pd.DataFrame(new_vals)
-
- df_orgz = pd.concat({ind: row.pipe(make_df) for ind, row in df.iterrows()})
+ def SeriesFromSubList(aList):
+ return pd.Series(aList)
+ df_orgz = pd.concat(
+ {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()}
+ )
df_orgz
`Rolling apply with a DataFrame returning a Series
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 75c816f66d5e4..92fa38c9da5c2 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -1207,6 +1207,19 @@ The dimension of the returned result can also change:
grouped.apply(f)
+``apply`` on a Series can operate on a returned value from the applied function
+that is itself a series, and possibly upcast the result to a DataFrame:
+
+.. ipython:: python
+
+ def f(x):
+ return pd.Series([x, x ** 2], index=["x", "x^2"])
+
+
+ s = pd.Series(np.random.rand(5))
+ s
+ s.apply(f)
+
Similar to :ref:`groupby.aggregate.agg`, the resulting dtype will reflect that of the
apply function. If the results from different groups have different dtypes, then
a common dtype will be determined in the same way as ``DataFrame`` construction.
diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
index 3425986a37743..3f1db2e3a9cd9 100644
--- a/doc/source/whatsnew/v0.10.0.rst
+++ b/doc/source/whatsnew/v0.10.0.rst
@@ -244,26 +244,15 @@ Convenience methods ``ffill`` and ``bfill`` have been added:
function, that is itself a series, and possibly upcast the result to a
DataFrame
- .. code-block:: python
-
- >>> def f(x):
- ... return pd.Series([x, x ** 2], index=["x", "x^2"])
- >>>
- >>> s = pd.Series(np.random.rand(5))
- >>> s
- 0 0.340445
- 1 0.984729
- 2 0.919540
- 3 0.037772
- 4 0.861549
- dtype: float64
- >>> s.apply(f)
- x x^2
- 0 0.340445 0.115903
- 1 0.984729 0.969691
- 2 0.919540 0.845555
- 3 0.037772 0.001427
- 4 0.861549 0.742267
+ .. ipython:: python
+
+ def f(x):
+ return pd.Series([x, x ** 2], index=["x", "x^2"])
+
+
+ s = pd.Series(np.random.rand(5))
+ s
+ s.apply(f)
- New API functions for working with pandas options (:issue:`2097`):
diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index 040ca048d1224..18054e0b01191 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -555,7 +555,6 @@ Other Deprecations
- Deprecated behavior of :func:`concat` when :class:`DataFrame` has columns that are all-NA, in a future version these will not be discarded when determining the resulting dtype (:issue:`40893`)
- Deprecated behavior of :meth:`Series.dt.to_pydatetime`, in a future version this will return a :class:`Series` containing python ``datetime`` objects instead of an ``ndarray`` of datetimes; this matches the behavior of other :attr:`Series.dt` properties (:issue:`20306`)
- Deprecated logical operations (``|``, ``&``, ``^``) between pandas objects and dtype-less sequences (e.g. ``list``, ``tuple``), wrap a sequence in a :class:`Series` or NumPy array before operating instead (:issue:`51521`)
-- Deprecated making :meth:`Series.apply` return a :class:`DataFrame` when the passed-in callable returns a :class:`Series` object. In the future this will return a :class:`Series` whose values are themselves :class:`Series`. This pattern was very slow and it's recommended to use alternative methods to archive the same goal (:issue:`52116`)
- Deprecated parameter ``convert_type`` in :meth:`Series.apply` (:issue:`52140`)
- Deprecated passing a dictionary to :meth:`.SeriesGroupBy.agg`; pass a list of aggregations instead (:issue:`50684`)
- Deprecated the ``fastpath`` keyword in :class:`Categorical` constructor, use :meth:`Categorical.from_codes` instead (:issue:`20110`)
diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index bb3fbd4ffc90f..42a7029b1fa77 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -44,7 +44,7 @@ Bug fixes
Other
~~~~~
--
+- Reverted the deprecation that disallowed :meth:`Series.apply` returning a :class:`DataFrame` when the passed-in callable returns a :class:`Series` object (:issue:`52116`)
.. ---------------------------------------------------------------------------
.. _whatsnew_211.contributors:
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index ad9933aff9eca..e5683359c2fb9 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -1289,14 +1289,6 @@ def curried(x):
)
if len(mapped) and isinstance(mapped[0], ABCSeries):
- warnings.warn(
- "Returning a DataFrame from Series.apply when the supplied function "
- "returns a Series is deprecated and will be removed in a future "
- "version.",
- FutureWarning,
- stacklevel=find_stack_level(),
- ) # GH52116
-
# GH#43986 Need to do list(mapped) in order to get treated as nested
# See also GH#25959 regarding EA support
return obj._constructor_expanddim(list(mapped), index=obj.index)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 885675e5caa5a..1d83643722ece 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4634,11 +4634,6 @@ def apply(
"""
Invoke function on values of Series.
- .. deprecated:: 2.1.0
-
- If the result from ``func`` is a ``Series``, wrapping the output in a
- ``DataFrame`` instead of a ``Series`` has been deprecated.
-
Can be ufunc (a NumPy function that applies to the entire Series)
or a Python function that only works on single values.
diff --git a/pandas/tests/apply/test_series_apply.py b/pandas/tests/apply/test_series_apply.py
index d3e5ac1b4ca7a..aeb6a01eb587a 100644
--- a/pandas/tests/apply/test_series_apply.py
+++ b/pandas/tests/apply/test_series_apply.py
@@ -420,8 +420,9 @@ def test_agg_evaluate_lambdas(string_series):
def test_with_nested_series(datetime_series, op_name):
# GH 2316
# .agg with a reducer and a transform, what to do
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ msg = "cannot aggregate"
+ warning = FutureWarning if op_name == "agg" else None
+ with tm.assert_produces_warning(warning, match=msg):
# GH52123
result = getattr(datetime_series, op_name)(
lambda x: Series([x, x**2], index=["x", "x^2"])
@@ -429,6 +430,10 @@ def test_with_nested_series(datetime_series, op_name):
expected = DataFrame({"x": datetime_series, "x^2": datetime_series**2})
tm.assert_frame_equal(result, expected)
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = datetime_series.agg(lambda x: Series([x, x**2], index=["x", "x^2"]))
+ tm.assert_frame_equal(result, expected)
+
def test_replicate_describe(string_series):
# this also tests a result set that is all scalars
@@ -512,10 +517,7 @@ def test_apply_series_on_date_time_index_aware_series(dti, exp, aware):
index = dti.tz_localize("UTC").index
else:
index = dti.index
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- # GH52123
- result = Series(index).apply(lambda x: Series([1, 2]))
+ result = Series(index).apply(lambda x: Series([1, 2]))
tm.assert_frame_equal(result, exp)
@@ -662,19 +664,7 @@ def test_apply_dictlike_lambda(ops, by_row, expected):
def test_apply_retains_column_name(by_row):
# GH 16380
df = DataFrame({"x": range(3)}, Index(range(3), name="x"))
- func = lambda x: Series(range(x + 1), Index(range(x + 1), name="y"))
-
- if not by_row:
- # GH53400
- msg = "'Series' object cannot be interpreted as an integer"
- with pytest.raises(TypeError, match=msg):
- df.x.apply(func, by_row=by_row)
- return
-
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- # GH52123
- result = df.x.apply(func, by_row=by_row)
+ result = df.x.apply(lambda x: Series(range(x + 1), Index(range(x + 1), name="y")))
expected = DataFrame(
[[0.0, np.nan, np.nan], [0.0, 1.0, np.nan], [0.0, 1.0, 2.0]],
columns=Index(range(3), name="y"),
@@ -689,3 +679,11 @@ def test_apply_type():
result = s.apply(type)
expected = Series([int, str, type], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
+
+
+def test_series_apply_unpack_nested_data():
+ # GH#55189
+ ser = Series([[1, 2, 3], [4, 5, 6, 7]])
+ result = ser.apply(lambda x: Series(x))
+ expected = DataFrame({0: [1.0, 4.0], 1: [2.0, 5.0], 2: [3.0, 6.0], 3: [np.nan, 7]})
+ tm.assert_frame_equal(result, expected)
| Backport PR #55189: Revert "DEPR: Deprecate returning a DataFrame in SeriesApply.apply_standard | https://api.github.com/repos/pandas-dev/pandas/pulls/55205 | 2023-09-19T22:26:42Z | 2023-09-20T15:53:48Z | 2023-09-20T15:53:48Z | 2023-09-20T15:53:49Z |
Backport PR #55175 on branch 2.1.x (DEPS: Bump to 1.26 proper for Python 3.12) | diff --git a/pyproject.toml b/pyproject.toml
index cfa767f808827..477c9be6274b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,8 +10,7 @@ requires = [
# we don't want to force users to compile with 1.25 though
# (Ideally, in the future, though, oldest-supported-numpy can be dropped when our min numpy is 1.25.x)
"oldest-supported-numpy>=2022.8.16; python_version<'3.12'",
- # TODO: This needs to be updated when the official numpy 1.26 comes out
- "numpy>=1.26.0b1; python_version>='3.12'",
+ "numpy>=1.26.0; python_version>='3.12'",
"versioneer[toml]"
]
@@ -32,8 +31,7 @@ requires-python = '>=3.9'
dependencies = [
"numpy>=1.22.4; python_version<'3.11'",
"numpy>=1.23.2; python_version=='3.11'",
- # TODO: This needs to be updated when the official numpy 1.26 comes out
- "numpy>=1.26.0b1; python_version>='3.12'",
+ "numpy>=1.26.0; python_version>='3.12'",
"python-dateutil>=2.8.2",
"pytz>=2020.1",
"tzdata>=2022.1"
| Backport PR #55175: DEPS: Bump to 1.26 proper for Python 3.12 | https://api.github.com/repos/pandas-dev/pandas/pulls/55203 | 2023-09-19T15:34:54Z | 2023-09-19T22:16:39Z | 2023-09-19T22:16:39Z | 2023-09-19T22:16:39Z |
BUG: RecursionError in loc.setitem | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 0fc4afc95a2ce..24bed22b3a3fe 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -266,6 +266,7 @@ Interval
Indexing
^^^^^^^^
- Bug in :meth:`Index.difference` not returning a unique set of values when ``other`` is empty or ``other`` is considered non-comparable (:issue:`55113`)
+- Bug in setting :class:`Categorical` values into a :class:`DataFrame` with numpy dtypes raising ``RecursionError`` (:issue:`52927`)
-
Missing
diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index 1d5db123068e2..74e785be06356 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -68,6 +68,7 @@
PeriodDtype,
)
from pandas.core.dtypes.generic import (
+ ABCExtensionArray,
ABCIndex,
ABCSeries,
)
@@ -1772,6 +1773,23 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
return casted
raise LossySetitemError
+ elif isinstance(element, ABCExtensionArray) and isinstance(
+ element.dtype, CategoricalDtype
+ ):
+ # GH#52927 setting Categorical value into non-EA frame
+ # TODO: general-case for EAs?
+ try:
+ casted = element.astype(dtype)
+ except (ValueError, TypeError):
+ raise LossySetitemError
+ # Check for cases of either
+ # a) lossy overflow/rounding or
+ # b) semantic changes like dt64->int64
+ comp = casted == element
+ if not comp.all():
+ raise LossySetitemError
+ return casted
+
# Anything other than integer we cannot hold
raise LossySetitemError
if (
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 66b01dfb59f7f..30f6507d02484 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -460,6 +460,12 @@ def coerce_to_target_dtype(self, other, warn_on_upcast: bool = False) -> Block:
and will receive the same block
"""
new_dtype = find_result_type(self.values.dtype, other)
+ if new_dtype == self.dtype:
+ # GH#52927 avoid RecursionError
+ raise AssertionError(
+ "Something has gone wrong, please report a bug at "
+ "https://github.com/pandas-dev/pandas/issues"
+ )
# In a future version of pandas, the default will be that
# setting `nan` into an integer series won't raise.
diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index d0b6adfda0241..a2693c85e507f 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -1657,6 +1657,14 @@ def test_loc_setitem_range_key(self, frame_or_series):
expected = frame_or_series([0, 1, 10, 9, 11], index=obj.index)
tm.assert_equal(obj, expected)
+ def test_loc_setitem_numpy_frame_categorical_value(self):
+ # GH#52927
+ df = DataFrame({"a": [1, 1, 1, 1, 1], "b": ["a", "a", "a", "a", "a"]})
+ df.loc[1:2, "a"] = Categorical([2, 2], categories=[1, 2])
+
+ expected = DataFrame({"a": [1, 2, 2, 1, 1], "b": ["a", "a", "a", "a", "a"]})
+ tm.assert_frame_equal(df, expected)
+
class TestLocWithEllipsis:
@pytest.fixture(params=[tm.loc, tm.iloc])
| - [x] closes #52927 (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/55201 | 2023-09-19T14:52:51Z | 2023-09-19T17:21:30Z | 2023-09-19T17:21:30Z | 2023-09-20T21:52:19Z |
Fix NameError in 'Evaluation order matters' | diff --git a/doc/source/user_guide/indexing.rst b/doc/source/user_guide/indexing.rst
index 52bc43f52b1d3..7541cf3c8af3c 100644
--- a/doc/source/user_guide/indexing.rst
+++ b/doc/source/user_guide/indexing.rst
@@ -1837,7 +1837,7 @@ This however is operating on a copy and will not work.
:okwarning:
:okexcept:
- with option_context('mode.chained_assignment','warn'):
+ with pd.option_context('mode.chained_assignment','warn'):
dfb[dfb['a'].str.startswith('o')]['c'] = 42
A chained assignment can also crop up in setting in a mixed dtype frame.
@@ -1879,7 +1879,7 @@ Last, the subsequent example will **not** work at all, and so should be avoided:
:okwarning:
:okexcept:
- with option_context('mode.chained_assignment','raise'):
+ with pd.option_context('mode.chained_assignment','raise'):
dfd.loc[0]['a'] = 1111
.. warning::
| This code in 'Indexing and selecting data' of doc wouldn't execute
- [ ] 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/55198 | 2023-09-19T10:35:25Z | 2023-09-19T20:04:56Z | 2023-09-19T20:04:56Z | 2023-09-19T21:45:39Z |
TST: Test GroupBy.__getitem__ with a column from grouper | diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index d05b60fd56b5f..9e899bf453548 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -131,6 +131,20 @@ def test_getitem_single_column(self):
tm.assert_series_equal(result, expected)
+ @pytest.mark.parametrize(
+ "func", [lambda x: x.sum(), lambda x: x.agg(lambda y: y.sum())]
+ )
+ def test_getitem_from_grouper(self, func):
+ # GH 50383
+ df = DataFrame({"a": [1, 1, 2], "b": 3, "c": 4, "d": 5})
+ gb = df.groupby(["a", "b"])[["a", "c"]]
+
+ idx = MultiIndex.from_tuples([(1, 3), (2, 3)], names=["a", "b"])
+ expected = DataFrame({"a": [2, 2], "c": [8, 4]}, index=idx)
+ result = func(gb)
+
+ tm.assert_frame_equal(result, expected)
+
def test_indices_grouped_by_tuple_with_lambda(self):
# GH 36158
df = DataFrame(
| - [x] closes #50383
| https://api.github.com/repos/pandas-dev/pandas/pulls/55193 | 2023-09-19T00:39:07Z | 2023-09-19T10:40:15Z | 2023-09-19T10:40:15Z | 2023-09-19T10:40:23Z |
CLN: do not import Axes/Artist/Figure from matplotlib.pyplot | diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py
index 0591394f5d9ed..d4e7e196dc2d4 100644
--- a/pandas/_testing/asserters.py
+++ b/pandas/_testing/asserters.py
@@ -416,7 +416,8 @@ def assert_attr_equal(attr: str, left, right, obj: str = "Attributes") -> None:
def assert_is_valid_plot_return_object(objs) -> None:
- import matplotlib.pyplot as plt
+ from matplotlib.artist import Artist
+ from matplotlib.axes import Axes
if isinstance(objs, (Series, np.ndarray)):
for el in objs.ravel():
@@ -424,14 +425,14 @@ def assert_is_valid_plot_return_object(objs) -> None:
"one of 'objs' is not a matplotlib Axes instance, "
f"type encountered {repr(type(el).__name__)}"
)
- assert isinstance(el, (plt.Axes, dict)), msg
+ assert isinstance(el, (Axes, dict)), msg
else:
msg = (
"objs is neither an ndarray of Artist instances nor a single "
"ArtistArtist instance, tuple, or dict, 'objs' is a "
f"{repr(type(objs).__name__)}"
)
- assert isinstance(objs, (plt.Artist, tuple, dict)), msg
+ assert isinstance(objs, (Artist, tuple, dict)), msg
def assert_is_sorted(seq) -> None:
| See https://github.com/matplotlib/matplotlib/issues/26812
These were the only cases that I found. | https://api.github.com/repos/pandas-dev/pandas/pulls/55192 | 2023-09-18T22:30:30Z | 2023-09-19T15:46:13Z | 2023-09-19T15:46:13Z | 2023-12-10T04:33:57Z |
Backport PR #55185 on branch 2.1.x (CI: Fix no BLAS error in 32 bit build) | diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index 1445e5e822017..c1a0352b5f5c0 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -230,11 +230,13 @@ jobs:
git -c user.email="you@example.com" merge --no-commit my_ref_name
fi
- name: Build environment and Run Tests
+ # https://github.com/numpy/numpy/issues/24703#issuecomment-1722379388
run: |
/opt/python/cp311-cp311/bin/python -m venv ~/virtualenvs/pandas-dev
. ~/virtualenvs/pandas-dev/bin/activate
python -m pip install --no-cache-dir -U pip wheel setuptools meson[ninja]==1.2.1 meson-python==0.13.1
- python -m pip install --no-cache-dir versioneer[toml] cython numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+ python -m pip install numpy --config-settings=setup-args="-Dallow-noblas=true"
+ python -m pip install --no-cache-dir versioneer[toml] cython python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
python -m pip install --no-cache-dir --no-build-isolation -e .
python -m pip list --no-cache-dir
export PANDAS_CI=1
| Backport PR #55185: CI: Fix no BLAS error in 32 bit build | https://api.github.com/repos/pandas-dev/pandas/pulls/55191 | 2023-09-18T22:03:03Z | 2023-09-18T23:32:21Z | 2023-09-18T23:32:21Z | 2023-09-18T23:32:21Z |
TYP: Allow None in Period.strftime | diff --git a/pandas/_libs/tslibs/period.pyi b/pandas/_libs/tslibs/period.pyi
index c85865fea8fd0..a4aecd2ce0a09 100644
--- a/pandas/_libs/tslibs/period.pyi
+++ b/pandas/_libs/tslibs/period.pyi
@@ -90,7 +90,7 @@ class Period(PeriodMixin):
def _from_ordinal(cls, ordinal: int, freq) -> Period: ...
@classmethod
def now(cls, freq: Frequency = ...) -> Period: ...
- def strftime(self, fmt: str) -> str: ...
+ def strftime(self, fmt: str | None) -> str: ...
def to_timestamp(
self,
freq: str | BaseOffset | None = ...,
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index c37e9cd7ef1f3..21937ab2519e4 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -2511,11 +2511,12 @@ cdef class _Period(PeriodMixin):
object_state = None, self.freq, self.ordinal
return (Period, object_state)
- def strftime(self, fmt: str) -> str:
+ def strftime(self, fmt: str | None) -> str:
r"""
Returns a formatted string representation of the :class:`Period`.
- ``fmt`` must be a string containing one or several directives.
+ ``fmt`` must be ``None`` or a string containing one or several directives.
+ When ``None``, the format will be determined from the frequency of the Period.
The method recognizes the same directives as the :func:`time.strftime`
function of the standard Python distribution, as well as the specific
additional directives ``%f``, ``%F``, ``%q``, ``%l``, ``%u``, ``%n``.
| - [ ] 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.
Fixes the ASV failures in the first two lines of: https://github.com/pandas-dev/pandas/pull/55179#issue-1899772995
While not familiar with this code, I believe this is the correct resolution because (a) of the ASV `tslibs.period.PeriodUnaryMethods.time_strftime_default` explicitly passes `None` and (b) the `fmt` argument is passed to `period_format` which has the signature `object fmt=None`. | https://api.github.com/repos/pandas-dev/pandas/pulls/55190 | 2023-09-18T21:50:56Z | 2023-09-19T15:47:07Z | 2023-09-19T15:47:07Z | 2023-09-19T20:33:44Z |
Revert "DEPR: Deprecate returning a DataFrame in SeriesApply.apply_standard | diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 002e88533ab93..2d2c0a4db4df6 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -794,12 +794,12 @@ Apply
index=["I", "II", "III"],
)
- def make_df(ser):
- new_vals = [pd.Series(value, name=name) for name, value in ser.items()]
- return pd.DataFrame(new_vals)
-
- df_orgz = pd.concat({ind: row.pipe(make_df) for ind, row in df.iterrows()})
+ def SeriesFromSubList(aList):
+ return pd.Series(aList)
+ df_orgz = pd.concat(
+ {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()}
+ )
df_orgz
`Rolling apply with a DataFrame returning a Series
diff --git a/doc/source/user_guide/groupby.rst b/doc/source/user_guide/groupby.rst
index 5dd14e243fbb3..4be62090ec645 100644
--- a/doc/source/user_guide/groupby.rst
+++ b/doc/source/user_guide/groupby.rst
@@ -1213,6 +1213,19 @@ The dimension of the returned result can also change:
grouped.apply(f)
+``apply`` on a Series can operate on a returned value from the applied function
+that is itself a series, and possibly upcast the result to a DataFrame:
+
+.. ipython:: python
+
+ def f(x):
+ return pd.Series([x, x ** 2], index=["x", "x^2"])
+
+
+ s = pd.Series(np.random.rand(5))
+ s
+ s.apply(f)
+
Similar to :ref:`groupby.aggregate.agg`, the resulting dtype will reflect that of the
apply function. If the results from different groups have different dtypes, then
a common dtype will be determined in the same way as ``DataFrame`` construction.
diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
index 422efc1b36946..be50c34d7d14c 100644
--- a/doc/source/whatsnew/v0.10.0.rst
+++ b/doc/source/whatsnew/v0.10.0.rst
@@ -261,26 +261,15 @@ Convenience methods ``ffill`` and ``bfill`` have been added:
function, that is itself a series, and possibly upcast the result to a
DataFrame
- .. code-block:: python
-
- >>> def f(x):
- ... return pd.Series([x, x ** 2], index=["x", "x^2"])
- >>>
- >>> s = pd.Series(np.random.rand(5))
- >>> s
- 0 0.340445
- 1 0.984729
- 2 0.919540
- 3 0.037772
- 4 0.861549
- dtype: float64
- >>> s.apply(f)
- x x^2
- 0 0.340445 0.115903
- 1 0.984729 0.969691
- 2 0.919540 0.845555
- 3 0.037772 0.001427
- 4 0.861549 0.742267
+ .. ipython:: python
+
+ def f(x):
+ return pd.Series([x, x ** 2], index=["x", "x^2"])
+
+
+ s = pd.Series(np.random.rand(5))
+ s
+ s.apply(f)
- New API functions for working with pandas options (:issue:`2097`):
diff --git a/doc/source/whatsnew/v2.1.0.rst b/doc/source/whatsnew/v2.1.0.rst
index 040ca048d1224..18054e0b01191 100644
--- a/doc/source/whatsnew/v2.1.0.rst
+++ b/doc/source/whatsnew/v2.1.0.rst
@@ -555,7 +555,6 @@ Other Deprecations
- Deprecated behavior of :func:`concat` when :class:`DataFrame` has columns that are all-NA, in a future version these will not be discarded when determining the resulting dtype (:issue:`40893`)
- Deprecated behavior of :meth:`Series.dt.to_pydatetime`, in a future version this will return a :class:`Series` containing python ``datetime`` objects instead of an ``ndarray`` of datetimes; this matches the behavior of other :attr:`Series.dt` properties (:issue:`20306`)
- Deprecated logical operations (``|``, ``&``, ``^``) between pandas objects and dtype-less sequences (e.g. ``list``, ``tuple``), wrap a sequence in a :class:`Series` or NumPy array before operating instead (:issue:`51521`)
-- Deprecated making :meth:`Series.apply` return a :class:`DataFrame` when the passed-in callable returns a :class:`Series` object. In the future this will return a :class:`Series` whose values are themselves :class:`Series`. This pattern was very slow and it's recommended to use alternative methods to archive the same goal (:issue:`52116`)
- Deprecated parameter ``convert_type`` in :meth:`Series.apply` (:issue:`52140`)
- Deprecated passing a dictionary to :meth:`.SeriesGroupBy.agg`; pass a list of aggregations instead (:issue:`50684`)
- Deprecated the ``fastpath`` keyword in :class:`Categorical` constructor, use :meth:`Categorical.from_codes` instead (:issue:`20110`)
diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 6d5da7cdff3b3..c9ab496295d85 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -43,7 +43,7 @@ Bug fixes
Other
~~~~~
--
+- Reverted the deprecation that disallowed :meth:`Series.apply` returning a :class:`DataFrame` when the passed-in callable returns a :class:`Series` object (:issue:`52116`)
.. ---------------------------------------------------------------------------
.. _whatsnew_211.contributors:
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 9748d4fe66739..1525e316f345f 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -1326,14 +1326,6 @@ def curried(x):
)
if len(mapped) and isinstance(mapped[0], ABCSeries):
- warnings.warn(
- "Returning a DataFrame from Series.apply when the supplied function "
- "returns a Series is deprecated and will be removed in a future "
- "version.",
- FutureWarning,
- stacklevel=find_stack_level(),
- ) # GH52116
-
# GH#43986 Need to do list(mapped) in order to get treated as nested
# See also GH#25959 regarding EA support
return obj._constructor_expanddim(list(mapped), index=obj.index)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index e0e27581ef7e2..b3ead03cd5e3f 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4638,11 +4638,6 @@ def apply(
"""
Invoke function on values of Series.
- .. deprecated:: 2.1.0
-
- If the result from ``func`` is a ``Series``, wrapping the output in a
- ``DataFrame`` instead of a ``Series`` has been deprecated.
-
Can be ufunc (a NumPy function that applies to the entire Series)
or a Python function that only works on single values.
diff --git a/pandas/tests/apply/test_series_apply.py b/pandas/tests/apply/test_series_apply.py
index d3e5ac1b4ca7a..aeb6a01eb587a 100644
--- a/pandas/tests/apply/test_series_apply.py
+++ b/pandas/tests/apply/test_series_apply.py
@@ -420,8 +420,9 @@ def test_agg_evaluate_lambdas(string_series):
def test_with_nested_series(datetime_series, op_name):
# GH 2316
# .agg with a reducer and a transform, what to do
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ msg = "cannot aggregate"
+ warning = FutureWarning if op_name == "agg" else None
+ with tm.assert_produces_warning(warning, match=msg):
# GH52123
result = getattr(datetime_series, op_name)(
lambda x: Series([x, x**2], index=["x", "x^2"])
@@ -429,6 +430,10 @@ def test_with_nested_series(datetime_series, op_name):
expected = DataFrame({"x": datetime_series, "x^2": datetime_series**2})
tm.assert_frame_equal(result, expected)
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = datetime_series.agg(lambda x: Series([x, x**2], index=["x", "x^2"]))
+ tm.assert_frame_equal(result, expected)
+
def test_replicate_describe(string_series):
# this also tests a result set that is all scalars
@@ -512,10 +517,7 @@ def test_apply_series_on_date_time_index_aware_series(dti, exp, aware):
index = dti.tz_localize("UTC").index
else:
index = dti.index
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- # GH52123
- result = Series(index).apply(lambda x: Series([1, 2]))
+ result = Series(index).apply(lambda x: Series([1, 2]))
tm.assert_frame_equal(result, exp)
@@ -662,19 +664,7 @@ def test_apply_dictlike_lambda(ops, by_row, expected):
def test_apply_retains_column_name(by_row):
# GH 16380
df = DataFrame({"x": range(3)}, Index(range(3), name="x"))
- func = lambda x: Series(range(x + 1), Index(range(x + 1), name="y"))
-
- if not by_row:
- # GH53400
- msg = "'Series' object cannot be interpreted as an integer"
- with pytest.raises(TypeError, match=msg):
- df.x.apply(func, by_row=by_row)
- return
-
- msg = "Returning a DataFrame from Series.apply when the supplied function"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- # GH52123
- result = df.x.apply(func, by_row=by_row)
+ result = df.x.apply(lambda x: Series(range(x + 1), Index(range(x + 1), name="y")))
expected = DataFrame(
[[0.0, np.nan, np.nan], [0.0, 1.0, np.nan], [0.0, 1.0, 2.0]],
columns=Index(range(3), name="y"),
@@ -689,3 +679,11 @@ def test_apply_type():
result = s.apply(type)
expected = Series([int, str, type], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
+
+
+def test_series_apply_unpack_nested_data():
+ # GH#55189
+ ser = Series([[1, 2, 3], [4, 5, 6, 7]])
+ result = ser.apply(lambda x: Series(x))
+ expected = DataFrame({0: [1.0, 4.0], 1: [2.0, 5.0], 2: [3.0, 6.0], 3: [np.nan, 7]})
+ tm.assert_frame_equal(result, expected)
| Reasons for reverting:
- This was done for performance reasons, but this kills the use case to unpack nested data (lists for example)
- The deprecation is poorly done (axis 1 does not emit a warning)
- There is no documentation what users should do, this is necessary if we deprecate something for performance reasons
#52116
closes #55134 | https://api.github.com/repos/pandas-dev/pandas/pulls/55189 | 2023-09-18T21:09:26Z | 2023-09-19T22:25:37Z | 2023-09-19T22:25:37Z | 2023-09-19T22:41:55Z |
STY: Fix precommit failure | diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 2178e972b4d08..2f6fa35cae070 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1942,7 +1942,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the rounding resolution.
- It uses the same units as class contructor :class:`~pandas.Timedelta`.
+ It uses the same units as class constructor :class:`~pandas.Timedelta`.
Returns
-------
@@ -1970,7 +1970,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the flooring resolution.
- It uses the same units as class contructor :class:`~pandas.Timedelta`.
+ It uses the same units as class constructor :class:`~pandas.Timedelta`.
Examples
--------
@@ -1990,7 +1990,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the ceiling resolution.
- It uses the same units as class contructor :class:`~pandas.Timedelta`.
+ It uses the same units as class constructor :class:`~pandas.Timedelta`.
Examples
--------
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/55186 | 2023-09-18T18:18:35Z | 2023-09-18T19:00:45Z | 2023-09-18T19:00:45Z | 2023-09-18T19:00:48Z |
CI: Fix no BLAS error in 32 bit build | diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index 98c6226b14075..139cfcde95b2c 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -230,11 +230,13 @@ jobs:
git -c user.email="you@example.com" merge --no-commit my_ref_name
fi
- name: Build environment and Run Tests
+ # https://github.com/numpy/numpy/issues/24703#issuecomment-1722379388
run: |
/opt/python/cp311-cp311/bin/python -m venv ~/virtualenvs/pandas-dev
. ~/virtualenvs/pandas-dev/bin/activate
python -m pip install --no-cache-dir -U pip wheel setuptools meson[ninja]==1.2.1 meson-python==0.13.1
- python -m pip install --no-cache-dir versioneer[toml] cython numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+ python -m pip install numpy --config-settings=setup-args="-Dallow-noblas=true"
+ python -m pip install --no-cache-dir versioneer[toml] cython python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
python -m pip install --no-cache-dir --no-build-isolation -e .
python -m pip list --no-cache-dir
export PANDAS_CI=1
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/55185 | 2023-09-18T18:16:39Z | 2023-09-18T22:02:54Z | 2023-09-18T22:02:54Z | 2023-09-18T22:02:58Z |
Backport PR #55180 on branch 2.1.x (MAINT: Cleanup expired ndarray methods) | diff --git a/doc/source/user_guide/gotchas.rst b/doc/source/user_guide/gotchas.rst
index c00a236ff4e9d..99c85ac66623d 100644
--- a/doc/source/user_guide/gotchas.rst
+++ b/doc/source/user_guide/gotchas.rst
@@ -379,7 +379,7 @@ constructors using something similar to the following:
.. ipython:: python
x = np.array(list(range(10)), ">i4") # big endian
- newx = x.byteswap().newbyteorder() # force native byteorder
+ newx = x.byteswap().view(x.dtype.newbyteorder()) # force native byteorder
s = pd.Series(newx)
See `the NumPy documentation on byte order
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 9fea451940930..5b190619c4ac3 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -1771,7 +1771,7 @@ def read(
self._data_read = True
# if necessary, swap the byte order to native here
if self._byteorder != self._native_byteorder:
- raw_data = raw_data.byteswap().newbyteorder()
+ raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder())
if convert_categoricals:
self._read_value_labels()
| Backport PR #55180: MAINT: Cleanup expired ndarray methods | https://api.github.com/repos/pandas-dev/pandas/pulls/55184 | 2023-09-18T16:18:37Z | 2023-09-18T18:20:06Z | 2023-09-18T18:20:06Z | 2023-09-18T18:20:06Z |
Faster ensure_string_array | diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 0c0610f72044e..23c8066b973f8 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -776,6 +776,7 @@ cpdef ndarray[object] ensure_string_array(
cdef:
Py_ssize_t i = 0, n = len(arr)
bint already_copied = True
+ ndarray[object] newarr
if hasattr(arr, "to_numpy"):
@@ -800,8 +801,30 @@ cpdef ndarray[object] ensure_string_array(
# short-circuit, all elements are str
return result
+ if arr.dtype.kind == "f": # non-optimized path
+ for i in range(n):
+ val = arr[i]
+
+ if not already_copied:
+ result = result.copy()
+ already_copied = True
+
+ if not checknull(val):
+ # f"{val}" is not always equivalent to str(val) for floats
+ result[i] = str(val)
+ else:
+ if convert_na_value:
+ val = na_value
+ if skipna:
+ result[i] = val
+ else:
+ result[i] = f"{val}"
+
+ return result
+
+ newarr = np.asarray(arr, dtype=object)
for i in range(n):
- val = arr[i]
+ val = newarr[i]
if isinstance(val, str):
continue
@@ -2831,9 +2854,14 @@ NoDefault = Literal[_NoDefault.no_default]
@cython.boundscheck(False)
@cython.wraparound(False)
-def map_infer_mask(ndarray arr, object f, const uint8_t[:] mask, bint convert=True,
- object na_value=no_default, cnp.dtype dtype=np.dtype(object)
- ) -> np.ndarray:
+def map_infer_mask(
+ ndarray[object] arr,
+ object f,
+ const uint8_t[:] mask,
+ bint convert=True,
+ object na_value=no_default,
+ cnp.dtype dtype=np.dtype(object)
+) -> np.ndarray:
"""
Substitute for np.vectorize with pandas-friendly dtype inference.
| Benchmarks are still using 0.29.x, but I think this also prevents a regression from upgrading to 3.X as noted in #55179
```sh
| Change | Before [f4f598fb] <main> | After [b12de6c8] <faster-ensure-string> | Ratio | Benchmark (Parameter) |
|----------|----------------------------|-------------------------------------------|---------|--------------------------------------------------------------------------------|
| - | 1.78±0.02ms | 1.42±0.01ms | 0.8 | strings.Construction.time_construction('categorical_series', 'string[python]') |
| - | 981±10μs | 694±6μs | 0.71 | strings.Construction.time_construction('frame', 'string[python]') |
| - | 860±7μs | 436±4μs | 0.51 | strings.Construction.time_construction('series', 'string[python]') |
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55183 | 2023-09-18T16:02:49Z | 2023-09-20T22:39:03Z | 2023-09-20T22:39:03Z | 2023-10-20T20:29:20Z |
DOC: correct an example in v0.14.0.rst to fix "NameError" | diff --git a/doc/source/whatsnew/v0.14.0.rst b/doc/source/whatsnew/v0.14.0.rst
index 9c537b3a48c74..33c3de577c063 100644
--- a/doc/source/whatsnew/v0.14.0.rst
+++ b/doc/source/whatsnew/v0.14.0.rst
@@ -349,9 +349,15 @@ More consistent behavior for some groupby methods:
- groupby head and tail respect column selection:
- .. ipython:: python
+ .. code-block:: ipython
+
+ In [19]: g[['B']].head(1)
+ Out[19]:
+ B
+ 0 2
+ 2 6
- g[['B']].head(1)
+ [2 rows x 1 columns]
- groupby ``nth`` now reduces by default; filtering can be achieved by passing ``as_index=False``. With an optional ``dropna`` argument to ignore
NaN. See :ref:`the docs <groupby.nth>`.
| while building whatsnew/v0.14.0.rst the error occurs:
```
----> 1 g[['B']].head(1)
NameError: name 'g' is not defined
```
corrected the example to fix the error. | https://api.github.com/repos/pandas-dev/pandas/pulls/55182 | 2023-09-18T15:51:25Z | 2023-09-18T20:19:01Z | 2023-09-18T20:19:01Z | 2023-09-18T22:53:30Z |
MAINT: Cleanup expired ndarray methods | diff --git a/doc/source/user_guide/gotchas.rst b/doc/source/user_guide/gotchas.rst
index c00a236ff4e9d..99c85ac66623d 100644
--- a/doc/source/user_guide/gotchas.rst
+++ b/doc/source/user_guide/gotchas.rst
@@ -379,7 +379,7 @@ constructors using something similar to the following:
.. ipython:: python
x = np.array(list(range(10)), ">i4") # big endian
- newx = x.byteswap().newbyteorder() # force native byteorder
+ newx = x.byteswap().view(x.dtype.newbyteorder()) # force native byteorder
s = pd.Series(newx)
See `the NumPy documentation on byte order
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 7f19e62f40774..a6d17a604a23f 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -1773,7 +1773,7 @@ def read(
self._data_read = True
# if necessary, swap the byte order to native here
if self._byteorder != self._native_byteorder:
- raw_data = raw_data.byteswap().newbyteorder()
+ raw_data = raw_data.byteswap().view(raw_data.dtype.newbyteorder())
if convert_categoricals:
self._read_value_labels()
| Hi!
This PR reflects changes introduced in https://github.com/numpy/numpy/pull/24682. | https://api.github.com/repos/pandas-dev/pandas/pulls/55180 | 2023-09-17T17:22:19Z | 2023-09-18T16:17:34Z | 2023-09-18T16:17:33Z | 2023-09-18T16:18:22Z |
DEP: Use Cython 3.0 | diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json
index 810764754b7e1..9b0dc14fe6747 100644
--- a/asv_bench/asv.conf.json
+++ b/asv_bench/asv.conf.json
@@ -41,7 +41,7 @@
// pip (with all the conda available packages installed first,
// followed by the pip installed packages).
"matrix": {
- "Cython": ["0.29.33"],
+ "Cython": ["3.0.5"],
"matplotlib": [],
"sqlalchemy": [],
"scipy": [],
diff --git a/environment.yml b/environment.yml
index e41389c7f262a..5b90b1620537f 100644
--- a/environment.yml
+++ b/environment.yml
@@ -8,7 +8,7 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython=0.29.33
+ - cython=3.0.5
- meson[ninja]=1.2.1
- meson-python=0.13.1
diff --git a/pandas/_libs/arrays.pyi b/pandas/_libs/arrays.pyi
index 78fee8f01319c..86f69c3cdfc75 100644
--- a/pandas/_libs/arrays.pyi
+++ b/pandas/_libs/arrays.pyi
@@ -26,7 +26,7 @@ class NDArrayBacked:
def size(self) -> int: ...
@property
def nbytes(self) -> int: ...
- def copy(self): ...
+ def copy(self, order=...): ...
def delete(self, loc, axis=...): ...
def swapaxes(self, axis1, axis2): ...
def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
diff --git a/pandas/_libs/groupby.pyi b/pandas/_libs/groupby.pyi
index 609663c721950..135828a23648a 100644
--- a/pandas/_libs/groupby.pyi
+++ b/pandas/_libs/groupby.pyi
@@ -44,7 +44,6 @@ def group_fillna_indexer(
labels: np.ndarray, # ndarray[int64_t]
sorted_labels: npt.NDArray[np.intp],
mask: npt.NDArray[np.uint8],
- direction: Literal["ffill", "bfill"],
limit: int, # int64_t
dropna: bool,
) -> None: ...
@@ -55,7 +54,7 @@ def group_any_all(
mask: np.ndarray, # const uint8_t[::1]
val_test: Literal["any", "all"],
skipna: bool,
- nullable: bool,
+ result_mask: np.ndarray | None,
) -> None: ...
def group_sum(
out: np.ndarray, # complexfloatingintuint_t[:, ::1]
diff --git a/pandas/_libs/hashtable.pyi b/pandas/_libs/hashtable.pyi
index 0ac914e86f699..555ec73acd9b2 100644
--- a/pandas/_libs/hashtable.pyi
+++ b/pandas/_libs/hashtable.pyi
@@ -20,7 +20,6 @@ class Factorizer:
def factorize(
self,
values: np.ndarray,
- sort: bool = ...,
na_sentinel=...,
na_value=...,
mask=...,
@@ -157,9 +156,9 @@ class HashTable:
def __contains__(self, key: Hashable) -> bool: ...
def sizeof(self, deep: bool = ...) -> int: ...
def get_state(self) -> dict[str, int]: ...
- # TODO: `item` type is subclass-specific
- def get_item(self, item): ... # TODO: return type?
- def set_item(self, item, val) -> None: ...
+ # TODO: `val/key` type is subclass-specific
+ def get_item(self, val): ... # TODO: return type?
+ def set_item(self, key, val) -> None: ...
def get_na(self): ... # TODO: return type?
def set_na(self, val) -> None: ...
def map_locations(
@@ -185,6 +184,7 @@ class HashTable:
self,
values: np.ndarray, # np.ndarray[subclass-specific]
return_inverse: bool = ...,
+ mask=...,
) -> (
tuple[
np.ndarray, # np.ndarray[subclass-specific]
@@ -198,6 +198,7 @@ class HashTable:
na_sentinel: int = ...,
na_value: object = ...,
mask=...,
+ ignore_na: bool = True,
) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific]
class Complex128HashTable(HashTable): ...
diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index 1cf5d734705af..c0723392496c1 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -1239,9 +1239,10 @@ cdef class StringHashTable(HashTable):
na_value=na_value, ignore_na=ignore_na,
return_inverse=True)
+ # Add unused mask parameter for compat with other signatures
def get_labels(self, ndarray[object] values, ObjectVector uniques,
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
- object na_value=None):
+ object na_value=None, object mask=None):
# -> np.ndarray[np.intp]
_, labels = self._unique(values, uniques, count_prior=count_prior,
na_sentinel=na_sentinel, na_value=na_value,
@@ -1496,9 +1497,10 @@ cdef class PyObjectHashTable(HashTable):
na_value=na_value, ignore_na=ignore_na,
return_inverse=True)
+ # Add unused mask parameter for compat with other signatures
def get_labels(self, ndarray[object] values, ObjectVector uniques,
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
- object na_value=None):
+ object na_value=None, object mask=None):
# -> np.ndarray[np.intp]
_, labels = self._unique(values, uniques, count_prior=count_prior,
na_sentinel=na_sentinel, na_value=na_value,
diff --git a/pandas/_libs/lib.pyi b/pandas/_libs/lib.pyi
index 33f6f16381639..b9fd970e68f5b 100644
--- a/pandas/_libs/lib.pyi
+++ b/pandas/_libs/lib.pyi
@@ -45,22 +45,24 @@ def is_scalar(val: object) -> bool: ...
def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
def is_pyarrow_array(obj: object) -> bool: ...
def is_period(val: object) -> TypeGuard[Period]: ...
-def is_interval(val: object) -> TypeGuard[Interval]: ...
-def is_decimal(val: object) -> TypeGuard[Decimal]: ...
-def is_complex(val: object) -> TypeGuard[complex]: ...
-def is_bool(val: object) -> TypeGuard[bool | np.bool_]: ...
-def is_integer(val: object) -> TypeGuard[int | np.integer]: ...
+def is_interval(obj: object) -> TypeGuard[Interval]: ...
+def is_decimal(obj: object) -> TypeGuard[Decimal]: ...
+def is_complex(obj: object) -> TypeGuard[complex]: ...
+def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ...
+def is_integer(obj: object) -> TypeGuard[int | np.integer]: ...
def is_int_or_none(obj) -> bool: ...
-def is_float(val: object) -> TypeGuard[float]: ...
+def is_float(obj: object) -> TypeGuard[float]: ...
def is_interval_array(values: np.ndarray) -> bool: ...
-def is_datetime64_array(values: np.ndarray) -> bool: ...
-def is_timedelta_or_timedelta64_array(values: np.ndarray) -> bool: ...
+def is_datetime64_array(values: np.ndarray, skipna: bool = True) -> bool: ...
+def is_timedelta_or_timedelta64_array(
+ values: np.ndarray, skipna: bool = True
+) -> bool: ...
def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
def is_time_array(values: np.ndarray, skipna: bool = ...): ...
def is_date_array(values: np.ndarray, skipna: bool = ...): ...
def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...
def is_string_array(values: np.ndarray, skipna: bool = ...): ...
-def is_float_array(values: np.ndarray, skipna: bool = ...): ...
+def is_float_array(values: np.ndarray): ...
def is_integer_array(values: np.ndarray, skipna: bool = ...): ...
def is_bool_array(values: np.ndarray, skipna: bool = ...): ...
def fast_multiget(
@@ -185,7 +187,7 @@ def count_level_2d(
max_bin: int,
) -> np.ndarray: ... # np.ndarray[np.int64, ndim=2]
def get_level_sorter(
- label: np.ndarray, # const int64_t[:]
+ codes: np.ndarray, # const int64_t[:]
starts: np.ndarray, # const intp_t[:]
) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1]
def generate_bins_dt64(
diff --git a/pandas/_libs/ops.pyi b/pandas/_libs/ops.pyi
index 515f7aa53ba15..6738a1dff4a9e 100644
--- a/pandas/_libs/ops.pyi
+++ b/pandas/_libs/ops.pyi
@@ -37,8 +37,8 @@ def vec_binop(
@overload
def maybe_convert_bool(
arr: npt.NDArray[np.object_],
- true_values: Iterable = ...,
- false_values: Iterable = ...,
+ true_values: Iterable | None = None,
+ false_values: Iterable | None = None,
convert_to_masked_nullable: Literal[False] = ...,
) -> tuple[np.ndarray, None]: ...
@overload
diff --git a/pandas/_libs/sparse.pyi b/pandas/_libs/sparse.pyi
index 9e5cecc61e5ca..536265b25425e 100644
--- a/pandas/_libs/sparse.pyi
+++ b/pandas/_libs/sparse.pyi
@@ -39,6 +39,10 @@ class BlockIndex(SparseIndex):
self, length: int, blocs: np.ndarray, blengths: np.ndarray
) -> None: ...
+ # Override to have correct parameters
+ def intersect(self, other: SparseIndex) -> Self: ...
+ def make_union(self, y: SparseIndex) -> Self: ...
+
def make_mask_object_ndarray(
arr: npt.NDArray[np.object_], fill_value
) -> npt.NDArray[np.bool_]: ...
diff --git a/pandas/_libs/tslibs/conversion.pyi b/pandas/_libs/tslibs/conversion.pyi
index be75c2f2f68ea..a5f5d04efeb76 100644
--- a/pandas/_libs/tslibs/conversion.pyi
+++ b/pandas/_libs/tslibs/conversion.pyi
@@ -9,6 +9,7 @@ DT64NS_DTYPE: np.dtype
TD64NS_DTYPE: np.dtype
def precision_from_unit(
- in_reso: int, # NPY_DATETIMEUNIT
+ in_reso: int,
+ out_reso: int = ...,
) -> tuple[int, int]: ... # (int64_t, _)
def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ...
diff --git a/pandas/_libs/tslibs/dtypes.pyi b/pandas/_libs/tslibs/dtypes.pyi
index d8680ed2d27b4..176e4295879c7 100644
--- a/pandas/_libs/tslibs/dtypes.pyi
+++ b/pandas/_libs/tslibs/dtypes.pyi
@@ -10,10 +10,10 @@ 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: ...
+def periods_per_day(reso: int = ...) -> int: ...
def periods_per_second(reso: int) -> int: ...
def is_supported_unit(reso: int) -> bool: ...
-def npy_unit_to_abbrev(reso: int) -> str: ...
+def npy_unit_to_abbrev(unit: int) -> str: ...
def get_supported_reso(reso: int) -> int: ...
def abbrev_to_npy_unit(abbrev: str) -> int: ...
def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ...
diff --git a/pandas/_libs/tslibs/np_datetime.pyi b/pandas/_libs/tslibs/np_datetime.pyi
index 0cb0e3b0237d7..c42bc43ac9d89 100644
--- a/pandas/_libs/tslibs/np_datetime.pyi
+++ b/pandas/_libs/tslibs/np_datetime.pyi
@@ -9,7 +9,7 @@ class OutOfBoundsTimedelta(ValueError): ...
def py_get_unit_from_dtype(dtype: np.dtype): ...
def py_td64_to_tdstruct(td64: int, unit: int) -> dict: ...
def astype_overflowsafe(
- arr: np.ndarray,
+ values: np.ndarray,
dtype: np.dtype,
copy: bool = ...,
round_ok: bool = ...,
diff --git a/pandas/_libs/tslibs/period.pyi b/pandas/_libs/tslibs/period.pyi
index a4aecd2ce0a09..846d238beadbd 100644
--- a/pandas/_libs/tslibs/period.pyi
+++ b/pandas/_libs/tslibs/period.pyi
@@ -89,7 +89,7 @@ class Period(PeriodMixin):
@classmethod
def _from_ordinal(cls, ordinal: int, freq) -> Period: ...
@classmethod
- def now(cls, freq: Frequency = ...) -> Period: ...
+ def now(cls, freq: Frequency) -> Period: ...
def strftime(self, fmt: str | None) -> str: ...
def to_timestamp(
self,
diff --git a/pandas/_libs/tslibs/strptime.pyi b/pandas/_libs/tslibs/strptime.pyi
index 4565bb7ecf959..0ec1a1e25a2b3 100644
--- a/pandas/_libs/tslibs/strptime.pyi
+++ b/pandas/_libs/tslibs/strptime.pyi
@@ -8,6 +8,7 @@ def array_strptime(
exact: bool = ...,
errors: str = ...,
utc: bool = ...,
+ creso: int = ..., # NPY_DATETIMEUNIT
) -> tuple[np.ndarray, np.ndarray]: ...
# first ndarray is M8[ns], second is object ndarray of tzinfo | None
diff --git a/pandas/_libs/tslibs/timedeltas.pyi b/pandas/_libs/tslibs/timedeltas.pyi
index 181703c5f55b2..24ec6c8891a89 100644
--- a/pandas/_libs/tslibs/timedeltas.pyi
+++ b/pandas/_libs/tslibs/timedeltas.pyi
@@ -71,7 +71,7 @@ _S = TypeVar("_S", bound=timedelta)
def get_unit_for_round(freq, creso: int) -> int: ...
def disallow_ambiguous_unit(unit: str | None) -> None: ...
def ints_to_pytimedelta(
- arr: npt.NDArray[np.timedelta64],
+ m8values: npt.NDArray[np.timedelta64],
box: bool = ...,
) -> npt.NDArray[np.object_]: ...
def array_to_timedelta64(
@@ -165,8 +165,10 @@ class Timedelta(timedelta):
def __gt__(self, other: timedelta) -> bool: ...
def __hash__(self) -> int: ...
def isoformat(self) -> str: ...
- def to_numpy(self) -> np.timedelta64: ...
- def view(self, dtype: npt.DTypeLike = ...) -> object: ...
+ def to_numpy(
+ self, dtype: npt.DTypeLike = ..., copy: bool = False
+ ) -> np.timedelta64: ...
+ def view(self, dtype: npt.DTypeLike) -> object: ...
@property
def unit(self) -> str: ...
def as_unit(self, unit: str, round_ok: bool = ...) -> Timedelta: ...
diff --git a/pandas/_libs/tslibs/timestamps.pyi b/pandas/_libs/tslibs/timestamps.pyi
index e23f01b800874..c78c174e27902 100644
--- a/pandas/_libs/tslibs/timestamps.pyi
+++ b/pandas/_libs/tslibs/timestamps.pyi
@@ -183,7 +183,7 @@ class Timestamp(datetime):
def is_year_end(self) -> bool: ...
def to_pydatetime(self, warn: bool = ...) -> datetime: ...
def to_datetime64(self) -> np.datetime64: ...
- def to_period(self, freq: BaseOffset | str = ...) -> Period: ...
+ def to_period(self, freq: BaseOffset | str | None = None) -> Period: ...
def to_julian_date(self) -> np.float64: ...
@property
def asm8(self) -> np.datetime64: ...
diff --git a/pandas/_libs/tslibs/tzconversion.pyi b/pandas/_libs/tslibs/tzconversion.pyi
index a354765a348ec..2108fa0f35547 100644
--- a/pandas/_libs/tslibs/tzconversion.pyi
+++ b/pandas/_libs/tslibs/tzconversion.pyi
@@ -10,7 +10,7 @@ from pandas._typing import npt
# tz_convert_from_utc_single exposed for testing
def tz_convert_from_utc_single(
- val: np.int64, tz: tzinfo, creso: int = ...
+ utc_val: np.int64, tz: tzinfo, creso: int = ...
) -> np.int64: ...
def tz_localize_to_utc(
vals: npt.NDArray[np.int64],
diff --git a/pandas/_libs/tslibs/vectorized.pyi b/pandas/_libs/tslibs/vectorized.pyi
index 3fd9e2501e611..de19f592da62b 100644
--- a/pandas/_libs/tslibs/vectorized.pyi
+++ b/pandas/_libs/tslibs/vectorized.pyi
@@ -31,7 +31,7 @@ def get_resolution(
reso: int = ..., # NPY_DATETIMEUNIT
) -> Resolution: ...
def ints_to_pydatetime(
- arr: npt.NDArray[np.int64],
+ stamps: npt.NDArray[np.int64],
tz: tzinfo | None = ...,
box: str = ...,
reso: int = ..., # NPY_DATETIMEUNIT
diff --git a/pandas/_libs/window/aggregations.pyi b/pandas/_libs/window/aggregations.pyi
index b926a7cb73425..a6cfbec9b15b9 100644
--- a/pandas/_libs/window/aggregations.pyi
+++ b/pandas/_libs/window/aggregations.pyi
@@ -111,8 +111,8 @@ def ewm(
com: float, # float64_t
adjust: bool,
ignore_na: bool,
- deltas: np.ndarray, # const float64_t[:]
- normalize: bool,
+ deltas: np.ndarray | None = None, # const float64_t[:]
+ normalize: bool = True,
) -> np.ndarray: ... # np.ndarray[np.float64]
def ewmcov(
input_x: np.ndarray, # const float64_t[:]
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 33b2f65340a3b..d984164c543cf 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -2281,8 +2281,7 @@ def _concat_same_type(
return new_obj
def copy(self, order: str = "C") -> Self:
- # error: Unexpected keyword argument "order" for "copy"
- new_obj = super().copy(order=order) # type: ignore[call-arg]
+ new_obj = super().copy(order=order)
new_obj._freq = self.freq
return new_obj
diff --git a/pyproject.toml b/pyproject.toml
index 26d52d97b0934..8ebd70762b2a5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,7 +5,7 @@ requires = [
"meson-python==0.13.1",
"meson==1.2.1",
"wheel",
- "Cython>=0.29.33,<3", # Note: sync with setup.py, environment.yml and asv.conf.json
+ "Cython==3.0.5", # Note: sync with setup.py, environment.yml and asv.conf.json
# Any NumPy version should be fine for compiling. Users are unlikely
# to get a NumPy<1.25 so the result will be compatible with all relevant
# NumPy versions (if not it is presumably compatible with their version).
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 490e170299030..d11f51c45afcd 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -3,7 +3,7 @@
pip
versioneer[toml]
-cython==0.29.33
+cython==3.0.5
meson[ninja]==1.2.1
meson-python==0.13.1
pytest>=7.3.2
diff --git a/scripts/run_stubtest.py b/scripts/run_stubtest.py
index dedcdb5532593..35cbbef08124e 100644
--- a/scripts/run_stubtest.py
+++ b/scripts/run_stubtest.py
@@ -47,6 +47,8 @@
# stubtest might be too sensitive
"pandas._libs.lib.NoDefault",
"pandas._libs.lib._NoDefault.no_default",
+ # stubtest/Cython is not recognizing the default value for the dtype parameter
+ "pandas._libs.lib.map_infer_mask",
# internal type alias (should probably be private)
"pandas._libs.lib.ndarray_obj_2d",
# runtime argument "owner" has a default value but stub argument does not
diff --git a/setup.py b/setup.py
index db3717efb738d..d6fb4b775a351 100755
--- a/setup.py
+++ b/setup.py
@@ -37,7 +37,7 @@ def is_platform_mac():
# note: sync with pyproject.toml, environment.yml and asv.conf.json
-min_cython_ver = "0.29.33"
+min_cython_ver = "3.0.5"
try:
from Cython import (
| - [ ] 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.
We're seeing some perf regressions (but also a lot of perf improvements) by using Cython 3.0. Not sure what the best way to approach this is - do we try to handle fixing these in a single branch along with changing to 3.0? Or do we just adopt 3.0 in one PR and try to address the regressions after?
<details>
<summary>ASV Regressions for Cython 3.0</summary>
```
| Change | Before [e89d18c8] <main> | After [71693857] <unpin_cython_everywhere> | Ratio | Benchmark (Parameter) |
|----------|----------------------------|----------------------------------------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| + | 1.14±0ms | 2.64±0.1ms | 2.32 | series_methods.Fillna.time_ffill('Int64') |
| + | 1.16±0.01ms | 2.66±0.1ms | 2.3 | series_methods.Fillna.time_ffill('Float64') |
| + | 1.24±0.3ms | 2.69±0.4ms | 2.17 | series_methods.Fillna.time_ffill('datetime64[ns]') |
| + | 1.14±0.01ms | 1.84±0.04ms | 1.61 | series_methods.Fillna.time_bfill('Float64') |
| + | 1.13±0.01ms | 1.78±0.05ms | 1.57 | series_methods.Fillna.time_bfill('Int64') |
| + | 6.87±0.2ms | 9.88±0.06ms | 1.44 | algos.isin.IsinWithRandomFloat.time_isin(<class 'numpy.object_'>, 80000, 'outside') |
| + | 111±7μs | 157±4μs | 1.42 | algos.isin.IsInForObjects.time_isin('short', 'nans') |
| + | 3.12±0.2ms | 4.12±0.03ms | 1.32 | algos.isin.IsInForObjects.time_isin('long_floats', 'long_floats') |
| + | 49.5±2ms | 61.8±2ms | 1.25 | index_object.IntervalIndexMethod.time_intersection_both_duplicate(100000) |
| + | 797±8ms | 999±20ms | 1.25 | strings.Dummies.time_get_dummies('str') |
| + | 13.3±0.2ms | 16.4±0.04ms | 1.23 | indexing.MultiIndexing.time_loc_all_lists(True) |
| + | 892±7ms | 1.10±0.01s | 1.23 | strings.Dummies.time_get_dummies('string[pyarrow]') |
| + | 8.71±0.06ms | 10.5±0.4ms | 1.21 | index_object.SetOperations.time_operation('non_monotonic', 'date_string', 'symmetric_difference') |
| + | 10.7±0.3ms | 12.6±0.1ms | 1.18 | algorithms.Duplicated.time_duplicated(False, False, 'float64') |
| + | 250±2μs | 294±5μs | 1.18 | arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function lt>) |
| + | 23.2±0.07ms | 27.1±0.09ms | 1.17 | groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'last') |
| + | 421±20ns | 491±20ns | 1.17 | index_cached_properties.IndexCache.time_shape('Int64Index') |
| + | 955±10ms | 1.12±0.02s | 1.17 | strings.Dummies.time_get_dummies('string[python]') |
| + | 187±6μs | 218±10μs | 1.16 | algos.isin.IsInForObjects.time_isin('nans', 'nans') |
| + | 29.6±0.1ms | 34.5±0.4ms | 1.16 | groupby.GroupByCythonAggEaDtypes.time_frame_agg('Float64', 'mean') |
| + | 32.0±0.3ms | 37.2±0.5ms | 1.16 | groupby.GroupByCythonAggEaDtypes.time_frame_agg('Int32', 'mean') |
| + | 347±10ns | 402±7ns | 1.16 | tslibs.timestamp.TimestampOps.time_to_pydatetime(datetime.timezone(datetime.timedelta(seconds=3600))) |
| + | 348±8ns | 404±8ns | 1.16 | tslibs.timestamp.TimestampOps.time_to_pydatetime(datetime.timezone.utc) |
| + | 117±0.5ns | 135±8ns | 1.16 | tslibs.timestamp.TimestampProperties.time_quarter(None) |
| + | 11.0±0.6ms | 12.6±0.5ms | 1.15 | algorithms.Duplicated.time_duplicated(False, False, 'datetime64[ns, tz]') |
| + | 11.0±0.4ms | 12.6±0.4ms | 1.15 | algorithms.Duplicated.time_duplicated(False, False, 'datetime64[ns]') |
| + | 256±6μs | 294±7μs | 1.15 | arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function gt>) |
| + | 11.8±0.2ms | 13.6±0.1ms | 1.15 | indexing.DataFrameNumericIndexing.time_loc_dups(<class 'numpy.float64'>, 'nonunique_monotonic_inc') |
| + | 202±20μs | 230±3μs | 1.14 | algos.isin.IsIn.time_isin('str') |
| + | 208±20μs | 237±3μs | 1.14 | algos.isin.IsIn.time_isin_categorical('str') |
| + | 345±9ns | 394±6ns | 1.14 | tslibs.timestamp.TimestampOps.time_to_pydatetime(None) |
| + | 352±9ns | 402±10ns | 1.14 | tslibs.timestamp.TimestampOps.time_to_pydatetime(tzfile('/usr/share/zoneinfo/Asia/Tokyo')) |
| + | 353±10ns | 404±10ns | 1.14 | tslibs.timestamp.TimestampOps.time_to_pydatetime(tzlocal()) |
| + | 5.20±0.3ms | 5.86±0.1ms | 1.13 | algorithms.Duplicated.time_duplicated(False, 'last', 'datetime64[ns]') |
| + | 207±20μs | 234±0.9μs | 1.13 | algos.isin.IsIn.time_isin('string[python]') |
| + | 496±40μs | 558±6μs | 1.13 | arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function add>) |
| + | 76.1±1μs | 86.1±3μs | 1.13 | indexing.MultiIndexing.time_xs_full_key(True) |
| + | 349±10ns | 394±6ns | 1.13 | tslibs.timestamp.TimestampOps.time_to_pydatetime(<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>) |
| + | 118±2ns | 133±10ns | 1.13 | tslibs.timestamp.TimestampProperties.time_is_year_start(<DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>) |
| + | 213±20μs | 239±2μs | 1.12 | algos.isin.IsIn.time_isin_categorical('string[python]') |
| + | 4.95±0.1ms | 5.56±0.06ms | 1.12 | tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone(datetime.timedelta(seconds=3600))) |
| + | 280±20ns | 311±30ns | 1.11 | index_cached_properties.IndexCache.time_is_unique('Int64Index') |
| + | 104±1ns | 116±3ns | 1.11 | tslibs.timestamp.TimestampProperties.time_is_year_end(None) |
| + | 7.48±0.05ms | 8.25±0.2ms | 1.1 | frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 0) |
| + | 1.89±0.01ms | 2.09±0.04ms | 1.1 | frame_methods.Fillna.time_bfill(False, 'Int64') |
| + | 21.3±0.2ms | 23.5±0.3ms | 1.1 | groupby.GroupByCythonAgg.time_frame_agg('float64', 'mean') |
| + | 51.6±0.4ms | 56.9±0.2ms | 1.1 | io.csv.ToCSV.time_frame('long') |
| + | 1.68±0.02ms | 1.85±0.1ms | 1.1 | join_merge.Concat.time_concat_series(0) |
| + | 52.8±0.6μs | 58.3±0.5μs | 1.1 | tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone(datetime.timedelta(seconds=3600))) |
| + | 115±1ns | 127±7ns | 1.1 | tslibs.timestamp.TimestampProperties.time_days_in_month(None) |
| + | 121±0.6ns | 133±8ns | 1.1 | tslibs.timestamp.TimestampProperties.time_quarter(datetime.timezone.utc)
```
</details>
~I'm hoping the `gil.*` regressions will be fixed by https://github.com/cython/cython/pull/5685~ GIL regressions are now gone. | https://api.github.com/repos/pandas-dev/pandas/pulls/55179 | 2023-09-17T12:47:58Z | 2023-11-16T20:27:32Z | 2023-11-16T20:27:32Z | 2023-11-16T21:10:34Z |
DOC: added deprecation message in docs for DataFrame.rolling | diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index becbba703f92c..72e94d049a9de 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -937,6 +937,11 @@ class Window(BaseWindow):
For `Series` this parameter is unused and defaults to 0.
+ .. deprecated:: 2.1.0
+
+ The axis keyword is deprecated. For ``axis=1``,
+ transpose the DataFrame first instead.
+
closed : str, default None
If ``'right'``, the first point in the window is excluded from calculations.
| - [ ] closes #55133
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit). | https://api.github.com/repos/pandas-dev/pandas/pulls/55177 | 2023-09-17T10:54:14Z | 2023-09-18T19:42:38Z | 2023-09-18T19:42:38Z | 2023-09-18T19:42:45Z |
DOC: Update read_csv docs to reflect behaviour with `parse_dates` | diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py
index e826aad478059..acf35ebd6afe5 100644
--- a/pandas/io/parsers/readers.py
+++ b/pandas/io/parsers/readers.py
@@ -238,7 +238,8 @@
default False
The behavior is as follows:
- * ``bool``. If ``True`` -> try parsing the index.
+ * ``bool``. If ``True`` -> try parsing the index. Note: Automatically set to
+ ``True`` if ``date_format`` or ``date_parser`` arguments have been passed.
* ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3
each as a separate date column.
* ``list`` of ``list``. e.g. If ``[[1, 3]]`` -> combine columns 1 and 3 and parse
| - [x] closes #55158
- [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/55176 | 2023-09-17T04:10:53Z | 2023-09-18T16:24:43Z | 2023-09-18T16:24:43Z | 2023-09-18T16:25:29Z |
DEPS: Bump to 1.26 proper for Python 3.12 | diff --git a/pyproject.toml b/pyproject.toml
index 7807a6cc6368d..4e1c77413efda 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,8 +10,7 @@ requires = [
# we don't want to force users to compile with 1.25 though
# (Ideally, in the future, though, oldest-supported-numpy can be dropped when our min numpy is 1.25.x)
"oldest-supported-numpy>=2022.8.16; python_version<'3.12'",
- # TODO: This needs to be updated when the official numpy 1.26 comes out
- "numpy>=1.26.0b1; python_version>='3.12'",
+ "numpy>=1.26.0; python_version>='3.12'",
"versioneer[toml]"
]
@@ -32,8 +31,7 @@ requires-python = '>=3.9'
dependencies = [
"numpy>=1.22.4; python_version<'3.11'",
"numpy>=1.23.2; python_version=='3.11'",
- # TODO: This needs to be updated when the official numpy 1.26 comes out
- "numpy>=1.26.0b1; python_version>='3.12'",
+ "numpy>=1.26.0; python_version>='3.12'",
"python-dateutil>=2.8.2",
"pytz>=2020.1",
"tzdata>=2022.1"
| - [ ] 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/55175 | 2023-09-16T20:50:00Z | 2023-09-19T15:33:35Z | 2023-09-19T15:33:35Z | 2023-09-19T18:26:40Z |
BUG: .rolling() returns incorrect values when ts index is not nano seconds | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 145c364728b40..418f3abe30647 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -14,6 +14,7 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression in :meth:`DataFrame.join` where result has missing values and dtype is arrow backed string (:issue:`55348`)
+- Fixed regression in :meth:`~DataFrame.rolling` where non-nanosecond index or ``on`` column would produce incorrect results (:issue:`55026`, :issue:`55106`, :issue:`55299`)
- Fixed regression in :meth:`DataFrame.resample` which was extrapolating back to ``origin`` when ``origin`` was outside its bounds (:issue:`55064`)
- Fixed regression in :meth:`DataFrame.sort_index` which was not sorting correctly when the index was a sliced :class:`MultiIndex` (:issue:`55379`)
- Fixed performance regression with wide DataFrames, typically involving methods where all columns were accessed individually (:issue:`55256`, :issue:`55245`)
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index 72e94d049a9de..f90863a8ea1ef 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -21,6 +21,7 @@
from pandas._libs.tslibs import (
BaseOffset,
+ Timedelta,
to_offset,
)
import pandas._libs.window.aggregations as window_aggregations
@@ -112,6 +113,8 @@
from pandas.core.generic import NDFrame
from pandas.core.groupby.ops import BaseGrouper
+from pandas.core.arrays.datetimelike import dtype_to_unit
+
class BaseWindow(SelectionMixin):
"""Provides utilities for performing windowing operations."""
@@ -1887,7 +1890,12 @@ def _validate(self):
self._on.freq.nanos / self._on.freq.n
)
else:
- self._win_freq_i8 = freq.nanos
+ try:
+ unit = dtype_to_unit(self._on.dtype) # type: ignore[arg-type]
+ except TypeError:
+ # if not a datetime dtype, eg for empty dataframes
+ unit = "ns"
+ self._win_freq_i8 = Timedelta(freq.nanos).as_unit(unit)._value
# min_periods must be an integer
if self.min_periods is None:
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index 3fe922539780d..b4f6fde6850a2 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1950,3 +1950,35 @@ def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype):
op2 = getattr(rolling2, kernel)
expected = op2(*arg2, numeric_only=numeric_only)
tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+@pytest.mark.parametrize("tz", [None, "UTC", "Europe/Prague"])
+def test_rolling_timedelta_window_non_nanoseconds(unit, tz):
+ # Test Sum, GH#55106
+ df_time = DataFrame(
+ {"A": range(5)}, index=date_range("2013-01-01", freq="1s", periods=5, tz=tz)
+ )
+ sum_in_nanosecs = df_time.rolling("1s").sum()
+ # microseconds / milliseconds should not break the correct rolling
+ df_time.index = df_time.index.as_unit(unit)
+ sum_in_microsecs = df_time.rolling("1s").sum()
+ sum_in_microsecs.index = sum_in_microsecs.index.as_unit("ns")
+ tm.assert_frame_equal(sum_in_nanosecs, sum_in_microsecs)
+
+ # Test max, GH#55026
+ ref_dates = date_range("2023-01-01", "2023-01-10", unit="ns", tz=tz)
+ ref_series = Series(0, index=ref_dates)
+ ref_series.iloc[0] = 1
+ ref_max_series = ref_series.rolling(Timedelta(days=4)).max()
+
+ dates = date_range("2023-01-01", "2023-01-10", unit=unit, tz=tz)
+ series = Series(0, index=dates)
+ series.iloc[0] = 1
+ max_series = series.rolling(Timedelta(days=4)).max()
+
+ ref_df = DataFrame(ref_max_series)
+ df = DataFrame(max_series)
+ df.index = df.index.as_unit("ns")
+
+ tm.assert_frame_equal(ref_df, df)
| - [X] closes #55106
- [X] closes #55026
- [X] [Tests added and passed]
The bug is referred to and fixed inside the code. It needs to be generalised. @jorisvandenbossche we need to have the first feedback if we can generalise the data type with some utils or more generally. Done during the in-person sprint check. | https://api.github.com/repos/pandas-dev/pandas/pulls/55173 | 2023-09-16T16:33:12Z | 2023-10-26T02:14:58Z | 2023-10-26T02:14:58Z | 2023-10-26T02:15:15Z |
DOC: complete parameter descriptions for DataFrame.insert's docstring | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8fcb91c846826..ce07210e735a4 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4915,7 +4915,9 @@ def insert(
column : str, number, or hashable object
Label of the inserted column.
value : Scalar, Series, or array-like
+ Content of the inserted column.
allow_duplicates : bool, optional, default lib.no_default
+ Allow duplicate column labels to be created.
See Also
--------
| issue: https://github.com/jorisvandenbossche/pydata-amsterdam-pandas-sprint/issues/1
- bullet point item #5: "complete the parameter descriptions"
- [x] closes https://github.com/jorisvandenbossche/pydata-amsterdam-pandas-sprint/issues/1, bullet point 5
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
@jorisvandenbossche
| https://api.github.com/repos/pandas-dev/pandas/pulls/55172 | 2023-09-16T16:07:39Z | 2023-09-18T16:26:39Z | 2023-09-18T16:26:39Z | 2023-09-18T16:26:46Z |
ENH: set __module__ for objects in pandas pd.DataFrame API | diff --git a/doc/source/development/contributing_docstring.rst b/doc/source/development/contributing_docstring.rst
index e2881c1087e60..0b8c1e16dce0e 100644
--- a/doc/source/development/contributing_docstring.rst
+++ b/doc/source/development/contributing_docstring.rst
@@ -940,7 +940,7 @@ Finally, docstrings can also be appended to with the ``doc`` decorator.
In this example, we'll create a parent docstring normally (this is like
``pandas.core.generic.NDFrame``). Then we'll have two children (like
-``pandas.core.series.Series`` and ``pandas.core.frame.DataFrame``). We'll
+``pandas.core.series.Series`` and ``pandas.DataFrame``). We'll
substitute the class names in this docstring.
.. code-block:: python
diff --git a/doc/source/user_guide/enhancingperf.rst b/doc/source/user_guide/enhancingperf.rst
index 8c510173819e0..c4721f3a6b09c 100644
--- a/doc/source/user_guide/enhancingperf.rst
+++ b/doc/source/user_guide/enhancingperf.rst
@@ -453,7 +453,7 @@ by evaluate arithmetic and boolean expression all at once for large :class:`~pan
:func:`~pandas.eval` is many orders of magnitude slower for
smaller expressions or objects than plain Python. A good rule of thumb is
to only use :func:`~pandas.eval` when you have a
- :class:`.DataFrame` with more than 10,000 rows.
+ :class:`~pandas.core.frame.DataFrame` with more than 10,000 rows.
Supported syntax
~~~~~~~~~~~~~~~~
diff --git a/doc/source/user_guide/io.rst b/doc/source/user_guide/io.rst
index 9c48e66daacf0..db2326d5b9754 100644
--- a/doc/source/user_guide/io.rst
+++ b/doc/source/user_guide/io.rst
@@ -6400,7 +6400,7 @@ ignored.
In [2]: df = pd.DataFrame({'A': np.random.randn(sz), 'B': [1] * sz})
In [3]: df.info()
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 2 columns):
A 1000000 non-null float64
diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index cb12962256a55..c63d047f03823 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -840,7 +840,7 @@ then all the columns are dummy-encoded, and a :class:`SparseDataFrame` was retur
In [2]: df = pd.DataFrame({"A": [1, 2], "B": ['a', 'b'], "C": ['a', 'a']})
In [3]: type(pd.get_dummies(df, sparse=True))
- Out[3]: pandas.core.frame.DataFrame
+ Out[3]: pandas.DataFrame
In [4]: type(pd.get_dummies(df[['B', 'C']], sparse=True))
Out[4]: pandas.core.sparse.frame.SparseDataFrame
diff --git a/doc/source/whatsnew/v1.0.0.rst b/doc/source/whatsnew/v1.0.0.rst
index 94a8ee7cd1a5d..5dbf6f1c60598 100755
--- a/doc/source/whatsnew/v1.0.0.rst
+++ b/doc/source/whatsnew/v1.0.0.rst
@@ -414,7 +414,7 @@ Extended verbose info output for :class:`~pandas.DataFrame`
... "text_col": ["a", "b", "c"],
... "float_col": [0.0, 0.1, 0.2]})
In [2]: df.info(verbose=True)
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
int_col 3 non-null int64
diff --git a/pandas/conftest.py b/pandas/conftest.py
index 50a94b35c2edc..65410c3c09494 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -125,7 +125,7 @@ def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None:
item : pytest.Item
pytest test item.
path : str
- Module path to Python object, e.g. "pandas.core.frame.DataFrame.append". A
+ Module path to Python object, e.g. "pandas.DataFrame.append". A
warning will be filtered when item.name ends with in given path. So it is
sufficient to specify e.g. "DataFrame.append".
message : str
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8fd0cd8c66e3c..5d10a5541f556 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -65,6 +65,7 @@
Appender,
Substitution,
doc,
+ set_module,
)
from pandas.util._exceptions import (
find_stack_level,
@@ -498,6 +499,7 @@
# DataFrame class
+@set_module("pandas")
class DataFrame(NDFrame, OpsMixin):
"""
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index c8a2e11dce3d7..6b4070ed6349c 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -227,7 +227,7 @@ def iloc(self) -> _iLocIndexer:
a b c d
0 1 2 3 4
>>> type(df.iloc[[0]])
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
>>> df.iloc[[0, 1]]
a b c d
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index 8566751b9f33e..c503121328f53 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -855,7 +855,7 @@ class DataFrameRenderer:
- to_csv
- to_latex
- Called in pandas.core.frame.DataFrame:
+ Called in pandas.DataFrame:
- to_html
- to_string
diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py
index ad595a2be8374..bb156f0fbf826 100644
--- a/pandas/io/formats/info.py
+++ b/pandas/io/formats/info.py
@@ -72,7 +72,7 @@
Prints information of all columns:
>>> df.info(verbose=True)
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
# Column Non-Null Count Dtype
@@ -87,7 +87,7 @@
information:
>>> df.info(verbose=False)
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Columns: 3 entries, int_col to float_col
dtypes: float64(1), int64(1), object(1)
@@ -115,7 +115,7 @@
... 'column_3': np.random.choice(['a', 'b', 'c'], 10 ** 6)
... })
>>> df.info()
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
@@ -127,7 +127,7 @@
memory usage: 22.9+ MB
>>> df.info(memory_usage='deep')
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 3 columns):
# Column Non-Null Count Dtype
diff --git a/pandas/tests/api/test_api.py b/pandas/tests/api/test_api.py
index 15b6c9abaea8f..82c5c305b574c 100644
--- a/pandas/tests/api/test_api.py
+++ b/pandas/tests/api/test_api.py
@@ -401,3 +401,7 @@ def test_pandas_array_alias():
res = pd.arrays.PandasArray
assert res is pd.arrays.NumpyExtensionArray
+
+
+def test_set_module():
+ assert pd.DataFrame.__module__ == "pandas"
diff --git a/pandas/tests/frame/methods/test_info.py b/pandas/tests/frame/methods/test_info.py
index fcb7677f03f27..4e3726f4dc51d 100644
--- a/pandas/tests/frame/methods/test_info.py
+++ b/pandas/tests/frame/methods/test_info.py
@@ -40,7 +40,7 @@ def test_info_empty():
result = buf.getvalue()
expected = textwrap.dedent(
"""\
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 0 entries
Empty DataFrame\n"""
)
@@ -208,7 +208,7 @@ def test_info_memory():
bytes = float(df.memory_usage().sum())
expected = textwrap.dedent(
f"""\
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 1 columns):
# Column Non-Null Count Dtype
@@ -501,7 +501,7 @@ def test_info_int_columns():
result = buf.getvalue()
expected = textwrap.dedent(
"""\
- <class 'pandas.core.frame.DataFrame'>
+ <class 'pandas.DataFrame'>
Index: 2 entries, A to B
Data columns (total 2 columns):
# Column Non-Null Count Dtype
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index 699fffe5d0488..9ce7a0818ac02 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -509,7 +509,7 @@ def test_groupby_with_datetime_key(self):
assert len(gb.groups.keys()) == 4
def test_grouping_error_on_multidim_input(self, df):
- msg = "Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional"
+ msg = "Grouper for '<class 'pandas.DataFrame'>' not 1-dimensional"
with pytest.raises(ValueError, match=msg):
Grouping(df.index, df[["A", "A"]])
diff --git a/pandas/util/_decorators.py b/pandas/util/_decorators.py
index 4f6fc0f3d8de3..d287fa72d552d 100644
--- a/pandas/util/_decorators.py
+++ b/pandas/util/_decorators.py
@@ -503,3 +503,24 @@ def indent(text: str | None, indents: int = 1) -> str:
"future_version_msg",
"Substitution",
]
+
+
+def set_module(module):
+ """Private decorator for overriding __module__ on a function or class.
+
+ Example usage::
+
+ @set_module("pandas")
+ def example():
+ pass
+
+
+ assert example.__module__ == "pandas"
+ """
+
+ def decorator(func):
+ if module is not None:
+ func.__module__ = module
+ return func
+
+ return decorator
| - [x] part of #55178
- [ ] [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.
This PR add ```@set_module``` decorator around ```class pd.DataFrame```
Before this PR
```
>>> import pandas as pd
>>> pd.DataFrame
<class 'pandas.core.frame.DataFrame'>
```
After this PR
```
>>> import pandas as pd
>>> pd.DataFrame
<class 'pandas.DataFrame'>
```
This change would discourage users from incorrectly using
```
from pandas.core.frame import DataFrame
```
Inspired by the similar implementation in numpy:
https://github.com/numpy/numpy/pull/12382
| https://api.github.com/repos/pandas-dev/pandas/pulls/55171 | 2023-09-16T15:46:31Z | 2024-03-23T12:25:15Z | 2024-03-23T12:25:15Z | 2024-03-23T12:25:15Z |
DOC: add alternative to docstring of deprecated Series.bool() method | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index bbaa6d3faeb05..b0063ca659a38 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1526,7 +1526,8 @@ def bool(self) -> bool_t:
.. deprecated:: 2.1.0
- bool is deprecated and will be removed in future version of pandas
+ bool is deprecated and will be removed in future version of pandas.
+ For ``Series`` use ``pandas.Series.item``.
This must be a boolean scalar value, either True or False. It will raise a
ValueError if the Series or DataFrame does not have exactly 1 element, or that
@@ -1556,6 +1557,14 @@ def bool(self) -> bool_t:
True
>>> pd.DataFrame({'col': [False]}).bool() # doctest: +SKIP
False
+
+ This is an alternative method and will only work
+ for single element objects with a boolean value:
+
+ >>> pd.Series([True]).item() # doctest: +SKIP
+ True
+ >>> pd.Series([False]).item() # doctest: +SKIP
+ False
"""
warnings.warn(
| Fixed! Docs adapted.
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.bool.html is deprecated; it would be nice to explain a potential alternative.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55168 | 2023-09-16T15:12:48Z | 2023-09-19T15:50:04Z | 2023-09-19T15:50:04Z | 2023-09-19T15:50:12Z |
DEV: don't install tables on MacOS ARM in dev requirements.txt (add env markers) | diff --git a/requirements-dev.txt b/requirements-dev.txt
index 77afa9005029c..2c3c1331e25e5 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -35,7 +35,7 @@ psycopg2-binary>=2.9.3
pyarrow>=7.0.0
pymysql>=1.0.2
pyreadstat>=1.1.5
-tables>=3.7.0
+tables>=3.7.0 ; sys_platform != 'darwin' and platform_machine != 'arm64'
python-calamine>=0.1.6
pyxlsb>=1.0.9
s3fs>=2022.05.0
diff --git a/scripts/generate_pip_deps_from_conda.py b/scripts/generate_pip_deps_from_conda.py
index 2ca4455158db5..44ed049483336 100755
--- a/scripts/generate_pip_deps_from_conda.py
+++ b/scripts/generate_pip_deps_from_conda.py
@@ -32,6 +32,30 @@
"seaborn-base": "seaborn",
"sqlalchemy": "SQLAlchemy",
}
+ADD_ENV_MARKERS = {
+ # pytables has no wheels available for macOS arm,
+ # and installing from source generally fails
+ # -> exclude from dev dependencies in this case
+ "pytables": " ; sys_platform != 'darwin' and platform_machine != 'arm64'",
+}
+
+
+def rename(orig_name: str) -> str:
+ if orig_name in RENAME:
+ return RENAME[orig_name]
+ return orig_name
+
+
+def remap_version(package: str, version: str) -> str:
+ if package in REMAP_VERSION:
+ return REMAP_VERSION[package]
+ return version
+
+
+def retrieve_env_markers(package: str) -> str:
+ if package in ADD_ENV_MARKERS:
+ return ADD_ENV_MARKERS[package]
+ return ""
def conda_package_to_pip(package: str):
@@ -46,23 +70,24 @@ def conda_package_to_pip(package: str):
"""
package = re.sub("(?<=[^<>])=", "==", package).strip()
+ if package in EXCLUDE:
+ return
+
for compare in ("<=", ">=", "=="):
if compare in package:
pkg, version = package.split(compare)
if pkg in EXCLUDE:
return
- if pkg in REMAP_VERSION:
- return "".join((pkg, compare, REMAP_VERSION[pkg]))
- if pkg in RENAME:
- return "".join((RENAME[pkg], compare, version))
-
- if package in EXCLUDE:
- return
-
- if package in RENAME:
- return RENAME[package]
+ return "".join(
+ (
+ rename(pkg),
+ compare,
+ remap_version(pkg, version),
+ retrieve_env_markers(pkg),
+ )
+ )
- return package
+ return "".join((rename(package), retrieve_env_markers(package)))
def generate_pip_from_conda(
@@ -103,8 +128,10 @@ def generate_pip_from_conda(
raise ValueError(f"Unexpected dependency {dep}")
header = (
- f"# This file is auto-generated from {conda_path.name}, do not modify.\n"
- "# See that file for comments about the need/usage of each dependency.\n\n"
+ f"# This file is auto-generated from {conda_path.name}, do not modify.\
+\n"
+ "# See that file for comments about the need/usage of each dependency.\
+\n\n"
)
pip_content = header + "\n".join(pip_deps) + "\n"
| When installing dependencies via pip on Mac M1 or M2 installing tables is not trivial and throws an error (see below) because wheels of tables for this hardware is not available and building depends on hdf5.
The solution is to exclude the install of tables via environment markers in the `requirements-dev.txt`. This file is generated by `scripts/generate_pip_deps_from_conda.py`.
This commit adds the following functionality to `scripts/generate_pip_deps_from_conda.py`:
- Adding environment markers to dependencies specified in `ADD_ENV_MARKERS`.
- Allow multiple operations on the same package (add env markers, rename package and remap version).
- [ ] 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).
- [x] 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.
# error installing tables
```pip install tables
Collecting tables
Using cached tables-3.8.0.tar.gz (8.0 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [11 lines of output]
<string>:19: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
ld: library not found for -lhdf5
clang: error: linker command failed with exit code 1 (use -v to see invocation)
cpuinfo failed, assuming no CPU features: 'flags'
* Using Python 3.10.10 (main, Apr 20 2023, 14:37:50) [Clang 14.0.0 (clang-1400.0.29.202)]
* Found cython 3.0.2
* USE_PKGCONFIG: True
.. ERROR:: Could not find a local HDF5 installation.
You may need to explicitly state where your local HDF5 headers and
library can be found by setting the ``HDF5_DIR`` environment
variable or by using the ``--hdf5`` command-line option.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55167 | 2023-09-16T15:12:42Z | 2024-02-28T17:56:57Z | null | 2024-02-28T17:56:57Z |
DOC: Added alternative examples for deprecated DataFrame.last() | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 5c303e2a73bd7..9ffeefa94213b 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9660,7 +9660,8 @@ def last(self, offset) -> Self:
Notes
-----
.. deprecated:: 2.1.0
- Please create a mask and filter using `.loc` instead
+ Please create a mask and filter using `.loc` instead.
+ See examples for both cases.
Examples
--------
@@ -9680,6 +9681,19 @@ def last(self, offset) -> Self:
2018-04-13 3
2018-04-15 4
+ This is an alternative example. If you are not sure the index is sorted.
+ >>> ts[ts.index >= ts.index.max() - pd.Timedelta(days=3)] # doctest: +SKIP
+ A
+ 2018-04-13 3
+ 2018-04-15 4
+
+ This is a faster alternative example. If you are sure the index is sorted.
+ >>> ts.loc[ts.index.max() - pd.Timedelta(days=3):] # doctest: +SKIP
+ A
+ 2018-04-13 3
+ 2018-04-15 4
+
+
Notice the data for 3 last calendar days were returned, not the last
3 observed days in the dataset, and therefore data for 2018-04-11 was
not returned.
| - [ ] 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/55166 | 2023-09-16T15:01:44Z | 2023-11-07T00:57:54Z | null | 2023-11-07T00:57:54Z |
DOC: remove duplicated sentence in return secion of pandas.DataFrame.agg /asof docstrings | diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 5c303e2a73bd7..bbaa6d3faeb05 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -8323,8 +8323,6 @@ def asof(self, where, subset=None):
* DataFrame : when `self` is a DataFrame and `where` is an
array-like
- Return scalar, Series, or DataFrame.
-
See Also
--------
merge_asof : Perform an asof merge. Similar to left join.
diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py
index 9da103e13f691..ec219941a3afc 100644
--- a/pandas/core/shared_docs.py
+++ b/pandas/core/shared_docs.py
@@ -34,8 +34,6 @@
* scalar : when Series.agg is called with single function
* Series : when DataFrame.agg is called with a single function
* DataFrame : when DataFrame.agg is called with several functions
-
- Return scalar, Series or DataFrame.
{see_also}
Notes
-----
| - [ ] 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/55165 | 2023-09-16T14:12:31Z | 2023-09-17T12:48:09Z | 2023-09-17T12:48:09Z | 2023-09-17T12:48:09Z |
DOC: fix the dev version label in the version dropdown | diff --git a/doc/source/conf.py b/doc/source/conf.py
index 86d2494707ce2..65ed3c1a63410 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -172,7 +172,8 @@
import pandas # isort:skip
# version = '%s r%s' % (pandas.__version__, svn_version())
-version = str(pandas.__version__)
+# version = str(pandas.__version__)
+version = str(pandas.__version__).split("+")[0]
# The full version, including alpha/beta/rc tags.
release = version
@@ -231,7 +232,7 @@
# documentation.
switcher_version = version
-if ".dev" in version:
+if "dev" in version:
switcher_version = "dev"
elif "rc" in version:
switcher_version = version.split("rc", maxsplit=1)[0] + " (rc)"
| Fixing the version in dev distributions
- [X] closes #55160 | https://api.github.com/repos/pandas-dev/pandas/pulls/55163 | 2023-09-16T14:02:58Z | 2023-10-07T15:23:09Z | null | 2023-10-07T15:23:09Z |
DOC: fix see also links in pandas.DataFrame.agg docstring | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8fcb91c846826..124c21839afe7 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -9829,11 +9829,11 @@ def _gotitem(
--------
DataFrame.apply : Perform any type of operations.
DataFrame.transform : Perform transformation type operations.
- pandas.core.groupby.GroupBy : Perform operations over groups.
- pandas.core.resample.Resampler : Perform operations over resampled bins.
- pandas.core.window.Rolling : Perform operations over rolling window.
- pandas.core.window.Expanding : Perform operations over expanding window.
- pandas.core.window.ExponentialMovingWindow : Perform operation over exponential
+ pandas.DataFrame.groupby : Perform operations over groups.
+ pandas.DataFrame.resample : Perform operations over resampled bins.
+ pandas.DataFrame.rolling : Perform operations over rolling window.
+ pandas.DataFrame.expanding : Perform operations over expanding window.
+ pandas.core.window.ewm.ExponentialMovingWindow : Perform operation over exponential
weighted window.
"""
)
| - [ ] 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/55162 | 2023-09-16T13:50:36Z | 2023-09-17T12:45:26Z | 2023-09-17T12:45:26Z | 2023-09-17T12:45:26Z |
Backport PR #55159 on branch 2.1.x (DOCS: temporary pin pydata-sphinx-theme to 0.13) | diff --git a/environment.yml b/environment.yml
index d194b11df7195..447abd22e29b4 100644
--- a/environment.yml
+++ b/environment.yml
@@ -84,7 +84,7 @@ dependencies:
- google-auth
- natsort # DataFrame.sort_values doctest
- numpydoc
- - pydata-sphinx-theme
+ - pydata-sphinx-theme=0.13
- pytest-cython # doctest
- sphinx
- sphinx-design
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 0177016806b0e..15a8633d09039 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -59,7 +59,7 @@ gitdb
google-auth
natsort
numpydoc
-pydata-sphinx-theme
+pydata-sphinx-theme==0.13
pytest-cython
sphinx
sphinx-design
| Backport PR #55159: DOCS: temporary pin pydata-sphinx-theme to 0.13 | https://api.github.com/repos/pandas-dev/pandas/pulls/55161 | 2023-09-16T13:47:39Z | 2023-09-16T16:47:14Z | 2023-09-16T16:47:14Z | 2023-09-16T16:47:15Z |
DOCS: temporary pin pydata-sphinx-theme to 0.13 | diff --git a/environment.yml b/environment.yml
index 8103ee29c84a7..5caa57ef37ee8 100644
--- a/environment.yml
+++ b/environment.yml
@@ -85,7 +85,7 @@ dependencies:
- google-auth
- natsort # DataFrame.sort_values doctest
- numpydoc
- - pydata-sphinx-theme
+ - pydata-sphinx-theme=0.13
- pytest-cython # doctest
- sphinx
- sphinx-design
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 77afa9005029c..b8e7e376378c6 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -60,7 +60,7 @@ gitdb
google-auth
natsort
numpydoc
-pydata-sphinx-theme
+pydata-sphinx-theme==0.13
pytest-cython
sphinx
sphinx-design
|
The pydata-sphinx-theme has a new release, and that causes some issues with the top bar:
So we can maybe temporarily pin the theme until that is fixed | https://api.github.com/repos/pandas-dev/pandas/pulls/55159 | 2023-09-16T09:26:33Z | 2023-09-16T13:46:33Z | 2023-09-16T13:46:33Z | 2023-09-16T20:34:45Z |
Update __init__.py | diff --git a/pandas/api/types/__init__.py b/pandas/api/types/__init__.py
index c601086bb9f86..d30a4fa8f9a86 100644
--- a/pandas/api/types/__init__.py
+++ b/pandas/api/types/__init__.py
@@ -3,15 +3,14 @@
"""
from pandas._libs.lib import infer_dtype
-
-from pandas.core.dtypes.api import * # noqa: F403
-from pandas.core.dtypes.concat import union_categoricals
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
+from pandas.core.dtypes.concat import union_categoricals
+from pandas.core.dtypes.api import * # noqa: F403
__all__ = [
"infer_dtype",
| - [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [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/55157 | 2023-09-16T05:09:50Z | 2023-09-18T16:29:55Z | null | 2023-09-18T16:29:56Z |
CLN: de-duplicate code in _matplotlib.converter | diff --git a/pandas/_libs/tslibs/dtypes.pyx b/pandas/_libs/tslibs/dtypes.pyx
index bafde9f3b237b..f0f73d242cdf0 100644
--- a/pandas/_libs/tslibs/dtypes.pyx
+++ b/pandas/_libs/tslibs/dtypes.pyx
@@ -460,7 +460,6 @@ cdef NPY_DATETIMEUNIT freq_group_code_to_npy_unit(int freq) noexcept nogil:
return NPY_DATETIMEUNIT.NPY_FR_D
-# TODO: use in _matplotlib.converter?
cpdef int64_t periods_per_day(
NPY_DATETIMEUNIT reso=NPY_DATETIMEUNIT.NPY_FR_ns
) except? -1:
diff --git a/pandas/plotting/_matplotlib/converter.py b/pandas/plotting/_matplotlib/converter.py
index 33aeaa6d81406..3f77a32014bff 100644
--- a/pandas/plotting/_matplotlib/converter.py
+++ b/pandas/plotting/_matplotlib/converter.py
@@ -11,7 +11,6 @@
from typing import (
TYPE_CHECKING,
Any,
- Final,
cast,
)
@@ -30,8 +29,14 @@
Timestamp,
to_offset,
)
-from pandas._libs.tslibs.dtypes import FreqGroup
-from pandas._typing import F
+from pandas._libs.tslibs.dtypes import (
+ FreqGroup,
+ periods_per_day,
+)
+from pandas._typing import (
+ F,
+ npt,
+)
from pandas.core.dtypes.common import (
is_float,
@@ -60,15 +65,6 @@
from pandas._libs.tslibs.offsets import BaseOffset
-# constants
-HOURS_PER_DAY: Final = 24.0
-MIN_PER_HOUR: Final = 60.0
-SEC_PER_MIN: Final = 60.0
-
-SEC_PER_HOUR: Final = SEC_PER_MIN * MIN_PER_HOUR
-SEC_PER_DAY: Final = SEC_PER_HOUR * HOURS_PER_DAY
-
-MUSEC_PER_DAY: Final = 10**6 * SEC_PER_DAY
_mpl_units = {} # Cache for units overwritten by us
@@ -495,7 +491,7 @@ def _get_default_annual_spacing(nyears) -> tuple[int, int]:
return (min_spacing, maj_spacing)
-def period_break(dates: PeriodIndex, period: str) -> np.ndarray:
+def _period_break(dates: PeriodIndex, period: str) -> npt.NDArray[np.intp]:
"""
Returns the indices where the given period changes.
@@ -506,12 +502,17 @@ def period_break(dates: PeriodIndex, period: str) -> np.ndarray:
period : str
Name of the period to monitor.
"""
+ mask = _period_break_mask(dates, period)
+ return np.nonzero(mask)[0]
+
+
+def _period_break_mask(dates: PeriodIndex, period: str) -> npt.NDArray[np.bool_]:
current = getattr(dates, period)
previous = getattr(dates - 1 * dates.freq, period)
- return np.nonzero(current - previous)[0]
+ return current != previous
-def has_level_label(label_flags: np.ndarray, vmin: float) -> bool:
+def has_level_label(label_flags: npt.NDArray[np.intp], vmin: float) -> bool:
"""
Returns true if the ``label_flags`` indicate there is at least one label
for this level.
@@ -527,54 +528,59 @@ def has_level_label(label_flags: np.ndarray, vmin: float) -> bool:
return True
-def _daily_finder(vmin, vmax, freq: BaseOffset):
+def _get_periods_per_ymd(freq: BaseOffset) -> tuple[int, int, int]:
# error: "BaseOffset" has no attribute "_period_dtype_code"
dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
freq_group = FreqGroup.from_period_dtype_code(dtype_code)
- periodsperday = -1
+ ppd = -1 # placeholder for above-day freqs
if dtype_code >= FreqGroup.FR_HR.value:
- if freq_group == FreqGroup.FR_NS:
- periodsperday = 24 * 60 * 60 * 1000000000
- elif freq_group == FreqGroup.FR_US:
- periodsperday = 24 * 60 * 60 * 1000000
- elif freq_group == FreqGroup.FR_MS:
- periodsperday = 24 * 60 * 60 * 1000
- elif freq_group == FreqGroup.FR_SEC:
- periodsperday = 24 * 60 * 60
- elif freq_group == FreqGroup.FR_MIN:
- periodsperday = 24 * 60
- elif freq_group == FreqGroup.FR_HR:
- periodsperday = 24
- else: # pragma: no cover
- raise ValueError(f"unexpected frequency: {dtype_code}")
- periodsperyear = 365 * periodsperday
- periodspermonth = 28 * periodsperday
-
+ # error: "BaseOffset" has no attribute "_creso"
+ ppd = periods_per_day(freq._creso) # type: ignore[attr-defined]
+ ppm = 28 * ppd
+ ppy = 365 * ppd
elif freq_group == FreqGroup.FR_BUS:
- periodsperyear = 261
- periodspermonth = 19
+ ppm = 19
+ ppy = 261
elif freq_group == FreqGroup.FR_DAY:
- periodsperyear = 365
- periodspermonth = 28
+ ppm = 28
+ ppy = 365
elif freq_group == FreqGroup.FR_WK:
- periodsperyear = 52
- periodspermonth = 3
- else: # pragma: no cover
- raise ValueError("unexpected frequency")
+ ppm = 3
+ ppy = 52
+ elif freq_group == FreqGroup.FR_MTH:
+ ppm = 1
+ ppy = 12
+ elif freq_group == FreqGroup.FR_QTR:
+ ppm = -1 # placerholder
+ ppy = 4
+ elif freq_group == FreqGroup.FR_ANN:
+ ppm = -1 # placeholder
+ ppy = 1
+ else:
+ raise NotImplementedError(f"Unsupported frequency: {dtype_code}")
+
+ return ppd, ppm, ppy
+
+
+def _daily_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
+ # error: "BaseOffset" has no attribute "_period_dtype_code"
+ dtype_code = freq._period_dtype_code # type: ignore[attr-defined]
+
+ periodsperday, periodspermonth, periodsperyear = _get_periods_per_ymd(freq)
# save this for later usage
vmin_orig = vmin
+ (vmin, vmax) = (int(vmin), int(vmax))
+ span = vmax - vmin + 1
- (vmin, vmax) = (
- Period(ordinal=int(vmin), freq=freq),
- Period(ordinal=int(vmax), freq=freq),
+ dates_ = period_range(
+ start=Period(ordinal=vmin, freq=freq),
+ end=Period(ordinal=vmax, freq=freq),
+ freq=freq,
)
- assert isinstance(vmin, Period)
- assert isinstance(vmax, Period)
- span = vmax.ordinal - vmin.ordinal + 1
- dates_ = period_range(start=vmin, end=vmax, freq=freq)
+
# Initialize the output
info = np.zeros(
span, dtype=[("val", np.int64), ("maj", bool), ("min", bool), ("fmt", "|S20")]
@@ -595,45 +601,38 @@ def first_label(label_flags):
# Case 1. Less than a month
if span <= periodspermonth:
- day_start = period_break(dates_, "day")
- month_start = period_break(dates_, "month")
+ day_start = _period_break(dates_, "day")
+ month_start = _period_break(dates_, "month")
+ year_start = _period_break(dates_, "year")
- def _hour_finder(label_interval, force_year_start) -> None:
- _hour = dates_.hour
- _prev_hour = (dates_ - 1 * dates_.freq).hour
- hour_start = (_hour - _prev_hour) != 0
+ def _hour_finder(label_interval: int, force_year_start: bool) -> None:
+ target = dates_.hour
+ mask = _period_break_mask(dates_, "hour")
info_maj[day_start] = True
- info_min[hour_start & (_hour % label_interval == 0)] = True
- year_start = period_break(dates_, "year")
- info_fmt[hour_start & (_hour % label_interval == 0)] = "%H:%M"
+ info_min[mask & (target % label_interval == 0)] = True
+ info_fmt[mask & (target % label_interval == 0)] = "%H:%M"
info_fmt[day_start] = "%H:%M\n%d-%b"
info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
if force_year_start and not has_level_label(year_start, vmin_orig):
info_fmt[first_label(day_start)] = "%H:%M\n%d-%b\n%Y"
- def _minute_finder(label_interval) -> None:
- hour_start = period_break(dates_, "hour")
- _minute = dates_.minute
- _prev_minute = (dates_ - 1 * dates_.freq).minute
- minute_start = (_minute - _prev_minute) != 0
+ def _minute_finder(label_interval: int) -> None:
+ target = dates_.minute
+ hour_start = _period_break(dates_, "hour")
+ mask = _period_break_mask(dates_, "minute")
info_maj[hour_start] = True
- info_min[minute_start & (_minute % label_interval == 0)] = True
- year_start = period_break(dates_, "year")
- info_fmt = info["fmt"]
- info_fmt[minute_start & (_minute % label_interval == 0)] = "%H:%M"
+ info_min[mask & (target % label_interval == 0)] = True
+ info_fmt[mask & (target % label_interval == 0)] = "%H:%M"
info_fmt[day_start] = "%H:%M\n%d-%b"
info_fmt[year_start] = "%H:%M\n%d-%b\n%Y"
- def _second_finder(label_interval) -> None:
- minute_start = period_break(dates_, "minute")
- _second = dates_.second
- _prev_second = (dates_ - 1 * dates_.freq).second
- second_start = (_second - _prev_second) != 0
- info["maj"][minute_start] = True
- info["min"][second_start & (_second % label_interval == 0)] = True
- year_start = period_break(dates_, "year")
- info_fmt = info["fmt"]
- info_fmt[second_start & (_second % label_interval == 0)] = "%H:%M:%S"
+ def _second_finder(label_interval: int) -> None:
+ target = dates_.second
+ minute_start = _period_break(dates_, "minute")
+ mask = _period_break_mask(dates_, "second")
+ info_maj[minute_start] = True
+ info_min[mask & (target % label_interval == 0)] = True
+ info_fmt[mask & (target % label_interval == 0)] = "%H:%M:%S"
info_fmt[day_start] = "%H:%M:%S\n%d-%b"
info_fmt[year_start] = "%H:%M:%S\n%d-%b\n%Y"
@@ -672,8 +671,6 @@ def _second_finder(label_interval) -> None:
else:
info_maj[month_start] = True
info_min[day_start] = True
- year_start = period_break(dates_, "year")
- info_fmt = info["fmt"]
info_fmt[day_start] = "%d"
info_fmt[month_start] = "%d\n%b"
info_fmt[year_start] = "%d\n%b\n%Y"
@@ -685,15 +682,15 @@ def _second_finder(label_interval) -> None:
# Case 2. Less than three months
elif span <= periodsperyear // 4:
- month_start = period_break(dates_, "month")
+ month_start = _period_break(dates_, "month")
info_maj[month_start] = True
if dtype_code < FreqGroup.FR_HR.value:
info["min"] = True
else:
- day_start = period_break(dates_, "day")
+ day_start = _period_break(dates_, "day")
info["min"][day_start] = True
- week_start = period_break(dates_, "week")
- year_start = period_break(dates_, "year")
+ week_start = _period_break(dates_, "week")
+ year_start = _period_break(dates_, "year")
info_fmt[week_start] = "%d"
info_fmt[month_start] = "\n\n%b"
info_fmt[year_start] = "\n\n%b\n%Y"
@@ -704,9 +701,9 @@ def _second_finder(label_interval) -> None:
info_fmt[first_label(month_start)] = "\n\n%b\n%Y"
# Case 3. Less than 14 months ...............
elif span <= 1.15 * periodsperyear:
- year_start = period_break(dates_, "year")
- month_start = period_break(dates_, "month")
- week_start = period_break(dates_, "week")
+ year_start = _period_break(dates_, "year")
+ month_start = _period_break(dates_, "month")
+ week_start = _period_break(dates_, "week")
info_maj[month_start] = True
info_min[week_start] = True
info_min[year_start] = False
@@ -717,17 +714,17 @@ def _second_finder(label_interval) -> None:
info_fmt[first_label(month_start)] = "%b\n%Y"
# Case 4. Less than 2.5 years ...............
elif span <= 2.5 * periodsperyear:
- year_start = period_break(dates_, "year")
- quarter_start = period_break(dates_, "quarter")
- month_start = period_break(dates_, "month")
+ year_start = _period_break(dates_, "year")
+ quarter_start = _period_break(dates_, "quarter")
+ month_start = _period_break(dates_, "month")
info_maj[quarter_start] = True
info_min[month_start] = True
info_fmt[quarter_start] = "%b"
info_fmt[year_start] = "%b\n%Y"
# Case 4. Less than 4 years .................
elif span <= 4 * periodsperyear:
- year_start = period_break(dates_, "year")
- month_start = period_break(dates_, "month")
+ year_start = _period_break(dates_, "year")
+ month_start = _period_break(dates_, "month")
info_maj[year_start] = True
info_min[month_start] = True
info_min[year_start] = False
@@ -738,15 +735,15 @@ def _second_finder(label_interval) -> None:
info_fmt[year_start] = "%b\n%Y"
# Case 5. Less than 11 years ................
elif span <= 11 * periodsperyear:
- year_start = period_break(dates_, "year")
- quarter_start = period_break(dates_, "quarter")
+ year_start = _period_break(dates_, "year")
+ quarter_start = _period_break(dates_, "quarter")
info_maj[year_start] = True
info_min[quarter_start] = True
info_min[year_start] = False
info_fmt[year_start] = "%Y"
# Case 6. More than 12 years ................
else:
- year_start = period_break(dates_, "year")
+ year_start = _period_break(dates_, "year")
year_break = dates_[year_start].year
nyears = span / periodsperyear
(min_anndef, maj_anndef) = _get_default_annual_spacing(nyears)
@@ -759,8 +756,8 @@ def _second_finder(label_interval) -> None:
return info
-def _monthly_finder(vmin, vmax, freq):
- periodsperyear = 12
+def _monthly_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
+ _, _, periodsperyear = _get_periods_per_ymd(freq)
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
@@ -795,6 +792,7 @@ def _monthly_finder(vmin, vmax, freq):
quarter_start = (dates_ % 3 == 0).nonzero()
info_maj[year_start] = True
# TODO: Check the following : is it really info['fmt'] ?
+ # 2023-09-15 this is reached in test_finder_monthly
info["fmt"][quarter_start] = True
info["min"] = True
@@ -829,8 +827,8 @@ def _monthly_finder(vmin, vmax, freq):
return info
-def _quarterly_finder(vmin, vmax, freq):
- periodsperyear = 4
+def _quarterly_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
+ _, _, periodsperyear = _get_periods_per_ymd(freq)
vmin_orig = vmin
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
@@ -876,7 +874,8 @@ def _quarterly_finder(vmin, vmax, freq):
return info
-def _annual_finder(vmin, vmax, freq):
+def _annual_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
+ # Note: small difference here vs other finders in adding 1 to vmax
(vmin, vmax) = (int(vmin), int(vmax + 1))
span = vmax - vmin + 1
@@ -889,8 +888,9 @@ def _annual_finder(vmin, vmax, freq):
(min_anndef, maj_anndef) = _get_default_annual_spacing(span)
major_idx = dates_ % maj_anndef == 0
+ minor_idx = dates_ % min_anndef == 0
info["maj"][major_idx] = True
- info["min"][(dates_ % min_anndef == 0)] = True
+ info["min"][minor_idx] = True
info["fmt"][major_idx] = "%Y"
return info
@@ -1087,7 +1087,7 @@ def format_timedelta_ticks(x, pos, n_decimals: int) -> str:
"""
Convert seconds to 'D days HH:MM:SS.F'
"""
- s, ns = divmod(x, 10**9)
+ s, ns = divmod(x, 10**9) # TODO(non-nano): this looks like it assumes ns
m, s = divmod(s, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
diff --git a/pandas/tests/plotting/test_converter.py b/pandas/tests/plotting/test_converter.py
index 0108079f1110f..7d574b86cef36 100644
--- a/pandas/tests/plotting/test_converter.py
+++ b/pandas/tests/plotting/test_converter.py
@@ -10,6 +10,8 @@
import pandas._config.config as cf
+from pandas._libs.tslibs import to_offset
+
from pandas import (
Index,
Period,
@@ -390,7 +392,7 @@ def test_quarterly_finder(year_span):
pytest.skip("the quarterly finder is only invoked if the span is >= 45")
nyears = span / 4
(min_anndef, maj_anndef) = converter._get_default_annual_spacing(nyears)
- result = converter._quarterly_finder(vmin, vmax, "Q")
+ result = converter._quarterly_finder(vmin, vmax, to_offset("Q"))
quarters = PeriodIndex(
arrays.PeriodArray(np.array([x[0] for x in result]), dtype="period[Q]")
)
| - [ ] 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/55155 | 2023-09-16T02:30:56Z | 2023-09-18T16:32:27Z | 2023-09-18T16:32:27Z | 2023-09-18T20:41:46Z |
Backport PR #54784 on branch 2.1.x (CI: Unpin Cython) | diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index 6410f2edd6175..1445e5e822017 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -233,8 +233,8 @@ jobs:
run: |
/opt/python/cp311-cp311/bin/python -m venv ~/virtualenvs/pandas-dev
. ~/virtualenvs/pandas-dev/bin/activate
- python -m pip install --no-cache-dir -U pip wheel setuptools meson[ninja]==1.0.1 meson-python==0.13.1
- python -m pip install --no-cache-dir versioneer[toml] "cython<3.0.1" numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+ python -m pip install --no-cache-dir -U pip wheel setuptools meson[ninja]==1.2.1 meson-python==0.13.1
+ python -m pip install --no-cache-dir versioneer[toml] cython numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
python -m pip install --no-cache-dir --no-build-isolation -e .
python -m pip list --no-cache-dir
export PANDAS_CI=1
@@ -271,8 +271,8 @@ jobs:
run: |
/opt/python/cp311-cp311/bin/python -m venv ~/virtualenvs/pandas-dev
. ~/virtualenvs/pandas-dev/bin/activate
- python -m pip install --no-cache-dir -U pip wheel setuptools meson-python==0.13.1 meson[ninja]==1.0.1
- python -m pip install --no-cache-dir versioneer[toml] "cython<3.0.1" numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
+ python -m pip install --no-cache-dir -U pip wheel setuptools meson-python==0.13.1 meson[ninja]==1.2.1
+ python -m pip install --no-cache-dir versioneer[toml] cython numpy python-dateutil pytz pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17 hypothesis>=6.46.1
python -m pip install --no-cache-dir --no-build-isolation -e .
python -m pip list --no-cache-dir
@@ -342,10 +342,10 @@ jobs:
- name: Build Environment
run: |
python --version
- python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.0.1 meson-python==0.13.1
+ python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.2.1 meson-python==0.13.1
python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
python -m pip install versioneer[toml]
- python -m pip install python-dateutil pytz tzdata "cython<3.0.1" hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
+ python -m pip install python-dateutil pytz tzdata cython hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
python -m pip install -ve . --no-build-isolation --no-index
python -m pip list
diff --git a/ci/deps/actions-310.yaml b/ci/deps/actions-310.yaml
index 2190136220c6c..edd48604dbfda 100644
--- a/ci/deps/actions-310.yaml
+++ b/ci/deps/actions-310.yaml
@@ -6,8 +6,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-311-downstream_compat.yaml b/ci/deps/actions-311-downstream_compat.yaml
index cf85345cb0cc2..2fb6072314b1e 100644
--- a/ci/deps/actions-311-downstream_compat.yaml
+++ b/ci/deps/actions-311-downstream_compat.yaml
@@ -7,8 +7,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-311-numpydev.yaml b/ci/deps/actions-311-numpydev.yaml
index acf94fce5288b..7fd3a65ec91f8 100644
--- a/ci/deps/actions-311-numpydev.yaml
+++ b/ci/deps/actions-311-numpydev.yaml
@@ -6,8 +6,9 @@ dependencies:
# build dependencies
- versioneer[toml]
- - meson[ninja]=1.0.1
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
+ - cython>=0.29.33
# test dependencies
- pytest>=7.3.2
@@ -25,7 +26,6 @@ dependencies:
- pip
- pip:
- - "cython<3.0.1"
- "--extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple"
- "--pre"
- "numpy"
diff --git a/ci/deps/actions-311-pyarrownightly.yaml b/ci/deps/actions-311-pyarrownightly.yaml
index e850f69f945c5..893341350f4ef 100644
--- a/ci/deps/actions-311-pyarrownightly.yaml
+++ b/ci/deps/actions-311-pyarrownightly.yaml
@@ -6,8 +6,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - meson[ninja]=1.0.1
- - cython>=0.29.33, <3.0.1
+ - meson[ninja]=1.2.1
+ - cython>=0.29.33
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-311.yaml b/ci/deps/actions-311.yaml
index 3c1630714a041..029312202bcaa 100644
--- a/ci/deps/actions-311.yaml
+++ b/ci/deps/actions-311.yaml
@@ -6,8 +6,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-39-minimum_versions.yaml b/ci/deps/actions-39-minimum_versions.yaml
index b1cea49e22d15..2a7312cefe619 100644
--- a/ci/deps/actions-39-minimum_versions.yaml
+++ b/ci/deps/actions-39-minimum_versions.yaml
@@ -8,8 +8,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-39.yaml b/ci/deps/actions-39.yaml
index b8a119ece4b03..12893698b5534 100644
--- a/ci/deps/actions-39.yaml
+++ b/ci/deps/actions-39.yaml
@@ -6,8 +6,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/actions-pypy-39.yaml b/ci/deps/actions-pypy-39.yaml
index 23c3706f43dad..4923c94ab08f3 100644
--- a/ci/deps/actions-pypy-39.yaml
+++ b/ci/deps/actions-pypy-39.yaml
@@ -9,8 +9,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/ci/deps/circle-310-arm64.yaml b/ci/deps/circle-310-arm64.yaml
index 71686837451b4..a945dbfe2ca20 100644
--- a/ci/deps/circle-310-arm64.yaml
+++ b/ci/deps/circle-310-arm64.yaml
@@ -6,8 +6,8 @@ dependencies:
# build dependencies
- versioneer[toml]
- - cython>=0.29.33, <3.0.1
- - meson[ninja]=1.0.1
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/environment.yml b/environment.yml
index 1a9dffb55bca7..d194b11df7195 100644
--- a/environment.yml
+++ b/environment.yml
@@ -9,7 +9,7 @@ dependencies:
# build dependencies
- versioneer[toml]
- cython=0.29.33
- - meson[ninja]=1.0.1
+ - meson[ninja]=1.2.1
- meson-python=0.13.1
# test dependencies
diff --git a/meson.build b/meson.build
index e0e533ffade97..68018046c081f 100644
--- a/meson.build
+++ b/meson.build
@@ -4,7 +4,7 @@ project(
'c', 'cpp', 'cython',
version: run_command(['generate_version.py', '--print'], check: true).stdout().strip(),
license: 'BSD-3',
- meson_version: '>=1.0.1',
+ meson_version: '>=1.2.1',
default_options: [
'buildtype=release',
'c_std=c99'
diff --git a/pyproject.toml b/pyproject.toml
index 752ee846eed33..cfa767f808827 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@
# See https://github.com/scipy/scipy/pull/12940 for the AIX issue.
requires = [
"meson-python==0.13.1",
- "meson==1.0.1",
+ "meson==1.2.1",
"wheel",
"Cython>=0.29.33,<3", # Note: sync with setup.py, environment.yml and asv.conf.json
# Note: numpy 1.25 has a backwards compatible C API by default
diff --git a/requirements-dev.txt b/requirements-dev.txt
index be02007a36333..0177016806b0e 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -4,7 +4,7 @@
pip
versioneer[toml]
cython==0.29.33
-meson[ninja]==1.0.1
+meson[ninja]==1.2.1
meson-python==0.13.1
pytest>=7.3.2
pytest-cov
| Backport PR #54784: CI: Unpin Cython | https://api.github.com/repos/pandas-dev/pandas/pulls/55154 | 2023-09-16T02:04:25Z | 2023-09-16T13:46:48Z | 2023-09-16T13:46:48Z | 2023-09-16T13:46:49Z |
BUG: Fix weighted rolling variance implementation | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 0fc4afc95a2ce..dde7f0e2cf828 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -222,6 +222,7 @@ 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.core.window.Window.var` and :meth:`pandas.core.window.Window.std` where implementation of the algorithm was incorrect (:issue:`53273` and :issue:`54333`)
Categorical
^^^^^^^^^^^
diff --git a/pandas/_libs/window/aggregations.pyx b/pandas/_libs/window/aggregations.pyx
index 6365c030b695b..6617945fee768 100644
--- a/pandas/_libs/window/aggregations.pyx
+++ b/pandas/_libs/window/aggregations.pyx
@@ -1558,7 +1558,7 @@ cdef float64_t calc_weighted_var(float64_t t,
if nobs == 1:
result = 0
else:
- result = t * win_n / ((win_n - ddof) * sum_w)
+ result = t * nobs / ((nobs - ddof) * sum_w)
if result < 0:
result = 0
else:
@@ -1599,68 +1599,21 @@ cdef void add_weighted_var(float64_t val,
cdef:
float64_t temp, q, r
- if val != val:
- return
-
nobs[0] = nobs[0] + 1
- q = val - mean[0]
- temp = sum_w[0] + w
- r = q * w / temp
-
- mean[0] = mean[0] + r
- t[0] = t[0] + r * sum_w[0] * q
- sum_w[0] = temp
-
-
-cdef void remove_weighted_var(float64_t val,
- float64_t w,
- float64_t *t,
- float64_t *sum_w,
- float64_t *mean,
- float64_t *nobs) noexcept nogil:
- """
- Update weighted mean, sum of weights and sum of weighted squared
- differences to remove value and weight pair from weighted variance
- calculation using West's method.
-
- Paper: https://dl.acm.org/citation.cfm?id=359153
-
- Parameters
- ----------
- val: float64_t
- window values
- w: float64_t
- window weights
- t: float64_t
- sum of weighted squared differences
- sum_w: float64_t
- sum of weights
- mean: float64_t
- weighted mean
- nobs: float64_t
- number of observations
- """
-
- cdef:
- float64_t temp, q, r
-
- if val == val:
- nobs[0] = nobs[0] - 1
-
- if nobs[0]:
- q = val - mean[0]
- temp = sum_w[0] - w
- r = q * w / temp
+ if nobs[0] == 1:
+ sum_w[0] = w
+ mean[0] = val
+ t[0] = 0
- mean[0] = mean[0] - r
- t[0] = t[0] - r * sum_w[0] * q
- sum_w[0] = temp
+ else:
+ q = val - mean[0]
+ temp = sum_w[0] + w
+ r = q * w / temp
- else:
- t[0] = 0
- sum_w[0] = 0
- mean[0] = 0
+ mean[0] = mean[0] + r
+ t[0] = t[0] + r * sum_w[0] * q
+ sum_w[0] = temp
def roll_weighted_var(const float64_t[:] values, const float64_t[:] weights,
@@ -1690,44 +1643,26 @@ def roll_weighted_var(const float64_t[:] values, const float64_t[:] weights,
"""
cdef:
- float64_t t = 0, sum_w = 0, mean = 0, nobs = 0
- float64_t val, pre_val, w, pre_w
- Py_ssize_t i, n, win_n
+ float64_t t, sum_w, mean, nobs
+ float64_t val, w
+ Py_ssize_t in_i, win_i, n, win_n
float64_t[:] output
n = len(values)
win_n = len(weights)
- output = np.empty(n, dtype=np.float64)
-
- with nogil:
-
- for i in range(min(win_n, n)):
- add_weighted_var(values[i], weights[i], &t,
- &sum_w, &mean, &nobs)
- output[i] = calc_weighted_var(t, sum_w, win_n,
- ddof, nobs, minp)
-
- for i in range(win_n, n):
- val = values[i]
- pre_val = values[i - win_n]
+ output = np.empty(n, dtype=np.float64)
- w = weights[i % win_n]
- pre_w = weights[(i - win_n) % win_n]
-
- if val == val:
- if pre_val == pre_val:
- remove_weighted_var(pre_val, pre_w, &t,
- &sum_w, &mean, &nobs)
+ for in_i in range(n):
+ nobs = 0
+ for win_i in range(max(0, win_n - in_i - 1), win_n):
+ w = weights[win_i]
+ val = values[win_i - win_n + in_i + 1]
+ if val == val and w == w:
add_weighted_var(val, w, &t, &sum_w, &mean, &nobs)
- elif pre_val == pre_val:
- remove_weighted_var(pre_val, pre_w, &t,
- &sum_w, &mean, &nobs)
-
- output[i] = calc_weighted_var(t, sum_w, win_n,
- ddof, nobs, minp)
+ output[in_i] = calc_weighted_var(t, sum_w, win_n, ddof, nobs, minp)
return output
diff --git a/pandas/tests/window/test_win_type.py b/pandas/tests/window/test_win_type.py
index 5052019ddb726..d325db0650c68 100644
--- a/pandas/tests/window/test_win_type.py
+++ b/pandas/tests/window/test_win_type.py
@@ -661,6 +661,23 @@ def test_cmov_window_special_linear_range(win_types_special, step):
tm.assert_series_equal(xp, rs)
+@pytest.mark.parametrize("size", [10, 100, 1000])
+@pytest.mark.parametrize("scale", [0, 0.1, 0.01, 0.001, 0.0001])
+def test_weighted_std_through_mean(win_types, step, scale, size):
+ # GH 53273
+ pytest.importorskip("scipy")
+ window = 5
+ s = Series(np.random.default_rng(0).normal(loc=1, scale=scale, size=size))
+
+ squared_mean = (s**2).rolling(window, win_type=win_types).mean()
+ mean = s.rolling(window, win_type=win_types).mean()
+ expected = (squared_mean - mean**2).pow(0.5)
+
+ result = s.rolling(window, win_type=win_types).std(ddof=0)
+
+ tm.assert_series_equal(expected, result)
+
+
def test_weighted_var_big_window_no_segfault(win_types, center):
# GitHub Issue #46772
pytest.importorskip("scipy")
| - [x] closes #53273, closes #54333
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [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.
---
Outputs from the code snippets in the given issues are below
### Issue #53273
#### Code
```python
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
np.random.seed(42)
df = pd.Series(np.random.normal(size=1200))
df_std_stock = (
df.rolling(window=220, min_periods=100, win_type='exponential')
.std(tau=-(25 / np.log(2)), center=0, sym=False)
)
df_std_custom = (
(df ** 2).rolling(window=220, min_periods=100, win_type='exponential')
.mean(tau=-(25 / np.log(2)), center=0, sym=False)
)
df_std_custom_mean = (
df.rolling(window=220, min_periods=100, win_type='exponential')
.mean(tau=-(25 / np.log(2)), center=0, sym=False)
)
df_std_custom = (df_std_custom - df_std_custom_mean ** 2).pow(0.5)
plt.plot(df_std_stock.index, df_std_stock.values, label="df_std_stock", linewidth=3)
plt.plot(df_std_custom.index, df_std_custom.values, label="df_std_custom", linestyle='--')
plt.legend()
plt.show()
```
#### Output
### Issue #54333
#### Code
```python
import numpy as np
import pandas as pd
from scipy import signal
def equal(win_len: int, **kwargs):
assert win_len == 5
return np.array([1.0, 1.0, 1.0, 1.0, 1.0])
def linear(win_len: int, **kwargs):
assert win_len == 5
return np.array([0.2, 0.4, 0.6, 0.8, 1.0])
signal.windows.equal_weight = equal
signal.windows.linear_decay = linear
s = pd.Series([1.0, 0.0] * 10)
print(list(s))
print(list(s.rolling(window=5).var(ddof=0)))
print(list(s.rolling(window=5, win_type="equal_weight").var(ddof=0)))
print(list(s.rolling(window=5, win_type="linear_decay").var(ddof=0)))
```
#### Output
```
[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]
[nan, nan, nan, nan, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24]
[nan, nan, nan, nan, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24, 0.24]
[nan, nan, nan, nan, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005, 0.24000000000000005]
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55153 | 2023-09-15T21:59:29Z | 2023-12-18T19:34:13Z | null | 2023-12-18T19:34:13Z |
Remove undefined behavior from npy_datetimestruct_to_datetime | diff --git a/asv_bench/benchmarks/tslibs/period.py b/asv_bench/benchmarks/tslibs/period.py
index 67f3b7736018d..af3bfac6d3d01 100644
--- a/asv_bench/benchmarks/tslibs/period.py
+++ b/asv_bench/benchmarks/tslibs/period.py
@@ -151,7 +151,11 @@ def setup(self, size, freq, tz):
# tzlocal is cumbersomely slow, so skip to keep runtime in check
raise NotImplementedError
- arr = np.arange(10, dtype="i8").repeat(size // 10)
+ # we pick 2**55 because smaller values end up returning
+ # -1 from npy_datetimestruct_to_datetime with NPY_FR_Y frequency
+ # this artificially slows down functions since -1 is also the
+ # error sentinel
+ arr = np.arange(2**55, 2**55 + 10, dtype="i8").repeat(size // 10)
self.i8values = arr
def time_dt64arr_to_periodarr(self, size, freq, tz):
diff --git a/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c b/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
index db1c735bd6094..01e11e5138a8e 100644
--- a/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
+++ b/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
@@ -29,6 +29,58 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt
#include <numpy/arrayscalars.h>
#include <numpy/ndarraytypes.h>
+#if defined(_WIN32)
+#ifndef ENABLE_INTSAFE_SIGNED_FUNCTIONS
+#define ENABLE_INTSAFE_SIGNED_FUNCTIONS
+#endif
+#include <intsafe.h>
+#define checked_int64_add(a, b, res) LongLongAdd(a, b, res)
+#define checked_int64_sub(a, b, res) LongLongSub(a, b, res)
+#define checked_int64_mul(a, b, res) LongLongMult(a, b, res)
+#else
+#if defined __has_builtin
+#if __has_builtin(__builtin_add_overflow)
+#if _LP64 || __LP64__ || _ILP64 || __ILP64__
+#define checked_int64_add(a, b, res) __builtin_saddl_overflow(a, b, res)
+#define checked_int64_sub(a, b, res) __builtin_ssubl_overflow(a, b, res)
+#define checked_int64_mul(a, b, res) __builtin_smull_overflow(a, b, res)
+#else
+#define checked_int64_add(a, b, res) __builtin_saddll_overflow(a, b, res)
+#define checked_int64_sub(a, b, res) __builtin_ssubll_overflow(a, b, res)
+#define checked_int64_mul(a, b, res) __builtin_smulll_overflow(a, b, res)
+#endif
+#else
+_Static_assert(0,
+ "Overflow checking not detected; please try a newer compiler");
+#endif
+// __has_builtin was added in gcc 10, but our muslinux_1_1 build environment
+// only has gcc-9.3, so fall back to __GNUC__ macro as long as we have that
+#elif __GNUC__ > 7
+#if _LP64 || __LP64__ || _ILP64 || __ILP64__
+#define checked_int64_add(a, b, res) __builtin_saddl_overflow(a, b, res)
+#define checked_int64_sub(a, b, res) __builtin_ssubl_overflow(a, b, res)
+#define checked_int64_mul(a, b, res) __builtin_smull_overflow(a, b, res)
+#else
+#define checked_int64_add(a, b, res) __builtin_saddll_overflow(a, b, res)
+#define checked_int64_sub(a, b, res) __builtin_ssubll_overflow(a, b, res)
+#define checked_int64_mul(a, b, res) __builtin_smulll_overflow(a, b, res)
+#endif
+#else
+_Static_assert(0, "__has_builtin not detected; please try a newer compiler");
+#endif
+#endif
+
+#define PD_CHECK_OVERFLOW(FUNC) \
+ do { \
+ if ((FUNC) != 0) { \
+ PyGILState_STATE gstate = PyGILState_Ensure(); \
+ PyErr_SetString(PyExc_OverflowError, \
+ "Overflow occurred in npy_datetimestruct_to_datetime"); \
+ PyGILState_Release(gstate); \
+ return -1; \
+ } \
+ } while (0)
+
const int days_per_month_table[2][12] = {
{31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31},
{31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31}};
@@ -299,96 +351,189 @@ PyObject *extract_utc_offset(PyObject *obj) {
return tmp;
}
+static inline int scaleYearToEpoch(int64_t year, int64_t *result) {
+ return checked_int64_sub(year, 1970, result);
+}
+
+static inline int scaleYearsToMonths(int64_t years, int64_t *result) {
+ return checked_int64_mul(years, 12, result);
+}
+
+static inline int scaleDaysToWeeks(int64_t days, int64_t *result) {
+ if (days >= 0) {
+ *result = days / 7;
+ return 0;
+ } else {
+ int res;
+ int64_t checked_days;
+ if ((res = checked_int64_sub(days, 6, &checked_days))) {
+ return res;
+ }
+
+ *result = checked_days / 7;
+ return 0;
+ }
+}
+
+static inline int scaleDaysToHours(int64_t days, int64_t *result) {
+ return checked_int64_mul(days, 24, result);
+}
+
+static inline int scaleHoursToMinutes(int64_t hours, int64_t *result) {
+ return checked_int64_mul(hours, 60, result);
+}
+
+static inline int scaleMinutesToSeconds(int64_t minutes, int64_t *result) {
+ return checked_int64_mul(minutes, 60, result);
+}
+
+static inline int scaleSecondsToMilliseconds(int64_t seconds, int64_t *result) {
+ return checked_int64_mul(seconds, 1000, result);
+}
+
+static inline int scaleSecondsToMicroseconds(int64_t seconds, int64_t *result) {
+ return checked_int64_mul(seconds, 1000000, result);
+}
+
+static inline int scaleMicrosecondsToNanoseconds(int64_t microseconds,
+ int64_t *result) {
+ return checked_int64_mul(microseconds, 1000, result);
+}
+
+static inline int scaleMicrosecondsToPicoseconds(int64_t microseconds,
+ int64_t *result) {
+ return checked_int64_mul(microseconds, 1000000, result);
+}
+
+static inline int64_t scalePicosecondsToFemtoseconds(int64_t picoseconds,
+ int64_t *result) {
+ return checked_int64_mul(picoseconds, 1000, result);
+}
+
+static inline int64_t scalePicosecondsToAttoseconds(int64_t picoseconds,
+ int64_t *result) {
+ return checked_int64_mul(picoseconds, 1000000, result);
+}
+
/*
* Converts a datetime from a datetimestruct to a datetime based
- * on a metadata unit. The date is assumed to be valid.
+ * on a metadata unit. Returns -1 on and sets PyErr on error.
*/
npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base,
const npy_datetimestruct *dts) {
- npy_datetime ret;
-
- if (base == NPY_FR_Y) {
- /* Truncate to the year */
- ret = dts->year - 1970;
- } else if (base == NPY_FR_M) {
- /* Truncate to the month */
- ret = 12 * (dts->year - 1970) + (dts->month - 1);
- } else {
- /* Otherwise calculate the number of days to start */
- npy_int64 days = get_datetimestruct_days(dts);
-
- switch (base) {
- case NPY_FR_W:
- /* Truncate to weeks */
- if (days >= 0) {
- ret = days / 7;
- } else {
- ret = (days - 6) / 7;
- }
- break;
- case NPY_FR_D:
- ret = days;
- break;
- case NPY_FR_h:
- ret = days * 24 + dts->hour;
- break;
- case NPY_FR_m:
- ret = (days * 24 + dts->hour) * 60 + dts->min;
- break;
- case NPY_FR_s:
- ret = ((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec;
- break;
- case NPY_FR_ms:
- ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) * 1000 +
- dts->us / 1000;
- break;
- case NPY_FR_us:
- ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
- 1000000 +
- dts->us;
- break;
- case NPY_FR_ns:
- ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
- 1000000 +
- dts->us) *
- 1000 +
- dts->ps / 1000;
- break;
- case NPY_FR_ps:
- ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps;
- break;
- case NPY_FR_fs:
- /* only 2.6 hours */
- ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps) *
- 1000 +
- dts->as / 1000;
- break;
- case NPY_FR_as:
- /* only 9.2 secs */
- ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps) *
- 1000000 +
- dts->as;
- break;
- default:
- /* Something got corrupted */
- PyErr_SetString(PyExc_ValueError,
- "NumPy datetime metadata with corrupt unit value");
- return -1;
- }
- }
- return ret;
+ if ((base == NPY_FR_Y) || (base == NPY_FR_M)) {
+ int64_t years;
+ PD_CHECK_OVERFLOW(scaleYearToEpoch(dts->year, &years));
+
+ if (base == NPY_FR_Y) {
+ return years;
+ }
+
+ int64_t months;
+ PD_CHECK_OVERFLOW(scaleYearsToMonths(years, &months));
+
+ int64_t months_adder;
+ PD_CHECK_OVERFLOW(checked_int64_sub(dts->month, 1, &months_adder));
+ PD_CHECK_OVERFLOW(checked_int64_add(months, months_adder, &months));
+
+ if (base == NPY_FR_M) {
+ return months;
+ }
+ }
+
+ const int64_t days = get_datetimestruct_days(dts);
+ if (base == NPY_FR_D) {
+ return days;
+ }
+
+ if (base == NPY_FR_W) {
+ int64_t weeks;
+ PD_CHECK_OVERFLOW(scaleDaysToWeeks(days, &weeks));
+ return weeks;
+ }
+
+ int64_t hours;
+ PD_CHECK_OVERFLOW(scaleDaysToHours(days, &hours));
+ PD_CHECK_OVERFLOW(checked_int64_add(hours, dts->hour, &hours));
+
+ if (base == NPY_FR_h) {
+ return hours;
+ }
+
+ int64_t minutes;
+ PD_CHECK_OVERFLOW(scaleHoursToMinutes(hours, &minutes));
+ PD_CHECK_OVERFLOW(checked_int64_add(minutes, dts->min, &minutes));
+
+ if (base == NPY_FR_m) {
+ return minutes;
+ }
+
+ int64_t seconds;
+ PD_CHECK_OVERFLOW(scaleMinutesToSeconds(minutes, &seconds));
+ PD_CHECK_OVERFLOW(checked_int64_add(seconds, dts->sec, &seconds));
+
+ if (base == NPY_FR_s) {
+ return seconds;
+ }
+
+ if (base == NPY_FR_ms) {
+ int64_t milliseconds;
+ PD_CHECK_OVERFLOW(scaleSecondsToMilliseconds(seconds, &milliseconds));
+ PD_CHECK_OVERFLOW(
+ checked_int64_add(milliseconds, dts->us / 1000, &milliseconds));
+
+ return milliseconds;
+ }
+
+ int64_t microseconds;
+ PD_CHECK_OVERFLOW(scaleSecondsToMicroseconds(seconds, µseconds));
+ PD_CHECK_OVERFLOW(checked_int64_add(microseconds, dts->us, µseconds));
+
+ if (base == NPY_FR_us) {
+ return microseconds;
+ }
+
+ if (base == NPY_FR_ns) {
+ int64_t nanoseconds;
+ PD_CHECK_OVERFLOW(
+ scaleMicrosecondsToNanoseconds(microseconds, &nanoseconds));
+ PD_CHECK_OVERFLOW(
+ checked_int64_add(nanoseconds, dts->ps / 1000, &nanoseconds));
+
+ return nanoseconds;
+ }
+
+ int64_t picoseconds;
+ PD_CHECK_OVERFLOW(scaleMicrosecondsToPicoseconds(microseconds, &picoseconds));
+ PD_CHECK_OVERFLOW(checked_int64_add(picoseconds, dts->ps, &picoseconds));
+
+ if (base == NPY_FR_ps) {
+ return picoseconds;
+ }
+
+ if (base == NPY_FR_fs) {
+ int64_t femtoseconds;
+ PD_CHECK_OVERFLOW(
+ scalePicosecondsToFemtoseconds(picoseconds, &femtoseconds));
+ PD_CHECK_OVERFLOW(
+ checked_int64_add(femtoseconds, dts->as / 1000, &femtoseconds));
+ return femtoseconds;
+ }
+
+ if (base == NPY_FR_as) {
+ int64_t attoseconds;
+ PD_CHECK_OVERFLOW(scalePicosecondsToAttoseconds(picoseconds, &attoseconds));
+ PD_CHECK_OVERFLOW(checked_int64_add(attoseconds, dts->as, &attoseconds));
+ return attoseconds;
+ }
+
+ /* Something got corrupted */
+ PyGILState_STATE gstate = PyGILState_Ensure();
+ PyErr_SetString(PyExc_ValueError,
+ "NumPy datetime metadata with corrupt unit value");
+ PyGILState_Release(gstate);
+
+ return -1;
}
/*
diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
index 72faf1757a7b0..f073d099d37ee 100644
--- a/pandas/_libs/tslib.pyx
+++ b/pandas/_libs/tslib.pyx
@@ -34,7 +34,6 @@ cnp.import_array()
from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
NPY_FR_ns,
- check_dts_bounds,
import_pandas_datetime,
npy_datetimestruct,
npy_datetimestruct_to_datetime,
@@ -99,8 +98,10 @@ def _test_parse_iso8601(ts: str):
obj = _TSObject()
string_to_dts(ts, &obj.dts, &out_bestunit, &out_local, &out_tzoffset, True)
- obj.value = npy_datetimestruct_to_datetime(NPY_FR_ns, &obj.dts)
- check_dts_bounds(&obj.dts)
+ try:
+ obj.value = npy_datetimestruct_to_datetime(NPY_FR_ns, &obj.dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(f"Out of bounds nanosecond timestamp: {ts}") from err
if out_local == 1:
obj.tzinfo = timezone(timedelta(minutes=out_tzoffset))
obj.value = tz_localize_to_utc_single(obj.value, obj.tzinfo)
@@ -488,7 +489,6 @@ cpdef array_to_datetime(
elif PyDate_Check(val):
iresult[i] = pydate_to_dt64(val, &dts, reso=creso)
- check_dts_bounds(&dts, creso)
state.found_other = True
elif is_datetime64_object(val):
diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index a2bc6d4d52f2e..4f14782d9efbb 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -212,8 +212,12 @@ cdef int64_t get_datetime64_nanos(object val, NPY_DATETIMEUNIT reso) except? -1:
if unit != reso:
pandas_datetime_to_datetimestruct(ival, unit, &dts)
- check_dts_bounds(&dts, reso)
- ival = npy_datetimestruct_to_datetime(reso, &dts)
+ try:
+ ival = npy_datetimestruct_to_datetime(reso, &dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(
+ "Out of bounds nanosecond timestamp: {val}"
+ ) from err
return ival
@@ -413,14 +417,16 @@ cdef _TSObject convert_datetime_to_tsobject(
if nanos:
obj.dts.ps = nanos * 1000
- obj.value = npy_datetimestruct_to_datetime(reso, &obj.dts)
+ try:
+ obj.value = npy_datetimestruct_to_datetime(reso, &obj.dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime("Out of bounds nanosecond timestamp") from err
if obj.tzinfo is not None and not is_utc(obj.tzinfo):
offset = get_utcoffset(obj.tzinfo, ts)
pps = periods_per_second(reso)
obj.value -= int(offset.total_seconds() * pps)
- check_dts_bounds(&obj.dts, reso)
check_overflows(obj, reso)
return obj
@@ -713,5 +719,4 @@ cdef int64_t parse_pydatetime(
result = (<_Timestamp>val)._as_creso(creso, round_ok=False)._value
else:
result = pydatetime_to_dt64(val, dts, reso=creso)
- check_dts_bounds(dts, creso)
return result
diff --git a/pandas/_libs/tslibs/np_datetime.pxd b/pandas/_libs/tslibs/np_datetime.pxd
index 445b832e8a5bd..9cc211b748f68 100644
--- a/pandas/_libs/tslibs/np_datetime.pxd
+++ b/pandas/_libs/tslibs/np_datetime.pxd
@@ -60,7 +60,7 @@ cdef extern from "pandas/datetime/pd_datetime.h":
npy_datetimestruct *result) nogil
npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT fr,
- npy_datetimestruct *d) nogil
+ npy_datetimestruct *d) except? -1 nogil
void pandas_timedelta_to_timedeltastruct(npy_timedelta val,
NPY_DATETIMEUNIT fr,
@@ -80,15 +80,17 @@ cdef inline void import_pandas_datetime() noexcept:
cdef bint cmp_scalar(int64_t lhs, int64_t rhs, int op) except -1
+cdef str dts_to_iso_string(npy_datetimestruct *dts)
+
cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=?)
cdef int64_t pydatetime_to_dt64(
datetime val, npy_datetimestruct *dts, NPY_DATETIMEUNIT reso=?
-)
+) except? -1
cdef void pydatetime_to_dtstruct(datetime dt, npy_datetimestruct *dts) noexcept
cdef int64_t pydate_to_dt64(
date val, npy_datetimestruct *dts, NPY_DATETIMEUNIT reso=?
-)
+) except? -1
cdef void pydate_to_dtstruct(date val, npy_datetimestruct *dts) noexcept
cdef NPY_DATETIMEUNIT get_datetime64_unit(object obj) noexcept nogil
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 3ffd70f83e88f..71a194177bf82 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -194,6 +194,11 @@ cdef get_implementation_bounds(
raise NotImplementedError(reso)
+cdef str dts_to_iso_string(npy_datetimestruct *dts):
+ return (f"{dts.year}-{dts.month:02d}-{dts.day:02d} "
+ f"{dts.hour:02d}:{dts.min:02d}:{dts.sec:02d}")
+
+
cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=NPY_FR_ns):
"""Raises OutOfBoundsDatetime if the given date is outside the range that
can be represented by nanosecond-resolution 64-bit integers."""
@@ -209,8 +214,7 @@ cdef check_dts_bounds(npy_datetimestruct *dts, NPY_DATETIMEUNIT unit=NPY_FR_ns):
error = True
if error:
- fmt = (f"{dts.year}-{dts.month:02d}-{dts.day:02d} "
- f"{dts.hour:02d}:{dts.min:02d}:{dts.sec:02d}")
+ fmt = dts_to_iso_string(dts)
# TODO: "nanosecond" in the message assumes NPY_FR_ns
raise OutOfBoundsDatetime(f"Out of bounds nanosecond timestamp: {fmt}")
@@ -246,12 +250,20 @@ cdef void pydatetime_to_dtstruct(datetime dt, npy_datetimestruct *dts) noexcept:
cdef int64_t pydatetime_to_dt64(datetime val,
npy_datetimestruct *dts,
- NPY_DATETIMEUNIT reso=NPY_FR_ns):
+ NPY_DATETIMEUNIT reso=NPY_FR_ns) except? -1:
"""
Note we are assuming that the datetime object is timezone-naive.
"""
+ cdef int64_t result
pydatetime_to_dtstruct(val, dts)
- return npy_datetimestruct_to_datetime(reso, dts)
+ try:
+ result = npy_datetimestruct_to_datetime(reso, dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(
+ f"Out of bounds nanosecond timestamp: {val}"
+ ) from err
+
+ return result
cdef void pydate_to_dtstruct(date val, npy_datetimestruct *dts) noexcept:
@@ -265,9 +277,16 @@ cdef void pydate_to_dtstruct(date val, npy_datetimestruct *dts) noexcept:
cdef int64_t pydate_to_dt64(
date val, npy_datetimestruct *dts, NPY_DATETIMEUNIT reso=NPY_FR_ns
-):
+) except? -1:
+ cdef int64_t result
pydate_to_dtstruct(val, dts)
- return npy_datetimestruct_to_datetime(reso, dts)
+
+ try:
+ result = npy_datetimestruct_to_datetime(reso, dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(f"Out of bounds nanosecond timestamp: {val}") from err
+
+ return result
cdef int string_to_dts(
@@ -641,7 +660,12 @@ cdef int64_t _convert_reso_with_dtstruct(
) except? -1:
cdef:
npy_datetimestruct dts
+ int64_t result
pandas_datetime_to_datetimestruct(value, from_unit, &dts)
- check_dts_bounds(&dts, to_unit)
- return npy_datetimestruct_to_datetime(to_unit, &dts)
+ try:
+ result = npy_datetimestruct_to_datetime(to_unit, &dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime from err
+
+ return result
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index d1f925f3a0b48..318e018689a78 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -43,6 +43,8 @@ from pandas._libs.tslibs.dtypes cimport (
freq_to_period_freqstr,
)
+from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
+
# import datetime C API
import_datetime()
@@ -52,7 +54,7 @@ from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
NPY_FR_D,
astype_overflowsafe,
- check_dts_bounds,
+ dts_to_iso_string,
import_pandas_datetime,
npy_datetimestruct,
npy_datetimestruct_to_datetime,
@@ -1156,14 +1158,20 @@ cpdef int64_t period_ordinal(int y, int m, int d, int h, int min,
cdef int64_t period_ordinal_to_dt64(int64_t ordinal, int freq) except? -1:
cdef:
npy_datetimestruct dts
+ int64_t result
if ordinal == NPY_NAT:
return NPY_NAT
get_date_info(ordinal, freq, &dts)
- check_dts_bounds(&dts)
- return npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT.NPY_FR_ns, &dts)
+ try:
+ result = npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT.NPY_FR_ns, &dts)
+ except OverflowError as err:
+ fmt = dts_to_iso_string(&dts)
+ raise OutOfBoundsDatetime(f"Out of bounds nanosecond timestamp: {fmt}") from err
+
+ return result
cdef str period_format(int64_t value, int freq, object fmt=None):
diff --git a/pandas/_libs/tslibs/strptime.pyx b/pandas/_libs/tslibs/strptime.pyx
index c8fd95be34cc0..8966c9e81699b 100644
--- a/pandas/_libs/tslibs/strptime.pyx
+++ b/pandas/_libs/tslibs/strptime.pyx
@@ -60,7 +60,6 @@ from pandas._libs.tslibs.nattype cimport (
from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
NPY_FR_ns,
- check_dts_bounds,
get_datetime64_unit,
import_pandas_datetime,
npy_datetimestruct,
@@ -369,7 +368,6 @@ def array_strptime(
iresult[i] = pydatetime_to_dt64(
val.replace(tzinfo=None), &dts, reso=creso
)
- check_dts_bounds(&dts, creso)
result_timezone[i] = val.tzinfo
continue
elif PyDate_Check(val):
@@ -378,7 +376,6 @@ def array_strptime(
if infer_reso:
creso = state.creso
iresult[i] = pydate_to_dt64(val, &dts, reso=creso)
- check_dts_bounds(&dts, creso)
continue
elif is_datetime64_object(val):
item_reso = get_supported_reso(get_datetime64_unit(val))
@@ -413,7 +410,12 @@ def array_strptime(
state.update_creso(item_reso)
if infer_reso:
creso = state.creso
- value = npy_datetimestruct_to_datetime(creso, &dts)
+ try:
+ value = npy_datetimestruct_to_datetime(creso, &dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(
+ f"Out of bounds nanosecond timestamp: {val}"
+ ) from err
if out_local == 1:
# Store the out_tzoffset in seconds
# since we store the total_seconds of
@@ -423,7 +425,6 @@ def array_strptime(
out_local = 0
out_tzoffset = 0
iresult[i] = value
- check_dts_bounds(&dts, creso)
continue
if parse_today_now(val, &iresult[i], utc, creso, infer_reso=infer_reso):
@@ -444,11 +445,16 @@ def array_strptime(
tz = _parse_with_format(
val, fmt, exact, format_regex, locale_time, &dts, &item_reso
)
+
state.update_creso(item_reso)
if infer_reso:
creso = state.creso
- iresult[i] = npy_datetimestruct_to_datetime(creso, &dts)
- check_dts_bounds(&dts, creso)
+ try:
+ iresult[i] = npy_datetimestruct_to_datetime(creso, &dts)
+ except OverflowError as err:
+ raise OutOfBoundsDatetime(
+ f"Out of bounds nanosecond timestamp: {val}"
+ ) from err
result_timezone[i] = tz
except (ValueError, OutOfBoundsDatetime) as ex:
diff --git a/pandas/_libs/tslibs/timestamps.pxd b/pandas/_libs/tslibs/timestamps.pxd
index 26018cd904249..bd73c713f6c04 100644
--- a/pandas/_libs/tslibs/timestamps.pxd
+++ b/pandas/_libs/tslibs/timestamps.pxd
@@ -26,7 +26,7 @@ cdef class _Timestamp(ABCTimestamp):
cdef bint _get_start_end_field(self, str field, freq)
cdef _get_date_name_field(self, str field, object locale)
- cdef int64_t _maybe_convert_value_to_local(self)
+ cdef int64_t _maybe_convert_value_to_local(self) except? -1
cdef bint _can_compare(self, datetime other)
cpdef to_datetime64(self)
cpdef datetime to_pydatetime(_Timestamp self, bint warn=*)
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index d0c3c5c23b272..56a6885d4a9e0 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -83,10 +83,10 @@ from pandas._libs.tslibs.nattype cimport (
from pandas._libs.tslibs.np_datetime cimport (
NPY_DATETIMEUNIT,
NPY_FR_ns,
- check_dts_bounds,
cmp_dtstructs,
cmp_scalar,
convert_reso,
+ dts_to_iso_string,
get_datetime64_unit,
get_unit_from_dtype,
import_pandas_datetime,
@@ -571,7 +571,7 @@ cdef class _Timestamp(ABCTimestamp):
# -----------------------------------------------------------------
- cdef int64_t _maybe_convert_value_to_local(self):
+ cdef int64_t _maybe_convert_value_to_local(self) except? -1:
"""Convert UTC i8 value to local i8 value if tz exists"""
cdef:
int64_t val
@@ -2489,8 +2489,13 @@ default 'raise'
# We can avoid going through pydatetime paths, which is robust
# to datetimes outside of pydatetime range.
ts = _TSObject()
- check_dts_bounds(&dts, self._creso)
- ts.value = npy_datetimestruct_to_datetime(self._creso, &dts)
+ try:
+ ts.value = npy_datetimestruct_to_datetime(self._creso, &dts)
+ except OverflowError as err:
+ fmt = dts_to_iso_string(&dts)
+ raise OutOfBoundsDatetime(
+ f"Out of bounds timestamp: {fmt} with frequency '{self.unit}'"
+ ) from err
ts.dts = dts
ts.creso = self._creso
ts.fold = fold
diff --git a/pandas/tests/scalar/timestamp/methods/test_replace.py b/pandas/tests/scalar/timestamp/methods/test_replace.py
index 9b2b21cf7f388..8a208455edc82 100644
--- a/pandas/tests/scalar/timestamp/methods/test_replace.py
+++ b/pandas/tests/scalar/timestamp/methods/test_replace.py
@@ -21,7 +21,7 @@ def test_replace_out_of_pydatetime_bounds(self):
# GH#50348
ts = Timestamp("2016-01-01").as_unit("ns")
- msg = "Out of bounds nanosecond timestamp: 99999-01-01 00:00:00"
+ msg = "Out of bounds timestamp: 99999-01-01 00:00:00 with frequency 'ns'"
with pytest.raises(OutOfBoundsDatetime, match=msg):
ts.replace(year=99_999)
diff --git a/pandas/tests/tslibs/test_array_to_datetime.py b/pandas/tests/tslibs/test_array_to_datetime.py
index 86560969d10ce..3ab214196a218 100644
--- a/pandas/tests/tslibs/test_array_to_datetime.py
+++ b/pandas/tests/tslibs/test_array_to_datetime.py
@@ -15,6 +15,7 @@
tslib,
)
from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
+from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime
from pandas import Timestamp
import pandas._testing as tm
@@ -162,7 +163,7 @@ def test_coerce_outside_ns_bounds(invalid_date, errors):
if errors == "raise":
msg = "^Out of bounds nanosecond timestamp: .*, at position 0$"
- with pytest.raises(ValueError, match=msg):
+ with pytest.raises(OutOfBoundsDatetime, match=msg):
tslib.array_to_datetime(**kwargs)
else: # coerce.
result, _ = tslib.array_to_datetime(**kwargs)
| This helps the readability of things as is, but also helps us better implement bounds checking in a later PR to avoid overflow | https://api.github.com/repos/pandas-dev/pandas/pulls/55151 | 2023-09-15T20:33:33Z | 2023-11-15T18:20:46Z | 2023-11-15T18:20:46Z | 2023-11-15T23:02:22Z |
Replace cpplint with clang-format | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c911edfa03670..a9a9baac6069a 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -70,19 +70,6 @@ repos:
- id: fix-encoding-pragma
args: [--remove]
- id: trailing-whitespace
-- repo: https://github.com/cpplint/cpplint
- rev: 1.6.1
- hooks:
- - id: cpplint
- exclude: ^pandas/_libs/include/pandas/vendored/klib
- args: [
- --quiet,
- '--extensions=c,h',
- '--headers=h',
- --recursive,
- --linelength=88,
- '--filter=-readability/casting,-runtime/int,-build/include_subdir,-readability/fn_size'
- ]
- repo: https://github.com/pylint-dev/pylint
rev: v3.0.0b0
hooks:
@@ -127,6 +114,13 @@ repos:
rev: v0.6.8
hooks:
- id: sphinx-lint
+- repo: https://github.com/pre-commit/mirrors-clang-format
+ rev: ea59a72
+ hooks:
+ - id: clang-format
+ files: ^pandas/_libs/src|^pandas/_libs/include
+ args: [-i]
+ types_or: [c, c++]
- repo: local
hooks:
- id: pyright
diff --git a/doc/source/development/contributing_codebase.rst b/doc/source/development/contributing_codebase.rst
index e22b57dbbff17..888cfc24aea5b 100644
--- a/doc/source/development/contributing_codebase.rst
+++ b/doc/source/development/contributing_codebase.rst
@@ -39,7 +39,7 @@ Pre-commit
Additionally, :ref:`Continuous Integration <contributing.ci>` will run code formatting checks
like ``black``, ``ruff``,
-``isort``, and ``cpplint`` and more using `pre-commit hooks <https://pre-commit.com/>`_.
+``isort``, and ``clang-format`` and more using `pre-commit hooks <https://pre-commit.com/>`_.
Any warnings from these checks will cause the :ref:`Continuous Integration <contributing.ci>` to fail; therefore,
it is helpful to run the check yourself before submitting code. This
can be done by installing ``pre-commit`` (which should already have happened if you followed the instructions
diff --git a/pandas/_libs/include/pandas/datetime/date_conversions.h b/pandas/_libs/include/pandas/datetime/date_conversions.h
index a5ad926924dc5..42a16f33cc2ea 100644
--- a/pandas/_libs/include/pandas/datetime/date_conversions.h
+++ b/pandas/_libs/include/pandas/datetime/date_conversions.h
@@ -18,10 +18,8 @@ int scaleNanosecToUnit(npy_int64 *value, NPY_DATETIMEUNIT unit);
// up to precision `base` e.g. base="s" yields 2020-01-03T00:00:00Z
// while base="ns" yields "2020-01-01T00:00:00.000000000Z"
// len is mutated to save the length of the returned string
-char *int64ToIso(int64_t value,
- NPY_DATETIMEUNIT valueUnit,
- NPY_DATETIMEUNIT base,
- size_t *len);
+char *int64ToIso(int64_t value, NPY_DATETIMEUNIT valueUnit,
+ NPY_DATETIMEUNIT base, size_t *len);
// TODO(username): this function doesn't do a lot; should augment or
// replace with scaleNanosecToUnit
diff --git a/pandas/_libs/include/pandas/datetime/pd_datetime.h b/pandas/_libs/include/pandas/datetime/pd_datetime.h
index 3e362deb87807..714d264924750 100644
--- a/pandas/_libs/include/pandas/datetime/pd_datetime.h
+++ b/pandas/_libs/include/pandas/datetime/pd_datetime.h
@@ -19,12 +19,12 @@ See NUMPY_LICENSE.txt for the license.
#ifndef NPY_NO_DEPRECATED_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#endif // NPY_NO_DEPRECATED_API
+#endif // NPY_NO_DEPRECATED_API
-#include <numpy/ndarraytypes.h>
+#include "pandas/datetime/date_conversions.h"
#include "pandas/vendored/numpy/datetime/np_datetime.h"
#include "pandas/vendored/numpy/datetime/np_datetime_strings.h"
-#include "pandas/datetime/date_conversions.h"
+#include <numpy/ndarraytypes.h>
#ifdef __cplusplus
extern "C" {
diff --git a/pandas/_libs/include/pandas/inline_helper.h b/pandas/_libs/include/pandas/inline_helper.h
index c77da0e52b9d3..1e03da1327470 100644
--- a/pandas/_libs/include/pandas/inline_helper.h
+++ b/pandas/_libs/include/pandas/inline_helper.h
@@ -10,15 +10,15 @@ The full license is in the LICENSE file, distributed with this software.
#pragma once
#ifndef PANDAS_INLINE
- #if defined(__clang__)
- #define PANDAS_INLINE static __inline__ __attribute__ ((__unused__))
- #elif defined(__GNUC__)
- #define PANDAS_INLINE static __inline__
- #elif defined(_MSC_VER)
- #define PANDAS_INLINE static __inline
- #elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define PANDAS_INLINE static inline
- #else
- #define PANDAS_INLINE
- #endif // __GNUC__
-#endif // PANDAS_INLINE
+#if defined(__clang__)
+#define PANDAS_INLINE static __inline__ __attribute__((__unused__))
+#elif defined(__GNUC__)
+#define PANDAS_INLINE static __inline__
+#elif defined(_MSC_VER)
+#define PANDAS_INLINE static __inline
+#elif defined(__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+#define PANDAS_INLINE static inline
+#else
+#define PANDAS_INLINE
+#endif // __GNUC__
+#endif // PANDAS_INLINE
diff --git a/pandas/_libs/include/pandas/parser/io.h b/pandas/_libs/include/pandas/parser/io.h
index 9032eb6759358..cbe6bc04b7663 100644
--- a/pandas/_libs/include/pandas/parser/io.h
+++ b/pandas/_libs/include/pandas/parser/io.h
@@ -10,15 +10,15 @@ The full license is in the LICENSE file, distributed with this software.
#pragma once
#define PY_SSIZE_T_CLEAN
-#include <Python.h>
#include "tokenizer.h"
+#include <Python.h>
#define FS(source) ((file_source *)source)
typedef struct _rd_source {
- PyObject *obj;
- PyObject *buffer;
- size_t position;
+ PyObject *obj;
+ PyObject *buffer;
+ size_t position;
} rd_source;
#define RDS(source) ((rd_source *)source)
diff --git a/pandas/_libs/include/pandas/parser/pd_parser.h b/pandas/_libs/include/pandas/parser/pd_parser.h
index 1ea94fde593ef..61f15dcef8d27 100644
--- a/pandas/_libs/include/pandas/parser/pd_parser.h
+++ b/pandas/_libs/include/pandas/parser/pd_parser.h
@@ -13,8 +13,8 @@ extern "C" {
#endif
#define PY_SSIZE_T_CLEAN
-#include <Python.h>
#include "pandas/parser/tokenizer.h"
+#include <Python.h>
typedef struct {
int (*to_double)(char *, double *, char, char, int *);
@@ -81,11 +81,10 @@ static PandasParser_CAPI *PandasParserAPI = NULL;
PandasParserAPI->parser_set_default_options((self))
#define parser_consume_rows(self, nrows) \
PandasParserAPI->parser_consume_rows((self), (nrows))
-#define parser_trim_buffers(self) \
- PandasParserAPI->parser_trim_buffers((self))
-#define tokenize_all_rows(self, encoding_errors) \
+#define parser_trim_buffers(self) PandasParserAPI->parser_trim_buffers((self))
+#define tokenize_all_rows(self, encoding_errors) \
PandasParserAPI->tokenize_all_rows((self), (encoding_errors))
-#define tokenize_nrows(self, nrows, encoding_errors) \
+#define tokenize_nrows(self, nrows, encoding_errors) \
PandasParserAPI->tokenize_nrows((self), (nrows), (encoding_errors))
#define str_to_int64(p_item, int_min, int_max, error, t_sep) \
PandasParserAPI->str_to_int64((p_item), (int_min), (int_max), (error), \
@@ -104,7 +103,7 @@ static PandasParser_CAPI *PandasParserAPI = NULL;
PandasParserAPI->round_trip((p), (q), (decimal), (sci), (tsep), \
(skip_trailing), (error), (maybe_int))
#define to_boolean(item, val) PandasParserAPI->to_boolean((item), (val))
-#endif /* !defined(_PANDAS_PARSER_IMPL) */
+#endif /* !defined(_PANDAS_PARSER_IMPL) */
#ifdef __cplusplus
}
diff --git a/pandas/_libs/include/pandas/parser/tokenizer.h b/pandas/_libs/include/pandas/parser/tokenizer.h
index a53d09012116d..6a46ad637a401 100644
--- a/pandas/_libs/include/pandas/parser/tokenizer.h
+++ b/pandas/_libs/include/pandas/parser/tokenizer.h
@@ -18,9 +18,9 @@ See LICENSE for the license
#define ERROR_OVERFLOW 2
#define ERROR_INVALID_CHARS 3
-#include <stdint.h>
#include "pandas/inline_helper.h"
#include "pandas/portable.h"
+#include <stdint.h>
#include "pandas/vendored/klib/khash.h"
@@ -29,7 +29,6 @@ See LICENSE for the license
#define REACHED_EOF 1
#define CALLING_READ_FAILED 2
-
/*
C flat file parsing low level code for pandas / NumPy
@@ -46,7 +45,7 @@ See LICENSE for the license
#define TRACE(X) printf X;
#else
#define TRACE(X)
-#endif // VERBOSE
+#endif // VERBOSE
#define PARSER_OUT_OF_MEMORY -1
@@ -56,131 +55,127 @@ See LICENSE for the license
*/
typedef enum {
- START_RECORD,
- START_FIELD,
- ESCAPED_CHAR,
- IN_FIELD,
- IN_QUOTED_FIELD,
- ESCAPE_IN_QUOTED_FIELD,
- QUOTE_IN_QUOTED_FIELD,
- EAT_CRNL,
- EAT_CRNL_NOP,
- EAT_WHITESPACE,
- EAT_COMMENT,
- EAT_LINE_COMMENT,
- WHITESPACE_LINE,
- START_FIELD_IN_SKIP_LINE,
- IN_FIELD_IN_SKIP_LINE,
- IN_QUOTED_FIELD_IN_SKIP_LINE,
- QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE,
- FINISHED
+ START_RECORD,
+ START_FIELD,
+ ESCAPED_CHAR,
+ IN_FIELD,
+ IN_QUOTED_FIELD,
+ ESCAPE_IN_QUOTED_FIELD,
+ QUOTE_IN_QUOTED_FIELD,
+ EAT_CRNL,
+ EAT_CRNL_NOP,
+ EAT_WHITESPACE,
+ EAT_COMMENT,
+ EAT_LINE_COMMENT,
+ WHITESPACE_LINE,
+ START_FIELD_IN_SKIP_LINE,
+ IN_FIELD_IN_SKIP_LINE,
+ IN_QUOTED_FIELD_IN_SKIP_LINE,
+ QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE,
+ FINISHED
} ParserState;
typedef enum {
- QUOTE_MINIMAL,
- QUOTE_ALL,
- QUOTE_NONNUMERIC,
- QUOTE_NONE
+ QUOTE_MINIMAL,
+ QUOTE_ALL,
+ QUOTE_NONNUMERIC,
+ QUOTE_NONE
} QuoteStyle;
-typedef enum {
- ERROR,
- WARN,
- SKIP
-} BadLineHandleMethod;
+typedef enum { ERROR, WARN, SKIP } BadLineHandleMethod;
typedef void *(*io_callback)(void *src, size_t nbytes, size_t *bytes_read,
int *status, const char *encoding_errors);
typedef int (*io_cleanup)(void *src);
typedef struct parser_t {
- void *source;
- io_callback cb_io;
- io_cleanup cb_cleanup;
-
- int64_t chunksize; // Number of bytes to prepare for each chunk
- char *data; // pointer to data to be processed
- int64_t datalen; // amount of data available
- int64_t datapos;
-
- // where to write out tokenized data
- char *stream;
- uint64_t stream_len;
- uint64_t stream_cap;
-
- // Store words in (potentially ragged) matrix for now, hmm
- char **words;
- int64_t *word_starts; // where we are in the stream
- uint64_t words_len;
- uint64_t words_cap;
- uint64_t max_words_cap; // maximum word cap encountered
-
- char *pword_start; // pointer to stream start of current field
- int64_t word_start; // position start of current field
-
- int64_t *line_start; // position in words for start of line
- int64_t *line_fields; // Number of fields in each line
- uint64_t lines; // Number of (good) lines observed
- uint64_t file_lines; // Number of lines (including bad or skipped)
- uint64_t lines_cap; // Vector capacity
-
- // Tokenizing stuff
- ParserState state;
- int doublequote; /* is " represented by ""? */
- char delimiter; /* field separator */
- int delim_whitespace; /* delimit by consuming space/tabs instead */
- char quotechar; /* quote character */
- char escapechar; /* escape character */
- char lineterminator;
- int skipinitialspace; /* ignore spaces following delimiter? */
- int quoting; /* style of quoting to write */
-
- char commentchar;
- int allow_embedded_newline;
-
- int usecols; // Boolean: 1: usecols provided, 0: none provided
-
- Py_ssize_t expected_fields;
- BadLineHandleMethod on_bad_lines;
-
- // floating point options
- char decimal;
- char sci;
-
- // thousands separator (comma, period)
- char thousands;
-
- int header; // Boolean: 1: has header, 0: no header
- int64_t header_start; // header row start
- uint64_t header_end; // header row end
-
- void *skipset;
- PyObject *skipfunc;
- int64_t skip_first_N_rows;
- int64_t skip_footer;
- double (*double_converter)(const char *, char **,
- char, char, char, int, int *, int *);
-
- // error handling
- char *warn_msg;
- char *error_msg;
-
- int skip_empty_lines;
+ void *source;
+ io_callback cb_io;
+ io_cleanup cb_cleanup;
+
+ int64_t chunksize; // Number of bytes to prepare for each chunk
+ char *data; // pointer to data to be processed
+ int64_t datalen; // amount of data available
+ int64_t datapos;
+
+ // where to write out tokenized data
+ char *stream;
+ uint64_t stream_len;
+ uint64_t stream_cap;
+
+ // Store words in (potentially ragged) matrix for now, hmm
+ char **words;
+ int64_t *word_starts; // where we are in the stream
+ uint64_t words_len;
+ uint64_t words_cap;
+ uint64_t max_words_cap; // maximum word cap encountered
+
+ char *pword_start; // pointer to stream start of current field
+ int64_t word_start; // position start of current field
+
+ int64_t *line_start; // position in words for start of line
+ int64_t *line_fields; // Number of fields in each line
+ uint64_t lines; // Number of (good) lines observed
+ uint64_t file_lines; // Number of lines (including bad or skipped)
+ uint64_t lines_cap; // Vector capacity
+
+ // Tokenizing stuff
+ ParserState state;
+ int doublequote; /* is " represented by ""? */
+ char delimiter; /* field separator */
+ int delim_whitespace; /* delimit by consuming space/tabs instead */
+ char quotechar; /* quote character */
+ char escapechar; /* escape character */
+ char lineterminator;
+ int skipinitialspace; /* ignore spaces following delimiter? */
+ int quoting; /* style of quoting to write */
+
+ char commentchar;
+ int allow_embedded_newline;
+
+ int usecols; // Boolean: 1: usecols provided, 0: none provided
+
+ Py_ssize_t expected_fields;
+ BadLineHandleMethod on_bad_lines;
+
+ // floating point options
+ char decimal;
+ char sci;
+
+ // thousands separator (comma, period)
+ char thousands;
+
+ int header; // Boolean: 1: has header, 0: no header
+ int64_t header_start; // header row start
+ uint64_t header_end; // header row end
+
+ void *skipset;
+ PyObject *skipfunc;
+ int64_t skip_first_N_rows;
+ int64_t skip_footer;
+ double (*double_converter)(const char *, char **, char, char, char, int,
+ int *, int *);
+
+ // error handling
+ char *warn_msg;
+ char *error_msg;
+
+ int skip_empty_lines;
} parser_t;
typedef struct coliter_t {
- char **words;
- int64_t *line_start;
- int64_t col;
+ char **words;
+ int64_t *line_start;
+ int64_t col;
} coliter_t;
void coliter_setup(coliter_t *self, parser_t *parser, int64_t i, int64_t start);
-#define COLITER_NEXT(iter, word) \
- do { \
- const int64_t i = *iter.line_start++ + iter.col; \
- word = i >= *iter.line_start ? "" : iter.words[i]; \
- } while (0)
+#define COLITER_NEXT(iter, word) \
+ do { \
+ const int64_t i = *iter.line_start++ + iter.col; \
+ word = i >= *iter.line_start ? "" : iter.words[i]; \
+ } while (0)
parser_t *parser_new(void);
@@ -208,9 +203,9 @@ int tokenize_all_rows(parser_t *self, const char *encoding_errors);
// and want to free memory from the token stream
typedef struct uint_state {
- int seen_sint;
- int seen_uint;
- int seen_null;
+ int seen_sint;
+ int seen_uint;
+ int seen_null;
} uint_state;
void uint_state_init(uint_state *self);
@@ -223,9 +218,9 @@ int64_t str_to_int64(const char *p_item, int64_t int_min, int64_t int_max,
int *error, char tsep);
double xstrtod(const char *p, char **q, char decimal, char sci, char tsep,
int skip_trailing, int *error, int *maybe_int);
-double precise_xstrtod(const char *p, char **q, char decimal,
- char sci, char tsep, int skip_trailing,
- int *error, int *maybe_int);
+double precise_xstrtod(const char *p, char **q, char decimal, char sci,
+ char tsep, int skip_trailing, int *error,
+ int *maybe_int);
// GH-15140 - round_trip requires and acquires the GIL on its own
double round_trip(const char *p, char **q, char decimal, char sci, char tsep,
diff --git a/pandas/_libs/include/pandas/portable.h b/pandas/_libs/include/pandas/portable.h
index 2569aa4fded08..588f070372a8a 100644
--- a/pandas/_libs/include/pandas/portable.h
+++ b/pandas/_libs/include/pandas/portable.h
@@ -18,7 +18,8 @@ The full license is in the LICENSE file, distributed with this software.
// GH-23516 - works around locale perf issues
// from MUSL libc, licence at LICENSES/MUSL_LICENSE
#define isdigit_ascii(c) (((unsigned)(c) - '0') < 10u)
-#define getdigit_ascii(c, default) (isdigit_ascii(c) ? ((int)((c) - '0')) : default)
+#define getdigit_ascii(c, default) \
+ (isdigit_ascii(c) ? ((int)((c) - '0')) : default)
#define isspace_ascii(c) (((c) == ' ') || (((unsigned)(c) - '\t') < 5))
-#define toupper_ascii(c) ((((unsigned)(c) - 'a') < 26) ? ((c) & 0x5f) : (c))
+#define toupper_ascii(c) ((((unsigned)(c) - 'a') < 26) ? ((c)&0x5f) : (c))
#define tolower_ascii(c) ((((unsigned)(c) - 'A') < 26) ? ((c) | 0x20) : (c))
diff --git a/pandas/_libs/include/pandas/skiplist.h b/pandas/_libs/include/pandas/skiplist.h
index 3be9e51f42e09..d002dba193279 100644
--- a/pandas/_libs/include/pandas/skiplist.h
+++ b/pandas/_libs/include/pandas/skiplist.h
@@ -15,18 +15,18 @@ Python recipe (https://rhettinger.wordpress.com/2010/02/06/lost-knowledge/)
#pragma once
+#include "pandas/inline_helper.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
-#include "pandas/inline_helper.h"
PANDAS_INLINE float __skiplist_nanf(void) {
- const union {
- int __i;
- float __f;
- } __bint = {0x7fc00000UL};
- return __bint.__f;
+ const union {
+ int __i;
+ float __f;
+ } __bint = {0x7fc00000UL};
+ return __bint.__f;
}
#define PANDAS_NAN ((double)__skiplist_nanf())
@@ -35,46 +35,46 @@ PANDAS_INLINE double Log2(double val) { return log(val) / log(2.); }
typedef struct node_t node_t;
struct node_t {
- node_t **next;
- int *width;
- double value;
- int is_nil;
- int levels;
- int ref_count;
+ node_t **next;
+ int *width;
+ double value;
+ int is_nil;
+ int levels;
+ int ref_count;
};
typedef struct {
- node_t *head;
- node_t **tmp_chain;
- int *tmp_steps;
- int size;
- int maxlevels;
+ node_t *head;
+ node_t **tmp_chain;
+ int *tmp_steps;
+ int size;
+ int maxlevels;
} skiplist_t;
PANDAS_INLINE double urand(void) {
- return ((double)rand() + 1) / ((double)RAND_MAX + 2);
+ return ((double)rand() + 1) / ((double)RAND_MAX + 2);
}
PANDAS_INLINE int int_min(int a, int b) { return a < b ? a : b; }
PANDAS_INLINE node_t *node_init(double value, int levels) {
- node_t *result;
- result = (node_t *)malloc(sizeof(node_t));
- if (result) {
- result->value = value;
- result->levels = levels;
- result->is_nil = 0;
- result->ref_count = 0;
- result->next = (node_t **)malloc(levels * sizeof(node_t *));
- result->width = (int *)malloc(levels * sizeof(int));
- if (!(result->next && result->width) && (levels != 0)) {
- free(result->next);
- free(result->width);
- free(result);
- return NULL;
- }
+ node_t *result;
+ result = (node_t *)malloc(sizeof(node_t));
+ if (result) {
+ result->value = value;
+ result->levels = levels;
+ result->is_nil = 0;
+ result->ref_count = 0;
+ result->next = (node_t **)malloc(levels * sizeof(node_t *));
+ result->width = (int *)malloc(levels * sizeof(int));
+ if (!(result->next && result->width) && (levels != 0)) {
+ free(result->next);
+ free(result->width);
+ free(result);
+ return NULL;
}
- return result;
+ }
+ return result;
}
// do this ourselves
@@ -83,215 +83,215 @@ PANDAS_INLINE void node_incref(node_t *node) { ++(node->ref_count); }
PANDAS_INLINE void node_decref(node_t *node) { --(node->ref_count); }
static void node_destroy(node_t *node) {
- int i;
- if (node) {
- if (node->ref_count <= 1) {
- for (i = 0; i < node->levels; ++i) {
- node_destroy(node->next[i]);
- }
- free(node->next);
- free(node->width);
- // printf("Reference count was 1, freeing\n");
- free(node);
- } else {
- node_decref(node);
- }
- // pretty sure that freeing the struct above will be enough
+ int i;
+ if (node) {
+ if (node->ref_count <= 1) {
+ for (i = 0; i < node->levels; ++i) {
+ node_destroy(node->next[i]);
+ }
+ free(node->next);
+ free(node->width);
+ // printf("Reference count was 1, freeing\n");
+ free(node);
+ } else {
+ node_decref(node);
}
+ // pretty sure that freeing the struct above will be enough
+ }
}
PANDAS_INLINE void skiplist_destroy(skiplist_t *skp) {
- if (skp) {
- node_destroy(skp->head);
- free(skp->tmp_steps);
- free(skp->tmp_chain);
- free(skp);
- }
+ if (skp) {
+ node_destroy(skp->head);
+ free(skp->tmp_steps);
+ free(skp->tmp_chain);
+ free(skp);
+ }
}
PANDAS_INLINE skiplist_t *skiplist_init(int expected_size) {
- skiplist_t *result;
- node_t *NIL, *head;
- int maxlevels, i;
-
- maxlevels = 1 + Log2((double)expected_size);
- result = (skiplist_t *)malloc(sizeof(skiplist_t));
- if (!result) {
- return NULL;
- }
- result->tmp_chain = (node_t **)malloc(maxlevels * sizeof(node_t *));
- result->tmp_steps = (int *)malloc(maxlevels * sizeof(int));
- result->maxlevels = maxlevels;
- result->size = 0;
-
- head = result->head = node_init(PANDAS_NAN, maxlevels);
- NIL = node_init(0.0, 0);
-
- if (!(result->tmp_chain && result->tmp_steps && result->head && NIL)) {
- skiplist_destroy(result);
- node_destroy(NIL);
- return NULL;
- }
-
- node_incref(head);
-
- NIL->is_nil = 1;
-
- for (i = 0; i < maxlevels; ++i) {
- head->next[i] = NIL;
- head->width[i] = 1;
- node_incref(NIL);
- }
-
- return result;
+ skiplist_t *result;
+ node_t *NIL, *head;
+ int maxlevels, i;
+
+ maxlevels = 1 + Log2((double)expected_size);
+ result = (skiplist_t *)malloc(sizeof(skiplist_t));
+ if (!result) {
+ return NULL;
+ }
+ result->tmp_chain = (node_t **)malloc(maxlevels * sizeof(node_t *));
+ result->tmp_steps = (int *)malloc(maxlevels * sizeof(int));
+ result->maxlevels = maxlevels;
+ result->size = 0;
+
+ head = result->head = node_init(PANDAS_NAN, maxlevels);
+ NIL = node_init(0.0, 0);
+
+ if (!(result->tmp_chain && result->tmp_steps && result->head && NIL)) {
+ skiplist_destroy(result);
+ node_destroy(NIL);
+ return NULL;
+ }
+
+ node_incref(head);
+
+ NIL->is_nil = 1;
+
+ for (i = 0; i < maxlevels; ++i) {
+ head->next[i] = NIL;
+ head->width[i] = 1;
+ node_incref(NIL);
+ }
+
+ return result;
}
// 1 if left < right, 0 if left == right, -1 if left > right
PANDAS_INLINE int _node_cmp(node_t *node, double value) {
- if (node->is_nil || node->value > value) {
- return -1;
- } else if (node->value < value) {
- return 1;
- } else {
- return 0;
- }
+ if (node->is_nil || node->value > value) {
+ return -1;
+ } else if (node->value < value) {
+ return 1;
+ } else {
+ return 0;
+ }
}
PANDAS_INLINE double skiplist_get(skiplist_t *skp, int i, int *ret) {
- node_t *node;
- int level;
-
- if (i < 0 || i >= skp->size) {
- *ret = 0;
- return 0;
- }
-
- node = skp->head;
- ++i;
- for (level = skp->maxlevels - 1; level >= 0; --level) {
- while (node->width[level] <= i) {
- i -= node->width[level];
- node = node->next[level];
- }
+ node_t *node;
+ int level;
+
+ if (i < 0 || i >= skp->size) {
+ *ret = 0;
+ return 0;
+ }
+
+ node = skp->head;
+ ++i;
+ for (level = skp->maxlevels - 1; level >= 0; --level) {
+ while (node->width[level] <= i) {
+ i -= node->width[level];
+ node = node->next[level];
}
+ }
- *ret = 1;
- return node->value;
+ *ret = 1;
+ return node->value;
}
// Returns the lowest rank of all elements with value `value`, as opposed to the
// highest rank returned by `skiplist_insert`.
PANDAS_INLINE int skiplist_min_rank(skiplist_t *skp, double value) {
- node_t *node;
- int level, rank = 0;
-
- node = skp->head;
- for (level = skp->maxlevels - 1; level >= 0; --level) {
- while (_node_cmp(node->next[level], value) > 0) {
- rank += node->width[level];
- node = node->next[level];
- }
+ node_t *node;
+ int level, rank = 0;
+
+ node = skp->head;
+ for (level = skp->maxlevels - 1; level >= 0; --level) {
+ while (_node_cmp(node->next[level], value) > 0) {
+ rank += node->width[level];
+ node = node->next[level];
}
+ }
- return rank + 1;
+ return rank + 1;
}
// Returns the rank of the inserted element. When there are duplicates,
// `rank` is the highest of the group, i.e. the 'max' method of
// https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rank.html
PANDAS_INLINE int skiplist_insert(skiplist_t *skp, double value) {
- node_t *node, *prevnode, *newnode, *next_at_level;
- int *steps_at_level;
- int size, steps, level, rank = 0;
- node_t **chain;
-
- chain = skp->tmp_chain;
-
- steps_at_level = skp->tmp_steps;
- memset(steps_at_level, 0, skp->maxlevels * sizeof(int));
-
- node = skp->head;
-
- for (level = skp->maxlevels - 1; level >= 0; --level) {
- next_at_level = node->next[level];
- while (_node_cmp(next_at_level, value) >= 0) {
- steps_at_level[level] += node->width[level];
- rank += node->width[level];
- node = next_at_level;
- next_at_level = node->next[level];
- }
- chain[level] = node;
- }
+ node_t *node, *prevnode, *newnode, *next_at_level;
+ int *steps_at_level;
+ int size, steps, level, rank = 0;
+ node_t **chain;
- size = int_min(skp->maxlevels, 1 - ((int)Log2(urand())));
+ chain = skp->tmp_chain;
- newnode = node_init(value, size);
- if (!newnode) {
- return -1;
+ steps_at_level = skp->tmp_steps;
+ memset(steps_at_level, 0, skp->maxlevels * sizeof(int));
+
+ node = skp->head;
+
+ for (level = skp->maxlevels - 1; level >= 0; --level) {
+ next_at_level = node->next[level];
+ while (_node_cmp(next_at_level, value) >= 0) {
+ steps_at_level[level] += node->width[level];
+ rank += node->width[level];
+ node = next_at_level;
+ next_at_level = node->next[level];
}
- steps = 0;
+ chain[level] = node;
+ }
- for (level = 0; level < size; ++level) {
- prevnode = chain[level];
- newnode->next[level] = prevnode->next[level];
+ size = int_min(skp->maxlevels, 1 - ((int)Log2(urand())));
- prevnode->next[level] = newnode;
- node_incref(newnode); // increment the reference count
+ newnode = node_init(value, size);
+ if (!newnode) {
+ return -1;
+ }
+ steps = 0;
- newnode->width[level] = prevnode->width[level] - steps;
- prevnode->width[level] = steps + 1;
+ for (level = 0; level < size; ++level) {
+ prevnode = chain[level];
+ newnode->next[level] = prevnode->next[level];
- steps += steps_at_level[level];
- }
+ prevnode->next[level] = newnode;
+ node_incref(newnode); // increment the reference count
- for (level = size; level < skp->maxlevels; ++level) {
- chain[level]->width[level] += 1;
- }
+ newnode->width[level] = prevnode->width[level] - steps;
+ prevnode->width[level] = steps + 1;
+
+ steps += steps_at_level[level];
+ }
- ++(skp->size);
+ for (level = size; level < skp->maxlevels; ++level) {
+ chain[level]->width[level] += 1;
+ }
- return rank + 1;
+ ++(skp->size);
+
+ return rank + 1;
}
PANDAS_INLINE int skiplist_remove(skiplist_t *skp, double value) {
- int level, size;
- node_t *node, *prevnode, *tmpnode, *next_at_level;
- node_t **chain;
-
- chain = skp->tmp_chain;
- node = skp->head;
-
- for (level = skp->maxlevels - 1; level >= 0; --level) {
- next_at_level = node->next[level];
- while (_node_cmp(next_at_level, value) > 0) {
- node = next_at_level;
- next_at_level = node->next[level];
- }
- chain[level] = node;
+ int level, size;
+ node_t *node, *prevnode, *tmpnode, *next_at_level;
+ node_t **chain;
+
+ chain = skp->tmp_chain;
+ node = skp->head;
+
+ for (level = skp->maxlevels - 1; level >= 0; --level) {
+ next_at_level = node->next[level];
+ while (_node_cmp(next_at_level, value) > 0) {
+ node = next_at_level;
+ next_at_level = node->next[level];
}
+ chain[level] = node;
+ }
- if (value != chain[0]->next[0]->value) {
- return 0;
- }
+ if (value != chain[0]->next[0]->value) {
+ return 0;
+ }
- size = chain[0]->next[0]->levels;
+ size = chain[0]->next[0]->levels;
- for (level = 0; level < size; ++level) {
- prevnode = chain[level];
+ for (level = 0; level < size; ++level) {
+ prevnode = chain[level];
- tmpnode = prevnode->next[level];
+ tmpnode = prevnode->next[level];
- prevnode->width[level] += tmpnode->width[level] - 1;
- prevnode->next[level] = tmpnode->next[level];
+ prevnode->width[level] += tmpnode->width[level] - 1;
+ prevnode->next[level] = tmpnode->next[level];
- tmpnode->next[level] = NULL;
- node_destroy(tmpnode); // decrement refcount or free
- }
+ tmpnode->next[level] = NULL;
+ node_destroy(tmpnode); // decrement refcount or free
+ }
- for (level = size; level < skp->maxlevels; ++level) {
- --(chain[level]->width[level]);
- }
+ for (level = size; level < skp->maxlevels; ++level) {
+ --(chain[level]->width[level]);
+ }
- --(skp->size);
- return 1;
+ --(skp->size);
+ return 1;
}
diff --git a/pandas/_libs/include/pandas/vendored/klib/khash.h b/pandas/_libs/include/pandas/vendored/klib/khash.h
index 758f089cc11a5..31d12a3b30001 100644
--- a/pandas/_libs/include/pandas/vendored/klib/khash.h
+++ b/pandas/_libs/include/pandas/vendored/klib/khash.h
@@ -1,4 +1,4 @@
-//Licence at LICENSES/KLIB_LICENSE
+// Licence at LICENSES/KLIB_LICENSE
/*
An example:
@@ -6,38 +6,38 @@
#include "khash.h"
KHASH_MAP_INIT_INT(32, char)
int main() {
- int ret, is_missing;
- khiter_t k;
- khash_t(32) *h = kh_init(32);
- k = kh_put(32, h, 5, &ret);
- if (!ret) kh_del(32, h, k);
- kh_value(h, k) = 10;
- k = kh_get(32, h, 10);
- is_missing = (k == kh_end(h));
- k = kh_get(32, h, 5);
- kh_del(32, h, k);
- for (k = kh_begin(h); k != kh_end(h); ++k)
- if (kh_exist(h, k)) kh_value(h, k) = 1;
- kh_destroy(32, h);
- return 0;
+ int ret, is_missing;
+ khiter_t k;
+ khash_t(32) *h = kh_init(32);
+ k = kh_put(32, h, 5, &ret);
+ if (!ret) kh_del(32, h, k);
+ kh_value(h, k) = 10;
+ k = kh_get(32, h, 10);
+ is_missing = (k == kh_end(h));
+ k = kh_get(32, h, 5);
+ kh_del(32, h, k);
+ for (k = kh_begin(h); k != kh_end(h); ++k)
+ if (kh_exist(h, k)) kh_value(h, k) = 1;
+ kh_destroy(32, h);
+ return 0;
}
*/
/*
2011-09-16 (0.2.6):
- * The capacity is a power of 2. This seems to dramatically improve the
- speed for simple keys. Thank Zilong Tan for the suggestion. Reference:
+ * The capacity is a power of 2. This seems to dramatically improve the
+ speed for simple keys. Thank Zilong Tan for the suggestion. Reference:
- - https://github.com/stefanocasazza/ULib
- - https://nothings.org/computer/judy/
+ - https://github.com/stefanocasazza/ULib
+ - https://nothings.org/computer/judy/
- * Allow to optionally use linear probing which usually has better
- performance for random input. Double hashing is still the default as it
- is more robust to certain non-random input.
+ * Allow to optionally use linear probing which usually has better
+ performance for random input. Double hashing is still the default as
+ it is more robust to certain non-random input.
- * Added Wang's integer hash function (not used by default). This hash
- function is more robust to certain non-random input.
+ * Added Wang's integer hash function (not used by default). This hash
+ function is more robust to certain non-random input.
2011-02-14 (0.2.5):
@@ -49,32 +49,31 @@ int main() {
2008-09-19 (0.2.3):
- * Corrected the example
- * Improved interfaces
+ * Corrected the example
+ * Improved interfaces
2008-09-11 (0.2.2):
- * Improved speed a little in kh_put()
+ * Improved speed a little in kh_put()
2008-09-10 (0.2.1):
- * Added kh_clear()
- * Fixed a compiling error
+ * Added kh_clear()
+ * Fixed a compiling error
2008-09-02 (0.2.0):
- * Changed to token concatenation which increases flexibility.
+ * Changed to token concatenation which increases flexibility.
2008-08-31 (0.1.2):
- * Fixed a bug in kh_get(), which has not been tested previously.
+ * Fixed a bug in kh_get(), which has not been tested previously.
2008-08-31 (0.1.1):
- * Added destructor
+ * Added destructor
*/
-
#ifndef __AC_KHASH_H
#define __AC_KHASH_H
@@ -86,11 +85,10 @@ int main() {
#define AC_VERSION_KHASH_H "0.2.6"
+#include "pandas/inline_helper.h"
+#include <limits.h>
#include <stdlib.h>
#include <string.h>
-#include <limits.h>
-#include "pandas/inline_helper.h"
-
// hooks for memory allocator, C-runtime allocator used per default
#ifndef KHASH_MALLOC
@@ -109,7 +107,6 @@ int main() {
#define KHASH_FREE free
#endif
-
#if UINT_MAX == 0xffffffffu
typedef unsigned int khuint32_t;
typedef signed int khint32_t;
@@ -145,262 +142,311 @@ typedef float khfloat32_t;
typedef khuint32_t khuint_t;
typedef khuint_t khiter_t;
-#define __ac_isempty(flag, i) ((flag[i>>5]>>(i&0x1fU))&1)
+#define __ac_isempty(flag, i) ((flag[i >> 5] >> (i & 0x1fU)) & 1)
#define __ac_isdel(flag, i) (0)
#define __ac_iseither(flag, i) __ac_isempty(flag, i)
#define __ac_set_isdel_false(flag, i) (0)
-#define __ac_set_isempty_false(flag, i) (flag[i>>5]&=~(1ul<<(i&0x1fU)))
-#define __ac_set_isempty_true(flag, i) (flag[i>>5]|=(1ul<<(i&0x1fU)))
+#define __ac_set_isempty_false(flag, i) (flag[i >> 5] &= ~(1ul << (i & 0x1fU)))
+#define __ac_set_isempty_true(flag, i) (flag[i >> 5] |= (1ul << (i & 0x1fU)))
#define __ac_set_isboth_false(flag, i) __ac_set_isempty_false(flag, i)
#define __ac_set_isdel_true(flag, i) ((void)0)
-
-// specializations of https://github.com/aappleby/smhasher/blob/master/src/MurmurHash2.cpp
-khuint32_t PANDAS_INLINE murmur2_32to32(khuint32_t k){
- const khuint32_t SEED = 0xc70f6907UL;
- // 'm' and 'r' are mixing constants generated offline.
- // They're not really 'magic', they just happen to work well.
- const khuint32_t M_32 = 0x5bd1e995;
- const int R_32 = 24;
-
- // Initialize the hash to a 'random' value
- khuint32_t h = SEED ^ 4;
-
- //handle 4 bytes:
- k *= M_32;
- k ^= k >> R_32;
- k *= M_32;
-
- h *= M_32;
- h ^= k;
-
- // Do a few final mixes of the hash to ensure the "last few
- // bytes" are well-incorporated. (Really needed here?)
- h ^= h >> 13;
- h *= M_32;
- h ^= h >> 15;
- return h;
+// specializations of
+// https://github.com/aappleby/smhasher/blob/master/src/MurmurHash2.cpp
+khuint32_t PANDAS_INLINE murmur2_32to32(khuint32_t k) {
+ const khuint32_t SEED = 0xc70f6907UL;
+ // 'm' and 'r' are mixing constants generated offline.
+ // They're not really 'magic', they just happen to work well.
+ const khuint32_t M_32 = 0x5bd1e995;
+ const int R_32 = 24;
+
+ // Initialize the hash to a 'random' value
+ khuint32_t h = SEED ^ 4;
+
+ // handle 4 bytes:
+ k *= M_32;
+ k ^= k >> R_32;
+ k *= M_32;
+
+ h *= M_32;
+ h ^= k;
+
+ // Do a few final mixes of the hash to ensure the "last few
+ // bytes" are well-incorporated. (Really needed here?)
+ h ^= h >> 13;
+ h *= M_32;
+ h ^= h >> 15;
+ return h;
}
-// it is possible to have a special x64-version, which would need less operations, but
-// using 32bit version always has also some benefits:
+// it is possible to have a special x64-version, which would need less
+// operations, but using 32bit version always has also some benefits:
// - one code for 32bit and 64bit builds
// - the same case for 32bit and 64bit builds
-// - no performance difference could be measured compared to a possible x64-version
-
-khuint32_t PANDAS_INLINE murmur2_32_32to32(khuint32_t k1, khuint32_t k2){
- const khuint32_t SEED = 0xc70f6907UL;
- // 'm' and 'r' are mixing constants generated offline.
- // They're not really 'magic', they just happen to work well.
- const khuint32_t M_32 = 0x5bd1e995;
- const int R_32 = 24;
-
- // Initialize the hash to a 'random' value
- khuint32_t h = SEED ^ 4;
-
- //handle first 4 bytes:
- k1 *= M_32;
- k1 ^= k1 >> R_32;
- k1 *= M_32;
-
- h *= M_32;
- h ^= k1;
-
- //handle second 4 bytes:
- k2 *= M_32;
- k2 ^= k2 >> R_32;
- k2 *= M_32;
-
- h *= M_32;
- h ^= k2;
-
- // Do a few final mixes of the hash to ensure the "last few
- // bytes" are well-incorporated.
- h ^= h >> 13;
- h *= M_32;
- h ^= h >> 15;
- return h;
+// - no performance difference could be measured compared to a possible
+// x64-version
+
+khuint32_t PANDAS_INLINE murmur2_32_32to32(khuint32_t k1, khuint32_t k2) {
+ const khuint32_t SEED = 0xc70f6907UL;
+ // 'm' and 'r' are mixing constants generated offline.
+ // They're not really 'magic', they just happen to work well.
+ const khuint32_t M_32 = 0x5bd1e995;
+ const int R_32 = 24;
+
+ // Initialize the hash to a 'random' value
+ khuint32_t h = SEED ^ 4;
+
+ // handle first 4 bytes:
+ k1 *= M_32;
+ k1 ^= k1 >> R_32;
+ k1 *= M_32;
+
+ h *= M_32;
+ h ^= k1;
+
+ // handle second 4 bytes:
+ k2 *= M_32;
+ k2 ^= k2 >> R_32;
+ k2 *= M_32;
+
+ h *= M_32;
+ h ^= k2;
+
+ // Do a few final mixes of the hash to ensure the "last few
+ // bytes" are well-incorporated.
+ h ^= h >> 13;
+ h *= M_32;
+ h ^= h >> 15;
+ return h;
}
-khuint32_t PANDAS_INLINE murmur2_64to32(khuint64_t k){
- khuint32_t k1 = (khuint32_t)k;
- khuint32_t k2 = (khuint32_t)(k >> 32);
+khuint32_t PANDAS_INLINE murmur2_64to32(khuint64_t k) {
+ khuint32_t k1 = (khuint32_t)k;
+ khuint32_t k2 = (khuint32_t)(k >> 32);
- return murmur2_32_32to32(k1, k2);
+ return murmur2_32_32to32(k1, k2);
}
-
#ifdef KHASH_LINEAR
#define __ac_inc(k, m) 1
#else
#define __ac_inc(k, m) (murmur2_32to32(k) | 1) & (m)
#endif
-#define __ac_fsize(m) ((m) < 32? 1 : (m)>>5)
+#define __ac_fsize(m) ((m) < 32 ? 1 : (m) >> 5)
#ifndef kroundup32
-#define kroundup32(x) (--(x), (x)|=(x)>>1, (x)|=(x)>>2, (x)|=(x)>>4, (x)|=(x)>>8, (x)|=(x)>>16, ++(x))
+#define kroundup32(x) \
+ (--(x), (x) |= (x) >> 1, (x) |= (x) >> 2, (x) |= (x) >> 4, (x) |= (x) >> 8, \
+ (x) |= (x) >> 16, ++(x))
#endif
static const double __ac_HASH_UPPER = 0.77;
-#define KHASH_DECLARE(name, khkey_t, khval_t) \
- typedef struct { \
- khuint_t n_buckets, size, n_occupied, upper_bound; \
- khuint32_t *flags; \
- khkey_t *keys; \
- khval_t *vals; \
- } kh_##name##_t; \
- extern kh_##name##_t *kh_init_##name(); \
- extern void kh_destroy_##name(kh_##name##_t *h); \
- extern void kh_clear_##name(kh_##name##_t *h); \
- extern khuint_t kh_get_##name(const kh_##name##_t *h, khkey_t key); \
- extern void kh_resize_##name(kh_##name##_t *h, khuint_t new_n_buckets); \
- extern khuint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret); \
- extern void kh_del_##name(kh_##name##_t *h, khuint_t x);
-
-#define KHASH_INIT2(name, SCOPE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal) \
- typedef struct { \
- khuint_t n_buckets, size, n_occupied, upper_bound; \
- khuint32_t *flags; \
- khkey_t *keys; \
- khval_t *vals; \
- } kh_##name##_t; \
- SCOPE kh_##name##_t *kh_init_##name(void) { \
- return (kh_##name##_t*)KHASH_CALLOC(1, sizeof(kh_##name##_t)); \
- } \
- SCOPE void kh_destroy_##name(kh_##name##_t *h) \
- { \
- if (h) { \
- KHASH_FREE(h->keys); KHASH_FREE(h->flags); \
- KHASH_FREE(h->vals); \
- KHASH_FREE(h); \
- } \
- } \
- SCOPE void kh_clear_##name(kh_##name##_t *h) \
- { \
- if (h && h->flags) { \
- memset(h->flags, 0xaa, __ac_fsize(h->n_buckets) * sizeof(khuint32_t)); \
- h->size = h->n_occupied = 0; \
- } \
- } \
- SCOPE khuint_t kh_get_##name(const kh_##name##_t *h, khkey_t key) \
- { \
- if (h->n_buckets) { \
- khuint_t inc, k, i, last, mask; \
- mask = h->n_buckets - 1; \
- k = __hash_func(key); i = k & mask; \
- inc = __ac_inc(k, mask); last = i; /* inc==1 for linear probing */ \
- while (!__ac_isempty(h->flags, i) && (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
- i = (i + inc) & mask; \
- if (i == last) return h->n_buckets; \
- } \
- return __ac_iseither(h->flags, i)? h->n_buckets : i; \
- } else return 0; \
- } \
- SCOPE void kh_resize_##name(kh_##name##_t *h, khuint_t new_n_buckets) \
- { /* This function uses 0.25*n_bucktes bytes of working space instead of [sizeof(key_t+val_t)+.25]*n_buckets. */ \
- khuint32_t *new_flags = 0; \
- khuint_t j = 1; \
- { \
- kroundup32(new_n_buckets); \
- if (new_n_buckets < 4) new_n_buckets = 4; \
- if (h->size >= (khuint_t)(new_n_buckets * __ac_HASH_UPPER + 0.5)) j = 0; /* requested size is too small */ \
- else { /* hash table size to be changed (shrink or expand); rehash */ \
- new_flags = (khuint32_t*)KHASH_MALLOC(__ac_fsize(new_n_buckets) * sizeof(khuint32_t)); \
- memset(new_flags, 0xff, __ac_fsize(new_n_buckets) * sizeof(khuint32_t)); \
- if (h->n_buckets < new_n_buckets) { /* expand */ \
- h->keys = (khkey_t*)KHASH_REALLOC(h->keys, new_n_buckets * sizeof(khkey_t)); \
- if (kh_is_map) h->vals = (khval_t*)KHASH_REALLOC(h->vals, new_n_buckets * sizeof(khval_t)); \
- } /* otherwise shrink */ \
- } \
- } \
- if (j) { /* rehashing is needed */ \
- for (j = 0; j != h->n_buckets; ++j) { \
- if (__ac_iseither(h->flags, j) == 0) { \
- khkey_t key = h->keys[j]; \
- khval_t val; \
- khuint_t new_mask; \
- new_mask = new_n_buckets - 1; \
- if (kh_is_map) val = h->vals[j]; \
- __ac_set_isempty_true(h->flags, j); \
- while (1) { /* kick-out process; sort of like in Cuckoo hashing */ \
- khuint_t inc, k, i; \
- k = __hash_func(key); \
- i = k & new_mask; \
- inc = __ac_inc(k, new_mask); \
- while (!__ac_isempty(new_flags, i)) i = (i + inc) & new_mask; \
- __ac_set_isempty_false(new_flags, i); \
- if (i < h->n_buckets && __ac_iseither(h->flags, i) == 0) { /* kick out the existing element */ \
- { khkey_t tmp = h->keys[i]; h->keys[i] = key; key = tmp; } \
- if (kh_is_map) { khval_t tmp = h->vals[i]; h->vals[i] = val; val = tmp; } \
- __ac_set_isempty_true(h->flags, i); /* mark it as deleted in the old hash table */ \
- } else { /* write the element and jump out of the loop */ \
- h->keys[i] = key; \
- if (kh_is_map) h->vals[i] = val; \
- break; \
- } \
- } \
- } \
- } \
- if (h->n_buckets > new_n_buckets) { /* shrink the hash table */ \
- h->keys = (khkey_t*)KHASH_REALLOC(h->keys, new_n_buckets * sizeof(khkey_t)); \
- if (kh_is_map) h->vals = (khval_t*)KHASH_REALLOC(h->vals, new_n_buckets * sizeof(khval_t)); \
- } \
- KHASH_FREE(h->flags); /* free the working space */ \
- h->flags = new_flags; \
- h->n_buckets = new_n_buckets; \
- h->n_occupied = h->size; \
- h->upper_bound = (khuint_t)(h->n_buckets * __ac_HASH_UPPER + 0.5); \
- } \
- } \
- SCOPE khuint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret) \
- { \
- khuint_t x; \
- if (h->n_occupied >= h->upper_bound) { /* update the hash table */ \
- if (h->n_buckets > (h->size<<1)) kh_resize_##name(h, h->n_buckets - 1); /* clear "deleted" elements */ \
- else kh_resize_##name(h, h->n_buckets + 1); /* expand the hash table */ \
- } /* TODO: to implement automatically shrinking; resize() already support shrinking */ \
- { \
- khuint_t inc, k, i, site, last, mask = h->n_buckets - 1; \
- x = site = h->n_buckets; k = __hash_func(key); i = k & mask; \
- if (__ac_isempty(h->flags, i)) x = i; /* for speed up */ \
- else { \
- inc = __ac_inc(k, mask); last = i; \
- while (!__ac_isempty(h->flags, i) && (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
- if (__ac_isdel(h->flags, i)) site = i; \
- i = (i + inc) & mask; \
- if (i == last) { x = site; break; } \
- } \
- if (x == h->n_buckets) { \
- if (__ac_isempty(h->flags, i) && site != h->n_buckets) x = site; \
- else x = i; \
- } \
- } \
- } \
- if (__ac_isempty(h->flags, x)) { /* not present at all */ \
- h->keys[x] = key; \
- __ac_set_isboth_false(h->flags, x); \
- ++h->size; ++h->n_occupied; \
- *ret = 1; \
- } else if (__ac_isdel(h->flags, x)) { /* deleted */ \
- h->keys[x] = key; \
- __ac_set_isboth_false(h->flags, x); \
- ++h->size; \
- *ret = 2; \
- } else *ret = 0; /* Don't touch h->keys[x] if present and not deleted */ \
- return x; \
- } \
- SCOPE void kh_del_##name(kh_##name##_t *h, khuint_t x) \
- { \
- if (x != h->n_buckets && !__ac_iseither(h->flags, x)) { \
- __ac_set_isdel_true(h->flags, x); \
- --h->size; \
- } \
- }
-
-#define KHASH_INIT(name, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal) \
- KHASH_INIT2(name, PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, __hash_equal)
+#define KHASH_DECLARE(name, khkey_t, khval_t) \
+ typedef struct { \
+ khuint_t n_buckets, size, n_occupied, upper_bound; \
+ khuint32_t *flags; \
+ khkey_t *keys; \
+ khval_t *vals; \
+ } kh_##name##_t; \
+ extern kh_##name##_t *kh_init_##name(); \
+ extern void kh_destroy_##name(kh_##name##_t *h); \
+ extern void kh_clear_##name(kh_##name##_t *h); \
+ extern khuint_t kh_get_##name(const kh_##name##_t *h, khkey_t key); \
+ extern void kh_resize_##name(kh_##name##_t *h, khuint_t new_n_buckets); \
+ extern khuint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret); \
+ extern void kh_del_##name(kh_##name##_t *h, khuint_t x);
+
+#define KHASH_INIT2(name, SCOPE, khkey_t, khval_t, kh_is_map, __hash_func, \
+ __hash_equal) \
+ typedef struct { \
+ khuint_t n_buckets, size, n_occupied, upper_bound; \
+ khuint32_t *flags; \
+ khkey_t *keys; \
+ khval_t *vals; \
+ } kh_##name##_t; \
+ SCOPE kh_##name##_t *kh_init_##name(void) { \
+ return (kh_##name##_t *)KHASH_CALLOC(1, sizeof(kh_##name##_t)); \
+ } \
+ SCOPE void kh_destroy_##name(kh_##name##_t *h) { \
+ if (h) { \
+ KHASH_FREE(h->keys); \
+ KHASH_FREE(h->flags); \
+ KHASH_FREE(h->vals); \
+ KHASH_FREE(h); \
+ } \
+ } \
+ SCOPE void kh_clear_##name(kh_##name##_t *h) { \
+ if (h && h->flags) { \
+ memset(h->flags, 0xaa, __ac_fsize(h->n_buckets) * sizeof(khuint32_t)); \
+ h->size = h->n_occupied = 0; \
+ } \
+ } \
+ SCOPE khuint_t kh_get_##name(const kh_##name##_t *h, khkey_t key) { \
+ if (h->n_buckets) { \
+ khuint_t inc, k, i, last, mask; \
+ mask = h->n_buckets - 1; \
+ k = __hash_func(key); \
+ i = k & mask; \
+ inc = __ac_inc(k, mask); \
+ last = i; /* inc==1 for linear probing */ \
+ while (!__ac_isempty(h->flags, i) && \
+ (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
+ i = (i + inc) & mask; \
+ if (i == last) \
+ return h->n_buckets; \
+ } \
+ return __ac_iseither(h->flags, i) ? h->n_buckets : i; \
+ } else \
+ return 0; \
+ } \
+ SCOPE void kh_resize_##name( \
+ kh_##name##_t *h, \
+ khuint_t new_n_buckets) { /* This function uses 0.25*n_bucktes bytes of \
+ working space instead of \
+ [sizeof(key_t+val_t)+.25]*n_buckets. */ \
+ khuint32_t *new_flags = 0; \
+ khuint_t j = 1; \
+ { \
+ kroundup32(new_n_buckets); \
+ if (new_n_buckets < 4) \
+ new_n_buckets = 4; \
+ if (h->size >= (khuint_t)(new_n_buckets * __ac_HASH_UPPER + 0.5)) \
+ j = 0; /* requested size is too small */ \
+ else { /* hash table size to be changed (shrink or expand); rehash */ \
+ new_flags = (khuint32_t *)KHASH_MALLOC(__ac_fsize(new_n_buckets) * \
+ sizeof(khuint32_t)); \
+ memset(new_flags, 0xff, \
+ __ac_fsize(new_n_buckets) * sizeof(khuint32_t)); \
+ if (h->n_buckets < new_n_buckets) { /* expand */ \
+ h->keys = (khkey_t *)KHASH_REALLOC(h->keys, \
+ new_n_buckets * sizeof(khkey_t)); \
+ if (kh_is_map) \
+ h->vals = (khval_t *)KHASH_REALLOC(h->vals, new_n_buckets * \
+ sizeof(khval_t)); \
+ } /* otherwise shrink */ \
+ } \
+ } \
+ if (j) { /* rehashing is needed */ \
+ for (j = 0; j != h->n_buckets; ++j) { \
+ if (__ac_iseither(h->flags, j) == 0) { \
+ khkey_t key = h->keys[j]; \
+ khval_t val; \
+ khuint_t new_mask; \
+ new_mask = new_n_buckets - 1; \
+ if (kh_is_map) \
+ val = h->vals[j]; \
+ __ac_set_isempty_true(h->flags, j); \
+ while (1) { /* kick-out process; sort of like in Cuckoo hashing */ \
+ khuint_t inc, k, i; \
+ k = __hash_func(key); \
+ i = k & new_mask; \
+ inc = __ac_inc(k, new_mask); \
+ while (!__ac_isempty(new_flags, i)) \
+ i = (i + inc) & new_mask; \
+ __ac_set_isempty_false(new_flags, i); \
+ if (i < h->n_buckets && \
+ __ac_iseither(h->flags, i) == \
+ 0) { /* kick out the existing element */ \
+ { \
+ khkey_t tmp = h->keys[i]; \
+ h->keys[i] = key; \
+ key = tmp; \
+ } \
+ if (kh_is_map) { \
+ khval_t tmp = h->vals[i]; \
+ h->vals[i] = val; \
+ val = tmp; \
+ } \
+ __ac_set_isempty_true( \
+ h->flags, i); /* mark it as deleted in the old hash table */ \
+ } else { /* write the element and jump out of the loop */ \
+ h->keys[i] = key; \
+ if (kh_is_map) \
+ h->vals[i] = val; \
+ break; \
+ } \
+ } \
+ } \
+ } \
+ if (h->n_buckets > new_n_buckets) { /* shrink the hash table */ \
+ h->keys = (khkey_t *)KHASH_REALLOC(h->keys, \
+ new_n_buckets * sizeof(khkey_t)); \
+ if (kh_is_map) \
+ h->vals = (khval_t *)KHASH_REALLOC(h->vals, \
+ new_n_buckets * sizeof(khval_t)); \
+ } \
+ KHASH_FREE(h->flags); /* free the working space */ \
+ h->flags = new_flags; \
+ h->n_buckets = new_n_buckets; \
+ h->n_occupied = h->size; \
+ h->upper_bound = (khuint_t)(h->n_buckets * __ac_HASH_UPPER + 0.5); \
+ } \
+ } \
+ SCOPE khuint_t kh_put_##name(kh_##name##_t *h, khkey_t key, int *ret) { \
+ khuint_t x; \
+ if (h->n_occupied >= h->upper_bound) { /* update the hash table */ \
+ if (h->n_buckets > (h->size << 1)) \
+ kh_resize_##name(h, h->n_buckets - 1); /* clear "deleted" elements */ \
+ else \
+ kh_resize_##name(h, h->n_buckets + 1); /* expand the hash table */ \
+ } /* TODO: to implement automatically shrinking; resize() already support \
+ shrinking */ \
+ { \
+ khuint_t inc, k, i, site, last, mask = h->n_buckets - 1; \
+ x = site = h->n_buckets; \
+ k = __hash_func(key); \
+ i = k & mask; \
+ if (__ac_isempty(h->flags, i)) \
+ x = i; /* for speed up */ \
+ else { \
+ inc = __ac_inc(k, mask); \
+ last = i; \
+ while (!__ac_isempty(h->flags, i) && \
+ (__ac_isdel(h->flags, i) || !__hash_equal(h->keys[i], key))) { \
+ if (__ac_isdel(h->flags, i)) \
+ site = i; \
+ i = (i + inc) & mask; \
+ if (i == last) { \
+ x = site; \
+ break; \
+ } \
+ } \
+ if (x == h->n_buckets) { \
+ if (__ac_isempty(h->flags, i) && site != h->n_buckets) \
+ x = site; \
+ else \
+ x = i; \
+ } \
+ } \
+ } \
+ if (__ac_isempty(h->flags, x)) { /* not present at all */ \
+ h->keys[x] = key; \
+ __ac_set_isboth_false(h->flags, x); \
+ ++h->size; \
+ ++h->n_occupied; \
+ *ret = 1; \
+ } else if (__ac_isdel(h->flags, x)) { /* deleted */ \
+ h->keys[x] = key; \
+ __ac_set_isboth_false(h->flags, x); \
+ ++h->size; \
+ *ret = 2; \
+ } else \
+ *ret = 0; /* Don't touch h->keys[x] if present and not deleted */ \
+ return x; \
+ } \
+ SCOPE void kh_del_##name(kh_##name##_t *h, khuint_t x) { \
+ if (x != h->n_buckets && !__ac_iseither(h->flags, x)) { \
+ __ac_set_isdel_true(h->flags, x); \
+ --h->size; \
+ } \
+ }
+
+#define KHASH_INIT(name, khkey_t, khval_t, kh_is_map, __hash_func, \
+ __hash_equal) \
+ KHASH_INIT2(name, PANDAS_INLINE, khkey_t, khval_t, kh_is_map, __hash_func, \
+ __hash_equal)
/* --- BEGIN OF HASH FUNCTIONS --- */
@@ -419,9 +465,8 @@ static const double __ac_HASH_UPPER = 0.77;
@param key The integer [khuint64_t]
@return The hash value [khuint_t]
*/
-PANDAS_INLINE khuint_t kh_int64_hash_func(khuint64_t key)
-{
- return (khuint_t)((key)>>33^(key)^(key)<<11);
+PANDAS_INLINE khuint_t kh_int64_hash_func(khuint64_t key) {
+ return (khuint_t)((key) >> 33 ^ (key) ^ (key) << 11);
}
/*! @function
@abstract 64-bit integer comparison function
@@ -433,11 +478,12 @@ PANDAS_INLINE khuint_t kh_int64_hash_func(khuint64_t key)
@param s Pointer to a null terminated string
@return The hash value
*/
-PANDAS_INLINE khuint_t __ac_X31_hash_string(const char *s)
-{
- khuint_t h = *s;
- if (h) for (++s ; *s; ++s) h = (h << 5) - h + *s;
- return h;
+PANDAS_INLINE khuint_t __ac_X31_hash_string(const char *s) {
+ khuint_t h = *s;
+ if (h)
+ for (++s; *s; ++s)
+ h = (h << 5) - h + *s;
+ return h;
}
/*! @function
@abstract Another interface to const char* hash function
@@ -450,15 +496,14 @@ PANDAS_INLINE khuint_t __ac_X31_hash_string(const char *s)
*/
#define kh_str_hash_equal(a, b) (strcmp(a, b) == 0)
-PANDAS_INLINE khuint_t __ac_Wang_hash(khuint_t key)
-{
- key += ~(key << 15);
- key ^= (key >> 10);
- key += (key << 3);
- key ^= (key >> 6);
- key += ~(key << 11);
- key ^= (key >> 16);
- return key;
+PANDAS_INLINE khuint_t __ac_Wang_hash(khuint_t key) {
+ key += ~(key << 15);
+ key ^= (key >> 10);
+ key += (key << 3);
+ key ^= (key >> 6);
+ key += ~(key << 11);
+ key ^= (key >> 16);
+ return key;
}
#define kh_int_hash_func2(k) __ac_Wang_hash((khuint_t)key)
@@ -508,7 +553,7 @@ PANDAS_INLINE khuint_t __ac_Wang_hash(khuint_t key)
@param k Key [type of keys]
@param r Extra return code: 0 if the key is present in the hash table;
1 if the bucket is empty (never used); 2 if the element in
- the bucket has been deleted [int*]
+ the bucket has been deleted [int*]
@return Iterator to the inserted element [khuint_t]
*/
#define kh_put(name, h, k, r) kh_put_##name(h, k, r)
@@ -518,7 +563,8 @@ PANDAS_INLINE khuint_t __ac_Wang_hash(khuint_t key)
@param name Name of the hash table [symbol]
@param h Pointer to the hash table [khash_t(name)*]
@param k Key [type of keys]
- @return Iterator to the found element, or kh_end(h) is the element is absent [khuint_t]
+ @return Iterator to the found element, or kh_end(h) is the element is
+ absent [khuint_t]
*/
#define kh_get(name, h, k) kh_get_##name(h, k)
@@ -594,81 +640,80 @@ PANDAS_INLINE khuint_t __ac_Wang_hash(khuint_t key)
@abstract Instantiate a hash set containing integer keys
@param name Name of the hash table [symbol]
*/
-#define KHASH_SET_INIT_INT(name) \
- KHASH_INIT(name, khint32_t, char, 0, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_SET_INIT_INT(name) \
+ KHASH_INIT(name, khint32_t, char, 0, kh_int_hash_func, kh_int_hash_equal)
/*! @function
@abstract Instantiate a hash map containing integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
-#define KHASH_MAP_INIT_INT(name, khval_t) \
- KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_MAP_INIT_INT(name, khval_t) \
+ KHASH_INIT(name, khint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
-#define KHASH_MAP_INIT_UINT(name, khval_t) \
- KHASH_INIT(name, khuint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_MAP_INIT_UINT(name, khval_t) \
+ KHASH_INIT(name, khuint32_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 64-bit integer keys
@param name Name of the hash table [symbol]
*/
-#define KHASH_SET_INIT_UINT64(name) \
- KHASH_INIT(name, khuint64_t, char, 0, kh_int64_hash_func, kh_int64_hash_equal)
+#define KHASH_SET_INIT_UINT64(name) \
+ KHASH_INIT(name, khuint64_t, char, 0, kh_int64_hash_func, kh_int64_hash_equal)
-#define KHASH_SET_INIT_INT64(name) \
- KHASH_INIT(name, khint64_t, char, 0, kh_int64_hash_func, kh_int64_hash_equal)
+#define KHASH_SET_INIT_INT64(name) \
+ KHASH_INIT(name, khint64_t, char, 0, kh_int64_hash_func, kh_int64_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 64-bit integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
-#define KHASH_MAP_INIT_UINT64(name, khval_t) \
- KHASH_INIT(name, khuint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
+#define KHASH_MAP_INIT_UINT64(name, khval_t) \
+ KHASH_INIT(name, khuint64_t, khval_t, 1, kh_int64_hash_func, \
+ kh_int64_hash_equal)
-#define KHASH_MAP_INIT_INT64(name, khval_t) \
- KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, kh_int64_hash_equal)
+#define KHASH_MAP_INIT_INT64(name, khval_t) \
+ KHASH_INIT(name, khint64_t, khval_t, 1, kh_int64_hash_func, \
+ kh_int64_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 16bit-integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
-#define KHASH_MAP_INIT_INT16(name, khval_t) \
- KHASH_INIT(name, khint16_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_MAP_INIT_INT16(name, khval_t) \
+ KHASH_INIT(name, khint16_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
-#define KHASH_MAP_INIT_UINT16(name, khval_t) \
- KHASH_INIT(name, khuint16_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_MAP_INIT_UINT16(name, khval_t) \
+ KHASH_INIT(name, khuint16_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
/*! @function
@abstract Instantiate a hash map containing 8bit-integer keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
-#define KHASH_MAP_INIT_INT8(name, khval_t) \
- KHASH_INIT(name, khint8_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
-
-#define KHASH_MAP_INIT_UINT8(name, khval_t) \
- KHASH_INIT(name, khuint8_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
-
+#define KHASH_MAP_INIT_INT8(name, khval_t) \
+ KHASH_INIT(name, khint8_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
+#define KHASH_MAP_INIT_UINT8(name, khval_t) \
+ KHASH_INIT(name, khuint8_t, khval_t, 1, kh_int_hash_func, kh_int_hash_equal)
typedef const char *kh_cstr_t;
/*! @function
@abstract Instantiate a hash map containing const char* keys
@param name Name of the hash table [symbol]
*/
-#define KHASH_SET_INIT_STR(name) \
- KHASH_INIT(name, kh_cstr_t, char, 0, kh_str_hash_func, kh_str_hash_equal)
+#define KHASH_SET_INIT_STR(name) \
+ KHASH_INIT(name, kh_cstr_t, char, 0, kh_str_hash_func, kh_str_hash_equal)
/*! @function
@abstract Instantiate a hash map containing const char* keys
@param name Name of the hash table [symbol]
@param khval_t Type of values [type]
*/
-#define KHASH_MAP_INIT_STR(name, khval_t) \
- KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
-
+#define KHASH_MAP_INIT_STR(name, khval_t) \
+ KHASH_INIT(name, kh_cstr_t, khval_t, 1, kh_str_hash_func, kh_str_hash_equal)
#define kh_exist_str(h, k) (kh_exist(h, k))
#define kh_exist_float64(h, k) (kh_exist(h, k))
@@ -692,5 +737,4 @@ KHASH_MAP_INIT_UINT16(uint16, size_t)
KHASH_MAP_INIT_INT8(int8, size_t)
KHASH_MAP_INIT_UINT8(uint8, size_t)
-
#endif /* __AC_KHASH_H */
diff --git a/pandas/_libs/include/pandas/vendored/klib/khash_python.h b/pandas/_libs/include/pandas/vendored/klib/khash_python.h
index 1009671adc56b..dc16a4ada1716 100644
--- a/pandas/_libs/include/pandas/vendored/klib/khash_python.h
+++ b/pandas/_libs/include/pandas/vendored/klib/khash_python.h
@@ -1,20 +1,17 @@
-//Licence at LICENSES/KLIB_LICENSE
+// Licence at LICENSES/KLIB_LICENSE
-#include <string.h>
#include <Python.h>
-
+#include <string.h>
typedef struct {
- float real;
- float imag;
+ float real;
+ float imag;
} khcomplex64_t;
typedef struct {
- double real;
- double imag;
+ double real;
+ double imag;
} khcomplex128_t;
-
-
// khash should report usage to tracemalloc
#if PY_VERSION_HEX >= 0x03060000
#include <pymem.h>
@@ -27,43 +24,41 @@ typedef struct {
#define PyTraceMalloc_Untrack(...)
#endif
-
static const int KHASH_TRACE_DOMAIN = 424242;
-void *traced_malloc(size_t size){
- void * ptr = malloc(size);
- if(ptr!=NULL){
- PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, size);
- }
- return ptr;
+void *traced_malloc(size_t size) {
+ void *ptr = malloc(size);
+ if (ptr != NULL) {
+ PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, size);
+ }
+ return ptr;
}
-void *traced_calloc(size_t num, size_t size){
- void * ptr = calloc(num, size);
- if(ptr!=NULL){
- PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, num*size);
- }
- return ptr;
+void *traced_calloc(size_t num, size_t size) {
+ void *ptr = calloc(num, size);
+ if (ptr != NULL) {
+ PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, num * size);
+ }
+ return ptr;
}
-void *traced_realloc(void* old_ptr, size_t size){
- void * ptr = realloc(old_ptr, size);
- if(ptr!=NULL){
- if(old_ptr != ptr){
- PyTraceMalloc_Untrack(KHASH_TRACE_DOMAIN, (uintptr_t)old_ptr);
- }
- PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, size);
+void *traced_realloc(void *old_ptr, size_t size) {
+ void *ptr = realloc(old_ptr, size);
+ if (ptr != NULL) {
+ if (old_ptr != ptr) {
+ PyTraceMalloc_Untrack(KHASH_TRACE_DOMAIN, (uintptr_t)old_ptr);
}
- return ptr;
+ PyTraceMalloc_Track(KHASH_TRACE_DOMAIN, (uintptr_t)ptr, size);
+ }
+ return ptr;
}
-void traced_free(void* ptr){
- if(ptr!=NULL){
- PyTraceMalloc_Untrack(KHASH_TRACE_DOMAIN, (uintptr_t)ptr);
- }
- free(ptr);
+void traced_free(void *ptr) {
+ if (ptr != NULL) {
+ PyTraceMalloc_Untrack(KHASH_TRACE_DOMAIN, (uintptr_t)ptr);
+ }
+ free(ptr);
}
-
#define KHASH_MALLOC traced_malloc
#define KHASH_REALLOC traced_realloc
#define KHASH_CALLOC traced_calloc
@@ -74,327 +69,295 @@ void traced_free(void* ptr){
// python 2.7 https://github.com/python/cpython/blob/2.7/Objects/object.c#L1021
// python 3.5 https://github.com/python/cpython/blob/3.5/Python/pyhash.c#L85
-// The python 3 hash function has the invariant hash(x) == hash(int(x)) == hash(decimal(x))
-// and the size of hash may be different by platform / version (long in py2, Py_ssize_t in py3).
-// We don't need those invariants because types will be cast before hashing, and if Py_ssize_t
-// is 64 bits the truncation causes collision issues. Given all that, we use our own
-// simple hash, viewing the double bytes as an int64 and using khash's default
-// hash for 64 bit integers.
-// GH 13436 showed that _Py_HashDouble doesn't work well with khash
-// GH 28303 showed, that the simple xoring-version isn't good enough
-// See GH 36729 for evaluation of the currently used murmur2-hash version
-// An interesting alternative to expensive murmur2-hash would be to change
-// the probing strategy and use e.g. the probing strategy from CPython's
+// The python 3 hash function has the invariant hash(x) == hash(int(x)) ==
+// hash(decimal(x)) and the size of hash may be different by platform / version
+// (long in py2, Py_ssize_t in py3). We don't need those invariants because
+// types will be cast before hashing, and if Py_ssize_t is 64 bits the
+// truncation causes collision issues. Given all that, we use our own simple
+// hash, viewing the double bytes as an int64 and using khash's default hash for
+// 64 bit integers. GH 13436 showed that _Py_HashDouble doesn't work well with
+// khash GH 28303 showed, that the simple xoring-version isn't good enough See
+// GH 36729 for evaluation of the currently used murmur2-hash version An
+// interesting alternative to expensive murmur2-hash would be to change the
+// probing strategy and use e.g. the probing strategy from CPython's
// implementation of dicts, which shines for smaller sizes but is more
// predisposed to superlinear running times (see GH 36729 for comparison)
-
khuint64_t PANDAS_INLINE asuint64(double key) {
- khuint64_t val;
- memcpy(&val, &key, sizeof(double));
- return val;
+ khuint64_t val;
+ memcpy(&val, &key, sizeof(double));
+ return val;
}
khuint32_t PANDAS_INLINE asuint32(float key) {
- khuint32_t val;
- memcpy(&val, &key, sizeof(float));
- return val;
+ khuint32_t val;
+ memcpy(&val, &key, sizeof(float));
+ return val;
}
#define ZERO_HASH 0
-#define NAN_HASH 0
-
-khuint32_t PANDAS_INLINE kh_float64_hash_func(double val){
- // 0.0 and -0.0 should have the same hash:
- if (val == 0.0){
- return ZERO_HASH;
- }
- // all nans should have the same hash:
- if ( val!=val ){
- return NAN_HASH;
- }
- khuint64_t as_int = asuint64(val);
- return murmur2_64to32(as_int);
+#define NAN_HASH 0
+
+khuint32_t PANDAS_INLINE kh_float64_hash_func(double val) {
+ // 0.0 and -0.0 should have the same hash:
+ if (val == 0.0) {
+ return ZERO_HASH;
+ }
+ // all nans should have the same hash:
+ if (val != val) {
+ return NAN_HASH;
+ }
+ khuint64_t as_int = asuint64(val);
+ return murmur2_64to32(as_int);
}
-khuint32_t PANDAS_INLINE kh_float32_hash_func(float val){
- // 0.0 and -0.0 should have the same hash:
- if (val == 0.0f){
- return ZERO_HASH;
- }
- // all nans should have the same hash:
- if ( val!=val ){
- return NAN_HASH;
- }
- khuint32_t as_int = asuint32(val);
- return murmur2_32to32(as_int);
+khuint32_t PANDAS_INLINE kh_float32_hash_func(float val) {
+ // 0.0 and -0.0 should have the same hash:
+ if (val == 0.0f) {
+ return ZERO_HASH;
+ }
+ // all nans should have the same hash:
+ if (val != val) {
+ return NAN_HASH;
+ }
+ khuint32_t as_int = asuint32(val);
+ return murmur2_32to32(as_int);
}
#define kh_floats_hash_equal(a, b) ((a) == (b) || ((b) != (b) && (a) != (a)))
-#define KHASH_MAP_INIT_FLOAT64(name, khval_t) \
- KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, kh_floats_hash_equal)
+#define KHASH_MAP_INIT_FLOAT64(name, khval_t) \
+ KHASH_INIT(name, khfloat64_t, khval_t, 1, kh_float64_hash_func, \
+ kh_floats_hash_equal)
KHASH_MAP_INIT_FLOAT64(float64, size_t)
-#define KHASH_MAP_INIT_FLOAT32(name, khval_t) \
- KHASH_INIT(name, khfloat32_t, khval_t, 1, kh_float32_hash_func, kh_floats_hash_equal)
+#define KHASH_MAP_INIT_FLOAT32(name, khval_t) \
+ KHASH_INIT(name, khfloat32_t, khval_t, 1, kh_float32_hash_func, \
+ kh_floats_hash_equal)
KHASH_MAP_INIT_FLOAT32(float32, size_t)
-khint32_t PANDAS_INLINE kh_complex128_hash_func(khcomplex128_t val){
- return kh_float64_hash_func(val.real)^kh_float64_hash_func(val.imag);
+khint32_t PANDAS_INLINE kh_complex128_hash_func(khcomplex128_t val) {
+ return kh_float64_hash_func(val.real) ^ kh_float64_hash_func(val.imag);
}
-khint32_t PANDAS_INLINE kh_complex64_hash_func(khcomplex64_t val){
- return kh_float32_hash_func(val.real)^kh_float32_hash_func(val.imag);
+khint32_t PANDAS_INLINE kh_complex64_hash_func(khcomplex64_t val) {
+ return kh_float32_hash_func(val.real) ^ kh_float32_hash_func(val.imag);
}
-#define kh_complex_hash_equal(a, b) \
+#define kh_complex_hash_equal(a, b) \
(kh_floats_hash_equal(a.real, b.real) && kh_floats_hash_equal(a.imag, b.imag))
-
-#define KHASH_MAP_INIT_COMPLEX64(name, khval_t) \
- KHASH_INIT(name, khcomplex64_t, khval_t, 1, kh_complex64_hash_func, kh_complex_hash_equal)
+#define KHASH_MAP_INIT_COMPLEX64(name, khval_t) \
+ KHASH_INIT(name, khcomplex64_t, khval_t, 1, kh_complex64_hash_func, \
+ kh_complex_hash_equal)
KHASH_MAP_INIT_COMPLEX64(complex64, size_t)
-
-#define KHASH_MAP_INIT_COMPLEX128(name, khval_t) \
- KHASH_INIT(name, khcomplex128_t, khval_t, 1, kh_complex128_hash_func, kh_complex_hash_equal)
+#define KHASH_MAP_INIT_COMPLEX128(name, khval_t) \
+ KHASH_INIT(name, khcomplex128_t, khval_t, 1, kh_complex128_hash_func, \
+ kh_complex_hash_equal)
KHASH_MAP_INIT_COMPLEX128(complex128, size_t)
-
#define kh_exist_complex64(h, k) (kh_exist(h, k))
#define kh_exist_complex128(h, k) (kh_exist(h, k))
-
// NaN-floats should be in the same equivalency class, see GH 22119
-int PANDAS_INLINE floatobject_cmp(PyFloatObject* a, PyFloatObject* b){
- return (
- Py_IS_NAN(PyFloat_AS_DOUBLE(a)) &&
- Py_IS_NAN(PyFloat_AS_DOUBLE(b))
- )
- ||
- ( PyFloat_AS_DOUBLE(a) == PyFloat_AS_DOUBLE(b) );
+int PANDAS_INLINE floatobject_cmp(PyFloatObject *a, PyFloatObject *b) {
+ return (Py_IS_NAN(PyFloat_AS_DOUBLE(a)) && Py_IS_NAN(PyFloat_AS_DOUBLE(b))) ||
+ (PyFloat_AS_DOUBLE(a) == PyFloat_AS_DOUBLE(b));
}
-
// NaNs should be in the same equivalency class, see GH 41836
// PyObject_RichCompareBool for complexobjects has a different behavior
// needs to be replaced
-int PANDAS_INLINE complexobject_cmp(PyComplexObject* a, PyComplexObject* b){
- return (
- Py_IS_NAN(a->cval.real) &&
- Py_IS_NAN(b->cval.real) &&
- Py_IS_NAN(a->cval.imag) &&
- Py_IS_NAN(b->cval.imag)
- )
- ||
- (
- Py_IS_NAN(a->cval.real) &&
- Py_IS_NAN(b->cval.real) &&
- a->cval.imag == b->cval.imag
- )
- ||
- (
- a->cval.real == b->cval.real &&
- Py_IS_NAN(a->cval.imag) &&
- Py_IS_NAN(b->cval.imag)
- )
- ||
- (
- a->cval.real == b->cval.real &&
- a->cval.imag == b->cval.imag
- );
+int PANDAS_INLINE complexobject_cmp(PyComplexObject *a, PyComplexObject *b) {
+ return (Py_IS_NAN(a->cval.real) && Py_IS_NAN(b->cval.real) &&
+ Py_IS_NAN(a->cval.imag) && Py_IS_NAN(b->cval.imag)) ||
+ (Py_IS_NAN(a->cval.real) && Py_IS_NAN(b->cval.real) &&
+ a->cval.imag == b->cval.imag) ||
+ (a->cval.real == b->cval.real && Py_IS_NAN(a->cval.imag) &&
+ Py_IS_NAN(b->cval.imag)) ||
+ (a->cval.real == b->cval.real && a->cval.imag == b->cval.imag);
}
-int PANDAS_INLINE pyobject_cmp(PyObject* a, PyObject* b);
-
+int PANDAS_INLINE pyobject_cmp(PyObject *a, PyObject *b);
// replacing PyObject_RichCompareBool (NaN!=NaN) with pyobject_cmp (NaN==NaN),
// which treats NaNs as equivalent
// see GH 41836
-int PANDAS_INLINE tupleobject_cmp(PyTupleObject* a, PyTupleObject* b){
- Py_ssize_t i;
+int PANDAS_INLINE tupleobject_cmp(PyTupleObject *a, PyTupleObject *b) {
+ Py_ssize_t i;
- if (Py_SIZE(a) != Py_SIZE(b)) {
- return 0;
- }
+ if (Py_SIZE(a) != Py_SIZE(b)) {
+ return 0;
+ }
- for (i = 0; i < Py_SIZE(a); ++i) {
- if (!pyobject_cmp(PyTuple_GET_ITEM(a, i), PyTuple_GET_ITEM(b, i))) {
- return 0;
- }
+ for (i = 0; i < Py_SIZE(a); ++i) {
+ if (!pyobject_cmp(PyTuple_GET_ITEM(a, i), PyTuple_GET_ITEM(b, i))) {
+ return 0;
}
- return 1;
+ }
+ return 1;
}
-
-int PANDAS_INLINE pyobject_cmp(PyObject* a, PyObject* b) {
- if (a == b) {
- return 1;
+int PANDAS_INLINE pyobject_cmp(PyObject *a, PyObject *b) {
+ if (a == b) {
+ return 1;
+ }
+ if (Py_TYPE(a) == Py_TYPE(b)) {
+ // special handling for some built-in types which could have NaNs
+ // as we would like to have them equivalent, but the usual
+ // PyObject_RichCompareBool would return False
+ if (PyFloat_CheckExact(a)) {
+ return floatobject_cmp((PyFloatObject *)a, (PyFloatObject *)b);
+ }
+ if (PyComplex_CheckExact(a)) {
+ return complexobject_cmp((PyComplexObject *)a, (PyComplexObject *)b);
}
- if (Py_TYPE(a) == Py_TYPE(b)) {
- // special handling for some built-in types which could have NaNs
- // as we would like to have them equivalent, but the usual
- // PyObject_RichCompareBool would return False
- if (PyFloat_CheckExact(a)) {
- return floatobject_cmp((PyFloatObject*)a, (PyFloatObject*)b);
- }
- if (PyComplex_CheckExact(a)) {
- return complexobject_cmp((PyComplexObject*)a, (PyComplexObject*)b);
- }
- if (PyTuple_CheckExact(a)) {
- return tupleobject_cmp((PyTupleObject*)a, (PyTupleObject*)b);
- }
- // frozenset isn't yet supported
+ if (PyTuple_CheckExact(a)) {
+ return tupleobject_cmp((PyTupleObject *)a, (PyTupleObject *)b);
}
+ // frozenset isn't yet supported
+ }
- int result = PyObject_RichCompareBool(a, b, Py_EQ);
- if (result < 0) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ int result = PyObject_RichCompareBool(a, b, Py_EQ);
+ if (result < 0) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
-
Py_hash_t PANDAS_INLINE _Pandas_HashDouble(double val) {
- //Since Python3.10, nan is no longer has hash 0
- if (Py_IS_NAN(val)) {
- return 0;
- }
+ // Since Python3.10, nan is no longer has hash 0
+ if (Py_IS_NAN(val)) {
+ return 0;
+ }
#if PY_VERSION_HEX < 0x030A0000
- return _Py_HashDouble(val);
+ return _Py_HashDouble(val);
#else
- return _Py_HashDouble(NULL, val);
+ return _Py_HashDouble(NULL, val);
#endif
}
-
-Py_hash_t PANDAS_INLINE floatobject_hash(PyFloatObject* key) {
- return _Pandas_HashDouble(PyFloat_AS_DOUBLE(key));
+Py_hash_t PANDAS_INLINE floatobject_hash(PyFloatObject *key) {
+ return _Pandas_HashDouble(PyFloat_AS_DOUBLE(key));
}
-
#define _PandasHASH_IMAG 1000003UL
// replaces _Py_HashDouble with _Pandas_HashDouble
-Py_hash_t PANDAS_INLINE complexobject_hash(PyComplexObject* key) {
- Py_uhash_t realhash = (Py_uhash_t)_Pandas_HashDouble(key->cval.real);
- Py_uhash_t imaghash = (Py_uhash_t)_Pandas_HashDouble(key->cval.imag);
- if (realhash == (Py_uhash_t)-1 || imaghash == (Py_uhash_t)-1) {
- return -1;
- }
- Py_uhash_t combined = realhash + _PandasHASH_IMAG * imaghash;
- if (combined == (Py_uhash_t)-1) {
- return -2;
- }
- return (Py_hash_t)combined;
+Py_hash_t PANDAS_INLINE complexobject_hash(PyComplexObject *key) {
+ Py_uhash_t realhash = (Py_uhash_t)_Pandas_HashDouble(key->cval.real);
+ Py_uhash_t imaghash = (Py_uhash_t)_Pandas_HashDouble(key->cval.imag);
+ if (realhash == (Py_uhash_t)-1 || imaghash == (Py_uhash_t)-1) {
+ return -1;
+ }
+ Py_uhash_t combined = realhash + _PandasHASH_IMAG * imaghash;
+ if (combined == (Py_uhash_t)-1) {
+ return -2;
+ }
+ return (Py_hash_t)combined;
}
+khuint32_t PANDAS_INLINE kh_python_hash_func(PyObject *key);
-khuint32_t PANDAS_INLINE kh_python_hash_func(PyObject* key);
-
-//we could use any hashing algorithm, this is the original CPython's for tuples
+// we could use any hashing algorithm, this is the original CPython's for tuples
#if SIZEOF_PY_UHASH_T > 4
#define _PandasHASH_XXPRIME_1 ((Py_uhash_t)11400714785074694791ULL)
#define _PandasHASH_XXPRIME_2 ((Py_uhash_t)14029467366897019727ULL)
#define _PandasHASH_XXPRIME_5 ((Py_uhash_t)2870177450012600261ULL)
-#define _PandasHASH_XXROTATE(x) ((x << 31) | (x >> 33)) /* Rotate left 31 bits */
+#define _PandasHASH_XXROTATE(x) \
+ ((x << 31) | (x >> 33)) /* Rotate left 31 bits */
#else
#define _PandasHASH_XXPRIME_1 ((Py_uhash_t)2654435761UL)
#define _PandasHASH_XXPRIME_2 ((Py_uhash_t)2246822519UL)
#define _PandasHASH_XXPRIME_5 ((Py_uhash_t)374761393UL)
-#define _PandasHASH_XXROTATE(x) ((x << 13) | (x >> 19)) /* Rotate left 13 bits */
+#define _PandasHASH_XXROTATE(x) \
+ ((x << 13) | (x >> 19)) /* Rotate left 13 bits */
#endif
-Py_hash_t PANDAS_INLINE tupleobject_hash(PyTupleObject* key) {
- Py_ssize_t i, len = Py_SIZE(key);
- PyObject **item = key->ob_item;
-
- Py_uhash_t acc = _PandasHASH_XXPRIME_5;
- for (i = 0; i < len; i++) {
- Py_uhash_t lane = kh_python_hash_func(item[i]);
- if (lane == (Py_uhash_t)-1) {
- return -1;
- }
- acc += lane * _PandasHASH_XXPRIME_2;
- acc = _PandasHASH_XXROTATE(acc);
- acc *= _PandasHASH_XXPRIME_1;
- }
-
- /* Add input length, mangled to keep the historical value of hash(()). */
- acc += len ^ (_PandasHASH_XXPRIME_5 ^ 3527539UL);
+Py_hash_t PANDAS_INLINE tupleobject_hash(PyTupleObject *key) {
+ Py_ssize_t i, len = Py_SIZE(key);
+ PyObject **item = key->ob_item;
- if (acc == (Py_uhash_t)-1) {
- return 1546275796;
+ Py_uhash_t acc = _PandasHASH_XXPRIME_5;
+ for (i = 0; i < len; i++) {
+ Py_uhash_t lane = kh_python_hash_func(item[i]);
+ if (lane == (Py_uhash_t)-1) {
+ return -1;
}
- return acc;
+ acc += lane * _PandasHASH_XXPRIME_2;
+ acc = _PandasHASH_XXROTATE(acc);
+ acc *= _PandasHASH_XXPRIME_1;
+ }
+
+ /* Add input length, mangled to keep the historical value of hash(()). */
+ acc += len ^ (_PandasHASH_XXPRIME_5 ^ 3527539UL);
+
+ if (acc == (Py_uhash_t)-1) {
+ return 1546275796;
+ }
+ return acc;
}
-
-khuint32_t PANDAS_INLINE kh_python_hash_func(PyObject* key) {
- Py_hash_t hash;
- // For PyObject_Hash holds:
- // hash(0.0) == 0 == hash(-0.0)
- // yet for different nan-objects different hash-values
- // are possible
- if (PyFloat_CheckExact(key)) {
- // we cannot use kh_float64_hash_func
- // because float(k) == k holds for any int-object k
- // and kh_float64_hash_func doesn't respect it
- hash = floatobject_hash((PyFloatObject*)key);
- }
- else if (PyComplex_CheckExact(key)) {
- // we cannot use kh_complex128_hash_func
- // because complex(k,0) == k holds for any int-object k
- // and kh_complex128_hash_func doesn't respect it
- hash = complexobject_hash((PyComplexObject*)key);
- }
- else if (PyTuple_CheckExact(key)) {
- hash = tupleobject_hash((PyTupleObject*)key);
- }
- else {
- hash = PyObject_Hash(key);
- }
-
- if (hash == -1) {
- PyErr_Clear();
- return 0;
- }
- #if SIZEOF_PY_HASH_T == 4
- // it is already 32bit value
- return hash;
- #else
- // for 64bit builds,
- // we need information of the upper 32bits as well
- // see GH 37615
- khuint64_t as_uint = (khuint64_t) hash;
- // uints avoid undefined behavior of signed ints
- return (as_uint>>32)^as_uint;
- #endif
+khuint32_t PANDAS_INLINE kh_python_hash_func(PyObject *key) {
+ Py_hash_t hash;
+ // For PyObject_Hash holds:
+ // hash(0.0) == 0 == hash(-0.0)
+ // yet for different nan-objects different hash-values
+ // are possible
+ if (PyFloat_CheckExact(key)) {
+ // we cannot use kh_float64_hash_func
+ // because float(k) == k holds for any int-object k
+ // and kh_float64_hash_func doesn't respect it
+ hash = floatobject_hash((PyFloatObject *)key);
+ } else if (PyComplex_CheckExact(key)) {
+ // we cannot use kh_complex128_hash_func
+ // because complex(k,0) == k holds for any int-object k
+ // and kh_complex128_hash_func doesn't respect it
+ hash = complexobject_hash((PyComplexObject *)key);
+ } else if (PyTuple_CheckExact(key)) {
+ hash = tupleobject_hash((PyTupleObject *)key);
+ } else {
+ hash = PyObject_Hash(key);
+ }
+
+ if (hash == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+#if SIZEOF_PY_HASH_T == 4
+ // it is already 32bit value
+ return hash;
+#else
+ // for 64bit builds,
+ // we need information of the upper 32bits as well
+ // see GH 37615
+ khuint64_t as_uint = (khuint64_t)hash;
+ // uints avoid undefined behavior of signed ints
+ return (as_uint >> 32) ^ as_uint;
+#endif
}
-
#define kh_python_hash_equal(a, b) (pyobject_cmp(a, b))
-
// Python object
-typedef PyObject* kh_pyobject_t;
+typedef PyObject *kh_pyobject_t;
-#define KHASH_MAP_INIT_PYOBJECT(name, khval_t) \
- KHASH_INIT(name, kh_pyobject_t, khval_t, 1, \
- kh_python_hash_func, kh_python_hash_equal)
+#define KHASH_MAP_INIT_PYOBJECT(name, khval_t) \
+ KHASH_INIT(name, kh_pyobject_t, khval_t, 1, kh_python_hash_func, \
+ kh_python_hash_equal)
KHASH_MAP_INIT_PYOBJECT(pymap, Py_ssize_t)
-#define KHASH_SET_INIT_PYOBJECT(name) \
- KHASH_INIT(name, kh_pyobject_t, char, 0, \
- kh_python_hash_func, kh_python_hash_equal)
+#define KHASH_SET_INIT_PYOBJECT(name) \
+ KHASH_INIT(name, kh_pyobject_t, char, 0, kh_python_hash_func, \
+ kh_python_hash_equal)
KHASH_SET_INIT_PYOBJECT(pyset)
@@ -404,49 +367,52 @@ KHASH_SET_INIT_PYOBJECT(pyset)
KHASH_MAP_INIT_STR(strbox, kh_pyobject_t)
typedef struct {
- kh_str_t *table;
- int starts[256];
+ kh_str_t *table;
+ int starts[256];
} kh_str_starts_t;
-typedef kh_str_starts_t* p_kh_str_starts_t;
+typedef kh_str_starts_t *p_kh_str_starts_t;
p_kh_str_starts_t PANDAS_INLINE kh_init_str_starts(void) {
- kh_str_starts_t *result = (kh_str_starts_t*)KHASH_CALLOC(1, sizeof(kh_str_starts_t));
- result->table = kh_init_str();
- return result;
+ kh_str_starts_t *result =
+ (kh_str_starts_t *)KHASH_CALLOC(1, sizeof(kh_str_starts_t));
+ result->table = kh_init_str();
+ return result;
}
-khuint_t PANDAS_INLINE kh_put_str_starts_item(kh_str_starts_t* table, char* key, int* ret) {
- khuint_t result = kh_put_str(table->table, key, ret);
- if (*ret != 0) {
- table->starts[(unsigned char)key[0]] = 1;
- }
- return result;
+khuint_t PANDAS_INLINE kh_put_str_starts_item(kh_str_starts_t *table, char *key,
+ int *ret) {
+ khuint_t result = kh_put_str(table->table, key, ret);
+ if (*ret != 0) {
+ table->starts[(unsigned char)key[0]] = 1;
+ }
+ return result;
}
-khuint_t PANDAS_INLINE kh_get_str_starts_item(const kh_str_starts_t* table, const char* key) {
- unsigned char ch = *key;
- if (table->starts[ch]) {
- if (ch == '\0' || kh_get_str(table->table, key) != table->table->n_buckets) return 1;
- }
- return 0;
+khuint_t PANDAS_INLINE kh_get_str_starts_item(const kh_str_starts_t *table,
+ const char *key) {
+ unsigned char ch = *key;
+ if (table->starts[ch]) {
+ if (ch == '\0' || kh_get_str(table->table, key) != table->table->n_buckets)
+ return 1;
+ }
+ return 0;
}
-void PANDAS_INLINE kh_destroy_str_starts(kh_str_starts_t* table) {
- kh_destroy_str(table->table);
- KHASH_FREE(table);
+void PANDAS_INLINE kh_destroy_str_starts(kh_str_starts_t *table) {
+ kh_destroy_str(table->table);
+ KHASH_FREE(table);
}
-void PANDAS_INLINE kh_resize_str_starts(kh_str_starts_t* table, khuint_t val) {
- kh_resize_str(table->table, val);
+void PANDAS_INLINE kh_resize_str_starts(kh_str_starts_t *table, khuint_t val) {
+ kh_resize_str(table->table, val);
}
// utility function: given the number of elements
// returns number of necessary buckets
-khuint_t PANDAS_INLINE kh_needed_n_buckets(khuint_t n_elements){
- khuint_t candidate = n_elements;
- kroundup32(candidate);
- khuint_t upper_bound = (khuint_t)(candidate * __ac_HASH_UPPER + 0.5);
- return (upper_bound < n_elements) ? 2*candidate : candidate;
-
+khuint_t PANDAS_INLINE kh_needed_n_buckets(khuint_t n_elements) {
+ khuint_t candidate = n_elements;
+ kroundup32(candidate);
+ khuint_t upper_bound = (khuint_t)(candidate * __ac_HASH_UPPER + 0.5);
+ return (upper_bound < n_elements) ? 2 * candidate : candidate;
}
diff --git a/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime.h b/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime.h
index 6b5135f559482..e4e90a7ea24cf 100644
--- a/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime.h
+++ b/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime.h
@@ -18,44 +18,44 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt
#ifndef NPY_NO_DEPRECATED_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#endif // NPY_NO_DEPRECATED_API
+#endif // NPY_NO_DEPRECATED_API
#include <numpy/ndarraytypes.h>
typedef struct {
- npy_int64 days;
- npy_int32 hrs, min, sec, ms, us, ns, seconds, microseconds, nanoseconds;
+ npy_int64 days;
+ npy_int32 hrs, min, sec, ms, us, ns, seconds, microseconds, nanoseconds;
} pandas_timedeltastruct;
-static const npy_datetimestruct _AS_MIN_DTS = {
- 1969, 12, 31, 23, 59, 50, 776627, 963145, 224193};
-static const npy_datetimestruct _FS_MIN_DTS = {
- 1969, 12, 31, 21, 26, 16, 627963, 145224, 193000};
-static const npy_datetimestruct _PS_MIN_DTS = {
- 1969, 9, 16, 5, 57, 7, 963145, 224193, 0};
-static const npy_datetimestruct _NS_MIN_DTS = {
- 1677, 9, 21, 0, 12, 43, 145224, 193000, 0};
-static const npy_datetimestruct _US_MIN_DTS = {
- -290308, 12, 21, 19, 59, 05, 224193, 0, 0};
-static const npy_datetimestruct _MS_MIN_DTS = {
- -292275055, 5, 16, 16, 47, 4, 193000, 0, 0};
+static const npy_datetimestruct _AS_MIN_DTS = {1969, 12, 31, 23, 59,
+ 50, 776627, 963145, 224193};
+static const npy_datetimestruct _FS_MIN_DTS = {1969, 12, 31, 21, 26,
+ 16, 627963, 145224, 193000};
+static const npy_datetimestruct _PS_MIN_DTS = {1969, 9, 16, 5, 57,
+ 7, 963145, 224193, 0};
+static const npy_datetimestruct _NS_MIN_DTS = {1677, 9, 21, 0, 12,
+ 43, 145224, 193000, 0};
+static const npy_datetimestruct _US_MIN_DTS = {-290308, 12, 21, 19, 59,
+ 05, 224193, 0, 0};
+static const npy_datetimestruct _MS_MIN_DTS = {-292275055, 5, 16, 16, 47,
+ 4, 193000, 0, 0};
static const npy_datetimestruct _S_MIN_DTS = {
-292277022657, 1, 27, 8, 29, 53, 0, 0, 0};
static const npy_datetimestruct _M_MIN_DTS = {
-17536621475646, 5, 4, 5, 53, 0, 0, 0, 0};
-static const npy_datetimestruct _AS_MAX_DTS = {
- 1970, 1, 1, 0, 0, 9, 223372, 36854, 775807};
-static const npy_datetimestruct _FS_MAX_DTS = {
- 1970, 1, 1, 2, 33, 43, 372036, 854775, 807000};
-static const npy_datetimestruct _PS_MAX_DTS = {
- 1970, 4, 17, 18, 2, 52, 36854, 775807, 0};
-static const npy_datetimestruct _NS_MAX_DTS = {
- 2262, 4, 11, 23, 47, 16, 854775, 807000, 0};
-static const npy_datetimestruct _US_MAX_DTS = {
- 294247, 1, 10, 4, 0, 54, 775807, 0, 0};
-static const npy_datetimestruct _MS_MAX_DTS = {
- 292278994, 8, 17, 7, 12, 55, 807000, 0, 0};
+static const npy_datetimestruct _AS_MAX_DTS = {1970, 1, 1, 0, 0,
+ 9, 223372, 36854, 775807};
+static const npy_datetimestruct _FS_MAX_DTS = {1970, 1, 1, 2, 33,
+ 43, 372036, 854775, 807000};
+static const npy_datetimestruct _PS_MAX_DTS = {1970, 4, 17, 18, 2,
+ 52, 36854, 775807, 0};
+static const npy_datetimestruct _NS_MAX_DTS = {2262, 4, 11, 23, 47,
+ 16, 854775, 807000, 0};
+static const npy_datetimestruct _US_MAX_DTS = {294247, 1, 10, 4, 0,
+ 54, 775807, 0, 0};
+static const npy_datetimestruct _MS_MAX_DTS = {292278994, 8, 17, 7, 12,
+ 55, 807000, 0, 0};
static const npy_datetimestruct _S_MAX_DTS = {
292277026596, 12, 4, 15, 30, 7, 0, 0, 0};
static const npy_datetimestruct _M_MAX_DTS = {
@@ -72,8 +72,7 @@ npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base,
void pandas_datetime_to_datetimestruct(npy_datetime val, NPY_DATETIMEUNIT fr,
npy_datetimestruct *result);
-void pandas_timedelta_to_timedeltastruct(npy_timedelta val,
- NPY_DATETIMEUNIT fr,
+void pandas_timedelta_to_timedeltastruct(npy_timedelta val, NPY_DATETIMEUNIT fr,
pandas_timedeltastruct *result);
extern const int days_per_month_table[2][12];
@@ -86,9 +85,7 @@ int is_leapyear(npy_int64 year);
/*
* Calculates the days offset from the 1970 epoch.
*/
-npy_int64
-get_datetimestruct_days(const npy_datetimestruct *dts);
-
+npy_int64 get_datetimestruct_days(const npy_datetimestruct *dts);
/*
* Compares two npy_datetimestruct objects chronologically
@@ -96,17 +93,14 @@ get_datetimestruct_days(const npy_datetimestruct *dts);
int cmp_npy_datetimestruct(const npy_datetimestruct *a,
const npy_datetimestruct *b);
-
/*
* Adjusts a datetimestruct based on a minutes offset. Assumes
* the current values are valid.
*/
-void
-add_minutes_to_datetimestruct(npy_datetimestruct *dts, int minutes);
+void add_minutes_to_datetimestruct(npy_datetimestruct *dts, int minutes);
/*
* This function returns the DateTimeMetaData
* contained within the provided datetime dtype.
*/
-PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(
- PyArray_Descr *dtype);
+PyArray_DatetimeMetaData get_datetime_metadata_from_dtype(PyArray_Descr *dtype);
diff --git a/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime_strings.h b/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime_strings.h
index 1098637e798fe..d96ca79d70cb7 100644
--- a/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime_strings.h
+++ b/pandas/_libs/include/pandas/vendored/numpy/datetime/np_datetime_strings.h
@@ -23,7 +23,7 @@ This file implements string parsing and creation for NumPy datetime.
#ifndef NPY_NO_DEPRECATED_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#endif // NPY_NO_DEPRECATED_API
+#endif // NPY_NO_DEPRECATED_API
/* 'format_requirement' can be one of three values:
* * PARTIAL_MATCH : Only require a partial match with 'format'.
@@ -34,11 +34,7 @@ This file implements string parsing and creation for NumPy datetime.
* be able to parse it without error is '%Y-%m-%d';
* * INFER_FORMAT: parse without comparing 'format' (i.e. infer it).
*/
-typedef enum {
- PARTIAL_MATCH,
- EXACT_MATCH,
- INFER_FORMAT
-} FormatRequirement;
+typedef enum { PARTIAL_MATCH, EXACT_MATCH, INFER_FORMAT } FormatRequirement;
/*
* Parses (almost) standard ISO 8601 date strings. The differences are:
@@ -58,31 +54,26 @@ typedef enum {
* 'str' must be a NULL-terminated string, and 'len' must be its length.
*
* 'out' gets filled with the parsed date-time.
- * 'out_local' gets whether returned value contains timezone. 0 for UTC, 1 for local time.
- * 'out_tzoffset' gets set to timezone offset by minutes
- * if the parsed time was in local time,
- * to 0 otherwise. The values 'now' and 'today' don't get counted
- * as local, and neither do UTC +/-#### timezone offsets, because
- * they aren't using the computer's local timezone offset.
+ * 'out_local' gets whether returned value contains timezone. 0 for UTC, 1 for
+ * local time. 'out_tzoffset' gets set to timezone offset by minutes if the
+ * parsed time was in local time, to 0 otherwise. The values 'now' and 'today'
+ * don't get counted as local, and neither do UTC +/-#### timezone offsets,
+ * because they aren't using the computer's local timezone offset.
*
* Returns 0 on success, -1 on failure.
*/
-int
-parse_iso_8601_datetime(const char *str, int len, int want_exc,
- npy_datetimestruct *out,
- NPY_DATETIMEUNIT *out_bestunit,
- int *out_local,
- int *out_tzoffset,
- const char* format,
- int format_len,
- FormatRequirement format_requirement);
+int parse_iso_8601_datetime(const char *str, int len, int want_exc,
+ npy_datetimestruct *out,
+ NPY_DATETIMEUNIT *out_bestunit, int *out_local,
+ int *out_tzoffset, const char *format,
+ int format_len,
+ FormatRequirement format_requirement);
/*
* Provides a string length to use for converting datetime
* objects with the given local and unit settings.
*/
-int
-get_datetime_iso_8601_strlen(int local, NPY_DATETIMEUNIT base);
+int get_datetime_iso_8601_strlen(int local, NPY_DATETIMEUNIT base);
/*
* Converts an npy_datetimestruct to an (almost) ISO 8601
@@ -94,9 +85,8 @@ get_datetime_iso_8601_strlen(int local, NPY_DATETIMEUNIT base);
* Returns 0 on success, -1 on failure (for example if the output
* string was too short).
*/
-int
-make_iso_8601_datetime(npy_datetimestruct *dts, char *outstr, int outlen,
- int utc, NPY_DATETIMEUNIT base);
+int make_iso_8601_datetime(npy_datetimestruct *dts, char *outstr, int outlen,
+ int utc, NPY_DATETIMEUNIT base);
/*
* Converts an pandas_timedeltastruct to an ISO 8601 string.
diff --git a/pandas/_libs/include/pandas/vendored/ujson/lib/ultrajson.h b/pandas/_libs/include/pandas/vendored/ujson/lib/ultrajson.h
index 54bcca9e4136c..d60335fbaee4d 100644
--- a/pandas/_libs/include/pandas/vendored/ujson/lib/ultrajson.h
+++ b/pandas/_libs/include/pandas/vendored/ujson/lib/ultrajson.h
@@ -16,18 +16,19 @@ modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
+THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
https://github.com/client9/stringencoders
-Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved.
+Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights
+reserved.
Numeric decoder derived from TCL library
https://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
@@ -51,9 +52,9 @@ tree doesn't have cyclic references.
#pragma once
+#include "pandas/portable.h"
#include <stdio.h>
#include <wchar.h>
-#include "pandas/portable.h"
// Don't output any extra whitespaces when encoding
#define JSON_NO_EXTRA_WHITESPACE
@@ -74,7 +75,8 @@ tree doesn't have cyclic references.
#endif
/*
-Dictates and limits how much stack space for buffers UltraJSON will use before resorting to provided heap functions */
+Dictates and limits how much stack space for buffers UltraJSON will use before
+resorting to provided heap functions */
#ifndef JSON_MAX_STACK_BUFFER_SIZE
#define JSON_MAX_STACK_BUFFER_SIZE 131072
#endif
@@ -138,23 +140,23 @@ typedef int64_t JSLONG;
#endif
enum JSTYPES {
- JT_NULL, // NULL
- JT_TRUE, // boolean true
- JT_FALSE, // boolean false
- JT_INT, // (JSINT32 (signed 32-bit))
- JT_LONG, // (JSINT64 (signed 64-bit))
- JT_DOUBLE, // (double)
- JT_BIGNUM, // integer larger than sys.maxsize
- JT_UTF8, // (char 8-bit)
- JT_ARRAY, // Array structure
- JT_OBJECT, // Key/Value structure
- JT_INVALID, // Internal, do not return nor expect
- JT_POS_INF, // Positive infinity
- JT_NEG_INF, // Negative infinity
+ JT_NULL, // NULL
+ JT_TRUE, // boolean true
+ JT_FALSE, // boolean false
+ JT_INT, // (JSINT32 (signed 32-bit))
+ JT_LONG, // (JSINT64 (signed 64-bit))
+ JT_DOUBLE, // (double)
+ JT_BIGNUM, // integer larger than sys.maxsize
+ JT_UTF8, // (char 8-bit)
+ JT_ARRAY, // Array structure
+ JT_OBJECT, // Key/Value structure
+ JT_INVALID, // Internal, do not return nor expect
+ JT_POS_INF, // Positive infinity
+ JT_NEG_INF, // Negative infinity
};
-typedef void * JSOBJ;
-typedef void * JSITER;
+typedef void *JSOBJ;
+typedef void *JSITER;
typedef struct __JSONTypeContext {
int type;
@@ -183,7 +185,7 @@ typedef struct __JSONObjectEncoder {
JSINT32 (*getIntValue)(JSOBJ obj, JSONTypeContext *tc);
double (*getDoubleValue)(JSOBJ obj, JSONTypeContext *tc);
const char *(*getBigNumStringValue)(JSOBJ obj, JSONTypeContext *tc,
- size_t *_outLen);
+ size_t *_outLen);
/*
Begin iteration of an iterable object (JS_ARRAY or JS_OBJECT)
@@ -192,8 +194,9 @@ typedef struct __JSONObjectEncoder {
JSPFN_ITERBEGIN iterBegin;
/*
- Retrieve next object in an iteration. Should return 0 to indicate iteration has reached end or 1 if there are more items.
- Implementor is responsible for keeping state of the iteration. Use ti->prv fields for this
+ Retrieve next object in an iteration. Should return 0 to indicate iteration
+ has reached end or 1 if there are more items. Implementor is responsible for
+ keeping state of the iteration. Use ti->prv fields for this
*/
JSPFN_ITERNEXT iterNext;
@@ -205,19 +208,22 @@ typedef struct __JSONObjectEncoder {
/*
Returns a reference to the value object of an iterator
- The is responsible for the life-cycle of the returned string. Use iterNext/iterEnd and ti->prv to keep track of current object
+ The is responsible for the life-cycle of the returned string. Use
+ iterNext/iterEnd and ti->prv to keep track of current object
*/
JSPFN_ITERGETVALUE iterGetValue;
/*
Return name of iterator.
- The is responsible for the life-cycle of the returned string. Use iterNext/iterEnd and ti->prv to keep track of current object
+ The is responsible for the life-cycle of the returned string. Use
+ iterNext/iterEnd and ti->prv to keep track of current object
*/
JSPFN_ITERGETNAME iterGetName;
/*
- Release a value as indicated by setting ti->release = 1 in the previous getValue call.
- The ti->prv array should contain the necessary context to release the value
+ Release a value as indicated by setting ti->release = 1 in the previous
+ getValue call. The ti->prv array should contain the necessary context to
+ release the value
*/
void (*releaseObject)(JSOBJ obj);
@@ -228,19 +234,23 @@ typedef struct __JSONObjectEncoder {
JSPFN_FREE free;
/*
- Configuration for max recursion, set to 0 to use default (see JSON_MAX_RECURSION_DEPTH)*/
+ Configuration for max recursion, set to 0 to use default (see
+ JSON_MAX_RECURSION_DEPTH)*/
int recursionMax;
/*
- Configuration for max decimals of double floating point numbers to encode (0-9) */
+ Configuration for max decimals of double floating point numbers to encode
+ (0-9) */
int doublePrecision;
/*
- If true output will be ASCII with all characters above 127 encoded as \uXXXX. If false output will be UTF-8 or what ever charset strings are brought as */
+ If true output will be ASCII with all characters above 127 encoded as \uXXXX.
+ If false output will be UTF-8 or what ever charset strings are brought as */
int forceASCII;
/*
- If true, '<', '>', and '&' characters will be encoded as \u003c, \u003e, and \u0026, respectively. If false, no special encoding will be used. */
+ If true, '<', '>', and '&' characters will be encoded as \u003c, \u003e, and
+ \u0026, respectively. If false, no special encoding will be used. */
int encodeHTMLChars;
/*
@@ -266,18 +276,20 @@ Encode an object structure into JSON.
Arguments:
obj - An anonymous type representing the object
enc - Function definitions for querying JSOBJ type
-buffer - Preallocated buffer to store result in. If NULL function allocates own buffer
-cbBuffer - Length of buffer (ignored if buffer is NULL)
+buffer - Preallocated buffer to store result in. If NULL function allocates own
+buffer cbBuffer - Length of buffer (ignored if buffer is NULL)
Returns:
Encoded JSON object as a null terminated char string.
NOTE:
-If the supplied buffer wasn't enough to hold the result the function will allocate a new buffer.
-Life cycle of the provided buffer must still be handled by caller.
+If the supplied buffer wasn't enough to hold the result the function will
+allocate a new buffer. Life cycle of the provided buffer must still be handled
+by caller.
-If the return value doesn't equal the specified buffer caller must release the memory using
-JSONObjectEncoder.free or free() as specified when calling this function.
+If the return value doesn't equal the specified buffer caller must release the
+memory using JSONObjectEncoder.free or free() as specified when calling this
+function.
*/
EXPORTFUNCTION char *JSON_EncodeObject(JSOBJ obj, JSONObjectEncoder *enc,
char *buffer, size_t cbBuffer);
diff --git a/pandas/_libs/include/pandas/vendored/ujson/python/version.h b/pandas/_libs/include/pandas/vendored/ujson/python/version.h
index 97232dd821387..4b00670c946af 100644
--- a/pandas/_libs/include/pandas/vendored/ujson/python/version.h
+++ b/pandas/_libs/include/pandas/vendored/ujson/python/version.h
@@ -16,18 +16,19 @@ modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
+THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
https://github.com/client9/stringencoders
-Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved.
+Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights
+reserved.
Numeric decoder derived from TCL library
https://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
diff --git a/pandas/_libs/src/datetime/date_conversions.c b/pandas/_libs/src/datetime/date_conversions.c
index 3bc3275be1cfe..4b172349de8d3 100644
--- a/pandas/_libs/src/datetime/date_conversions.c
+++ b/pandas/_libs/src/datetime/date_conversions.c
@@ -21,83 +21,81 @@ The full license is in the LICENSE file, distributed with this software.
* Mutates the provided value directly. Returns 0 on success, non-zero on error.
*/
int scaleNanosecToUnit(npy_int64 *value, NPY_DATETIMEUNIT unit) {
- switch (unit) {
- case NPY_FR_ns:
- break;
- case NPY_FR_us:
- *value /= 1000LL;
- break;
- case NPY_FR_ms:
- *value /= 1000000LL;
- break;
- case NPY_FR_s:
- *value /= 1000000000LL;
- break;
- default:
- return -1;
- }
+ switch (unit) {
+ case NPY_FR_ns:
+ break;
+ case NPY_FR_us:
+ *value /= 1000LL;
+ break;
+ case NPY_FR_ms:
+ *value /= 1000000LL;
+ break;
+ case NPY_FR_s:
+ *value /= 1000000000LL;
+ break;
+ default:
+ return -1;
+ }
- return 0;
+ return 0;
}
/* Converts the int64_t representation of a datetime to ISO; mutates len */
-char *int64ToIso(int64_t value,
- NPY_DATETIMEUNIT valueUnit,
- NPY_DATETIMEUNIT base,
- size_t *len) {
- npy_datetimestruct dts;
- int ret_code;
+char *int64ToIso(int64_t value, NPY_DATETIMEUNIT valueUnit,
+ NPY_DATETIMEUNIT base, size_t *len) {
+ npy_datetimestruct dts;
+ int ret_code;
- pandas_datetime_to_datetimestruct(value, valueUnit, &dts);
+ pandas_datetime_to_datetimestruct(value, valueUnit, &dts);
- *len = (size_t)get_datetime_iso_8601_strlen(0, base);
- char *result = PyObject_Malloc(*len);
+ *len = (size_t)get_datetime_iso_8601_strlen(0, base);
+ char *result = PyObject_Malloc(*len);
- if (result == NULL) {
- PyErr_NoMemory();
- return NULL;
- }
- // datetime64 is always naive
- ret_code = make_iso_8601_datetime(&dts, result, *len, 0, base);
- if (ret_code != 0) {
- PyErr_SetString(PyExc_ValueError,
- "Could not convert datetime value to string");
- PyObject_Free(result);
- }
+ if (result == NULL) {
+ PyErr_NoMemory();
+ return NULL;
+ }
+ // datetime64 is always naive
+ ret_code = make_iso_8601_datetime(&dts, result, *len, 0, base);
+ if (ret_code != 0) {
+ PyErr_SetString(PyExc_ValueError,
+ "Could not convert datetime value to string");
+ PyObject_Free(result);
+ }
- // Note that get_datetime_iso_8601_strlen just gives a generic size
- // for ISO string conversion, not the actual size used
- *len = strlen(result);
- return result;
+ // Note that get_datetime_iso_8601_strlen just gives a generic size
+ // for ISO string conversion, not the actual size used
+ *len = strlen(result);
+ return result;
}
npy_datetime NpyDateTimeToEpoch(npy_datetime dt, NPY_DATETIMEUNIT base) {
- scaleNanosecToUnit(&dt, base);
- return dt;
+ scaleNanosecToUnit(&dt, base);
+ return dt;
}
/* Converts the int64_t representation of a duration to ISO; mutates len */
char *int64ToIsoDuration(int64_t value, size_t *len) {
- pandas_timedeltastruct tds;
- int ret_code;
+ pandas_timedeltastruct tds;
+ int ret_code;
- pandas_timedelta_to_timedeltastruct(value, NPY_FR_ns, &tds);
+ pandas_timedelta_to_timedeltastruct(value, NPY_FR_ns, &tds);
- // Max theoretical length of ISO Duration with 64 bit day
- // as the largest unit is 70 characters + 1 for a null terminator
- char *result = PyObject_Malloc(71);
- if (result == NULL) {
- PyErr_NoMemory();
- return NULL;
- }
+ // Max theoretical length of ISO Duration with 64 bit day
+ // as the largest unit is 70 characters + 1 for a null terminator
+ char *result = PyObject_Malloc(71);
+ if (result == NULL) {
+ PyErr_NoMemory();
+ return NULL;
+ }
- ret_code = make_iso_8601_timedelta(&tds, result, len);
- if (ret_code == -1) {
- PyErr_SetString(PyExc_ValueError,
- "Could not convert timedelta value to string");
- PyObject_Free(result);
- return NULL;
- }
+ ret_code = make_iso_8601_timedelta(&tds, result, len);
+ if (ret_code == -1) {
+ PyErr_SetString(PyExc_ValueError,
+ "Could not convert timedelta value to string");
+ PyObject_Free(result);
+ return NULL;
+ }
- return result;
+ return result;
}
diff --git a/pandas/_libs/src/datetime/pd_datetime.c b/pandas/_libs/src/datetime/pd_datetime.c
index fc2cbcab90174..b201023114f8a 100644
--- a/pandas/_libs/src/datetime/pd_datetime.c
+++ b/pandas/_libs/src/datetime/pd_datetime.c
@@ -22,7 +22,6 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt
#include "datetime.h"
#include "pandas/datetime/pd_datetime.h"
-
static void pandas_datetime_destructor(PyObject *op) {
void *ptr = PyCapsule_GetPointer(op, PandasDateTime_CAPSULE_NAME);
PyMem_Free(ptr);
@@ -42,77 +41,77 @@ static void pandas_datetime_destructor(PyObject *op) {
* if obj doesn't have the needed date or datetime attributes.
*/
static int convert_pydatetime_to_datetimestruct(PyObject *dtobj,
- npy_datetimestruct *out) {
- // Assumes that obj is a valid datetime object
- PyObject *tmp;
- PyObject *obj = (PyObject*)dtobj;
-
- /* Initialize the output to all zeros */
- memset(out, 0, sizeof(npy_datetimestruct));
- out->month = 1;
- out->day = 1;
-
- out->year = PyLong_AsLong(PyObject_GetAttrString(obj, "year"));
- out->month = PyLong_AsLong(PyObject_GetAttrString(obj, "month"));
- out->day = PyLong_AsLong(PyObject_GetAttrString(obj, "day"));
-
- // TODO(anyone): If we can get PyDateTime_IMPORT to work, we could use
- // PyDateTime_Check here, and less verbose attribute lookups.
-
- /* Check for time attributes (if not there, return success as a date) */
- if (!PyObject_HasAttrString(obj, "hour") ||
- !PyObject_HasAttrString(obj, "minute") ||
- !PyObject_HasAttrString(obj, "second") ||
- !PyObject_HasAttrString(obj, "microsecond")) {
- return 0;
- }
+ npy_datetimestruct *out) {
+ // Assumes that obj is a valid datetime object
+ PyObject *tmp;
+ PyObject *obj = (PyObject *)dtobj;
+
+ /* Initialize the output to all zeros */
+ memset(out, 0, sizeof(npy_datetimestruct));
+ out->month = 1;
+ out->day = 1;
+
+ out->year = PyLong_AsLong(PyObject_GetAttrString(obj, "year"));
+ out->month = PyLong_AsLong(PyObject_GetAttrString(obj, "month"));
+ out->day = PyLong_AsLong(PyObject_GetAttrString(obj, "day"));
+
+ // TODO(anyone): If we can get PyDateTime_IMPORT to work, we could use
+ // PyDateTime_Check here, and less verbose attribute lookups.
+
+ /* Check for time attributes (if not there, return success as a date) */
+ if (!PyObject_HasAttrString(obj, "hour") ||
+ !PyObject_HasAttrString(obj, "minute") ||
+ !PyObject_HasAttrString(obj, "second") ||
+ !PyObject_HasAttrString(obj, "microsecond")) {
+ return 0;
+ }
- out->hour = PyLong_AsLong(PyObject_GetAttrString(obj, "hour"));
- out->min = PyLong_AsLong(PyObject_GetAttrString(obj, "minute"));
- out->sec = PyLong_AsLong(PyObject_GetAttrString(obj, "second"));
- out->us = PyLong_AsLong(PyObject_GetAttrString(obj, "microsecond"));
-
- if (PyObject_HasAttrString(obj, "tzinfo")) {
- PyObject *offset = extract_utc_offset(obj);
- /* Apply the time zone offset if datetime obj is tz-aware */
- if (offset != NULL) {
- if (offset == Py_None) {
- Py_DECREF(offset);
- return 0;
- }
- PyObject *tmp_int;
- int seconds_offset, minutes_offset;
- /*
- * The timedelta should have a function "total_seconds"
- * which contains the value we want.
- */
- tmp = PyObject_CallMethod(offset, "total_seconds", "");
- Py_DECREF(offset);
- if (tmp == NULL) {
- return -1;
- }
- tmp_int = PyNumber_Long(tmp);
- if (tmp_int == NULL) {
- Py_DECREF(tmp);
- return -1;
- }
- seconds_offset = PyLong_AsLong(tmp_int);
- if (seconds_offset == -1 && PyErr_Occurred()) {
- Py_DECREF(tmp_int);
- Py_DECREF(tmp);
- return -1;
- }
- Py_DECREF(tmp_int);
- Py_DECREF(tmp);
-
- /* Convert to a minutes offset and apply it */
- minutes_offset = seconds_offset / 60;
-
- add_minutes_to_datetimestruct(out, -minutes_offset);
- }
+ out->hour = PyLong_AsLong(PyObject_GetAttrString(obj, "hour"));
+ out->min = PyLong_AsLong(PyObject_GetAttrString(obj, "minute"));
+ out->sec = PyLong_AsLong(PyObject_GetAttrString(obj, "second"));
+ out->us = PyLong_AsLong(PyObject_GetAttrString(obj, "microsecond"));
+
+ if (PyObject_HasAttrString(obj, "tzinfo")) {
+ PyObject *offset = extract_utc_offset(obj);
+ /* Apply the time zone offset if datetime obj is tz-aware */
+ if (offset != NULL) {
+ if (offset == Py_None) {
+ Py_DECREF(offset);
+ return 0;
+ }
+ PyObject *tmp_int;
+ int seconds_offset, minutes_offset;
+ /*
+ * The timedelta should have a function "total_seconds"
+ * which contains the value we want.
+ */
+ tmp = PyObject_CallMethod(offset, "total_seconds", "");
+ Py_DECREF(offset);
+ if (tmp == NULL) {
+ return -1;
+ }
+ tmp_int = PyNumber_Long(tmp);
+ if (tmp_int == NULL) {
+ Py_DECREF(tmp);
+ return -1;
+ }
+ seconds_offset = PyLong_AsLong(tmp_int);
+ if (seconds_offset == -1 && PyErr_Occurred()) {
+ Py_DECREF(tmp_int);
+ Py_DECREF(tmp);
+ return -1;
+ }
+ Py_DECREF(tmp_int);
+ Py_DECREF(tmp);
+
+ /* Convert to a minutes offset and apply it */
+ minutes_offset = seconds_offset / 60;
+
+ add_minutes_to_datetimestruct(out, -minutes_offset);
}
+ }
- return 0;
+ return 0;
}
// Converts a Python object representing a Date / Datetime to ISO format
@@ -120,66 +119,66 @@ static int convert_pydatetime_to_datetimestruct(PyObject *dtobj,
// while base="ns" yields "2020-01-01T00:00:00.000000000Z"
// len is mutated to save the length of the returned string
static char *PyDateTimeToIso(PyObject *obj, NPY_DATETIMEUNIT base,
- size_t *len) {
- npy_datetimestruct dts;
- int ret;
-
- ret = convert_pydatetime_to_datetimestruct(obj, &dts);
- if (ret != 0) {
- if (!PyErr_Occurred()) {
- PyErr_SetString(PyExc_ValueError,
- "Could not convert PyDateTime to numpy datetime");
- }
- return NULL;
+ size_t *len) {
+ npy_datetimestruct dts;
+ int ret;
+
+ ret = convert_pydatetime_to_datetimestruct(obj, &dts);
+ if (ret != 0) {
+ if (!PyErr_Occurred()) {
+ PyErr_SetString(PyExc_ValueError,
+ "Could not convert PyDateTime to numpy datetime");
}
+ return NULL;
+ }
- *len = (size_t)get_datetime_iso_8601_strlen(0, base);
- char *result = PyObject_Malloc(*len);
- // Check to see if PyDateTime has a timezone.
- // Don't convert to UTC if it doesn't.
- int is_tz_aware = 0;
- if (PyObject_HasAttrString(obj, "tzinfo")) {
- PyObject *offset = extract_utc_offset(obj);
- if (offset == NULL) {
- PyObject_Free(result);
- return NULL;
- }
- is_tz_aware = offset != Py_None;
- Py_DECREF(offset);
+ *len = (size_t)get_datetime_iso_8601_strlen(0, base);
+ char *result = PyObject_Malloc(*len);
+ // Check to see if PyDateTime has a timezone.
+ // Don't convert to UTC if it doesn't.
+ int is_tz_aware = 0;
+ if (PyObject_HasAttrString(obj, "tzinfo")) {
+ PyObject *offset = extract_utc_offset(obj);
+ if (offset == NULL) {
+ PyObject_Free(result);
+ return NULL;
}
- ret = make_iso_8601_datetime(&dts, result, *len, is_tz_aware, base);
+ is_tz_aware = offset != Py_None;
+ Py_DECREF(offset);
+ }
+ ret = make_iso_8601_datetime(&dts, result, *len, is_tz_aware, base);
- if (ret != 0) {
- PyErr_SetString(PyExc_ValueError,
- "Could not convert datetime value to string");
- PyObject_Free(result);
- return NULL;
- }
+ if (ret != 0) {
+ PyErr_SetString(PyExc_ValueError,
+ "Could not convert datetime value to string");
+ PyObject_Free(result);
+ return NULL;
+ }
- // Note that get_datetime_iso_8601_strlen just gives a generic size
- // for ISO string conversion, not the actual size used
- *len = strlen(result);
- return result;
+ // Note that get_datetime_iso_8601_strlen just gives a generic size
+ // for ISO string conversion, not the actual size used
+ *len = strlen(result);
+ return result;
}
// Convert a Python Date/Datetime to Unix epoch with resolution base
static npy_datetime PyDateTimeToEpoch(PyObject *dt, NPY_DATETIMEUNIT base) {
- npy_datetimestruct dts;
- int ret;
-
- ret = convert_pydatetime_to_datetimestruct(dt, &dts);
- if (ret != 0) {
- if (!PyErr_Occurred()) {
- PyErr_SetString(PyExc_ValueError,
- "Could not convert PyDateTime to numpy datetime");
- }
- // TODO(username): is setting errMsg required?
- // ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
- // return NULL;
+ npy_datetimestruct dts;
+ int ret;
+
+ ret = convert_pydatetime_to_datetimestruct(dt, &dts);
+ if (ret != 0) {
+ if (!PyErr_Occurred()) {
+ PyErr_SetString(PyExc_ValueError,
+ "Could not convert PyDateTime to numpy datetime");
}
+ // TODO(username): is setting errMsg required?
+ // ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
+ // return NULL;
+ }
- npy_datetime npy_dt = npy_datetimestruct_to_datetime(NPY_FR_ns, &dts);
- return NpyDateTimeToEpoch(npy_dt, base);
+ npy_datetime npy_dt = npy_datetimestruct_to_datetime(NPY_FR_ns, &dts);
+ return NpyDateTimeToEpoch(npy_dt, base);
}
static int pandas_datetime_exec(PyObject *module) {
diff --git a/pandas/_libs/src/parser/io.c b/pandas/_libs/src/parser/io.c
index e00c5c1e807a7..29c2c8d095907 100644
--- a/pandas/_libs/src/parser/io.c
+++ b/pandas/_libs/src/parser/io.c
@@ -14,19 +14,19 @@ The full license is in the LICENSE file, distributed with this software.
*/
void *new_rd_source(PyObject *obj) {
- rd_source *rds = (rd_source *)malloc(sizeof(rd_source));
-
- if (rds == NULL) {
- PyErr_NoMemory();
- return NULL;
- }
- /* hold on to this object */
- Py_INCREF(obj);
- rds->obj = obj;
- rds->buffer = NULL;
- rds->position = 0;
-
- return (void *)rds;
+ rd_source *rds = (rd_source *)malloc(sizeof(rd_source));
+
+ if (rds == NULL) {
+ PyErr_NoMemory();
+ return NULL;
+ }
+ /* hold on to this object */
+ Py_INCREF(obj);
+ rds->obj = obj;
+ rds->buffer = NULL;
+ rds->position = 0;
+
+ return (void *)rds;
}
/*
@@ -36,11 +36,11 @@ void *new_rd_source(PyObject *obj) {
*/
int del_rd_source(void *rds) {
- Py_XDECREF(RDS(rds)->obj);
- Py_XDECREF(RDS(rds)->buffer);
- free(rds);
+ Py_XDECREF(RDS(rds)->obj);
+ Py_XDECREF(RDS(rds)->buffer);
+ free(rds);
- return 0;
+ return 0;
}
/*
@@ -51,57 +51,57 @@ int del_rd_source(void *rds) {
void *buffer_rd_bytes(void *source, size_t nbytes, size_t *bytes_read,
int *status, const char *encoding_errors) {
- PyGILState_STATE state;
- PyObject *result, *func, *args, *tmp;
-
- void *retval;
-
- size_t length;
- rd_source *src = RDS(source);
- state = PyGILState_Ensure();
-
- /* delete old object */
- Py_XDECREF(src->buffer);
- src->buffer = NULL;
- args = Py_BuildValue("(i)", nbytes);
-
- func = PyObject_GetAttrString(src->obj, "read");
-
- /* Note: PyObject_CallObject requires the GIL */
- result = PyObject_CallObject(func, args);
- Py_XDECREF(args);
- Py_XDECREF(func);
-
- if (result == NULL) {
- PyGILState_Release(state);
- *bytes_read = 0;
- *status = CALLING_READ_FAILED;
- return NULL;
- } else if (!PyBytes_Check(result)) {
- tmp = PyUnicode_AsEncodedString(result, "utf-8", encoding_errors);
- Py_DECREF(result);
- if (tmp == NULL) {
- PyGILState_Release(state);
- return NULL;
- }
- result = tmp;
- }
+ PyGILState_STATE state;
+ PyObject *result, *func, *args, *tmp;
+
+ void *retval;
+
+ size_t length;
+ rd_source *src = RDS(source);
+ state = PyGILState_Ensure();
- length = PySequence_Length(result);
+ /* delete old object */
+ Py_XDECREF(src->buffer);
+ src->buffer = NULL;
+ args = Py_BuildValue("(i)", nbytes);
- if (length == 0)
- *status = REACHED_EOF;
- else
- *status = 0;
+ func = PyObject_GetAttrString(src->obj, "read");
- /* hang on to the Python object */
- src->buffer = result;
- retval = (void *)PyBytes_AsString(result);
+ /* Note: PyObject_CallObject requires the GIL */
+ result = PyObject_CallObject(func, args);
+ Py_XDECREF(args);
+ Py_XDECREF(func);
+ if (result == NULL) {
PyGILState_Release(state);
+ *bytes_read = 0;
+ *status = CALLING_READ_FAILED;
+ return NULL;
+ } else if (!PyBytes_Check(result)) {
+ tmp = PyUnicode_AsEncodedString(result, "utf-8", encoding_errors);
+ Py_DECREF(result);
+ if (tmp == NULL) {
+ PyGILState_Release(state);
+ return NULL;
+ }
+ result = tmp;
+ }
+
+ length = PySequence_Length(result);
+
+ if (length == 0)
+ *status = REACHED_EOF;
+ else
+ *status = 0;
+
+ /* hang on to the Python object */
+ src->buffer = result;
+ retval = (void *)PyBytes_AsString(result);
+
+ PyGILState_Release(state);
- /* TODO: more error handling */
- *bytes_read = length;
+ /* TODO: more error handling */
+ *bytes_read = length;
- return retval;
+ return retval;
}
diff --git a/pandas/_libs/src/parser/pd_parser.c b/pandas/_libs/src/parser/pd_parser.c
index c429f17c1cb8b..41689704ccffc 100644
--- a/pandas/_libs/src/parser/pd_parser.c
+++ b/pandas/_libs/src/parser/pd_parser.c
@@ -12,7 +12,7 @@ Distributed under the terms of the BSD Simplified License.
#include "pandas/parser/io.h"
static int to_double(char *item, double *p_value, char sci, char decimal,
- int *maybe_int) {
+ int *maybe_int) {
char *p_end = NULL;
int error = 0;
@@ -95,7 +95,6 @@ static int floatify(PyObject *str, double *result, int *maybe_int) {
return -1;
}
-
static void pandas_parser_destructor(PyObject *op) {
void *ptr = PyCapsule_GetPointer(op, PandasParser_CAPSULE_NAME);
PyMem_Free(ptr);
diff --git a/pandas/_libs/src/parser/tokenizer.c b/pandas/_libs/src/parser/tokenizer.c
index ce8a38df172ef..c9466c485ae94 100644
--- a/pandas/_libs/src/parser/tokenizer.c
+++ b/pandas/_libs/src/parser/tokenizer.c
@@ -27,18 +27,18 @@ GitHub. See Python Software Foundation License and BSD licenses for these.
void coliter_setup(coliter_t *self, parser_t *parser, int64_t i,
int64_t start) {
- // column i, starting at 0
- self->words = parser->words;
- self->col = i;
- self->line_start = parser->line_start + start;
+ // column i, starting at 0
+ self->words = parser->words;
+ self->col = i;
+ self->line_start = parser->line_start + start;
}
static void free_if_not_null(void **ptr) {
- TRACE(("free_if_not_null %p\n", *ptr))
- if (*ptr != NULL) {
- free(*ptr);
- *ptr = NULL;
- }
+ TRACE(("free_if_not_null %p\n", *ptr))
+ if (*ptr != NULL) {
+ free(*ptr);
+ *ptr = NULL;
+ }
}
/*
@@ -49,542 +49,529 @@ static void free_if_not_null(void **ptr) {
static void *grow_buffer(void *buffer, uint64_t length, uint64_t *capacity,
int64_t space, int64_t elsize, int *error) {
- uint64_t cap = *capacity;
- void *newbuffer = buffer;
-
- // Can we fit potentially nbytes tokens (+ null terminators) in the stream?
- while ((length + space >= cap) && (newbuffer != NULL)) {
- cap = cap ? cap << 1 : 2;
- buffer = newbuffer;
- newbuffer = realloc(newbuffer, elsize * cap);
- }
-
- if (newbuffer == NULL) {
- // realloc failed so don't change *capacity, set *error to errno
- // and return the last good realloc'd buffer so it can be freed
- *error = errno;
- newbuffer = buffer;
- } else {
- // realloc worked, update *capacity and set *error to 0
- // sigh, multiple return values
- *capacity = cap;
- *error = 0;
- }
- return newbuffer;
+ uint64_t cap = *capacity;
+ void *newbuffer = buffer;
+
+ // Can we fit potentially nbytes tokens (+ null terminators) in the stream?
+ while ((length + space >= cap) && (newbuffer != NULL)) {
+ cap = cap ? cap << 1 : 2;
+ buffer = newbuffer;
+ newbuffer = realloc(newbuffer, elsize * cap);
+ }
+
+ if (newbuffer == NULL) {
+ // realloc failed so don't change *capacity, set *error to errno
+ // and return the last good realloc'd buffer so it can be freed
+ *error = errno;
+ newbuffer = buffer;
+ } else {
+ // realloc worked, update *capacity and set *error to 0
+ // sigh, multiple return values
+ *capacity = cap;
+ *error = 0;
+ }
+ return newbuffer;
}
void parser_set_default_options(parser_t *self) {
- self->decimal = '.';
- self->sci = 'E';
+ self->decimal = '.';
+ self->sci = 'E';
- // For tokenization
- self->state = START_RECORD;
+ // For tokenization
+ self->state = START_RECORD;
- self->delimiter = ','; // XXX
- self->delim_whitespace = 0;
+ self->delimiter = ','; // XXX
+ self->delim_whitespace = 0;
- self->doublequote = 0;
- self->quotechar = '"';
- self->escapechar = 0;
+ self->doublequote = 0;
+ self->quotechar = '"';
+ self->escapechar = 0;
- self->lineterminator = '\0'; /* NUL->standard logic */
+ self->lineterminator = '\0'; /* NUL->standard logic */
- self->skipinitialspace = 0;
- self->quoting = QUOTE_MINIMAL;
- self->allow_embedded_newline = 1;
+ self->skipinitialspace = 0;
+ self->quoting = QUOTE_MINIMAL;
+ self->allow_embedded_newline = 1;
- self->expected_fields = -1;
- self->on_bad_lines = ERROR;
+ self->expected_fields = -1;
+ self->on_bad_lines = ERROR;
- self->commentchar = '#';
- self->thousands = '\0';
+ self->commentchar = '#';
+ self->thousands = '\0';
- self->skipset = NULL;
- self->skipfunc = NULL;
- self->skip_first_N_rows = -1;
- self->skip_footer = 0;
+ self->skipset = NULL;
+ self->skipfunc = NULL;
+ self->skip_first_N_rows = -1;
+ self->skip_footer = 0;
}
parser_t *parser_new(void) { return (parser_t *)calloc(1, sizeof(parser_t)); }
int parser_clear_data_buffers(parser_t *self) {
- free_if_not_null((void *)&self->stream);
- free_if_not_null((void *)&self->words);
- free_if_not_null((void *)&self->word_starts);
- free_if_not_null((void *)&self->line_start);
- free_if_not_null((void *)&self->line_fields);
- return 0;
+ free_if_not_null((void *)&self->stream);
+ free_if_not_null((void *)&self->words);
+ free_if_not_null((void *)&self->word_starts);
+ free_if_not_null((void *)&self->line_start);
+ free_if_not_null((void *)&self->line_fields);
+ return 0;
}
int parser_cleanup(parser_t *self) {
- int status = 0;
+ int status = 0;
- // XXX where to put this
- free_if_not_null((void *)&self->error_msg);
- free_if_not_null((void *)&self->warn_msg);
+ // XXX where to put this
+ free_if_not_null((void *)&self->error_msg);
+ free_if_not_null((void *)&self->warn_msg);
- if (self->skipset != NULL) {
- kh_destroy_int64((kh_int64_t *)self->skipset);
- self->skipset = NULL;
- }
+ if (self->skipset != NULL) {
+ kh_destroy_int64((kh_int64_t *)self->skipset);
+ self->skipset = NULL;
+ }
- if (parser_clear_data_buffers(self) < 0) {
- status = -1;
- }
+ if (parser_clear_data_buffers(self) < 0) {
+ status = -1;
+ }
- if (self->cb_cleanup != NULL) {
- if (self->cb_cleanup(self->source) < 0) {
- status = -1;
- }
- self->cb_cleanup = NULL;
+ if (self->cb_cleanup != NULL) {
+ if (self->cb_cleanup(self->source) < 0) {
+ status = -1;
}
+ self->cb_cleanup = NULL;
+ }
- return status;
+ return status;
}
int parser_init(parser_t *self) {
- int64_t sz;
-
- /*
- Initialize data buffers
- */
-
- self->stream = NULL;
- self->words = NULL;
- self->word_starts = NULL;
- self->line_start = NULL;
- self->line_fields = NULL;
- self->error_msg = NULL;
- self->warn_msg = NULL;
-
- // token stream
- self->stream = malloc(STREAM_INIT_SIZE * sizeof(char));
- if (self->stream == NULL) {
- parser_cleanup(self);
- return PARSER_OUT_OF_MEMORY;
- }
- self->stream_cap = STREAM_INIT_SIZE;
- self->stream_len = 0;
-
- // word pointers and metadata
- sz = STREAM_INIT_SIZE / 10;
- sz = sz ? sz : 1;
- self->words = malloc(sz * sizeof(char *));
- self->word_starts = malloc(sz * sizeof(int64_t));
- self->max_words_cap = sz;
- self->words_cap = sz;
- self->words_len = 0;
-
- // line pointers and metadata
- self->line_start = malloc(sz * sizeof(int64_t));
-
- self->line_fields = malloc(sz * sizeof(int64_t));
-
- self->lines_cap = sz;
- self->lines = 0;
- self->file_lines = 0;
-
- if (self->stream == NULL || self->words == NULL ||
- self->word_starts == NULL || self->line_start == NULL ||
- self->line_fields == NULL) {
- parser_cleanup(self);
+ int64_t sz;
+
+ /*
+ Initialize data buffers
+ */
+
+ self->stream = NULL;
+ self->words = NULL;
+ self->word_starts = NULL;
+ self->line_start = NULL;
+ self->line_fields = NULL;
+ self->error_msg = NULL;
+ self->warn_msg = NULL;
+
+ // token stream
+ self->stream = malloc(STREAM_INIT_SIZE * sizeof(char));
+ if (self->stream == NULL) {
+ parser_cleanup(self);
+ return PARSER_OUT_OF_MEMORY;
+ }
+ self->stream_cap = STREAM_INIT_SIZE;
+ self->stream_len = 0;
+
+ // word pointers and metadata
+ sz = STREAM_INIT_SIZE / 10;
+ sz = sz ? sz : 1;
+ self->words = malloc(sz * sizeof(char *));
+ self->word_starts = malloc(sz * sizeof(int64_t));
+ self->max_words_cap = sz;
+ self->words_cap = sz;
+ self->words_len = 0;
+
+ // line pointers and metadata
+ self->line_start = malloc(sz * sizeof(int64_t));
+
+ self->line_fields = malloc(sz * sizeof(int64_t));
+
+ self->lines_cap = sz;
+ self->lines = 0;
+ self->file_lines = 0;
+
+ if (self->stream == NULL || self->words == NULL ||
+ self->word_starts == NULL || self->line_start == NULL ||
+ self->line_fields == NULL) {
+ parser_cleanup(self);
- return PARSER_OUT_OF_MEMORY;
- }
+ return PARSER_OUT_OF_MEMORY;
+ }
- /* amount of bytes buffered */
- self->datalen = 0;
- self->datapos = 0;
+ /* amount of bytes buffered */
+ self->datalen = 0;
+ self->datapos = 0;
- self->line_start[0] = 0;
- self->line_fields[0] = 0;
+ self->line_start[0] = 0;
+ self->line_fields[0] = 0;
- self->pword_start = self->stream;
- self->word_start = 0;
+ self->pword_start = self->stream;
+ self->word_start = 0;
- self->state = START_RECORD;
+ self->state = START_RECORD;
- self->error_msg = NULL;
- self->warn_msg = NULL;
+ self->error_msg = NULL;
+ self->warn_msg = NULL;
- self->commentchar = '\0';
+ self->commentchar = '\0';
- return 0;
+ return 0;
}
void parser_free(parser_t *self) {
- // opposite of parser_init
- parser_cleanup(self);
+ // opposite of parser_init
+ parser_cleanup(self);
}
-void parser_del(parser_t *self) {
- free(self);
-}
+void parser_del(parser_t *self) { free(self); }
static int make_stream_space(parser_t *self, size_t nbytes) {
- uint64_t i, cap, length;
- int status;
- void *orig_ptr, *newptr;
+ uint64_t i, cap, length;
+ int status;
+ void *orig_ptr, *newptr;
- // Can we fit potentially nbytes tokens (+ null terminators) in the stream?
+ // Can we fit potentially nbytes tokens (+ null terminators) in the stream?
- /*
- TOKEN STREAM
- */
+ /*
+ TOKEN STREAM
+ */
- orig_ptr = (void *)self->stream;
- TRACE(
- ("\n\nmake_stream_space: nbytes = %zu. grow_buffer(self->stream...)\n",
+ orig_ptr = (void *)self->stream;
+ TRACE(("\n\nmake_stream_space: nbytes = %zu. grow_buffer(self->stream...)\n",
nbytes))
- self->stream = (char *)grow_buffer((void *)self->stream, self->stream_len,
- &self->stream_cap, nbytes * 2,
- sizeof(char), &status);
- TRACE(
- ("make_stream_space: self->stream=%p, self->stream_len = %zu, "
+ self->stream =
+ (char *)grow_buffer((void *)self->stream, self->stream_len,
+ &self->stream_cap, nbytes * 2, sizeof(char), &status);
+ TRACE(("make_stream_space: self->stream=%p, self->stream_len = %zu, "
"self->stream_cap=%zu, status=%zu\n",
self->stream, self->stream_len, self->stream_cap, status))
- if (status != 0) {
- return PARSER_OUT_OF_MEMORY;
- }
-
- // realloc sets errno when moving buffer?
- if (self->stream != orig_ptr) {
- self->pword_start = self->stream + self->word_start;
-
- for (i = 0; i < self->words_len; ++i) {
- self->words[i] = self->stream + self->word_starts[i];
- }
- }
-
- /*
- WORD VECTORS
- */
-
- cap = self->words_cap;
-
- /**
- * If we are reading in chunks, we need to be aware of the maximum number
- * of words we have seen in previous chunks (self->max_words_cap), so
- * that way, we can properly allocate when reading subsequent ones.
- *
- * Otherwise, we risk a buffer overflow if we mistakenly under-allocate
- * just because a recent chunk did not have as many words.
- */
- if (self->words_len + nbytes < self->max_words_cap) {
- length = self->max_words_cap - nbytes - 1;
- } else {
- length = self->words_len;
- }
-
- self->words =
- (char **)grow_buffer((void *)self->words, length,
- &self->words_cap, nbytes,
- sizeof(char *), &status);
- TRACE(
- ("make_stream_space: grow_buffer(self->self->words, %zu, %zu, %zu, "
+ if (status != 0) {
+ return PARSER_OUT_OF_MEMORY;
+ }
+
+ // realloc sets errno when moving buffer?
+ if (self->stream != orig_ptr) {
+ self->pword_start = self->stream + self->word_start;
+
+ for (i = 0; i < self->words_len; ++i) {
+ self->words[i] = self->stream + self->word_starts[i];
+ }
+ }
+
+ /*
+ WORD VECTORS
+ */
+
+ cap = self->words_cap;
+
+ /**
+ * If we are reading in chunks, we need to be aware of the maximum number
+ * of words we have seen in previous chunks (self->max_words_cap), so
+ * that way, we can properly allocate when reading subsequent ones.
+ *
+ * Otherwise, we risk a buffer overflow if we mistakenly under-allocate
+ * just because a recent chunk did not have as many words.
+ */
+ if (self->words_len + nbytes < self->max_words_cap) {
+ length = self->max_words_cap - nbytes - 1;
+ } else {
+ length = self->words_len;
+ }
+
+ self->words =
+ (char **)grow_buffer((void *)self->words, length, &self->words_cap,
+ nbytes, sizeof(char *), &status);
+ TRACE(("make_stream_space: grow_buffer(self->self->words, %zu, %zu, %zu, "
"%d)\n",
self->words_len, self->words_cap, nbytes, status))
- if (status != 0) {
- return PARSER_OUT_OF_MEMORY;
- }
-
- // realloc took place
- if (cap != self->words_cap) {
- TRACE(
- ("make_stream_space: cap != self->words_cap, nbytes = %d, "
- "self->words_cap=%d\n",
- nbytes, self->words_cap))
- newptr = realloc((void *)self->word_starts,
- sizeof(int64_t) * self->words_cap);
- if (newptr == NULL) {
- return PARSER_OUT_OF_MEMORY;
- } else {
- self->word_starts = (int64_t *)newptr;
- }
- }
-
- /*
- LINE VECTORS
- */
- cap = self->lines_cap;
- self->line_start =
- (int64_t *)grow_buffer((void *)self->line_start, self->lines + 1,
- &self->lines_cap, nbytes,
- sizeof(int64_t), &status);
- TRACE((
- "make_stream_space: grow_buffer(self->line_start, %zu, %zu, %zu, %d)\n",
- self->lines + 1, self->lines_cap, nbytes, status))
- if (status != 0) {
- return PARSER_OUT_OF_MEMORY;
- }
-
- // realloc took place
- if (cap != self->lines_cap) {
- TRACE(("make_stream_space: cap != self->lines_cap, nbytes = %d\n",
- nbytes))
- newptr = realloc((void *)self->line_fields,
- sizeof(int64_t) * self->lines_cap);
- if (newptr == NULL) {
- return PARSER_OUT_OF_MEMORY;
- } else {
- self->line_fields = (int64_t *)newptr;
- }
+ if (status != 0) {
+ return PARSER_OUT_OF_MEMORY;
+ }
+
+ // realloc took place
+ if (cap != self->words_cap) {
+ TRACE(("make_stream_space: cap != self->words_cap, nbytes = %d, "
+ "self->words_cap=%d\n",
+ nbytes, self->words_cap))
+ newptr =
+ realloc((void *)self->word_starts, sizeof(int64_t) * self->words_cap);
+ if (newptr == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ } else {
+ self->word_starts = (int64_t *)newptr;
+ }
+ }
+
+ /*
+ LINE VECTORS
+ */
+ cap = self->lines_cap;
+ self->line_start = (int64_t *)grow_buffer((void *)self->line_start,
+ self->lines + 1, &self->lines_cap,
+ nbytes, sizeof(int64_t), &status);
+ TRACE(
+ ("make_stream_space: grow_buffer(self->line_start, %zu, %zu, %zu, %d)\n",
+ self->lines + 1, self->lines_cap, nbytes, status))
+ if (status != 0) {
+ return PARSER_OUT_OF_MEMORY;
+ }
+
+ // realloc took place
+ if (cap != self->lines_cap) {
+ TRACE(("make_stream_space: cap != self->lines_cap, nbytes = %d\n", nbytes))
+ newptr =
+ realloc((void *)self->line_fields, sizeof(int64_t) * self->lines_cap);
+ if (newptr == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ } else {
+ self->line_fields = (int64_t *)newptr;
}
+ }
- return 0;
+ return 0;
}
static int push_char(parser_t *self, char c) {
- TRACE(("push_char: self->stream[%zu] = %x, stream_cap=%zu\n",
- self->stream_len + 1, c, self->stream_cap))
- if (self->stream_len >= self->stream_cap) {
- TRACE(
- ("push_char: ERROR!!! self->stream_len(%d) >= "
- "self->stream_cap(%d)\n",
- self->stream_len, self->stream_cap))
- int64_t bufsize = 100;
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "Buffer overflow caught - possible malformed input file.\n");
- return PARSER_OUT_OF_MEMORY;
- }
- self->stream[self->stream_len++] = c;
- return 0;
+ TRACE(("push_char: self->stream[%zu] = %x, stream_cap=%zu\n",
+ self->stream_len + 1, c, self->stream_cap))
+ if (self->stream_len >= self->stream_cap) {
+ TRACE(("push_char: ERROR!!! self->stream_len(%d) >= "
+ "self->stream_cap(%d)\n",
+ self->stream_len, self->stream_cap))
+ int64_t bufsize = 100;
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize,
+ "Buffer overflow caught - possible malformed input file.\n");
+ return PARSER_OUT_OF_MEMORY;
+ }
+ self->stream[self->stream_len++] = c;
+ return 0;
}
int PANDAS_INLINE end_field(parser_t *self) {
- // XXX cruft
- if (self->words_len >= self->words_cap) {
- TRACE(
- ("end_field: ERROR!!! self->words_len(%zu) >= "
- "self->words_cap(%zu)\n",
- self->words_len, self->words_cap))
- int64_t bufsize = 100;
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "Buffer overflow caught - possible malformed input file.\n");
- return PARSER_OUT_OF_MEMORY;
- }
+ // XXX cruft
+ if (self->words_len >= self->words_cap) {
+ TRACE(("end_field: ERROR!!! self->words_len(%zu) >= "
+ "self->words_cap(%zu)\n",
+ self->words_len, self->words_cap))
+ int64_t bufsize = 100;
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize,
+ "Buffer overflow caught - possible malformed input file.\n");
+ return PARSER_OUT_OF_MEMORY;
+ }
- // null terminate token
- push_char(self, '\0');
+ // null terminate token
+ push_char(self, '\0');
- // set pointer and metadata
- self->words[self->words_len] = self->pword_start;
+ // set pointer and metadata
+ self->words[self->words_len] = self->pword_start;
- TRACE(("end_field: Char diff: %d\n", self->pword_start - self->words[0]));
+ TRACE(("end_field: Char diff: %d\n", self->pword_start - self->words[0]));
- TRACE(("end_field: Saw word %s at: %d. Total: %d\n", self->pword_start,
- self->word_start, self->words_len + 1))
+ TRACE(("end_field: Saw word %s at: %d. Total: %d\n", self->pword_start,
+ self->word_start, self->words_len + 1))
- self->word_starts[self->words_len] = self->word_start;
- self->words_len++;
+ self->word_starts[self->words_len] = self->word_start;
+ self->words_len++;
- // increment line field count
- self->line_fields[self->lines]++;
+ // increment line field count
+ self->line_fields[self->lines]++;
- // New field begin in stream
- self->pword_start = self->stream + self->stream_len;
- self->word_start = self->stream_len;
+ // New field begin in stream
+ self->pword_start = self->stream + self->stream_len;
+ self->word_start = self->stream_len;
- return 0;
+ return 0;
}
static void append_warning(parser_t *self, const char *msg) {
- int64_t ex_length;
- int64_t length = strlen(msg);
- void *newptr;
-
- if (self->warn_msg == NULL) {
- self->warn_msg = malloc(length + 1);
- snprintf(self->warn_msg, length + 1, "%s", msg);
- } else {
- ex_length = strlen(self->warn_msg);
- newptr = realloc(self->warn_msg, ex_length + length + 1);
- if (newptr != NULL) {
- self->warn_msg = (char *)newptr;
- snprintf(self->warn_msg + ex_length, length + 1, "%s", msg);
- }
- }
+ int64_t ex_length;
+ int64_t length = strlen(msg);
+ void *newptr;
+
+ if (self->warn_msg == NULL) {
+ self->warn_msg = malloc(length + 1);
+ snprintf(self->warn_msg, length + 1, "%s", msg);
+ } else {
+ ex_length = strlen(self->warn_msg);
+ newptr = realloc(self->warn_msg, ex_length + length + 1);
+ if (newptr != NULL) {
+ self->warn_msg = (char *)newptr;
+ snprintf(self->warn_msg + ex_length, length + 1, "%s", msg);
+ }
+ }
}
static int end_line(parser_t *self) {
- char *msg;
- int64_t fields;
- int64_t ex_fields = self->expected_fields;
- int64_t bufsize = 100; // for error or warning messages
+ char *msg;
+ int64_t fields;
+ int64_t ex_fields = self->expected_fields;
+ int64_t bufsize = 100; // for error or warning messages
- fields = self->line_fields[self->lines];
+ fields = self->line_fields[self->lines];
- TRACE(("end_line: Line end, nfields: %d\n", fields));
+ TRACE(("end_line: Line end, nfields: %d\n", fields));
- TRACE(("end_line: lines: %d\n", self->lines));
- if (self->lines > 0) {
- if (self->expected_fields >= 0) {
- ex_fields = self->expected_fields;
- } else {
- ex_fields = self->line_fields[self->lines - 1];
- }
+ TRACE(("end_line: lines: %d\n", self->lines));
+ if (self->lines > 0) {
+ if (self->expected_fields >= 0) {
+ ex_fields = self->expected_fields;
+ } else {
+ ex_fields = self->line_fields[self->lines - 1];
}
- TRACE(("end_line: ex_fields: %d\n", ex_fields));
-
- if (self->state == START_FIELD_IN_SKIP_LINE ||
- self->state == IN_FIELD_IN_SKIP_LINE ||
- self->state == IN_QUOTED_FIELD_IN_SKIP_LINE ||
- self->state == QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE) {
- TRACE(("end_line: Skipping row %d\n", self->file_lines));
- // increment file line count
- self->file_lines++;
+ }
+ TRACE(("end_line: ex_fields: %d\n", ex_fields));
- // skip the tokens from this bad line
- self->line_start[self->lines] += fields;
+ if (self->state == START_FIELD_IN_SKIP_LINE ||
+ self->state == IN_FIELD_IN_SKIP_LINE ||
+ self->state == IN_QUOTED_FIELD_IN_SKIP_LINE ||
+ self->state == QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE) {
+ TRACE(("end_line: Skipping row %d\n", self->file_lines));
+ // increment file line count
+ self->file_lines++;
- // reset field count
- self->line_fields[self->lines] = 0;
- return 0;
- }
+ // skip the tokens from this bad line
+ self->line_start[self->lines] += fields;
- if (!(self->lines <= self->header_end + 1) &&
- (fields > ex_fields) && !(self->usecols)) {
- // increment file line count
- self->file_lines++;
+ // reset field count
+ self->line_fields[self->lines] = 0;
+ return 0;
+ }
- // skip the tokens from this bad line
- self->line_start[self->lines] += fields;
+ if (!(self->lines <= self->header_end + 1) && (fields > ex_fields) &&
+ !(self->usecols)) {
+ // increment file line count
+ self->file_lines++;
- // reset field count
- self->line_fields[self->lines] = 0;
+ // skip the tokens from this bad line
+ self->line_start[self->lines] += fields;
- // file_lines is now the actual file line number (starting at 1)
- if (self->on_bad_lines == ERROR) {
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "Expected %" PRId64 " fields in line %" PRIu64 ", saw %"
- PRId64 "\n", ex_fields, self->file_lines, fields);
+ // reset field count
+ self->line_fields[self->lines] = 0;
- TRACE(("Error at line %d, %d fields\n", self->file_lines, fields));
+ // file_lines is now the actual file line number (starting at 1)
+ if (self->on_bad_lines == ERROR) {
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize,
+ "Expected %" PRId64 " fields in line %" PRIu64 ", saw %" PRId64
+ "\n",
+ ex_fields, self->file_lines, fields);
- return -1;
- } else {
- // simply skip bad lines
- if (self->on_bad_lines == WARN) {
- // pass up error message
- msg = malloc(bufsize);
- snprintf(msg, bufsize,
- "Skipping line %" PRIu64 ": expected %" PRId64
- " fields, saw %" PRId64 "\n",
- self->file_lines, ex_fields, fields);
- append_warning(self, msg);
- free(msg);
- }
- }
+ TRACE(("Error at line %d, %d fields\n", self->file_lines, fields));
+
+ return -1;
} else {
- // missing trailing delimiters
- if ((self->lines >= self->header_end + 1) &&
- fields < ex_fields) {
- // might overrun the buffer when closing fields
- if (make_stream_space(self, ex_fields - fields) < 0) {
- int64_t bufsize = 100;
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize, "out of memory");
- return -1;
- }
-
- while (fields < ex_fields) {
- end_field(self);
- fields++;
- }
- }
+ // simply skip bad lines
+ if (self->on_bad_lines == WARN) {
+ // pass up error message
+ msg = malloc(bufsize);
+ snprintf(msg, bufsize,
+ "Skipping line %" PRIu64 ": expected %" PRId64
+ " fields, saw %" PRId64 "\n",
+ self->file_lines, ex_fields, fields);
+ append_warning(self, msg);
+ free(msg);
+ }
+ }
+ } else {
+ // missing trailing delimiters
+ if ((self->lines >= self->header_end + 1) && fields < ex_fields) {
+ // might overrun the buffer when closing fields
+ if (make_stream_space(self, ex_fields - fields) < 0) {
+ int64_t bufsize = 100;
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize, "out of memory");
+ return -1;
+ }
- // increment both line counts
- self->file_lines++;
- self->lines++;
-
- // good line, set new start point
- if (self->lines >= self->lines_cap) {
- TRACE((
- "end_line: ERROR!!! self->lines(%zu) >= self->lines_cap(%zu)\n",
- self->lines, self->lines_cap))
- int64_t bufsize = 100;
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "Buffer overflow caught - "
- "possible malformed input file.\n");
- return PARSER_OUT_OF_MEMORY;
- }
- self->line_start[self->lines] =
- (self->line_start[self->lines - 1] + fields);
+ while (fields < ex_fields) {
+ end_field(self);
+ fields++;
+ }
+ }
- TRACE(
- ("end_line: new line start: %d\n", self->line_start[self->lines]));
+ // increment both line counts
+ self->file_lines++;
+ self->lines++;
- // new line start with 0 fields
- self->line_fields[self->lines] = 0;
+ // good line, set new start point
+ if (self->lines >= self->lines_cap) {
+ TRACE(("end_line: ERROR!!! self->lines(%zu) >= self->lines_cap(%zu)\n",
+ self->lines, self->lines_cap))
+ int64_t bufsize = 100;
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize,
+ "Buffer overflow caught - "
+ "possible malformed input file.\n");
+ return PARSER_OUT_OF_MEMORY;
}
+ self->line_start[self->lines] =
+ (self->line_start[self->lines - 1] + fields);
- TRACE(("end_line: Finished line, at %d\n", self->lines));
+ TRACE(("end_line: new line start: %d\n", self->line_start[self->lines]));
- return 0;
+ // new line start with 0 fields
+ self->line_fields[self->lines] = 0;
+ }
+
+ TRACE(("end_line: Finished line, at %d\n", self->lines));
+
+ return 0;
}
int parser_add_skiprow(parser_t *self, int64_t row) {
- khiter_t k;
- kh_int64_t *set;
- int ret = 0;
+ khiter_t k;
+ kh_int64_t *set;
+ int ret = 0;
- if (self->skipset == NULL) {
- self->skipset = (void *)kh_init_int64();
- }
+ if (self->skipset == NULL) {
+ self->skipset = (void *)kh_init_int64();
+ }
- set = (kh_int64_t *)self->skipset;
+ set = (kh_int64_t *)self->skipset;
- k = kh_put_int64(set, row, &ret);
- set->keys[k] = row;
+ k = kh_put_int64(set, row, &ret);
+ set->keys[k] = row;
- return 0;
+ return 0;
}
int parser_set_skipfirstnrows(parser_t *self, int64_t nrows) {
- // self->file_lines is zero based so subtract 1 from nrows
- if (nrows > 0) {
- self->skip_first_N_rows = nrows - 1;
- }
+ // self->file_lines is zero based so subtract 1 from nrows
+ if (nrows > 0) {
+ self->skip_first_N_rows = nrows - 1;
+ }
- return 0;
+ return 0;
}
static int parser_buffer_bytes(parser_t *self, size_t nbytes,
const char *encoding_errors) {
- int status;
- size_t bytes_read;
-
- status = 0;
- self->datapos = 0;
- self->data = self->cb_io(self->source, nbytes, &bytes_read, &status,
- encoding_errors);
- TRACE((
- "parser_buffer_bytes self->cb_io: nbytes=%zu, datalen: %d, status=%d\n",
- nbytes, bytes_read, status));
- self->datalen = bytes_read;
-
- if (status != REACHED_EOF && self->data == NULL) {
- int64_t bufsize = 200;
- self->error_msg = malloc(bufsize);
-
- if (status == CALLING_READ_FAILED) {
- snprintf(self->error_msg, bufsize,
- "Calling read(nbytes) on source failed. "
- "Try engine='python'.");
- } else {
- snprintf(self->error_msg, bufsize, "Unknown error in IO callback");
- }
- return -1;
+ int status;
+ size_t bytes_read;
+
+ status = 0;
+ self->datapos = 0;
+ self->data =
+ self->cb_io(self->source, nbytes, &bytes_read, &status, encoding_errors);
+ TRACE(
+ ("parser_buffer_bytes self->cb_io: nbytes=%zu, datalen: %d, status=%d\n",
+ nbytes, bytes_read, status));
+ self->datalen = bytes_read;
+
+ if (status != REACHED_EOF && self->data == NULL) {
+ int64_t bufsize = 200;
+ self->error_msg = malloc(bufsize);
+
+ if (status == CALLING_READ_FAILED) {
+ snprintf(self->error_msg, bufsize,
+ "Calling read(nbytes) on source failed. "
+ "Try engine='python'.");
+ } else {
+ snprintf(self->error_msg, bufsize, "Unknown error in IO callback");
}
+ return -1;
+ }
- TRACE(("datalen: %d\n", self->datalen));
+ TRACE(("datalen: %d\n", self->datalen));
- return status;
+ return status;
}
/*
@@ -593,63 +580,61 @@ static int parser_buffer_bytes(parser_t *self, size_t nbytes,
*/
-#define PUSH_CHAR(c) \
- TRACE( \
- ("PUSH_CHAR: Pushing %c, slen= %d, stream_cap=%zu, stream_len=%zu\n", \
- c, slen, self->stream_cap, self->stream_len)) \
- if (slen >= self->stream_cap) { \
- TRACE(("PUSH_CHAR: ERROR!!! slen(%d) >= stream_cap(%d)\n", slen, \
- self->stream_cap)) \
- int64_t bufsize = 100; \
- self->error_msg = malloc(bufsize); \
- snprintf(self->error_msg, bufsize, \
- "Buffer overflow caught - possible malformed input file.\n");\
- return PARSER_OUT_OF_MEMORY; \
- } \
- *stream++ = c; \
- slen++;
+#define PUSH_CHAR(c) \
+ TRACE(("PUSH_CHAR: Pushing %c, slen= %d, stream_cap=%zu, stream_len=%zu\n", \
+ c, slen, self->stream_cap, self->stream_len)) \
+ if (slen >= self->stream_cap) { \
+ TRACE(("PUSH_CHAR: ERROR!!! slen(%d) >= stream_cap(%d)\n", slen, \
+ self->stream_cap)) \
+ int64_t bufsize = 100; \
+ self->error_msg = malloc(bufsize); \
+ snprintf(self->error_msg, bufsize, \
+ "Buffer overflow caught - possible malformed input file.\n"); \
+ return PARSER_OUT_OF_MEMORY; \
+ } \
+ *stream++ = c; \
+ slen++;
// This is a little bit of a hack but works for now
-#define END_FIELD() \
- self->stream_len = slen; \
- if (end_field(self) < 0) { \
- goto parsingerror; \
- } \
- stream = self->stream + self->stream_len; \
- slen = self->stream_len;
-
-#define END_LINE_STATE(STATE) \
- self->stream_len = slen; \
- if (end_line(self) < 0) { \
- goto parsingerror; \
- } \
- stream = self->stream + self->stream_len; \
- slen = self->stream_len; \
- self->state = STATE; \
- if (line_limit > 0 && self->lines == start_lines + line_limit) { \
- goto linelimit; \
- }
-
-#define END_LINE_AND_FIELD_STATE(STATE) \
- self->stream_len = slen; \
- if (end_line(self) < 0) { \
- goto parsingerror; \
- } \
- if (end_field(self) < 0) { \
- goto parsingerror; \
- } \
- stream = self->stream + self->stream_len; \
- slen = self->stream_len; \
- self->state = STATE; \
- if (line_limit > 0 && self->lines == start_lines + line_limit) { \
- goto linelimit; \
- }
+#define END_FIELD() \
+ self->stream_len = slen; \
+ if (end_field(self) < 0) { \
+ goto parsingerror; \
+ } \
+ stream = self->stream + self->stream_len; \
+ slen = self->stream_len;
+
+#define END_LINE_STATE(STATE) \
+ self->stream_len = slen; \
+ if (end_line(self) < 0) { \
+ goto parsingerror; \
+ } \
+ stream = self->stream + self->stream_len; \
+ slen = self->stream_len; \
+ self->state = STATE; \
+ if (line_limit > 0 && self->lines == start_lines + line_limit) { \
+ goto linelimit; \
+ }
+
+#define END_LINE_AND_FIELD_STATE(STATE) \
+ self->stream_len = slen; \
+ if (end_line(self) < 0) { \
+ goto parsingerror; \
+ } \
+ if (end_field(self) < 0) { \
+ goto parsingerror; \
+ } \
+ stream = self->stream + self->stream_len; \
+ slen = self->stream_len; \
+ self->state = STATE; \
+ if (line_limit > 0 && self->lines == start_lines + line_limit) { \
+ goto linelimit; \
+ }
#define END_LINE() END_LINE_STATE(START_RECORD)
-#define IS_TERMINATOR(c) \
- (c == lineterminator)
+#define IS_TERMINATOR(c) (c == lineterminator)
#define IS_QUOTE(c) ((c == self->quotechar && self->quoting != QUOTE_NONE))
@@ -660,678 +645,671 @@ static int parser_buffer_bytes(parser_t *self, size_t nbytes,
#define IS_ESCAPE_CHAR(c) (c == escape_symbol)
-#define IS_SKIPPABLE_SPACE(c) \
- ((!self->delim_whitespace && c == ' ' && self->skipinitialspace))
+#define IS_SKIPPABLE_SPACE(c) \
+ ((!self->delim_whitespace && c == ' ' && self->skipinitialspace))
// applied when in a field
-#define IS_DELIMITER(c) \
- ((!delim_whitespace && c == delimiter) || (delim_whitespace && isblank(c)))
-
-#define _TOKEN_CLEANUP() \
- self->stream_len = slen; \
- self->datapos = i; \
- TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, \
- self->datalen));
-
-#define CHECK_FOR_BOM() \
- if (*buf == '\xef' && *(buf + 1) == '\xbb' && *(buf + 2) == '\xbf') { \
- buf += 3; \
- self->datapos += 3; \
- }
+#define IS_DELIMITER(c) \
+ ((!delim_whitespace && c == delimiter) || (delim_whitespace && isblank(c)))
-int skip_this_line(parser_t *self, int64_t rownum) {
- int should_skip;
- PyObject *result;
- PyGILState_STATE state;
-
- if (self->skipfunc != NULL) {
- state = PyGILState_Ensure();
- result = PyObject_CallFunction(self->skipfunc, "i", rownum);
-
- // Error occurred. It will be processed
- // and caught at the Cython level.
- if (result == NULL) {
- should_skip = -1;
- } else {
- should_skip = PyObject_IsTrue(result);
- }
+#define _TOKEN_CLEANUP() \
+ self->stream_len = slen; \
+ self->datapos = i; \
+ TRACE(("_TOKEN_CLEANUP: datapos: %d, datalen: %d\n", self->datapos, \
+ self->datalen));
- Py_XDECREF(result);
- PyGILState_Release(state);
+#define CHECK_FOR_BOM() \
+ if (*buf == '\xef' && *(buf + 1) == '\xbb' && *(buf + 2) == '\xbf') { \
+ buf += 3; \
+ self->datapos += 3; \
+ }
- return should_skip;
- } else if (self->skipset != NULL) {
- return (kh_get_int64((kh_int64_t *)self->skipset, self->file_lines) !=
- ((kh_int64_t *)self->skipset)->n_buckets);
+int skip_this_line(parser_t *self, int64_t rownum) {
+ int should_skip;
+ PyObject *result;
+ PyGILState_STATE state;
+
+ if (self->skipfunc != NULL) {
+ state = PyGILState_Ensure();
+ result = PyObject_CallFunction(self->skipfunc, "i", rownum);
+
+ // Error occurred. It will be processed
+ // and caught at the Cython level.
+ if (result == NULL) {
+ should_skip = -1;
} else {
- return (rownum <= self->skip_first_N_rows);
+ should_skip = PyObject_IsTrue(result);
}
+
+ Py_XDECREF(result);
+ PyGILState_Release(state);
+
+ return should_skip;
+ } else if (self->skipset != NULL) {
+ return (kh_get_int64((kh_int64_t *)self->skipset, self->file_lines) !=
+ ((kh_int64_t *)self->skipset)->n_buckets);
+ } else {
+ return (rownum <= self->skip_first_N_rows);
+ }
}
-int tokenize_bytes(parser_t *self,
- size_t line_limit, uint64_t start_lines) {
- int64_t i;
- uint64_t slen;
- int should_skip;
- char c;
- char *stream;
- char *buf = self->data + self->datapos;
-
- const char lineterminator = (self->lineterminator == '\0') ?
- '\n' : self->lineterminator;
-
- const int delim_whitespace = self->delim_whitespace;
- const char delimiter = self->delimiter;
-
- // 1000 is something that couldn't fit in "char"
- // thus comparing a char to it would always be "false"
- const int carriage_symbol = (self->lineterminator == '\0') ? '\r' : 1000;
- const int comment_symbol = (self->commentchar != '\0') ?
- self->commentchar : 1000;
- const int escape_symbol = (self->escapechar != '\0') ?
- self->escapechar : 1000;
-
- if (make_stream_space(self, self->datalen - self->datapos) < 0) {
- int64_t bufsize = 100;
- self->error_msg = malloc(bufsize);
- snprintf(self->error_msg, bufsize, "out of memory");
- return -1;
- }
+int tokenize_bytes(parser_t *self, size_t line_limit, uint64_t start_lines) {
+ int64_t i;
+ uint64_t slen;
+ int should_skip;
+ char c;
+ char *stream;
+ char *buf = self->data + self->datapos;
+
+ const char lineterminator =
+ (self->lineterminator == '\0') ? '\n' : self->lineterminator;
+
+ const int delim_whitespace = self->delim_whitespace;
+ const char delimiter = self->delimiter;
+
+ // 1000 is something that couldn't fit in "char"
+ // thus comparing a char to it would always be "false"
+ const int carriage_symbol = (self->lineterminator == '\0') ? '\r' : 1000;
+ const int comment_symbol =
+ (self->commentchar != '\0') ? self->commentchar : 1000;
+ const int escape_symbol =
+ (self->escapechar != '\0') ? self->escapechar : 1000;
+
+ if (make_stream_space(self, self->datalen - self->datapos) < 0) {
+ int64_t bufsize = 100;
+ self->error_msg = malloc(bufsize);
+ snprintf(self->error_msg, bufsize, "out of memory");
+ return -1;
+ }
- stream = self->stream + self->stream_len;
- slen = self->stream_len;
+ stream = self->stream + self->stream_len;
+ slen = self->stream_len;
- TRACE(("%s\n", buf));
+ TRACE(("%s\n", buf));
- if (self->file_lines == 0) {
- CHECK_FOR_BOM();
- }
+ if (self->file_lines == 0) {
+ CHECK_FOR_BOM();
+ }
+
+ for (i = self->datapos; i < self->datalen; ++i) {
+ // next character in file
+ c = *buf++;
+
+ TRACE(("tokenize_bytes - Iter: %d Char: 0x%x Line %d field_count %d, "
+ "state %d\n",
+ i, c, self->file_lines + 1, self->line_fields[self->lines],
+ self->state));
+
+ switch (self->state) {
+ case START_FIELD_IN_SKIP_LINE:
+ if (IS_TERMINATOR(c)) {
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ } else if (IS_QUOTE(c)) {
+ self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
+ } else if (IS_DELIMITER(c)) {
+ // Do nothing, we're starting a new field again.
+ } else {
+ self->state = IN_FIELD_IN_SKIP_LINE;
+ }
+ break;
+
+ case IN_FIELD_IN_SKIP_LINE:
+ if (IS_TERMINATOR(c)) {
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ } else if (IS_DELIMITER(c)) {
+ self->state = START_FIELD_IN_SKIP_LINE;
+ }
+ break;
+
+ case IN_QUOTED_FIELD_IN_SKIP_LINE:
+ if (IS_QUOTE(c)) {
+ if (self->doublequote) {
+ self->state = QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE;
+ } else {
+ self->state = IN_FIELD_IN_SKIP_LINE;
+ }
+ }
+ break;
+
+ case QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE:
+ if (IS_QUOTE(c)) {
+ self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
+ } else if (IS_TERMINATOR(c)) {
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ } else if (IS_DELIMITER(c)) {
+ self->state = START_FIELD_IN_SKIP_LINE;
+ } else {
+ self->state = IN_FIELD_IN_SKIP_LINE;
+ }
+ break;
+
+ case WHITESPACE_LINE:
+ if (IS_TERMINATOR(c)) {
+ self->file_lines++;
+ self->state = START_RECORD;
+ break;
+ } else if (IS_CARRIAGE(c)) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ break;
+ } else if (!self->delim_whitespace) {
+ if (isblank(c) && c != self->delimiter) {
+ } else { // backtrack
+ // use i + 1 because buf has been incremented but not i
+ do {
+ --buf;
+ --i;
+ } while (i + 1 > self->datapos && !IS_TERMINATOR(*buf));
+
+ // reached a newline rather than the beginning
+ if (IS_TERMINATOR(*buf)) {
+ ++buf; // move pointer to first char after newline
+ ++i;
+ }
+ self->state = START_FIELD;
+ }
+ break;
+ }
+ // fall through
+
+ case EAT_WHITESPACE:
+ if (IS_TERMINATOR(c)) {
+ END_LINE();
+ self->state = START_RECORD;
+ break;
+ } else if (IS_CARRIAGE(c)) {
+ self->state = EAT_CRNL;
+ break;
+ } else if (IS_COMMENT_CHAR(c)) {
+ self->state = EAT_COMMENT;
+ break;
+ } else if (!isblank(c)) {
+ self->state = START_FIELD;
+ // fall through to subsequent state
+ } else {
+ // if whitespace char, keep slurping
+ break;
+ }
+
+ case START_RECORD:
+ // start of record
+ should_skip = skip_this_line(self, self->file_lines);
+
+ if (should_skip == -1) {
+ goto parsingerror;
+ } else if (should_skip) {
+ if (IS_QUOTE(c)) {
+ self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
+ } else {
+ self->state = IN_FIELD_IN_SKIP_LINE;
- for (i = self->datapos; i < self->datalen; ++i) {
- // next character in file
- c = *buf++;
-
- TRACE(
- ("tokenize_bytes - Iter: %d Char: 0x%x Line %d field_count %d, "
- "state %d\n",
- i, c, self->file_lines + 1, self->line_fields[self->lines],
- self->state));
-
- switch (self->state) {
- case START_FIELD_IN_SKIP_LINE:
- if (IS_TERMINATOR(c)) {
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- } else if (IS_QUOTE(c)) {
- self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
- } else if (IS_DELIMITER(c)) {
- // Do nothing, we're starting a new field again.
- } else {
- self->state = IN_FIELD_IN_SKIP_LINE;
- }
- break;
-
- case IN_FIELD_IN_SKIP_LINE:
- if (IS_TERMINATOR(c)) {
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- } else if (IS_DELIMITER(c)) {
- self->state = START_FIELD_IN_SKIP_LINE;
- }
- break;
-
- case IN_QUOTED_FIELD_IN_SKIP_LINE:
- if (IS_QUOTE(c)) {
- if (self->doublequote) {
- self->state = QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE;
- } else {
- self->state = IN_FIELD_IN_SKIP_LINE;
- }
- }
- break;
-
- case QUOTE_IN_QUOTED_FIELD_IN_SKIP_LINE:
- if (IS_QUOTE(c)) {
- self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
- } else if (IS_TERMINATOR(c)) {
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- } else if (IS_DELIMITER(c)) {
- self->state = START_FIELD_IN_SKIP_LINE;
- } else {
- self->state = IN_FIELD_IN_SKIP_LINE;
- }
- break;
-
- case WHITESPACE_LINE:
- if (IS_TERMINATOR(c)) {
- self->file_lines++;
- self->state = START_RECORD;
- break;
- } else if (IS_CARRIAGE(c)) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- break;
- } else if (!self->delim_whitespace) {
- if (isblank(c) && c != self->delimiter) {
- } else { // backtrack
- // use i + 1 because buf has been incremented but not i
- do {
- --buf;
- --i;
- } while (i + 1 > self->datapos && !IS_TERMINATOR(*buf));
-
- // reached a newline rather than the beginning
- if (IS_TERMINATOR(*buf)) {
- ++buf; // move pointer to first char after newline
- ++i;
- }
- self->state = START_FIELD;
- }
- break;
- }
- // fall through
-
- case EAT_WHITESPACE:
- if (IS_TERMINATOR(c)) {
- END_LINE();
- self->state = START_RECORD;
- break;
- } else if (IS_CARRIAGE(c)) {
- self->state = EAT_CRNL;
- break;
- } else if (IS_COMMENT_CHAR(c)) {
- self->state = EAT_COMMENT;
- break;
- } else if (!isblank(c)) {
- self->state = START_FIELD;
- // fall through to subsequent state
- } else {
- // if whitespace char, keep slurping
- break;
- }
-
- case START_RECORD:
- // start of record
- should_skip = skip_this_line(self, self->file_lines);
-
- if (should_skip == -1) {
- goto parsingerror;
- } else if (should_skip) {
- if (IS_QUOTE(c)) {
- self->state = IN_QUOTED_FIELD_IN_SKIP_LINE;
- } else {
- self->state = IN_FIELD_IN_SKIP_LINE;
-
- if (IS_TERMINATOR(c)) {
- END_LINE();
- }
- }
- break;
- } else if (IS_TERMINATOR(c)) {
- // \n\r possible?
- if (self->skip_empty_lines) {
- self->file_lines++;
- } else {
- END_LINE();
- }
- break;
- } else if (IS_CARRIAGE(c)) {
- if (self->skip_empty_lines) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- } else {
- self->state = EAT_CRNL;
- }
- break;
- } else if (IS_COMMENT_CHAR(c)) {
- self->state = EAT_LINE_COMMENT;
- break;
- } else if (isblank(c)) {
- if (self->delim_whitespace) {
- if (self->skip_empty_lines) {
- self->state = WHITESPACE_LINE;
- } else {
- self->state = EAT_WHITESPACE;
- }
- break;
- } else if (c != self->delimiter && self->skip_empty_lines) {
- self->state = WHITESPACE_LINE;
- break;
- }
- // fall through
- }
-
- // normal character - fall through
- // to handle as START_FIELD
- self->state = START_FIELD;
-
- case START_FIELD:
- // expecting field
- if (IS_TERMINATOR(c)) {
- END_FIELD();
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- END_FIELD();
- self->state = EAT_CRNL;
- } else if (IS_QUOTE(c)) {
- // start quoted field
- self->state = IN_QUOTED_FIELD;
- } else if (IS_ESCAPE_CHAR(c)) {
- // possible escaped character
- self->state = ESCAPED_CHAR;
- } else if (IS_SKIPPABLE_SPACE(c)) {
- // ignore space at start of field
- } else if (IS_DELIMITER(c)) {
- if (self->delim_whitespace) {
- self->state = EAT_WHITESPACE;
- } else {
- // save empty field
- END_FIELD();
- }
- } else if (IS_COMMENT_CHAR(c)) {
- END_FIELD();
- self->state = EAT_COMMENT;
- } else {
- // begin new unquoted field
- PUSH_CHAR(c);
- self->state = IN_FIELD;
- }
- break;
-
- case ESCAPED_CHAR:
- PUSH_CHAR(c);
- self->state = IN_FIELD;
- break;
-
- case EAT_LINE_COMMENT:
- if (IS_TERMINATOR(c)) {
- self->file_lines++;
- self->state = START_RECORD;
- } else if (IS_CARRIAGE(c)) {
- self->file_lines++;
- self->state = EAT_CRNL_NOP;
- }
- break;
-
- case IN_FIELD:
- // in unquoted field
- if (IS_TERMINATOR(c)) {
- END_FIELD();
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- END_FIELD();
- self->state = EAT_CRNL;
- } else if (IS_ESCAPE_CHAR(c)) {
- // possible escaped character
- self->state = ESCAPED_CHAR;
- } else if (IS_DELIMITER(c)) {
- // end of field - end of line not reached yet
- END_FIELD();
-
- if (self->delim_whitespace) {
- self->state = EAT_WHITESPACE;
- } else {
- self->state = START_FIELD;
- }
- } else if (IS_COMMENT_CHAR(c)) {
- END_FIELD();
- self->state = EAT_COMMENT;
- } else {
- // normal character - save in field
- PUSH_CHAR(c);
- }
- break;
-
- case IN_QUOTED_FIELD:
- // in quoted field
- if (IS_ESCAPE_CHAR(c)) {
- // possible escape character
- self->state = ESCAPE_IN_QUOTED_FIELD;
- } else if (IS_QUOTE(c)) {
- if (self->doublequote) {
- // double quote - " represented by ""
- self->state = QUOTE_IN_QUOTED_FIELD;
- } else {
- // end of quote part of field
- self->state = IN_FIELD;
- }
- } else {
- // normal character - save in field
- PUSH_CHAR(c);
- }
- break;
-
- case ESCAPE_IN_QUOTED_FIELD:
- PUSH_CHAR(c);
- self->state = IN_QUOTED_FIELD;
- break;
-
- case QUOTE_IN_QUOTED_FIELD:
- // double quote - seen a quote in an quoted field
- if (IS_QUOTE(c)) {
- // save "" as "
-
- PUSH_CHAR(c);
- self->state = IN_QUOTED_FIELD;
- } else if (IS_DELIMITER(c)) {
- // end of field - end of line not reached yet
- END_FIELD();
-
- if (self->delim_whitespace) {
- self->state = EAT_WHITESPACE;
- } else {
- self->state = START_FIELD;
- }
- } else if (IS_TERMINATOR(c)) {
- END_FIELD();
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- END_FIELD();
- self->state = EAT_CRNL;
- } else {
- PUSH_CHAR(c);
- self->state = IN_FIELD;
- }
- break;
-
- case EAT_COMMENT:
- if (IS_TERMINATOR(c)) {
- END_LINE();
- } else if (IS_CARRIAGE(c)) {
- self->state = EAT_CRNL;
- }
- break;
-
- // only occurs with non-custom line terminator,
- // which is why we directly check for '\n'
- case EAT_CRNL:
- if (c == '\n') {
- END_LINE();
- } else if (IS_DELIMITER(c)) {
- if (self->delim_whitespace) {
- END_LINE_STATE(EAT_WHITESPACE);
- } else {
- // Handle \r-delimited files
- END_LINE_AND_FIELD_STATE(START_FIELD);
- }
- } else {
- if (self->delim_whitespace) {
- /* XXX
- * first character of a new record--need to back up and
- * reread
- * to handle properly...
- */
- i--;
- buf--; // back up one character (HACK!)
- END_LINE_STATE(START_RECORD);
- } else {
- // \r line terminator
- // UGH. we don't actually want
- // to consume the token. fix this later
- self->stream_len = slen;
- if (end_line(self) < 0) {
- goto parsingerror;
- }
-
- stream = self->stream + self->stream_len;
- slen = self->stream_len;
- self->state = START_RECORD;
-
- --i;
- buf--; // let's try this character again (HACK!)
- if (line_limit > 0 &&
- self->lines == start_lines + line_limit) {
- goto linelimit;
- }
- }
- }
- break;
-
- // only occurs with non-custom line terminator,
- // which is why we directly check for '\n'
- case EAT_CRNL_NOP: // inside an ignored comment line
- self->state = START_RECORD;
- // \r line terminator -- parse this character again
- if (c != '\n' && !IS_DELIMITER(c)) {
- --i;
- --buf;
- }
- break;
- default:
- break;
+ if (IS_TERMINATOR(c)) {
+ END_LINE();
+ }
+ }
+ break;
+ } else if (IS_TERMINATOR(c)) {
+ // \n\r possible?
+ if (self->skip_empty_lines) {
+ self->file_lines++;
+ } else {
+ END_LINE();
+ }
+ break;
+ } else if (IS_CARRIAGE(c)) {
+ if (self->skip_empty_lines) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ } else {
+ self->state = EAT_CRNL;
+ }
+ break;
+ } else if (IS_COMMENT_CHAR(c)) {
+ self->state = EAT_LINE_COMMENT;
+ break;
+ } else if (isblank(c)) {
+ if (self->delim_whitespace) {
+ if (self->skip_empty_lines) {
+ self->state = WHITESPACE_LINE;
+ } else {
+ self->state = EAT_WHITESPACE;
+ }
+ break;
+ } else if (c != self->delimiter && self->skip_empty_lines) {
+ self->state = WHITESPACE_LINE;
+ break;
}
+ // fall through
+ }
+
+ // normal character - fall through
+ // to handle as START_FIELD
+ self->state = START_FIELD;
+
+ case START_FIELD:
+ // expecting field
+ if (IS_TERMINATOR(c)) {
+ END_FIELD();
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ END_FIELD();
+ self->state = EAT_CRNL;
+ } else if (IS_QUOTE(c)) {
+ // start quoted field
+ self->state = IN_QUOTED_FIELD;
+ } else if (IS_ESCAPE_CHAR(c)) {
+ // possible escaped character
+ self->state = ESCAPED_CHAR;
+ } else if (IS_SKIPPABLE_SPACE(c)) {
+ // ignore space at start of field
+ } else if (IS_DELIMITER(c)) {
+ if (self->delim_whitespace) {
+ self->state = EAT_WHITESPACE;
+ } else {
+ // save empty field
+ END_FIELD();
+ }
+ } else if (IS_COMMENT_CHAR(c)) {
+ END_FIELD();
+ self->state = EAT_COMMENT;
+ } else {
+ // begin new unquoted field
+ PUSH_CHAR(c);
+ self->state = IN_FIELD;
+ }
+ break;
+
+ case ESCAPED_CHAR:
+ PUSH_CHAR(c);
+ self->state = IN_FIELD;
+ break;
+
+ case EAT_LINE_COMMENT:
+ if (IS_TERMINATOR(c)) {
+ self->file_lines++;
+ self->state = START_RECORD;
+ } else if (IS_CARRIAGE(c)) {
+ self->file_lines++;
+ self->state = EAT_CRNL_NOP;
+ }
+ break;
+
+ case IN_FIELD:
+ // in unquoted field
+ if (IS_TERMINATOR(c)) {
+ END_FIELD();
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ END_FIELD();
+ self->state = EAT_CRNL;
+ } else if (IS_ESCAPE_CHAR(c)) {
+ // possible escaped character
+ self->state = ESCAPED_CHAR;
+ } else if (IS_DELIMITER(c)) {
+ // end of field - end of line not reached yet
+ END_FIELD();
+
+ if (self->delim_whitespace) {
+ self->state = EAT_WHITESPACE;
+ } else {
+ self->state = START_FIELD;
+ }
+ } else if (IS_COMMENT_CHAR(c)) {
+ END_FIELD();
+ self->state = EAT_COMMENT;
+ } else {
+ // normal character - save in field
+ PUSH_CHAR(c);
+ }
+ break;
+
+ case IN_QUOTED_FIELD:
+ // in quoted field
+ if (IS_ESCAPE_CHAR(c)) {
+ // possible escape character
+ self->state = ESCAPE_IN_QUOTED_FIELD;
+ } else if (IS_QUOTE(c)) {
+ if (self->doublequote) {
+ // double quote - " represented by ""
+ self->state = QUOTE_IN_QUOTED_FIELD;
+ } else {
+ // end of quote part of field
+ self->state = IN_FIELD;
+ }
+ } else {
+ // normal character - save in field
+ PUSH_CHAR(c);
+ }
+ break;
+
+ case ESCAPE_IN_QUOTED_FIELD:
+ PUSH_CHAR(c);
+ self->state = IN_QUOTED_FIELD;
+ break;
+
+ case QUOTE_IN_QUOTED_FIELD:
+ // double quote - seen a quote in an quoted field
+ if (IS_QUOTE(c)) {
+ // save "" as "
+
+ PUSH_CHAR(c);
+ self->state = IN_QUOTED_FIELD;
+ } else if (IS_DELIMITER(c)) {
+ // end of field - end of line not reached yet
+ END_FIELD();
+
+ if (self->delim_whitespace) {
+ self->state = EAT_WHITESPACE;
+ } else {
+ self->state = START_FIELD;
+ }
+ } else if (IS_TERMINATOR(c)) {
+ END_FIELD();
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ END_FIELD();
+ self->state = EAT_CRNL;
+ } else {
+ PUSH_CHAR(c);
+ self->state = IN_FIELD;
+ }
+ break;
+
+ case EAT_COMMENT:
+ if (IS_TERMINATOR(c)) {
+ END_LINE();
+ } else if (IS_CARRIAGE(c)) {
+ self->state = EAT_CRNL;
+ }
+ break;
+
+ // only occurs with non-custom line terminator,
+ // which is why we directly check for '\n'
+ case EAT_CRNL:
+ if (c == '\n') {
+ END_LINE();
+ } else if (IS_DELIMITER(c)) {
+ if (self->delim_whitespace) {
+ END_LINE_STATE(EAT_WHITESPACE);
+ } else {
+ // Handle \r-delimited files
+ END_LINE_AND_FIELD_STATE(START_FIELD);
+ }
+ } else {
+ if (self->delim_whitespace) {
+ /* XXX
+ * first character of a new record--need to back up and
+ * reread
+ * to handle properly...
+ */
+ i--;
+ buf--; // back up one character (HACK!)
+ END_LINE_STATE(START_RECORD);
+ } else {
+ // \r line terminator
+ // UGH. we don't actually want
+ // to consume the token. fix this later
+ self->stream_len = slen;
+ if (end_line(self) < 0) {
+ goto parsingerror;
+ }
+
+ stream = self->stream + self->stream_len;
+ slen = self->stream_len;
+ self->state = START_RECORD;
+
+ --i;
+ buf--; // let's try this character again (HACK!)
+ if (line_limit > 0 && self->lines == start_lines + line_limit) {
+ goto linelimit;
+ }
+ }
+ }
+ break;
+
+ // only occurs with non-custom line terminator,
+ // which is why we directly check for '\n'
+ case EAT_CRNL_NOP: // inside an ignored comment line
+ self->state = START_RECORD;
+ // \r line terminator -- parse this character again
+ if (c != '\n' && !IS_DELIMITER(c)) {
+ --i;
+ --buf;
+ }
+ break;
+ default:
+ break;
}
+ }
- _TOKEN_CLEANUP();
+ _TOKEN_CLEANUP();
- TRACE(("Finished tokenizing input\n"))
+ TRACE(("Finished tokenizing input\n"))
- return 0;
+ return 0;
parsingerror:
- i++;
- _TOKEN_CLEANUP();
+ i++;
+ _TOKEN_CLEANUP();
- return -1;
+ return -1;
linelimit:
- i++;
- _TOKEN_CLEANUP();
+ i++;
+ _TOKEN_CLEANUP();
- return 0;
+ return 0;
}
static int parser_handle_eof(parser_t *self) {
- int64_t bufsize = 100;
-
- TRACE(
- ("handling eof, datalen: %d, pstate: %d\n", self->datalen, self->state))
+ int64_t bufsize = 100;
- if (self->datalen != 0) return -1;
+ TRACE(("handling eof, datalen: %d, pstate: %d\n", self->datalen, self->state))
- switch (self->state) {
- case START_RECORD:
- case WHITESPACE_LINE:
- case EAT_CRNL_NOP:
- case EAT_LINE_COMMENT:
- return 0;
-
- case ESCAPE_IN_QUOTED_FIELD:
- case IN_QUOTED_FIELD:
- self->error_msg = (char *)malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "EOF inside string starting at row %" PRIu64,
- self->file_lines);
- return -1;
-
- case ESCAPED_CHAR:
- self->error_msg = (char *)malloc(bufsize);
- snprintf(self->error_msg, bufsize,
- "EOF following escape character");
- return -1;
-
- case IN_FIELD:
- case START_FIELD:
- case QUOTE_IN_QUOTED_FIELD:
- if (end_field(self) < 0) return -1;
- break;
-
- default:
- break;
- }
-
- if (end_line(self) < 0)
- return -1;
- else
- return 0;
-}
+ if (self->datalen != 0)
+ return -1;
-int parser_consume_rows(parser_t *self, size_t nrows) {
- int64_t offset, word_deletions;
- uint64_t char_count, i;
+ switch (self->state) {
+ case START_RECORD:
+ case WHITESPACE_LINE:
+ case EAT_CRNL_NOP:
+ case EAT_LINE_COMMENT:
+ return 0;
- if (nrows > self->lines) {
- nrows = self->lines;
- }
+ case ESCAPE_IN_QUOTED_FIELD:
+ case IN_QUOTED_FIELD:
+ self->error_msg = (char *)malloc(bufsize);
+ snprintf(self->error_msg, bufsize,
+ "EOF inside string starting at row %" PRIu64, self->file_lines);
+ return -1;
- /* do nothing */
- if (nrows == 0) return 0;
+ case ESCAPED_CHAR:
+ self->error_msg = (char *)malloc(bufsize);
+ snprintf(self->error_msg, bufsize, "EOF following escape character");
+ return -1;
- /* cannot guarantee that nrows + 1 has been observed */
- word_deletions = self->line_start[nrows - 1] + self->line_fields[nrows - 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;
- }
+ case IN_FIELD:
+ case START_FIELD:
+ case QUOTE_IN_QUOTED_FIELD:
+ if (end_field(self) < 0)
+ return -1;
+ break;
- TRACE(("parser_consume_rows: Deleting %d words, %d chars\n", word_deletions,
- char_count));
+ default:
+ break;
+ }
- /* move stream, only if something to move */
- if (char_count < self->stream_len) {
- memmove(self->stream, (self->stream + char_count),
- self->stream_len - char_count);
- }
- /* buffer counts */
- self->stream_len -= char_count;
+ if (end_line(self) < 0)
+ return -1;
+ else
+ return 0;
+}
- /* move token metadata */
- // Note: We should always have words_len < word_deletions, so this
- // subtraction will remain appropriately-typed.
- for (i = 0; i < self->words_len - word_deletions; ++i) {
- offset = i + word_deletions;
+int parser_consume_rows(parser_t *self, size_t nrows) {
+ int64_t offset, word_deletions;
+ uint64_t char_count, i;
- self->words[i] = self->words[offset] - char_count;
- self->word_starts[i] = self->word_starts[offset] - char_count;
- }
- self->words_len -= word_deletions;
-
- /* move current word pointer to stream */
- self->pword_start -= char_count;
- self->word_start -= char_count;
-
- /* move line metadata */
- // Note: We should always have self->lines - nrows + 1 >= 0, so this
- // subtraction will remain appropriately-typed.
- for (i = 0; i < self->lines - nrows + 1; ++i) {
- offset = i + nrows;
- self->line_start[i] = self->line_start[offset] - word_deletions;
- self->line_fields[i] = self->line_fields[offset];
- }
- self->lines -= nrows;
+ if (nrows > self->lines) {
+ nrows = self->lines;
+ }
+ /* do nothing */
+ if (nrows == 0)
return 0;
+
+ /* cannot guarantee that nrows + 1 has been observed */
+ word_deletions = self->line_start[nrows - 1] + self->line_fields[nrows - 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));
+
+ /* move stream, only if something to move */
+ if (char_count < self->stream_len) {
+ memmove(self->stream, (self->stream + char_count),
+ self->stream_len - char_count);
+ }
+ /* buffer counts */
+ self->stream_len -= char_count;
+
+ /* move token metadata */
+ // Note: We should always have words_len < word_deletions, so this
+ // subtraction will remain appropriately-typed.
+ for (i = 0; i < self->words_len - word_deletions; ++i) {
+ offset = i + word_deletions;
+
+ self->words[i] = self->words[offset] - char_count;
+ self->word_starts[i] = self->word_starts[offset] - char_count;
+ }
+ self->words_len -= word_deletions;
+
+ /* move current word pointer to stream */
+ self->pword_start -= char_count;
+ self->word_start -= char_count;
+
+ /* move line metadata */
+ // Note: We should always have self->lines - nrows + 1 >= 0, so this
+ // subtraction will remain appropriately-typed.
+ for (i = 0; i < self->lines - nrows + 1; ++i) {
+ offset = i + nrows;
+ self->line_start[i] = self->line_start[offset] - word_deletions;
+ self->line_fields[i] = self->line_fields[offset];
+ }
+ self->lines -= nrows;
+
+ return 0;
}
static size_t _next_pow2(size_t sz) {
- size_t result = 1;
- while (result < sz) result *= 2;
- return result;
+ size_t result = 1;
+ while (result < sz)
+ result *= 2;
+ return result;
}
int parser_trim_buffers(parser_t *self) {
- /*
- Free memory
- */
- size_t new_cap;
- void *newptr;
-
- uint64_t i;
-
- /**
- * Before we free up space and trim, we should
- * save how many words we saw when parsing, if
- * it exceeds the maximum number we saw before.
- *
- * This is important for when we read in chunks,
- * so that we can inform subsequent chunk parsing
- * as to how many words we could possibly see.
- */
- if (self->words_cap > self->max_words_cap) {
- self->max_words_cap = self->words_cap;
- }
-
- /* trim words, word_starts */
- new_cap = _next_pow2(self->words_len) + 1;
- if (new_cap < self->words_cap) {
- TRACE(("parser_trim_buffers: new_cap < self->words_cap\n"));
- self->words = realloc(self->words, new_cap * sizeof(char *));
- if (self->words == NULL) {
- return PARSER_OUT_OF_MEMORY;
- }
- self->word_starts = realloc(self->word_starts,
- new_cap * sizeof(int64_t));
- if (self->word_starts == NULL) {
- return PARSER_OUT_OF_MEMORY;
- }
- self->words_cap = new_cap;
- }
-
- /* trim stream */
- new_cap = _next_pow2(self->stream_len) + 1;
- TRACE(
- ("parser_trim_buffers: new_cap = %zu, stream_cap = %zu, lines_cap = "
+ /*
+ Free memory
+ */
+ size_t new_cap;
+ void *newptr;
+
+ uint64_t i;
+
+ /**
+ * Before we free up space and trim, we should
+ * save how many words we saw when parsing, if
+ * it exceeds the maximum number we saw before.
+ *
+ * This is important for when we read in chunks,
+ * so that we can inform subsequent chunk parsing
+ * as to how many words we could possibly see.
+ */
+ if (self->words_cap > self->max_words_cap) {
+ self->max_words_cap = self->words_cap;
+ }
+
+ /* trim words, word_starts */
+ new_cap = _next_pow2(self->words_len) + 1;
+ if (new_cap < self->words_cap) {
+ TRACE(("parser_trim_buffers: new_cap < self->words_cap\n"));
+ self->words = realloc(self->words, new_cap * sizeof(char *));
+ if (self->words == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ }
+ self->word_starts = realloc(self->word_starts, new_cap * sizeof(int64_t));
+ if (self->word_starts == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ }
+ self->words_cap = new_cap;
+ }
+
+ /* trim stream */
+ new_cap = _next_pow2(self->stream_len) + 1;
+ TRACE(("parser_trim_buffers: new_cap = %zu, stream_cap = %zu, lines_cap = "
"%zu\n",
new_cap, self->stream_cap, self->lines_cap));
- if (new_cap < self->stream_cap) {
- TRACE(
- ("parser_trim_buffers: new_cap < self->stream_cap, calling "
- "realloc\n"));
- newptr = realloc(self->stream, new_cap);
- if (newptr == NULL) {
- return PARSER_OUT_OF_MEMORY;
- } else {
- // Update the pointers in the self->words array (char **) if
- // `realloc`
- // moved the `self->stream` buffer. This block mirrors a similar
- // block in
- // `make_stream_space`.
- if (self->stream != newptr) {
- self->pword_start = (char *)newptr + self->word_start;
-
- for (i = 0; i < self->words_len; ++i) {
- self->words[i] = (char *)newptr + self->word_starts[i];
- }
- }
-
- self->stream = newptr;
- self->stream_cap = new_cap;
+ if (new_cap < self->stream_cap) {
+ TRACE(("parser_trim_buffers: new_cap < self->stream_cap, calling "
+ "realloc\n"));
+ newptr = realloc(self->stream, new_cap);
+ if (newptr == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ } else {
+ // Update the pointers in the self->words array (char **) if
+ // `realloc`
+ // moved the `self->stream` buffer. This block mirrors a similar
+ // block in
+ // `make_stream_space`.
+ if (self->stream != newptr) {
+ self->pword_start = (char *)newptr + self->word_start;
+
+ for (i = 0; i < self->words_len; ++i) {
+ self->words[i] = (char *)newptr + self->word_starts[i];
}
+ }
+
+ self->stream = newptr;
+ self->stream_cap = new_cap;
}
+ }
- /* trim line_start, line_fields */
- new_cap = _next_pow2(self->lines) + 1;
- if (new_cap < self->lines_cap) {
- TRACE(("parser_trim_buffers: new_cap < self->lines_cap\n"));
- newptr = realloc(self->line_start,
- new_cap * sizeof(int64_t));
- if (newptr == NULL) {
- return PARSER_OUT_OF_MEMORY;
- } else {
- self->line_start = newptr;
- }
- newptr = realloc(self->line_fields,
- new_cap * sizeof(int64_t));
- if (newptr == NULL) {
- return PARSER_OUT_OF_MEMORY;
- } else {
- self->line_fields = newptr;
- self->lines_cap = new_cap;
- }
+ /* trim line_start, line_fields */
+ new_cap = _next_pow2(self->lines) + 1;
+ if (new_cap < self->lines_cap) {
+ TRACE(("parser_trim_buffers: new_cap < self->lines_cap\n"));
+ newptr = realloc(self->line_start, new_cap * sizeof(int64_t));
+ if (newptr == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ } else {
+ self->line_start = newptr;
+ }
+ newptr = realloc(self->line_fields, new_cap * sizeof(int64_t));
+ if (newptr == NULL) {
+ return PARSER_OUT_OF_MEMORY;
+ } else {
+ self->line_fields = newptr;
+ self->lines_cap = new_cap;
}
+ }
- return 0;
+ return 0;
}
/*
@@ -1341,63 +1319,61 @@ int parser_trim_buffers(parser_t *self) {
int _tokenize_helper(parser_t *self, size_t nrows, int all,
const char *encoding_errors) {
- int status = 0;
- uint64_t start_lines = self->lines;
-
- if (self->state == FINISHED) {
- return 0;
- }
-
- TRACE((
- "_tokenize_helper: Asked to tokenize %d rows, datapos=%d, datalen=%d\n",
- nrows, self->datapos, self->datalen));
+ int status = 0;
+ uint64_t start_lines = self->lines;
- while (1) {
- if (!all && self->lines - start_lines >= nrows) break;
-
- if (self->datapos == self->datalen) {
- status = parser_buffer_bytes(self, self->chunksize,
- encoding_errors);
-
- if (status == REACHED_EOF) {
- // close out last line
- status = parser_handle_eof(self);
- self->state = FINISHED;
- break;
- } else if (status != 0) {
- return status;
- }
- }
-
- TRACE(
- ("_tokenize_helper: Trying to process %d bytes, datalen=%d, "
- "datapos= %d\n",
- self->datalen - self->datapos, self->datalen, self->datapos));
-
- status = tokenize_bytes(self, nrows, start_lines);
-
- if (status < 0) {
- // XXX
- TRACE(
- ("_tokenize_helper: Status %d returned from tokenize_bytes, "
- "breaking\n",
- status));
- status = -1;
- break;
- }
- }
- TRACE(("leaving tokenize_helper\n"));
- return status;
+ if (self->state == FINISHED) {
+ return 0;
+ }
+
+ TRACE(
+ ("_tokenize_helper: Asked to tokenize %d rows, datapos=%d, datalen=%d\n",
+ nrows, self->datapos, self->datalen));
+
+ while (1) {
+ if (!all && self->lines - start_lines >= nrows)
+ break;
+
+ if (self->datapos == self->datalen) {
+ status = parser_buffer_bytes(self, self->chunksize, encoding_errors);
+
+ if (status == REACHED_EOF) {
+ // close out last line
+ status = parser_handle_eof(self);
+ self->state = FINISHED;
+ break;
+ } else if (status != 0) {
+ return status;
+ }
+ }
+
+ TRACE(("_tokenize_helper: Trying to process %d bytes, datalen=%d, "
+ "datapos= %d\n",
+ self->datalen - self->datapos, self->datalen, self->datapos));
+
+ status = tokenize_bytes(self, nrows, start_lines);
+
+ if (status < 0) {
+ // XXX
+ TRACE(("_tokenize_helper: Status %d returned from tokenize_bytes, "
+ "breaking\n",
+ status));
+ status = -1;
+ break;
+ }
+ }
+ TRACE(("leaving tokenize_helper\n"));
+ return status;
}
int tokenize_nrows(parser_t *self, size_t nrows, const char *encoding_errors) {
- int status = _tokenize_helper(self, nrows, 0, encoding_errors);
- return status;
+ int status = _tokenize_helper(self, nrows, 0, encoding_errors);
+ return status;
}
int tokenize_all_rows(parser_t *self, const char *encoding_errors) {
- int status = _tokenize_helper(self, -1, 1, encoding_errors);
- return status;
+ int status = _tokenize_helper(self, -1, 1, encoding_errors);
+ return status;
}
/*
@@ -1415,15 +1391,15 @@ int tokenize_all_rows(parser_t *self, const char *encoding_errors) {
* leaves the value of *val unmodified.
*/
int to_boolean(const char *item, uint8_t *val) {
- if (strcasecmp(item, "TRUE") == 0) {
- *val = 1;
- return 0;
- } else if (strcasecmp(item, "FALSE") == 0) {
- *val = 0;
- return 0;
- }
+ if (strcasecmp(item, "TRUE") == 0) {
+ *val = 1;
+ return 0;
+ } else if (strcasecmp(item, "FALSE") == 0) {
+ *val = 0;
+ return 0;
+ }
- return -1;
+ return -1;
}
// ---------------------------------------------------------------------------
@@ -1479,301 +1455,320 @@ const int max_int_decimal_digits = (sizeof(unsigned int) * 8) / 4;
double xstrtod(const char *str, char **endptr, char decimal, char sci,
char tsep, int skip_trailing, int *error, int *maybe_int) {
- double number;
- unsigned int i_number = 0;
- int exponent;
- int negative;
- char *p = (char *)str;
- double p10;
- int n;
- int num_digits;
- int num_decimals;
-
- if (maybe_int != NULL) *maybe_int = 1;
- // Skip leading whitespace.
- while (isspace_ascii(*p)) p++;
-
- // Handle optional sign.
- negative = 0;
- switch (*p) {
- case '-':
- negative = 1; // Fall through to increment position.
- case '+':
- p++;
- }
-
- exponent = 0;
- num_digits = 0;
- num_decimals = 0;
-
- // Process string of digits.
- while (isdigit_ascii(*p) && num_digits <= max_int_decimal_digits) {
- i_number = i_number * 10 + (*p - '0');
- p++;
- num_digits++;
-
- p += (tsep != '\0' && *p == tsep);
- }
- number = i_number;
-
- if (num_digits > max_int_decimal_digits) {
- // process what's left as double
- while (isdigit_ascii(*p)) {
- number = number * 10. + (*p - '0');
- p++;
- num_digits++;
-
- p += (tsep != '\0' && *p == tsep);
- }
- }
-
- // Process decimal part.
- if (*p == decimal) {
- if (maybe_int != NULL) *maybe_int = 0;
- p++;
-
- while (isdigit_ascii(*p)) {
- number = number * 10. + (*p - '0');
- p++;
- num_digits++;
- num_decimals++;
- }
-
- exponent -= num_decimals;
- }
-
- if (num_digits == 0) {
- *error = ERANGE;
- return 0.0;
- }
-
- // Correct for sign.
- if (negative) number = -number;
-
- // Process an exponent string.
- if (toupper_ascii(*p) == toupper_ascii(sci)) {
- if (maybe_int != NULL) *maybe_int = 0;
-
- // Handle optional sign.
- negative = 0;
- switch (*++p) {
- case '-':
- negative = 1; // Fall through to increment pos.
- case '+':
- p++;
- }
-
- // Process string of digits.
- num_digits = 0;
- n = 0;
- while (isdigit_ascii(*p)) {
- n = n * 10 + (*p - '0');
- num_digits++;
- p++;
- }
-
- if (negative)
- exponent -= n;
- else
- exponent += n;
+ double number;
+ unsigned int i_number = 0;
+ int exponent;
+ int negative;
+ char *p = (char *)str;
+ double p10;
+ int n;
+ int num_digits;
+ int num_decimals;
+
+ if (maybe_int != NULL)
+ *maybe_int = 1;
+ // Skip leading whitespace.
+ while (isspace_ascii(*p))
+ p++;
+
+ // Handle optional sign.
+ negative = 0;
+ switch (*p) {
+ case '-':
+ negative = 1; // Fall through to increment position.
+ case '+':
+ p++;
+ }
+
+ exponent = 0;
+ num_digits = 0;
+ num_decimals = 0;
+
+ // Process string of digits.
+ while (isdigit_ascii(*p) && num_digits <= max_int_decimal_digits) {
+ i_number = i_number * 10 + (*p - '0');
+ p++;
+ num_digits++;
+
+ p += (tsep != '\0' && *p == tsep);
+ }
+ number = i_number;
+
+ if (num_digits > max_int_decimal_digits) {
+ // process what's left as double
+ while (isdigit_ascii(*p)) {
+ number = number * 10. + (*p - '0');
+ p++;
+ num_digits++;
- // If no digits, after the 'e'/'E', un-consume it
- if (num_digits == 0) p--;
+ p += (tsep != '\0' && *p == tsep);
}
+ }
- if (exponent < DBL_MIN_EXP || exponent > DBL_MAX_EXP) {
- *error = ERANGE;
- return HUGE_VAL;
- }
+ // Process decimal part.
+ if (*p == decimal) {
+ if (maybe_int != NULL)
+ *maybe_int = 0;
+ p++;
- // Scale the result.
- p10 = 10.;
- n = exponent;
- if (n < 0) n = -n;
- while (n) {
- if (n & 1) {
- if (exponent < 0)
- number /= p10;
- else
- number *= p10;
- }
- n >>= 1;
- p10 *= p10;
+ while (isdigit_ascii(*p)) {
+ number = number * 10. + (*p - '0');
+ p++;
+ num_digits++;
+ num_decimals++;
}
- if (number == HUGE_VAL) {
- *error = ERANGE;
- }
+ exponent -= num_decimals;
+ }
- if (skip_trailing) {
- // Skip trailing whitespace.
- while (isspace_ascii(*p)) p++;
- }
+ if (num_digits == 0) {
+ *error = ERANGE;
+ return 0.0;
+ }
- if (endptr) *endptr = p;
- return number;
-}
+ // Correct for sign.
+ if (negative)
+ number = -number;
-double precise_xstrtod(const char *str, char **endptr, char decimal,
- char sci, char tsep, int skip_trailing,
- int *error, int *maybe_int) {
- double number;
- int exponent;
- int negative;
- char *p = (char *)str;
- int num_digits;
- int num_decimals;
- int max_digits = 17;
- int n;
-
- if (maybe_int != NULL) *maybe_int = 1;
- // Cache powers of 10 in memory.
- static double e[] = {
- 1., 1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9,
- 1e10, 1e11, 1e12, 1e13, 1e14, 1e15, 1e16, 1e17, 1e18, 1e19,
- 1e20, 1e21, 1e22, 1e23, 1e24, 1e25, 1e26, 1e27, 1e28, 1e29,
- 1e30, 1e31, 1e32, 1e33, 1e34, 1e35, 1e36, 1e37, 1e38, 1e39,
- 1e40, 1e41, 1e42, 1e43, 1e44, 1e45, 1e46, 1e47, 1e48, 1e49,
- 1e50, 1e51, 1e52, 1e53, 1e54, 1e55, 1e56, 1e57, 1e58, 1e59,
- 1e60, 1e61, 1e62, 1e63, 1e64, 1e65, 1e66, 1e67, 1e68, 1e69,
- 1e70, 1e71, 1e72, 1e73, 1e74, 1e75, 1e76, 1e77, 1e78, 1e79,
- 1e80, 1e81, 1e82, 1e83, 1e84, 1e85, 1e86, 1e87, 1e88, 1e89,
- 1e90, 1e91, 1e92, 1e93, 1e94, 1e95, 1e96, 1e97, 1e98, 1e99,
- 1e100, 1e101, 1e102, 1e103, 1e104, 1e105, 1e106, 1e107, 1e108, 1e109,
- 1e110, 1e111, 1e112, 1e113, 1e114, 1e115, 1e116, 1e117, 1e118, 1e119,
- 1e120, 1e121, 1e122, 1e123, 1e124, 1e125, 1e126, 1e127, 1e128, 1e129,
- 1e130, 1e131, 1e132, 1e133, 1e134, 1e135, 1e136, 1e137, 1e138, 1e139,
- 1e140, 1e141, 1e142, 1e143, 1e144, 1e145, 1e146, 1e147, 1e148, 1e149,
- 1e150, 1e151, 1e152, 1e153, 1e154, 1e155, 1e156, 1e157, 1e158, 1e159,
- 1e160, 1e161, 1e162, 1e163, 1e164, 1e165, 1e166, 1e167, 1e168, 1e169,
- 1e170, 1e171, 1e172, 1e173, 1e174, 1e175, 1e176, 1e177, 1e178, 1e179,
- 1e180, 1e181, 1e182, 1e183, 1e184, 1e185, 1e186, 1e187, 1e188, 1e189,
- 1e190, 1e191, 1e192, 1e193, 1e194, 1e195, 1e196, 1e197, 1e198, 1e199,
- 1e200, 1e201, 1e202, 1e203, 1e204, 1e205, 1e206, 1e207, 1e208, 1e209,
- 1e210, 1e211, 1e212, 1e213, 1e214, 1e215, 1e216, 1e217, 1e218, 1e219,
- 1e220, 1e221, 1e222, 1e223, 1e224, 1e225, 1e226, 1e227, 1e228, 1e229,
- 1e230, 1e231, 1e232, 1e233, 1e234, 1e235, 1e236, 1e237, 1e238, 1e239,
- 1e240, 1e241, 1e242, 1e243, 1e244, 1e245, 1e246, 1e247, 1e248, 1e249,
- 1e250, 1e251, 1e252, 1e253, 1e254, 1e255, 1e256, 1e257, 1e258, 1e259,
- 1e260, 1e261, 1e262, 1e263, 1e264, 1e265, 1e266, 1e267, 1e268, 1e269,
- 1e270, 1e271, 1e272, 1e273, 1e274, 1e275, 1e276, 1e277, 1e278, 1e279,
- 1e280, 1e281, 1e282, 1e283, 1e284, 1e285, 1e286, 1e287, 1e288, 1e289,
- 1e290, 1e291, 1e292, 1e293, 1e294, 1e295, 1e296, 1e297, 1e298, 1e299,
- 1e300, 1e301, 1e302, 1e303, 1e304, 1e305, 1e306, 1e307, 1e308};
-
- // Skip leading whitespace.
- while (isspace_ascii(*p)) p++;
+ // Process an exponent string.
+ if (toupper_ascii(*p) == toupper_ascii(sci)) {
+ if (maybe_int != NULL)
+ *maybe_int = 0;
// Handle optional sign.
negative = 0;
- switch (*p) {
- case '-':
- negative = 1; // Fall through to increment position.
- case '+':
- p++;
+ switch (*++p) {
+ case '-':
+ negative = 1; // Fall through to increment pos.
+ case '+':
+ p++;
}
- number = 0.;
- exponent = 0;
- num_digits = 0;
- num_decimals = 0;
-
// Process string of digits.
+ num_digits = 0;
+ n = 0;
while (isdigit_ascii(*p)) {
- if (num_digits < max_digits) {
- number = number * 10. + (*p - '0');
- num_digits++;
- } else {
- ++exponent;
- }
-
- p++;
- p += (tsep != '\0' && *p == tsep);
+ n = n * 10 + (*p - '0');
+ num_digits++;
+ p++;
}
- // Process decimal part
- if (*p == decimal) {
- if (maybe_int != NULL) *maybe_int = 0;
- p++;
+ if (negative)
+ exponent -= n;
+ else
+ exponent += n;
+
+ // If no digits, after the 'e'/'E', un-consume it
+ if (num_digits == 0)
+ p--;
+ }
+
+ if (exponent < DBL_MIN_EXP || exponent > DBL_MAX_EXP) {
+ *error = ERANGE;
+ return HUGE_VAL;
+ }
+
+ // Scale the result.
+ p10 = 10.;
+ n = exponent;
+ if (n < 0)
+ n = -n;
+ while (n) {
+ if (n & 1) {
+ if (exponent < 0)
+ number /= p10;
+ else
+ number *= p10;
+ }
+ n >>= 1;
+ p10 *= p10;
+ }
+
+ if (number == HUGE_VAL) {
+ *error = ERANGE;
+ }
+
+ if (skip_trailing) {
+ // Skip trailing whitespace.
+ while (isspace_ascii(*p))
+ p++;
+ }
+
+ if (endptr)
+ *endptr = p;
+ return number;
+}
- while (num_digits < max_digits && isdigit_ascii(*p)) {
- number = number * 10. + (*p - '0');
- p++;
- num_digits++;
- num_decimals++;
- }
+double precise_xstrtod(const char *str, char **endptr, char decimal, char sci,
+ char tsep, int skip_trailing, int *error,
+ int *maybe_int) {
+ double number;
+ int exponent;
+ int negative;
+ char *p = (char *)str;
+ int num_digits;
+ int num_decimals;
+ int max_digits = 17;
+ int n;
+
+ if (maybe_int != NULL)
+ *maybe_int = 1;
+ // Cache powers of 10 in memory.
+ static double e[] = {
+ 1., 1e1, 1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9,
+ 1e10, 1e11, 1e12, 1e13, 1e14, 1e15, 1e16, 1e17, 1e18, 1e19,
+ 1e20, 1e21, 1e22, 1e23, 1e24, 1e25, 1e26, 1e27, 1e28, 1e29,
+ 1e30, 1e31, 1e32, 1e33, 1e34, 1e35, 1e36, 1e37, 1e38, 1e39,
+ 1e40, 1e41, 1e42, 1e43, 1e44, 1e45, 1e46, 1e47, 1e48, 1e49,
+ 1e50, 1e51, 1e52, 1e53, 1e54, 1e55, 1e56, 1e57, 1e58, 1e59,
+ 1e60, 1e61, 1e62, 1e63, 1e64, 1e65, 1e66, 1e67, 1e68, 1e69,
+ 1e70, 1e71, 1e72, 1e73, 1e74, 1e75, 1e76, 1e77, 1e78, 1e79,
+ 1e80, 1e81, 1e82, 1e83, 1e84, 1e85, 1e86, 1e87, 1e88, 1e89,
+ 1e90, 1e91, 1e92, 1e93, 1e94, 1e95, 1e96, 1e97, 1e98, 1e99,
+ 1e100, 1e101, 1e102, 1e103, 1e104, 1e105, 1e106, 1e107, 1e108, 1e109,
+ 1e110, 1e111, 1e112, 1e113, 1e114, 1e115, 1e116, 1e117, 1e118, 1e119,
+ 1e120, 1e121, 1e122, 1e123, 1e124, 1e125, 1e126, 1e127, 1e128, 1e129,
+ 1e130, 1e131, 1e132, 1e133, 1e134, 1e135, 1e136, 1e137, 1e138, 1e139,
+ 1e140, 1e141, 1e142, 1e143, 1e144, 1e145, 1e146, 1e147, 1e148, 1e149,
+ 1e150, 1e151, 1e152, 1e153, 1e154, 1e155, 1e156, 1e157, 1e158, 1e159,
+ 1e160, 1e161, 1e162, 1e163, 1e164, 1e165, 1e166, 1e167, 1e168, 1e169,
+ 1e170, 1e171, 1e172, 1e173, 1e174, 1e175, 1e176, 1e177, 1e178, 1e179,
+ 1e180, 1e181, 1e182, 1e183, 1e184, 1e185, 1e186, 1e187, 1e188, 1e189,
+ 1e190, 1e191, 1e192, 1e193, 1e194, 1e195, 1e196, 1e197, 1e198, 1e199,
+ 1e200, 1e201, 1e202, 1e203, 1e204, 1e205, 1e206, 1e207, 1e208, 1e209,
+ 1e210, 1e211, 1e212, 1e213, 1e214, 1e215, 1e216, 1e217, 1e218, 1e219,
+ 1e220, 1e221, 1e222, 1e223, 1e224, 1e225, 1e226, 1e227, 1e228, 1e229,
+ 1e230, 1e231, 1e232, 1e233, 1e234, 1e235, 1e236, 1e237, 1e238, 1e239,
+ 1e240, 1e241, 1e242, 1e243, 1e244, 1e245, 1e246, 1e247, 1e248, 1e249,
+ 1e250, 1e251, 1e252, 1e253, 1e254, 1e255, 1e256, 1e257, 1e258, 1e259,
+ 1e260, 1e261, 1e262, 1e263, 1e264, 1e265, 1e266, 1e267, 1e268, 1e269,
+ 1e270, 1e271, 1e272, 1e273, 1e274, 1e275, 1e276, 1e277, 1e278, 1e279,
+ 1e280, 1e281, 1e282, 1e283, 1e284, 1e285, 1e286, 1e287, 1e288, 1e289,
+ 1e290, 1e291, 1e292, 1e293, 1e294, 1e295, 1e296, 1e297, 1e298, 1e299,
+ 1e300, 1e301, 1e302, 1e303, 1e304, 1e305, 1e306, 1e307, 1e308};
+
+ // Skip leading whitespace.
+ while (isspace_ascii(*p))
+ p++;
+
+ // Handle optional sign.
+ negative = 0;
+ switch (*p) {
+ case '-':
+ negative = 1; // Fall through to increment position.
+ case '+':
+ p++;
+ }
+
+ number = 0.;
+ exponent = 0;
+ num_digits = 0;
+ num_decimals = 0;
+
+ // Process string of digits.
+ while (isdigit_ascii(*p)) {
+ if (num_digits < max_digits) {
+ number = number * 10. + (*p - '0');
+ num_digits++;
+ } else {
+ ++exponent;
+ }
- if (num_digits >= max_digits) // Consume extra decimal digits.
- while (isdigit_ascii(*p)) ++p;
+ p++;
+ p += (tsep != '\0' && *p == tsep);
+ }
- exponent -= num_decimals;
- }
+ // Process decimal part
+ if (*p == decimal) {
+ if (maybe_int != NULL)
+ *maybe_int = 0;
+ p++;
- if (num_digits == 0) {
- *error = ERANGE;
- return 0.0;
+ while (num_digits < max_digits && isdigit_ascii(*p)) {
+ number = number * 10. + (*p - '0');
+ p++;
+ num_digits++;
+ num_decimals++;
}
- // Correct for sign.
- if (negative) number = -number;
+ if (num_digits >= max_digits) // Consume extra decimal digits.
+ while (isdigit_ascii(*p))
+ ++p;
- // Process an exponent string.
- if (toupper_ascii(*p) == toupper_ascii(sci)) {
- if (maybe_int != NULL) *maybe_int = 0;
+ exponent -= num_decimals;
+ }
- // Handle optional sign
- negative = 0;
- switch (*++p) {
- case '-':
- negative = 1; // Fall through to increment pos.
- case '+':
- p++;
- }
+ if (num_digits == 0) {
+ *error = ERANGE;
+ return 0.0;
+ }
- // Process string of digits.
- num_digits = 0;
- n = 0;
- while (num_digits < max_digits && isdigit_ascii(*p)) {
- n = n * 10 + (*p - '0');
- num_digits++;
- p++;
- }
+ // Correct for sign.
+ if (negative)
+ number = -number;
- if (negative)
- exponent -= n;
- else
- exponent += n;
+ // Process an exponent string.
+ if (toupper_ascii(*p) == toupper_ascii(sci)) {
+ if (maybe_int != NULL)
+ *maybe_int = 0;
- // If no digits after the 'e'/'E', un-consume it.
- if (num_digits == 0) p--;
+ // Handle optional sign
+ negative = 0;
+ switch (*++p) {
+ case '-':
+ negative = 1; // Fall through to increment pos.
+ case '+':
+ p++;
}
- if (exponent > 308) {
- *error = ERANGE;
- return HUGE_VAL;
- } else if (exponent > 0) {
- number *= e[exponent];
- } else if (exponent < -308) { // Subnormal
- if (exponent < -616) { // Prevent invalid array access.
- number = 0.;
- } else {
- number /= e[-308 - exponent];
- number /= e[308];
- }
+ // Process string of digits.
+ num_digits = 0;
+ n = 0;
+ while (num_digits < max_digits && isdigit_ascii(*p)) {
+ n = n * 10 + (*p - '0');
+ num_digits++;
+ p++;
+ }
+ if (negative)
+ exponent -= n;
+ else
+ exponent += n;
+
+ // If no digits after the 'e'/'E', un-consume it.
+ if (num_digits == 0)
+ p--;
+ }
+
+ if (exponent > 308) {
+ *error = ERANGE;
+ return HUGE_VAL;
+ } else if (exponent > 0) {
+ number *= e[exponent];
+ } else if (exponent < -308) { // Subnormal
+ if (exponent < -616) { // Prevent invalid array access.
+ number = 0.;
} else {
- number /= e[-exponent];
+ number /= e[-308 - exponent];
+ number /= e[308];
}
- if (number == HUGE_VAL || number == -HUGE_VAL) *error = ERANGE;
+ } else {
+ number /= e[-exponent];
+ }
- if (skip_trailing) {
- // Skip trailing whitespace.
- while (isspace_ascii(*p)) p++;
- }
+ if (number == HUGE_VAL || number == -HUGE_VAL)
+ *error = ERANGE;
+
+ if (skip_trailing) {
+ // Skip trailing whitespace.
+ while (isspace_ascii(*p))
+ p++;
+ }
- if (endptr) *endptr = p;
- return number;
+ if (endptr)
+ *endptr = p;
+ return number;
}
/* copy a decimal number string with `decimal`, `tsep` as decimal point
@@ -1782,306 +1777,309 @@ double precise_xstrtod(const char *str, char **endptr, char decimal,
with a call to `free`.
*/
-char* _str_copy_decimal_str_c(const char *s, char **endpos, char decimal,
+char *_str_copy_decimal_str_c(const char *s, char **endpos, char decimal,
char tsep) {
- const char *p = s;
- size_t length = strlen(s);
- char *s_copy = malloc(length + 1);
- char *dst = s_copy;
- // Skip leading whitespace.
- while (isspace_ascii(*p)) p++;
- // Copy Leading sign
+ const char *p = s;
+ size_t length = strlen(s);
+ char *s_copy = malloc(length + 1);
+ char *dst = s_copy;
+ // Skip leading whitespace.
+ while (isspace_ascii(*p))
+ p++;
+ // Copy Leading sign
+ if (*p == '+' || *p == '-') {
+ *dst++ = *p++;
+ }
+ // Copy integer part dropping `tsep`
+ while (isdigit_ascii(*p)) {
+ *dst++ = *p++;
+ p += (tsep != '\0' && *p == tsep);
+ }
+ // Replace `decimal` with '.'
+ if (*p == decimal) {
+ *dst++ = '.';
+ p++;
+ }
+ // Copy fractional part after decimal (if any)
+ while (isdigit_ascii(*p)) {
+ *dst++ = *p++;
+ }
+ // Copy exponent if any
+ if (toupper_ascii(*p) == toupper_ascii('E')) {
+ *dst++ = *p++;
+ // Copy leading exponent sign (if any)
if (*p == '+' || *p == '-') {
- *dst++ = *p++;
- }
- // Copy integer part dropping `tsep`
- while (isdigit_ascii(*p)) {
- *dst++ = *p++;
- p += (tsep != '\0' && *p == tsep);
- }
- // Replace `decimal` with '.'
- if (*p == decimal) {
- *dst++ = '.';
- p++;
+ *dst++ = *p++;
}
- // Copy fractional part after decimal (if any)
+ // Copy exponent digits
while (isdigit_ascii(*p)) {
- *dst++ = *p++;
+ *dst++ = *p++;
}
- // Copy exponent if any
- if (toupper_ascii(*p) == toupper_ascii('E')) {
- *dst++ = *p++;
- // Copy leading exponent sign (if any)
- if (*p == '+' || *p == '-') {
- *dst++ = *p++;
- }
- // Copy exponent digits
- while (isdigit_ascii(*p)) {
- *dst++ = *p++;
- }
- }
- *dst++ = '\0'; // terminate
- if (endpos != NULL)
- *endpos = (char *)p;
- return s_copy;
+ }
+ *dst++ = '\0'; // terminate
+ if (endpos != NULL)
+ *endpos = (char *)p;
+ return s_copy;
}
-
double round_trip(const char *p, char **q, char decimal, char sci, char tsep,
int skip_trailing, int *error, int *maybe_int) {
- // 'normalize' representation to C-locale; replace decimal with '.' and
- // remove thousands separator.
- char *endptr;
- char *pc = _str_copy_decimal_str_c(p, &endptr, decimal, tsep);
- // This is called from a nogil block in parsers.pyx
- // so need to explicitly get GIL before Python calls
- PyGILState_STATE gstate;
- gstate = PyGILState_Ensure();
- char *endpc;
- double r = PyOS_string_to_double(pc, &endpc, 0);
- // PyOS_string_to_double needs to consume the whole string
- if (endpc == pc + strlen(pc)) {
- if (q != NULL) {
- // report endptr from source string (p)
- *q = endptr;
- }
- } else {
- *error = -1;
- if (q != NULL) {
- // p and pc are different len due to tsep removal. Can't report
- // how much it has consumed of p. Just rewind to beginning.
- *q = (char *)p; // TODO(willayd): this could be undefined behavior
- }
- }
- if (maybe_int != NULL) *maybe_int = 0;
- if (PyErr_Occurred() != NULL) *error = -1;
- else if (r == Py_HUGE_VAL) *error = (int)Py_HUGE_VAL;
- PyErr_Clear();
-
- PyGILState_Release(gstate);
- free(pc);
- if (skip_trailing && q != NULL && *q != p) {
- while (isspace_ascii(**q)) {
- (*q)++;
- }
- }
- return r;
+ // 'normalize' representation to C-locale; replace decimal with '.' and
+ // remove thousands separator.
+ char *endptr;
+ char *pc = _str_copy_decimal_str_c(p, &endptr, decimal, tsep);
+ // This is called from a nogil block in parsers.pyx
+ // so need to explicitly get GIL before Python calls
+ PyGILState_STATE gstate;
+ gstate = PyGILState_Ensure();
+ char *endpc;
+ double r = PyOS_string_to_double(pc, &endpc, 0);
+ // PyOS_string_to_double needs to consume the whole string
+ if (endpc == pc + strlen(pc)) {
+ if (q != NULL) {
+ // report endptr from source string (p)
+ *q = endptr;
+ }
+ } else {
+ *error = -1;
+ if (q != NULL) {
+ // p and pc are different len due to tsep removal. Can't report
+ // how much it has consumed of p. Just rewind to beginning.
+ *q = (char *)p; // TODO(willayd): this could be undefined behavior
+ }
+ }
+ if (maybe_int != NULL)
+ *maybe_int = 0;
+ if (PyErr_Occurred() != NULL)
+ *error = -1;
+ else if (r == Py_HUGE_VAL)
+ *error = (int)Py_HUGE_VAL;
+ PyErr_Clear();
+
+ PyGILState_Release(gstate);
+ free(pc);
+ if (skip_trailing && q != NULL && *q != p) {
+ while (isspace_ascii(**q)) {
+ (*q)++;
+ }
+ }
+ return r;
}
// End of xstrtod code
// ---------------------------------------------------------------------------
void uint_state_init(uint_state *self) {
- self->seen_sint = 0;
- self->seen_uint = 0;
- self->seen_null = 0;
+ self->seen_sint = 0;
+ self->seen_uint = 0;
+ self->seen_null = 0;
}
int uint64_conflict(uint_state *self) {
- return self->seen_uint && (self->seen_sint || self->seen_null);
+ return self->seen_uint && (self->seen_sint || self->seen_null);
}
int64_t str_to_int64(const char *p_item, int64_t int_min, int64_t int_max,
int *error, char tsep) {
- const char *p = p_item;
- int isneg = 0;
- int64_t number = 0;
- int d;
+ const char *p = p_item;
+ int isneg = 0;
+ int64_t number = 0;
+ int d;
+
+ // Skip leading spaces.
+ while (isspace_ascii(*p)) {
+ ++p;
+ }
+
+ // Handle sign.
+ if (*p == '-') {
+ isneg = 1;
+ ++p;
+ } else if (*p == '+') {
+ p++;
+ }
+
+ // Check that there is a first digit.
+ if (!isdigit_ascii(*p)) {
+ // Error...
+ *error = ERROR_NO_DIGITS;
+ return 0;
+ }
- // Skip leading spaces.
- while (isspace_ascii(*p)) {
- ++p;
- }
+ if (isneg) {
+ // If number is greater than pre_min, at least one more digit
+ // can be processed without overflowing.
+ int dig_pre_min = -(int_min % 10);
+ int64_t pre_min = int_min / 10;
- // Handle sign.
- if (*p == '-') {
- isneg = 1;
- ++p;
- } else if (*p == '+') {
- p++;
+ // Process the digits.
+ d = *p;
+ if (tsep != '\0') {
+ while (1) {
+ if (d == tsep) {
+ d = *++p;
+ continue;
+ } else if (!isdigit_ascii(d)) {
+ break;
+ }
+ if ((number > pre_min) ||
+ ((number == pre_min) && (d - '0' <= dig_pre_min))) {
+ number = number * 10 - (d - '0');
+ d = *++p;
+ } else {
+ *error = ERROR_OVERFLOW;
+ return 0;
+ }
+ }
+ } else {
+ while (isdigit_ascii(d)) {
+ if ((number > pre_min) ||
+ ((number == pre_min) && (d - '0' <= dig_pre_min))) {
+ number = number * 10 - (d - '0');
+ d = *++p;
+ } else {
+ *error = ERROR_OVERFLOW;
+ return 0;
+ }
+ }
}
+ } else {
+ // If number is less than pre_max, at least one more digit
+ // can be processed without overflowing.
+ int64_t pre_max = int_max / 10;
+ int dig_pre_max = int_max % 10;
- // Check that there is a first digit.
- if (!isdigit_ascii(*p)) {
- // Error...
- *error = ERROR_NO_DIGITS;
- return 0;
- }
+ // Process the digits.
+ d = *p;
+ if (tsep != '\0') {
+ while (1) {
+ if (d == tsep) {
+ d = *++p;
+ continue;
+ } else if (!isdigit_ascii(d)) {
+ break;
+ }
+ if ((number < pre_max) ||
+ ((number == pre_max) && (d - '0' <= dig_pre_max))) {
+ number = number * 10 + (d - '0');
+ d = *++p;
- if (isneg) {
- // If number is greater than pre_min, at least one more digit
- // can be processed without overflowing.
- int dig_pre_min = -(int_min % 10);
- int64_t pre_min = int_min / 10;
-
- // Process the digits.
- d = *p;
- if (tsep != '\0') {
- while (1) {
- if (d == tsep) {
- d = *++p;
- continue;
- } else if (!isdigit_ascii(d)) {
- break;
- }
- if ((number > pre_min) ||
- ((number == pre_min) && (d - '0' <= dig_pre_min))) {
- number = number * 10 - (d - '0');
- d = *++p;
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
} else {
- while (isdigit_ascii(d)) {
- if ((number > pre_min) ||
- ((number == pre_min) && (d - '0' <= dig_pre_min))) {
- number = number * 10 - (d - '0');
- d = *++p;
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
+ *error = ERROR_OVERFLOW;
+ return 0;
}
+ }
} else {
- // If number is less than pre_max, at least one more digit
- // can be processed without overflowing.
- int64_t pre_max = int_max / 10;
- int dig_pre_max = int_max % 10;
-
- // Process the digits.
- d = *p;
- if (tsep != '\0') {
- while (1) {
- if (d == tsep) {
- d = *++p;
- continue;
- } else if (!isdigit_ascii(d)) {
- break;
- }
- if ((number < pre_max) ||
- ((number == pre_max) && (d - '0' <= dig_pre_max))) {
- number = number * 10 + (d - '0');
- d = *++p;
-
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
+ while (isdigit_ascii(d)) {
+ if ((number < pre_max) ||
+ ((number == pre_max) && (d - '0' <= dig_pre_max))) {
+ number = number * 10 + (d - '0');
+ d = *++p;
+
} else {
- while (isdigit_ascii(d)) {
- if ((number < pre_max) ||
- ((number == pre_max) && (d - '0' <= dig_pre_max))) {
- number = number * 10 + (d - '0');
- d = *++p;
-
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
+ *error = ERROR_OVERFLOW;
+ return 0;
}
+ }
}
+ }
- // Skip trailing spaces.
- while (isspace_ascii(*p)) {
- ++p;
- }
+ // Skip trailing spaces.
+ while (isspace_ascii(*p)) {
+ ++p;
+ }
- // Did we use up all the characters?
- if (*p) {
- *error = ERROR_INVALID_CHARS;
- return 0;
- }
+ // Did we use up all the characters?
+ if (*p) {
+ *error = ERROR_INVALID_CHARS;
+ return 0;
+ }
- *error = 0;
- return number;
+ *error = 0;
+ return number;
}
uint64_t str_to_uint64(uint_state *state, const char *p_item, int64_t int_max,
uint64_t uint_max, int *error, char tsep) {
- const char *p = p_item;
- uint64_t pre_max = uint_max / 10;
- int dig_pre_max = uint_max % 10;
- uint64_t number = 0;
- int d;
-
- // Skip leading spaces.
- while (isspace_ascii(*p)) {
- ++p;
- }
-
- // Handle sign.
- if (*p == '-') {
- state->seen_sint = 1;
- *error = 0;
+ const char *p = p_item;
+ uint64_t pre_max = uint_max / 10;
+ int dig_pre_max = uint_max % 10;
+ uint64_t number = 0;
+ int d;
+
+ // Skip leading spaces.
+ while (isspace_ascii(*p)) {
+ ++p;
+ }
+
+ // Handle sign.
+ if (*p == '-') {
+ state->seen_sint = 1;
+ *error = 0;
+ return 0;
+ } else if (*p == '+') {
+ p++;
+ }
+
+ // Check that there is a first digit.
+ if (!isdigit_ascii(*p)) {
+ // Error...
+ *error = ERROR_NO_DIGITS;
+ return 0;
+ }
+
+ // If number is less than pre_max, at least one more digit
+ // can be processed without overflowing.
+ //
+ // Process the digits.
+ d = *p;
+ if (tsep != '\0') {
+ while (1) {
+ if (d == tsep) {
+ d = *++p;
+ continue;
+ } else if (!isdigit_ascii(d)) {
+ break;
+ }
+ if ((number < pre_max) ||
+ ((number == pre_max) && (d - '0' <= dig_pre_max))) {
+ number = number * 10 + (d - '0');
+ d = *++p;
+
+ } else {
+ *error = ERROR_OVERFLOW;
return 0;
- } else if (*p == '+') {
- p++;
+ }
}
+ } else {
+ while (isdigit_ascii(d)) {
+ if ((number < pre_max) ||
+ ((number == pre_max) && (d - '0' <= dig_pre_max))) {
+ number = number * 10 + (d - '0');
+ d = *++p;
- // Check that there is a first digit.
- if (!isdigit_ascii(*p)) {
- // Error...
- *error = ERROR_NO_DIGITS;
+ } else {
+ *error = ERROR_OVERFLOW;
return 0;
+ }
}
+ }
- // If number is less than pre_max, at least one more digit
- // can be processed without overflowing.
- //
- // Process the digits.
- d = *p;
- if (tsep != '\0') {
- while (1) {
- if (d == tsep) {
- d = *++p;
- continue;
- } else if (!isdigit_ascii(d)) {
- break;
- }
- if ((number < pre_max) ||
- ((number == pre_max) && (d - '0' <= dig_pre_max))) {
- number = number * 10 + (d - '0');
- d = *++p;
-
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
- } else {
- while (isdigit_ascii(d)) {
- if ((number < pre_max) ||
- ((number == pre_max) && (d - '0' <= dig_pre_max))) {
- number = number * 10 + (d - '0');
- d = *++p;
-
- } else {
- *error = ERROR_OVERFLOW;
- return 0;
- }
- }
- }
-
- // Skip trailing spaces.
- while (isspace_ascii(*p)) {
- ++p;
- }
+ // Skip trailing spaces.
+ while (isspace_ascii(*p)) {
+ ++p;
+ }
- // Did we use up all the characters?
- if (*p) {
- *error = ERROR_INVALID_CHARS;
- return 0;
- }
+ // Did we use up all the characters?
+ if (*p) {
+ *error = ERROR_INVALID_CHARS;
+ return 0;
+ }
- if (number > (uint64_t)int_max) {
- state->seen_uint = 1;
- }
+ if (number > (uint64_t)int_max) {
+ state->seen_uint = 1;
+ }
- *error = 0;
- return number;
+ *error = 0;
+ return number;
}
diff --git a/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c b/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
index 49016f79de5b9..db1c735bd6094 100644
--- a/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
+++ b/pandas/_libs/src/vendored/numpy/datetime/np_datetime.c
@@ -20,15 +20,14 @@ This file is derived from NumPy 1.7. See NUMPY_LICENSE.txt
#ifndef NPY_NO_DEPRECATED_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#endif // NPY_NO_DEPRECATED_API
+#endif // NPY_NO_DEPRECATED_API
#include <Python.h>
+#include "pandas/vendored/numpy/datetime/np_datetime.h"
#include <numpy/arrayobject.h>
#include <numpy/arrayscalars.h>
#include <numpy/ndarraytypes.h>
-#include "pandas/vendored/numpy/datetime/np_datetime.h"
-
const int days_per_month_table[2][12] = {
{31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31},
@@ -38,8 +37,8 @@ const int days_per_month_table[2][12] = {
* Returns 1 if the given year is a leap year, 0 otherwise.
*/
int is_leapyear(npy_int64 year) {
- return (year & 0x3) == 0 && /* year % 4 == 0 */
- ((year % 100) != 0 || (year % 400) == 0);
+ return (year & 0x3) == 0 && /* year % 4 == 0 */
+ ((year % 100) != 0 || (year % 400) == 0);
}
/*
@@ -47,108 +46,108 @@ int is_leapyear(npy_int64 year) {
* the current values are valid.g
*/
void add_minutes_to_datetimestruct(npy_datetimestruct *dts, int minutes) {
- int isleap;
-
- /* MINUTES */
- dts->min += minutes;
- while (dts->min < 0) {
- dts->min += 60;
- dts->hour--;
- }
- while (dts->min >= 60) {
- dts->min -= 60;
- dts->hour++;
- }
-
- /* HOURS */
- while (dts->hour < 0) {
- dts->hour += 24;
- dts->day--;
- }
- while (dts->hour >= 24) {
- dts->hour -= 24;
- dts->day++;
- }
-
- /* DAYS */
- if (dts->day < 1) {
- dts->month--;
- if (dts->month < 1) {
- dts->year--;
- dts->month = 12;
- }
- isleap = is_leapyear(dts->year);
- dts->day += days_per_month_table[isleap][dts->month - 1];
- } else if (dts->day > 28) {
- isleap = is_leapyear(dts->year);
- if (dts->day > days_per_month_table[isleap][dts->month - 1]) {
- dts->day -= days_per_month_table[isleap][dts->month - 1];
- dts->month++;
- if (dts->month > 12) {
- dts->year++;
- dts->month = 1;
- }
- }
+ int isleap;
+
+ /* MINUTES */
+ dts->min += minutes;
+ while (dts->min < 0) {
+ dts->min += 60;
+ dts->hour--;
+ }
+ while (dts->min >= 60) {
+ dts->min -= 60;
+ dts->hour++;
+ }
+
+ /* HOURS */
+ while (dts->hour < 0) {
+ dts->hour += 24;
+ dts->day--;
+ }
+ while (dts->hour >= 24) {
+ dts->hour -= 24;
+ dts->day++;
+ }
+
+ /* DAYS */
+ if (dts->day < 1) {
+ dts->month--;
+ if (dts->month < 1) {
+ dts->year--;
+ dts->month = 12;
}
+ isleap = is_leapyear(dts->year);
+ dts->day += days_per_month_table[isleap][dts->month - 1];
+ } else if (dts->day > 28) {
+ isleap = is_leapyear(dts->year);
+ if (dts->day > days_per_month_table[isleap][dts->month - 1]) {
+ dts->day -= days_per_month_table[isleap][dts->month - 1];
+ dts->month++;
+ if (dts->month > 12) {
+ dts->year++;
+ dts->month = 1;
+ }
+ }
+ }
}
/*
* Calculates the days offset from the 1970 epoch.
*/
npy_int64 get_datetimestruct_days(const npy_datetimestruct *dts) {
- int i, month;
- npy_int64 year, days = 0;
- const int *month_lengths;
-
- year = dts->year - 1970;
- days = year * 365;
-
- /* Adjust for leap years */
- if (days >= 0) {
- /*
- * 1968 is the closest leap year before 1970.
- * Exclude the current year, so add 1.
- */
- year += 1;
- /* Add one day for each 4 years */
- days += year / 4;
- /* 1900 is the closest previous year divisible by 100 */
- year += 68;
- /* Subtract one day for each 100 years */
- days -= year / 100;
- /* 1600 is the closest previous year divisible by 400 */
- year += 300;
- /* Add one day for each 400 years */
- days += year / 400;
- } else {
- /*
- * 1972 is the closest later year after 1970.
- * Include the current year, so subtract 2.
- */
- year -= 2;
- /* Subtract one day for each 4 years */
- days += year / 4;
- /* 2000 is the closest later year divisible by 100 */
- year -= 28;
- /* Add one day for each 100 years */
- days -= year / 100;
- /* 2000 is also the closest later year divisible by 400 */
- /* Subtract one day for each 400 years */
- days += year / 400;
- }
-
- month_lengths = days_per_month_table[is_leapyear(dts->year)];
- month = dts->month - 1;
-
- /* Add the months */
- for (i = 0; i < month; ++i) {
- days += month_lengths[i];
- }
-
- /* Add the days */
- days += dts->day - 1;
-
- return days;
+ int i, month;
+ npy_int64 year, days = 0;
+ const int *month_lengths;
+
+ year = dts->year - 1970;
+ days = year * 365;
+
+ /* Adjust for leap years */
+ if (days >= 0) {
+ /*
+ * 1968 is the closest leap year before 1970.
+ * Exclude the current year, so add 1.
+ */
+ year += 1;
+ /* Add one day for each 4 years */
+ days += year / 4;
+ /* 1900 is the closest previous year divisible by 100 */
+ year += 68;
+ /* Subtract one day for each 100 years */
+ days -= year / 100;
+ /* 1600 is the closest previous year divisible by 400 */
+ year += 300;
+ /* Add one day for each 400 years */
+ days += year / 400;
+ } else {
+ /*
+ * 1972 is the closest later year after 1970.
+ * Include the current year, so subtract 2.
+ */
+ year -= 2;
+ /* Subtract one day for each 4 years */
+ days += year / 4;
+ /* 2000 is the closest later year divisible by 100 */
+ year -= 28;
+ /* Add one day for each 100 years */
+ days -= year / 100;
+ /* 2000 is also the closest later year divisible by 400 */
+ /* Subtract one day for each 400 years */
+ days += year / 400;
+ }
+
+ month_lengths = days_per_month_table[is_leapyear(dts->year)];
+ month = dts->month - 1;
+
+ /* Add the months */
+ for (i = 0; i < month; ++i) {
+ days += month_lengths[i];
+ }
+
+ /* Add the days */
+ days += dts->day - 1;
+
+ return days;
}
/*
@@ -156,62 +155,61 @@ npy_int64 get_datetimestruct_days(const npy_datetimestruct *dts) {
* and returns the year.
*/
static npy_int64 days_to_yearsdays(npy_int64 *days_) {
- const npy_int64 days_per_400years = (400 * 365 + 100 - 4 + 1);
- /* Adjust so it's relative to the year 2000 (divisible by 400) */
- npy_int64 days = (*days_) - (365 * 30 + 7);
- npy_int64 year;
-
- /* Break down the 400 year cycle to get the year and day within the year */
- if (days >= 0) {
- year = 400 * (days / days_per_400years);
- days = days % days_per_400years;
- } else {
- year = 400 * ((days - (days_per_400years - 1)) / days_per_400years);
- days = days % days_per_400years;
- if (days < 0) {
- days += days_per_400years;
- }
- }
-
- /* Work out the year/day within the 400 year cycle */
- if (days >= 366) {
- year += 100 * ((days - 1) / (100 * 365 + 25 - 1));
- days = (days - 1) % (100 * 365 + 25 - 1);
- if (days >= 365) {
- year += 4 * ((days + 1) / (4 * 365 + 1));
- days = (days + 1) % (4 * 365 + 1);
- if (days >= 366) {
- year += (days - 1) / 365;
- days = (days - 1) % 365;
- }
- }
- }
-
- *days_ = days;
- return year + 2000;
-}
+ const npy_int64 days_per_400years = (400 * 365 + 100 - 4 + 1);
+ /* Adjust so it's relative to the year 2000 (divisible by 400) */
+ npy_int64 days = (*days_) - (365 * 30 + 7);
+ npy_int64 year;
+
+ /* Break down the 400 year cycle to get the year and day within the year */
+ if (days >= 0) {
+ year = 400 * (days / days_per_400years);
+ days = days % days_per_400years;
+ } else {
+ year = 400 * ((days - (days_per_400years - 1)) / days_per_400years);
+ days = days % days_per_400years;
+ if (days < 0) {
+ days += days_per_400years;
+ }
+ }
+
+ /* Work out the year/day within the 400 year cycle */
+ if (days >= 366) {
+ year += 100 * ((days - 1) / (100 * 365 + 25 - 1));
+ days = (days - 1) % (100 * 365 + 25 - 1);
+ if (days >= 365) {
+ year += 4 * ((days + 1) / (4 * 365 + 1));
+ days = (days + 1) % (4 * 365 + 1);
+ if (days >= 366) {
+ year += (days - 1) / 365;
+ days = (days - 1) % 365;
+ }
+ }
+ }
+ *days_ = days;
+ return year + 2000;
+}
/*
* Fills in the year, month, day in 'dts' based on the days
* offset from 1970.
*/
static void set_datetimestruct_days(npy_int64 days, npy_datetimestruct *dts) {
- const int *month_lengths;
- int i;
+ const int *month_lengths;
+ int i;
- dts->year = days_to_yearsdays(&days);
- month_lengths = days_per_month_table[is_leapyear(dts->year)];
+ dts->year = days_to_yearsdays(&days);
+ month_lengths = days_per_month_table[is_leapyear(dts->year)];
- for (i = 0; i < 12; ++i) {
- if (days < month_lengths[i]) {
- dts->month = i + 1;
- dts->day = days + 1;
- return;
- } else {
- days -= month_lengths[i];
- }
+ for (i = 0; i < 12; ++i) {
+ if (days < month_lengths[i]) {
+ dts->month = i + 1;
+ dts->day = days + 1;
+ return;
+ } else {
+ days -= month_lengths[i];
}
+ }
}
/*
@@ -219,86 +217,86 @@ static void set_datetimestruct_days(npy_int64 days, npy_datetimestruct *dts) {
*/
int cmp_npy_datetimestruct(const npy_datetimestruct *a,
const npy_datetimestruct *b) {
- if (a->year > b->year) {
- return 1;
- } else if (a->year < b->year) {
- return -1;
- }
-
- if (a->month > b->month) {
- return 1;
- } else if (a->month < b->month) {
- return -1;
- }
-
- if (a->day > b->day) {
- return 1;
- } else if (a->day < b->day) {
- return -1;
- }
-
- if (a->hour > b->hour) {
- return 1;
- } else if (a->hour < b->hour) {
- return -1;
- }
-
- if (a->min > b->min) {
- return 1;
- } else if (a->min < b->min) {
- return -1;
- }
-
- if (a->sec > b->sec) {
- return 1;
- } else if (a->sec < b->sec) {
- return -1;
- }
-
- if (a->us > b->us) {
- return 1;
- } else if (a->us < b->us) {
- return -1;
- }
-
- if (a->ps > b->ps) {
- return 1;
- } else if (a->ps < b->ps) {
- return -1;
- }
-
- if (a->as > b->as) {
- return 1;
- } else if (a->as < b->as) {
- return -1;
- }
-
- return 0;
+ if (a->year > b->year) {
+ return 1;
+ } else if (a->year < b->year) {
+ return -1;
+ }
+
+ if (a->month > b->month) {
+ return 1;
+ } else if (a->month < b->month) {
+ return -1;
+ }
+
+ if (a->day > b->day) {
+ return 1;
+ } else if (a->day < b->day) {
+ return -1;
+ }
+
+ if (a->hour > b->hour) {
+ return 1;
+ } else if (a->hour < b->hour) {
+ return -1;
+ }
+
+ if (a->min > b->min) {
+ return 1;
+ } else if (a->min < b->min) {
+ return -1;
+ }
+
+ if (a->sec > b->sec) {
+ return 1;
+ } else if (a->sec < b->sec) {
+ return -1;
+ }
+
+ if (a->us > b->us) {
+ return 1;
+ } else if (a->us < b->us) {
+ return -1;
+ }
+
+ if (a->ps > b->ps) {
+ return 1;
+ } else if (a->ps < b->ps) {
+ return -1;
+ }
+
+ if (a->as > b->as) {
+ return 1;
+ } else if (a->as < b->as) {
+ return -1;
+ }
+
+ return 0;
}
/*
-* Returns the offset from utc of the timezone as a timedelta.
-* The caller is responsible for ensuring that the tzinfo
-* attribute exists on the datetime object.
-*
-* If the passed object is timezone naive, Py_None is returned.
-* If extraction of the offset fails, NULL is returned.
-*
-* NOTE: This function is not vendored from numpy.
-*/
+ * Returns the offset from utc of the timezone as a timedelta.
+ * The caller is responsible for ensuring that the tzinfo
+ * attribute exists on the datetime object.
+ *
+ * If the passed object is timezone naive, Py_None is returned.
+ * If extraction of the offset fails, NULL is returned.
+ *
+ * NOTE: This function is not vendored from numpy.
+ */
PyObject *extract_utc_offset(PyObject *obj) {
- PyObject *tmp = PyObject_GetAttrString(obj, "tzinfo");
- if (tmp == NULL) {
- return NULL;
- }
- if (tmp != Py_None) {
- PyObject *offset = PyObject_CallMethod(tmp, "utcoffset", "O", obj);
- if (offset == NULL) {
- Py_DECREF(tmp);
- return NULL;
- }
- return offset;
- }
- return tmp;
+ PyObject *tmp = PyObject_GetAttrString(obj, "tzinfo");
+ if (tmp == NULL) {
+ return NULL;
+ }
+ if (tmp != Py_None) {
+ PyObject *offset = PyObject_CallMethod(tmp, "utcoffset", "O", obj);
+ if (offset == NULL) {
+ Py_DECREF(tmp);
+ return NULL;
+ }
+ return offset;
+ }
+ return tmp;
}
/*
@@ -307,98 +305,90 @@ PyObject *extract_utc_offset(PyObject *obj) {
*/
npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base,
const npy_datetimestruct *dts) {
- npy_datetime ret;
-
- if (base == NPY_FR_Y) {
- /* Truncate to the year */
- ret = dts->year - 1970;
- } else if (base == NPY_FR_M) {
- /* Truncate to the month */
- ret = 12 * (dts->year - 1970) + (dts->month - 1);
- } else {
- /* Otherwise calculate the number of days to start */
- npy_int64 days = get_datetimestruct_days(dts);
-
- switch (base) {
- case NPY_FR_W:
- /* Truncate to weeks */
- if (days >= 0) {
- ret = days / 7;
- } else {
- ret = (days - 6) / 7;
- }
- break;
- case NPY_FR_D:
- ret = days;
- break;
- case NPY_FR_h:
- ret = days * 24 + dts->hour;
- break;
- case NPY_FR_m:
- ret = (days * 24 + dts->hour) * 60 + dts->min;
- break;
- case NPY_FR_s:
- ret = ((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec;
- break;
- case NPY_FR_ms:
- ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000 +
- dts->us / 1000;
- break;
- case NPY_FR_us:
- ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000000 +
- dts->us;
- break;
- case NPY_FR_ns:
- ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000000 +
- dts->us) *
- 1000 +
- dts->ps / 1000;
- break;
- case NPY_FR_ps:
- ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps;
- break;
- case NPY_FR_fs:
- /* only 2.6 hours */
- ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps) *
- 1000 +
- dts->as / 1000;
- break;
- case NPY_FR_as:
- /* only 9.2 secs */
- ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 +
- dts->sec) *
- 1000000 +
- dts->us) *
- 1000000 +
- dts->ps) *
- 1000000 +
- dts->as;
- break;
- default:
- /* Something got corrupted */
- PyErr_SetString(
- PyExc_ValueError,
- "NumPy datetime metadata with corrupt unit value");
- return -1;
- }
- }
- return ret;
+ npy_datetime ret;
+
+ if (base == NPY_FR_Y) {
+ /* Truncate to the year */
+ ret = dts->year - 1970;
+ } else if (base == NPY_FR_M) {
+ /* Truncate to the month */
+ ret = 12 * (dts->year - 1970) + (dts->month - 1);
+ } else {
+ /* Otherwise calculate the number of days to start */
+ npy_int64 days = get_datetimestruct_days(dts);
+
+ switch (base) {
+ case NPY_FR_W:
+ /* Truncate to weeks */
+ if (days >= 0) {
+ ret = days / 7;
+ } else {
+ ret = (days - 6) / 7;
+ }
+ break;
+ case NPY_FR_D:
+ ret = days;
+ break;
+ case NPY_FR_h:
+ ret = days * 24 + dts->hour;
+ break;
+ case NPY_FR_m:
+ ret = (days * 24 + dts->hour) * 60 + dts->min;
+ break;
+ case NPY_FR_s:
+ ret = ((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec;
+ break;
+ case NPY_FR_ms:
+ ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) * 1000 +
+ dts->us / 1000;
+ break;
+ case NPY_FR_us:
+ ret = (((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
+ 1000000 +
+ dts->us;
+ break;
+ case NPY_FR_ns:
+ ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
+ 1000000 +
+ dts->us) *
+ 1000 +
+ dts->ps / 1000;
+ break;
+ case NPY_FR_ps:
+ ret = ((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
+ 1000000 +
+ dts->us) *
+ 1000000 +
+ dts->ps;
+ break;
+ case NPY_FR_fs:
+ /* only 2.6 hours */
+ ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
+ 1000000 +
+ dts->us) *
+ 1000000 +
+ dts->ps) *
+ 1000 +
+ dts->as / 1000;
+ break;
+ case NPY_FR_as:
+ /* only 9.2 secs */
+ ret = (((((days * 24 + dts->hour) * 60 + dts->min) * 60 + dts->sec) *
+ 1000000 +
+ dts->us) *
+ 1000000 +
+ dts->ps) *
+ 1000000 +
+ dts->as;
+ break;
+ default:
+ /* Something got corrupted */
+ PyErr_SetString(PyExc_ValueError,
+ "NumPy datetime metadata with corrupt unit value");
+ return -1;
+ }
+ }
+ return ret;
}
/*
@@ -410,164 +400,161 @@ npy_datetime npy_datetimestruct_to_datetime(NPY_DATETIMEUNIT base,
* for subsequent calls to this command - it is able to deduce that `*d >= 0`.
*/
npy_int64 extract_unit(npy_datetime *d, npy_datetime unit) {
- assert(unit > 0);
- npy_int64 div = *d / unit;
- npy_int64 mod = *d % unit;
- if (mod < 0) {
- mod += unit;
- div -= 1;
- }
- assert(mod >= 0);
- *d = mod;
- return div;
+ assert(unit > 0);
+ npy_int64 div = *d / unit;
+ npy_int64 mod = *d % unit;
+ if (mod < 0) {
+ mod += unit;
+ div -= 1;
+ }
+ assert(mod >= 0);
+ *d = mod;
+ return div;
}
/*
* Converts a datetime based on the given metadata into a datetimestruct
*/
-void pandas_datetime_to_datetimestruct(npy_datetime dt,
- NPY_DATETIMEUNIT base,
+void pandas_datetime_to_datetimestruct(npy_datetime dt, NPY_DATETIMEUNIT base,
npy_datetimestruct *out) {
- npy_int64 perday;
-
- /* Initialize the output to all zeros */
- memset(out, 0, sizeof(npy_datetimestruct));
- out->year = 1970;
- out->month = 1;
- out->day = 1;
-
- /*
- * Note that care must be taken with the / and % operators
- * for negative values.
- */
- switch (base) {
- case NPY_FR_Y:
- out->year = 1970 + dt;
- break;
-
- case NPY_FR_M:
- out->year = 1970 + extract_unit(&dt, 12);
- out->month = dt + 1;
- break;
-
- case NPY_FR_W:
- /* A week is 7 days */
- set_datetimestruct_days(dt * 7, out);
- break;
-
- case NPY_FR_D:
- set_datetimestruct_days(dt, out);
- break;
-
- case NPY_FR_h:
- perday = 24LL;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = dt;
- break;
-
- case NPY_FR_m:
- perday = 24LL * 60;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 60);
- out->min = (int)dt;
- break;
-
- case NPY_FR_s:
- perday = 24LL * 60 * 60;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 60 * 60);
- out->min = (int)extract_unit(&dt, 60);
- out->sec = (int)dt;
- break;
-
- case NPY_FR_ms:
- perday = 24LL * 60 * 60 * 1000;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 1000LL * 60 * 60);
- out->min = (int)extract_unit(&dt, 1000LL * 60);
- out->sec = (int)extract_unit(&dt, 1000LL);
- out->us = (int)(dt * 1000);
- break;
-
- case NPY_FR_us:
- perday = 24LL * 60LL * 60LL * 1000LL * 1000LL;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 60 * 60);
- out->min = (int)extract_unit(&dt, 1000LL * 1000 * 60);
- out->sec = (int)extract_unit(&dt, 1000LL * 1000);
- out->us = (int)dt;
- break;
-
- case NPY_FR_ns:
- perday = 24LL * 60LL * 60LL * 1000LL * 1000LL * 1000LL;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60 * 60);
- out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60);
- out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
- out->us = (int)extract_unit(&dt, 1000LL);
- out->ps = (int)(dt * 1000);
- break;
-
- case NPY_FR_ps:
- perday = 24LL * 60 * 60 * 1000 * 1000 * 1000 * 1000;
-
- set_datetimestruct_days(extract_unit(&dt, perday), out);
- out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60 * 60);
- out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60);
- out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
- out->us = (int)extract_unit(&dt, 1000LL);
- out->ps = (int)(dt * 1000);
- break;
-
- case NPY_FR_fs:
- /* entire range is only +- 2.6 hours */
- out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 *
- 1000 * 60 * 60);
- if (out->hour < 0) {
- out->year = 1969;
- out->month = 12;
- out->day = 31;
- out->hour += 24;
- assert(out->hour >= 0);
- }
- out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 *
- 1000 * 60);
- out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 *
- 1000);
- out->us = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
- out->ps = (int)extract_unit(&dt, 1000LL);
- out->as = (int)(dt * 1000);
- break;
-
- case NPY_FR_as:
- /* entire range is only +- 9.2 seconds */
- out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 *
- 1000 * 1000);
- if (out->sec < 0) {
- out->year = 1969;
- out->month = 12;
- out->day = 31;
- out->hour = 23;
- out->min = 59;
- out->sec += 60;
- assert(out->sec >= 0);
- }
- out->us = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000);
- out->ps = (int)extract_unit(&dt, 1000LL * 1000);
- out->as = (int)dt;
- break;
-
- default:
- PyErr_SetString(PyExc_RuntimeError,
- "NumPy datetime metadata is corrupted with invalid "
- "base unit");
+ npy_int64 perday;
+
+ /* Initialize the output to all zeros */
+ memset(out, 0, sizeof(npy_datetimestruct));
+ out->year = 1970;
+ out->month = 1;
+ out->day = 1;
+
+ /*
+ * Note that care must be taken with the / and % operators
+ * for negative values.
+ */
+ switch (base) {
+ case NPY_FR_Y:
+ out->year = 1970 + dt;
+ break;
+
+ case NPY_FR_M:
+ out->year = 1970 + extract_unit(&dt, 12);
+ out->month = dt + 1;
+ break;
+
+ case NPY_FR_W:
+ /* A week is 7 days */
+ set_datetimestruct_days(dt * 7, out);
+ break;
+
+ case NPY_FR_D:
+ set_datetimestruct_days(dt, out);
+ break;
+
+ case NPY_FR_h:
+ perday = 24LL;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = dt;
+ break;
+
+ case NPY_FR_m:
+ perday = 24LL * 60;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 60);
+ out->min = (int)dt;
+ break;
+
+ case NPY_FR_s:
+ perday = 24LL * 60 * 60;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 60 * 60);
+ out->min = (int)extract_unit(&dt, 60);
+ out->sec = (int)dt;
+ break;
+
+ case NPY_FR_ms:
+ perday = 24LL * 60 * 60 * 1000;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 1000LL * 60 * 60);
+ out->min = (int)extract_unit(&dt, 1000LL * 60);
+ out->sec = (int)extract_unit(&dt, 1000LL);
+ out->us = (int)(dt * 1000);
+ break;
+
+ case NPY_FR_us:
+ perday = 24LL * 60LL * 60LL * 1000LL * 1000LL;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 60 * 60);
+ out->min = (int)extract_unit(&dt, 1000LL * 1000 * 60);
+ out->sec = (int)extract_unit(&dt, 1000LL * 1000);
+ out->us = (int)dt;
+ break;
+
+ case NPY_FR_ns:
+ perday = 24LL * 60LL * 60LL * 1000LL * 1000LL * 1000LL;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60 * 60);
+ out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60);
+ out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
+ out->us = (int)extract_unit(&dt, 1000LL);
+ out->ps = (int)(dt * 1000);
+ break;
+
+ case NPY_FR_ps:
+ perday = 24LL * 60 * 60 * 1000 * 1000 * 1000 * 1000;
+
+ set_datetimestruct_days(extract_unit(&dt, perday), out);
+ out->hour = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60 * 60);
+ out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 60);
+ out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
+ out->us = (int)extract_unit(&dt, 1000LL);
+ out->ps = (int)(dt * 1000);
+ break;
+
+ case NPY_FR_fs:
+ /* entire range is only +- 2.6 hours */
+ out->hour =
+ (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 1000 * 60 * 60);
+ if (out->hour < 0) {
+ out->year = 1969;
+ out->month = 12;
+ out->day = 31;
+ out->hour += 24;
+ assert(out->hour >= 0);
}
+ out->min = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 1000 * 60);
+ out->sec = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 1000);
+ out->us = (int)extract_unit(&dt, 1000LL * 1000 * 1000);
+ out->ps = (int)extract_unit(&dt, 1000LL);
+ out->as = (int)(dt * 1000);
+ break;
+
+ case NPY_FR_as:
+ /* entire range is only +- 9.2 seconds */
+ out->sec =
+ (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000 * 1000 * 1000);
+ if (out->sec < 0) {
+ out->year = 1969;
+ out->month = 12;
+ out->day = 31;
+ out->hour = 23;
+ out->min = 59;
+ out->sec += 60;
+ assert(out->sec >= 0);
+ }
+ out->us = (int)extract_unit(&dt, 1000LL * 1000 * 1000 * 1000);
+ out->ps = (int)extract_unit(&dt, 1000LL * 1000);
+ out->as = (int)dt;
+ break;
+
+ default:
+ PyErr_SetString(PyExc_RuntimeError,
+ "NumPy datetime metadata is corrupted with invalid "
+ "base unit");
+ }
}
/*
@@ -579,363 +566,358 @@ void pandas_datetime_to_datetimestruct(npy_datetime dt,
void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
NPY_DATETIMEUNIT base,
pandas_timedeltastruct *out) {
- npy_int64 frac;
- npy_int64 sfrac;
- npy_int64 ifrac;
- int sign;
- npy_int64 per_day;
- npy_int64 per_sec;
+ npy_int64 frac;
+ npy_int64 sfrac;
+ npy_int64 ifrac;
+ int sign;
+ npy_int64 per_day;
+ npy_int64 per_sec;
+
+ /* Initialize the output to all zeros */
+ memset(out, 0, sizeof(pandas_timedeltastruct));
+
+ switch (base) {
+ case NPY_FR_ns:
+
+ per_day = 86400000000000LL;
+ per_sec = 1000LL * 1000LL * 1000LL;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
- /* Initialize the output to all zeros */
- memset(out, 0, sizeof(pandas_timedeltastruct));
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
- switch (base) {
- case NPY_FR_ns:
-
- per_day = 86400000000000LL;
- per_sec = 1000LL * 1000LL * 1000LL;
-
- // put frac in seconds
- if (td < 0 && td % per_sec != 0)
- frac = td / per_sec - 1;
- else
- frac = td / per_sec;
-
- if (frac < 0) {
- sign = -1;
-
- // even fraction
- if ((-frac % 86400LL) != 0) {
- out->days = -frac / 86400LL + 1;
- frac += 86400LL * out->days;
- } else {
- frac = -frac;
- }
- } else {
- sign = 1;
- out->days = 0;
- }
-
- if (frac >= 86400) {
- out->days += frac / 86400LL;
- frac -= out->days * 86400LL;
- }
-
- if (frac >= 3600) {
- out->hrs = frac / 3600LL;
- frac -= out->hrs * 3600LL;
- } else {
- out->hrs = 0;
- }
-
- if (frac >= 60) {
- out->min = frac / 60LL;
- frac -= out->min * 60LL;
- } else {
- out->min = 0;
- }
-
- if (frac >= 0) {
- out->sec = frac;
- frac -= out->sec;
- } else {
- out->sec = 0;
- }
-
- sfrac = (out->hrs * 3600LL + out->min * 60LL
- + out->sec) * per_sec;
-
- if (sign < 0)
- out->days = -out->days;
-
- ifrac = td - (out->days * per_day + sfrac);
-
- if (ifrac != 0) {
- out->ms = ifrac / (1000LL * 1000LL);
- ifrac -= out->ms * 1000LL * 1000LL;
- out->us = ifrac / 1000LL;
- ifrac -= out->us * 1000LL;
- out->ns = ifrac;
- } else {
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- }
- break;
-
- case NPY_FR_us:
-
- per_day = 86400000000LL;
- per_sec = 1000LL * 1000LL;
-
- // put frac in seconds
- if (td < 0 && td % per_sec != 0)
- frac = td / per_sec - 1;
- else
- frac = td / per_sec;
-
- if (frac < 0) {
- sign = -1;
-
- // even fraction
- if ((-frac % 86400LL) != 0) {
- out->days = -frac / 86400LL + 1;
- frac += 86400LL * out->days;
- } else {
- frac = -frac;
- }
- } else {
- sign = 1;
- out->days = 0;
- }
-
- if (frac >= 86400) {
- out->days += frac / 86400LL;
- frac -= out->days * 86400LL;
- }
-
- if (frac >= 3600) {
- out->hrs = frac / 3600LL;
- frac -= out->hrs * 3600LL;
- } else {
- out->hrs = 0;
- }
-
- if (frac >= 60) {
- out->min = frac / 60LL;
- frac -= out->min * 60LL;
- } else {
- out->min = 0;
- }
-
- if (frac >= 0) {
- out->sec = frac;
- frac -= out->sec;
- } else {
- out->sec = 0;
- }
-
- sfrac = (out->hrs * 3600LL + out->min * 60LL
- + out->sec) * per_sec;
-
- if (sign < 0)
- out->days = -out->days;
-
- ifrac = td - (out->days * per_day + sfrac);
-
- if (ifrac != 0) {
- out->ms = ifrac / 1000LL;
- ifrac -= out->ms * 1000LL;
- out->us = ifrac / 1L;
- ifrac -= out->us * 1L;
- out->ns = ifrac;
- } else {
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- }
- break;
-
- case NPY_FR_ms:
-
- per_day = 86400000LL;
- per_sec = 1000LL;
-
- // put frac in seconds
- if (td < 0 && td % per_sec != 0)
- frac = td / per_sec - 1;
- else
- frac = td / per_sec;
-
- if (frac < 0) {
- sign = -1;
-
- // even fraction
- if ((-frac % 86400LL) != 0) {
- out->days = -frac / 86400LL + 1;
- frac += 86400LL * out->days;
- } else {
- frac = -frac;
- }
- } else {
- sign = 1;
- out->days = 0;
- }
-
- if (frac >= 86400) {
- out->days += frac / 86400LL;
- frac -= out->days * 86400LL;
- }
-
- if (frac >= 3600) {
- out->hrs = frac / 3600LL;
- frac -= out->hrs * 3600LL;
- } else {
- out->hrs = 0;
- }
-
- if (frac >= 60) {
- out->min = frac / 60LL;
- frac -= out->min * 60LL;
- } else {
- out->min = 0;
- }
-
- if (frac >= 0) {
- out->sec = frac;
- frac -= out->sec;
- } else {
- out->sec = 0;
- }
-
- sfrac = (out->hrs * 3600LL + out->min * 60LL
- + out->sec) * per_sec;
-
- if (sign < 0)
- out->days = -out->days;
-
- ifrac = td - (out->days * per_day + sfrac);
-
- if (ifrac != 0) {
- out->ms = ifrac;
- out->us = 0;
- out->ns = 0;
- } else {
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- }
- break;
-
- case NPY_FR_s:
- // special case where we can simplify many expressions bc per_sec=1
-
- per_day = 86400LL;
- per_sec = 1L;
-
- // put frac in seconds
- if (td < 0 && td % per_sec != 0)
- frac = td / per_sec - 1;
- else
- frac = td / per_sec;
-
- if (frac < 0) {
- sign = -1;
-
- // even fraction
- if ((-frac % 86400LL) != 0) {
- out->days = -frac / 86400LL + 1;
- frac += 86400LL * out->days;
- } else {
- frac = -frac;
- }
- } else {
- sign = 1;
- out->days = 0;
- }
-
- if (frac >= 86400) {
- out->days += frac / 86400LL;
- frac -= out->days * 86400LL;
- }
-
- if (frac >= 3600) {
- out->hrs = frac / 3600LL;
- frac -= out->hrs * 3600LL;
- } else {
- out->hrs = 0;
- }
-
- if (frac >= 60) {
- out->min = frac / 60LL;
- frac -= out->min * 60LL;
- } else {
- out->min = 0;
- }
-
- if (frac >= 0) {
- out->sec = frac;
- frac -= out->sec;
- } else {
- out->sec = 0;
- }
-
- sfrac = (out->hrs * 3600LL + out->min * 60LL
- + out->sec) * per_sec;
-
- if (sign < 0)
- out->days = -out->days;
-
- ifrac = td - (out->days * per_day + sfrac);
-
- if (ifrac != 0) {
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- } else {
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- }
- break;
-
- case NPY_FR_m:
-
- out->days = td / 1440LL;
- td -= out->days * 1440LL;
- out->hrs = td / 60LL;
- td -= out->hrs * 60LL;
- out->min = td;
-
- out->sec = 0;
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- break;
-
- case NPY_FR_h:
- out->days = td / 24LL;
- td -= out->days * 24LL;
- out->hrs = td;
-
- out->min = 0;
- out->sec = 0;
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- break;
-
- case NPY_FR_D:
- out->days = td;
- out->hrs = 0;
- out->min = 0;
- out->sec = 0;
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- break;
-
- case NPY_FR_W:
- out->days = 7 * td;
- out->hrs = 0;
- out->min = 0;
- out->sec = 0;
- out->ms = 0;
- out->us = 0;
- out->ns = 0;
- break;
-
- default:
- PyErr_SetString(PyExc_RuntimeError,
- "NumPy timedelta metadata is corrupted with "
- "invalid base unit");
- }
-
- out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
- out->microseconds = out->ms * 1000 + out->us;
- out->nanoseconds = out->ns;
-}
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = ifrac / (1000LL * 1000LL);
+ ifrac -= out->ms * 1000LL * 1000LL;
+ out->us = ifrac / 1000LL;
+ ifrac -= out->us * 1000LL;
+ out->ns = ifrac;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_us:
+
+ per_day = 86400000000LL;
+ per_sec = 1000LL * 1000LL;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = ifrac / 1000LL;
+ ifrac -= out->ms * 1000LL;
+ out->us = ifrac / 1L;
+ ifrac -= out->us * 1L;
+ out->ns = ifrac;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_ms:
+
+ per_day = 86400000LL;
+ per_sec = 1000LL;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = ifrac;
+ out->us = 0;
+ out->ns = 0;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_s:
+ // special case where we can simplify many expressions bc per_sec=1
+
+ per_day = 86400LL;
+ per_sec = 1L;
+
+ // put frac in seconds
+ if (td < 0 && td % per_sec != 0)
+ frac = td / per_sec - 1;
+ else
+ frac = td / per_sec;
+
+ if (frac < 0) {
+ sign = -1;
+
+ // even fraction
+ if ((-frac % 86400LL) != 0) {
+ out->days = -frac / 86400LL + 1;
+ frac += 86400LL * out->days;
+ } else {
+ frac = -frac;
+ }
+ } else {
+ sign = 1;
+ out->days = 0;
+ }
+
+ if (frac >= 86400) {
+ out->days += frac / 86400LL;
+ frac -= out->days * 86400LL;
+ }
+
+ if (frac >= 3600) {
+ out->hrs = frac / 3600LL;
+ frac -= out->hrs * 3600LL;
+ } else {
+ out->hrs = 0;
+ }
+
+ if (frac >= 60) {
+ out->min = frac / 60LL;
+ frac -= out->min * 60LL;
+ } else {
+ out->min = 0;
+ }
+
+ if (frac >= 0) {
+ out->sec = frac;
+ frac -= out->sec;
+ } else {
+ out->sec = 0;
+ }
+
+ sfrac = (out->hrs * 3600LL + out->min * 60LL + out->sec) * per_sec;
+
+ if (sign < 0)
+ out->days = -out->days;
+
+ ifrac = td - (out->days * per_day + sfrac);
+
+ if (ifrac != 0) {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ } else {
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ }
+ break;
+
+ case NPY_FR_m:
+
+ out->days = td / 1440LL;
+ td -= out->days * 1440LL;
+ out->hrs = td / 60LL;
+ td -= out->hrs * 60LL;
+ out->min = td;
+
+ out->sec = 0;
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ break;
+
+ case NPY_FR_h:
+ out->days = td / 24LL;
+ td -= out->days * 24LL;
+ out->hrs = td;
+
+ out->min = 0;
+ out->sec = 0;
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ break;
+
+ case NPY_FR_D:
+ out->days = td;
+ out->hrs = 0;
+ out->min = 0;
+ out->sec = 0;
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ break;
+
+ case NPY_FR_W:
+ out->days = 7 * td;
+ out->hrs = 0;
+ out->min = 0;
+ out->sec = 0;
+ out->ms = 0;
+ out->us = 0;
+ out->ns = 0;
+ break;
+
+ default:
+ PyErr_SetString(PyExc_RuntimeError,
+ "NumPy timedelta metadata is corrupted with "
+ "invalid base unit");
+ }
+
+ out->seconds = out->hrs * 3600 + out->min * 60 + out->sec;
+ out->microseconds = out->ms * 1000 + out->us;
+ out->nanoseconds = out->ns;
+}
/*
* This function returns a pointer to the DateTimeMetaData
@@ -945,5 +927,5 @@ void pandas_timedelta_to_timedeltastruct(npy_timedelta td,
*/
PyArray_DatetimeMetaData
get_datetime_metadata_from_dtype(PyArray_Descr *dtype) {
- return (((PyArray_DatetimeDTypeMetaData *)dtype->c_metadata)->meta);
+ return (((PyArray_DatetimeDTypeMetaData *)dtype->c_metadata)->meta);
}
diff --git a/pandas/_libs/src/vendored/numpy/datetime/np_datetime_strings.c b/pandas/_libs/src/vendored/numpy/datetime/np_datetime_strings.c
index 9646183fa1786..a0d56efc14bd9 100644
--- a/pandas/_libs/src/vendored/numpy/datetime/np_datetime_strings.c
+++ b/pandas/_libs/src/vendored/numpy/datetime/np_datetime_strings.c
@@ -26,7 +26,7 @@ This file implements string parsing and creation for NumPy datetime.
#ifndef NPY_NO_DEPRECATED_API
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
-#endif // NPY_NO_DEPRECATED_API
+#endif // NPY_NO_DEPRECATED_API
#include <Python.h>
@@ -39,7 +39,6 @@ This file implements string parsing and creation for NumPy datetime.
#include "pandas/vendored/numpy/datetime/np_datetime.h"
#include "pandas/vendored/numpy/datetime/np_datetime_strings.h"
-
/*
* Parses (almost) standard ISO 8601 date strings. The differences are:
*
@@ -70,22 +69,19 @@ This file implements string parsing and creation for NumPy datetime.
*/
typedef enum {
- COMPARISON_SUCCESS,
- COMPLETED_PARTIAL_MATCH,
- COMPARISON_ERROR
+ COMPARISON_SUCCESS,
+ COMPLETED_PARTIAL_MATCH,
+ COMPARISON_ERROR
} DatetimePartParseResult;
// This function will advance the pointer on format
// and decrement characters_remaining by n on success
// On failure will return COMPARISON_ERROR without incrementing
// If `format_requirement` is PARTIAL_MATCH, and the `format` string has
// been exhausted, then return COMPLETED_PARTIAL_MATCH.
-static DatetimePartParseResult compare_format(
- const char **format,
- int *characters_remaining,
- const char *compare_to,
- int n,
- const FormatRequirement format_requirement
-) {
+static DatetimePartParseResult
+compare_format(const char **format, int *characters_remaining,
+ const char *compare_to, int n,
+ const FormatRequirement format_requirement) {
if (format_requirement == INFER_FORMAT) {
return COMPARISON_SUCCESS;
}
@@ -113,636 +109,649 @@ static DatetimePartParseResult compare_format(
int parse_iso_8601_datetime(const char *str, int len, int want_exc,
npy_datetimestruct *out,
- NPY_DATETIMEUNIT *out_bestunit,
- int *out_local, int *out_tzoffset,
- const char* format, int format_len,
+ NPY_DATETIMEUNIT *out_bestunit, int *out_local,
+ int *out_tzoffset, const char *format,
+ int format_len,
FormatRequirement format_requirement) {
- if (len < 0 || format_len < 0)
- goto parse_error;
- int year_leap = 0;
- int i, numdigits;
- const char *substr;
- int sublen;
- NPY_DATETIMEUNIT bestunit = NPY_FR_GENERIC;
- DatetimePartParseResult comparison;
-
- /* If year-month-day are separated by a valid separator,
- * months/days without leading zeroes will be parsed
- * (though not iso8601). If the components aren't separated,
- * 4 (YYYY) or 8 (YYYYMMDD) digits are expected. 6 digits are
- * forbidden here (but parsed as YYMMDD elsewhere).
- */
- int has_ymd_sep = 0;
- char ymd_sep = '\0';
- char valid_ymd_sep[] = {'-', '.', '/', '\\', ' '};
- int valid_ymd_sep_len = sizeof(valid_ymd_sep);
-
- /* hour-minute-second may or may not separated by ':'. If not, then
- * each component must be 2 digits. */
- int has_hms_sep = 0;
- int hour_was_2_digits = 0;
-
- /* Initialize the output to all zeros */
- memset(out, 0, sizeof(npy_datetimestruct));
- out->month = 1;
- out->day = 1;
-
- substr = str;
- sublen = len;
-
- /* Skip leading whitespace */
- while (sublen > 0 && isspace(*substr)) {
- ++substr;
- --sublen;
- comparison = compare_format(&format, &format_len, " ", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- }
-
- /* Leading '-' sign for negative year */
- if (*substr == '-') {
- ++substr;
- --sublen;
- }
-
- if (sublen == 0) {
- goto parse_error;
- }
-
- /* PARSE THE YEAR (4 digits) */
- comparison = compare_format(&format, &format_len, "%Y", 2, format_requirement);
+ if (len < 0 || format_len < 0)
+ goto parse_error;
+ int year_leap = 0;
+ int i, numdigits;
+ const char *substr;
+ int sublen;
+ NPY_DATETIMEUNIT bestunit = NPY_FR_GENERIC;
+ DatetimePartParseResult comparison;
+
+ /* If year-month-day are separated by a valid separator,
+ * months/days without leading zeroes will be parsed
+ * (though not iso8601). If the components aren't separated,
+ * 4 (YYYY) or 8 (YYYYMMDD) digits are expected. 6 digits are
+ * forbidden here (but parsed as YYMMDD elsewhere).
+ */
+ int has_ymd_sep = 0;
+ char ymd_sep = '\0';
+ char valid_ymd_sep[] = {'-', '.', '/', '\\', ' '};
+ int valid_ymd_sep_len = sizeof(valid_ymd_sep);
+
+ /* hour-minute-second may or may not separated by ':'. If not, then
+ * each component must be 2 digits. */
+ int has_hms_sep = 0;
+ int hour_was_2_digits = 0;
+
+ /* Initialize the output to all zeros */
+ memset(out, 0, sizeof(npy_datetimestruct));
+ out->month = 1;
+ out->day = 1;
+
+ substr = str;
+ sublen = len;
+
+ /* Skip leading whitespace */
+ while (sublen > 0 && isspace(*substr)) {
+ ++substr;
+ --sublen;
+ comparison =
+ compare_format(&format, &format_len, " ", 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ goto finish;
}
+ }
- out->year = 0;
- if (sublen >= 4 && isdigit(substr[0]) && isdigit(substr[1]) &&
- isdigit(substr[2]) && isdigit(substr[3])) {
- out->year = 1000 * (substr[0] - '0') + 100 * (substr[1] - '0') +
- 10 * (substr[2] - '0') + (substr[3] - '0');
+ /* Leading '-' sign for negative year */
+ if (*substr == '-') {
+ ++substr;
+ --sublen;
+ }
- substr += 4;
- sublen -= 4;
- }
+ if (sublen == 0) {
+ goto parse_error;
+ }
- /* Negate the year if necessary */
- if (str[0] == '-') {
- out->year = -out->year;
- }
- /* Check whether it's a leap-year */
- year_leap = is_leapyear(out->year);
+ /* PARSE THE YEAR (4 digits) */
+ comparison =
+ compare_format(&format, &format_len, "%Y", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
- /* Next character must be a separator, start of month, or end of string */
- if (sublen == 0) {
- if (out_local != NULL) {
- *out_local = 0;
- }
- if (format_len) {
- goto parse_error;
- }
- bestunit = NPY_FR_Y;
- goto finish;
- }
+ out->year = 0;
+ if (sublen >= 4 && isdigit(substr[0]) && isdigit(substr[1]) &&
+ isdigit(substr[2]) && isdigit(substr[3])) {
+ out->year = 1000 * (substr[0] - '0') + 100 * (substr[1] - '0') +
+ 10 * (substr[2] - '0') + (substr[3] - '0');
- if (!isdigit(*substr)) {
- for (i = 0; i < valid_ymd_sep_len; ++i) {
- if (*substr == valid_ymd_sep[i]) {
- break;
- }
- }
- if (i == valid_ymd_sep_len) {
- goto parse_error;
- }
- has_ymd_sep = 1;
- ymd_sep = valid_ymd_sep[i];
- ++substr;
- --sublen;
+ substr += 4;
+ sublen -= 4;
+ }
- comparison = compare_format(&format, &format_len, &ymd_sep, 1,
- format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- /* Cannot have trailing separator */
- if (sublen == 0 || !isdigit(*substr)) {
- goto parse_error;
- }
- }
+ /* Negate the year if necessary */
+ if (str[0] == '-') {
+ out->year = -out->year;
+ }
+ /* Check whether it's a leap-year */
+ year_leap = is_leapyear(out->year);
- /* PARSE THE MONTH */
- comparison = compare_format(&format, &format_len, "%m", 2, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- /* First digit required */
- out->month = (*substr - '0');
- ++substr;
- --sublen;
- /* Second digit optional if there was a separator */
- if (isdigit(*substr)) {
- out->month = 10 * out->month + (*substr - '0');
- ++substr;
- --sublen;
- } else if (!has_ymd_sep) {
- goto parse_error;
+ /* Next character must be a separator, start of month, or end of string */
+ if (sublen == 0) {
+ if (out_local != NULL) {
+ *out_local = 0;
}
- if (out->month < 1 || out->month > 12) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Month out of range in datetime string \"%s\"", str);
- }
- goto error;
+ if (format_len) {
+ goto parse_error;
}
+ bestunit = NPY_FR_Y;
+ goto finish;
+ }
- /* Next character must be the separator, start of day, or end of string */
- if (sublen == 0) {
- bestunit = NPY_FR_M;
- /* Forbid YYYYMM. Parsed instead as YYMMDD by someone else. */
- if (!has_ymd_sep) {
- goto parse_error;
- }
- if (format_len) {
- goto parse_error;
- }
- if (out_local != NULL) {
- *out_local = 0;
- }
- goto finish;
+ if (!isdigit(*substr)) {
+ for (i = 0; i < valid_ymd_sep_len; ++i) {
+ if (*substr == valid_ymd_sep[i]) {
+ break;
+ }
}
-
- if (has_ymd_sep) {
- /* Must have separator, but cannot be trailing */
- if (*substr != ymd_sep || sublen == 1) {
- goto parse_error;
- }
- ++substr;
- --sublen;
- comparison = compare_format(&format, &format_len, &ymd_sep, 1,
- format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
+ if (i == valid_ymd_sep_len) {
+ goto parse_error;
}
+ has_ymd_sep = 1;
+ ymd_sep = valid_ymd_sep[i];
+ ++substr;
+ --sublen;
- /* PARSE THE DAY */
- comparison = compare_format(&format, &format_len, "%d", 2, format_requirement);
+ comparison =
+ compare_format(&format, &format_len, &ymd_sep, 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ goto finish;
}
- /* First digit required */
- if (!isdigit(*substr)) {
- goto parse_error;
+ /* Cannot have trailing separator */
+ if (sublen == 0 || !isdigit(*substr)) {
+ goto parse_error;
}
- out->day = (*substr - '0');
+ }
+
+ /* PARSE THE MONTH */
+ comparison =
+ compare_format(&format, &format_len, "%m", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* First digit required */
+ out->month = (*substr - '0');
+ ++substr;
+ --sublen;
+ /* Second digit optional if there was a separator */
+ if (isdigit(*substr)) {
+ out->month = 10 * out->month + (*substr - '0');
++substr;
--sublen;
- /* Second digit optional if there was a separator */
- if (isdigit(*substr)) {
- out->day = 10 * out->day + (*substr - '0');
- ++substr;
- --sublen;
- } else if (!has_ymd_sep) {
- goto parse_error;
- }
- if (out->day < 1 ||
- out->day > days_per_month_table[year_leap][out->month - 1]) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Day out of range in datetime string \"%s\"", str);
- }
- goto error;
+ } else if (!has_ymd_sep) {
+ goto parse_error;
+ }
+ if (out->month < 1 || out->month > 12) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Month out of range in datetime string \"%s\"", str);
}
+ goto error;
+ }
- /* Next character must be a 'T', ' ', or end of string */
- if (sublen == 0) {
- if (out_local != NULL) {
- *out_local = 0;
- }
- if (format_len) {
- goto parse_error;
- }
- bestunit = NPY_FR_D;
- goto finish;
+ /* Next character must be the separator, start of day, or end of string */
+ if (sublen == 0) {
+ bestunit = NPY_FR_M;
+ /* Forbid YYYYMM. Parsed instead as YYMMDD by someone else. */
+ if (!has_ymd_sep) {
+ goto parse_error;
}
+ if (format_len) {
+ goto parse_error;
+ }
+ if (out_local != NULL) {
+ *out_local = 0;
+ }
+ goto finish;
+ }
- if ((*substr != 'T' && *substr != ' ') || sublen == 1) {
- goto parse_error;
+ if (has_ymd_sep) {
+ /* Must have separator, but cannot be trailing */
+ if (*substr != ymd_sep || sublen == 1) {
+ goto parse_error;
}
- comparison = compare_format(&format, &format_len, substr, 1, format_requirement);
+ ++substr;
+ --sublen;
+ comparison =
+ compare_format(&format, &format_len, &ymd_sep, 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ goto finish;
}
+ }
+
+ /* PARSE THE DAY */
+ comparison =
+ compare_format(&format, &format_len, "%d", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* First digit required */
+ if (!isdigit(*substr)) {
+ goto parse_error;
+ }
+ out->day = (*substr - '0');
+ ++substr;
+ --sublen;
+ /* Second digit optional if there was a separator */
+ if (isdigit(*substr)) {
+ out->day = 10 * out->day + (*substr - '0');
++substr;
--sublen;
+ } else if (!has_ymd_sep) {
+ goto parse_error;
+ }
+ if (out->day < 1 ||
+ out->day > days_per_month_table[year_leap][out->month - 1]) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Day out of range in datetime string \"%s\"", str);
+ }
+ goto error;
+ }
- /* PARSE THE HOURS */
- comparison = compare_format(&format, &format_len, "%H", 2, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ /* Next character must be a 'T', ' ', or end of string */
+ if (sublen == 0) {
+ if (out_local != NULL) {
+ *out_local = 0;
}
- /* First digit required */
- if (!isdigit(*substr)) {
- goto parse_error;
+ if (format_len) {
+ goto parse_error;
}
- out->hour = (*substr - '0');
+ bestunit = NPY_FR_D;
+ goto finish;
+ }
+
+ if ((*substr != 'T' && *substr != ' ') || sublen == 1) {
+ goto parse_error;
+ }
+ comparison =
+ compare_format(&format, &format_len, substr, 1, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ ++substr;
+ --sublen;
+
+ /* PARSE THE HOURS */
+ comparison =
+ compare_format(&format, &format_len, "%H", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* First digit required */
+ if (!isdigit(*substr)) {
+ goto parse_error;
+ }
+ out->hour = (*substr - '0');
+ ++substr;
+ --sublen;
+ /* Second digit optional */
+ if (isdigit(*substr)) {
+ hour_was_2_digits = 1;
+ out->hour = 10 * out->hour + (*substr - '0');
++substr;
--sublen;
- /* Second digit optional */
- if (isdigit(*substr)) {
- hour_was_2_digits = 1;
- out->hour = 10 * out->hour + (*substr - '0');
- ++substr;
- --sublen;
- if (out->hour >= 24) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Hours out of range in datetime string \"%s\"",
- str);
- }
- goto error;
- }
+ if (out->hour >= 24) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Hours out of range in datetime string \"%s\"", str);
+ }
+ goto error;
}
+ }
- /* Next character must be a ':' or the end of the string */
- if (sublen == 0) {
- if (!hour_was_2_digits) {
- goto parse_error;
- }
- if (format_len) {
- goto parse_error;
- }
- bestunit = NPY_FR_h;
- goto finish;
+ /* Next character must be a ':' or the end of the string */
+ if (sublen == 0) {
+ if (!hour_was_2_digits) {
+ goto parse_error;
}
-
- if (*substr == ':') {
- has_hms_sep = 1;
- ++substr;
- --sublen;
- /* Cannot have a trailing separator */
- if (sublen == 0 || !isdigit(*substr)) {
- goto parse_error;
- }
- comparison = compare_format(&format, &format_len, ":", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- } else if (!isdigit(*substr)) {
- if (!hour_was_2_digits) {
- goto parse_error;
- }
- goto parse_timezone;
+ if (format_len) {
+ goto parse_error;
}
+ bestunit = NPY_FR_h;
+ goto finish;
+ }
- /* PARSE THE MINUTES */
- comparison = compare_format(&format, &format_len, "%M", 2, format_requirement);
+ if (*substr == ':') {
+ has_hms_sep = 1;
+ ++substr;
+ --sublen;
+ /* Cannot have a trailing separator */
+ if (sublen == 0 || !isdigit(*substr)) {
+ goto parse_error;
+ }
+ comparison =
+ compare_format(&format, &format_len, ":", 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ goto finish;
}
- /* First digit required */
- out->min = (*substr - '0');
- ++substr;
- --sublen;
- /* Second digit optional if there was a separator */
- if (isdigit(*substr)) {
- out->min = 10 * out->min + (*substr - '0');
- ++substr;
- --sublen;
- if (out->min >= 60) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Minutes out of range in datetime string \"%s\"",
- str);
- }
- goto error;
- }
- } else if (!has_hms_sep) {
- goto parse_error;
+ } else if (!isdigit(*substr)) {
+ if (!hour_was_2_digits) {
+ goto parse_error;
}
+ goto parse_timezone;
+ }
- if (sublen == 0) {
- bestunit = NPY_FR_m;
- if (format_len) {
- goto parse_error;
- }
- goto finish;
+ /* PARSE THE MINUTES */
+ comparison =
+ compare_format(&format, &format_len, "%M", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* First digit required */
+ out->min = (*substr - '0');
+ ++substr;
+ --sublen;
+ /* Second digit optional if there was a separator */
+ if (isdigit(*substr)) {
+ out->min = 10 * out->min + (*substr - '0');
+ ++substr;
+ --sublen;
+ if (out->min >= 60) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Minutes out of range in datetime string \"%s\"", str);
+ }
+ goto error;
}
+ } else if (!has_hms_sep) {
+ goto parse_error;
+ }
- /* If we make it through this condition block, then the next
- * character is a digit. */
- if (has_hms_sep && *substr == ':') {
- comparison = compare_format(&format, &format_len, ":", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- ++substr;
- --sublen;
- /* Cannot have a trailing ':' */
- if (sublen == 0 || !isdigit(*substr)) {
- goto parse_error;
- }
- } else if (!has_hms_sep && isdigit(*substr)) {
- } else {
- goto parse_timezone;
+ if (sublen == 0) {
+ bestunit = NPY_FR_m;
+ if (format_len) {
+ goto parse_error;
}
+ goto finish;
+ }
- /* PARSE THE SECONDS */
- comparison = compare_format(&format, &format_len, "%S", 2, format_requirement);
+ /* If we make it through this condition block, then the next
+ * character is a digit. */
+ if (has_hms_sep && *substr == ':') {
+ comparison =
+ compare_format(&format, &format_len, ":", 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ goto finish;
}
- /* First digit required */
- out->sec = (*substr - '0');
++substr;
--sublen;
- /* Second digit optional if there was a separator */
- if (isdigit(*substr)) {
- out->sec = 10 * out->sec + (*substr - '0');
- ++substr;
- --sublen;
- if (out->sec >= 60) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Seconds out of range in datetime string \"%s\"",
- str);
- }
- goto error;
- }
- } else if (!has_hms_sep) {
- goto parse_error;
+ /* Cannot have a trailing ':' */
+ if (sublen == 0 || !isdigit(*substr)) {
+ goto parse_error;
}
+ } else if (!has_hms_sep && isdigit(*substr)) {
+ } else {
+ goto parse_timezone;
+ }
- /* Next character may be a '.' indicating fractional seconds */
- if (sublen > 0 && *substr == '.') {
- ++substr;
- --sublen;
- comparison = compare_format(&format, &format_len, ".", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- } else {
- bestunit = NPY_FR_s;
- goto parse_timezone;
+ /* PARSE THE SECONDS */
+ comparison =
+ compare_format(&format, &format_len, "%S", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* First digit required */
+ out->sec = (*substr - '0');
+ ++substr;
+ --sublen;
+ /* Second digit optional if there was a separator */
+ if (isdigit(*substr)) {
+ out->sec = 10 * out->sec + (*substr - '0');
+ ++substr;
+ --sublen;
+ if (out->sec >= 60) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Seconds out of range in datetime string \"%s\"", str);
+ }
+ goto error;
}
+ } else if (!has_hms_sep) {
+ goto parse_error;
+ }
- /* PARSE THE MICROSECONDS (0 to 6 digits) */
- comparison = compare_format(&format, &format_len, "%f", 2, format_requirement);
+ /* Next character may be a '.' indicating fractional seconds */
+ if (sublen > 0 && *substr == '.') {
+ ++substr;
+ --sublen;
+ comparison =
+ compare_format(&format, &format_len, ".", 1, format_requirement);
if (comparison == COMPARISON_ERROR) {
- goto parse_error;
+ goto parse_error;
} else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- numdigits = 0;
- for (i = 0; i < 6; ++i) {
- out->us *= 10;
- if (sublen > 0 && isdigit(*substr)) {
- out->us += (*substr - '0');
- ++substr;
- --sublen;
- ++numdigits;
- }
- }
-
- if (sublen == 0 || !isdigit(*substr)) {
- if (numdigits > 3) {
- bestunit = NPY_FR_us;
- } else {
- bestunit = NPY_FR_ms;
- }
- goto parse_timezone;
+ goto finish;
}
+ } else {
+ bestunit = NPY_FR_s;
+ goto parse_timezone;
+ }
- /* PARSE THE PICOSECONDS (0 to 6 digits) */
- numdigits = 0;
- for (i = 0; i < 6; ++i) {
- out->ps *= 10;
- if (sublen > 0 && isdigit(*substr)) {
- out->ps += (*substr - '0');
- ++substr;
- --sublen;
- ++numdigits;
- }
+ /* PARSE THE MICROSECONDS (0 to 6 digits) */
+ comparison =
+ compare_format(&format, &format_len, "%f", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ numdigits = 0;
+ for (i = 0; i < 6; ++i) {
+ out->us *= 10;
+ if (sublen > 0 && isdigit(*substr)) {
+ out->us += (*substr - '0');
+ ++substr;
+ --sublen;
+ ++numdigits;
}
+ }
- if (sublen == 0 || !isdigit(*substr)) {
- if (numdigits > 3) {
- bestunit = NPY_FR_ps;
- } else {
- bestunit = NPY_FR_ns;
- }
- goto parse_timezone;
+ if (sublen == 0 || !isdigit(*substr)) {
+ if (numdigits > 3) {
+ bestunit = NPY_FR_us;
+ } else {
+ bestunit = NPY_FR_ms;
}
+ goto parse_timezone;
+ }
- /* PARSE THE ATTOSECONDS (0 to 6 digits) */
- numdigits = 0;
- for (i = 0; i < 6; ++i) {
- out->as *= 10;
- if (sublen > 0 && isdigit(*substr)) {
- out->as += (*substr - '0');
- ++substr;
- --sublen;
- ++numdigits;
- }
+ /* PARSE THE PICOSECONDS (0 to 6 digits) */
+ numdigits = 0;
+ for (i = 0; i < 6; ++i) {
+ out->ps *= 10;
+ if (sublen > 0 && isdigit(*substr)) {
+ out->ps += (*substr - '0');
+ ++substr;
+ --sublen;
+ ++numdigits;
}
+ }
+ if (sublen == 0 || !isdigit(*substr)) {
if (numdigits > 3) {
- bestunit = NPY_FR_as;
+ bestunit = NPY_FR_ps;
} else {
- bestunit = NPY_FR_fs;
+ bestunit = NPY_FR_ns;
}
+ goto parse_timezone;
+ }
-parse_timezone:
- /* trim any whitespace between time/timezone */
- while (sublen > 0 && isspace(*substr)) {
- ++substr;
- --sublen;
- comparison = compare_format(&format, &format_len, " ", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
+ /* PARSE THE ATTOSECONDS (0 to 6 digits) */
+ numdigits = 0;
+ for (i = 0; i < 6; ++i) {
+ out->as *= 10;
+ if (sublen > 0 && isdigit(*substr)) {
+ out->as += (*substr - '0');
+ ++substr;
+ --sublen;
+ ++numdigits;
}
+ }
- if (sublen == 0) {
- // Unlike NumPy, treating no time zone as naive
- if (format_len > 0) {
- goto parse_error;
- }
- goto finish;
- }
+ if (numdigits > 3) {
+ bestunit = NPY_FR_as;
+ } else {
+ bestunit = NPY_FR_fs;
+ }
- /* UTC specifier */
- if (*substr == 'Z') {
- comparison = compare_format(&format, &format_len, "%z", 2, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- /* "Z" should be equivalent to tz offset "+00:00" */
- if (out_local != NULL) {
- *out_local = 1;
- }
+parse_timezone:
+ /* trim any whitespace between time/timezone */
+ while (sublen > 0 && isspace(*substr)) {
+ ++substr;
+ --sublen;
+ comparison =
+ compare_format(&format, &format_len, " ", 1, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ }
- if (out_tzoffset != NULL) {
- *out_tzoffset = 0;
- }
+ if (sublen == 0) {
+ // Unlike NumPy, treating no time zone as naive
+ if (format_len > 0) {
+ goto parse_error;
+ }
+ goto finish;
+ }
- if (sublen == 1) {
- if (format_len > 0) {
- goto parse_error;
- }
- goto finish;
- } else {
- ++substr;
- --sublen;
- }
- } else if (*substr == '-' || *substr == '+') {
- comparison = compare_format(&format, &format_len, "%z", 2, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
- }
- /* Time zone offset */
- int offset_neg = 0, offset_hour = 0, offset_minute = 0;
+ /* UTC specifier */
+ if (*substr == 'Z') {
+ comparison =
+ compare_format(&format, &format_len, "%z", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* "Z" should be equivalent to tz offset "+00:00" */
+ if (out_local != NULL) {
+ *out_local = 1;
+ }
- /*
- * Since "local" means local with respect to the current
- * machine, we say this is non-local.
- */
+ if (out_tzoffset != NULL) {
+ *out_tzoffset = 0;
+ }
- if (*substr == '-') {
- offset_neg = 1;
- }
- ++substr;
- --sublen;
+ if (sublen == 1) {
+ if (format_len > 0) {
+ goto parse_error;
+ }
+ goto finish;
+ } else {
+ ++substr;
+ --sublen;
+ }
+ } else if (*substr == '-' || *substr == '+') {
+ comparison =
+ compare_format(&format, &format_len, "%z", 2, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
+ }
+ /* Time zone offset */
+ int offset_neg = 0, offset_hour = 0, offset_minute = 0;
- /* The hours offset */
- if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) {
- offset_hour = 10 * (substr[0] - '0') + (substr[1] - '0');
- substr += 2;
- sublen -= 2;
- if (offset_hour >= 24) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Timezone hours offset out of range "
- "in datetime string \"%s\"",
- str);
- }
- goto error;
- }
- } else if (sublen >= 1 && isdigit(substr[0])) {
- offset_hour = substr[0] - '0';
- ++substr;
- --sublen;
- } else {
- goto parse_error;
- }
+ /*
+ * Since "local" means local with respect to the current
+ * machine, we say this is non-local.
+ */
- /* The minutes offset is optional */
- if (sublen > 0) {
- /* Optional ':' */
- if (*substr == ':') {
- ++substr;
- --sublen;
- }
-
- /* The minutes offset (at the end of the string) */
- if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) {
- offset_minute = 10 * (substr[0] - '0') + (substr[1] - '0');
- substr += 2;
- sublen -= 2;
- if (offset_minute >= 60) {
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Timezone minutes offset out of range "
- "in datetime string \"%s\"",
- str);
- }
- goto error;
- }
- } else if (sublen >= 1 && isdigit(substr[0])) {
- offset_minute = substr[0] - '0';
- ++substr;
- --sublen;
- } else {
- goto parse_error;
- }
- }
+ if (*substr == '-') {
+ offset_neg = 1;
+ }
+ ++substr;
+ --sublen;
- /* Apply the time zone offset */
- if (offset_neg) {
- offset_hour = -offset_hour;
- offset_minute = -offset_minute;
- }
- if (out_local != NULL) {
- *out_local = 1;
- // Unlike NumPy, do not change internal value to local time
- *out_tzoffset = 60 * offset_hour + offset_minute;
+ /* The hours offset */
+ if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) {
+ offset_hour = 10 * (substr[0] - '0') + (substr[1] - '0');
+ substr += 2;
+ sublen -= 2;
+ if (offset_hour >= 24) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Timezone hours offset out of range "
+ "in datetime string \"%s\"",
+ str);
}
+ goto error;
+ }
+ } else if (sublen >= 1 && isdigit(substr[0])) {
+ offset_hour = substr[0] - '0';
+ ++substr;
+ --sublen;
+ } else {
+ goto parse_error;
}
- /* Skip trailing whitespace */
- while (sublen > 0 && isspace(*substr)) {
+ /* The minutes offset is optional */
+ if (sublen > 0) {
+ /* Optional ':' */
+ if (*substr == ':') {
++substr;
--sublen;
- comparison = compare_format(&format, &format_len, " ", 1, format_requirement);
- if (comparison == COMPARISON_ERROR) {
- goto parse_error;
- } else if (comparison == COMPLETED_PARTIAL_MATCH) {
- goto finish;
+ }
+
+ /* The minutes offset (at the end of the string) */
+ if (sublen >= 2 && isdigit(substr[0]) && isdigit(substr[1])) {
+ offset_minute = 10 * (substr[0] - '0') + (substr[1] - '0');
+ substr += 2;
+ sublen -= 2;
+ if (offset_minute >= 60) {
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Timezone minutes offset out of range "
+ "in datetime string \"%s\"",
+ str);
+ }
+ goto error;
}
+ } else if (sublen >= 1 && isdigit(substr[0])) {
+ offset_minute = substr[0] - '0';
+ ++substr;
+ --sublen;
+ } else {
+ goto parse_error;
+ }
}
- if ((sublen != 0) || (format_len != 0)) {
- goto parse_error;
+ /* Apply the time zone offset */
+ if (offset_neg) {
+ offset_hour = -offset_hour;
+ offset_minute = -offset_minute;
+ }
+ if (out_local != NULL) {
+ *out_local = 1;
+ // Unlike NumPy, do not change internal value to local time
+ *out_tzoffset = 60 * offset_hour + offset_minute;
}
+ }
-finish:
- if (out_bestunit != NULL) {
- *out_bestunit = bestunit;
+ /* Skip trailing whitespace */
+ while (sublen > 0 && isspace(*substr)) {
+ ++substr;
+ --sublen;
+ comparison =
+ compare_format(&format, &format_len, " ", 1, format_requirement);
+ if (comparison == COMPARISON_ERROR) {
+ goto parse_error;
+ } else if (comparison == COMPLETED_PARTIAL_MATCH) {
+ goto finish;
}
- return 0;
+ }
+
+ if ((sublen != 0) || (format_len != 0)) {
+ goto parse_error;
+ }
+
+finish:
+ if (out_bestunit != NULL) {
+ *out_bestunit = bestunit;
+ }
+ return 0;
parse_error:
- if (want_exc) {
- PyErr_Format(PyExc_ValueError,
- "Error parsing datetime string \"%s\" at position %d", str,
- (int)(substr - str));
- }
- return -1;
+ if (want_exc) {
+ PyErr_Format(PyExc_ValueError,
+ "Error parsing datetime string \"%s\" at position %d", str,
+ (int)(substr - str));
+ }
+ return -1;
error:
- return -1;
+ return -1;
}
/*
@@ -750,56 +759,55 @@ int parse_iso_8601_datetime(const char *str, int len, int want_exc,
* objects with the given local and unit settings.
*/
int get_datetime_iso_8601_strlen(int local, NPY_DATETIMEUNIT base) {
- int len = 0;
-
- switch (base) {
- /* Generic units can only be used to represent NaT */
- /* return 4;*/
- case NPY_FR_as:
- len += 3; /* "###" */
- case NPY_FR_fs:
- len += 3; /* "###" */
- case NPY_FR_ps:
- len += 3; /* "###" */
- case NPY_FR_ns:
- len += 3; /* "###" */
- case NPY_FR_us:
- len += 3; /* "###" */
- case NPY_FR_ms:
- len += 4; /* ".###" */
- case NPY_FR_s:
- len += 3; /* ":##" */
- case NPY_FR_m:
- len += 3; /* ":##" */
- case NPY_FR_h:
- len += 3; /* "T##" */
- case NPY_FR_D:
- case NPY_FR_W:
- len += 3; /* "-##" */
- case NPY_FR_M:
- len += 3; /* "-##" */
- case NPY_FR_Y:
- len += 21; /* 64-bit year */
- break;
- default:
- len += 3; /* handle the now defunct NPY_FR_B */
- break;
- }
+ int len = 0;
+
+ switch (base) {
+ /* Generic units can only be used to represent NaT */
+ /* return 4;*/
+ case NPY_FR_as:
+ len += 3; /* "###" */
+ case NPY_FR_fs:
+ len += 3; /* "###" */
+ case NPY_FR_ps:
+ len += 3; /* "###" */
+ case NPY_FR_ns:
+ len += 3; /* "###" */
+ case NPY_FR_us:
+ len += 3; /* "###" */
+ case NPY_FR_ms:
+ len += 4; /* ".###" */
+ case NPY_FR_s:
+ len += 3; /* ":##" */
+ case NPY_FR_m:
+ len += 3; /* ":##" */
+ case NPY_FR_h:
+ len += 3; /* "T##" */
+ case NPY_FR_D:
+ case NPY_FR_W:
+ len += 3; /* "-##" */
+ case NPY_FR_M:
+ len += 3; /* "-##" */
+ case NPY_FR_Y:
+ len += 21; /* 64-bit year */
+ break;
+ default:
+ len += 3; /* handle the now defunct NPY_FR_B */
+ break;
+ }
- if (base >= NPY_FR_h) {
- if (local) {
- len += 5; /* "+####" or "-####" */
- } else {
- len += 1; /* "Z" */
- }
+ if (base >= NPY_FR_h) {
+ if (local) {
+ len += 5; /* "+####" or "-####" */
+ } else {
+ len += 1; /* "Z" */
}
+ }
- len += 1; /* NULL terminator */
+ len += 1; /* NULL terminator */
- return len;
+ return len;
}
-
/*
* Converts an npy_datetimestruct to an (almost) ISO 8601
* NULL-terminated string using timezone Z (UTC). If the string fits in
@@ -816,19 +824,19 @@ int get_datetime_iso_8601_strlen(int local, NPY_DATETIMEUNIT base) {
*/
int make_iso_8601_datetime(npy_datetimestruct *dts, char *outstr, int outlen,
int utc, NPY_DATETIMEUNIT base) {
- char *substr = outstr;
- int sublen = outlen;
- int tmplen;
-
- /*
- * Print weeks with the same precision as days.
- *
- * TODO: Could print weeks with YYYY-Www format if the week
- * epoch is a Monday.
- */
- if (base == NPY_FR_W) {
- base = NPY_FR_D;
- }
+ char *substr = outstr;
+ int sublen = outlen;
+ int tmplen;
+
+ /*
+ * Print weeks with the same precision as days.
+ *
+ * TODO: Could print weeks with YYYY-Www format if the week
+ * epoch is a Monday.
+ */
+ if (base == NPY_FR_W) {
+ base = NPY_FR_D;
+ }
/* YEAR */
/*
@@ -837,314 +845,309 @@ int make_iso_8601_datetime(npy_datetimestruct *dts, char *outstr, int outlen,
* to have data all the way to the end of the buffer.
*/
#ifdef _WIN32
- tmplen = _snprintf(substr, sublen, "%04" NPY_INT64_FMT, dts->year);
+ tmplen = _snprintf(substr, sublen, "%04" NPY_INT64_FMT, dts->year);
#else
- tmplen = snprintf(substr, sublen, "%04" NPY_INT64_FMT, dts->year);
-#endif // _WIN32
- /* If it ran out of space or there isn't space for the NULL terminator */
- if (tmplen < 0 || tmplen > sublen) {
- goto string_too_short;
- }
- substr += tmplen;
- sublen -= tmplen;
+ tmplen = snprintf(substr, sublen, "%04" NPY_INT64_FMT, dts->year);
+#endif // _WIN32
+ /* If it ran out of space or there isn't space for the NULL terminator */
+ if (tmplen < 0 || tmplen > sublen) {
+ goto string_too_short;
+ }
+ substr += tmplen;
+ sublen -= tmplen;
- /* Stop if the unit is years */
- if (base == NPY_FR_Y) {
- if (sublen > 0) {
- *substr = '\0';
- }
- return 0;
+ /* Stop if the unit is years */
+ if (base == NPY_FR_Y) {
+ if (sublen > 0) {
+ *substr = '\0';
}
+ return 0;
+ }
- /* MONTH */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = '-';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->month / 10) + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->month % 10) + '0');
- substr += 3;
- sublen -= 3;
-
- /* Stop if the unit is months */
- if (base == NPY_FR_M) {
- if (sublen > 0) {
- *substr = '\0';
- }
- return 0;
- }
+ /* MONTH */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = '-';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->month / 10) + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->month % 10) + '0');
+ substr += 3;
+ sublen -= 3;
- /* DAY */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = '-';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->day / 10) + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->day % 10) + '0');
- substr += 3;
- sublen -= 3;
-
- /* Stop if the unit is days */
- if (base == NPY_FR_D) {
- if (sublen > 0) {
- *substr = '\0';
- }
- return 0;
+ /* Stop if the unit is months */
+ if (base == NPY_FR_M) {
+ if (sublen > 0) {
+ *substr = '\0';
}
+ return 0;
+ }
- /* HOUR */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = 'T';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->hour / 10) + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->hour % 10) + '0');
- substr += 3;
- sublen -= 3;
+ /* DAY */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = '-';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->day / 10) + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->day % 10) + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is hours */
- if (base == NPY_FR_h) {
- goto add_time_zone;
+ /* Stop if the unit is days */
+ if (base == NPY_FR_D) {
+ if (sublen > 0) {
+ *substr = '\0';
}
+ return 0;
+ }
- /* MINUTE */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = ':';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->min / 10) + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->min % 10) + '0');
- substr += 3;
- sublen -= 3;
+ /* HOUR */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = 'T';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->hour / 10) + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->hour % 10) + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is minutes */
- if (base == NPY_FR_m) {
- goto add_time_zone;
- }
+ /* Stop if the unit is hours */
+ if (base == NPY_FR_h) {
+ goto add_time_zone;
+ }
- /* SECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = ':';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->sec / 10) + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->sec % 10) + '0');
- substr += 3;
- sublen -= 3;
+ /* MINUTE */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = ':';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->min / 10) + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->min % 10) + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is seconds */
- if (base == NPY_FR_s) {
- goto add_time_zone;
- }
+ /* Stop if the unit is minutes */
+ if (base == NPY_FR_m) {
+ goto add_time_zone;
+ }
- /* MILLISECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = '.';
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->us / 100000) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->us / 10000) % 10 + '0');
- if (sublen < 4) {
- goto string_too_short;
- }
- substr[3] = (char)((dts->us / 1000) % 10 + '0');
- substr += 4;
- sublen -= 4;
+ /* SECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = ':';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->sec / 10) + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->sec % 10) + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is milliseconds */
- if (base == NPY_FR_ms) {
- goto add_time_zone;
- }
+ /* Stop if the unit is seconds */
+ if (base == NPY_FR_s) {
+ goto add_time_zone;
+ }
- /* MICROSECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = (char)((dts->us / 100) % 10 + '0');
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->us / 10) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)(dts->us % 10 + '0');
- substr += 3;
- sublen -= 3;
+ /* MILLISECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = '.';
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->us / 100000) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->us / 10000) % 10 + '0');
+ if (sublen < 4) {
+ goto string_too_short;
+ }
+ substr[3] = (char)((dts->us / 1000) % 10 + '0');
+ substr += 4;
+ sublen -= 4;
- /* Stop if the unit is microseconds */
- if (base == NPY_FR_us) {
- goto add_time_zone;
- }
+ /* Stop if the unit is milliseconds */
+ if (base == NPY_FR_ms) {
+ goto add_time_zone;
+ }
- /* NANOSECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = (char)((dts->ps / 100000) % 10 + '0');
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->ps / 10000) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->ps / 1000) % 10 + '0');
- substr += 3;
- sublen -= 3;
+ /* MICROSECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = (char)((dts->us / 100) % 10 + '0');
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->us / 10) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)(dts->us % 10 + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is nanoseconds */
- if (base == NPY_FR_ns) {
- goto add_time_zone;
- }
+ /* Stop if the unit is microseconds */
+ if (base == NPY_FR_us) {
+ goto add_time_zone;
+ }
- /* PICOSECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = (char)((dts->ps / 100) % 10 + '0');
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->ps / 10) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)(dts->ps % 10 + '0');
- substr += 3;
- sublen -= 3;
+ /* NANOSECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = (char)((dts->ps / 100000) % 10 + '0');
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->ps / 10000) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->ps / 1000) % 10 + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is picoseconds */
- if (base == NPY_FR_ps) {
- goto add_time_zone;
- }
+ /* Stop if the unit is nanoseconds */
+ if (base == NPY_FR_ns) {
+ goto add_time_zone;
+ }
- /* FEMTOSECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = (char)((dts->as / 100000) % 10 + '0');
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->as / 10000) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)((dts->as / 1000) % 10 + '0');
- substr += 3;
- sublen -= 3;
+ /* PICOSECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = (char)((dts->ps / 100) % 10 + '0');
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->ps / 10) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)(dts->ps % 10 + '0');
+ substr += 3;
+ sublen -= 3;
- /* Stop if the unit is femtoseconds */
- if (base == NPY_FR_fs) {
- goto add_time_zone;
- }
+ /* Stop if the unit is picoseconds */
+ if (base == NPY_FR_ps) {
+ goto add_time_zone;
+ }
- /* ATTOSECOND */
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = (char)((dts->as / 100) % 10 + '0');
- if (sublen < 2) {
- goto string_too_short;
- }
- substr[1] = (char)((dts->as / 10) % 10 + '0');
- if (sublen < 3) {
- goto string_too_short;
- }
- substr[2] = (char)(dts->as % 10 + '0');
- substr += 3;
- sublen -= 3;
+ /* FEMTOSECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = (char)((dts->as / 100000) % 10 + '0');
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->as / 10000) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)((dts->as / 1000) % 10 + '0');
+ substr += 3;
+ sublen -= 3;
+
+ /* Stop if the unit is femtoseconds */
+ if (base == NPY_FR_fs) {
+ goto add_time_zone;
+ }
+
+ /* ATTOSECOND */
+ if (sublen < 1) {
+ goto string_too_short;
+ }
+ substr[0] = (char)((dts->as / 100) % 10 + '0');
+ if (sublen < 2) {
+ goto string_too_short;
+ }
+ substr[1] = (char)((dts->as / 10) % 10 + '0');
+ if (sublen < 3) {
+ goto string_too_short;
+ }
+ substr[2] = (char)(dts->as % 10 + '0');
+ substr += 3;
+ sublen -= 3;
add_time_zone:
- /* UTC "Zulu" time */
- if (utc) {
- if (sublen < 1) {
- goto string_too_short;
- }
- substr[0] = 'Z';
- substr += 1;
- sublen -= 1;
- }
- /* Add a NULL terminator, and return */
- if (sublen > 0) {
- substr[0] = '\0';
+ /* UTC "Zulu" time */
+ if (utc) {
+ if (sublen < 1) {
+ goto string_too_short;
}
+ substr[0] = 'Z';
+ substr += 1;
+ sublen -= 1;
+ }
+ /* Add a NULL terminator, and return */
+ if (sublen > 0) {
+ substr[0] = '\0';
+ }
- return 0;
+ return 0;
string_too_short:
- PyErr_Format(PyExc_RuntimeError,
- "The string provided for NumPy ISO datetime formatting "
- "was too short, with length %d",
- outlen);
- return -1;
+ PyErr_Format(PyExc_RuntimeError,
+ "The string provided for NumPy ISO datetime formatting "
+ "was too short, with length %d",
+ outlen);
+ return -1;
}
-
-int make_iso_8601_timedelta(pandas_timedeltastruct *tds,
- char *outstr, size_t *outlen) {
+int make_iso_8601_timedelta(pandas_timedeltastruct *tds, char *outstr,
+ size_t *outlen) {
*outlen = 0;
- *outlen += snprintf(outstr, 60, // NOLINT
- "P%" NPY_INT64_FMT
- "DT%" NPY_INT32_FMT
- "H%" NPY_INT32_FMT
- "M%" NPY_INT32_FMT,
- tds->days, tds->hrs, tds->min, tds->sec);
+ *outlen += snprintf(outstr, 60, // NOLINT
+ "P%" NPY_INT64_FMT "DT%" NPY_INT32_FMT "H%" NPY_INT32_FMT
+ "M%" NPY_INT32_FMT,
+ tds->days, tds->hrs, tds->min, tds->sec);
outstr += *outlen;
if (tds->ns != 0) {
- *outlen += snprintf(outstr, 12, // NOLINT
- ".%03" NPY_INT32_FMT
- "%03" NPY_INT32_FMT
- "%03" NPY_INT32_FMT
- "S", tds->ms, tds->us, tds->ns);
+ *outlen += snprintf(outstr, 12, // NOLINT
+ ".%03" NPY_INT32_FMT "%03" NPY_INT32_FMT
+ "%03" NPY_INT32_FMT "S",
+ tds->ms, tds->us, tds->ns);
} else if (tds->us != 0) {
- *outlen += snprintf(outstr, 9, // NOLINT
- ".%03" NPY_INT32_FMT
- "%03" NPY_INT32_FMT
- "S", tds->ms, tds->us);
+ *outlen += snprintf(outstr, 9, // NOLINT
+ ".%03" NPY_INT32_FMT "%03" NPY_INT32_FMT "S", tds->ms,
+ tds->us);
} else if (tds->ms != 0) {
- *outlen += snprintf(outstr, 6, // NOLINT
+ *outlen += snprintf(outstr, 6, // NOLINT
".%03" NPY_INT32_FMT "S", tds->ms);
} else {
- *outlen += snprintf(outstr, 2, // NOLINT
+ *outlen += snprintf(outstr, 2, // NOLINT
"%s", "S");
}
diff --git a/pandas/_libs/src/vendored/ujson/lib/ultrajsondec.c b/pandas/_libs/src/vendored/ujson/lib/ultrajsondec.c
index 70794786016fb..a858abf46a598 100644
--- a/pandas/_libs/src/vendored/ujson/lib/ultrajsondec.c
+++ b/pandas/_libs/src/vendored/ujson/lib/ultrajsondec.c
@@ -40,6 +40,7 @@ Numeric decoder derived from TCL library
// Licence at LICENSES/ULTRAJSON_LICENSE
+#include "pandas/vendored/ujson/lib/ultrajson.h"
#include <assert.h>
#include <errno.h>
#include <limits.h>
@@ -48,7 +49,6 @@ Numeric decoder derived from TCL library
#include <stdlib.h>
#include <string.h>
#include <wchar.h>
-#include "pandas/vendored/ujson/lib/ultrajson.h"
#ifndef TRUE
#define TRUE 1
@@ -59,15 +59,15 @@ Numeric decoder derived from TCL library
#endif
struct DecoderState {
- char *start;
- char *end;
- wchar_t *escStart;
- wchar_t *escEnd;
- int escHeap;
- int lastType;
- JSUINT32 objDepth;
- void *prv;
- JSONObjectDecoder *dec;
+ char *start;
+ char *end;
+ wchar_t *escStart;
+ wchar_t *escEnd;
+ int escHeap;
+ int lastType;
+ JSUINT32 objDepth;
+ void *prv;
+ JSONObjectDecoder *dec;
};
JSOBJ FASTCALL_MSVC decode_any(struct DecoderState *ds);
@@ -75,349 +75,372 @@ typedef JSOBJ (*PFN_DECODER)(struct DecoderState *ds);
static JSOBJ SetError(struct DecoderState *ds, int offset,
const char *message) {
- ds->dec->errorOffset = ds->start + offset;
- ds->dec->errorStr = (char *)message;
- return NULL;
+ ds->dec->errorOffset = ds->start + offset;
+ ds->dec->errorStr = (char *)message;
+ return NULL;
}
double createDouble(double intNeg, double intValue, double frcValue,
int frcDecimalCount) {
- static const double g_pow10[] = {1.0,
- 0.1,
- 0.01,
- 0.001,
- 0.0001,
- 0.00001,
- 0.000001,
- 0.0000001,
- 0.00000001,
- 0.000000001,
- 0.0000000001,
- 0.00000000001,
- 0.000000000001,
- 0.0000000000001,
- 0.00000000000001,
- 0.000000000000001};
- return (intValue + (frcValue * g_pow10[frcDecimalCount])) * intNeg;
+ static const double g_pow10[] = {1.0,
+ 0.1,
+ 0.01,
+ 0.001,
+ 0.0001,
+ 0.00001,
+ 0.000001,
+ 0.0000001,
+ 0.00000001,
+ 0.000000001,
+ 0.0000000001,
+ 0.00000000001,
+ 0.000000000001,
+ 0.0000000000001,
+ 0.00000000000001,
+ 0.000000000000001};
+ return (intValue + (frcValue * g_pow10[frcDecimalCount])) * intNeg;
}
JSOBJ FASTCALL_MSVC decodePreciseFloat(struct DecoderState *ds) {
- char *end;
- double value;
- errno = 0;
+ char *end;
+ double value;
+ errno = 0;
- value = strtod(ds->start, &end);
+ value = strtod(ds->start, &end);
- if (errno == ERANGE) {
- return SetError(ds, -1, "Range error when decoding numeric as double");
- }
+ if (errno == ERANGE) {
+ return SetError(ds, -1, "Range error when decoding numeric as double");
+ }
- ds->start = end;
- return ds->dec->newDouble(ds->prv, value);
+ ds->start = end;
+ return ds->dec->newDouble(ds->prv, value);
}
JSOBJ FASTCALL_MSVC decode_numeric(struct DecoderState *ds) {
- int intNeg = 1;
- JSUINT64 intValue;
- JSUINT64 prevIntValue;
- int chr;
- int decimalCount = 0;
- double frcValue = 0.0;
- double expNeg;
- double expValue;
- char *offset = ds->start;
-
- JSUINT64 overflowLimit = LLONG_MAX;
-
+ int intNeg = 1;
+ JSUINT64 intValue;
+ JSUINT64 prevIntValue;
+ int chr;
+ int decimalCount = 0;
+ double frcValue = 0.0;
+ double expNeg;
+ double expValue;
+ char *offset = ds->start;
+
+ JSUINT64 overflowLimit = LLONG_MAX;
+
+ if (*(offset) == 'I') {
+ goto DECODE_INF;
+ } else if (*(offset) == 'N') {
+ goto DECODE_NAN;
+ } else if (*(offset) == '-') {
+ offset++;
+ intNeg = -1;
+ overflowLimit = LLONG_MIN;
if (*(offset) == 'I') {
goto DECODE_INF;
- } else if (*(offset) == 'N') {
- goto DECODE_NAN;
- } else if (*(offset) == '-') {
- offset++;
- intNeg = -1;
- overflowLimit = LLONG_MIN;
- if (*(offset) == 'I') {
- goto DECODE_INF;
- }
+ }
+ }
+
+ // Scan integer part
+ intValue = 0;
+
+ while (1) {
+ chr = (int)(unsigned char)*(offset);
+
+ switch (chr) {
+ case '0':
+ case '1':
+ case '2':
+ case '3':
+ case '4':
+ case '5':
+ case '6':
+ case '7':
+ case '8':
+ case '9': {
+ // PERF: Don't do 64-bit arithmetic here unless we have to
+ prevIntValue = intValue;
+ intValue = intValue * 10ULL + (JSLONG)(chr - 48);
+
+ if (intNeg == 1 && prevIntValue > intValue) {
+ return SetError(ds, -1, "Value is too big!");
+ } else if (intNeg == -1 && intValue > overflowLimit) {
+ return SetError(ds, -1,
+ overflowLimit == LLONG_MAX ? "Value is too big!"
+ : "Value is too small");
+ }
+
+ offset++;
+ break;
+ }
+ case '.': {
+ offset++;
+ goto DECODE_FRACTION;
+ break;
+ }
+ case 'e':
+ case 'E': {
+ offset++;
+ goto DECODE_EXPONENT;
+ break;
}
- // Scan integer part
- intValue = 0;
-
- while (1) {
- chr = (int)(unsigned char)*(offset);
-
- switch (chr) {
- case '0':
- case '1':
- case '2':
- case '3':
- case '4':
- case '5':
- case '6':
- case '7':
- case '8':
- case '9': {
- // PERF: Don't do 64-bit arithmetic here unless we have to
- prevIntValue = intValue;
- intValue = intValue * 10ULL + (JSLONG) (chr - 48);
-
- if (intNeg == 1 && prevIntValue > intValue) {
- return SetError(ds, -1, "Value is too big!");
- } else if (intNeg == -1 && intValue > overflowLimit) {
- return SetError(ds, -1, overflowLimit == LLONG_MAX ?
- "Value is too big!" : "Value is too small");
- }
-
- offset++;
- break;
- }
- case '.': {
- offset++;
- goto DECODE_FRACTION;
- break;
- }
- case 'e':
- case 'E': {
- offset++;
- goto DECODE_EXPONENT;
- break;
- }
-
- default: {
- goto BREAK_INT_LOOP;
- break;
- }
- }
+ default: {
+ goto BREAK_INT_LOOP;
+ break;
+ }
}
+ }
BREAK_INT_LOOP:
- ds->lastType = JT_INT;
- ds->start = offset;
+ ds->lastType = JT_INT;
+ ds->start = offset;
- if (intNeg == 1 && (intValue & 0x8000000000000000ULL) != 0)
- return ds->dec->newUnsignedLong(ds->prv, intValue);
- else if ((intValue >> 31))
- return ds->dec->newLong(ds->prv, (JSINT64)(intValue * (JSINT64)intNeg));
- else
- return ds->dec->newInt(ds->prv, (JSINT32)(intValue * intNeg));
+ if (intNeg == 1 && (intValue & 0x8000000000000000ULL) != 0)
+ return ds->dec->newUnsignedLong(ds->prv, intValue);
+ else if ((intValue >> 31))
+ return ds->dec->newLong(ds->prv, (JSINT64)(intValue * (JSINT64)intNeg));
+ else
+ return ds->dec->newInt(ds->prv, (JSINT32)(intValue * intNeg));
DECODE_FRACTION:
- if (ds->dec->preciseFloat) {
- return decodePreciseFloat(ds);
+ if (ds->dec->preciseFloat) {
+ return decodePreciseFloat(ds);
+ }
+
+ // Scan fraction part
+ frcValue = 0.0;
+ for (;;) {
+ chr = (int)(unsigned char)*(offset);
+
+ switch (chr) {
+ case '0':
+ case '1':
+ case '2':
+ case '3':
+ case '4':
+ case '5':
+ case '6':
+ case '7':
+ case '8':
+ case '9': {
+ if (decimalCount < JSON_DOUBLE_MAX_DECIMALS) {
+ frcValue = frcValue * 10.0 + (double)(chr - 48);
+ decimalCount++;
+ }
+ offset++;
+ break;
}
-
- // Scan fraction part
- frcValue = 0.0;
- for (;;) {
- chr = (int)(unsigned char)*(offset);
-
- switch (chr) {
- case '0':
- case '1':
- case '2':
- case '3':
- case '4':
- case '5':
- case '6':
- case '7':
- case '8':
- case '9': {
- if (decimalCount < JSON_DOUBLE_MAX_DECIMALS) {
- frcValue = frcValue * 10.0 + (double)(chr - 48);
- decimalCount++;
- }
- offset++;
- break;
- }
- case 'e':
- case 'E': {
- offset++;
- goto DECODE_EXPONENT;
- break;
- }
- default: { goto BREAK_FRC_LOOP; }
- }
+ case 'e':
+ case 'E': {
+ offset++;
+ goto DECODE_EXPONENT;
+ break;
+ }
+ default: {
+ goto BREAK_FRC_LOOP;
}
+ }
+ }
BREAK_FRC_LOOP:
- // FIXME: Check for arithmetic overflow here
- ds->lastType = JT_DOUBLE;
- ds->start = offset;
- return ds->dec->newDouble(
- ds->prv,
- createDouble((double)intNeg, (double)intValue, frcValue, decimalCount));
+ // FIXME: Check for arithmetic overflow here
+ ds->lastType = JT_DOUBLE;
+ ds->start = offset;
+ return ds->dec->newDouble(
+ ds->prv,
+ createDouble((double)intNeg, (double)intValue, frcValue, decimalCount));
DECODE_EXPONENT:
- if (ds->dec->preciseFloat) {
- return decodePreciseFloat(ds);
- }
+ if (ds->dec->preciseFloat) {
+ return decodePreciseFloat(ds);
+ }
- expNeg = 1.0;
+ expNeg = 1.0;
- if (*(offset) == '-') {
- expNeg = -1.0;
- offset++;
- } else if (*(offset) == '+') {
- expNeg = +1.0;
- offset++;
+ if (*(offset) == '-') {
+ expNeg = -1.0;
+ offset++;
+ } else if (*(offset) == '+') {
+ expNeg = +1.0;
+ offset++;
+ }
+
+ expValue = 0.0;
+
+ for (;;) {
+ chr = (int)(unsigned char)*(offset);
+
+ switch (chr) {
+ case '0':
+ case '1':
+ case '2':
+ case '3':
+ case '4':
+ case '5':
+ case '6':
+ case '7':
+ case '8':
+ case '9': {
+ expValue = expValue * 10.0 + (double)(chr - 48);
+ offset++;
+ break;
+ }
+ default: {
+ goto BREAK_EXP_LOOP;
}
-
- expValue = 0.0;
-
- for (;;) {
- chr = (int)(unsigned char)*(offset);
-
- switch (chr) {
- case '0':
- case '1':
- case '2':
- case '3':
- case '4':
- case '5':
- case '6':
- case '7':
- case '8':
- case '9': {
- expValue = expValue * 10.0 + (double)(chr - 48);
- offset++;
- break;
- }
- default: { goto BREAK_EXP_LOOP; }
- }
}
+ }
DECODE_NAN:
- offset++;
- if (*(offset++) != 'a') goto SET_NAN_ERROR;
- if (*(offset++) != 'N') goto SET_NAN_ERROR;
+ offset++;
+ if (*(offset++) != 'a')
+ goto SET_NAN_ERROR;
+ if (*(offset++) != 'N')
+ goto SET_NAN_ERROR;
- ds->lastType = JT_NULL;
- ds->start = offset;
- return ds->dec->newNull(ds->prv);
+ ds->lastType = JT_NULL;
+ ds->start = offset;
+ return ds->dec->newNull(ds->prv);
SET_NAN_ERROR:
- return SetError(ds, -1, "Unexpected character found when decoding 'NaN'");
+ return SetError(ds, -1, "Unexpected character found when decoding 'NaN'");
DECODE_INF:
- offset++;
- if (*(offset++) != 'n') goto SET_INF_ERROR;
- if (*(offset++) != 'f') goto SET_INF_ERROR;
- if (*(offset++) != 'i') goto SET_INF_ERROR;
- if (*(offset++) != 'n') goto SET_INF_ERROR;
- if (*(offset++) != 'i') goto SET_INF_ERROR;
- if (*(offset++) != 't') goto SET_INF_ERROR;
- if (*(offset++) != 'y') goto SET_INF_ERROR;
-
- ds->start = offset;
-
- if (intNeg == 1) {
- ds->lastType = JT_POS_INF;
- return ds->dec->newPosInf(ds->prv);
- } else {
- ds->lastType = JT_NEG_INF;
- return ds->dec->newNegInf(ds->prv);
- }
+ offset++;
+ if (*(offset++) != 'n')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 'f')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 'i')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 'n')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 'i')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 't')
+ goto SET_INF_ERROR;
+ if (*(offset++) != 'y')
+ goto SET_INF_ERROR;
+
+ ds->start = offset;
+
+ if (intNeg == 1) {
+ ds->lastType = JT_POS_INF;
+ return ds->dec->newPosInf(ds->prv);
+ } else {
+ ds->lastType = JT_NEG_INF;
+ return ds->dec->newNegInf(ds->prv);
+ }
SET_INF_ERROR:
- if (intNeg == 1) {
- const char *msg = "Unexpected character found when decoding 'Infinity'";
- return SetError(ds, -1, msg);
- } else {
- const char *msg = "Unexpected character found when decoding '-Infinity'";
- return SetError(ds, -1, msg);
- }
-
+ if (intNeg == 1) {
+ const char *msg = "Unexpected character found when decoding 'Infinity'";
+ return SetError(ds, -1, msg);
+ } else {
+ const char *msg = "Unexpected character found when decoding '-Infinity'";
+ return SetError(ds, -1, msg);
+ }
BREAK_EXP_LOOP:
- // FIXME: Check for arithmetic overflow here
- ds->lastType = JT_DOUBLE;
- ds->start = offset;
- return ds->dec->newDouble(
- ds->prv,
- createDouble((double)intNeg, (double)intValue, frcValue, decimalCount) *
- pow(10.0, expValue * expNeg));
+ // FIXME: Check for arithmetic overflow here
+ ds->lastType = JT_DOUBLE;
+ ds->start = offset;
+ return ds->dec->newDouble(
+ ds->prv,
+ createDouble((double)intNeg, (double)intValue, frcValue, decimalCount) *
+ pow(10.0, expValue * expNeg));
}
JSOBJ FASTCALL_MSVC decode_true(struct DecoderState *ds) {
- char *offset = ds->start;
- offset++;
+ char *offset = ds->start;
+ offset++;
- if (*(offset++) != 'r') goto SETERROR;
- if (*(offset++) != 'u') goto SETERROR;
- if (*(offset++) != 'e') goto SETERROR;
+ if (*(offset++) != 'r')
+ goto SETERROR;
+ if (*(offset++) != 'u')
+ goto SETERROR;
+ if (*(offset++) != 'e')
+ goto SETERROR;
- ds->lastType = JT_TRUE;
- ds->start = offset;
- return ds->dec->newTrue(ds->prv);
+ ds->lastType = JT_TRUE;
+ ds->start = offset;
+ return ds->dec->newTrue(ds->prv);
SETERROR:
- return SetError(ds, -1, "Unexpected character found when decoding 'true'");
+ return SetError(ds, -1, "Unexpected character found when decoding 'true'");
}
JSOBJ FASTCALL_MSVC decode_false(struct DecoderState *ds) {
- char *offset = ds->start;
- offset++;
-
- if (*(offset++) != 'a') goto SETERROR;
- if (*(offset++) != 'l') goto SETERROR;
- if (*(offset++) != 's') goto SETERROR;
- if (*(offset++) != 'e') goto SETERROR;
-
- ds->lastType = JT_FALSE;
- ds->start = offset;
- return ds->dec->newFalse(ds->prv);
+ char *offset = ds->start;
+ offset++;
+
+ if (*(offset++) != 'a')
+ goto SETERROR;
+ if (*(offset++) != 'l')
+ goto SETERROR;
+ if (*(offset++) != 's')
+ goto SETERROR;
+ if (*(offset++) != 'e')
+ goto SETERROR;
+
+ ds->lastType = JT_FALSE;
+ ds->start = offset;
+ return ds->dec->newFalse(ds->prv);
SETERROR:
- return SetError(ds, -1, "Unexpected character found when decoding 'false'");
+ return SetError(ds, -1, "Unexpected character found when decoding 'false'");
}
JSOBJ FASTCALL_MSVC decode_null(struct DecoderState *ds) {
- char *offset = ds->start;
- offset++;
+ char *offset = ds->start;
+ offset++;
- if (*(offset++) != 'u') goto SETERROR;
- if (*(offset++) != 'l') goto SETERROR;
- if (*(offset++) != 'l') goto SETERROR;
+ if (*(offset++) != 'u')
+ goto SETERROR;
+ if (*(offset++) != 'l')
+ goto SETERROR;
+ if (*(offset++) != 'l')
+ goto SETERROR;
- ds->lastType = JT_NULL;
- ds->start = offset;
- return ds->dec->newNull(ds->prv);
+ ds->lastType = JT_NULL;
+ ds->start = offset;
+ return ds->dec->newNull(ds->prv);
SETERROR:
- return SetError(ds, -1, "Unexpected character found when decoding 'null'");
+ return SetError(ds, -1, "Unexpected character found when decoding 'null'");
}
void FASTCALL_MSVC SkipWhitespace(struct DecoderState *ds) {
- char *offset;
-
- for (offset = ds->start; (ds->end - offset) > 0; offset++) {
- switch (*offset) {
- case ' ':
- case '\t':
- case '\r':
- case '\n':
- break;
-
- default:
- ds->start = offset;
- return;
- }
+ char *offset;
+
+ for (offset = ds->start; (ds->end - offset) > 0; offset++) {
+ switch (*offset) {
+ case ' ':
+ case '\t':
+ case '\r':
+ case '\n':
+ break;
+
+ default:
+ ds->start = offset;
+ return;
}
+ }
- if (offset == ds->end) {
- ds->start = ds->end;
- }
+ if (offset == ds->end) {
+ ds->start = ds->end;
+ }
}
enum DECODESTRINGSTATE {
- DS_ISNULL = 0x32,
- DS_ISQUOTE,
- DS_ISESCAPE,
- DS_UTFLENERROR,
+ DS_ISNULL = 0x32,
+ DS_ISQUOTE,
+ DS_ISESCAPE,
+ DS_UTFLENERROR,
};
static const JSUINT8 g_decoderLookup[256] = {
@@ -680,531 +703,520 @@ static const JSUINT8 g_decoderLookup[256] = {
};
JSOBJ FASTCALL_MSVC decode_string(struct DecoderState *ds) {
- JSUTF16 sur[2] = {0};
- int iSur = 0;
- int index;
- wchar_t *escOffset;
- wchar_t *escStart;
- size_t escLen = (ds->escEnd - ds->escStart);
- JSUINT8 *inputOffset;
- JSUINT8 oct;
- JSUTF32 ucs;
- ds->lastType = JT_INVALID;
- ds->start++;
-
- if ((size_t)(ds->end - ds->start) > escLen) {
- size_t newSize = (ds->end - ds->start);
-
- if (ds->escHeap) {
- if (newSize > (SIZE_MAX / sizeof(wchar_t))) {
- return SetError(ds, -1, "Could not reserve memory block");
- }
- escStart = (wchar_t *)ds->dec->realloc(ds->escStart,
- newSize * sizeof(wchar_t));
- if (!escStart) {
- ds->dec->free(ds->escStart);
- return SetError(ds, -1, "Could not reserve memory block");
- }
- ds->escStart = escStart;
- } else {
- wchar_t *oldStart = ds->escStart;
- if (newSize > (SIZE_MAX / sizeof(wchar_t))) {
- return SetError(ds, -1, "Could not reserve memory block");
- }
- ds->escStart =
- (wchar_t *)ds->dec->malloc(newSize * sizeof(wchar_t));
- if (!ds->escStart) {
- return SetError(ds, -1, "Could not reserve memory block");
- }
- ds->escHeap = 1;
- memcpy(ds->escStart, oldStart, escLen * sizeof(wchar_t));
- }
-
- ds->escEnd = ds->escStart + newSize;
+ JSUTF16 sur[2] = {0};
+ int iSur = 0;
+ int index;
+ wchar_t *escOffset;
+ wchar_t *escStart;
+ size_t escLen = (ds->escEnd - ds->escStart);
+ JSUINT8 *inputOffset;
+ JSUINT8 oct;
+ JSUTF32 ucs;
+ ds->lastType = JT_INVALID;
+ ds->start++;
+
+ if ((size_t)(ds->end - ds->start) > escLen) {
+ size_t newSize = (ds->end - ds->start);
+
+ if (ds->escHeap) {
+ if (newSize > (SIZE_MAX / sizeof(wchar_t))) {
+ return SetError(ds, -1, "Could not reserve memory block");
+ }
+ escStart =
+ (wchar_t *)ds->dec->realloc(ds->escStart, newSize * sizeof(wchar_t));
+ if (!escStart) {
+ ds->dec->free(ds->escStart);
+ return SetError(ds, -1, "Could not reserve memory block");
+ }
+ ds->escStart = escStart;
+ } else {
+ wchar_t *oldStart = ds->escStart;
+ if (newSize > (SIZE_MAX / sizeof(wchar_t))) {
+ return SetError(ds, -1, "Could not reserve memory block");
+ }
+ ds->escStart = (wchar_t *)ds->dec->malloc(newSize * sizeof(wchar_t));
+ if (!ds->escStart) {
+ return SetError(ds, -1, "Could not reserve memory block");
+ }
+ ds->escHeap = 1;
+ memcpy(ds->escStart, oldStart, escLen * sizeof(wchar_t));
}
- escOffset = ds->escStart;
- inputOffset = (JSUINT8 *)ds->start;
-
- for (;;) {
- switch (g_decoderLookup[(JSUINT8)(*inputOffset)]) {
- case DS_ISNULL: {
- return SetError(ds, -1,
- "Unmatched ''\"' when when decoding 'string'");
- }
- case DS_ISQUOTE: {
- ds->lastType = JT_UTF8;
- inputOffset++;
- ds->start += ((char *)inputOffset - (ds->start));
- return ds->dec->newString(ds->prv, ds->escStart, escOffset);
- }
- case DS_UTFLENERROR: {
- return SetError(
- ds, -1,
- "Invalid UTF-8 sequence length when decoding 'string'");
- }
- case DS_ISESCAPE:
- inputOffset++;
- switch (*inputOffset) {
- case '\\':
- *(escOffset++) = L'\\';
- inputOffset++;
- continue;
- case '\"':
- *(escOffset++) = L'\"';
- inputOffset++;
- continue;
- case '/':
- *(escOffset++) = L'/';
- inputOffset++;
- continue;
- case 'b':
- *(escOffset++) = L'\b';
- inputOffset++;
- continue;
- case 'f':
- *(escOffset++) = L'\f';
- inputOffset++;
- continue;
- case 'n':
- *(escOffset++) = L'\n';
- inputOffset++;
- continue;
- case 'r':
- *(escOffset++) = L'\r';
- inputOffset++;
- continue;
- case 't':
- *(escOffset++) = L'\t';
- inputOffset++;
- continue;
-
- case 'u': {
- int index;
- inputOffset++;
-
- for (index = 0; index < 4; index++) {
- switch (*inputOffset) {
- case '\0':
- return SetError(ds, -1,
- "Unterminated unicode "
- "escape sequence when "
- "decoding 'string'");
- default:
- return SetError(ds, -1,
- "Unexpected character in "
- "unicode escape sequence "
- "when decoding 'string'");
-
- case '0':
- case '1':
- case '2':
- case '3':
- case '4':
- case '5':
- case '6':
- case '7':
- case '8':
- case '9':
- sur[iSur] = (sur[iSur] << 4) +
- (JSUTF16)(*inputOffset - '0');
- break;
-
- case 'a':
- case 'b':
- case 'c':
- case 'd':
- case 'e':
- case 'f':
- sur[iSur] = (sur[iSur] << 4) + 10 +
- (JSUTF16)(*inputOffset - 'a');
- break;
-
- case 'A':
- case 'B':
- case 'C':
- case 'D':
- case 'E':
- case 'F':
- sur[iSur] = (sur[iSur] << 4) + 10 +
- (JSUTF16)(*inputOffset - 'A');
- break;
- }
-
- inputOffset++;
- }
-
- if (iSur == 0) {
- if ((sur[iSur] & 0xfc00) == 0xd800) {
- // First of a surrogate pair, continue parsing
- iSur++;
- break;
- }
- (*escOffset++) = (wchar_t)sur[iSur];
- iSur = 0;
- } else {
- // Decode pair
- if ((sur[1] & 0xfc00) != 0xdc00) {
- return SetError(ds, -1,
- "Unpaired high surrogate when "
- "decoding 'string'");
- }
-#if WCHAR_MAX == 0xffff
- (*escOffset++) = (wchar_t)sur[0];
- (*escOffset++) = (wchar_t)sur[1];
-#else
- (*escOffset++) =
- (wchar_t)0x10000 +
- (((sur[0] - 0xd800) << 10) | (sur[1] - 0xdc00));
-#endif
- iSur = 0;
- }
- break;
- }
-
- case '\0':
- return SetError(ds, -1,
- "Unterminated escape sequence when "
- "decoding 'string'");
- default:
- return SetError(ds, -1,
- "Unrecognized escape sequence when "
- "decoding 'string'");
- }
- break;
-
- case 1: {
- *(escOffset++) = (wchar_t)(*inputOffset++);
- break;
- }
-
- case 2: {
- ucs = (*inputOffset++) & 0x1f;
- ucs <<= 6;
- if (((*inputOffset) & 0x80) != 0x80) {
- return SetError(ds, -1,
- "Invalid octet in UTF-8 sequence when "
- "decoding 'string'");
- }
- ucs |= (*inputOffset++) & 0x3f;
- if (ucs < 0x80)
- return SetError(ds, -1,
- "Overlong 2 byte UTF-8 sequence detected "
- "when decoding 'string'");
- *(escOffset++) = (wchar_t)ucs;
- break;
- }
-
- case 3: {
- JSUTF32 ucs = 0;
- ucs |= (*inputOffset++) & 0x0f;
-
- for (index = 0; index < 2; index++) {
- ucs <<= 6;
- oct = (*inputOffset++);
-
- if ((oct & 0x80) != 0x80) {
- return SetError(ds, -1,
- "Invalid octet in UTF-8 sequence when "
- "decoding 'string'");
- }
-
- ucs |= oct & 0x3f;
- }
-
- if (ucs < 0x800)
- return SetError(ds, -1,
- "Overlong 3 byte UTF-8 sequence detected "
- "when encoding string");
- *(escOffset++) = (wchar_t)ucs;
- break;
- }
-
- case 4: {
- JSUTF32 ucs = 0;
- ucs |= (*inputOffset++) & 0x07;
-
- for (index = 0; index < 3; index++) {
- ucs <<= 6;
- oct = (*inputOffset++);
-
- if ((oct & 0x80) != 0x80) {
- return SetError(ds, -1,
- "Invalid octet in UTF-8 sequence when "
- "decoding 'string'");
- }
-
- ucs |= oct & 0x3f;
- }
-
- if (ucs < 0x10000)
- return SetError(ds, -1,
- "Overlong 4 byte UTF-8 sequence detected "
- "when decoding 'string'");
+ ds->escEnd = ds->escStart + newSize;
+ }
+
+ escOffset = ds->escStart;
+ inputOffset = (JSUINT8 *)ds->start;
+ for (;;) {
+ switch (g_decoderLookup[(JSUINT8)(*inputOffset)]) {
+ case DS_ISNULL: {
+ return SetError(ds, -1, "Unmatched ''\"' when when decoding 'string'");
+ }
+ case DS_ISQUOTE: {
+ ds->lastType = JT_UTF8;
+ inputOffset++;
+ ds->start += ((char *)inputOffset - (ds->start));
+ return ds->dec->newString(ds->prv, ds->escStart, escOffset);
+ }
+ case DS_UTFLENERROR: {
+ return SetError(ds, -1,
+ "Invalid UTF-8 sequence length when decoding 'string'");
+ }
+ case DS_ISESCAPE:
+ inputOffset++;
+ switch (*inputOffset) {
+ case '\\':
+ *(escOffset++) = L'\\';
+ inputOffset++;
+ continue;
+ case '\"':
+ *(escOffset++) = L'\"';
+ inputOffset++;
+ continue;
+ case '/':
+ *(escOffset++) = L'/';
+ inputOffset++;
+ continue;
+ case 'b':
+ *(escOffset++) = L'\b';
+ inputOffset++;
+ continue;
+ case 'f':
+ *(escOffset++) = L'\f';
+ inputOffset++;
+ continue;
+ case 'n':
+ *(escOffset++) = L'\n';
+ inputOffset++;
+ continue;
+ case 'r':
+ *(escOffset++) = L'\r';
+ inputOffset++;
+ continue;
+ case 't':
+ *(escOffset++) = L'\t';
+ inputOffset++;
+ continue;
+
+ case 'u': {
+ int index;
+ inputOffset++;
+
+ for (index = 0; index < 4; index++) {
+ switch (*inputOffset) {
+ case '\0':
+ return SetError(ds, -1,
+ "Unterminated unicode "
+ "escape sequence when "
+ "decoding 'string'");
+ default:
+ return SetError(ds, -1,
+ "Unexpected character in "
+ "unicode escape sequence "
+ "when decoding 'string'");
+
+ case '0':
+ case '1':
+ case '2':
+ case '3':
+ case '4':
+ case '5':
+ case '6':
+ case '7':
+ case '8':
+ case '9':
+ sur[iSur] = (sur[iSur] << 4) + (JSUTF16)(*inputOffset - '0');
+ break;
+
+ case 'a':
+ case 'b':
+ case 'c':
+ case 'd':
+ case 'e':
+ case 'f':
+ sur[iSur] = (sur[iSur] << 4) + 10 + (JSUTF16)(*inputOffset - 'a');
+ break;
+
+ case 'A':
+ case 'B':
+ case 'C':
+ case 'D':
+ case 'E':
+ case 'F':
+ sur[iSur] = (sur[iSur] << 4) + 10 + (JSUTF16)(*inputOffset - 'A');
+ break;
+ }
+
+ inputOffset++;
+ }
+
+ if (iSur == 0) {
+ if ((sur[iSur] & 0xfc00) == 0xd800) {
+ // First of a surrogate pair, continue parsing
+ iSur++;
+ break;
+ }
+ (*escOffset++) = (wchar_t)sur[iSur];
+ iSur = 0;
+ } else {
+ // Decode pair
+ if ((sur[1] & 0xfc00) != 0xdc00) {
+ return SetError(ds, -1,
+ "Unpaired high surrogate when "
+ "decoding 'string'");
+ }
#if WCHAR_MAX == 0xffff
- if (ucs >= 0x10000) {
- ucs -= 0x10000;
- *(escOffset++) = (wchar_t)(ucs >> 10) + 0xd800;
- *(escOffset++) = (wchar_t)(ucs & 0x3ff) + 0xdc00;
- } else {
- *(escOffset++) = (wchar_t)ucs;
- }
+ (*escOffset++) = (wchar_t)sur[0];
+ (*escOffset++) = (wchar_t)sur[1];
#else
- *(escOffset++) = (wchar_t)ucs;
+ (*escOffset++) = (wchar_t)0x10000 +
+ (((sur[0] - 0xd800) << 10) | (sur[1] - 0xdc00));
#endif
- break;
- }
+ iSur = 0;
}
+ break;
+ }
+
+ case '\0':
+ return SetError(ds, -1,
+ "Unterminated escape sequence when "
+ "decoding 'string'");
+ default:
+ return SetError(ds, -1,
+ "Unrecognized escape sequence when "
+ "decoding 'string'");
+ }
+ break;
+
+ case 1: {
+ *(escOffset++) = (wchar_t)(*inputOffset++);
+ break;
}
-}
-JSOBJ FASTCALL_MSVC decode_array(struct DecoderState *ds) {
- JSOBJ itemValue;
- JSOBJ newObj;
- int len;
- ds->objDepth++;
- if (ds->objDepth > JSON_MAX_OBJECT_DEPTH) {
- return SetError(ds, -1, "Reached object decoding depth limit");
+ case 2: {
+ ucs = (*inputOffset++) & 0x1f;
+ ucs <<= 6;
+ if (((*inputOffset) & 0x80) != 0x80) {
+ return SetError(ds, -1,
+ "Invalid octet in UTF-8 sequence when "
+ "decoding 'string'");
+ }
+ ucs |= (*inputOffset++) & 0x3f;
+ if (ucs < 0x80)
+ return SetError(ds, -1,
+ "Overlong 2 byte UTF-8 sequence detected "
+ "when decoding 'string'");
+ *(escOffset++) = (wchar_t)ucs;
+ break;
}
- newObj = ds->dec->newArray(ds->prv, ds->dec);
- len = 0;
+ case 3: {
+ JSUTF32 ucs = 0;
+ ucs |= (*inputOffset++) & 0x0f;
- ds->lastType = JT_INVALID;
- ds->start++;
-
- for (;;) {
- SkipWhitespace(ds);
-
- if ((*ds->start) == ']') {
- ds->objDepth--;
- if (len == 0) {
- ds->start++;
- return ds->dec->endArray(ds->prv, newObj);
- }
-
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return SetError(
- ds, -1,
- "Unexpected character found when decoding array value (1)");
+ for (index = 0; index < 2; index++) {
+ ucs <<= 6;
+ oct = (*inputOffset++);
+
+ if ((oct & 0x80) != 0x80) {
+ return SetError(ds, -1,
+ "Invalid octet in UTF-8 sequence when "
+ "decoding 'string'");
}
- itemValue = decode_any(ds);
+ ucs |= oct & 0x3f;
+ }
- if (itemValue == NULL) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return NULL;
- }
+ if (ucs < 0x800)
+ return SetError(ds, -1,
+ "Overlong 3 byte UTF-8 sequence detected "
+ "when encoding string");
+ *(escOffset++) = (wchar_t)ucs;
+ break;
+ }
- if (!ds->dec->arrayAddItem(ds->prv, newObj, itemValue)) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return NULL;
- }
+ case 4: {
+ JSUTF32 ucs = 0;
+ ucs |= (*inputOffset++) & 0x07;
- SkipWhitespace(ds);
-
- switch (*(ds->start++)) {
- case ']': {
- ds->objDepth--;
- return ds->dec->endArray(ds->prv, newObj);
- }
- case ',':
- break;
-
- default:
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return SetError(
- ds, -1,
- "Unexpected character found when decoding array value (2)");
+ for (index = 0; index < 3; index++) {
+ ucs <<= 6;
+ oct = (*inputOffset++);
+
+ if ((oct & 0x80) != 0x80) {
+ return SetError(ds, -1,
+ "Invalid octet in UTF-8 sequence when "
+ "decoding 'string'");
}
- len++;
+ ucs |= oct & 0x3f;
+ }
+
+ if (ucs < 0x10000)
+ return SetError(ds, -1,
+ "Overlong 4 byte UTF-8 sequence detected "
+ "when decoding 'string'");
+
+#if WCHAR_MAX == 0xffff
+ if (ucs >= 0x10000) {
+ ucs -= 0x10000;
+ *(escOffset++) = (wchar_t)(ucs >> 10) + 0xd800;
+ *(escOffset++) = (wchar_t)(ucs & 0x3ff) + 0xdc00;
+ } else {
+ *(escOffset++) = (wchar_t)ucs;
+ }
+#else
+ *(escOffset++) = (wchar_t)ucs;
+#endif
+ break;
}
+ }
+ }
}
-JSOBJ FASTCALL_MSVC decode_object(struct DecoderState *ds) {
- JSOBJ itemName;
- JSOBJ itemValue;
- JSOBJ newObj;
+JSOBJ FASTCALL_MSVC decode_array(struct DecoderState *ds) {
+ JSOBJ itemValue;
+ JSOBJ newObj;
+ int len;
+ ds->objDepth++;
+ if (ds->objDepth > JSON_MAX_OBJECT_DEPTH) {
+ return SetError(ds, -1, "Reached object decoding depth limit");
+ }
+
+ newObj = ds->dec->newArray(ds->prv, ds->dec);
+ len = 0;
+
+ ds->lastType = JT_INVALID;
+ ds->start++;
+
+ for (;;) {
+ SkipWhitespace(ds);
+
+ if ((*ds->start) == ']') {
+ ds->objDepth--;
+ if (len == 0) {
+ ds->start++;
+ return ds->dec->endArray(ds->prv, newObj);
+ }
+
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return SetError(
+ ds, -1, "Unexpected character found when decoding array value (1)");
+ }
- ds->objDepth++;
- if (ds->objDepth > JSON_MAX_OBJECT_DEPTH) {
- return SetError(ds, -1, "Reached object decoding depth limit");
+ itemValue = decode_any(ds);
+
+ if (itemValue == NULL) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return NULL;
}
- newObj = ds->dec->newObject(ds->prv, ds->dec);
+ if (!ds->dec->arrayAddItem(ds->prv, newObj, itemValue)) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return NULL;
+ }
- ds->start++;
+ SkipWhitespace(ds);
- for (;;) {
- SkipWhitespace(ds);
+ switch (*(ds->start++)) {
+ case ']': {
+ ds->objDepth--;
+ return ds->dec->endArray(ds->prv, newObj);
+ }
+ case ',':
+ break;
- if ((*ds->start) == '}') {
- ds->objDepth--;
- ds->start++;
- return ds->dec->endObject(ds->prv, newObj);
- }
+ default:
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return SetError(
+ ds, -1, "Unexpected character found when decoding array value (2)");
+ }
- ds->lastType = JT_INVALID;
- itemName = decode_any(ds);
+ len++;
+ }
+}
- if (itemName == NULL) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return NULL;
- }
+JSOBJ FASTCALL_MSVC decode_object(struct DecoderState *ds) {
+ JSOBJ itemName;
+ JSOBJ itemValue;
+ JSOBJ newObj;
- if (ds->lastType != JT_UTF8) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- ds->dec->releaseObject(ds->prv, itemName, ds->dec);
- return SetError(
- ds, -1,
- "Key name of object must be 'string' when decoding 'object'");
- }
+ ds->objDepth++;
+ if (ds->objDepth > JSON_MAX_OBJECT_DEPTH) {
+ return SetError(ds, -1, "Reached object decoding depth limit");
+ }
- SkipWhitespace(ds);
+ newObj = ds->dec->newObject(ds->prv, ds->dec);
- if (*(ds->start++) != ':') {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- ds->dec->releaseObject(ds->prv, itemName, ds->dec);
- return SetError(ds, -1, "No ':' found when decoding object value");
- }
+ ds->start++;
- SkipWhitespace(ds);
+ for (;;) {
+ SkipWhitespace(ds);
- itemValue = decode_any(ds);
+ if ((*ds->start) == '}') {
+ ds->objDepth--;
+ ds->start++;
+ return ds->dec->endObject(ds->prv, newObj);
+ }
- if (itemValue == NULL) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- ds->dec->releaseObject(ds->prv, itemName, ds->dec);
- return NULL;
- }
+ ds->lastType = JT_INVALID;
+ itemName = decode_any(ds);
- if (!ds->dec->objectAddKey(ds->prv, newObj, itemName, itemValue)) {
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- ds->dec->releaseObject(ds->prv, itemName, ds->dec);
- ds->dec->releaseObject(ds->prv, itemValue, ds->dec);
- return NULL;
- }
+ if (itemName == NULL) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return NULL;
+ }
- SkipWhitespace(ds);
-
- switch (*(ds->start++)) {
- case '}': {
- ds->objDepth--;
- return ds->dec->endObject(ds->prv, newObj);
- }
- case ',':
- break;
-
- default:
- ds->dec->releaseObject(ds->prv, newObj, ds->dec);
- return SetError(
- ds, -1,
- "Unexpected character found when decoding object value");
- }
+ if (ds->lastType != JT_UTF8) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ ds->dec->releaseObject(ds->prv, itemName, ds->dec);
+ return SetError(
+ ds, -1, "Key name of object must be 'string' when decoding 'object'");
}
-}
-JSOBJ FASTCALL_MSVC decode_any(struct DecoderState *ds) {
- for (;;) {
- switch (*ds->start) {
- case '\"':
- return decode_string(ds);
- case '0':
- case '1':
- case '2':
- case '3':
- case '4':
- case '5':
- case '6':
- case '7':
- case '8':
- case '9':
- case 'I':
- case 'N':
- case '-':
- return decode_numeric(ds);
-
- case '[':
- return decode_array(ds);
- case '{':
- return decode_object(ds);
- case 't':
- return decode_true(ds);
- case 'f':
- return decode_false(ds);
- case 'n':
- return decode_null(ds);
-
- case ' ':
- case '\t':
- case '\r':
- case '\n':
- // White space
- ds->start++;
- break;
-
- default:
- return SetError(ds, -1, "Expected object or value");
- }
+ SkipWhitespace(ds);
+
+ if (*(ds->start++) != ':') {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ ds->dec->releaseObject(ds->prv, itemName, ds->dec);
+ return SetError(ds, -1, "No ':' found when decoding object value");
}
-}
-JSOBJ JSON_DecodeObject(JSONObjectDecoder *dec, const char *buffer,
- size_t cbBuffer) {
- /*
- FIXME: Base the size of escBuffer of that of cbBuffer so that the unicode
- escaping doesn't run into the wall each time */
- char *locale;
- struct DecoderState ds;
- wchar_t escBuffer[(JSON_MAX_STACK_BUFFER_SIZE / sizeof(wchar_t))];
- JSOBJ ret;
-
- ds.start = (char *)buffer;
- ds.end = ds.start + cbBuffer;
-
- ds.escStart = escBuffer;
- ds.escEnd = ds.escStart + (JSON_MAX_STACK_BUFFER_SIZE / sizeof(wchar_t));
- ds.escHeap = 0;
- ds.prv = dec->prv;
- ds.dec = dec;
- ds.dec->errorStr = NULL;
- ds.dec->errorOffset = NULL;
- ds.objDepth = 0;
-
- ds.dec = dec;
-
- locale = setlocale(LC_NUMERIC, NULL);
- if (!locale) {
- return SetError(&ds, -1, "setlocale call failed");
+ SkipWhitespace(ds);
+
+ itemValue = decode_any(ds);
+
+ if (itemValue == NULL) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ ds->dec->releaseObject(ds->prv, itemName, ds->dec);
+ return NULL;
}
- if (strcmp(locale, "C")) {
- size_t len = strlen(locale) + 1;
- char *saved_locale = malloc(len);
- if (saved_locale == NULL) {
- return SetError(&ds, -1, "Could not reserve memory block");
- }
- memcpy(saved_locale, locale, len);
- setlocale(LC_NUMERIC, "C");
- ret = decode_any(&ds);
- setlocale(LC_NUMERIC, saved_locale);
- free(saved_locale);
- } else {
- ret = decode_any(&ds);
+ if (!ds->dec->objectAddKey(ds->prv, newObj, itemName, itemValue)) {
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ ds->dec->releaseObject(ds->prv, itemName, ds->dec);
+ ds->dec->releaseObject(ds->prv, itemValue, ds->dec);
+ return NULL;
}
- if (ds.escHeap) {
- dec->free(ds.escStart);
+ SkipWhitespace(ds);
+
+ switch (*(ds->start++)) {
+ case '}': {
+ ds->objDepth--;
+ return ds->dec->endObject(ds->prv, newObj);
}
+ case ',':
+ break;
- SkipWhitespace(&ds);
+ default:
+ ds->dec->releaseObject(ds->prv, newObj, ds->dec);
+ return SetError(ds, -1,
+ "Unexpected character found when decoding object value");
+ }
+ }
+}
- if (ds.start != ds.end && ret) {
- dec->releaseObject(ds.prv, ret, ds.dec);
- return SetError(&ds, -1, "Trailing data");
+JSOBJ FASTCALL_MSVC decode_any(struct DecoderState *ds) {
+ for (;;) {
+ switch (*ds->start) {
+ case '\"':
+ return decode_string(ds);
+ case '0':
+ case '1':
+ case '2':
+ case '3':
+ case '4':
+ case '5':
+ case '6':
+ case '7':
+ case '8':
+ case '9':
+ case 'I':
+ case 'N':
+ case '-':
+ return decode_numeric(ds);
+
+ case '[':
+ return decode_array(ds);
+ case '{':
+ return decode_object(ds);
+ case 't':
+ return decode_true(ds);
+ case 'f':
+ return decode_false(ds);
+ case 'n':
+ return decode_null(ds);
+
+ case ' ':
+ case '\t':
+ case '\r':
+ case '\n':
+ // White space
+ ds->start++;
+ break;
+
+ default:
+ return SetError(ds, -1, "Expected object or value");
}
+ }
+}
- return ret;
+JSOBJ JSON_DecodeObject(JSONObjectDecoder *dec, const char *buffer,
+ size_t cbBuffer) {
+ /*
+ FIXME: Base the size of escBuffer of that of cbBuffer so that the unicode
+ escaping doesn't run into the wall each time */
+ char *locale;
+ struct DecoderState ds;
+ wchar_t escBuffer[(JSON_MAX_STACK_BUFFER_SIZE / sizeof(wchar_t))];
+ JSOBJ ret;
+
+ ds.start = (char *)buffer;
+ ds.end = ds.start + cbBuffer;
+
+ ds.escStart = escBuffer;
+ ds.escEnd = ds.escStart + (JSON_MAX_STACK_BUFFER_SIZE / sizeof(wchar_t));
+ ds.escHeap = 0;
+ ds.prv = dec->prv;
+ ds.dec = dec;
+ ds.dec->errorStr = NULL;
+ ds.dec->errorOffset = NULL;
+ ds.objDepth = 0;
+
+ ds.dec = dec;
+
+ locale = setlocale(LC_NUMERIC, NULL);
+ if (!locale) {
+ return SetError(&ds, -1, "setlocale call failed");
+ }
+
+ if (strcmp(locale, "C")) {
+ size_t len = strlen(locale) + 1;
+ char *saved_locale = malloc(len);
+ if (saved_locale == NULL) {
+ return SetError(&ds, -1, "Could not reserve memory block");
+ }
+ memcpy(saved_locale, locale, len);
+ setlocale(LC_NUMERIC, "C");
+ ret = decode_any(&ds);
+ setlocale(LC_NUMERIC, saved_locale);
+ free(saved_locale);
+ } else {
+ ret = decode_any(&ds);
+ }
+
+ if (ds.escHeap) {
+ dec->free(ds.escStart);
+ }
+
+ SkipWhitespace(&ds);
+
+ if (ds.start != ds.end && ret) {
+ dec->releaseObject(ds.prv, ret, ds.dec);
+ return SetError(&ds, -1, "Trailing data");
+ }
+
+ return ret;
}
diff --git a/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c b/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
index 942bd0b518144..917af4872ecfe 100644
--- a/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
+++ b/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
@@ -40,6 +40,7 @@ Numeric decoder derived from TCL library
// Licence at LICENSES/ULTRAJSON_LICENSE
+#include "pandas/vendored/ujson/lib/ultrajson.h"
#include <assert.h>
#include <float.h>
#include <locale.h>
@@ -48,7 +49,6 @@ Numeric decoder derived from TCL library
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
-#include "pandas/vendored/ujson/lib/ultrajson.h"
#ifndef TRUE
#define TRUE 1
@@ -359,8 +359,8 @@ static const JSUINT8 g_asciiOutputTable[256] = {
1};
static void SetError(JSOBJ obj, JSONObjectEncoder *enc, const char *message) {
- enc->errorMsg = message;
- enc->errorObj = obj;
+ enc->errorMsg = message;
+ enc->errorObj = obj;
}
/*
@@ -368,371 +368,362 @@ FIXME: Keep track of how big these get across several encoder calls and try to
make an estimate
That way we won't run our head into the wall each call */
void Buffer_Realloc(JSONObjectEncoder *enc, size_t cbNeeded) {
- size_t curSize = enc->end - enc->start;
- size_t newSize = curSize * 2;
- size_t offset = enc->offset - enc->start;
+ size_t curSize = enc->end - enc->start;
+ size_t newSize = curSize * 2;
+ size_t offset = enc->offset - enc->start;
- while (newSize < curSize + cbNeeded) {
- newSize *= 2;
- }
+ while (newSize < curSize + cbNeeded) {
+ newSize *= 2;
+ }
- if (enc->heap) {
- enc->start = (char *)enc->realloc(enc->start, newSize);
- if (!enc->start) {
- SetError(NULL, enc, "Could not reserve memory block");
- return;
- }
- } else {
- char *oldStart = enc->start;
- enc->heap = 1;
- enc->start = (char *)enc->malloc(newSize);
- if (!enc->start) {
- SetError(NULL, enc, "Could not reserve memory block");
- return;
- }
- memcpy(enc->start, oldStart, offset);
+ if (enc->heap) {
+ enc->start = (char *)enc->realloc(enc->start, newSize);
+ if (!enc->start) {
+ SetError(NULL, enc, "Could not reserve memory block");
+ return;
}
- enc->offset = enc->start + offset;
- enc->end = enc->start + newSize;
+ } else {
+ char *oldStart = enc->start;
+ enc->heap = 1;
+ enc->start = (char *)enc->malloc(newSize);
+ if (!enc->start) {
+ SetError(NULL, enc, "Could not reserve memory block");
+ return;
+ }
+ memcpy(enc->start, oldStart, offset);
+ }
+ enc->offset = enc->start + offset;
+ enc->end = enc->start + newSize;
}
INLINE_PREFIX void FASTCALL_MSVC
Buffer_AppendShortHexUnchecked(char *outputOffset, unsigned short value) {
- *(outputOffset++) = g_hexChars[(value & 0xf000) >> 12];
- *(outputOffset++) = g_hexChars[(value & 0x0f00) >> 8];
- *(outputOffset++) = g_hexChars[(value & 0x00f0) >> 4];
- *(outputOffset++) = g_hexChars[(value & 0x000f) >> 0];
+ *(outputOffset++) = g_hexChars[(value & 0xf000) >> 12];
+ *(outputOffset++) = g_hexChars[(value & 0x0f00) >> 8];
+ *(outputOffset++) = g_hexChars[(value & 0x00f0) >> 4];
+ *(outputOffset++) = g_hexChars[(value & 0x000f) >> 0];
}
int Buffer_EscapeStringUnvalidated(JSONObjectEncoder *enc, const char *io,
const char *end) {
- char *of = (char *)enc->offset;
-
- for (;;) {
- switch (*io) {
- case 0x00: {
- if (io < end) {
- *(of++) = '\\';
- *(of++) = 'u';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = '0';
- break;
- } else {
- enc->offset += (of - enc->offset);
- return TRUE;
- }
- }
- case '\"':
- (*of++) = '\\';
- (*of++) = '\"';
- break;
- case '\\':
- (*of++) = '\\';
- (*of++) = '\\';
- break;
- case '/':
- (*of++) = '\\';
- (*of++) = '/';
- break;
- case '\b':
- (*of++) = '\\';
- (*of++) = 'b';
- break;
- case '\f':
- (*of++) = '\\';
- (*of++) = 'f';
- break;
- case '\n':
- (*of++) = '\\';
- (*of++) = 'n';
- break;
- case '\r':
- (*of++) = '\\';
- (*of++) = 'r';
- break;
- case '\t':
- (*of++) = '\\';
- (*of++) = 't';
- break;
-
- case 0x26: // '/'
- case 0x3c: // '<'
- case 0x3e: // '>'
- {
- if (enc->encodeHTMLChars) {
- // Fall through to \u00XX case below.
- } else {
- // Same as default case below.
- (*of++) = (*io);
- break;
- }
- }
- case 0x01:
- case 0x02:
- case 0x03:
- case 0x04:
- case 0x05:
- case 0x06:
- case 0x07:
- case 0x0b:
- case 0x0e:
- case 0x0f:
- case 0x10:
- case 0x11:
- case 0x12:
- case 0x13:
- case 0x14:
- case 0x15:
- case 0x16:
- case 0x17:
- case 0x18:
- case 0x19:
- case 0x1a:
- case 0x1b:
- case 0x1c:
- case 0x1d:
- case 0x1e:
- case 0x1f: {
- *(of++) = '\\';
- *(of++) = 'u';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = g_hexChars[(unsigned char)(((*io) & 0xf0) >> 4)];
- *(of++) = g_hexChars[(unsigned char)((*io) & 0x0f)];
- break;
- }
- default:
- (*of++) = (*io);
- break;
- }
- io++;
+ char *of = (char *)enc->offset;
+
+ for (;;) {
+ switch (*io) {
+ case 0x00: {
+ if (io < end) {
+ *(of++) = '\\';
+ *(of++) = 'u';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = '0';
+ break;
+ } else {
+ enc->offset += (of - enc->offset);
+ return TRUE;
+ }
+ }
+ case '\"':
+ (*of++) = '\\';
+ (*of++) = '\"';
+ break;
+ case '\\':
+ (*of++) = '\\';
+ (*of++) = '\\';
+ break;
+ case '/':
+ (*of++) = '\\';
+ (*of++) = '/';
+ break;
+ case '\b':
+ (*of++) = '\\';
+ (*of++) = 'b';
+ break;
+ case '\f':
+ (*of++) = '\\';
+ (*of++) = 'f';
+ break;
+ case '\n':
+ (*of++) = '\\';
+ (*of++) = 'n';
+ break;
+ case '\r':
+ (*of++) = '\\';
+ (*of++) = 'r';
+ break;
+ case '\t':
+ (*of++) = '\\';
+ (*of++) = 't';
+ break;
+
+ case 0x26: // '/'
+ case 0x3c: // '<'
+ case 0x3e: // '>'
+ {
+ if (enc->encodeHTMLChars) {
+ // Fall through to \u00XX case below.
+ } else {
+ // Same as default case below.
+ (*of++) = (*io);
+ break;
+ }
}
+ case 0x01:
+ case 0x02:
+ case 0x03:
+ case 0x04:
+ case 0x05:
+ case 0x06:
+ case 0x07:
+ case 0x0b:
+ case 0x0e:
+ case 0x0f:
+ case 0x10:
+ case 0x11:
+ case 0x12:
+ case 0x13:
+ case 0x14:
+ case 0x15:
+ case 0x16:
+ case 0x17:
+ case 0x18:
+ case 0x19:
+ case 0x1a:
+ case 0x1b:
+ case 0x1c:
+ case 0x1d:
+ case 0x1e:
+ case 0x1f: {
+ *(of++) = '\\';
+ *(of++) = 'u';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = g_hexChars[(unsigned char)(((*io) & 0xf0) >> 4)];
+ *(of++) = g_hexChars[(unsigned char)((*io) & 0x0f)];
+ break;
+ }
+ default:
+ (*of++) = (*io);
+ break;
+ }
+ io++;
+ }
}
int Buffer_EscapeStringValidated(JSOBJ obj, JSONObjectEncoder *enc,
const char *io, const char *end) {
- JSUTF32 ucs;
- char *of = (char *)enc->offset;
-
- for (;;) {
- JSUINT8 utflen = g_asciiOutputTable[(unsigned char)*io];
-
- switch (utflen) {
- case 0: {
- if (io < end) {
- *(of++) = '\\';
- *(of++) = 'u';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = '0';
- io++;
- continue;
- } else {
- enc->offset += (of - enc->offset);
- return TRUE;
- }
- }
-
- case 1: {
- *(of++) = (*io++);
- continue;
- }
-
- case 2: {
- JSUTF32 in;
- JSUTF16 in16;
-
- if (end - io < 1) {
- enc->offset += (of - enc->offset);
- SetError(
- obj, enc,
- "Unterminated UTF-8 sequence when encoding string");
- return FALSE;
- }
-
- memcpy(&in16, io, sizeof(JSUTF16));
- in = (JSUTF32)in16;
+ JSUTF32 ucs;
+ char *of = (char *)enc->offset;
+
+ for (;;) {
+ JSUINT8 utflen = g_asciiOutputTable[(unsigned char)*io];
+
+ switch (utflen) {
+ case 0: {
+ if (io < end) {
+ *(of++) = '\\';
+ *(of++) = 'u';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = '0';
+ io++;
+ continue;
+ } else {
+ enc->offset += (of - enc->offset);
+ return TRUE;
+ }
+ }
+
+ case 1: {
+ *(of++) = (*io++);
+ continue;
+ }
+
+ case 2: {
+ JSUTF32 in;
+ JSUTF16 in16;
+
+ if (end - io < 1) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc, "Unterminated UTF-8 sequence when encoding string");
+ return FALSE;
+ }
+
+ memcpy(&in16, io, sizeof(JSUTF16));
+ in = (JSUTF32)in16;
#ifdef __LITTLE_ENDIAN__
- ucs = ((in & 0x1f) << 6) | ((in >> 8) & 0x3f);
+ ucs = ((in & 0x1f) << 6) | ((in >> 8) & 0x3f);
#else
- ucs = ((in & 0x1f00) >> 2) | (in & 0x3f);
+ ucs = ((in & 0x1f00) >> 2) | (in & 0x3f);
#endif
- if (ucs < 0x80) {
- enc->offset += (of - enc->offset);
- SetError(obj, enc,
- "Overlong 2 byte UTF-8 sequence detected when "
- "encoding string");
- return FALSE;
- }
-
- io += 2;
- break;
- }
-
- case 3: {
- JSUTF32 in;
- JSUTF16 in16;
- JSUINT8 in8;
-
- if (end - io < 2) {
- enc->offset += (of - enc->offset);
- SetError(
- obj, enc,
- "Unterminated UTF-8 sequence when encoding string");
- return FALSE;
- }
-
- memcpy(&in16, io, sizeof(JSUTF16));
- memcpy(&in8, io + 2, sizeof(JSUINT8));
+ if (ucs < 0x80) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc,
+ "Overlong 2 byte UTF-8 sequence detected when "
+ "encoding string");
+ return FALSE;
+ }
+
+ io += 2;
+ break;
+ }
+
+ case 3: {
+ JSUTF32 in;
+ JSUTF16 in16;
+ JSUINT8 in8;
+
+ if (end - io < 2) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc, "Unterminated UTF-8 sequence when encoding string");
+ return FALSE;
+ }
+
+ memcpy(&in16, io, sizeof(JSUTF16));
+ memcpy(&in8, io + 2, sizeof(JSUINT8));
#ifdef __LITTLE_ENDIAN__
- in = (JSUTF32)in16;
- in |= in8 << 16;
- ucs = ((in & 0x0f) << 12) | ((in & 0x3f00) >> 2) |
- ((in & 0x3f0000) >> 16);
+ in = (JSUTF32)in16;
+ in |= in8 << 16;
+ ucs =
+ ((in & 0x0f) << 12) | ((in & 0x3f00) >> 2) | ((in & 0x3f0000) >> 16);
#else
- in = in16 << 8;
- in |= in8;
- ucs =
- ((in & 0x0f0000) >> 4) | ((in & 0x3f00) >> 2) | (in & 0x3f);
+ in = in16 << 8;
+ in |= in8;
+ ucs = ((in & 0x0f0000) >> 4) | ((in & 0x3f00) >> 2) | (in & 0x3f);
#endif
- if (ucs < 0x800) {
- enc->offset += (of - enc->offset);
- SetError(obj, enc,
- "Overlong 3 byte UTF-8 sequence detected when "
- "encoding string");
- return FALSE;
- }
-
- io += 3;
- break;
- }
- case 4: {
- JSUTF32 in;
-
- if (end - io < 3) {
- enc->offset += (of - enc->offset);
- SetError(
- obj, enc,
- "Unterminated UTF-8 sequence when encoding string");
- return FALSE;
- }
-
- memcpy(&in, io, sizeof(JSUTF32));
+ if (ucs < 0x800) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc,
+ "Overlong 3 byte UTF-8 sequence detected when "
+ "encoding string");
+ return FALSE;
+ }
+
+ io += 3;
+ break;
+ }
+ case 4: {
+ JSUTF32 in;
+
+ if (end - io < 3) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc, "Unterminated UTF-8 sequence when encoding string");
+ return FALSE;
+ }
+
+ memcpy(&in, io, sizeof(JSUTF32));
#ifdef __LITTLE_ENDIAN__
- ucs = ((in & 0x07) << 18) | ((in & 0x3f00) << 4) |
- ((in & 0x3f0000) >> 10) | ((in & 0x3f000000) >> 24);
+ ucs = ((in & 0x07) << 18) | ((in & 0x3f00) << 4) |
+ ((in & 0x3f0000) >> 10) | ((in & 0x3f000000) >> 24);
#else
- ucs = ((in & 0x07000000) >> 6) | ((in & 0x3f0000) >> 4) |
- ((in & 0x3f00) >> 2) | (in & 0x3f);
+ ucs = ((in & 0x07000000) >> 6) | ((in & 0x3f0000) >> 4) |
+ ((in & 0x3f00) >> 2) | (in & 0x3f);
#endif
- if (ucs < 0x10000) {
- enc->offset += (of - enc->offset);
- SetError(obj, enc,
- "Overlong 4 byte UTF-8 sequence detected when "
- "encoding string");
- return FALSE;
- }
-
- io += 4;
- break;
- }
-
- case 5:
- case 6: {
- enc->offset += (of - enc->offset);
- SetError(
- obj, enc,
- "Unsupported UTF-8 sequence length when encoding string");
- return FALSE;
- }
-
- case 29: {
- if (enc->encodeHTMLChars) {
- // Fall through to \u00XX case 30 below.
- } else {
- // Same as case 1 above.
- *(of++) = (*io++);
- continue;
- }
- }
-
- case 30: {
- // \uXXXX encode
- *(of++) = '\\';
- *(of++) = 'u';
- *(of++) = '0';
- *(of++) = '0';
- *(of++) = g_hexChars[(unsigned char)(((*io) & 0xf0) >> 4)];
- *(of++) = g_hexChars[(unsigned char)((*io) & 0x0f)];
- io++;
- continue;
- }
- case 10:
- case 12:
- case 14:
- case 16:
- case 18:
- case 20:
- case 22:
- case 24: {
- *(of++) = *((char *)(g_escapeChars + utflen + 0));
- *(of++) = *((char *)(g_escapeChars + utflen + 1));
- io++;
- continue;
- }
- // This can never happen, it's here to make L4 VC++ happy
- default: {
- ucs = 0;
- break;
- }
- }
-
- /*
- If the character is a UTF8 sequence of length > 1 we end up here */
- if (ucs >= 0x10000) {
- ucs -= 0x10000;
- *(of++) = '\\';
- *(of++) = 'u';
- Buffer_AppendShortHexUnchecked(
- of, (unsigned short)(ucs >> 10) + 0xd800);
- of += 4;
-
- *(of++) = '\\';
- *(of++) = 'u';
- Buffer_AppendShortHexUnchecked(
- of, (unsigned short)(ucs & 0x3ff) + 0xdc00);
- of += 4;
- } else {
- *(of++) = '\\';
- *(of++) = 'u';
- Buffer_AppendShortHexUnchecked(of, (unsigned short)ucs);
- of += 4;
- }
+ if (ucs < 0x10000) {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc,
+ "Overlong 4 byte UTF-8 sequence detected when "
+ "encoding string");
+ return FALSE;
+ }
+
+ io += 4;
+ break;
+ }
+
+ case 5:
+ case 6: {
+ enc->offset += (of - enc->offset);
+ SetError(obj, enc,
+ "Unsupported UTF-8 sequence length when encoding string");
+ return FALSE;
}
+
+ case 29: {
+ if (enc->encodeHTMLChars) {
+ // Fall through to \u00XX case 30 below.
+ } else {
+ // Same as case 1 above.
+ *(of++) = (*io++);
+ continue;
+ }
+ }
+
+ case 30: {
+ // \uXXXX encode
+ *(of++) = '\\';
+ *(of++) = 'u';
+ *(of++) = '0';
+ *(of++) = '0';
+ *(of++) = g_hexChars[(unsigned char)(((*io) & 0xf0) >> 4)];
+ *(of++) = g_hexChars[(unsigned char)((*io) & 0x0f)];
+ io++;
+ continue;
+ }
+ case 10:
+ case 12:
+ case 14:
+ case 16:
+ case 18:
+ case 20:
+ case 22:
+ case 24: {
+ *(of++) = *((char *)(g_escapeChars + utflen + 0));
+ *(of++) = *((char *)(g_escapeChars + utflen + 1));
+ io++;
+ continue;
+ }
+ // This can never happen, it's here to make L4 VC++ happy
+ default: {
+ ucs = 0;
+ break;
+ }
+ }
+
+ /*
+ If the character is a UTF8 sequence of length > 1 we end up here */
+ if (ucs >= 0x10000) {
+ ucs -= 0x10000;
+ *(of++) = '\\';
+ *(of++) = 'u';
+ Buffer_AppendShortHexUnchecked(of, (unsigned short)(ucs >> 10) + 0xd800);
+ of += 4;
+
+ *(of++) = '\\';
+ *(of++) = 'u';
+ Buffer_AppendShortHexUnchecked(of,
+ (unsigned short)(ucs & 0x3ff) + 0xdc00);
+ of += 4;
+ } else {
+ *(of++) = '\\';
+ *(of++) = 'u';
+ Buffer_AppendShortHexUnchecked(of, (unsigned short)ucs);
+ of += 4;
+ }
+ }
}
-#define Buffer_Reserve(__enc, __len) \
- if ( (size_t) ((__enc)->end - (__enc)->offset) < (size_t) (__len)) \
- { \
- Buffer_Realloc((__enc), (__len));\
- } \
+#define Buffer_Reserve(__enc, __len) \
+ if ((size_t)((__enc)->end - (__enc)->offset) < (size_t)(__len)) { \
+ Buffer_Realloc((__enc), (__len)); \
+ }
#define Buffer_AppendCharUnchecked(__enc, __chr) *((__enc)->offset++) = __chr;
-INLINE_PREFIX void FASTCALL_MSVC strreverse(char *begin,
- char *end) {
- char aux;
- while (end > begin) aux = *end, *end-- = *begin, *begin++ = aux;
+INLINE_PREFIX void FASTCALL_MSVC strreverse(char *begin, char *end) {
+ char aux;
+ while (end > begin)
+ aux = *end, *end-- = *begin, *begin++ = aux;
}
void Buffer_AppendIndentNewlineUnchecked(JSONObjectEncoder *enc) {
- if (enc->indent > 0) Buffer_AppendCharUnchecked(enc, '\n');
+ if (enc->indent > 0)
+ Buffer_AppendCharUnchecked(enc, '\n');
}
// This function could be refactored to only accept enc as an argument,
@@ -747,172 +738,174 @@ void Buffer_AppendIndentUnchecked(JSONObjectEncoder *enc, JSINT32 value) {
}
void Buffer_AppendIntUnchecked(JSONObjectEncoder *enc, JSINT32 value) {
- char *wstr;
- JSUINT32 uvalue = (value < 0) ? -value : value;
- wstr = enc->offset;
-
- // Conversion. Number is reversed.
- do {
- *wstr++ = (char)(48 + (uvalue % 10));
- } while (uvalue /= 10);
- if (value < 0) *wstr++ = '-';
-
- // Reverse string
- strreverse(enc->offset, wstr - 1);
- enc->offset += (wstr - (enc->offset));
+ char *wstr;
+ JSUINT32 uvalue = (value < 0) ? -value : value;
+ wstr = enc->offset;
+
+ // Conversion. Number is reversed.
+ do {
+ *wstr++ = (char)(48 + (uvalue % 10));
+ } while (uvalue /= 10);
+ if (value < 0)
+ *wstr++ = '-';
+
+ // Reverse string
+ strreverse(enc->offset, wstr - 1);
+ enc->offset += (wstr - (enc->offset));
}
void Buffer_AppendLongUnchecked(JSONObjectEncoder *enc, JSINT64 value) {
- char *wstr;
- JSUINT64 uvalue;
- if (value == INT64_MIN) {
- uvalue = INT64_MAX + UINT64_C(1);
- } else {
- uvalue = (value < 0) ? -value : value;
- }
+ char *wstr;
+ JSUINT64 uvalue;
+ if (value == INT64_MIN) {
+ uvalue = INT64_MAX + UINT64_C(1);
+ } else {
+ uvalue = (value < 0) ? -value : value;
+ }
- wstr = enc->offset;
- // Conversion. Number is reversed.
+ wstr = enc->offset;
+ // Conversion. Number is reversed.
- do {
- *wstr++ = (char)(48 + (uvalue % 10ULL));
- } while (uvalue /= 10ULL);
- if (value < 0) *wstr++ = '-';
+ do {
+ *wstr++ = (char)(48 + (uvalue % 10ULL));
+ } while (uvalue /= 10ULL);
+ if (value < 0)
+ *wstr++ = '-';
- // Reverse string
- strreverse(enc->offset, wstr - 1);
- enc->offset += (wstr - (enc->offset));
+ // Reverse string
+ strreverse(enc->offset, wstr - 1);
+ enc->offset += (wstr - (enc->offset));
}
int Buffer_AppendDoubleUnchecked(JSOBJ obj, JSONObjectEncoder *enc,
double value) {
- /* if input is beyond the thresholds, revert to exponential */
- const double thres_max = (double)1e16 - 1;
- const double thres_min = (double)1e-15;
- char precision_str[20];
- int count;
- double diff = 0.0;
- char *str = enc->offset;
- char *wstr = str;
- unsigned long long whole;
- double tmp;
- unsigned long long frac;
- int neg;
- double pow10;
-
- if (value == HUGE_VAL || value == -HUGE_VAL) {
- SetError(obj, enc, "Invalid Inf value when encoding double");
- return FALSE;
- }
+ /* if input is beyond the thresholds, revert to exponential */
+ const double thres_max = (double)1e16 - 1;
+ const double thres_min = (double)1e-15;
+ char precision_str[20];
+ int count;
+ double diff = 0.0;
+ char *str = enc->offset;
+ char *wstr = str;
+ unsigned long long whole;
+ double tmp;
+ unsigned long long frac;
+ int neg;
+ double pow10;
+
+ if (value == HUGE_VAL || value == -HUGE_VAL) {
+ SetError(obj, enc, "Invalid Inf value when encoding double");
+ return FALSE;
+ }
- if (!(value == value)) {
- SetError(obj, enc, "Invalid Nan value when encoding double");
- return FALSE;
- }
+ if (!(value == value)) {
+ SetError(obj, enc, "Invalid Nan value when encoding double");
+ return FALSE;
+ }
- /* we'll work in positive values and deal with the
- negative sign issue later */
- neg = 0;
- if (value < 0) {
- neg = 1;
- value = -value;
- }
+ /* we'll work in positive values and deal with the
+ negative sign issue later */
+ neg = 0;
+ if (value < 0) {
+ neg = 1;
+ value = -value;
+ }
- /*
- for very large or small numbers switch back to native sprintf for
- exponentials. anyone want to write code to replace this? */
- if (value > thres_max || (value != 0.0 && fabs(value) < thres_min)) {
- precision_str[0] = '%';
- precision_str[1] = '.';
+ /*
+ for very large or small numbers switch back to native sprintf for
+ exponentials. anyone want to write code to replace this? */
+ if (value > thres_max || (value != 0.0 && fabs(value) < thres_min)) {
+ precision_str[0] = '%';
+ precision_str[1] = '.';
#if defined(_WIN32) && defined(_MSC_VER)
- sprintf_s(precision_str + 2, sizeof(precision_str) - 2, "%ug",
- enc->doublePrecision);
- enc->offset += sprintf_s(str, enc->end - enc->offset, precision_str,
- neg ? -value : value);
+ sprintf_s(precision_str + 2, sizeof(precision_str) - 2, "%ug",
+ enc->doublePrecision);
+ enc->offset += sprintf_s(str, enc->end - enc->offset, precision_str,
+ neg ? -value : value);
#else
- snprintf(precision_str + 2, sizeof(precision_str) - 2, "%ug",
- enc->doublePrecision);
- enc->offset += snprintf(str, enc->end - enc->offset, precision_str,
- neg ? -value : value);
+ snprintf(precision_str + 2, sizeof(precision_str) - 2, "%ug",
+ enc->doublePrecision);
+ enc->offset += snprintf(str, enc->end - enc->offset, precision_str,
+ neg ? -value : value);
#endif
- return TRUE;
- }
+ return TRUE;
+ }
- pow10 = g_pow10[enc->doublePrecision];
+ pow10 = g_pow10[enc->doublePrecision];
- whole = (unsigned long long)value;
- tmp = (value - whole) * pow10;
- frac = (unsigned long long)(tmp);
- diff = tmp - frac;
+ whole = (unsigned long long)value;
+ tmp = (value - whole) * pow10;
+ frac = (unsigned long long)(tmp);
+ diff = tmp - frac;
+
+ if (diff > 0.5) {
+ ++frac;
+ } else if (diff == 0.5 && ((frac == 0) || (frac & 1))) {
+ /* if halfway, round up if odd, OR
+ if last digit is 0. That last part is strange */
+ ++frac;
+ }
+
+ // handle rollover, e.g.
+ // case 0.99 with prec 1 is 1.0 and case 0.95 with prec is 1.0 as well
+ if (frac >= pow10) {
+ frac = 0;
+ ++whole;
+ }
+
+ if (enc->doublePrecision == 0) {
+ diff = value - whole;
if (diff > 0.5) {
- ++frac;
- } else if (diff == 0.5 && ((frac == 0) || (frac & 1))) {
- /* if halfway, round up if odd, OR
- if last digit is 0. That last part is strange */
- ++frac;
+ /* greater than 0.5, round up, e.g. 1.6 -> 2 */
+ ++whole;
+ } else if (diff == 0.5 && (whole & 1)) {
+ /* exactly 0.5 and ODD, then round up */
+ /* 1.5 -> 2, but 2.5 -> 2 */
+ ++whole;
}
- // handle rollover, e.g.
- // case 0.99 with prec 1 is 1.0 and case 0.95 with prec is 1.0 as well
- if (frac >= pow10) {
- frac = 0;
- ++whole;
+ // vvvvvvvvvvvvvvvvvvv Diff from modp_dto2
+ } else if (frac) {
+ count = enc->doublePrecision;
+ // now do fractional part, as an unsigned number
+ // we know it is not 0 but we can have leading zeros, these
+ // should be removed
+ while (!(frac % 10)) {
+ --count;
+ frac /= 10;
}
+ //^^^^^^^^^^^^^^^^^^^ Diff from modp_dto2
- if (enc->doublePrecision == 0) {
- diff = value - whole;
-
- if (diff > 0.5) {
- /* greater than 0.5, round up, e.g. 1.6 -> 2 */
- ++whole;
- } else if (diff == 0.5 && (whole & 1)) {
- /* exactly 0.5 and ODD, then round up */
- /* 1.5 -> 2, but 2.5 -> 2 */
- ++whole;
- }
-
- // vvvvvvvvvvvvvvvvvvv Diff from modp_dto2
- } else if (frac) {
- count = enc->doublePrecision;
- // now do fractional part, as an unsigned number
- // we know it is not 0 but we can have leading zeros, these
- // should be removed
- while (!(frac % 10)) {
- --count;
- frac /= 10;
- }
- //^^^^^^^^^^^^^^^^^^^ Diff from modp_dto2
-
- // now do fractional part, as an unsigned number
- do {
- --count;
- *wstr++ = (char)(48 + (frac % 10));
- } while (frac /= 10);
- // add extra 0s
- while (count-- > 0) {
- *wstr++ = '0';
- }
- // add decimal
- *wstr++ = '.';
- } else {
- *wstr++ = '0';
- *wstr++ = '.';
+ // now do fractional part, as an unsigned number
+ do {
+ --count;
+ *wstr++ = (char)(48 + (frac % 10));
+ } while (frac /= 10);
+ // add extra 0s
+ while (count-- > 0) {
+ *wstr++ = '0';
}
+ // add decimal
+ *wstr++ = '.';
+ } else {
+ *wstr++ = '0';
+ *wstr++ = '.';
+ }
- // Do whole part. Take care of sign
- // conversion. Number is reversed.
- do {
- *wstr++ = (char)(48 + (whole % 10));
- } while (whole /= 10);
+ // Do whole part. Take care of sign
+ // conversion. Number is reversed.
+ do {
+ *wstr++ = (char)(48 + (whole % 10));
+ } while (whole /= 10);
- if (neg) {
- *wstr++ = '-';
- }
- strreverse(str, wstr - 1);
- enc->offset += (wstr - (enc->offset));
+ if (neg) {
+ *wstr++ = '-';
+ }
+ strreverse(str, wstr - 1);
+ enc->offset += (wstr - (enc->offset));
- return TRUE;
+ return TRUE;
}
/*
@@ -925,291 +918,287 @@ Perhaps implement recursion detection */
void encode(JSOBJ obj, JSONObjectEncoder *enc, const char *name,
size_t cbName) {
- const char *value;
- char *objName;
- int count;
- JSOBJ iterObj;
- size_t szlen;
- JSONTypeContext tc;
- tc.encoder = enc;
-
- if (enc->level > enc->recursionMax) {
- SetError(obj, enc, "Maximum recursion level reached");
- return;
- }
+ const char *value;
+ char *objName;
+ int count;
+ JSOBJ iterObj;
+ size_t szlen;
+ JSONTypeContext tc;
+ tc.encoder = enc;
+
+ if (enc->level > enc->recursionMax) {
+ SetError(obj, enc, "Maximum recursion level reached");
+ return;
+ }
- /*
- This reservation must hold
+ /*
+ This reservation must hold
- length of _name as encoded worst case +
- maxLength of double to string OR maxLength of JSLONG to string
- */
+ length of _name as encoded worst case +
+ maxLength of double to string OR maxLength of JSLONG to string
+ */
- Buffer_Reserve(enc, 256 + RESERVE_STRING(cbName));
- if (enc->errorMsg) {
- return;
- }
+ Buffer_Reserve(enc, 256 + RESERVE_STRING(cbName));
+ if (enc->errorMsg) {
+ return;
+ }
- if (name) {
- Buffer_AppendCharUnchecked(enc, '\"');
+ if (name) {
+ Buffer_AppendCharUnchecked(enc, '\"');
- if (enc->forceASCII) {
- if (!Buffer_EscapeStringValidated(obj, enc, name, name + cbName)) {
- return;
- }
- } else {
- if (!Buffer_EscapeStringUnvalidated(enc, name, name + cbName)) {
- return;
- }
- }
+ if (enc->forceASCII) {
+ if (!Buffer_EscapeStringValidated(obj, enc, name, name + cbName)) {
+ return;
+ }
+ } else {
+ if (!Buffer_EscapeStringUnvalidated(enc, name, name + cbName)) {
+ return;
+ }
+ }
- Buffer_AppendCharUnchecked(enc, '\"');
+ Buffer_AppendCharUnchecked(enc, '\"');
- Buffer_AppendCharUnchecked(enc, ':');
+ Buffer_AppendCharUnchecked(enc, ':');
#ifndef JSON_NO_EXTRA_WHITESPACE
- Buffer_AppendCharUnchecked(enc, ' ');
+ Buffer_AppendCharUnchecked(enc, ' ');
#endif
- }
+ }
- enc->beginTypeContext(obj, &tc);
+ enc->beginTypeContext(obj, &tc);
- switch (tc.type) {
- case JT_INVALID: {
- return;
- }
+ switch (tc.type) {
+ case JT_INVALID: {
+ return;
+ }
- case JT_ARRAY: {
- count = 0;
- enc->iterBegin(obj, &tc);
+ case JT_ARRAY: {
+ count = 0;
+ enc->iterBegin(obj, &tc);
- Buffer_AppendCharUnchecked(enc, '[');
- Buffer_AppendIndentNewlineUnchecked(enc);
+ Buffer_AppendCharUnchecked(enc, '[');
+ Buffer_AppendIndentNewlineUnchecked(enc);
- while (enc->iterNext(obj, &tc)) {
- if (count > 0) {
- Buffer_AppendCharUnchecked(enc, ',');
+ while (enc->iterNext(obj, &tc)) {
+ if (count > 0) {
+ Buffer_AppendCharUnchecked(enc, ',');
#ifndef JSON_NO_EXTRA_WHITESPACE
- Buffer_AppendCharUnchecked(buffer, ' ');
+ Buffer_AppendCharUnchecked(buffer, ' ');
#endif
- Buffer_AppendIndentNewlineUnchecked(enc);
- }
-
- iterObj = enc->iterGetValue(obj, &tc);
-
- enc->level++;
- Buffer_AppendIndentUnchecked(enc, enc->level);
- encode(iterObj, enc, NULL, 0);
- count++;
- }
-
- enc->iterEnd(obj, &tc);
- Buffer_AppendIndentNewlineUnchecked(enc);
- Buffer_AppendIndentUnchecked(enc, enc->level);
- Buffer_AppendCharUnchecked(enc, ']');
- break;
- }
-
- case JT_OBJECT: {
- count = 0;
- enc->iterBegin(obj, &tc);
-
- Buffer_AppendCharUnchecked(enc, '{');
- Buffer_AppendIndentNewlineUnchecked(enc);
-
- while (enc->iterNext(obj, &tc)) {
- if (count > 0) {
- Buffer_AppendCharUnchecked(enc, ',');
+ Buffer_AppendIndentNewlineUnchecked(enc);
+ }
+
+ iterObj = enc->iterGetValue(obj, &tc);
+
+ enc->level++;
+ Buffer_AppendIndentUnchecked(enc, enc->level);
+ encode(iterObj, enc, NULL, 0);
+ count++;
+ }
+
+ enc->iterEnd(obj, &tc);
+ Buffer_AppendIndentNewlineUnchecked(enc);
+ Buffer_AppendIndentUnchecked(enc, enc->level);
+ Buffer_AppendCharUnchecked(enc, ']');
+ break;
+ }
+
+ case JT_OBJECT: {
+ count = 0;
+ enc->iterBegin(obj, &tc);
+
+ Buffer_AppendCharUnchecked(enc, '{');
+ Buffer_AppendIndentNewlineUnchecked(enc);
+
+ while (enc->iterNext(obj, &tc)) {
+ if (count > 0) {
+ Buffer_AppendCharUnchecked(enc, ',');
#ifndef JSON_NO_EXTRA_WHITESPACE
- Buffer_AppendCharUnchecked(enc, ' ');
+ Buffer_AppendCharUnchecked(enc, ' ');
#endif
- Buffer_AppendIndentNewlineUnchecked(enc);
- }
-
- iterObj = enc->iterGetValue(obj, &tc);
- objName = enc->iterGetName(obj, &tc, &szlen);
-
- enc->level++;
- Buffer_AppendIndentUnchecked(enc, enc->level);
- encode(iterObj, enc, objName, szlen);
- count++;
- }
-
- enc->iterEnd(obj, &tc);
- Buffer_AppendIndentNewlineUnchecked(enc);
- Buffer_AppendIndentUnchecked(enc, enc->level);
- Buffer_AppendCharUnchecked(enc, '}');
- break;
- }
-
- case JT_LONG: {
- Buffer_AppendLongUnchecked(enc, enc->getLongValue(obj, &tc));
- break;
- }
-
- case JT_INT: {
- Buffer_AppendIntUnchecked(enc, enc->getIntValue(obj, &tc));
- break;
- }
-
- case JT_TRUE: {
- Buffer_AppendCharUnchecked(enc, 't');
- Buffer_AppendCharUnchecked(enc, 'r');
- Buffer_AppendCharUnchecked(enc, 'u');
- Buffer_AppendCharUnchecked(enc, 'e');
- break;
- }
-
- case JT_FALSE: {
- Buffer_AppendCharUnchecked(enc, 'f');
- Buffer_AppendCharUnchecked(enc, 'a');
- Buffer_AppendCharUnchecked(enc, 'l');
- Buffer_AppendCharUnchecked(enc, 's');
- Buffer_AppendCharUnchecked(enc, 'e');
- break;
- }
-
- case JT_NULL: {
- Buffer_AppendCharUnchecked(enc, 'n');
- Buffer_AppendCharUnchecked(enc, 'u');
- Buffer_AppendCharUnchecked(enc, 'l');
- Buffer_AppendCharUnchecked(enc, 'l');
- break;
- }
-
- case JT_DOUBLE: {
- if (!Buffer_AppendDoubleUnchecked(obj, enc,
- enc->getDoubleValue(obj, &tc))) {
- enc->endTypeContext(obj, &tc);
- enc->level--;
- return;
- }
- break;
- }
-
- case JT_UTF8: {
- value = enc->getStringValue(obj, &tc, &szlen);
- if (enc->errorMsg) {
- enc->endTypeContext(obj, &tc);
- return;
- }
- Buffer_Reserve(enc, RESERVE_STRING(szlen));
- Buffer_AppendCharUnchecked(enc, '\"');
-
- if (enc->forceASCII) {
- if (!Buffer_EscapeStringValidated(obj, enc, value,
- value + szlen)) {
- enc->endTypeContext(obj, &tc);
- enc->level--;
- return;
- }
- } else {
- if (!Buffer_EscapeStringUnvalidated(enc, value,
- value + szlen)) {
- enc->endTypeContext(obj, &tc);
- enc->level--;
- return;
- }
- }
-
- Buffer_AppendCharUnchecked(enc, '\"');
- break;
- }
-
- case JT_BIGNUM: {
- value = enc->getBigNumStringValue(obj, &tc, &szlen);
-
- Buffer_Reserve(enc, RESERVE_STRING(szlen));
- if (enc->errorMsg) {
- enc->endTypeContext(obj, &tc);
- return;
- }
-
- if (enc->forceASCII) {
- if (!Buffer_EscapeStringValidated(obj, enc, value,
- value + szlen)) {
- enc->endTypeContext(obj, &tc);
- enc->level--;
- return;
- }
- } else {
- if (!Buffer_EscapeStringUnvalidated(enc, value,
- value + szlen)) {
- enc->endTypeContext(obj, &tc);
- enc->level--;
- return;
- }
- }
-
- break;
- }
+ Buffer_AppendIndentNewlineUnchecked(enc);
+ }
+
+ iterObj = enc->iterGetValue(obj, &tc);
+ objName = enc->iterGetName(obj, &tc, &szlen);
+
+ enc->level++;
+ Buffer_AppendIndentUnchecked(enc, enc->level);
+ encode(iterObj, enc, objName, szlen);
+ count++;
}
- enc->endTypeContext(obj, &tc);
- enc->level--;
-}
+ enc->iterEnd(obj, &tc);
+ Buffer_AppendIndentNewlineUnchecked(enc);
+ Buffer_AppendIndentUnchecked(enc, enc->level);
+ Buffer_AppendCharUnchecked(enc, '}');
+ break;
+ }
-char *JSON_EncodeObject(JSOBJ obj, JSONObjectEncoder *enc, char *_buffer,
- size_t _cbBuffer) {
- char *locale;
- enc->malloc = enc->malloc ? enc->malloc : malloc;
- enc->free = enc->free ? enc->free : free;
- enc->realloc = enc->realloc ? enc->realloc : realloc;
- enc->errorMsg = NULL;
- enc->errorObj = NULL;
- enc->level = 0;
-
- if (enc->recursionMax < 1) {
- enc->recursionMax = JSON_MAX_RECURSION_DEPTH;
+ case JT_LONG: {
+ Buffer_AppendLongUnchecked(enc, enc->getLongValue(obj, &tc));
+ break;
+ }
+
+ case JT_INT: {
+ Buffer_AppendIntUnchecked(enc, enc->getIntValue(obj, &tc));
+ break;
+ }
+
+ case JT_TRUE: {
+ Buffer_AppendCharUnchecked(enc, 't');
+ Buffer_AppendCharUnchecked(enc, 'r');
+ Buffer_AppendCharUnchecked(enc, 'u');
+ Buffer_AppendCharUnchecked(enc, 'e');
+ break;
+ }
+
+ case JT_FALSE: {
+ Buffer_AppendCharUnchecked(enc, 'f');
+ Buffer_AppendCharUnchecked(enc, 'a');
+ Buffer_AppendCharUnchecked(enc, 'l');
+ Buffer_AppendCharUnchecked(enc, 's');
+ Buffer_AppendCharUnchecked(enc, 'e');
+ break;
+ }
+
+ case JT_NULL: {
+ Buffer_AppendCharUnchecked(enc, 'n');
+ Buffer_AppendCharUnchecked(enc, 'u');
+ Buffer_AppendCharUnchecked(enc, 'l');
+ Buffer_AppendCharUnchecked(enc, 'l');
+ break;
+ }
+
+ case JT_DOUBLE: {
+ if (!Buffer_AppendDoubleUnchecked(obj, enc,
+ enc->getDoubleValue(obj, &tc))) {
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+ return;
}
+ break;
+ }
- if (enc->doublePrecision < 0 ||
- enc->doublePrecision > JSON_DOUBLE_MAX_DECIMALS) {
- enc->doublePrecision = JSON_DOUBLE_MAX_DECIMALS;
+ case JT_UTF8: {
+ value = enc->getStringValue(obj, &tc, &szlen);
+ if (enc->errorMsg) {
+ enc->endTypeContext(obj, &tc);
+ return;
}
+ Buffer_Reserve(enc, RESERVE_STRING(szlen));
+ Buffer_AppendCharUnchecked(enc, '\"');
- if (_buffer == NULL) {
- _cbBuffer = 32768;
- enc->start = (char *)enc->malloc(_cbBuffer);
- if (!enc->start) {
- SetError(obj, enc, "Could not reserve memory block");
- return NULL;
- }
- enc->heap = 1;
+ if (enc->forceASCII) {
+ if (!Buffer_EscapeStringValidated(obj, enc, value, value + szlen)) {
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+ return;
+ }
} else {
- enc->start = _buffer;
- enc->heap = 0;
+ if (!Buffer_EscapeStringUnvalidated(enc, value, value + szlen)) {
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+ return;
+ }
}
- enc->end = enc->start + _cbBuffer;
- enc->offset = enc->start;
+ Buffer_AppendCharUnchecked(enc, '\"');
+ break;
+ }
- locale = setlocale(LC_NUMERIC, NULL);
- if (!locale) {
- SetError(NULL, enc, "setlocale call failed");
- return NULL;
+ case JT_BIGNUM: {
+ value = enc->getBigNumStringValue(obj, &tc, &szlen);
+
+ Buffer_Reserve(enc, RESERVE_STRING(szlen));
+ if (enc->errorMsg) {
+ enc->endTypeContext(obj, &tc);
+ return;
}
- if (strcmp(locale, "C")) {
- size_t len = strlen(locale) + 1;
- char *saved_locale = malloc(len);
- if (saved_locale == NULL) {
- SetError(NULL, enc, "Could not reserve memory block");
- return NULL;
- }
- memcpy(saved_locale, locale, len);
- setlocale(LC_NUMERIC, "C");
- encode(obj, enc, NULL, 0);
- setlocale(LC_NUMERIC, saved_locale);
- free(saved_locale);
+ if (enc->forceASCII) {
+ if (!Buffer_EscapeStringValidated(obj, enc, value, value + szlen)) {
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+ return;
+ }
} else {
- encode(obj, enc, NULL, 0);
+ if (!Buffer_EscapeStringUnvalidated(enc, value, value + szlen)) {
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+ return;
+ }
}
- Buffer_Reserve(enc, 1);
- if (enc->errorMsg) {
- return NULL;
+ break;
+ }
+ }
+
+ enc->endTypeContext(obj, &tc);
+ enc->level--;
+}
+
+char *JSON_EncodeObject(JSOBJ obj, JSONObjectEncoder *enc, char *_buffer,
+ size_t _cbBuffer) {
+ char *locale;
+ enc->malloc = enc->malloc ? enc->malloc : malloc;
+ enc->free = enc->free ? enc->free : free;
+ enc->realloc = enc->realloc ? enc->realloc : realloc;
+ enc->errorMsg = NULL;
+ enc->errorObj = NULL;
+ enc->level = 0;
+
+ if (enc->recursionMax < 1) {
+ enc->recursionMax = JSON_MAX_RECURSION_DEPTH;
+ }
+
+ if (enc->doublePrecision < 0 ||
+ enc->doublePrecision > JSON_DOUBLE_MAX_DECIMALS) {
+ enc->doublePrecision = JSON_DOUBLE_MAX_DECIMALS;
+ }
+
+ if (_buffer == NULL) {
+ _cbBuffer = 32768;
+ enc->start = (char *)enc->malloc(_cbBuffer);
+ if (!enc->start) {
+ SetError(obj, enc, "Could not reserve memory block");
+ return NULL;
}
- Buffer_AppendCharUnchecked(enc, '\0');
+ enc->heap = 1;
+ } else {
+ enc->start = _buffer;
+ enc->heap = 0;
+ }
+
+ enc->end = enc->start + _cbBuffer;
+ enc->offset = enc->start;
+
+ locale = setlocale(LC_NUMERIC, NULL);
+ if (!locale) {
+ SetError(NULL, enc, "setlocale call failed");
+ return NULL;
+ }
+
+ if (strcmp(locale, "C")) {
+ size_t len = strlen(locale) + 1;
+ char *saved_locale = malloc(len);
+ if (saved_locale == NULL) {
+ SetError(NULL, enc, "Could not reserve memory block");
+ return NULL;
+ }
+ memcpy(saved_locale, locale, len);
+ setlocale(LC_NUMERIC, "C");
+ encode(obj, enc, NULL, 0);
+ setlocale(LC_NUMERIC, saved_locale);
+ free(saved_locale);
+ } else {
+ encode(obj, enc, NULL, 0);
+ }
+
+ Buffer_Reserve(enc, 1);
+ if (enc->errorMsg) {
+ return NULL;
+ }
+ Buffer_AppendCharUnchecked(enc, '\0');
- return enc->start;
+ return enc->start;
}
diff --git a/pandas/_libs/src/vendored/ujson/python/JSONtoObj.c b/pandas/_libs/src/vendored/ujson/python/JSONtoObj.c
index d230642b0632d..147282c476c3b 100644
--- a/pandas/_libs/src/vendored/ujson/python/JSONtoObj.c
+++ b/pandas/_libs/src/vendored/ujson/python/JSONtoObj.c
@@ -16,18 +16,19 @@ modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
+THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
https://github.com/client9/stringencoders
-Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved.
+Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights
+reserved.
Numeric decoder derived from TCL library
https://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
@@ -40,32 +41,32 @@ Numeric decoder derived from TCL library
#define PY_ARRAY_UNIQUE_SYMBOL UJSON_NUMPY
#define NO_IMPORT_ARRAY
#define PY_SSIZE_T_CLEAN
+#include "pandas/vendored/ujson/lib/ultrajson.h"
#include <Python.h>
#include <numpy/arrayobject.h>
-#include "pandas/vendored/ujson/lib/ultrajson.h"
#define PRINTMARK()
typedef struct __PyObjectDecoder {
- JSONObjectDecoder dec;
+ JSONObjectDecoder dec;
- void *npyarr; // Numpy context buffer
- void *npyarr_addr; // Ref to npyarr ptr to track DECREF calls
- npy_intp curdim; // Current array dimension
+ void *npyarr; // Numpy context buffer
+ void *npyarr_addr; // Ref to npyarr ptr to track DECREF calls
+ npy_intp curdim; // Current array dimension
- PyArray_Descr *dtype;
+ PyArray_Descr *dtype;
} PyObjectDecoder;
typedef struct __NpyArrContext {
- PyObject *ret;
- PyObject *labels[2];
- PyArray_Dims shape;
+ PyObject *ret;
+ PyObject *labels[2];
+ PyArray_Dims shape;
- PyObjectDecoder *dec;
+ PyObjectDecoder *dec;
- npy_intp i;
- npy_intp elsize;
- npy_intp elcount;
+ npy_intp i;
+ npy_intp elsize;
+ npy_intp elcount;
} NpyArrContext;
// Numpy handling based on numpy internal code, specifically the function
@@ -87,304 +88,301 @@ int Object_npyArrayListAddItem(void *prv, JSOBJ obj, JSOBJ value);
// free the numpy context buffer
void Npy_releaseContext(NpyArrContext *npyarr) {
- PRINTMARK();
- if (npyarr) {
- if (npyarr->shape.ptr) {
- PyObject_Free(npyarr->shape.ptr);
- }
- if (npyarr->dec) {
- npyarr->dec->npyarr = NULL;
- npyarr->dec->curdim = 0;
- }
- Py_XDECREF(npyarr->labels[0]);
- Py_XDECREF(npyarr->labels[1]);
- Py_XDECREF(npyarr->ret);
- PyObject_Free(npyarr);
+ PRINTMARK();
+ if (npyarr) {
+ if (npyarr->shape.ptr) {
+ PyObject_Free(npyarr->shape.ptr);
}
+ if (npyarr->dec) {
+ npyarr->dec->npyarr = NULL;
+ npyarr->dec->curdim = 0;
+ }
+ Py_XDECREF(npyarr->labels[0]);
+ Py_XDECREF(npyarr->labels[1]);
+ Py_XDECREF(npyarr->ret);
+ PyObject_Free(npyarr);
+ }
}
JSOBJ Object_npyNewArray(void *prv, void *_decoder) {
- NpyArrContext *npyarr;
- PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
- PRINTMARK();
- if (decoder->curdim <= 0) {
- // start of array - initialise the context buffer
- npyarr = decoder->npyarr = PyObject_Malloc(sizeof(NpyArrContext));
- decoder->npyarr_addr = npyarr;
-
- if (!npyarr) {
- PyErr_NoMemory();
- return NULL;
- }
-
- npyarr->dec = decoder;
- npyarr->labels[0] = npyarr->labels[1] = NULL;
-
- npyarr->shape.ptr = PyObject_Malloc(sizeof(npy_intp) * NPY_MAXDIMS);
- npyarr->shape.len = 1;
- npyarr->ret = NULL;
-
- npyarr->elsize = 0;
- npyarr->elcount = 4;
- npyarr->i = 0;
- } else {
- // starting a new dimension continue the current array (and reshape
- // after)
- npyarr = (NpyArrContext *)decoder->npyarr;
- if (decoder->curdim >= npyarr->shape.len) {
- npyarr->shape.len++;
- }
+ NpyArrContext *npyarr;
+ PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
+ PRINTMARK();
+ if (decoder->curdim <= 0) {
+ // start of array - initialise the context buffer
+ npyarr = decoder->npyarr = PyObject_Malloc(sizeof(NpyArrContext));
+ decoder->npyarr_addr = npyarr;
+
+ if (!npyarr) {
+ PyErr_NoMemory();
+ return NULL;
+ }
+
+ npyarr->dec = decoder;
+ npyarr->labels[0] = npyarr->labels[1] = NULL;
+
+ npyarr->shape.ptr = PyObject_Malloc(sizeof(npy_intp) * NPY_MAXDIMS);
+ npyarr->shape.len = 1;
+ npyarr->ret = NULL;
+
+ npyarr->elsize = 0;
+ npyarr->elcount = 4;
+ npyarr->i = 0;
+ } else {
+ // starting a new dimension continue the current array (and reshape
+ // after)
+ npyarr = (NpyArrContext *)decoder->npyarr;
+ if (decoder->curdim >= npyarr->shape.len) {
+ npyarr->shape.len++;
}
+ }
- npyarr->shape.ptr[decoder->curdim] = 0;
- decoder->curdim++;
- return npyarr;
+ npyarr->shape.ptr[decoder->curdim] = 0;
+ decoder->curdim++;
+ return npyarr;
}
PyObject *Npy_returnLabelled(NpyArrContext *npyarr) {
- PyObject *ret = npyarr->ret;
- npy_intp i;
-
- if (npyarr->labels[0] || npyarr->labels[1]) {
- // finished decoding, build tuple with values and labels
- ret = PyTuple_New(npyarr->shape.len + 1);
- for (i = 0; i < npyarr->shape.len; i++) {
- if (npyarr->labels[i]) {
- PyTuple_SET_ITEM(ret, i + 1, npyarr->labels[i]);
- npyarr->labels[i] = NULL;
- } else {
- Py_INCREF(Py_None);
- PyTuple_SET_ITEM(ret, i + 1, Py_None);
- }
- }
- PyTuple_SET_ITEM(ret, 0, npyarr->ret);
+ PyObject *ret = npyarr->ret;
+ npy_intp i;
+
+ if (npyarr->labels[0] || npyarr->labels[1]) {
+ // finished decoding, build tuple with values and labels
+ ret = PyTuple_New(npyarr->shape.len + 1);
+ for (i = 0; i < npyarr->shape.len; i++) {
+ if (npyarr->labels[i]) {
+ PyTuple_SET_ITEM(ret, i + 1, npyarr->labels[i]);
+ npyarr->labels[i] = NULL;
+ } else {
+ Py_INCREF(Py_None);
+ PyTuple_SET_ITEM(ret, i + 1, Py_None);
+ }
}
+ PyTuple_SET_ITEM(ret, 0, npyarr->ret);
+ }
- return ret;
+ return ret;
}
JSOBJ Object_npyEndArray(void *prv, JSOBJ obj) {
- PyObject *ret;
- char *new_data;
- NpyArrContext *npyarr = (NpyArrContext *)obj;
- int emptyType = NPY_DEFAULT_TYPE;
- npy_intp i;
- PRINTMARK();
- if (!npyarr) {
- return NULL;
- }
-
- ret = npyarr->ret;
- i = npyarr->i;
-
- npyarr->dec->curdim--;
-
- if (i == 0 || !npyarr->ret) {
- // empty array would not have been initialised so do it now.
- if (npyarr->dec->dtype) {
- emptyType = npyarr->dec->dtype->type_num;
- }
- npyarr->ret = ret =
- PyArray_EMPTY(npyarr->shape.len, npyarr->shape.ptr, emptyType, 0);
- } else if (npyarr->dec->curdim <= 0) {
- // realloc to final size
- new_data = PyDataMem_RENEW(PyArray_DATA(ret), i * npyarr->elsize);
- if (new_data == NULL) {
- PyErr_NoMemory();
- Npy_releaseContext(npyarr);
- return NULL;
- }
- ((PyArrayObject *)ret)->data = (void *)new_data;
- // PyArray_BYTES(ret) = new_data;
- }
+ PyObject *ret;
+ char *new_data;
+ NpyArrContext *npyarr = (NpyArrContext *)obj;
+ int emptyType = NPY_DEFAULT_TYPE;
+ npy_intp i;
+ PRINTMARK();
+ if (!npyarr) {
+ return NULL;
+ }
- if (npyarr->dec->curdim <= 0) {
- // finished decoding array, reshape if necessary
- if (npyarr->shape.len > 1) {
- npyarr->ret = PyArray_Newshape((PyArrayObject *)ret, &npyarr->shape,
- NPY_ANYORDER);
- Py_DECREF(ret);
- }
+ ret = npyarr->ret;
+ i = npyarr->i;
- ret = Npy_returnLabelled(npyarr);
+ npyarr->dec->curdim--;
- npyarr->ret = NULL;
- Npy_releaseContext(npyarr);
+ if (i == 0 || !npyarr->ret) {
+ // empty array would not have been initialised so do it now.
+ if (npyarr->dec->dtype) {
+ emptyType = npyarr->dec->dtype->type_num;
+ }
+ npyarr->ret = ret =
+ PyArray_EMPTY(npyarr->shape.len, npyarr->shape.ptr, emptyType, 0);
+ } else if (npyarr->dec->curdim <= 0) {
+ // realloc to final size
+ new_data = PyDataMem_RENEW(PyArray_DATA(ret), i * npyarr->elsize);
+ if (new_data == NULL) {
+ PyErr_NoMemory();
+ Npy_releaseContext(npyarr);
+ return NULL;
+ }
+ ((PyArrayObject *)ret)->data = (void *)new_data;
+ // PyArray_BYTES(ret) = new_data;
+ }
+
+ if (npyarr->dec->curdim <= 0) {
+ // finished decoding array, reshape if necessary
+ if (npyarr->shape.len > 1) {
+ npyarr->ret =
+ PyArray_Newshape((PyArrayObject *)ret, &npyarr->shape, NPY_ANYORDER);
+ Py_DECREF(ret);
}
- return ret;
+ ret = Npy_returnLabelled(npyarr);
+
+ npyarr->ret = NULL;
+ Npy_releaseContext(npyarr);
+ }
+
+ return ret;
}
int Object_npyArrayAddItem(void *prv, JSOBJ obj, JSOBJ value) {
- PyObject *type;
- PyArray_Descr *dtype;
- npy_intp i;
- char *new_data, *item;
- NpyArrContext *npyarr = (NpyArrContext *)obj;
- PRINTMARK();
- if (!npyarr) {
- return 0;
- }
+ PyObject *type;
+ PyArray_Descr *dtype;
+ npy_intp i;
+ char *new_data, *item;
+ NpyArrContext *npyarr = (NpyArrContext *)obj;
+ PRINTMARK();
+ if (!npyarr) {
+ return 0;
+ }
- i = npyarr->i;
+ i = npyarr->i;
- npyarr->shape.ptr[npyarr->dec->curdim - 1]++;
+ npyarr->shape.ptr[npyarr->dec->curdim - 1]++;
- if (PyArray_Check((PyObject *)value)) {
- // multidimensional array, keep decoding values.
- return 1;
+ if (PyArray_Check((PyObject *)value)) {
+ // multidimensional array, keep decoding values.
+ return 1;
+ }
+
+ if (!npyarr->ret) {
+ // Array not initialised yet.
+ // We do it here so we can 'sniff' the data type if none was provided
+ if (!npyarr->dec->dtype) {
+ type = PyObject_Type(value);
+ if (!PyArray_DescrConverter(type, &dtype)) {
+ Py_DECREF(type);
+ goto fail;
+ }
+ Py_INCREF(dtype);
+ Py_DECREF(type);
+ } else {
+ dtype = PyArray_DescrNew(npyarr->dec->dtype);
}
+ // If it's an object or string then fill a Python list and subsequently
+ // convert. Otherwise we would need to somehow mess about with
+ // reference counts when renewing memory.
+ npyarr->elsize = dtype->elsize;
+ if (PyDataType_REFCHK(dtype) || npyarr->elsize == 0) {
+ Py_XDECREF(dtype);
+
+ if (npyarr->dec->curdim > 1) {
+ PyErr_SetString(PyExc_ValueError,
+ "Cannot decode multidimensional arrays with "
+ "variable length elements to numpy");
+ goto fail;
+ }
+ npyarr->elcount = 0;
+ npyarr->ret = PyList_New(0);
+ if (!npyarr->ret) {
+ goto fail;
+ }
+ ((JSONObjectDecoder *)npyarr->dec)->newArray = Object_npyNewArrayList;
+ ((JSONObjectDecoder *)npyarr->dec)->arrayAddItem =
+ Object_npyArrayListAddItem;
+ ((JSONObjectDecoder *)npyarr->dec)->endArray = Object_npyEndArrayList;
+ return Object_npyArrayListAddItem(prv, obj, value);
+ }
+
+ npyarr->ret = PyArray_NewFromDescr(&PyArray_Type, dtype, 1,
+ &npyarr->elcount, NULL, NULL, 0, NULL);
+
if (!npyarr->ret) {
- // Array not initialised yet.
- // We do it here so we can 'sniff' the data type if none was provided
- if (!npyarr->dec->dtype) {
- type = PyObject_Type(value);
- if (!PyArray_DescrConverter(type, &dtype)) {
- Py_DECREF(type);
- goto fail;
- }
- Py_INCREF(dtype);
- Py_DECREF(type);
- } else {
- dtype = PyArray_DescrNew(npyarr->dec->dtype);
- }
-
- // If it's an object or string then fill a Python list and subsequently
- // convert. Otherwise we would need to somehow mess about with
- // reference counts when renewing memory.
- npyarr->elsize = dtype->elsize;
- if (PyDataType_REFCHK(dtype) || npyarr->elsize == 0) {
- Py_XDECREF(dtype);
-
- if (npyarr->dec->curdim > 1) {
- PyErr_SetString(PyExc_ValueError,
- "Cannot decode multidimensional arrays with "
- "variable length elements to numpy");
- goto fail;
- }
- npyarr->elcount = 0;
- npyarr->ret = PyList_New(0);
- if (!npyarr->ret) {
- goto fail;
- }
- ((JSONObjectDecoder *)npyarr->dec)->newArray =
- Object_npyNewArrayList;
- ((JSONObjectDecoder *)npyarr->dec)->arrayAddItem =
- Object_npyArrayListAddItem;
- ((JSONObjectDecoder *)npyarr->dec)->endArray =
- Object_npyEndArrayList;
- return Object_npyArrayListAddItem(prv, obj, value);
- }
-
- npyarr->ret = PyArray_NewFromDescr(
- &PyArray_Type, dtype, 1, &npyarr->elcount, NULL, NULL, 0, NULL);
-
- if (!npyarr->ret) {
- goto fail;
- }
+ goto fail;
+ }
+ }
+
+ if (i >= npyarr->elcount) {
+ // Grow PyArray_DATA(ret):
+ // this is similar for the strategy for PyListObject, but we use
+ // 50% overallocation => 0, 4, 8, 14, 23, 36, 56, 86 ...
+ if (npyarr->elsize == 0) {
+ PyErr_SetString(PyExc_ValueError,
+ "Cannot decode multidimensional arrays with "
+ "variable length elements to numpy");
+ goto fail;
}
- if (i >= npyarr->elcount) {
- // Grow PyArray_DATA(ret):
- // this is similar for the strategy for PyListObject, but we use
- // 50% overallocation => 0, 4, 8, 14, 23, 36, 56, 86 ...
- if (npyarr->elsize == 0) {
- PyErr_SetString(PyExc_ValueError,
- "Cannot decode multidimensional arrays with "
- "variable length elements to numpy");
- goto fail;
- }
-
- npyarr->elcount = (i >> 1) + (i < 4 ? 4 : 2) + i;
- if (npyarr->elcount <= NPY_MAX_INTP / npyarr->elsize) {
- new_data = PyDataMem_RENEW(PyArray_DATA(npyarr->ret),
- npyarr->elcount * npyarr->elsize);
- } else {
- PyErr_NoMemory();
- goto fail;
- }
- ((PyArrayObject *)npyarr->ret)->data = (void *)new_data;
-
- // PyArray_BYTES(npyarr->ret) = new_data;
+ npyarr->elcount = (i >> 1) + (i < 4 ? 4 : 2) + i;
+ if (npyarr->elcount <= NPY_MAX_INTP / npyarr->elsize) {
+ new_data = PyDataMem_RENEW(PyArray_DATA(npyarr->ret),
+ npyarr->elcount * npyarr->elsize);
+ } else {
+ PyErr_NoMemory();
+ goto fail;
}
+ ((PyArrayObject *)npyarr->ret)->data = (void *)new_data;
- PyArray_DIMS(npyarr->ret)[0] = i + 1;
+ // PyArray_BYTES(npyarr->ret) = new_data;
+ }
- if ((item = PyArray_GETPTR1(npyarr->ret, i)) == NULL ||
- PyArray_SETITEM(npyarr->ret, item, value) == -1) {
- goto fail;
- }
+ PyArray_DIMS(npyarr->ret)[0] = i + 1;
- Py_DECREF((PyObject *)value);
- npyarr->i++;
- return 1;
+ if ((item = PyArray_GETPTR1(npyarr->ret, i)) == NULL ||
+ PyArray_SETITEM(npyarr->ret, item, value) == -1) {
+ goto fail;
+ }
+
+ Py_DECREF((PyObject *)value);
+ npyarr->i++;
+ return 1;
fail:
- Npy_releaseContext(npyarr);
- return 0;
+ Npy_releaseContext(npyarr);
+ return 0;
}
JSOBJ Object_npyNewArrayList(void *prv, void *_decoder) {
- PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
- PRINTMARK();
- PyErr_SetString(
- PyExc_ValueError,
- "nesting not supported for object or variable length dtypes");
- Npy_releaseContext(decoder->npyarr);
- return NULL;
+ PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
+ PRINTMARK();
+ PyErr_SetString(PyExc_ValueError,
+ "nesting not supported for object or variable length dtypes");
+ Npy_releaseContext(decoder->npyarr);
+ return NULL;
}
JSOBJ Object_npyEndArrayList(void *prv, JSOBJ obj) {
- PyObject *list, *ret;
- NpyArrContext *npyarr = (NpyArrContext *)obj;
- PRINTMARK();
- if (!npyarr) {
- return NULL;
- }
+ PyObject *list, *ret;
+ NpyArrContext *npyarr = (NpyArrContext *)obj;
+ PRINTMARK();
+ if (!npyarr) {
+ return NULL;
+ }
- // convert decoded list to numpy array
- list = (PyObject *)npyarr->ret;
- npyarr->ret = PyArray_FROM_O(list);
+ // convert decoded list to numpy array
+ list = (PyObject *)npyarr->ret;
+ npyarr->ret = PyArray_FROM_O(list);
- ret = Npy_returnLabelled(npyarr);
- npyarr->ret = list;
+ ret = Npy_returnLabelled(npyarr);
+ npyarr->ret = list;
- ((JSONObjectDecoder *)npyarr->dec)->newArray = Object_npyNewArray;
- ((JSONObjectDecoder *)npyarr->dec)->arrayAddItem = Object_npyArrayAddItem;
- ((JSONObjectDecoder *)npyarr->dec)->endArray = Object_npyEndArray;
- Npy_releaseContext(npyarr);
- return ret;
+ ((JSONObjectDecoder *)npyarr->dec)->newArray = Object_npyNewArray;
+ ((JSONObjectDecoder *)npyarr->dec)->arrayAddItem = Object_npyArrayAddItem;
+ ((JSONObjectDecoder *)npyarr->dec)->endArray = Object_npyEndArray;
+ Npy_releaseContext(npyarr);
+ return ret;
}
int Object_npyArrayListAddItem(void *prv, JSOBJ obj, JSOBJ value) {
- NpyArrContext *npyarr = (NpyArrContext *)obj;
- PRINTMARK();
- if (!npyarr) {
- return 0;
- }
- PyList_Append((PyObject *)npyarr->ret, value);
- Py_DECREF((PyObject *)value);
- npyarr->elcount++;
- return 1;
+ NpyArrContext *npyarr = (NpyArrContext *)obj;
+ PRINTMARK();
+ if (!npyarr) {
+ return 0;
+ }
+ PyList_Append((PyObject *)npyarr->ret, value);
+ Py_DECREF((PyObject *)value);
+ npyarr->elcount++;
+ return 1;
}
int Object_objectAddKey(void *prv, JSOBJ obj, JSOBJ name, JSOBJ value) {
- int ret = PyDict_SetItem(obj, name, value);
- Py_DECREF((PyObject *)name);
- Py_DECREF((PyObject *)value);
- return ret == 0 ? 1 : 0;
+ int ret = PyDict_SetItem(obj, name, value);
+ Py_DECREF((PyObject *)name);
+ Py_DECREF((PyObject *)value);
+ return ret == 0 ? 1 : 0;
}
int Object_arrayAddItem(void *prv, JSOBJ obj, JSOBJ value) {
- int ret = PyList_Append(obj, value);
- Py_DECREF((PyObject *)value);
- return ret == 0 ? 1 : 0;
+ int ret = PyList_Append(obj, value);
+ Py_DECREF((PyObject *)value);
+ return ret == 0 ? 1 : 0;
}
JSOBJ Object_newString(void *prv, wchar_t *start, wchar_t *end) {
- return PyUnicode_FromWideChar(start, (end - start));
+ return PyUnicode_FromWideChar(start, (end - start));
}
JSOBJ Object_newTrue(void *prv) { Py_RETURN_TRUE; }
@@ -406,117 +404,115 @@ JSOBJ Object_newArray(void *prv, void *decoder) { return PyList_New(0); }
JSOBJ Object_endArray(void *prv, JSOBJ obj) { return obj; }
JSOBJ Object_newInteger(void *prv, JSINT32 value) {
- return PyLong_FromLong((long)value);
+ return PyLong_FromLong((long)value);
}
JSOBJ Object_newLong(void *prv, JSINT64 value) {
- return PyLong_FromLongLong(value);
+ return PyLong_FromLongLong(value);
}
JSOBJ Object_newUnsignedLong(void *prv, JSUINT64 value) {
- return PyLong_FromUnsignedLongLong(value);
+ return PyLong_FromUnsignedLongLong(value);
}
JSOBJ Object_newDouble(void *prv, double value) {
- return PyFloat_FromDouble(value);
+ return PyFloat_FromDouble(value);
}
static void Object_releaseObject(void *prv, JSOBJ obj, void *_decoder) {
- PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
- if (obj != decoder->npyarr_addr) {
- Py_XDECREF(((PyObject *)obj));
- }
+ PyObjectDecoder *decoder = (PyObjectDecoder *)_decoder;
+ if (obj != decoder->npyarr_addr) {
+ Py_XDECREF(((PyObject *)obj));
+ }
}
-static char *g_kwlist[] = {"obj", "precise_float",
- "labelled", "dtype", NULL};
+static char *g_kwlist[] = {"obj", "precise_float", "labelled", "dtype", NULL};
PyObject *JSONToObj(PyObject *self, PyObject *args, PyObject *kwargs) {
- PyObject *ret;
- PyObject *sarg;
- PyObject *arg;
- PyObject *opreciseFloat = NULL;
- JSONObjectDecoder *decoder;
- PyObjectDecoder pyDecoder;
- PyArray_Descr *dtype = NULL;
- int labelled = 0;
-
- JSONObjectDecoder dec = {
- Object_newString, Object_objectAddKey, Object_arrayAddItem,
- Object_newTrue, Object_newFalse, Object_newNull,
- Object_newPosInf, Object_newNegInf, Object_newObject,
- Object_endObject, Object_newArray, Object_endArray,
- Object_newInteger, Object_newLong, Object_newUnsignedLong,
- Object_newDouble,
- Object_releaseObject, PyObject_Malloc, PyObject_Free,
- PyObject_Realloc};
-
- dec.preciseFloat = 0;
- dec.prv = NULL;
-
- pyDecoder.dec = dec;
- pyDecoder.curdim = 0;
- pyDecoder.npyarr = NULL;
- pyDecoder.npyarr_addr = NULL;
-
- decoder = (JSONObjectDecoder *)&pyDecoder;
-
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|OiiO&", g_kwlist, &arg,
- &opreciseFloat, &labelled,
- PyArray_DescrConverter2, &dtype)) {
- Npy_releaseContext(pyDecoder.npyarr);
- return NULL;
- }
-
- if (opreciseFloat && PyObject_IsTrue(opreciseFloat)) {
- decoder->preciseFloat = 1;
- }
-
- if (PyBytes_Check(arg)) {
- sarg = arg;
- } else if (PyUnicode_Check(arg)) {
- sarg = PyUnicode_AsUTF8String(arg);
- if (sarg == NULL) {
- // Exception raised above us by codec according to docs
- return NULL;
- }
- } else {
- PyErr_Format(PyExc_TypeError, "Expected 'str' or 'bytes'");
- return NULL;
+ PyObject *ret;
+ PyObject *sarg;
+ PyObject *arg;
+ PyObject *opreciseFloat = NULL;
+ JSONObjectDecoder *decoder;
+ PyObjectDecoder pyDecoder;
+ PyArray_Descr *dtype = NULL;
+ int labelled = 0;
+
+ JSONObjectDecoder dec = {
+ Object_newString, Object_objectAddKey, Object_arrayAddItem,
+ Object_newTrue, Object_newFalse, Object_newNull,
+ Object_newPosInf, Object_newNegInf, Object_newObject,
+ Object_endObject, Object_newArray, Object_endArray,
+ Object_newInteger, Object_newLong, Object_newUnsignedLong,
+ Object_newDouble, Object_releaseObject, PyObject_Malloc,
+ PyObject_Free, PyObject_Realloc};
+
+ dec.preciseFloat = 0;
+ dec.prv = NULL;
+
+ pyDecoder.dec = dec;
+ pyDecoder.curdim = 0;
+ pyDecoder.npyarr = NULL;
+ pyDecoder.npyarr_addr = NULL;
+
+ decoder = (JSONObjectDecoder *)&pyDecoder;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|OiiO&", g_kwlist, &arg,
+ &opreciseFloat, &labelled,
+ PyArray_DescrConverter2, &dtype)) {
+ Npy_releaseContext(pyDecoder.npyarr);
+ return NULL;
+ }
+
+ if (opreciseFloat && PyObject_IsTrue(opreciseFloat)) {
+ decoder->preciseFloat = 1;
+ }
+
+ if (PyBytes_Check(arg)) {
+ sarg = arg;
+ } else if (PyUnicode_Check(arg)) {
+ sarg = PyUnicode_AsUTF8String(arg);
+ if (sarg == NULL) {
+ // Exception raised above us by codec according to docs
+ return NULL;
}
+ } else {
+ PyErr_Format(PyExc_TypeError, "Expected 'str' or 'bytes'");
+ return NULL;
+ }
- decoder->errorStr = NULL;
- decoder->errorOffset = NULL;
+ decoder->errorStr = NULL;
+ decoder->errorOffset = NULL;
- ret = JSON_DecodeObject(decoder, PyBytes_AS_STRING(sarg),
- PyBytes_GET_SIZE(sarg));
+ ret = JSON_DecodeObject(decoder, PyBytes_AS_STRING(sarg),
+ PyBytes_GET_SIZE(sarg));
- if (sarg != arg) {
- Py_DECREF(sarg);
- }
+ if (sarg != arg) {
+ Py_DECREF(sarg);
+ }
- if (PyErr_Occurred()) {
- if (ret) {
- Py_DECREF((PyObject *)ret);
- }
- Npy_releaseContext(pyDecoder.npyarr);
- return NULL;
+ if (PyErr_Occurred()) {
+ if (ret) {
+ Py_DECREF((PyObject *)ret);
}
+ Npy_releaseContext(pyDecoder.npyarr);
+ return NULL;
+ }
- if (decoder->errorStr) {
- /*
- FIXME: It's possible to give a much nicer error message here with actual
- failing element in input etc*/
-
- PyErr_Format(PyExc_ValueError, "%s", decoder->errorStr);
+ if (decoder->errorStr) {
+ /*
+ FIXME: It's possible to give a much nicer error message here with actual
+ failing element in input etc*/
- if (ret) {
- Py_DECREF((PyObject *)ret);
- }
- Npy_releaseContext(pyDecoder.npyarr);
+ PyErr_Format(PyExc_ValueError, "%s", decoder->errorStr);
- return NULL;
+ if (ret) {
+ Py_DECREF((PyObject *)ret);
}
+ Npy_releaseContext(pyDecoder.npyarr);
+
+ return NULL;
+ }
- return ret;
+ return ret;
}
diff --git a/pandas/_libs/src/vendored/ujson/python/objToJSON.c b/pandas/_libs/src/vendored/ujson/python/objToJSON.c
index a4c93f1560a0e..22ffec51c82f1 100644
--- a/pandas/_libs/src/vendored/ujson/python/objToJSON.c
+++ b/pandas/_libs/src/vendored/ujson/python/objToJSON.c
@@ -44,13 +44,13 @@ Numeric decoder derived from TCL library
#define NO_IMPORT_ARRAY
#define PY_ARRAY_UNIQUE_SYMBOL UJSON_NUMPY
+#include "datetime.h"
+#include "pandas/datetime/pd_datetime.h"
+#include "pandas/vendored/ujson/lib/ultrajson.h"
#include <numpy/arrayobject.h>
#include <numpy/arrayscalars.h>
#include <numpy/ndarraytypes.h>
#include <numpy/npy_math.h>
-#include "pandas/vendored/ujson/lib/ultrajson.h"
-#include "datetime.h"
-#include "pandas/datetime/pd_datetime.h"
npy_int64 get_nat(void) { return NPY_MIN_INT64; }
@@ -65,81 +65,81 @@ int object_is_nat_type(PyObject *obj);
int object_is_na_type(PyObject *obj);
typedef struct __NpyArrContext {
- PyObject *array;
- char *dataptr;
- int curdim; // current dimension in array's order
- int stridedim; // dimension we are striding over
- int inc; // stride dimension increment (+/- 1)
- npy_intp dim;
- npy_intp stride;
- npy_intp ndim;
- npy_intp index[NPY_MAXDIMS];
- int type_num;
- PyArray_GetItemFunc *getitem;
-
- char **rowLabels;
- char **columnLabels;
+ PyObject *array;
+ char *dataptr;
+ int curdim; // current dimension in array's order
+ int stridedim; // dimension we are striding over
+ int inc; // stride dimension increment (+/- 1)
+ npy_intp dim;
+ npy_intp stride;
+ npy_intp ndim;
+ npy_intp index[NPY_MAXDIMS];
+ int type_num;
+ PyArray_GetItemFunc *getitem;
+
+ char **rowLabels;
+ char **columnLabels;
} NpyArrContext;
typedef struct __PdBlockContext {
- int colIdx;
- int ncols;
- int transpose;
+ int colIdx;
+ int ncols;
+ int transpose;
- NpyArrContext **npyCtxts; // NpyArrContext for each column
+ NpyArrContext **npyCtxts; // NpyArrContext for each column
} PdBlockContext;
typedef struct __TypeContext {
- JSPFN_ITERBEGIN iterBegin;
- JSPFN_ITEREND iterEnd;
- JSPFN_ITERNEXT iterNext;
- JSPFN_ITERGETNAME iterGetName;
- JSPFN_ITERGETVALUE iterGetValue;
- PFN_PyTypeToUTF8 PyTypeToUTF8;
- PyObject *newObj;
- PyObject *dictObj;
- Py_ssize_t index;
- Py_ssize_t size;
- PyObject *itemValue;
- PyObject *itemName;
- PyObject *attrList;
- PyObject *iterator;
-
- double doubleValue;
- JSINT64 longValue;
-
- char *cStr;
- NpyArrContext *npyarr;
- PdBlockContext *pdblock;
- int transpose;
- char **rowLabels;
- char **columnLabels;
- npy_intp rowLabelsLen;
- npy_intp columnLabelsLen;
+ JSPFN_ITERBEGIN iterBegin;
+ JSPFN_ITEREND iterEnd;
+ JSPFN_ITERNEXT iterNext;
+ JSPFN_ITERGETNAME iterGetName;
+ JSPFN_ITERGETVALUE iterGetValue;
+ PFN_PyTypeToUTF8 PyTypeToUTF8;
+ PyObject *newObj;
+ PyObject *dictObj;
+ Py_ssize_t index;
+ Py_ssize_t size;
+ PyObject *itemValue;
+ PyObject *itemName;
+ PyObject *attrList;
+ PyObject *iterator;
+
+ double doubleValue;
+ JSINT64 longValue;
+
+ char *cStr;
+ NpyArrContext *npyarr;
+ PdBlockContext *pdblock;
+ int transpose;
+ char **rowLabels;
+ char **columnLabels;
+ npy_intp rowLabelsLen;
+ npy_intp columnLabelsLen;
} TypeContext;
typedef struct __PyObjectEncoder {
- JSONObjectEncoder enc;
+ JSONObjectEncoder enc;
- // pass through the NpyArrContext when encoding multi-dimensional arrays
- NpyArrContext *npyCtxtPassthru;
+ // pass through the NpyArrContext when encoding multi-dimensional arrays
+ NpyArrContext *npyCtxtPassthru;
- // pass through the PdBlockContext when encoding blocks
- PdBlockContext *blkCtxtPassthru;
+ // pass through the PdBlockContext when encoding blocks
+ PdBlockContext *blkCtxtPassthru;
- // pass-through to encode numpy data directly
- int npyType;
- void *npyValue;
+ // pass-through to encode numpy data directly
+ int npyType;
+ void *npyValue;
- int datetimeIso;
- NPY_DATETIMEUNIT datetimeUnit;
- NPY_DATETIMEUNIT valueUnit;
+ int datetimeIso;
+ NPY_DATETIMEUNIT datetimeUnit;
+ NPY_DATETIMEUNIT valueUnit;
- // output format style for pandas data types
- int outputFormat;
- int originalOutputFormat;
+ // output format style for pandas data types
+ int outputFormat;
+ int originalOutputFormat;
- PyObject *defaultHandler;
+ PyObject *defaultHandler;
} PyObjectEncoder;
#define GET_TC(__ptrtc) ((TypeContext *)((__ptrtc)->prv))
@@ -149,245 +149,241 @@ enum PANDAS_FORMAT { SPLIT, RECORDS, INDEX, COLUMNS, VALUES };
int PdBlock_iterNext(JSOBJ, JSONTypeContext *);
static TypeContext *createTypeContext(void) {
- TypeContext *pc;
+ TypeContext *pc;
- pc = PyObject_Malloc(sizeof(TypeContext));
- if (!pc) {
- PyErr_NoMemory();
- return NULL;
- }
- pc->newObj = NULL;
- pc->dictObj = NULL;
- pc->itemValue = NULL;
- pc->itemName = NULL;
- pc->attrList = NULL;
- pc->index = 0;
- pc->size = 0;
- pc->longValue = 0;
- pc->doubleValue = 0.0;
- pc->cStr = NULL;
- pc->npyarr = NULL;
- pc->pdblock = NULL;
- pc->rowLabels = NULL;
- pc->columnLabels = NULL;
- pc->transpose = 0;
- pc->rowLabelsLen = 0;
- pc->columnLabelsLen = 0;
-
- return pc;
+ pc = PyObject_Malloc(sizeof(TypeContext));
+ if (!pc) {
+ PyErr_NoMemory();
+ return NULL;
+ }
+ pc->newObj = NULL;
+ pc->dictObj = NULL;
+ pc->itemValue = NULL;
+ pc->itemName = NULL;
+ pc->attrList = NULL;
+ pc->index = 0;
+ pc->size = 0;
+ pc->longValue = 0;
+ pc->doubleValue = 0.0;
+ pc->cStr = NULL;
+ pc->npyarr = NULL;
+ pc->pdblock = NULL;
+ pc->rowLabels = NULL;
+ pc->columnLabels = NULL;
+ pc->transpose = 0;
+ pc->rowLabelsLen = 0;
+ pc->columnLabelsLen = 0;
+
+ return pc;
}
static PyObject *get_values(PyObject *obj) {
- PyObject *values = NULL;
-
- if (object_is_index_type(obj) || object_is_series_type(obj)) {
- // The special cases to worry about are dt64tz and category[dt64tz].
- // In both cases we want the UTC-localized datetime64 ndarray,
- // without going through and object array of Timestamps.
- if (PyObject_HasAttrString(obj, "tz")) {
- PyObject *tz = PyObject_GetAttrString(obj, "tz");
- if (tz != Py_None) {
- // Go through object array if we have dt64tz, since tz info will
- // be lost if values is used directly.
- Py_DECREF(tz);
- values = PyObject_CallMethod(obj, "__array__", NULL);
- return values;
- }
- Py_DECREF(tz);
- }
- values = PyObject_GetAttrString(obj, "values");
- if (values == NULL) {
- // Clear so we can subsequently try another method
- PyErr_Clear();
- } else if (PyObject_HasAttrString(values, "__array__")) {
- // We may have gotten a Categorical or Sparse array so call np.array
- PyObject *array_values = PyObject_CallMethod(values, "__array__",
- NULL);
- Py_DECREF(values);
- values = array_values;
- } else if (!PyArray_CheckExact(values)) {
- // Didn't get a numpy array, so keep trying
- Py_DECREF(values);
- values = NULL;
- }
- }
-
+ PyObject *values = NULL;
+
+ if (object_is_index_type(obj) || object_is_series_type(obj)) {
+ // The special cases to worry about are dt64tz and category[dt64tz].
+ // In both cases we want the UTC-localized datetime64 ndarray,
+ // without going through and object array of Timestamps.
+ if (PyObject_HasAttrString(obj, "tz")) {
+ PyObject *tz = PyObject_GetAttrString(obj, "tz");
+ if (tz != Py_None) {
+ // Go through object array if we have dt64tz, since tz info will
+ // be lost if values is used directly.
+ Py_DECREF(tz);
+ values = PyObject_CallMethod(obj, "__array__", NULL);
+ return values;
+ }
+ Py_DECREF(tz);
+ }
+ values = PyObject_GetAttrString(obj, "values");
if (values == NULL) {
- PyObject *typeRepr = PyObject_Repr((PyObject *)Py_TYPE(obj));
- PyObject *repr;
- if (PyObject_HasAttrString(obj, "dtype")) {
- PyObject *dtype = PyObject_GetAttrString(obj, "dtype");
- repr = PyObject_Repr(dtype);
- Py_DECREF(dtype);
- } else {
- repr = PyUnicode_FromString("<unknown dtype>");
- }
+ // Clear so we can subsequently try another method
+ PyErr_Clear();
+ } else if (PyObject_HasAttrString(values, "__array__")) {
+ // We may have gotten a Categorical or Sparse array so call np.array
+ PyObject *array_values = PyObject_CallMethod(values, "__array__", NULL);
+ Py_DECREF(values);
+ values = array_values;
+ } else if (!PyArray_CheckExact(values)) {
+ // Didn't get a numpy array, so keep trying
+ Py_DECREF(values);
+ values = NULL;
+ }
+ }
+
+ if (values == NULL) {
+ PyObject *typeRepr = PyObject_Repr((PyObject *)Py_TYPE(obj));
+ PyObject *repr;
+ if (PyObject_HasAttrString(obj, "dtype")) {
+ PyObject *dtype = PyObject_GetAttrString(obj, "dtype");
+ repr = PyObject_Repr(dtype);
+ Py_DECREF(dtype);
+ } else {
+ repr = PyUnicode_FromString("<unknown dtype>");
+ }
- PyErr_Format(PyExc_ValueError, "%R or %R are not JSON serializable yet",
- repr, typeRepr);
- Py_DECREF(repr);
- Py_DECREF(typeRepr);
+ PyErr_Format(PyExc_ValueError, "%R or %R are not JSON serializable yet",
+ repr, typeRepr);
+ Py_DECREF(repr);
+ Py_DECREF(typeRepr);
- return NULL;
- }
+ return NULL;
+ }
- return values;
+ return values;
}
static PyObject *get_sub_attr(PyObject *obj, char *attr, char *subAttr) {
- PyObject *tmp = PyObject_GetAttrString(obj, attr);
- PyObject *ret;
+ PyObject *tmp = PyObject_GetAttrString(obj, attr);
+ PyObject *ret;
- if (tmp == 0) {
- return 0;
- }
- ret = PyObject_GetAttrString(tmp, subAttr);
- Py_DECREF(tmp);
+ if (tmp == 0) {
+ return 0;
+ }
+ ret = PyObject_GetAttrString(tmp, subAttr);
+ Py_DECREF(tmp);
- return ret;
+ return ret;
}
static Py_ssize_t get_attr_length(PyObject *obj, char *attr) {
- PyObject *tmp = PyObject_GetAttrString(obj, attr);
- Py_ssize_t ret;
+ PyObject *tmp = PyObject_GetAttrString(obj, attr);
+ Py_ssize_t ret;
- if (tmp == 0) {
- return 0;
- }
- ret = PyObject_Length(tmp);
- Py_DECREF(tmp);
+ if (tmp == 0) {
+ return 0;
+ }
+ ret = PyObject_Length(tmp);
+ Py_DECREF(tmp);
- if (ret == -1) {
- return 0;
- }
+ if (ret == -1) {
+ return 0;
+ }
- return ret;
+ return ret;
}
-
static npy_int64 get_long_attr(PyObject *o, const char *attr) {
- // NB we are implicitly assuming that o is a Timedelta or Timestamp, or NaT
+ // NB we are implicitly assuming that o is a Timedelta or Timestamp, or NaT
- npy_int64 long_val;
- PyObject *value = PyObject_GetAttrString(o, attr);
- long_val =
- (PyLong_Check(value) ? PyLong_AsLongLong(value) : PyLong_AsLong(value));
+ npy_int64 long_val;
+ PyObject *value = PyObject_GetAttrString(o, attr);
+ long_val =
+ (PyLong_Check(value) ? PyLong_AsLongLong(value) : PyLong_AsLong(value));
- Py_DECREF(value);
-
- if (object_is_nat_type(o)) {
- // i.e. o is NaT, long_val will be NPY_MIN_INT64
- return long_val;
- }
+ Py_DECREF(value);
- // ensure we are in nanoseconds, similar to Timestamp._as_creso or _as_unit
- PyObject* reso = PyObject_GetAttrString(o, "_creso");
- if (!PyLong_Check(reso)) {
- // https://github.com/pandas-dev/pandas/pull/49034#discussion_r1023165139
- Py_DECREF(reso);
- return -1;
- }
+ if (object_is_nat_type(o)) {
+ // i.e. o is NaT, long_val will be NPY_MIN_INT64
+ return long_val;
+ }
- long cReso = PyLong_AsLong(reso);
+ // ensure we are in nanoseconds, similar to Timestamp._as_creso or _as_unit
+ PyObject *reso = PyObject_GetAttrString(o, "_creso");
+ if (!PyLong_Check(reso)) {
+ // https://github.com/pandas-dev/pandas/pull/49034#discussion_r1023165139
Py_DECREF(reso);
- if (cReso == -1 && PyErr_Occurred()) {
- return -1;
- }
+ return -1;
+ }
- if (cReso == NPY_FR_us) {
- long_val = long_val * 1000L;
- } else if (cReso == NPY_FR_ms) {
- long_val = long_val * 1000000L;
- } else if (cReso == NPY_FR_s) {
- long_val = long_val * 1000000000L;
- }
+ long cReso = PyLong_AsLong(reso);
+ Py_DECREF(reso);
+ if (cReso == -1 && PyErr_Occurred()) {
+ return -1;
+ }
- return long_val;
+ if (cReso == NPY_FR_us) {
+ long_val = long_val * 1000L;
+ } else if (cReso == NPY_FR_ms) {
+ long_val = long_val * 1000000L;
+ } else if (cReso == NPY_FR_s) {
+ long_val = long_val * 1000000000L;
+ }
+
+ return long_val;
}
static npy_float64 total_seconds(PyObject *td) {
- npy_float64 double_val;
- PyObject *value = PyObject_CallMethod(td, "total_seconds", NULL);
- double_val = PyFloat_AS_DOUBLE(value);
- Py_DECREF(value);
- return double_val;
+ npy_float64 double_val;
+ PyObject *value = PyObject_CallMethod(td, "total_seconds", NULL);
+ double_val = PyFloat_AS_DOUBLE(value);
+ Py_DECREF(value);
+ return double_val;
}
static char *PyBytesToUTF8(JSOBJ _obj, JSONTypeContext *Py_UNUSED(tc),
size_t *_outLen) {
- PyObject *obj = (PyObject *)_obj;
- *_outLen = PyBytes_GET_SIZE(obj);
- return PyBytes_AS_STRING(obj);
-}
-
-static char *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc,
- size_t *_outLen) {
- char *encoded = (char *)PyUnicode_AsUTF8AndSize(_obj,
- (Py_ssize_t *)_outLen);
- if (encoded == NULL) {
- /* Something went wrong.
- Set errorMsg(to tell encoder to stop),
- and let Python exception propagate. */
- JSONObjectEncoder *enc = (JSONObjectEncoder *)tc->encoder;
- enc->errorMsg = "Encoding failed.";
- }
- return encoded;
+ PyObject *obj = (PyObject *)_obj;
+ *_outLen = PyBytes_GET_SIZE(obj);
+ return PyBytes_AS_STRING(obj);
+}
+
+static char *PyUnicodeToUTF8(JSOBJ _obj, JSONTypeContext *tc, size_t *_outLen) {
+ char *encoded = (char *)PyUnicode_AsUTF8AndSize(_obj, (Py_ssize_t *)_outLen);
+ if (encoded == NULL) {
+ /* Something went wrong.
+ Set errorMsg(to tell encoder to stop),
+ and let Python exception propagate. */
+ JSONObjectEncoder *enc = (JSONObjectEncoder *)tc->encoder;
+ enc->errorMsg = "Encoding failed.";
+ }
+ return encoded;
}
/* JSON callback. returns a char* and mutates the pointer to *len */
static char *NpyDateTimeToIsoCallback(JSOBJ Py_UNUSED(unused),
JSONTypeContext *tc, size_t *len) {
- NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- NPY_DATETIMEUNIT valueUnit = ((PyObjectEncoder *)tc->encoder)->valueUnit;
- GET_TC(tc)->cStr = int64ToIso(GET_TC(tc)->longValue, valueUnit, base, len);
- return GET_TC(tc)->cStr;
+ NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ NPY_DATETIMEUNIT valueUnit = ((PyObjectEncoder *)tc->encoder)->valueUnit;
+ GET_TC(tc)->cStr = int64ToIso(GET_TC(tc)->longValue, valueUnit, base, len);
+ return GET_TC(tc)->cStr;
}
/* JSON callback. returns a char* and mutates the pointer to *len */
static char *NpyTimeDeltaToIsoCallback(JSOBJ Py_UNUSED(unused),
JSONTypeContext *tc, size_t *len) {
- GET_TC(tc)->cStr = int64ToIsoDuration(GET_TC(tc)->longValue, len);
- return GET_TC(tc)->cStr;
+ GET_TC(tc)->cStr = int64ToIsoDuration(GET_TC(tc)->longValue, len);
+ return GET_TC(tc)->cStr;
}
/* JSON callback */
static char *PyDateTimeToIsoCallback(JSOBJ obj, JSONTypeContext *tc,
size_t *len) {
- if (!PyDate_Check(obj) && !PyDateTime_Check(obj)) {
- PyErr_SetString(PyExc_TypeError, "Expected date or datetime object");
- ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
- return NULL;
- }
+ if (!PyDate_Check(obj) && !PyDateTime_Check(obj)) {
+ PyErr_SetString(PyExc_TypeError, "Expected date or datetime object");
+ ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
+ return NULL;
+ }
- NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- return PyDateTimeToIso(obj, base, len);
+ NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ return PyDateTimeToIso(obj, base, len);
}
static char *PyTimeToJSON(JSOBJ _obj, JSONTypeContext *tc, size_t *outLen) {
- PyObject *obj = (PyObject *)_obj;
- PyObject *str;
- PyObject *tmp;
-
- str = PyObject_CallMethod(obj, "isoformat", NULL);
- if (str == NULL) {
- *outLen = 0;
- if (!PyErr_Occurred()) {
- PyErr_SetString(PyExc_ValueError, "Failed to convert time");
- }
- ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
- return NULL;
- }
- if (PyUnicode_Check(str)) {
- tmp = str;
- str = PyUnicode_AsUTF8String(str);
- Py_DECREF(tmp);
+ PyObject *obj = (PyObject *)_obj;
+ PyObject *str;
+ PyObject *tmp;
+
+ str = PyObject_CallMethod(obj, "isoformat", NULL);
+ if (str == NULL) {
+ *outLen = 0;
+ if (!PyErr_Occurred()) {
+ PyErr_SetString(PyExc_ValueError, "Failed to convert time");
}
+ ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
+ return NULL;
+ }
+ if (PyUnicode_Check(str)) {
+ tmp = str;
+ str = PyUnicode_AsUTF8String(str);
+ Py_DECREF(tmp);
+ }
- GET_TC(tc)->newObj = str;
+ GET_TC(tc)->newObj = str;
- *outLen = PyBytes_GET_SIZE(str);
- char *outValue = PyBytes_AS_STRING(str);
- return outValue;
+ *outLen = PyBytes_GET_SIZE(str);
+ char *outValue = PyBytes_AS_STRING(str);
+ return outValue;
}
//=============================================================================
@@ -395,167 +391,167 @@ static char *PyTimeToJSON(JSOBJ _obj, JSONTypeContext *tc, size_t *outLen) {
//=============================================================================
static void NpyArr_freeItemValue(JSOBJ Py_UNUSED(_obj), JSONTypeContext *tc) {
- if (GET_TC(tc)->npyarr &&
- GET_TC(tc)->itemValue != GET_TC(tc)->npyarr->array) {
- Py_XDECREF(GET_TC(tc)->itemValue);
- GET_TC(tc)->itemValue = NULL;
- }
+ if (GET_TC(tc)->npyarr &&
+ GET_TC(tc)->itemValue != GET_TC(tc)->npyarr->array) {
+ Py_XDECREF(GET_TC(tc)->itemValue);
+ GET_TC(tc)->itemValue = NULL;
+ }
}
int NpyArr_iterNextNone(JSOBJ Py_UNUSED(_obj), JSONTypeContext *Py_UNUSED(tc)) {
- return 0;
+ return 0;
}
void NpyArr_iterBegin(JSOBJ _obj, JSONTypeContext *tc) {
- PyArrayObject *obj;
- NpyArrContext *npyarr;
-
- if (GET_TC(tc)->newObj) {
- obj = (PyArrayObject *)GET_TC(tc)->newObj;
- } else {
- obj = (PyArrayObject *)_obj;
- }
+ PyArrayObject *obj;
+ NpyArrContext *npyarr;
- npyarr = PyObject_Malloc(sizeof(NpyArrContext));
- GET_TC(tc)->npyarr = npyarr;
+ if (GET_TC(tc)->newObj) {
+ obj = (PyArrayObject *)GET_TC(tc)->newObj;
+ } else {
+ obj = (PyArrayObject *)_obj;
+ }
- if (!npyarr) {
- PyErr_NoMemory();
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
+ npyarr = PyObject_Malloc(sizeof(NpyArrContext));
+ GET_TC(tc)->npyarr = npyarr;
- npyarr->array = (PyObject *)obj;
- npyarr->getitem = (PyArray_GetItemFunc *)PyArray_DESCR(obj)->f->getitem;
- npyarr->dataptr = PyArray_DATA(obj);
- npyarr->ndim = PyArray_NDIM(obj) - 1;
- npyarr->curdim = 0;
- npyarr->type_num = PyArray_DESCR(obj)->type_num;
-
- if (GET_TC(tc)->transpose) {
- npyarr->dim = PyArray_DIM(obj, npyarr->ndim);
- npyarr->stride = PyArray_STRIDE(obj, npyarr->ndim);
- npyarr->stridedim = npyarr->ndim;
- npyarr->index[npyarr->ndim] = 0;
- npyarr->inc = -1;
- } else {
- npyarr->dim = PyArray_DIM(obj, 0);
- npyarr->stride = PyArray_STRIDE(obj, 0);
- npyarr->stridedim = 0;
- npyarr->index[0] = 0;
- npyarr->inc = 1;
- }
-
- npyarr->columnLabels = GET_TC(tc)->columnLabels;
- npyarr->rowLabels = GET_TC(tc)->rowLabels;
+ if (!npyarr) {
+ PyErr_NoMemory();
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ return;
+ }
+
+ npyarr->array = (PyObject *)obj;
+ npyarr->getitem = (PyArray_GetItemFunc *)PyArray_DESCR(obj)->f->getitem;
+ npyarr->dataptr = PyArray_DATA(obj);
+ npyarr->ndim = PyArray_NDIM(obj) - 1;
+ npyarr->curdim = 0;
+ npyarr->type_num = PyArray_DESCR(obj)->type_num;
+
+ if (GET_TC(tc)->transpose) {
+ npyarr->dim = PyArray_DIM(obj, npyarr->ndim);
+ npyarr->stride = PyArray_STRIDE(obj, npyarr->ndim);
+ npyarr->stridedim = npyarr->ndim;
+ npyarr->index[npyarr->ndim] = 0;
+ npyarr->inc = -1;
+ } else {
+ npyarr->dim = PyArray_DIM(obj, 0);
+ npyarr->stride = PyArray_STRIDE(obj, 0);
+ npyarr->stridedim = 0;
+ npyarr->index[0] = 0;
+ npyarr->inc = 1;
+ }
+
+ npyarr->columnLabels = GET_TC(tc)->columnLabels;
+ npyarr->rowLabels = GET_TC(tc)->rowLabels;
}
void NpyArr_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
- NpyArrContext *npyarr = GET_TC(tc)->npyarr;
+ NpyArrContext *npyarr = GET_TC(tc)->npyarr;
- if (npyarr) {
- NpyArr_freeItemValue(obj, tc);
- PyObject_Free(npyarr);
- }
+ if (npyarr) {
+ NpyArr_freeItemValue(obj, tc);
+ PyObject_Free(npyarr);
+ }
}
void NpyArrPassThru_iterBegin(JSOBJ Py_UNUSED(obj),
JSONTypeContext *Py_UNUSED(tc)) {}
void NpyArrPassThru_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
- NpyArrContext *npyarr = GET_TC(tc)->npyarr;
- // finished this dimension, reset the data pointer
- npyarr->curdim--;
- npyarr->dataptr -= npyarr->stride * npyarr->index[npyarr->stridedim];
- npyarr->stridedim -= npyarr->inc;
- npyarr->dim = PyArray_DIM(npyarr->array, npyarr->stridedim);
- npyarr->stride = PyArray_STRIDE(npyarr->array, npyarr->stridedim);
- npyarr->dataptr += npyarr->stride;
+ NpyArrContext *npyarr = GET_TC(tc)->npyarr;
+ // finished this dimension, reset the data pointer
+ npyarr->curdim--;
+ npyarr->dataptr -= npyarr->stride * npyarr->index[npyarr->stridedim];
+ npyarr->stridedim -= npyarr->inc;
+ npyarr->dim = PyArray_DIM(npyarr->array, npyarr->stridedim);
+ npyarr->stride = PyArray_STRIDE(npyarr->array, npyarr->stridedim);
+ npyarr->dataptr += npyarr->stride;
- NpyArr_freeItemValue(obj, tc);
+ NpyArr_freeItemValue(obj, tc);
}
int NpyArr_iterNextItem(JSOBJ obj, JSONTypeContext *tc) {
- NpyArrContext *npyarr = GET_TC(tc)->npyarr;
+ NpyArrContext *npyarr = GET_TC(tc)->npyarr;
- if (PyErr_Occurred()) {
- return 0;
- }
+ if (PyErr_Occurred()) {
+ return 0;
+ }
- if (npyarr->index[npyarr->stridedim] >= npyarr->dim) {
- return 0;
- }
+ if (npyarr->index[npyarr->stridedim] >= npyarr->dim) {
+ return 0;
+ }
- NpyArr_freeItemValue(obj, tc);
+ NpyArr_freeItemValue(obj, tc);
- if (PyArray_ISDATETIME(npyarr->array)) {
- GET_TC(tc)->itemValue = obj;
- Py_INCREF(obj);
- ((PyObjectEncoder *)tc->encoder)->npyType = PyArray_TYPE(npyarr->array);
- // Also write the resolution (unit) of the ndarray
- PyArray_Descr *dtype = PyArray_DESCR(npyarr->array);
- ((PyObjectEncoder *)tc->encoder)->valueUnit =
- get_datetime_metadata_from_dtype(dtype).base;
- ((PyObjectEncoder *)tc->encoder)->npyValue = npyarr->dataptr;
- ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = npyarr;
- } else {
- GET_TC(tc)->itemValue = npyarr->getitem(npyarr->dataptr, npyarr->array);
- }
+ if (PyArray_ISDATETIME(npyarr->array)) {
+ GET_TC(tc)->itemValue = obj;
+ Py_INCREF(obj);
+ ((PyObjectEncoder *)tc->encoder)->npyType = PyArray_TYPE(npyarr->array);
+ // Also write the resolution (unit) of the ndarray
+ PyArray_Descr *dtype = PyArray_DESCR(npyarr->array);
+ ((PyObjectEncoder *)tc->encoder)->valueUnit =
+ get_datetime_metadata_from_dtype(dtype).base;
+ ((PyObjectEncoder *)tc->encoder)->npyValue = npyarr->dataptr;
+ ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = npyarr;
+ } else {
+ GET_TC(tc)->itemValue = npyarr->getitem(npyarr->dataptr, npyarr->array);
+ }
- npyarr->dataptr += npyarr->stride;
- npyarr->index[npyarr->stridedim]++;
- return 1;
+ npyarr->dataptr += npyarr->stride;
+ npyarr->index[npyarr->stridedim]++;
+ return 1;
}
int NpyArr_iterNext(JSOBJ _obj, JSONTypeContext *tc) {
- NpyArrContext *npyarr = GET_TC(tc)->npyarr;
+ NpyArrContext *npyarr = GET_TC(tc)->npyarr;
- if (PyErr_Occurred()) {
- return 0;
- }
+ if (PyErr_Occurred()) {
+ return 0;
+ }
- if (npyarr->curdim >= npyarr->ndim ||
- npyarr->index[npyarr->stridedim] >= npyarr->dim) {
- // innermost dimension, start retrieving item values
- GET_TC(tc)->iterNext = NpyArr_iterNextItem;
- return NpyArr_iterNextItem(_obj, tc);
- }
+ if (npyarr->curdim >= npyarr->ndim ||
+ npyarr->index[npyarr->stridedim] >= npyarr->dim) {
+ // innermost dimension, start retrieving item values
+ GET_TC(tc)->iterNext = NpyArr_iterNextItem;
+ return NpyArr_iterNextItem(_obj, tc);
+ }
- // dig a dimension deeper
- npyarr->index[npyarr->stridedim]++;
+ // dig a dimension deeper
+ npyarr->index[npyarr->stridedim]++;
- npyarr->curdim++;
- npyarr->stridedim += npyarr->inc;
- npyarr->dim = PyArray_DIM(npyarr->array, npyarr->stridedim);
- npyarr->stride = PyArray_STRIDE(npyarr->array, npyarr->stridedim);
- npyarr->index[npyarr->stridedim] = 0;
+ npyarr->curdim++;
+ npyarr->stridedim += npyarr->inc;
+ npyarr->dim = PyArray_DIM(npyarr->array, npyarr->stridedim);
+ npyarr->stride = PyArray_STRIDE(npyarr->array, npyarr->stridedim);
+ npyarr->index[npyarr->stridedim] = 0;
- ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = npyarr;
- GET_TC(tc)->itemValue = npyarr->array;
- return 1;
+ ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = npyarr;
+ GET_TC(tc)->itemValue = npyarr->array;
+ return 1;
}
JSOBJ NpyArr_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *NpyArr_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- NpyArrContext *npyarr = GET_TC(tc)->npyarr;
- npy_intp idx;
- char *cStr;
+ NpyArrContext *npyarr = GET_TC(tc)->npyarr;
+ npy_intp idx;
+ char *cStr;
- if (GET_TC(tc)->iterNext == NpyArr_iterNextItem) {
- idx = npyarr->index[npyarr->stridedim] - 1;
- cStr = npyarr->columnLabels[idx];
- } else {
- idx = npyarr->index[npyarr->stridedim - npyarr->inc] - 1;
- cStr = npyarr->rowLabels[idx];
- }
+ if (GET_TC(tc)->iterNext == NpyArr_iterNextItem) {
+ idx = npyarr->index[npyarr->stridedim] - 1;
+ cStr = npyarr->columnLabels[idx];
+ } else {
+ idx = npyarr->index[npyarr->stridedim - npyarr->inc] - 1;
+ cStr = npyarr->rowLabels[idx];
+ }
- *outLen = strlen(cStr);
+ *outLen = strlen(cStr);
- return cStr;
+ return cStr;
}
//=============================================================================
@@ -568,217 +564,216 @@ char *NpyArr_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
//=============================================================================
void PdBlockPassThru_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- if (blkCtxt->transpose) {
- blkCtxt->colIdx++;
- } else {
- blkCtxt->colIdx = 0;
- }
+ if (blkCtxt->transpose) {
+ blkCtxt->colIdx++;
+ } else {
+ blkCtxt->colIdx = 0;
+ }
- NpyArr_freeItemValue(obj, tc);
+ NpyArr_freeItemValue(obj, tc);
}
int PdBlock_iterNextItem(JSOBJ obj, JSONTypeContext *tc) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- if (blkCtxt->colIdx >= blkCtxt->ncols) {
- return 0;
- }
+ if (blkCtxt->colIdx >= blkCtxt->ncols) {
+ return 0;
+ }
- GET_TC(tc)->npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
- blkCtxt->colIdx++;
- return NpyArr_iterNextItem(obj, tc);
+ GET_TC(tc)->npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
+ blkCtxt->colIdx++;
+ return NpyArr_iterNextItem(obj, tc);
}
char *PdBlock_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- NpyArrContext *npyarr = blkCtxt->npyCtxts[0];
- npy_intp idx;
- char *cStr;
-
- if (GET_TC(tc)->iterNext == PdBlock_iterNextItem) {
- idx = blkCtxt->colIdx - 1;
- cStr = npyarr->columnLabels[idx];
- } else {
- idx = GET_TC(tc)->iterNext != PdBlock_iterNext
- ? npyarr->index[npyarr->stridedim - npyarr->inc] - 1
- : npyarr->index[npyarr->stridedim];
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ NpyArrContext *npyarr = blkCtxt->npyCtxts[0];
+ npy_intp idx;
+ char *cStr;
- cStr = npyarr->rowLabels[idx];
- }
+ if (GET_TC(tc)->iterNext == PdBlock_iterNextItem) {
+ idx = blkCtxt->colIdx - 1;
+ cStr = npyarr->columnLabels[idx];
+ } else {
+ idx = GET_TC(tc)->iterNext != PdBlock_iterNext
+ ? npyarr->index[npyarr->stridedim - npyarr->inc] - 1
+ : npyarr->index[npyarr->stridedim];
- *outLen = strlen(cStr);
- return cStr;
+ cStr = npyarr->rowLabels[idx];
+ }
+
+ *outLen = strlen(cStr);
+ return cStr;
}
char *PdBlock_iterGetName_Transpose(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- NpyArrContext *npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
- npy_intp idx;
- char *cStr;
-
- if (GET_TC(tc)->iterNext == NpyArr_iterNextItem) {
- idx = npyarr->index[npyarr->stridedim] - 1;
- cStr = npyarr->columnLabels[idx];
- } else {
- idx = blkCtxt->colIdx;
- cStr = npyarr->rowLabels[idx];
- }
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ NpyArrContext *npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
+ npy_intp idx;
+ char *cStr;
+
+ if (GET_TC(tc)->iterNext == NpyArr_iterNextItem) {
+ idx = npyarr->index[npyarr->stridedim] - 1;
+ cStr = npyarr->columnLabels[idx];
+ } else {
+ idx = blkCtxt->colIdx;
+ cStr = npyarr->rowLabels[idx];
+ }
- *outLen = strlen(cStr);
- return cStr;
+ *outLen = strlen(cStr);
+ return cStr;
}
int PdBlock_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- NpyArrContext *npyarr;
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ NpyArrContext *npyarr;
- if (PyErr_Occurred() || ((JSONObjectEncoder *)tc->encoder)->errorMsg) {
- return 0;
- }
+ if (PyErr_Occurred() || ((JSONObjectEncoder *)tc->encoder)->errorMsg) {
+ return 0;
+ }
- if (blkCtxt->transpose) {
- if (blkCtxt->colIdx >= blkCtxt->ncols) {
- return 0;
- }
- } else {
- npyarr = blkCtxt->npyCtxts[0];
- if (npyarr->index[npyarr->stridedim] >= npyarr->dim) {
- return 0;
- }
+ if (blkCtxt->transpose) {
+ if (blkCtxt->colIdx >= blkCtxt->ncols) {
+ return 0;
}
+ } else {
+ npyarr = blkCtxt->npyCtxts[0];
+ if (npyarr->index[npyarr->stridedim] >= npyarr->dim) {
+ return 0;
+ }
+ }
- ((PyObjectEncoder *)tc->encoder)->blkCtxtPassthru = blkCtxt;
- GET_TC(tc)->itemValue = obj;
+ ((PyObjectEncoder *)tc->encoder)->blkCtxtPassthru = blkCtxt;
+ GET_TC(tc)->itemValue = obj;
- return 1;
+ return 1;
}
void PdBlockPassThru_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
+ PdBlockContext *blkCtxt = GET_TC(tc)->pdblock;
- if (blkCtxt->transpose) {
- // if transposed we exhaust each column before moving to the next
- GET_TC(tc)->iterNext = NpyArr_iterNextItem;
- GET_TC(tc)->iterGetName = PdBlock_iterGetName_Transpose;
- GET_TC(tc)->npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
- }
+ if (blkCtxt->transpose) {
+ // if transposed we exhaust each column before moving to the next
+ GET_TC(tc)->iterNext = NpyArr_iterNextItem;
+ GET_TC(tc)->iterGetName = PdBlock_iterGetName_Transpose;
+ GET_TC(tc)->npyarr = blkCtxt->npyCtxts[blkCtxt->colIdx];
+ }
}
void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) {
- PyObject *obj, *values, *arrays, *array;
- PdBlockContext *blkCtxt;
- NpyArrContext *npyarr;
- Py_ssize_t i;
-
- obj = (PyObject *)_obj;
-
- GET_TC(tc)->iterGetName = GET_TC(tc)->transpose
- ? PdBlock_iterGetName_Transpose
- : PdBlock_iterGetName;
-
- blkCtxt = PyObject_Malloc(sizeof(PdBlockContext));
- if (!blkCtxt) {
- PyErr_NoMemory();
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
- GET_TC(tc)->pdblock = blkCtxt;
+ PyObject *obj, *values, *arrays, *array;
+ PdBlockContext *blkCtxt;
+ NpyArrContext *npyarr;
+ Py_ssize_t i;
- blkCtxt->colIdx = 0;
- blkCtxt->transpose = GET_TC(tc)->transpose;
- blkCtxt->ncols = get_attr_length(obj, "columns");
-
- if (blkCtxt->ncols == 0) {
- blkCtxt->npyCtxts = NULL;
-
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
+ obj = (PyObject *)_obj;
- blkCtxt->npyCtxts =
- PyObject_Malloc(sizeof(NpyArrContext *) * blkCtxt->ncols);
- if (!blkCtxt->npyCtxts) {
- PyErr_NoMemory();
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
+ GET_TC(tc)->iterGetName = GET_TC(tc)->transpose
+ ? PdBlock_iterGetName_Transpose
+ : PdBlock_iterGetName;
- arrays = get_sub_attr(obj, "_mgr", "column_arrays");
- if (!arrays) {
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
+ blkCtxt = PyObject_Malloc(sizeof(PdBlockContext));
+ if (!blkCtxt) {
+ PyErr_NoMemory();
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ return;
+ }
+ GET_TC(tc)->pdblock = blkCtxt;
- for (i = 0; i < PyObject_Length(arrays); i++) {
- array = PyList_GET_ITEM(arrays, i);
- if (!array) {
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto ARR_RET;
- }
+ blkCtxt->colIdx = 0;
+ blkCtxt->transpose = GET_TC(tc)->transpose;
+ blkCtxt->ncols = get_attr_length(obj, "columns");
- // ensure we have a numpy array (i.e. np.asarray)
- values = PyObject_CallMethod(array, "__array__", NULL);
- if ((!values) || (!PyArray_CheckExact(values))) {
- // Didn't get a numpy array
- ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto ARR_RET;
- }
+ if (blkCtxt->ncols == 0) {
+ blkCtxt->npyCtxts = NULL;
- GET_TC(tc)->newObj = values;
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ return;
+ }
- // init a dedicated context for this column
- NpyArr_iterBegin(obj, tc);
- npyarr = GET_TC(tc)->npyarr;
+ blkCtxt->npyCtxts = PyObject_Malloc(sizeof(NpyArrContext *) * blkCtxt->ncols);
+ if (!blkCtxt->npyCtxts) {
+ PyErr_NoMemory();
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ return;
+ }
- GET_TC(tc)->itemValue = NULL;
- ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = NULL;
+ arrays = get_sub_attr(obj, "_mgr", "column_arrays");
+ if (!arrays) {
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ return;
+ }
- blkCtxt->npyCtxts[i] = npyarr;
- GET_TC(tc)->newObj = NULL;
+ for (i = 0; i < PyObject_Length(arrays); i++) {
+ array = PyList_GET_ITEM(arrays, i);
+ if (!array) {
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ goto ARR_RET;
}
- GET_TC(tc)->npyarr = blkCtxt->npyCtxts[0];
- goto ARR_RET;
-ARR_RET:
- Py_DECREF(arrays);
-}
+ // ensure we have a numpy array (i.e. np.asarray)
+ values = PyObject_CallMethod(array, "__array__", NULL);
+ if ((!values) || (!PyArray_CheckExact(values))) {
+ // Didn't get a numpy array
+ ((JSONObjectEncoder *)tc->encoder)->errorMsg = "";
+ GET_TC(tc)->iterNext = NpyArr_iterNextNone;
+ goto ARR_RET;
+ }
-void PdBlock_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
- PdBlockContext *blkCtxt;
- NpyArrContext *npyarr;
- int i;
+ GET_TC(tc)->newObj = values;
- GET_TC(tc)->itemValue = NULL;
+ // init a dedicated context for this column
+ NpyArr_iterBegin(obj, tc);
npyarr = GET_TC(tc)->npyarr;
- blkCtxt = GET_TC(tc)->pdblock;
+ GET_TC(tc)->itemValue = NULL;
+ ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = NULL;
- if (blkCtxt) {
- for (i = 0; i < blkCtxt->ncols; i++) {
- npyarr = blkCtxt->npyCtxts[i];
- if (npyarr) {
- if (npyarr->array) {
- Py_DECREF(npyarr->array);
- npyarr->array = NULL;
- }
+ blkCtxt->npyCtxts[i] = npyarr;
+ GET_TC(tc)->newObj = NULL;
+ }
+ GET_TC(tc)->npyarr = blkCtxt->npyCtxts[0];
+ goto ARR_RET;
- GET_TC(tc)->npyarr = npyarr;
- NpyArr_iterEnd(obj, tc);
+ARR_RET:
+ Py_DECREF(arrays);
+}
- blkCtxt->npyCtxts[i] = NULL;
- }
+void PdBlock_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
+ PdBlockContext *blkCtxt;
+ NpyArrContext *npyarr;
+ int i;
+
+ GET_TC(tc)->itemValue = NULL;
+ npyarr = GET_TC(tc)->npyarr;
+
+ blkCtxt = GET_TC(tc)->pdblock;
+
+ if (blkCtxt) {
+ for (i = 0; i < blkCtxt->ncols; i++) {
+ npyarr = blkCtxt->npyCtxts[i];
+ if (npyarr) {
+ if (npyarr->array) {
+ Py_DECREF(npyarr->array);
+ npyarr->array = NULL;
}
- if (blkCtxt->npyCtxts) {
- PyObject_Free(blkCtxt->npyCtxts);
- }
- PyObject_Free(blkCtxt);
+ GET_TC(tc)->npyarr = npyarr;
+ NpyArr_iterEnd(obj, tc);
+
+ blkCtxt->npyCtxts[i] = NULL;
+ }
}
+
+ if (blkCtxt->npyCtxts) {
+ PyObject_Free(blkCtxt->npyCtxts);
+ }
+ PyObject_Free(blkCtxt);
+ }
}
//=============================================================================
@@ -786,34 +781,34 @@ void PdBlock_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
// itemValue is borrowed reference, no ref counting
//=============================================================================
void Tuple_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->index = 0;
- GET_TC(tc)->size = PyTuple_GET_SIZE((PyObject *)obj);
- GET_TC(tc)->itemValue = NULL;
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->size = PyTuple_GET_SIZE((PyObject *)obj);
+ GET_TC(tc)->itemValue = NULL;
}
int Tuple_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- PyObject *item;
+ PyObject *item;
- if (GET_TC(tc)->index >= GET_TC(tc)->size) {
- return 0;
- }
+ if (GET_TC(tc)->index >= GET_TC(tc)->size) {
+ return 0;
+ }
- item = PyTuple_GET_ITEM(obj, GET_TC(tc)->index);
+ item = PyTuple_GET_ITEM(obj, GET_TC(tc)->index);
- GET_TC(tc)->itemValue = item;
- GET_TC(tc)->index++;
- return 1;
+ GET_TC(tc)->itemValue = item;
+ GET_TC(tc)->index++;
+ return 1;
}
void Tuple_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc)) {}
JSOBJ Tuple_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Tuple_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc),
size_t *Py_UNUSED(outLen)) {
- return NULL;
+ return NULL;
}
//=============================================================================
@@ -821,47 +816,47 @@ char *Tuple_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc),
// itemValue is borrowed reference, no ref counting
//=============================================================================
void Set_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->itemValue = NULL;
- GET_TC(tc)->iterator = PyObject_GetIter(obj);
+ GET_TC(tc)->itemValue = NULL;
+ GET_TC(tc)->iterator = PyObject_GetIter(obj);
}
int Set_iterNext(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObject *item;
+ PyObject *item;
- if (GET_TC(tc)->itemValue) {
- Py_DECREF(GET_TC(tc)->itemValue);
- GET_TC(tc)->itemValue = NULL;
- }
+ if (GET_TC(tc)->itemValue) {
+ Py_DECREF(GET_TC(tc)->itemValue);
+ GET_TC(tc)->itemValue = NULL;
+ }
- item = PyIter_Next(GET_TC(tc)->iterator);
+ item = PyIter_Next(GET_TC(tc)->iterator);
- if (item == NULL) {
- return 0;
- }
+ if (item == NULL) {
+ return 0;
+ }
- GET_TC(tc)->itemValue = item;
- return 1;
+ GET_TC(tc)->itemValue = item;
+ return 1;
}
void Set_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- if (GET_TC(tc)->itemValue) {
- Py_DECREF(GET_TC(tc)->itemValue);
- GET_TC(tc)->itemValue = NULL;
- }
+ if (GET_TC(tc)->itemValue) {
+ Py_DECREF(GET_TC(tc)->itemValue);
+ GET_TC(tc)->itemValue = NULL;
+ }
- if (GET_TC(tc)->iterator) {
- Py_DECREF(GET_TC(tc)->iterator);
- GET_TC(tc)->iterator = NULL;
- }
+ if (GET_TC(tc)->iterator) {
+ Py_DECREF(GET_TC(tc)->iterator);
+ GET_TC(tc)->iterator = NULL;
+ }
}
JSOBJ Set_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Set_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc),
size_t *Py_UNUSED(outLen)) {
- return NULL;
+ return NULL;
}
//=============================================================================
@@ -870,98 +865,98 @@ char *Set_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc),
// itemValue ref is from PyObject_GetAttr. Ref counted
//=============================================================================
void Dir_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->attrList = PyObject_Dir(obj);
- GET_TC(tc)->index = 0;
- GET_TC(tc)->size = PyList_GET_SIZE(GET_TC(tc)->attrList);
+ GET_TC(tc)->attrList = PyObject_Dir(obj);
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->size = PyList_GET_SIZE(GET_TC(tc)->attrList);
}
void Dir_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- if (GET_TC(tc)->itemValue) {
- Py_DECREF(GET_TC(tc)->itemValue);
- GET_TC(tc)->itemValue = NULL;
- }
+ if (GET_TC(tc)->itemValue) {
+ Py_DECREF(GET_TC(tc)->itemValue);
+ GET_TC(tc)->itemValue = NULL;
+ }
- if (GET_TC(tc)->itemName) {
- Py_DECREF(GET_TC(tc)->itemName);
- GET_TC(tc)->itemName = NULL;
- }
+ if (GET_TC(tc)->itemName) {
+ Py_DECREF(GET_TC(tc)->itemName);
+ GET_TC(tc)->itemName = NULL;
+ }
- Py_DECREF((PyObject *)GET_TC(tc)->attrList);
+ Py_DECREF((PyObject *)GET_TC(tc)->attrList);
}
int Dir_iterNext(JSOBJ _obj, JSONTypeContext *tc) {
- PyObject *obj = (PyObject *)_obj;
- PyObject *itemValue = GET_TC(tc)->itemValue;
- PyObject *itemName = GET_TC(tc)->itemName;
- PyObject *attr;
- PyObject *attrName;
- char *attrStr;
-
- if (PyErr_Occurred() || ((JSONObjectEncoder *)tc->encoder)->errorMsg) {
- return 0;
- }
-
- if (itemValue) {
- Py_DECREF(GET_TC(tc)->itemValue);
- GET_TC(tc)->itemValue = itemValue = NULL;
- }
-
- if (itemName) {
- Py_DECREF(GET_TC(tc)->itemName);
- GET_TC(tc)->itemName = itemName = NULL;
- }
+ PyObject *obj = (PyObject *)_obj;
+ PyObject *itemValue = GET_TC(tc)->itemValue;
+ PyObject *itemName = GET_TC(tc)->itemName;
+ PyObject *attr;
+ PyObject *attrName;
+ char *attrStr;
+
+ if (PyErr_Occurred() || ((JSONObjectEncoder *)tc->encoder)->errorMsg) {
+ return 0;
+ }
- for (; GET_TC(tc)->index < GET_TC(tc)->size; GET_TC(tc)->index++) {
- attrName = PyList_GET_ITEM(GET_TC(tc)->attrList, GET_TC(tc)->index);
- attr = PyUnicode_AsUTF8String(attrName);
- attrStr = PyBytes_AS_STRING(attr);
+ if (itemValue) {
+ Py_DECREF(GET_TC(tc)->itemValue);
+ GET_TC(tc)->itemValue = itemValue = NULL;
+ }
- if (attrStr[0] == '_') {
- Py_DECREF(attr);
- continue;
- }
+ if (itemName) {
+ Py_DECREF(GET_TC(tc)->itemName);
+ GET_TC(tc)->itemName = itemName = NULL;
+ }
- itemValue = PyObject_GetAttr(obj, attrName);
- if (itemValue == NULL) {
- PyErr_Clear();
- Py_DECREF(attr);
- continue;
- }
+ for (; GET_TC(tc)->index < GET_TC(tc)->size; GET_TC(tc)->index++) {
+ attrName = PyList_GET_ITEM(GET_TC(tc)->attrList, GET_TC(tc)->index);
+ attr = PyUnicode_AsUTF8String(attrName);
+ attrStr = PyBytes_AS_STRING(attr);
- if (PyCallable_Check(itemValue)) {
- Py_DECREF(itemValue);
- Py_DECREF(attr);
- continue;
- }
-
- GET_TC(tc)->itemName = itemName;
- GET_TC(tc)->itemValue = itemValue;
+ if (attrStr[0] == '_') {
+ Py_DECREF(attr);
+ continue;
+ }
- itemName = attr;
- break;
+ itemValue = PyObject_GetAttr(obj, attrName);
+ if (itemValue == NULL) {
+ PyErr_Clear();
+ Py_DECREF(attr);
+ continue;
}
- if (itemName == NULL) {
- GET_TC(tc)->index = GET_TC(tc)->size;
- GET_TC(tc)->itemValue = NULL;
- return 0;
+ if (PyCallable_Check(itemValue)) {
+ Py_DECREF(itemValue);
+ Py_DECREF(attr);
+ continue;
}
GET_TC(tc)->itemName = itemName;
GET_TC(tc)->itemValue = itemValue;
- GET_TC(tc)->index++;
- return 1;
+ itemName = attr;
+ break;
+ }
+
+ if (itemName == NULL) {
+ GET_TC(tc)->index = GET_TC(tc)->size;
+ GET_TC(tc)->itemValue = NULL;
+ return 0;
+ }
+
+ GET_TC(tc)->itemName = itemName;
+ GET_TC(tc)->itemValue = itemValue;
+ GET_TC(tc)->index++;
+
+ return 1;
}
JSOBJ Dir_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Dir_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- *outLen = PyBytes_GET_SIZE(GET_TC(tc)->itemName);
- return PyBytes_AS_STRING(GET_TC(tc)->itemName);
+ *outLen = PyBytes_GET_SIZE(GET_TC(tc)->itemName);
+ return PyBytes_AS_STRING(GET_TC(tc)->itemName);
}
//=============================================================================
@@ -969,187 +964,187 @@ char *Dir_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
// itemValue is borrowed from object (which is list). No refcounting
//=============================================================================
void List_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->index = 0;
- GET_TC(tc)->size = PyList_GET_SIZE((PyObject *)obj);
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->size = PyList_GET_SIZE((PyObject *)obj);
}
int List_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- if (GET_TC(tc)->index >= GET_TC(tc)->size) {
- return 0;
- }
+ if (GET_TC(tc)->index >= GET_TC(tc)->size) {
+ return 0;
+ }
- GET_TC(tc)->itemValue = PyList_GET_ITEM(obj, GET_TC(tc)->index);
- GET_TC(tc)->index++;
- return 1;
+ GET_TC(tc)->itemValue = PyList_GET_ITEM(obj, GET_TC(tc)->index);
+ GET_TC(tc)->index++;
+ return 1;
}
void List_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc)) {}
JSOBJ List_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *List_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc),
size_t *Py_UNUSED(outLen)) {
- return NULL;
+ return NULL;
}
//=============================================================================
// pandas Index iteration functions
//=============================================================================
void Index_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- GET_TC(tc)->index = 0;
- GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
- if (!GET_TC(tc)->cStr) {
- PyErr_NoMemory();
- }
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
+ if (!GET_TC(tc)->cStr) {
+ PyErr_NoMemory();
+ }
}
int Index_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- Py_ssize_t index;
- if (!GET_TC(tc)->cStr) {
- return 0;
- }
-
- index = GET_TC(tc)->index;
- Py_XDECREF(GET_TC(tc)->itemValue);
- if (index == 0) {
- memcpy(GET_TC(tc)->cStr, "name", sizeof(char) * 5);
- GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "name");
- } else if (index == 1) {
- memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
- GET_TC(tc)->itemValue = get_values(obj);
- if (!GET_TC(tc)->itemValue) {
- return 0;
- }
- } else {
- return 0;
- }
+ Py_ssize_t index;
+ if (!GET_TC(tc)->cStr) {
+ return 0;
+ }
+
+ index = GET_TC(tc)->index;
+ Py_XDECREF(GET_TC(tc)->itemValue);
+ if (index == 0) {
+ memcpy(GET_TC(tc)->cStr, "name", sizeof(char) * 5);
+ GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "name");
+ } else if (index == 1) {
+ memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
+ GET_TC(tc)->itemValue = get_values(obj);
+ if (!GET_TC(tc)->itemValue) {
+ return 0;
+ }
+ } else {
+ return 0;
+ }
- GET_TC(tc)->index++;
- return 1;
+ GET_TC(tc)->index++;
+ return 1;
}
void Index_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *Py_UNUSED(tc)) {}
JSOBJ Index_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Index_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- *outLen = strlen(GET_TC(tc)->cStr);
- return GET_TC(tc)->cStr;
+ *outLen = strlen(GET_TC(tc)->cStr);
+ return GET_TC(tc)->cStr;
}
//=============================================================================
// pandas Series iteration functions
//=============================================================================
void Series_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
- GET_TC(tc)->index = 0;
- GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
- enc->outputFormat = VALUES; // for contained series
- if (!GET_TC(tc)->cStr) {
- PyErr_NoMemory();
- }
+ PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
+ enc->outputFormat = VALUES; // for contained series
+ if (!GET_TC(tc)->cStr) {
+ PyErr_NoMemory();
+ }
}
int Series_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- Py_ssize_t index;
- if (!GET_TC(tc)->cStr) {
- return 0;
- }
-
- index = GET_TC(tc)->index;
- Py_XDECREF(GET_TC(tc)->itemValue);
- if (index == 0) {
- memcpy(GET_TC(tc)->cStr, "name", sizeof(char) * 5);
- GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "name");
- } else if (index == 1) {
- memcpy(GET_TC(tc)->cStr, "index", sizeof(char) * 6);
- GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "index");
- } else if (index == 2) {
- memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
- GET_TC(tc)->itemValue = get_values(obj);
- if (!GET_TC(tc)->itemValue) {
- return 0;
- }
- } else {
- return 0;
- }
+ Py_ssize_t index;
+ if (!GET_TC(tc)->cStr) {
+ return 0;
+ }
+
+ index = GET_TC(tc)->index;
+ Py_XDECREF(GET_TC(tc)->itemValue);
+ if (index == 0) {
+ memcpy(GET_TC(tc)->cStr, "name", sizeof(char) * 5);
+ GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "name");
+ } else if (index == 1) {
+ memcpy(GET_TC(tc)->cStr, "index", sizeof(char) * 6);
+ GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "index");
+ } else if (index == 2) {
+ memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
+ GET_TC(tc)->itemValue = get_values(obj);
+ if (!GET_TC(tc)->itemValue) {
+ return 0;
+ }
+ } else {
+ return 0;
+ }
- GET_TC(tc)->index++;
- return 1;
+ GET_TC(tc)->index++;
+ return 1;
}
void Series_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
- enc->outputFormat = enc->originalOutputFormat;
+ PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
+ enc->outputFormat = enc->originalOutputFormat;
}
JSOBJ Series_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Series_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- *outLen = strlen(GET_TC(tc)->cStr);
- return GET_TC(tc)->cStr;
+ *outLen = strlen(GET_TC(tc)->cStr);
+ return GET_TC(tc)->cStr;
}
//=============================================================================
// pandas DataFrame iteration functions
//=============================================================================
void DataFrame_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
- GET_TC(tc)->index = 0;
- GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
- enc->outputFormat = VALUES; // for contained series & index
- if (!GET_TC(tc)->cStr) {
- PyErr_NoMemory();
- }
+ PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
+ GET_TC(tc)->index = 0;
+ GET_TC(tc)->cStr = PyObject_Malloc(20 * sizeof(char));
+ enc->outputFormat = VALUES; // for contained series & index
+ if (!GET_TC(tc)->cStr) {
+ PyErr_NoMemory();
+ }
}
int DataFrame_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- Py_ssize_t index;
- if (!GET_TC(tc)->cStr) {
- return 0;
- }
-
- index = GET_TC(tc)->index;
- Py_XDECREF(GET_TC(tc)->itemValue);
- if (index == 0) {
- memcpy(GET_TC(tc)->cStr, "columns", sizeof(char) * 8);
- GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "columns");
- } else if (index == 1) {
- memcpy(GET_TC(tc)->cStr, "index", sizeof(char) * 6);
- GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "index");
- } else if (index == 2) {
- memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
- Py_INCREF(obj);
- GET_TC(tc)->itemValue = obj;
- } else {
- return 0;
- }
+ Py_ssize_t index;
+ if (!GET_TC(tc)->cStr) {
+ return 0;
+ }
+
+ index = GET_TC(tc)->index;
+ Py_XDECREF(GET_TC(tc)->itemValue);
+ if (index == 0) {
+ memcpy(GET_TC(tc)->cStr, "columns", sizeof(char) * 8);
+ GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "columns");
+ } else if (index == 1) {
+ memcpy(GET_TC(tc)->cStr, "index", sizeof(char) * 6);
+ GET_TC(tc)->itemValue = PyObject_GetAttrString(obj, "index");
+ } else if (index == 2) {
+ memcpy(GET_TC(tc)->cStr, "data", sizeof(char) * 5);
+ Py_INCREF(obj);
+ GET_TC(tc)->itemValue = obj;
+ } else {
+ return 0;
+ }
- GET_TC(tc)->index++;
- return 1;
+ GET_TC(tc)->index++;
+ return 1;
}
void DataFrame_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
- enc->outputFormat = enc->originalOutputFormat;
+ PyObjectEncoder *enc = (PyObjectEncoder *)tc->encoder;
+ enc->outputFormat = enc->originalOutputFormat;
}
JSOBJ DataFrame_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *DataFrame_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- *outLen = strlen(GET_TC(tc)->cStr);
- return GET_TC(tc)->cStr;
+ *outLen = strlen(GET_TC(tc)->cStr);
+ return GET_TC(tc)->cStr;
}
//=============================================================================
@@ -1158,62 +1153,62 @@ char *DataFrame_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
// itemValue is borrowed from object (which is dict). No refCounting
//=============================================================================
void Dict_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- GET_TC(tc)->index = 0;
+ GET_TC(tc)->index = 0;
}
int Dict_iterNext(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- PyObject *itemNameTmp;
+ PyObject *itemNameTmp;
- if (GET_TC(tc)->itemName) {
- Py_DECREF(GET_TC(tc)->itemName);
- GET_TC(tc)->itemName = NULL;
- }
+ if (GET_TC(tc)->itemName) {
+ Py_DECREF(GET_TC(tc)->itemName);
+ GET_TC(tc)->itemName = NULL;
+ }
- if (!PyDict_Next((PyObject *)GET_TC(tc)->dictObj, &GET_TC(tc)->index,
- &GET_TC(tc)->itemName, &GET_TC(tc)->itemValue)) {
- return 0;
- }
+ if (!PyDict_Next((PyObject *)GET_TC(tc)->dictObj, &GET_TC(tc)->index,
+ &GET_TC(tc)->itemName, &GET_TC(tc)->itemValue)) {
+ return 0;
+ }
- if (PyUnicode_Check(GET_TC(tc)->itemName)) {
- GET_TC(tc)->itemName = PyUnicode_AsUTF8String(GET_TC(tc)->itemName);
- } else if (!PyBytes_Check(GET_TC(tc)->itemName)) {
- GET_TC(tc)->itemName = PyObject_Str(GET_TC(tc)->itemName);
- itemNameTmp = GET_TC(tc)->itemName;
- GET_TC(tc)->itemName = PyUnicode_AsUTF8String(GET_TC(tc)->itemName);
- Py_DECREF(itemNameTmp);
- } else {
- Py_INCREF(GET_TC(tc)->itemName);
- }
- return 1;
+ if (PyUnicode_Check(GET_TC(tc)->itemName)) {
+ GET_TC(tc)->itemName = PyUnicode_AsUTF8String(GET_TC(tc)->itemName);
+ } else if (!PyBytes_Check(GET_TC(tc)->itemName)) {
+ GET_TC(tc)->itemName = PyObject_Str(GET_TC(tc)->itemName);
+ itemNameTmp = GET_TC(tc)->itemName;
+ GET_TC(tc)->itemName = PyUnicode_AsUTF8String(GET_TC(tc)->itemName);
+ Py_DECREF(itemNameTmp);
+ } else {
+ Py_INCREF(GET_TC(tc)->itemName);
+ }
+ return 1;
}
void Dict_iterEnd(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- if (GET_TC(tc)->itemName) {
- Py_DECREF(GET_TC(tc)->itemName);
- GET_TC(tc)->itemName = NULL;
- }
- Py_DECREF(GET_TC(tc)->dictObj);
+ if (GET_TC(tc)->itemName) {
+ Py_DECREF(GET_TC(tc)->itemName);
+ GET_TC(tc)->itemName = NULL;
+ }
+ Py_DECREF(GET_TC(tc)->dictObj);
}
JSOBJ Dict_iterGetValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->itemValue;
+ return GET_TC(tc)->itemValue;
}
char *Dict_iterGetName(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc,
size_t *outLen) {
- *outLen = PyBytes_GET_SIZE(GET_TC(tc)->itemName);
- return PyBytes_AS_STRING(GET_TC(tc)->itemName);
+ *outLen = PyBytes_GET_SIZE(GET_TC(tc)->itemName);
+ return PyBytes_AS_STRING(GET_TC(tc)->itemName);
}
void NpyArr_freeLabels(char **labels, npy_intp len) {
- npy_intp i;
+ npy_intp i;
- if (labels) {
- for (i = 0; i < len; i++) {
- PyObject_Free(labels[i]);
- }
- PyObject_Free(labels);
+ if (labels) {
+ for (i = 0; i < len; i++) {
+ PyObject_Free(labels[i]);
}
+ PyObject_Free(labels);
+ }
}
/*
@@ -1235,895 +1230,885 @@ void NpyArr_freeLabels(char **labels, npy_intp len) {
*/
char **NpyArr_encodeLabels(PyArrayObject *labels, PyObjectEncoder *enc,
npy_intp num) {
- // NOTE this function steals a reference to labels.
- PyObject *item = NULL;
- size_t len;
- npy_intp i, stride;
- char **ret;
- char *dataptr, *cLabel;
- int type_num;
- PyArray_Descr *dtype;
- NPY_DATETIMEUNIT base = enc->datetimeUnit;
-
- if (!labels) {
- return 0;
- }
-
- if (PyArray_SIZE(labels) < num) {
- PyErr_SetString(
- PyExc_ValueError,
- "Label array sizes do not match corresponding data shape");
- Py_DECREF(labels);
- return 0;
- }
-
- ret = PyObject_Malloc(sizeof(char *) * num);
- if (!ret) {
- PyErr_NoMemory();
- Py_DECREF(labels);
- return 0;
- }
-
- for (i = 0; i < num; i++) {
- ret[i] = NULL;
- }
-
- stride = PyArray_STRIDE(labels, 0);
- dataptr = PyArray_DATA(labels);
- type_num = PyArray_TYPE(labels);
- dtype = PyArray_DESCR(labels);
+ // NOTE this function steals a reference to labels.
+ PyObject *item = NULL;
+ size_t len;
+ npy_intp i, stride;
+ char **ret;
+ char *dataptr, *cLabel;
+ int type_num;
+ PyArray_Descr *dtype;
+ NPY_DATETIMEUNIT base = enc->datetimeUnit;
+
+ if (!labels) {
+ return 0;
+ }
- for (i = 0; i < num; i++) {
- item = PyArray_GETITEM(labels, dataptr);
- if (!item) {
- NpyArr_freeLabels(ret, num);
- ret = 0;
- break;
- }
+ if (PyArray_SIZE(labels) < num) {
+ PyErr_SetString(PyExc_ValueError,
+ "Label array sizes do not match corresponding data shape");
+ Py_DECREF(labels);
+ return 0;
+ }
- int is_datetimelike = 0;
- npy_int64 i8date;
- NPY_DATETIMEUNIT dateUnit = NPY_FR_ns;
- if (PyTypeNum_ISDATETIME(type_num)) {
- is_datetimelike = 1;
- PyArray_VectorUnaryFunc *castfunc =
- PyArray_GetCastFunc(PyArray_DescrFromType(type_num), NPY_INT64);
- if (!castfunc) {
- PyErr_Format(PyExc_ValueError,
- "Cannot cast numpy dtype %d to long",
- enc->npyType);
- }
- castfunc(dataptr, &i8date, 1, NULL, NULL);
- dateUnit = get_datetime_metadata_from_dtype(dtype).base;
- } else if (PyDate_Check(item) || PyDelta_Check(item)) {
- is_datetimelike = 1;
- if (PyObject_HasAttrString(item, "_value")) {
- // pd.Timestamp object or pd.NaT
- // see test_date_index_and_values for case with non-nano
- i8date = get_long_attr(item, "_value");
- } else {
- if (PyDelta_Check(item)) {
- i8date = total_seconds(item) *
- 1000000000LL; // nanoseconds per second
- } else {
- // datetime.* objects don't follow above rules
- i8date = PyDateTimeToEpoch(item, NPY_FR_ns);
- }
- }
+ ret = PyObject_Malloc(sizeof(char *) * num);
+ if (!ret) {
+ PyErr_NoMemory();
+ Py_DECREF(labels);
+ return 0;
+ }
+
+ for (i = 0; i < num; i++) {
+ ret[i] = NULL;
+ }
+
+ stride = PyArray_STRIDE(labels, 0);
+ dataptr = PyArray_DATA(labels);
+ type_num = PyArray_TYPE(labels);
+ dtype = PyArray_DESCR(labels);
+
+ for (i = 0; i < num; i++) {
+ item = PyArray_GETITEM(labels, dataptr);
+ if (!item) {
+ NpyArr_freeLabels(ret, num);
+ ret = 0;
+ break;
+ }
+
+ int is_datetimelike = 0;
+ npy_int64 i8date;
+ NPY_DATETIMEUNIT dateUnit = NPY_FR_ns;
+ if (PyTypeNum_ISDATETIME(type_num)) {
+ is_datetimelike = 1;
+ PyArray_VectorUnaryFunc *castfunc =
+ PyArray_GetCastFunc(PyArray_DescrFromType(type_num), NPY_INT64);
+ if (!castfunc) {
+ PyErr_Format(PyExc_ValueError, "Cannot cast numpy dtype %d to long",
+ enc->npyType);
+ }
+ castfunc(dataptr, &i8date, 1, NULL, NULL);
+ dateUnit = get_datetime_metadata_from_dtype(dtype).base;
+ } else if (PyDate_Check(item) || PyDelta_Check(item)) {
+ is_datetimelike = 1;
+ if (PyObject_HasAttrString(item, "_value")) {
+ // pd.Timestamp object or pd.NaT
+ // see test_date_index_and_values for case with non-nano
+ i8date = get_long_attr(item, "_value");
+ } else {
+ if (PyDelta_Check(item)) {
+ i8date = total_seconds(item) * 1000000000LL; // nanoseconds per second
+ } else {
+ // datetime.* objects don't follow above rules
+ i8date = PyDateTimeToEpoch(item, NPY_FR_ns);
}
+ }
+ }
- if (is_datetimelike) {
- if (i8date == get_nat()) {
- len = 4;
- cLabel = PyObject_Malloc(len + 1);
- strncpy(cLabel, "null", len + 1);
+ if (is_datetimelike) {
+ if (i8date == get_nat()) {
+ len = 4;
+ cLabel = PyObject_Malloc(len + 1);
+ strncpy(cLabel, "null", len + 1);
+ } else {
+ if (enc->datetimeIso) {
+ if ((type_num == NPY_TIMEDELTA) || (PyDelta_Check(item))) {
+ // TODO(username): non-nano timedelta support?
+ cLabel = int64ToIsoDuration(i8date, &len);
+ } else {
+ if (type_num == NPY_DATETIME) {
+ cLabel = int64ToIso(i8date, dateUnit, base, &len);
} else {
- if (enc->datetimeIso) {
- if ((type_num == NPY_TIMEDELTA) || (PyDelta_Check(item))) {
- // TODO(username): non-nano timedelta support?
- cLabel = int64ToIsoDuration(i8date, &len);
- } else {
- if (type_num == NPY_DATETIME) {
- cLabel = int64ToIso(i8date, dateUnit, base, &len);
- } else {
- cLabel = PyDateTimeToIso(item, base, &len);
- }
- }
- if (cLabel == NULL) {
- Py_DECREF(item);
- NpyArr_freeLabels(ret, num);
- ret = 0;
- break;
- }
- } else {
- int size_of_cLabel = 21; // 21 chars for int 64
- cLabel = PyObject_Malloc(size_of_cLabel);
- snprintf(cLabel, size_of_cLabel, "%" NPY_DATETIME_FMT,
- NpyDateTimeToEpoch(i8date, base));
- len = strlen(cLabel);
- }
- }
- } else { // Fallback to string representation
- // Replace item with the string to keep it alive.
- Py_SETREF(item, PyObject_Str(item));
- if (item == NULL) {
- NpyArr_freeLabels(ret, num);
- ret = 0;
- break;
+ cLabel = PyDateTimeToIso(item, base, &len);
}
-
- cLabel = (char *)PyUnicode_AsUTF8(item);
- len = strlen(cLabel);
- }
-
- // Add 1 to include NULL terminator
- ret[i] = PyObject_Malloc(len + 1);
- memcpy(ret[i], cLabel, len + 1);
- Py_DECREF(item);
-
- if (is_datetimelike) {
- PyObject_Free(cLabel);
- }
-
- if (PyErr_Occurred()) {
+ }
+ if (cLabel == NULL) {
+ Py_DECREF(item);
NpyArr_freeLabels(ret, num);
ret = 0;
break;
+ }
+ } else {
+ int size_of_cLabel = 21; // 21 chars for int 64
+ cLabel = PyObject_Malloc(size_of_cLabel);
+ snprintf(cLabel, size_of_cLabel, "%" NPY_DATETIME_FMT,
+ NpyDateTimeToEpoch(i8date, base));
+ len = strlen(cLabel);
}
+ }
+ } else { // Fallback to string representation
+ // Replace item with the string to keep it alive.
+ Py_SETREF(item, PyObject_Str(item));
+ if (item == NULL) {
+ NpyArr_freeLabels(ret, num);
+ ret = 0;
+ break;
+ }
- if (!ret[i]) {
- PyErr_NoMemory();
- ret = 0;
- break;
- }
-
- dataptr += stride;
+ cLabel = (char *)PyUnicode_AsUTF8(item);
+ len = strlen(cLabel);
}
- Py_DECREF(labels);
- return ret;
-}
+ // Add 1 to include NULL terminator
+ ret[i] = PyObject_Malloc(len + 1);
+ memcpy(ret[i], cLabel, len + 1);
+ Py_DECREF(item);
-void Object_invokeDefaultHandler(PyObject *obj, PyObjectEncoder *enc) {
- PyObject *tmpObj = NULL;
- tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL);
- if (!PyErr_Occurred()) {
- if (tmpObj == NULL) {
- PyErr_SetString(PyExc_TypeError,
- "Failed to execute default handler");
- } else {
- encode(tmpObj, (JSONObjectEncoder *)enc, NULL, 0);
- }
+ if (is_datetimelike) {
+ PyObject_Free(cLabel);
}
- Py_XDECREF(tmpObj);
- return;
-}
-
-void Object_beginTypeContext(JSOBJ _obj, JSONTypeContext *tc) {
- PyObject *obj, *exc, *toDictFunc, *tmpObj, *values;
- TypeContext *pc;
- PyObjectEncoder *enc;
- double val;
- npy_int64 value;
- int unit;
- tc->prv = NULL;
-
- if (!_obj) {
- tc->type = JT_INVALID;
- return;
+ if (PyErr_Occurred()) {
+ NpyArr_freeLabels(ret, num);
+ ret = 0;
+ break;
}
- obj = (PyObject *)_obj;
- enc = (PyObjectEncoder *)tc->encoder;
-
- if (PyBool_Check(obj)) {
- tc->type = (obj == Py_True) ? JT_TRUE : JT_FALSE;
- return;
- } else if (obj == Py_None) {
- tc->type = JT_NULL;
- return;
+ if (!ret[i]) {
+ PyErr_NoMemory();
+ ret = 0;
+ break;
}
- pc = createTypeContext();
- if (!pc) {
- tc->type = JT_INVALID;
- return;
- }
- tc->prv = pc;
-
- if (PyTypeNum_ISDATETIME(enc->npyType)) {
- int64_t longVal;
- PyArray_VectorUnaryFunc *castfunc =
- PyArray_GetCastFunc(PyArray_DescrFromType(enc->npyType), NPY_INT64);
- if (!castfunc) {
- PyErr_Format(PyExc_ValueError, "Cannot cast numpy dtype %d to long",
- enc->npyType);
- }
- castfunc(enc->npyValue, &longVal, 1, NULL, NULL);
- if (longVal == get_nat()) {
- tc->type = JT_NULL;
- } else {
- if (enc->datetimeIso) {
- if (enc->npyType == NPY_TIMEDELTA) {
- pc->PyTypeToUTF8 = NpyTimeDeltaToIsoCallback;
- } else {
- pc->PyTypeToUTF8 = NpyDateTimeToIsoCallback;
- }
- // Currently no way to pass longVal to iso function, so use
- // state management
- pc->longValue = longVal;
- tc->type = JT_UTF8;
- } else {
- NPY_DATETIMEUNIT base =
- ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- pc->longValue = NpyDateTimeToEpoch(longVal, base);
- tc->type = JT_LONG;
- }
- }
+ dataptr += stride;
+ }
- // TODO(username): this prevents infinite loop with
- // mixed-type DataFrames;
- // refactor
- enc->npyCtxtPassthru = NULL;
- enc->npyType = -1;
- return;
- }
+ Py_DECREF(labels);
+ return ret;
+}
- if (PyIter_Check(obj) ||
- (PyArray_Check(obj) && !PyArray_CheckScalar(obj))) {
- goto ISITERABLE;
+void Object_invokeDefaultHandler(PyObject *obj, PyObjectEncoder *enc) {
+ PyObject *tmpObj = NULL;
+ tmpObj = PyObject_CallFunctionObjArgs(enc->defaultHandler, obj, NULL);
+ if (!PyErr_Occurred()) {
+ if (tmpObj == NULL) {
+ PyErr_SetString(PyExc_TypeError, "Failed to execute default handler");
+ } else {
+ encode(tmpObj, (JSONObjectEncoder *)enc, NULL, 0);
}
+ }
+ Py_XDECREF(tmpObj);
+ return;
+}
- if (PyLong_Check(obj)) {
- tc->type = JT_LONG;
- int overflow = 0;
- pc->longValue = PyLong_AsLongLongAndOverflow(obj, &overflow);
- int err;
- err = (pc->longValue == -1) && PyErr_Occurred();
-
- if (overflow) {
- tc->type = JT_BIGNUM;
- } else if (err) {
- goto INVALID;
- }
+void Object_beginTypeContext(JSOBJ _obj, JSONTypeContext *tc) {
+ PyObject *obj, *exc, *toDictFunc, *tmpObj, *values;
+ TypeContext *pc;
+ PyObjectEncoder *enc;
+ double val;
+ npy_int64 value;
+ int unit;
- return;
- } else if (PyFloat_Check(obj)) {
- val = PyFloat_AS_DOUBLE(obj);
- if (npy_isnan(val) || npy_isinf(val)) {
- tc->type = JT_NULL;
- } else {
- pc->doubleValue = val;
- tc->type = JT_DOUBLE;
- }
- return;
- } else if (PyBytes_Check(obj)) {
- pc->PyTypeToUTF8 = PyBytesToUTF8;
- tc->type = JT_UTF8;
- return;
- } else if (PyUnicode_Check(obj)) {
- pc->PyTypeToUTF8 = PyUnicodeToUTF8;
- tc->type = JT_UTF8;
- return;
- } else if (object_is_decimal_type(obj)) {
- pc->doubleValue = PyFloat_AsDouble(obj);
- tc->type = JT_DOUBLE;
- return;
- } else if (PyDateTime_Check(obj) || PyDate_Check(obj)) {
- if (object_is_nat_type(obj)) {
- tc->type = JT_NULL;
- return;
- }
+ tc->prv = NULL;
- if (enc->datetimeIso) {
- pc->PyTypeToUTF8 = PyDateTimeToIsoCallback;
- tc->type = JT_UTF8;
- } else {
- NPY_DATETIMEUNIT base =
- ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- pc->longValue = PyDateTimeToEpoch(obj, base);
- tc->type = JT_LONG;
- }
- return;
- } else if (PyTime_Check(obj)) {
- pc->PyTypeToUTF8 = PyTimeToJSON;
- tc->type = JT_UTF8;
- return;
- } else if (PyArray_IsScalar(obj, Datetime)) {
- npy_int64 longVal;
- if (((PyDatetimeScalarObject *)obj)->obval == get_nat()) {
- tc->type = JT_NULL;
- return;
- }
- PyArray_Descr *dtype = PyArray_DescrFromScalar(obj);
- if (!PyTypeNum_ISDATETIME(dtype->type_num)) {
- PyErr_Format(PyExc_ValueError, "Could not get resolution of datetime");
- return;
- }
+ if (!_obj) {
+ tc->type = JT_INVALID;
+ return;
+ }
- PyArray_Descr *outcode = PyArray_DescrFromType(NPY_INT64);
- PyArray_CastScalarToCtype(obj, &longVal, outcode);
- Py_DECREF(outcode);
+ obj = (PyObject *)_obj;
+ enc = (PyObjectEncoder *)tc->encoder;
- if (enc->datetimeIso) {
- GET_TC(tc)->longValue = longVal;
- pc->PyTypeToUTF8 = NpyDateTimeToIsoCallback;
- enc->valueUnit = get_datetime_metadata_from_dtype(dtype).base;
- tc->type = JT_UTF8;
- } else {
- NPY_DATETIMEUNIT base =
- ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- pc->longValue = PyDateTimeToEpoch(obj, base);
- tc->type = JT_LONG;
- }
- return;
- } else if (PyDelta_Check(obj)) {
- if (PyObject_HasAttrString(obj, "_value")) {
- // pd.Timedelta object or pd.NaT
- value = get_long_attr(obj, "_value");
- } else {
- value = total_seconds(obj) * 1000000000LL; // nanoseconds per sec
- }
+ if (PyBool_Check(obj)) {
+ tc->type = (obj == Py_True) ? JT_TRUE : JT_FALSE;
+ return;
+ } else if (obj == Py_None) {
+ tc->type = JT_NULL;
+ return;
+ }
- if (value == get_nat()) {
- tc->type = JT_NULL;
- return;
- } else if (enc->datetimeIso) {
- pc->PyTypeToUTF8 = NpyTimeDeltaToIsoCallback;
- tc->type = JT_UTF8;
+ pc = createTypeContext();
+ if (!pc) {
+ tc->type = JT_INVALID;
+ return;
+ }
+ tc->prv = pc;
+
+ if (PyTypeNum_ISDATETIME(enc->npyType)) {
+ int64_t longVal;
+ PyArray_VectorUnaryFunc *castfunc =
+ PyArray_GetCastFunc(PyArray_DescrFromType(enc->npyType), NPY_INT64);
+ if (!castfunc) {
+ PyErr_Format(PyExc_ValueError, "Cannot cast numpy dtype %d to long",
+ enc->npyType);
+ }
+ castfunc(enc->npyValue, &longVal, 1, NULL, NULL);
+ if (longVal == get_nat()) {
+ tc->type = JT_NULL;
+ } else {
+ if (enc->datetimeIso) {
+ if (enc->npyType == NPY_TIMEDELTA) {
+ pc->PyTypeToUTF8 = NpyTimeDeltaToIsoCallback;
} else {
- unit = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
- if (scaleNanosecToUnit(&value, unit) != 0) {
- // TODO(username): Add some kind of error handling here
- }
+ pc->PyTypeToUTF8 = NpyDateTimeToIsoCallback;
+ }
+ // Currently no way to pass longVal to iso function, so use
+ // state management
+ pc->longValue = longVal;
+ tc->type = JT_UTF8;
+ } else {
+ NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ pc->longValue = NpyDateTimeToEpoch(longVal, base);
+ tc->type = JT_LONG;
+ }
+ }
- exc = PyErr_Occurred();
+ // TODO(username): this prevents infinite loop with
+ // mixed-type DataFrames;
+ // refactor
+ enc->npyCtxtPassthru = NULL;
+ enc->npyType = -1;
+ return;
+ }
- if (exc && PyErr_ExceptionMatches(PyExc_OverflowError)) {
- goto INVALID;
- }
+ if (PyIter_Check(obj) || (PyArray_Check(obj) && !PyArray_CheckScalar(obj))) {
+ goto ISITERABLE;
+ }
- tc->type = JT_LONG;
- }
- pc->longValue = value;
- return;
- } else if (PyArray_IsScalar(obj, Integer)) {
- tc->type = JT_LONG;
- PyArray_CastScalarToCtype(obj, &(pc->longValue),
- PyArray_DescrFromType(NPY_INT64));
+ if (PyLong_Check(obj)) {
+ tc->type = JT_LONG;
+ int overflow = 0;
+ pc->longValue = PyLong_AsLongLongAndOverflow(obj, &overflow);
+ int err;
+ err = (pc->longValue == -1) && PyErr_Occurred();
- exc = PyErr_Occurred();
+ if (overflow) {
+ tc->type = JT_BIGNUM;
+ } else if (err) {
+ goto INVALID;
+ }
- if (exc && PyErr_ExceptionMatches(PyExc_OverflowError)) {
- goto INVALID;
- }
+ return;
+ } else if (PyFloat_Check(obj)) {
+ val = PyFloat_AS_DOUBLE(obj);
+ if (npy_isnan(val) || npy_isinf(val)) {
+ tc->type = JT_NULL;
+ } else {
+ pc->doubleValue = val;
+ tc->type = JT_DOUBLE;
+ }
+ return;
+ } else if (PyBytes_Check(obj)) {
+ pc->PyTypeToUTF8 = PyBytesToUTF8;
+ tc->type = JT_UTF8;
+ return;
+ } else if (PyUnicode_Check(obj)) {
+ pc->PyTypeToUTF8 = PyUnicodeToUTF8;
+ tc->type = JT_UTF8;
+ return;
+ } else if (object_is_decimal_type(obj)) {
+ pc->doubleValue = PyFloat_AsDouble(obj);
+ tc->type = JT_DOUBLE;
+ return;
+ } else if (PyDateTime_Check(obj) || PyDate_Check(obj)) {
+ if (object_is_nat_type(obj)) {
+ tc->type = JT_NULL;
+ return;
+ }
- return;
- } else if (PyArray_IsScalar(obj, Bool)) {
- PyArray_CastScalarToCtype(obj, &(pc->longValue),
- PyArray_DescrFromType(NPY_BOOL));
- tc->type = (pc->longValue) ? JT_TRUE : JT_FALSE;
- return;
- } else if (PyArray_IsScalar(obj, Float) || PyArray_IsScalar(obj, Double)) {
- PyArray_CastScalarToCtype(obj, &(pc->doubleValue),
- PyArray_DescrFromType(NPY_DOUBLE));
- tc->type = JT_DOUBLE;
- return;
- } else if (PyArray_CheckScalar(obj)) {
- PyErr_Format(PyExc_TypeError,
- "%R (numpy-scalar) is not JSON serializable at the moment",
- obj);
- goto INVALID;
- } else if (object_is_na_type(obj)) {
- tc->type = JT_NULL;
- return;
+ if (enc->datetimeIso) {
+ pc->PyTypeToUTF8 = PyDateTimeToIsoCallback;
+ tc->type = JT_UTF8;
+ } else {
+ NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ pc->longValue = PyDateTimeToEpoch(obj, base);
+ tc->type = JT_LONG;
+ }
+ return;
+ } else if (PyTime_Check(obj)) {
+ pc->PyTypeToUTF8 = PyTimeToJSON;
+ tc->type = JT_UTF8;
+ return;
+ } else if (PyArray_IsScalar(obj, Datetime)) {
+ npy_int64 longVal;
+ if (((PyDatetimeScalarObject *)obj)->obval == get_nat()) {
+ tc->type = JT_NULL;
+ return;
+ }
+ PyArray_Descr *dtype = PyArray_DescrFromScalar(obj);
+ if (!PyTypeNum_ISDATETIME(dtype->type_num)) {
+ PyErr_Format(PyExc_ValueError, "Could not get resolution of datetime");
+ return;
+ }
+
+ PyArray_Descr *outcode = PyArray_DescrFromType(NPY_INT64);
+ PyArray_CastScalarToCtype(obj, &longVal, outcode);
+ Py_DECREF(outcode);
+
+ if (enc->datetimeIso) {
+ GET_TC(tc)->longValue = longVal;
+ pc->PyTypeToUTF8 = NpyDateTimeToIsoCallback;
+ enc->valueUnit = get_datetime_metadata_from_dtype(dtype).base;
+ tc->type = JT_UTF8;
+ } else {
+ NPY_DATETIMEUNIT base = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ pc->longValue = PyDateTimeToEpoch(obj, base);
+ tc->type = JT_LONG;
+ }
+ return;
+ } else if (PyDelta_Check(obj)) {
+ if (PyObject_HasAttrString(obj, "_value")) {
+ // pd.Timedelta object or pd.NaT
+ value = get_long_attr(obj, "_value");
+ } else {
+ value = total_seconds(obj) * 1000000000LL; // nanoseconds per sec
}
-ISITERABLE:
+ if (value == get_nat()) {
+ tc->type = JT_NULL;
+ return;
+ } else if (enc->datetimeIso) {
+ pc->PyTypeToUTF8 = NpyTimeDeltaToIsoCallback;
+ tc->type = JT_UTF8;
+ } else {
+ unit = ((PyObjectEncoder *)tc->encoder)->datetimeUnit;
+ if (scaleNanosecToUnit(&value, unit) != 0) {
+ // TODO(username): Add some kind of error handling here
+ }
- if (object_is_index_type(obj)) {
- if (enc->outputFormat == SPLIT) {
- tc->type = JT_OBJECT;
- pc->iterBegin = Index_iterBegin;
- pc->iterEnd = Index_iterEnd;
- pc->iterNext = Index_iterNext;
- pc->iterGetValue = Index_iterGetValue;
- pc->iterGetName = Index_iterGetName;
- return;
- }
+ exc = PyErr_Occurred();
- pc->newObj = get_values(obj);
- if (pc->newObj) {
- tc->type = JT_ARRAY;
- pc->iterBegin = NpyArr_iterBegin;
- pc->iterEnd = NpyArr_iterEnd;
- pc->iterNext = NpyArr_iterNext;
- pc->iterGetValue = NpyArr_iterGetValue;
- pc->iterGetName = NpyArr_iterGetName;
- } else {
- goto INVALID;
- }
+ if (exc && PyErr_ExceptionMatches(PyExc_OverflowError)) {
+ goto INVALID;
+ }
- return;
- } else if (object_is_series_type(obj)) {
- if (enc->outputFormat == SPLIT) {
- tc->type = JT_OBJECT;
- pc->iterBegin = Series_iterBegin;
- pc->iterEnd = Series_iterEnd;
- pc->iterNext = Series_iterNext;
- pc->iterGetValue = Series_iterGetValue;
- pc->iterGetName = Series_iterGetName;
- return;
- }
+ tc->type = JT_LONG;
+ }
+ pc->longValue = value;
+ return;
+ } else if (PyArray_IsScalar(obj, Integer)) {
+ tc->type = JT_LONG;
+ PyArray_CastScalarToCtype(obj, &(pc->longValue),
+ PyArray_DescrFromType(NPY_INT64));
- pc->newObj = get_values(obj);
- if (!pc->newObj) {
- goto INVALID;
- }
+ exc = PyErr_Occurred();
- if (enc->outputFormat == INDEX || enc->outputFormat == COLUMNS) {
- tc->type = JT_OBJECT;
- tmpObj = PyObject_GetAttrString(obj, "index");
- if (!tmpObj) {
- goto INVALID;
- }
- values = get_values(tmpObj);
- Py_DECREF(tmpObj);
- if (!values) {
- goto INVALID;
- }
- pc->columnLabelsLen = PyArray_DIM(pc->newObj, 0);
- pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
- pc->columnLabelsLen);
- if (!pc->columnLabels) {
- goto INVALID;
- }
- } else {
- tc->type = JT_ARRAY;
- }
- pc->iterBegin = NpyArr_iterBegin;
- pc->iterEnd = NpyArr_iterEnd;
- pc->iterNext = NpyArr_iterNext;
- pc->iterGetValue = NpyArr_iterGetValue;
- pc->iterGetName = NpyArr_iterGetName;
- return;
- } else if (PyArray_Check(obj)) {
- if (enc->npyCtxtPassthru) {
- pc->npyarr = enc->npyCtxtPassthru;
- tc->type = (pc->npyarr->columnLabels ? JT_OBJECT : JT_ARRAY);
-
- pc->iterBegin = NpyArrPassThru_iterBegin;
- pc->iterNext = NpyArr_iterNext;
- pc->iterEnd = NpyArrPassThru_iterEnd;
- pc->iterGetValue = NpyArr_iterGetValue;
- pc->iterGetName = NpyArr_iterGetName;
-
- enc->npyCtxtPassthru = NULL;
- return;
- }
+ if (exc && PyErr_ExceptionMatches(PyExc_OverflowError)) {
+ goto INVALID;
+ }
- tc->type = JT_ARRAY;
- pc->iterBegin = NpyArr_iterBegin;
- pc->iterEnd = NpyArr_iterEnd;
- pc->iterNext = NpyArr_iterNext;
- pc->iterGetValue = NpyArr_iterGetValue;
- pc->iterGetName = NpyArr_iterGetName;
- return;
- } else if (object_is_dataframe_type(obj)) {
- if (enc->blkCtxtPassthru) {
- pc->pdblock = enc->blkCtxtPassthru;
- tc->type =
- (pc->pdblock->npyCtxts[0]->columnLabels ? JT_OBJECT : JT_ARRAY);
-
- pc->iterBegin = PdBlockPassThru_iterBegin;
- pc->iterEnd = PdBlockPassThru_iterEnd;
- pc->iterNext = PdBlock_iterNextItem;
- pc->iterGetName = PdBlock_iterGetName;
- pc->iterGetValue = NpyArr_iterGetValue;
-
- enc->blkCtxtPassthru = NULL;
- return;
- }
+ return;
+ } else if (PyArray_IsScalar(obj, Bool)) {
+ PyArray_CastScalarToCtype(obj, &(pc->longValue),
+ PyArray_DescrFromType(NPY_BOOL));
+ tc->type = (pc->longValue) ? JT_TRUE : JT_FALSE;
+ return;
+ } else if (PyArray_IsScalar(obj, Float) || PyArray_IsScalar(obj, Double)) {
+ PyArray_CastScalarToCtype(obj, &(pc->doubleValue),
+ PyArray_DescrFromType(NPY_DOUBLE));
+ tc->type = JT_DOUBLE;
+ return;
+ } else if (PyArray_CheckScalar(obj)) {
+ PyErr_Format(PyExc_TypeError,
+ "%R (numpy-scalar) is not JSON serializable at the moment",
+ obj);
+ goto INVALID;
+ } else if (object_is_na_type(obj)) {
+ tc->type = JT_NULL;
+ return;
+ }
- if (enc->outputFormat == SPLIT) {
- tc->type = JT_OBJECT;
- pc->iterBegin = DataFrame_iterBegin;
- pc->iterEnd = DataFrame_iterEnd;
- pc->iterNext = DataFrame_iterNext;
- pc->iterGetValue = DataFrame_iterGetValue;
- pc->iterGetName = DataFrame_iterGetName;
- return;
- }
+ISITERABLE:
- pc->iterBegin = PdBlock_iterBegin;
- pc->iterEnd = PdBlock_iterEnd;
- pc->iterNext = PdBlock_iterNext;
- pc->iterGetName = PdBlock_iterGetName;
- pc->iterGetValue = NpyArr_iterGetValue;
-
- if (enc->outputFormat == VALUES) {
- tc->type = JT_ARRAY;
- } else if (enc->outputFormat == RECORDS) {
- tc->type = JT_ARRAY;
- tmpObj = PyObject_GetAttrString(obj, "columns");
- if (!tmpObj) {
- goto INVALID;
- }
- values = get_values(tmpObj);
- if (!values) {
- Py_DECREF(tmpObj);
- goto INVALID;
- }
- pc->columnLabelsLen = PyObject_Size(tmpObj);
- pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
- pc->columnLabelsLen);
- Py_DECREF(tmpObj);
- if (!pc->columnLabels) {
- goto INVALID;
- }
- } else if (enc->outputFormat == INDEX || enc->outputFormat == COLUMNS) {
- tc->type = JT_OBJECT;
- tmpObj = (enc->outputFormat == INDEX
- ? PyObject_GetAttrString(obj, "index")
- : PyObject_GetAttrString(obj, "columns"));
- if (!tmpObj) {
- goto INVALID;
- }
- values = get_values(tmpObj);
- if (!values) {
- Py_DECREF(tmpObj);
- goto INVALID;
- }
- pc->rowLabelsLen = PyObject_Size(tmpObj);
- pc->rowLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
- pc->rowLabelsLen);
- Py_DECREF(tmpObj);
- tmpObj = (enc->outputFormat == INDEX
- ? PyObject_GetAttrString(obj, "columns")
- : PyObject_GetAttrString(obj, "index"));
- if (!tmpObj) {
- NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
- pc->rowLabels = NULL;
- goto INVALID;
- }
- values = get_values(tmpObj);
- if (!values) {
- Py_DECREF(tmpObj);
- NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
- pc->rowLabels = NULL;
- goto INVALID;
- }
- pc->columnLabelsLen = PyObject_Size(tmpObj);
- pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
- pc->columnLabelsLen);
- Py_DECREF(tmpObj);
- if (!pc->columnLabels) {
- NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
- pc->rowLabels = NULL;
- goto INVALID;
- }
+ if (object_is_index_type(obj)) {
+ if (enc->outputFormat == SPLIT) {
+ tc->type = JT_OBJECT;
+ pc->iterBegin = Index_iterBegin;
+ pc->iterEnd = Index_iterEnd;
+ pc->iterNext = Index_iterNext;
+ pc->iterGetValue = Index_iterGetValue;
+ pc->iterGetName = Index_iterGetName;
+ return;
+ }
+
+ pc->newObj = get_values(obj);
+ if (pc->newObj) {
+ tc->type = JT_ARRAY;
+ pc->iterBegin = NpyArr_iterBegin;
+ pc->iterEnd = NpyArr_iterEnd;
+ pc->iterNext = NpyArr_iterNext;
+ pc->iterGetValue = NpyArr_iterGetValue;
+ pc->iterGetName = NpyArr_iterGetName;
+ } else {
+ goto INVALID;
+ }
- if (enc->outputFormat == COLUMNS) {
- pc->transpose = 1;
- }
- } else {
- goto INVALID;
- }
- return;
- } else if (PyDict_Check(obj)) {
- tc->type = JT_OBJECT;
- pc->iterBegin = Dict_iterBegin;
- pc->iterEnd = Dict_iterEnd;
- pc->iterNext = Dict_iterNext;
- pc->iterGetValue = Dict_iterGetValue;
- pc->iterGetName = Dict_iterGetName;
- pc->dictObj = obj;
- Py_INCREF(obj);
-
- return;
- } else if (PyList_Check(obj)) {
- tc->type = JT_ARRAY;
- pc->iterBegin = List_iterBegin;
- pc->iterEnd = List_iterEnd;
- pc->iterNext = List_iterNext;
- pc->iterGetValue = List_iterGetValue;
- pc->iterGetName = List_iterGetName;
- return;
- } else if (PyTuple_Check(obj)) {
- tc->type = JT_ARRAY;
- pc->iterBegin = Tuple_iterBegin;
- pc->iterEnd = Tuple_iterEnd;
- pc->iterNext = Tuple_iterNext;
- pc->iterGetValue = Tuple_iterGetValue;
- pc->iterGetName = Tuple_iterGetName;
- return;
- } else if (PyAnySet_Check(obj)) {
- tc->type = JT_ARRAY;
- pc->iterBegin = Set_iterBegin;
- pc->iterEnd = Set_iterEnd;
- pc->iterNext = Set_iterNext;
- pc->iterGetValue = Set_iterGetValue;
- pc->iterGetName = Set_iterGetName;
- return;
+ return;
+ } else if (object_is_series_type(obj)) {
+ if (enc->outputFormat == SPLIT) {
+ tc->type = JT_OBJECT;
+ pc->iterBegin = Series_iterBegin;
+ pc->iterEnd = Series_iterEnd;
+ pc->iterNext = Series_iterNext;
+ pc->iterGetValue = Series_iterGetValue;
+ pc->iterGetName = Series_iterGetName;
+ return;
+ }
+
+ pc->newObj = get_values(obj);
+ if (!pc->newObj) {
+ goto INVALID;
+ }
+
+ if (enc->outputFormat == INDEX || enc->outputFormat == COLUMNS) {
+ tc->type = JT_OBJECT;
+ tmpObj = PyObject_GetAttrString(obj, "index");
+ if (!tmpObj) {
+ goto INVALID;
+ }
+ values = get_values(tmpObj);
+ Py_DECREF(tmpObj);
+ if (!values) {
+ goto INVALID;
+ }
+ pc->columnLabelsLen = PyArray_DIM(pc->newObj, 0);
+ pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
+ pc->columnLabelsLen);
+ if (!pc->columnLabels) {
+ goto INVALID;
+ }
+ } else {
+ tc->type = JT_ARRAY;
}
+ pc->iterBegin = NpyArr_iterBegin;
+ pc->iterEnd = NpyArr_iterEnd;
+ pc->iterNext = NpyArr_iterNext;
+ pc->iterGetValue = NpyArr_iterGetValue;
+ pc->iterGetName = NpyArr_iterGetName;
+ return;
+ } else if (PyArray_Check(obj)) {
+ if (enc->npyCtxtPassthru) {
+ pc->npyarr = enc->npyCtxtPassthru;
+ tc->type = (pc->npyarr->columnLabels ? JT_OBJECT : JT_ARRAY);
+
+ pc->iterBegin = NpyArrPassThru_iterBegin;
+ pc->iterNext = NpyArr_iterNext;
+ pc->iterEnd = NpyArrPassThru_iterEnd;
+ pc->iterGetValue = NpyArr_iterGetValue;
+ pc->iterGetName = NpyArr_iterGetName;
+
+ enc->npyCtxtPassthru = NULL;
+ return;
+ }
+
+ tc->type = JT_ARRAY;
+ pc->iterBegin = NpyArr_iterBegin;
+ pc->iterEnd = NpyArr_iterEnd;
+ pc->iterNext = NpyArr_iterNext;
+ pc->iterGetValue = NpyArr_iterGetValue;
+ pc->iterGetName = NpyArr_iterGetName;
+ return;
+ } else if (object_is_dataframe_type(obj)) {
+ if (enc->blkCtxtPassthru) {
+ pc->pdblock = enc->blkCtxtPassthru;
+ tc->type =
+ (pc->pdblock->npyCtxts[0]->columnLabels ? JT_OBJECT : JT_ARRAY);
+
+ pc->iterBegin = PdBlockPassThru_iterBegin;
+ pc->iterEnd = PdBlockPassThru_iterEnd;
+ pc->iterNext = PdBlock_iterNextItem;
+ pc->iterGetName = PdBlock_iterGetName;
+ pc->iterGetValue = NpyArr_iterGetValue;
+
+ enc->blkCtxtPassthru = NULL;
+ return;
+ }
+
+ if (enc->outputFormat == SPLIT) {
+ tc->type = JT_OBJECT;
+ pc->iterBegin = DataFrame_iterBegin;
+ pc->iterEnd = DataFrame_iterEnd;
+ pc->iterNext = DataFrame_iterNext;
+ pc->iterGetValue = DataFrame_iterGetValue;
+ pc->iterGetName = DataFrame_iterGetName;
+ return;
+ }
+
+ pc->iterBegin = PdBlock_iterBegin;
+ pc->iterEnd = PdBlock_iterEnd;
+ pc->iterNext = PdBlock_iterNext;
+ pc->iterGetName = PdBlock_iterGetName;
+ pc->iterGetValue = NpyArr_iterGetValue;
+
+ if (enc->outputFormat == VALUES) {
+ tc->type = JT_ARRAY;
+ } else if (enc->outputFormat == RECORDS) {
+ tc->type = JT_ARRAY;
+ tmpObj = PyObject_GetAttrString(obj, "columns");
+ if (!tmpObj) {
+ goto INVALID;
+ }
+ values = get_values(tmpObj);
+ if (!values) {
+ Py_DECREF(tmpObj);
+ goto INVALID;
+ }
+ pc->columnLabelsLen = PyObject_Size(tmpObj);
+ pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
+ pc->columnLabelsLen);
+ Py_DECREF(tmpObj);
+ if (!pc->columnLabels) {
+ goto INVALID;
+ }
+ } else if (enc->outputFormat == INDEX || enc->outputFormat == COLUMNS) {
+ tc->type = JT_OBJECT;
+ tmpObj =
+ (enc->outputFormat == INDEX ? PyObject_GetAttrString(obj, "index")
+ : PyObject_GetAttrString(obj, "columns"));
+ if (!tmpObj) {
+ goto INVALID;
+ }
+ values = get_values(tmpObj);
+ if (!values) {
+ Py_DECREF(tmpObj);
+ goto INVALID;
+ }
+ pc->rowLabelsLen = PyObject_Size(tmpObj);
+ pc->rowLabels =
+ NpyArr_encodeLabels((PyArrayObject *)values, enc, pc->rowLabelsLen);
+ Py_DECREF(tmpObj);
+ tmpObj =
+ (enc->outputFormat == INDEX ? PyObject_GetAttrString(obj, "columns")
+ : PyObject_GetAttrString(obj, "index"));
+ if (!tmpObj) {
+ NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
+ pc->rowLabels = NULL;
+ goto INVALID;
+ }
+ values = get_values(tmpObj);
+ if (!values) {
+ Py_DECREF(tmpObj);
+ NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
+ pc->rowLabels = NULL;
+ goto INVALID;
+ }
+ pc->columnLabelsLen = PyObject_Size(tmpObj);
+ pc->columnLabels = NpyArr_encodeLabels((PyArrayObject *)values, enc,
+ pc->columnLabelsLen);
+ Py_DECREF(tmpObj);
+ if (!pc->columnLabels) {
+ NpyArr_freeLabels(pc->rowLabels, pc->rowLabelsLen);
+ pc->rowLabels = NULL;
+ goto INVALID;
+ }
- toDictFunc = PyObject_GetAttrString(obj, "toDict");
+ if (enc->outputFormat == COLUMNS) {
+ pc->transpose = 1;
+ }
+ } else {
+ goto INVALID;
+ }
+ return;
+ } else if (PyDict_Check(obj)) {
+ tc->type = JT_OBJECT;
+ pc->iterBegin = Dict_iterBegin;
+ pc->iterEnd = Dict_iterEnd;
+ pc->iterNext = Dict_iterNext;
+ pc->iterGetValue = Dict_iterGetValue;
+ pc->iterGetName = Dict_iterGetName;
+ pc->dictObj = obj;
+ Py_INCREF(obj);
- if (toDictFunc) {
- PyObject *tuple = PyTuple_New(0);
- PyObject *toDictResult = PyObject_Call(toDictFunc, tuple, NULL);
- Py_DECREF(tuple);
- Py_DECREF(toDictFunc);
+ return;
+ } else if (PyList_Check(obj)) {
+ tc->type = JT_ARRAY;
+ pc->iterBegin = List_iterBegin;
+ pc->iterEnd = List_iterEnd;
+ pc->iterNext = List_iterNext;
+ pc->iterGetValue = List_iterGetValue;
+ pc->iterGetName = List_iterGetName;
+ return;
+ } else if (PyTuple_Check(obj)) {
+ tc->type = JT_ARRAY;
+ pc->iterBegin = Tuple_iterBegin;
+ pc->iterEnd = Tuple_iterEnd;
+ pc->iterNext = Tuple_iterNext;
+ pc->iterGetValue = Tuple_iterGetValue;
+ pc->iterGetName = Tuple_iterGetName;
+ return;
+ } else if (PyAnySet_Check(obj)) {
+ tc->type = JT_ARRAY;
+ pc->iterBegin = Set_iterBegin;
+ pc->iterEnd = Set_iterEnd;
+ pc->iterNext = Set_iterNext;
+ pc->iterGetValue = Set_iterGetValue;
+ pc->iterGetName = Set_iterGetName;
+ return;
+ }
- if (toDictResult == NULL) {
- PyErr_Clear();
- tc->type = JT_NULL;
- return;
- }
+ toDictFunc = PyObject_GetAttrString(obj, "toDict");
- if (!PyDict_Check(toDictResult)) {
- Py_DECREF(toDictResult);
- tc->type = JT_NULL;
- return;
- }
+ if (toDictFunc) {
+ PyObject *tuple = PyTuple_New(0);
+ PyObject *toDictResult = PyObject_Call(toDictFunc, tuple, NULL);
+ Py_DECREF(tuple);
+ Py_DECREF(toDictFunc);
- tc->type = JT_OBJECT;
- pc->iterBegin = Dict_iterBegin;
- pc->iterEnd = Dict_iterEnd;
- pc->iterNext = Dict_iterNext;
- pc->iterGetValue = Dict_iterGetValue;
- pc->iterGetName = Dict_iterGetName;
- pc->dictObj = toDictResult;
- return;
+ if (toDictResult == NULL) {
+ PyErr_Clear();
+ tc->type = JT_NULL;
+ return;
}
- PyErr_Clear();
-
- if (enc->defaultHandler) {
- Object_invokeDefaultHandler(obj, enc);
- goto INVALID;
+ if (!PyDict_Check(toDictResult)) {
+ Py_DECREF(toDictResult);
+ tc->type = JT_NULL;
+ return;
}
tc->type = JT_OBJECT;
- pc->iterBegin = Dir_iterBegin;
- pc->iterEnd = Dir_iterEnd;
- pc->iterNext = Dir_iterNext;
- pc->iterGetValue = Dir_iterGetValue;
- pc->iterGetName = Dir_iterGetName;
+ pc->iterBegin = Dict_iterBegin;
+ pc->iterEnd = Dict_iterEnd;
+ pc->iterNext = Dict_iterNext;
+ pc->iterGetValue = Dict_iterGetValue;
+ pc->iterGetName = Dict_iterGetName;
+ pc->dictObj = toDictResult;
return;
+ }
+
+ PyErr_Clear();
+
+ if (enc->defaultHandler) {
+ Object_invokeDefaultHandler(obj, enc);
+ goto INVALID;
+ }
+
+ tc->type = JT_OBJECT;
+ pc->iterBegin = Dir_iterBegin;
+ pc->iterEnd = Dir_iterEnd;
+ pc->iterNext = Dir_iterNext;
+ pc->iterGetValue = Dir_iterGetValue;
+ pc->iterGetName = Dir_iterGetName;
+ return;
INVALID:
- tc->type = JT_INVALID;
- PyObject_Free(tc->prv);
- tc->prv = NULL;
- return;
+ tc->type = JT_INVALID;
+ PyObject_Free(tc->prv);
+ tc->prv = NULL;
+ return;
}
void Object_endTypeContext(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- if (tc->prv) {
- Py_XDECREF(GET_TC(tc)->newObj);
- GET_TC(tc)->newObj = NULL;
- NpyArr_freeLabels(GET_TC(tc)->rowLabels, GET_TC(tc)->rowLabelsLen);
- GET_TC(tc)->rowLabels = NULL;
- NpyArr_freeLabels(GET_TC(tc)->columnLabels,
- GET_TC(tc)->columnLabelsLen);
- GET_TC(tc)->columnLabels = NULL;
- PyObject_Free(GET_TC(tc)->cStr);
- GET_TC(tc)->cStr = NULL;
- PyObject_Free(tc->prv);
- tc->prv = NULL;
- }
+ if (tc->prv) {
+ Py_XDECREF(GET_TC(tc)->newObj);
+ GET_TC(tc)->newObj = NULL;
+ NpyArr_freeLabels(GET_TC(tc)->rowLabels, GET_TC(tc)->rowLabelsLen);
+ GET_TC(tc)->rowLabels = NULL;
+ NpyArr_freeLabels(GET_TC(tc)->columnLabels, GET_TC(tc)->columnLabelsLen);
+ GET_TC(tc)->columnLabels = NULL;
+ PyObject_Free(GET_TC(tc)->cStr);
+ GET_TC(tc)->cStr = NULL;
+ PyObject_Free(tc->prv);
+ tc->prv = NULL;
+ }
}
const char *Object_getStringValue(JSOBJ obj, JSONTypeContext *tc,
size_t *_outLen) {
- return GET_TC(tc)->PyTypeToUTF8(obj, tc, _outLen);
+ return GET_TC(tc)->PyTypeToUTF8(obj, tc, _outLen);
}
JSINT64 Object_getLongValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->longValue;
+ return GET_TC(tc)->longValue;
}
double Object_getDoubleValue(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
- return GET_TC(tc)->doubleValue;
+ return GET_TC(tc)->doubleValue;
}
const char *Object_getBigNumStringValue(JSOBJ obj, JSONTypeContext *tc,
size_t *_outLen) {
- PyObject *repr = PyObject_Str(obj);
- const char *str = PyUnicode_AsUTF8AndSize(repr, (Py_ssize_t *)_outLen);
- char *bytes = PyObject_Malloc(*_outLen + 1);
- memcpy(bytes, str, *_outLen + 1);
- GET_TC(tc)->cStr = bytes;
+ PyObject *repr = PyObject_Str(obj);
+ const char *str = PyUnicode_AsUTF8AndSize(repr, (Py_ssize_t *)_outLen);
+ char *bytes = PyObject_Malloc(*_outLen + 1);
+ memcpy(bytes, str, *_outLen + 1);
+ GET_TC(tc)->cStr = bytes;
- Py_DECREF(repr);
+ Py_DECREF(repr);
- return GET_TC(tc)->cStr;
+ return GET_TC(tc)->cStr;
}
static void Object_releaseObject(JSOBJ _obj) { Py_DECREF((PyObject *)_obj); }
void Object_iterBegin(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->iterBegin(obj, tc);
+ GET_TC(tc)->iterBegin(obj, tc);
}
int Object_iterNext(JSOBJ obj, JSONTypeContext *tc) {
- return GET_TC(tc)->iterNext(obj, tc);
+ return GET_TC(tc)->iterNext(obj, tc);
}
void Object_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
- GET_TC(tc)->iterEnd(obj, tc);
+ GET_TC(tc)->iterEnd(obj, tc);
}
JSOBJ Object_iterGetValue(JSOBJ obj, JSONTypeContext *tc) {
- return GET_TC(tc)->iterGetValue(obj, tc);
+ return GET_TC(tc)->iterGetValue(obj, tc);
}
char *Object_iterGetName(JSOBJ obj, JSONTypeContext *tc, size_t *outLen) {
- return GET_TC(tc)->iterGetName(obj, tc, outLen);
+ return GET_TC(tc)->iterGetName(obj, tc, outLen);
}
PyObject *objToJSON(PyObject *Py_UNUSED(self), PyObject *args,
PyObject *kwargs) {
- PyDateTime_IMPORT;
- if (PyDateTimeAPI == NULL) {
- return NULL;
- }
-
- PandasDateTime_IMPORT;
- if (PandasDateTimeAPI == NULL) {
- return NULL;
- }
-
- static char *kwlist[] = {"obj",
- "ensure_ascii",
- "double_precision",
- "encode_html_chars",
- "orient",
- "date_unit",
- "iso_dates",
- "default_handler",
- "indent",
- NULL};
-
- char buffer[65536];
- char *ret;
- PyObject *newobj;
- PyObject *oinput = NULL;
- PyObject *oensureAscii = NULL;
- int idoublePrecision = 10; // default double precision setting
- PyObject *oencodeHTMLChars = NULL;
- char *sOrient = NULL;
- char *sdateFormat = NULL;
- PyObject *oisoDates = 0;
- PyObject *odefHandler = 0;
- int indent = 0;
-
- PyObjectEncoder pyEncoder = {{
- Object_beginTypeContext,
- Object_endTypeContext,
- Object_getStringValue,
- Object_getLongValue,
- NULL, // getIntValue is unused
- Object_getDoubleValue,
- Object_getBigNumStringValue,
- Object_iterBegin,
- Object_iterNext,
- Object_iterEnd,
- Object_iterGetValue,
- Object_iterGetName,
- Object_releaseObject,
- PyObject_Malloc,
- PyObject_Realloc,
- PyObject_Free,
- -1, // recursionMax
- idoublePrecision,
- 1, // forceAscii
- 0, // encodeHTMLChars
- 0, // indent
- }};
- JSONObjectEncoder *encoder = (JSONObjectEncoder *)&pyEncoder;
-
- pyEncoder.npyCtxtPassthru = NULL;
- pyEncoder.blkCtxtPassthru = NULL;
- pyEncoder.npyType = -1;
- pyEncoder.npyValue = NULL;
- pyEncoder.datetimeIso = 0;
- pyEncoder.datetimeUnit = NPY_FR_ms;
- pyEncoder.outputFormat = COLUMNS;
- pyEncoder.defaultHandler = 0;
-
- if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|OiOssOOi", kwlist,
- &oinput, &oensureAscii, &idoublePrecision,
- &oencodeHTMLChars, &sOrient, &sdateFormat,
- &oisoDates, &odefHandler, &indent)) {
- return NULL;
- }
+ PyDateTime_IMPORT;
+ if (PyDateTimeAPI == NULL) {
+ return NULL;
+ }
- if (oensureAscii != NULL && !PyObject_IsTrue(oensureAscii)) {
- encoder->forceASCII = 0;
- }
+ PandasDateTime_IMPORT;
+ if (PandasDateTimeAPI == NULL) {
+ return NULL;
+ }
+
+ static char *kwlist[] = {"obj",
+ "ensure_ascii",
+ "double_precision",
+ "encode_html_chars",
+ "orient",
+ "date_unit",
+ "iso_dates",
+ "default_handler",
+ "indent",
+ NULL};
+
+ char buffer[65536];
+ char *ret;
+ PyObject *newobj;
+ PyObject *oinput = NULL;
+ PyObject *oensureAscii = NULL;
+ int idoublePrecision = 10; // default double precision setting
+ PyObject *oencodeHTMLChars = NULL;
+ char *sOrient = NULL;
+ char *sdateFormat = NULL;
+ PyObject *oisoDates = 0;
+ PyObject *odefHandler = 0;
+ int indent = 0;
+
+ PyObjectEncoder pyEncoder = {{
+ Object_beginTypeContext,
+ Object_endTypeContext,
+ Object_getStringValue,
+ Object_getLongValue,
+ NULL, // getIntValue is unused
+ Object_getDoubleValue,
+ Object_getBigNumStringValue,
+ Object_iterBegin,
+ Object_iterNext,
+ Object_iterEnd,
+ Object_iterGetValue,
+ Object_iterGetName,
+ Object_releaseObject,
+ PyObject_Malloc,
+ PyObject_Realloc,
+ PyObject_Free,
+ -1, // recursionMax
+ idoublePrecision,
+ 1, // forceAscii
+ 0, // encodeHTMLChars
+ 0, // indent
+ }};
+ JSONObjectEncoder *encoder = (JSONObjectEncoder *)&pyEncoder;
+
+ pyEncoder.npyCtxtPassthru = NULL;
+ pyEncoder.blkCtxtPassthru = NULL;
+ pyEncoder.npyType = -1;
+ pyEncoder.npyValue = NULL;
+ pyEncoder.datetimeIso = 0;
+ pyEncoder.datetimeUnit = NPY_FR_ms;
+ pyEncoder.outputFormat = COLUMNS;
+ pyEncoder.defaultHandler = 0;
+
+ if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|OiOssOOi", kwlist, &oinput,
+ &oensureAscii, &idoublePrecision,
+ &oencodeHTMLChars, &sOrient, &sdateFormat,
+ &oisoDates, &odefHandler, &indent)) {
+ return NULL;
+ }
- if (oencodeHTMLChars != NULL && PyObject_IsTrue(oencodeHTMLChars)) {
- encoder->encodeHTMLChars = 1;
- }
+ if (oensureAscii != NULL && !PyObject_IsTrue(oensureAscii)) {
+ encoder->forceASCII = 0;
+ }
- if (idoublePrecision > JSON_DOUBLE_MAX_DECIMALS || idoublePrecision < 0) {
- PyErr_Format(
- PyExc_ValueError,
- "Invalid value '%d' for option 'double_precision', max is '%u'",
- idoublePrecision, JSON_DOUBLE_MAX_DECIMALS);
- return NULL;
- }
- encoder->doublePrecision = idoublePrecision;
-
- if (sOrient != NULL) {
- if (strcmp(sOrient, "records") == 0) {
- pyEncoder.outputFormat = RECORDS;
- } else if (strcmp(sOrient, "index") == 0) {
- pyEncoder.outputFormat = INDEX;
- } else if (strcmp(sOrient, "split") == 0) {
- pyEncoder.outputFormat = SPLIT;
- } else if (strcmp(sOrient, "values") == 0) {
- pyEncoder.outputFormat = VALUES;
- } else if (strcmp(sOrient, "columns") != 0) {
- PyErr_Format(PyExc_ValueError,
- "Invalid value '%s' for option 'orient'", sOrient);
- return NULL;
- }
- }
+ if (oencodeHTMLChars != NULL && PyObject_IsTrue(oencodeHTMLChars)) {
+ encoder->encodeHTMLChars = 1;
+ }
- if (sdateFormat != NULL) {
- if (strcmp(sdateFormat, "s") == 0) {
- pyEncoder.datetimeUnit = NPY_FR_s;
- } else if (strcmp(sdateFormat, "ms") == 0) {
- pyEncoder.datetimeUnit = NPY_FR_ms;
- } else if (strcmp(sdateFormat, "us") == 0) {
- pyEncoder.datetimeUnit = NPY_FR_us;
- } else if (strcmp(sdateFormat, "ns") == 0) {
- pyEncoder.datetimeUnit = NPY_FR_ns;
- } else {
- PyErr_Format(PyExc_ValueError,
- "Invalid value '%s' for option 'date_unit'",
- sdateFormat);
- return NULL;
- }
+ if (idoublePrecision > JSON_DOUBLE_MAX_DECIMALS || idoublePrecision < 0) {
+ PyErr_Format(
+ PyExc_ValueError,
+ "Invalid value '%d' for option 'double_precision', max is '%u'",
+ idoublePrecision, JSON_DOUBLE_MAX_DECIMALS);
+ return NULL;
+ }
+ encoder->doublePrecision = idoublePrecision;
+
+ if (sOrient != NULL) {
+ if (strcmp(sOrient, "records") == 0) {
+ pyEncoder.outputFormat = RECORDS;
+ } else if (strcmp(sOrient, "index") == 0) {
+ pyEncoder.outputFormat = INDEX;
+ } else if (strcmp(sOrient, "split") == 0) {
+ pyEncoder.outputFormat = SPLIT;
+ } else if (strcmp(sOrient, "values") == 0) {
+ pyEncoder.outputFormat = VALUES;
+ } else if (strcmp(sOrient, "columns") != 0) {
+ PyErr_Format(PyExc_ValueError, "Invalid value '%s' for option 'orient'",
+ sOrient);
+ return NULL;
+ }
+ }
+
+ if (sdateFormat != NULL) {
+ if (strcmp(sdateFormat, "s") == 0) {
+ pyEncoder.datetimeUnit = NPY_FR_s;
+ } else if (strcmp(sdateFormat, "ms") == 0) {
+ pyEncoder.datetimeUnit = NPY_FR_ms;
+ } else if (strcmp(sdateFormat, "us") == 0) {
+ pyEncoder.datetimeUnit = NPY_FR_us;
+ } else if (strcmp(sdateFormat, "ns") == 0) {
+ pyEncoder.datetimeUnit = NPY_FR_ns;
+ } else {
+ PyErr_Format(PyExc_ValueError,
+ "Invalid value '%s' for option 'date_unit'", sdateFormat);
+ return NULL;
}
+ }
- if (oisoDates != NULL && PyObject_IsTrue(oisoDates)) {
- pyEncoder.datetimeIso = 1;
- }
+ if (oisoDates != NULL && PyObject_IsTrue(oisoDates)) {
+ pyEncoder.datetimeIso = 1;
+ }
- if (odefHandler != NULL && odefHandler != Py_None) {
- if (!PyCallable_Check(odefHandler)) {
- PyErr_SetString(PyExc_TypeError, "Default handler is not callable");
- return NULL;
- }
- pyEncoder.defaultHandler = odefHandler;
+ if (odefHandler != NULL && odefHandler != Py_None) {
+ if (!PyCallable_Check(odefHandler)) {
+ PyErr_SetString(PyExc_TypeError, "Default handler is not callable");
+ return NULL;
}
+ pyEncoder.defaultHandler = odefHandler;
+ }
- encoder->indent = indent;
+ encoder->indent = indent;
- pyEncoder.originalOutputFormat = pyEncoder.outputFormat;
- ret = JSON_EncodeObject(oinput, encoder, buffer, sizeof(buffer));
- if (PyErr_Occurred()) {
- return NULL;
- }
+ pyEncoder.originalOutputFormat = pyEncoder.outputFormat;
+ ret = JSON_EncodeObject(oinput, encoder, buffer, sizeof(buffer));
+ if (PyErr_Occurred()) {
+ return NULL;
+ }
- if (encoder->errorMsg) {
- if (ret != buffer) {
- encoder->free(ret);
- }
- PyErr_Format(PyExc_OverflowError, "%s", encoder->errorMsg);
- return NULL;
+ if (encoder->errorMsg) {
+ if (ret != buffer) {
+ encoder->free(ret);
}
+ PyErr_Format(PyExc_OverflowError, "%s", encoder->errorMsg);
+ return NULL;
+ }
- newobj = PyUnicode_FromString(ret);
+ newobj = PyUnicode_FromString(ret);
- if (ret != buffer) {
- encoder->free(ret);
- }
+ if (ret != buffer) {
+ encoder->free(ret);
+ }
- return newobj;
+ return newobj;
}
diff --git a/pandas/_libs/src/vendored/ujson/python/ujson.c b/pandas/_libs/src/vendored/ujson/python/ujson.c
index 12dbb33f2874f..736089c48e3ee 100644
--- a/pandas/_libs/src/vendored/ujson/python/ujson.c
+++ b/pandas/_libs/src/vendored/ujson/python/ujson.c
@@ -16,18 +16,19 @@ derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+DISCLAIMED. IN NO EVENT SHALL ESN SOCIAL SOFTWARE AB OR JONAS TARNSTROM BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF
+THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc)
https://github.com/client9/stringencoders
-Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights reserved.
+Copyright (c) 2007 Nick Galbreath -- nickg [at] modp [dot] com. All rights
+reserved.
Numeric decoder derived from TCL library
https://www.opensource.apple.com/source/tcl/tcl-14/tcl/license.terms
@@ -50,10 +51,10 @@ void *initObjToJSON(void);
/* JSONToObj */
PyObject *JSONToObj(PyObject *self, PyObject *args, PyObject *kwargs);
-#define ENCODER_HELP_TEXT \
- "Use ensure_ascii=false to output UTF-8. Pass in double_precision to " \
- "alter the maximum digit precision of doubles. Set " \
- "encode_html_chars=True to encode < > & as unicode escape sequences."
+#define ENCODER_HELP_TEXT \
+ "Use ensure_ascii=false to output UTF-8. Pass in double_precision to " \
+ "alter the maximum digit precision of doubles. Set " \
+ "encode_html_chars=True to encode < > & as unicode escape sequences."
static PyMethodDef ujsonMethods[] = {
{"ujson_dumps", (PyCFunction)objToJSON, METH_VARARGS | METH_KEYWORDS,
@@ -65,12 +66,12 @@ static PyMethodDef ujsonMethods[] = {
};
typedef struct {
- PyObject *type_decimal;
- PyObject *type_dataframe;
- PyObject *type_series;
- PyObject *type_index;
- PyObject *type_nat;
- PyObject *type_na;
+ PyObject *type_decimal;
+ PyObject *type_dataframe;
+ PyObject *type_series;
+ PyObject *type_index;
+ PyObject *type_nat;
+ PyObject *type_na;
} modulestate;
#define modulestate(o) ((modulestate *)PyModule_GetState(o))
@@ -90,359 +91,356 @@ static struct PyModuleDef moduledef = {.m_base = PyModuleDef_HEAD_INIT,
#ifndef PYPY_VERSION
/* Used in objToJSON.c */
int object_is_decimal_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_decimal = state->type_decimal;
- if (type_decimal == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_decimal);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_decimal = state->type_decimal;
+ if (type_decimal == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_decimal);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_dataframe_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_dataframe = state->type_dataframe;
- if (type_dataframe == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_dataframe);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_dataframe = state->type_dataframe;
+ if (type_dataframe == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_dataframe);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_series_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_series = state->type_series;
- if (type_series == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_series);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_series = state->type_series;
+ if (type_series == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_series);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_index_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_index = state->type_index;
- if (type_index == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_index);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_index = state->type_index;
+ if (type_index == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_index);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_nat_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_nat = state->type_nat;
- if (type_nat == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_nat);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_nat = state->type_nat;
+ if (type_nat == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_nat);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_na_type(PyObject *obj) {
- PyObject *module = PyState_FindModule(&moduledef);
- if (module == NULL)
- return 0;
- modulestate *state = modulestate(module);
- if (state == NULL)
- return 0;
- PyObject *type_na = state->type_na;
- if (type_na == NULL) {
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_na);
- if (result == -1) {
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyState_FindModule(&moduledef);
+ if (module == NULL)
+ return 0;
+ modulestate *state = modulestate(module);
+ if (state == NULL)
+ return 0;
+ PyObject *type_na = state->type_na;
+ if (type_na == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_na);
+ if (result == -1) {
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
#else
- /* Used in objToJSON.c */
+/* Used in objToJSON.c */
int object_is_decimal_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("decimal");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_decimal = PyObject_GetAttrString(module, "Decimal");
- if (type_decimal == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_decimal);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_decimal);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("decimal");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_decimal = PyObject_GetAttrString(module, "Decimal");
+ if (type_decimal == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_decimal);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_decimal);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_dataframe_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("pandas");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_dataframe = PyObject_GetAttrString(module, "DataFrame");
- if (type_dataframe == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_dataframe);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_dataframe);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("pandas");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_dataframe = PyObject_GetAttrString(module, "DataFrame");
+ if (type_dataframe == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_dataframe);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_dataframe);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_series_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("pandas");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_series = PyObject_GetAttrString(module, "Series");
- if (type_series == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_series);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_series);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("pandas");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_series = PyObject_GetAttrString(module, "Series");
+ if (type_series == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_series);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_series);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_index_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("pandas");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_index = PyObject_GetAttrString(module, "Index");
- if (type_index == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_index);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_index);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("pandas");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_index = PyObject_GetAttrString(module, "Index");
+ if (type_index == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_index);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_index);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_nat_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("pandas._libs.tslibs.nattype");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_nat = PyObject_GetAttrString(module, "NaTType");
- if (type_nat == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_nat);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_nat);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("pandas._libs.tslibs.nattype");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_nat = PyObject_GetAttrString(module, "NaTType");
+ if (type_nat == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_nat);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_nat);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
int object_is_na_type(PyObject *obj) {
- PyObject *module = PyImport_ImportModule("pandas._libs.missing");
- if (module == NULL) {
- PyErr_Clear();
- return 0;
- }
- PyObject *type_na = PyObject_GetAttrString(module, "NAType");
- if (type_na == NULL) {
- Py_DECREF(module);
- PyErr_Clear();
- return 0;
- }
- int result = PyObject_IsInstance(obj, type_na);
- if (result == -1) {
- Py_DECREF(module);
- Py_DECREF(type_na);
- PyErr_Clear();
- return 0;
- }
- return result;
+ PyObject *module = PyImport_ImportModule("pandas._libs.missing");
+ if (module == NULL) {
+ PyErr_Clear();
+ return 0;
+ }
+ PyObject *type_na = PyObject_GetAttrString(module, "NAType");
+ if (type_na == NULL) {
+ Py_DECREF(module);
+ PyErr_Clear();
+ return 0;
+ }
+ int result = PyObject_IsInstance(obj, type_na);
+ if (result == -1) {
+ Py_DECREF(module);
+ Py_DECREF(type_na);
+ PyErr_Clear();
+ return 0;
+ }
+ return result;
}
#endif
static int module_traverse(PyObject *m, visitproc visit, void *arg) {
- Py_VISIT(modulestate(m)->type_decimal);
- Py_VISIT(modulestate(m)->type_dataframe);
- Py_VISIT(modulestate(m)->type_series);
- Py_VISIT(modulestate(m)->type_index);
- Py_VISIT(modulestate(m)->type_nat);
- Py_VISIT(modulestate(m)->type_na);
- return 0;
+ Py_VISIT(modulestate(m)->type_decimal);
+ Py_VISIT(modulestate(m)->type_dataframe);
+ Py_VISIT(modulestate(m)->type_series);
+ Py_VISIT(modulestate(m)->type_index);
+ Py_VISIT(modulestate(m)->type_nat);
+ Py_VISIT(modulestate(m)->type_na);
+ return 0;
}
static int module_clear(PyObject *m) {
- Py_CLEAR(modulestate(m)->type_decimal);
- Py_CLEAR(modulestate(m)->type_dataframe);
- Py_CLEAR(modulestate(m)->type_series);
- Py_CLEAR(modulestate(m)->type_index);
- Py_CLEAR(modulestate(m)->type_nat);
- Py_CLEAR(modulestate(m)->type_na);
- return 0;
+ Py_CLEAR(modulestate(m)->type_decimal);
+ Py_CLEAR(modulestate(m)->type_dataframe);
+ Py_CLEAR(modulestate(m)->type_series);
+ Py_CLEAR(modulestate(m)->type_index);
+ Py_CLEAR(modulestate(m)->type_nat);
+ Py_CLEAR(modulestate(m)->type_na);
+ return 0;
}
static void module_free(void *module) { module_clear((PyObject *)module); }
PyMODINIT_FUNC PyInit_json(void) {
- import_array()
- PyObject *module;
+ import_array() PyObject *module;
#ifndef PYPY_VERSION
- // This function is not supported in PyPy.
- if ((module = PyState_FindModule(&moduledef)) != NULL) {
- Py_INCREF(module);
- return module;
- }
+ // This function is not supported in PyPy.
+ if ((module = PyState_FindModule(&moduledef)) != NULL) {
+ Py_INCREF(module);
+ return module;
+ }
#endif
- module = PyModule_Create(&moduledef);
- if (module == NULL) {
- return NULL;
- }
+ module = PyModule_Create(&moduledef);
+ if (module == NULL) {
+ return NULL;
+ }
#ifndef PYPY_VERSION
- PyObject *mod_decimal = PyImport_ImportModule("decimal");
- if (mod_decimal) {
- PyObject *type_decimal = PyObject_GetAttrString(mod_decimal, "Decimal");
- assert(type_decimal != NULL);
- modulestate(module)->type_decimal = type_decimal;
- Py_DECREF(mod_decimal);
- }
-
- PyObject *mod_pandas = PyImport_ImportModule("pandas");
- if (mod_pandas) {
- PyObject *type_dataframe =
- PyObject_GetAttrString(mod_pandas, "DataFrame");
- assert(type_dataframe != NULL);
- modulestate(module)->type_dataframe = type_dataframe;
-
- PyObject *type_series = PyObject_GetAttrString(mod_pandas, "Series");
- assert(type_series != NULL);
- modulestate(module)->type_series = type_series;
-
- PyObject *type_index = PyObject_GetAttrString(mod_pandas, "Index");
- assert(type_index != NULL);
- modulestate(module)->type_index = type_index;
-
- Py_DECREF(mod_pandas);
- }
-
- PyObject *mod_nattype =
- PyImport_ImportModule("pandas._libs.tslibs.nattype");
- if (mod_nattype) {
- PyObject *type_nat = PyObject_GetAttrString(mod_nattype, "NaTType");
- assert(type_nat != NULL);
- modulestate(module)->type_nat = type_nat;
-
- Py_DECREF(mod_nattype);
- }
-
- PyObject *mod_natype = PyImport_ImportModule("pandas._libs.missing");
- if (mod_natype) {
- PyObject *type_na = PyObject_GetAttrString(mod_natype, "NAType");
- assert(type_na != NULL);
- modulestate(module)->type_na = type_na;
-
- Py_DECREF(mod_natype);
- } else {
- PyErr_Clear();
- }
+ PyObject *mod_decimal = PyImport_ImportModule("decimal");
+ if (mod_decimal) {
+ PyObject *type_decimal = PyObject_GetAttrString(mod_decimal, "Decimal");
+ assert(type_decimal != NULL);
+ modulestate(module)->type_decimal = type_decimal;
+ Py_DECREF(mod_decimal);
+ }
+
+ PyObject *mod_pandas = PyImport_ImportModule("pandas");
+ if (mod_pandas) {
+ PyObject *type_dataframe = PyObject_GetAttrString(mod_pandas, "DataFrame");
+ assert(type_dataframe != NULL);
+ modulestate(module)->type_dataframe = type_dataframe;
+
+ PyObject *type_series = PyObject_GetAttrString(mod_pandas, "Series");
+ assert(type_series != NULL);
+ modulestate(module)->type_series = type_series;
+
+ PyObject *type_index = PyObject_GetAttrString(mod_pandas, "Index");
+ assert(type_index != NULL);
+ modulestate(module)->type_index = type_index;
+
+ Py_DECREF(mod_pandas);
+ }
+
+ PyObject *mod_nattype = PyImport_ImportModule("pandas._libs.tslibs.nattype");
+ if (mod_nattype) {
+ PyObject *type_nat = PyObject_GetAttrString(mod_nattype, "NaTType");
+ assert(type_nat != NULL);
+ modulestate(module)->type_nat = type_nat;
+
+ Py_DECREF(mod_nattype);
+ }
+
+ PyObject *mod_natype = PyImport_ImportModule("pandas._libs.missing");
+ if (mod_natype) {
+ PyObject *type_na = PyObject_GetAttrString(mod_natype, "NAType");
+ assert(type_na != NULL);
+ modulestate(module)->type_na = type_na;
+
+ Py_DECREF(mod_natype);
+ } else {
+ PyErr_Clear();
+ }
#endif
- /* Not vendored for now
- JSONDecodeError = PyErr_NewException("ujson.JSONDecodeError",
- PyExc_ValueError, NULL); Py_XINCREF(JSONDecodeError); if
- (PyModule_AddObject(module, "JSONDecodeError", JSONDecodeError) < 0)
- {
- Py_XDECREF(JSONDecodeError);
- Py_CLEAR(JSONDecodeError);
- Py_DECREF(module);
- return NULL;
- }
- */
-
- return module;
+ /* Not vendored for now
+ JSONDecodeError = PyErr_NewException("ujson.JSONDecodeError",
+ PyExc_ValueError, NULL); Py_XINCREF(JSONDecodeError); if
+ (PyModule_AddObject(module, "JSONDecodeError", JSONDecodeError) < 0)
+ {
+ Py_XDECREF(JSONDecodeError);
+ Py_CLEAR(JSONDecodeError);
+ Py_DECREF(module);
+ return NULL;
+ }
+ */
+
+ return module;
}
| cpplint isn't seemingly well maintained and focuses on C++, which we aren't using. I think clang-format is a much better tool for our formatting | https://api.github.com/repos/pandas-dev/pandas/pulls/55150 | 2023-09-15T19:15:24Z | 2023-10-22T19:35:23Z | 2023-10-22T19:35:23Z | 2023-10-22T19:35:31Z |
docs: add explanation about `freq` units in `Timedelta.round()` | diff --git a/pandas/_libs/tslibs/timedeltas.pyx b/pandas/_libs/tslibs/timedeltas.pyx
index 2d9fe93c397cb..2178e972b4d08 100644
--- a/pandas/_libs/tslibs/timedeltas.pyx
+++ b/pandas/_libs/tslibs/timedeltas.pyx
@@ -1942,6 +1942,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the rounding resolution.
+ It uses the same units as class contructor :class:`~pandas.Timedelta`.
Returns
-------
@@ -1969,6 +1970,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the flooring resolution.
+ It uses the same units as class contructor :class:`~pandas.Timedelta`.
Examples
--------
@@ -1988,6 +1990,7 @@ class Timedelta(_Timedelta):
----------
freq : str
Frequency string indicating the ceiling resolution.
+ It uses the same units as class contructor :class:`~pandas.Timedelta`.
Examples
--------
| - ~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/55149 | 2023-09-15T18:30:09Z | 2023-09-18T16:37:34Z | 2023-09-18T16:37:34Z | 2023-09-18T17:15:05Z |
DEPR: deprecate exposing blocks in core.internals | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 579d3a20506f3..6ce33fd1f572c 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -270,6 +270,7 @@ Other Deprecations
- Deprecated :func:`read_gbq` and :meth:`DataFrame.to_gbq`. Use ``pandas_gbq.read_gbq`` and ``pandas_gbq.to_gbq`` instead https://pandas-gbq.readthedocs.io/en/latest/api.html (:issue:`55525`)
- Deprecated :meth:`.DataFrameGroupBy.fillna` and :meth:`.SeriesGroupBy.fillna`; use :meth:`.DataFrameGroupBy.ffill`, :meth:`.DataFrameGroupBy.bfill` for forward and backward filling or :meth:`.DataFrame.fillna` to fill with a single value (or the Series equivalents) (:issue:`55718`)
- Deprecated :meth:`Index.format`, use ``index.astype(str)`` or ``index.map(formatter)`` instead (:issue:`55413`)
+- Deprecated ``core.internals`` members ``Block``, ``ExtensionBlock``, and ``DatetimeTZBlock``, use public APIs instead (:issue:`55139`)
- Deprecated ``year``, ``month``, ``quarter``, ``day``, ``hour``, ``minute``, and ``second`` keywords in the :class:`PeriodIndex` constructor, use :meth:`PeriodIndex.from_fields` instead (:issue:`55960`)
- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_clipboard`. (:issue:`54229`)
- Deprecated allowing non-keyword arguments in :meth:`DataFrame.to_csv` except ``path_or_buf``. (:issue:`54229`)
diff --git a/pandas/core/internals/__init__.py b/pandas/core/internals/__init__.py
index 0f8cb9f053174..2eb413440ba9c 100644
--- a/pandas/core/internals/__init__.py
+++ b/pandas/core/internals/__init__.py
@@ -1,4 +1,4 @@
-from pandas.core.internals.api import make_block
+from pandas.core.internals.api import make_block # 2023-09-18 pyarrow uses this
from pandas.core.internals.array_manager import (
ArrayManager,
SingleArrayManager,
@@ -7,11 +7,6 @@
DataManager,
SingleDataManager,
)
-from pandas.core.internals.blocks import ( # io.pytables, io.packers
- Block,
- DatetimeTZBlock,
- ExtensionBlock,
-)
from pandas.core.internals.concat import concatenate_managers
from pandas.core.internals.managers import (
BlockManager,
@@ -19,9 +14,9 @@
)
__all__ = [
- "Block",
- "DatetimeTZBlock",
- "ExtensionBlock",
+ "Block", # pylint: disable=undefined-all-variable
+ "DatetimeTZBlock", # pylint: disable=undefined-all-variable
+ "ExtensionBlock", # pylint: disable=undefined-all-variable
"make_block",
"DataManager",
"ArrayManager",
@@ -34,33 +29,54 @@
def __getattr__(name: str):
+ # GH#55139
import warnings
- from pandas.util._exceptions import find_stack_level
-
if name == "create_block_manager_from_blocks":
# GH#33892
warnings.warn(
f"{name} is deprecated and will be removed in a future version. "
"Use public APIs instead.",
DeprecationWarning,
- stacklevel=find_stack_level(),
+ # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+ # on hard-coding stacklevel
+ stacklevel=2,
)
from pandas.core.internals.managers import create_block_manager_from_blocks
return create_block_manager_from_blocks
- if name in ["NumericBlock", "ObjectBlock"]:
+ if name in [
+ "NumericBlock",
+ "ObjectBlock",
+ "Block",
+ "ExtensionBlock",
+ "DatetimeTZBlock",
+ ]:
warnings.warn(
f"{name} is deprecated and will be removed in a future version. "
"Use public APIs instead.",
DeprecationWarning,
- stacklevel=find_stack_level(),
+ # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+ # on hard-coding stacklevel
+ stacklevel=2,
)
if name == "NumericBlock":
from pandas.core.internals.blocks import NumericBlock
return NumericBlock
+ elif name == "DatetimeTZBlock":
+ from pandas.core.internals.blocks import DatetimeTZBlock
+
+ return DatetimeTZBlock
+ elif name == "ExtensionBlock":
+ from pandas.core.internals.blocks import ExtensionBlock
+
+ return ExtensionBlock
+ elif name == "Block":
+ from pandas.core.internals.blocks import Block
+
+ return Block
else:
from pandas.core.internals.blocks import ObjectBlock
diff --git a/pandas/core/internals/api.py b/pandas/core/internals/api.py
index a3fd77fc8d9ea..b0b3937ca47ea 100644
--- a/pandas/core/internals/api.py
+++ b/pandas/core/internals/api.py
@@ -23,9 +23,6 @@
from pandas.core.arrays import DatetimeArray
from pandas.core.construction import extract_array
from pandas.core.internals.blocks import (
- Block,
- DatetimeTZBlock,
- ExtensionBlock,
check_ndim,
ensure_block_shape,
extract_pandas_array,
@@ -36,6 +33,8 @@
if TYPE_CHECKING:
from pandas._typing import Dtype
+ from pandas.core.internals.blocks import Block
+
def make_block(
values, placement, klass=None, ndim=None, dtype: Dtype | None = None
@@ -56,6 +55,11 @@ def make_block(
values, dtype = extract_pandas_array(values, dtype, ndim)
+ from pandas.core.internals.blocks import (
+ DatetimeTZBlock,
+ ExtensionBlock,
+ )
+
if klass is ExtensionBlock and isinstance(values.dtype, PeriodDtype):
# GH-44681 changed PeriodArray to be stored in the 2D
# NDArrayBackedExtensionBlock instead of ExtensionBlock
@@ -108,21 +112,44 @@ def maybe_infer_ndim(values, placement: BlockPlacement, ndim: int | None) -> int
def __getattr__(name: str):
+ # GH#55139
import warnings
- from pandas.util._exceptions import find_stack_level
-
- if name == "create_block_manager_from_blocks":
+ if name in [
+ "Block",
+ "ExtensionBlock",
+ "DatetimeTZBlock",
+ "create_block_manager_from_blocks",
+ ]:
# GH#33892
warnings.warn(
f"{name} is deprecated and will be removed in a future version. "
"Use public APIs instead.",
DeprecationWarning,
- stacklevel=find_stack_level(),
+ # https://github.com/pandas-dev/pandas/pull/55139#pullrequestreview-1720690758
+ # on hard-coding stacklevel
+ stacklevel=2,
)
- from pandas.core.internals.managers import create_block_manager_from_blocks
- return create_block_manager_from_blocks
+ if name == "create_block_manager_from_blocks":
+ from pandas.core.internals.managers import create_block_manager_from_blocks
+
+ return create_block_manager_from_blocks
+
+ elif name == "Block":
+ from pandas.core.internals.blocks import Block
+
+ return Block
+
+ elif name == "DatetimeTZBlock":
+ from pandas.core.internals.blocks import DatetimeTZBlock
+
+ return DatetimeTZBlock
+
+ elif name == "ExtensionBlock":
+ from pandas.core.internals.blocks import ExtensionBlock
+
+ return ExtensionBlock
raise AttributeError(
f"module 'pandas.core.internals.api' has no attribute '{name}'"
diff --git a/pandas/tests/internals/test_api.py b/pandas/tests/internals/test_api.py
index ffc672cc748be..1251a6ae97a1c 100644
--- a/pandas/tests/internals/test_api.py
+++ b/pandas/tests/internals/test_api.py
@@ -3,6 +3,8 @@
in core.internals
"""
+import pytest
+
import pandas as pd
import pandas._testing as tm
from pandas.core import internals
@@ -27,9 +29,6 @@ def test_namespace():
"ops",
]
expected = [
- "Block",
- "DatetimeTZBlock",
- "ExtensionBlock",
"make_block",
"DataManager",
"ArrayManager",
@@ -44,6 +43,28 @@ def test_namespace():
assert set(result) == set(expected + modules)
+@pytest.mark.parametrize(
+ "name",
+ [
+ "NumericBlock",
+ "ObjectBlock",
+ "Block",
+ "ExtensionBlock",
+ "DatetimeTZBlock",
+ ],
+)
+def test_deprecations(name):
+ # GH#55139
+ msg = f"{name} is deprecated.* Use public APIs instead"
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
+ getattr(internals, name)
+
+ if name not in ["NumericBlock", "ObjectBlock"]:
+ # NumericBlock and ObjectBlock are not in the internals.api namespace
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
+ getattr(api, name)
+
+
def test_make_block_2d_with_dti():
# GH#41168
dti = pd.date_range("2012", periods=3, tz="UTC")
| Motivation: tried to clean up the blocks code bc DatetimeTZBlock and DatetimeLikeBlock are no longer really needed. Found that we can't do that bc DatetimeTZBlock is exposed in the pseudo-public API. That got me looking at `internals.__init__`, where comments suggest that we are importing the block subclasses for io.pytables (which no longer needs them) and io.packers (which no longer exists). So we could remove them directly, but apparently a recent PR deprecated NumericBlock and ObjectBlock so this piggy-backs on that deprecation (and adds a missing test).
In internals.api I need to deprecate DatetimeTZBlock in order to eventually get rid of it entirely. Also deprecating Block and ExtensionBlock is orthogonal just to wean downstream packages off of them.
cc @jorisvandenbossche
xref https://github.com/pandas-dev/pandas/issues/40226 | https://api.github.com/repos/pandas-dev/pandas/pulls/55139 | 2023-09-14T22:53:54Z | 2023-11-20T21:15:40Z | 2023-11-20T21:15:40Z | 2023-11-20T21:48:49Z |
BUG: Silence `Period[B]` warnings in plotting code | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index 1a25b848e0f84..9119a0bc90949 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -24,6 +24,7 @@ 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`)
+- Silence ``Period[B]`` warnings introduced by :issue:`53446` during normal plotting activity (:issue:`55138`)
-
.. ---------------------------------------------------------------------------
diff --git a/pandas/plotting/_matplotlib/converter.py b/pandas/plotting/_matplotlib/converter.py
index e6408a0eae841..5a7ceabbf554e 100644
--- a/pandas/plotting/_matplotlib/converter.py
+++ b/pandas/plotting/_matplotlib/converter.py
@@ -13,6 +13,7 @@
Any,
cast,
)
+import warnings
import matplotlib.dates as mdates
from matplotlib.ticker import (
@@ -239,18 +240,29 @@ def _convert_1d(values, units, axis):
if not hasattr(axis, "freq"):
raise TypeError("Axis must have `freq` set to convert to Periods")
valid_types = (str, datetime, Period, pydt.date, pydt.time, np.datetime64)
- if isinstance(values, valid_types) or is_integer(values) or is_float(values):
- return get_datevalue(values, axis.freq)
- elif isinstance(values, PeriodIndex):
- return values.asfreq(axis.freq).asi8
- elif isinstance(values, Index):
- return values.map(lambda x: get_datevalue(x, axis.freq))
- elif lib.infer_dtype(values, skipna=False) == "period":
- # https://github.com/pandas-dev/pandas/issues/24304
- # convert ndarray[period] -> PeriodIndex
- return PeriodIndex(values, freq=axis.freq).asi8
- elif isinstance(values, (list, tuple, np.ndarray, Index)):
- return [get_datevalue(x, axis.freq) for x in values]
+ with warnings.catch_warnings():
+ warnings.filterwarnings(
+ "ignore", "Period with BDay freq is deprecated", category=FutureWarning
+ )
+ warnings.filterwarnings(
+ "ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
+ )
+ if (
+ isinstance(values, valid_types)
+ or is_integer(values)
+ or is_float(values)
+ ):
+ return get_datevalue(values, axis.freq)
+ elif isinstance(values, PeriodIndex):
+ return values.asfreq(axis.freq).asi8
+ elif isinstance(values, Index):
+ return values.map(lambda x: get_datevalue(x, axis.freq))
+ elif lib.infer_dtype(values, skipna=False) == "period":
+ # https://github.com/pandas-dev/pandas/issues/24304
+ # convert ndarray[period] -> PeriodIndex
+ return PeriodIndex(values, freq=axis.freq).asi8
+ elif isinstance(values, (list, tuple, np.ndarray, Index)):
+ return [get_datevalue(x, axis.freq) for x in values]
return values
@@ -575,11 +587,18 @@ def _daily_finder(vmin, vmax, freq: BaseOffset) -> np.ndarray:
(vmin, vmax) = (int(vmin), int(vmax))
span = vmax - vmin + 1
- dates_ = period_range(
- start=Period(ordinal=vmin, freq=freq),
- end=Period(ordinal=vmax, freq=freq),
- freq=freq,
- )
+ with warnings.catch_warnings():
+ warnings.filterwarnings(
+ "ignore", "Period with BDay freq is deprecated", category=FutureWarning
+ )
+ warnings.filterwarnings(
+ "ignore", r"PeriodDtype\[B\] is deprecated", category=FutureWarning
+ )
+ dates_ = period_range(
+ start=Period(ordinal=vmin, freq=freq),
+ end=Period(ordinal=vmax, freq=freq),
+ freq=freq,
+ )
# Initialize the output
info = np.zeros(
@@ -1072,7 +1091,13 @@ def __call__(self, x, pos: int = 0) -> str:
fmt = self.formatdict.pop(x, "")
if isinstance(fmt, np.bytes_):
fmt = fmt.decode("utf-8")
- period = Period(ordinal=int(x), freq=self.freq)
+ with warnings.catch_warnings():
+ warnings.filterwarnings(
+ "ignore",
+ "Period with BDay freq is deprecated",
+ category=FutureWarning,
+ )
+ period = Period(ordinal=int(x), freq=self.freq)
assert isinstance(period, Period)
return period.strftime(fmt)
diff --git a/pandas/tests/plotting/frame/test_frame.py b/pandas/tests/plotting/frame/test_frame.py
index 47114cab47619..2ef1f065f603d 100644
--- a/pandas/tests/plotting/frame/test_frame.py
+++ b/pandas/tests/plotting/frame/test_frame.py
@@ -2487,6 +2487,15 @@ def test_secondary_y(self, secondary_y):
assert ax.get_ylim() == (0, 100)
assert ax.get_yticks()[0] == 99
+ @pytest.mark.slow
+ def test_plot_no_warning(self):
+ # GH 55138
+ # TODO(3.0): this can be removed once Period[B] deprecation is enforced
+ df = tm.makeTimeDataFrame()
+ with tm.assert_produces_warning(False):
+ _ = df.plot()
+ _ = df.T.plot()
+
def _generate_4_axes_via_gridspec():
import matplotlib.pyplot as plt
diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py
index e77dc0b305171..6719e39612f5d 100644
--- a/pandas/tests/plotting/test_series.py
+++ b/pandas/tests/plotting/test_series.py
@@ -973,3 +973,10 @@ def test_series_none_color(self):
ax = series.plot(color=None)
expected = _unpack_cycler(mpl.pyplot.rcParams)[:1]
_check_colors(ax.get_lines(), linecolors=expected)
+
+ @pytest.mark.slow
+ def test_plot_no_warning(self, ts):
+ # GH 55138
+ # TODO(3.0): this can be removed once Period[B] deprecation is enforced
+ with tm.assert_produces_warning(False):
+ _ = ts.plot()
| - [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.
This silences warnings introduced by https://github.com/pandas-dev/pandas/issues/53446 that would happen during normal plotting activity.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55138 | 2023-09-14T15:52:50Z | 2023-10-03T15:46:01Z | 2023-10-03T15:46:01Z | 2023-10-04T16:34:54Z |
PERF: groupby aggregations on pyarrow timestamp and duration types | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 54e855f61905a..03b69b53836ad 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -177,6 +177,7 @@ Performance improvements
~~~~~~~~~~~~~~~~~~~~~~~~
- Performance improvement in :func:`concat` with ``axis=1`` and objects with unaligned indexes (:issue:`55084`)
- Performance improvement in :func:`to_dict` on converting DataFrame to dictionary (:issue:`50990`)
+- 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 when indexing with more than 4 keys (:issue:`54550`)
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 2b2e0c843564f..a329c37c77449 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -1993,9 +1993,17 @@ def _groupby_op(
**kwargs,
)
- masked = self._to_masked()
+ # maybe convert to a compatible dtype optimized for groupby
+ values: ExtensionArray
+ pa_type = self._pa_array.type
+ if pa.types.is_timestamp(pa_type):
+ values = self._to_datetimearray()
+ elif pa.types.is_duration(pa_type):
+ values = self._to_timedeltaarray()
+ else:
+ values = self._to_masked()
- result = masked._groupby_op(
+ result = values._groupby_op(
how=how,
has_dropped_na=has_dropped_na,
min_count=min_count,
| - [x] closes #55031
- [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.
Seeing ~identical timing between pyarrow and numpy in the OP example with this PR.
```
import pandas as pd
import numpy as np
N = 1_000_000
df = pd.DataFrame(
{
"group": np.arange(N//1000).repeat(1000),
"timestamp": pd.array(np.arange(N), dtype="timestamp[ns][pyarrow]"),
"duration": pd.array(np.arange(N), dtype="duration[s][pyarrow]"),
}
)
gb = df.groupby("group")
%timeit gb["timestamp"].max()
# 215 ms ± 9.72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <- main
# 13 ms ± 839 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- PR
%timeit gb["duration"].max()
# 272 ms ± 2.72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <- main
# 12.8 ms ± 341 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- PR
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55131 | 2023-09-13T22:04:06Z | 2023-09-15T20:06:25Z | 2023-09-15T20:06:24Z | 2023-11-16T12:56:53Z |
TYP: Correct type annotation for to_dict. | diff --git a/pandas/_typing.py b/pandas/_typing.py
index 0e2a0881f0122..f18c67fcb0c90 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -4,6 +4,7 @@
Hashable,
Iterator,
Mapping,
+ MutableMapping,
Sequence,
)
from datetime import (
@@ -103,6 +104,7 @@
TypeGuard: Any = None
HashableT = TypeVar("HashableT", bound=Hashable)
+MutableMappingT = TypeVar("MutableMappingT", bound=MutableMapping)
# array-like
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index a16597221ac92..51fceb1f09a62 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -230,6 +230,7 @@
Level,
MergeHow,
MergeValidate,
+ MutableMappingT,
NaAction,
NaPosition,
NsmallestNlargestKeep,
@@ -1927,6 +1928,27 @@ def _create_data_for_split_and_tight_to_dict(
def to_dict(
self,
orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+ *,
+ into: type[MutableMappingT] | MutableMappingT,
+ index: bool = ...,
+ ) -> MutableMappingT:
+ ...
+
+ @overload
+ def to_dict(
+ self,
+ orient: Literal["records"],
+ *,
+ into: type[MutableMappingT] | MutableMappingT,
+ index: bool = ...,
+ ) -> list[MutableMappingT]:
+ ...
+
+ @overload
+ def to_dict(
+ self,
+ orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+ *,
into: type[dict] = ...,
index: bool = ...,
) -> dict:
@@ -1936,11 +1958,14 @@ def to_dict(
def to_dict(
self,
orient: Literal["records"],
+ *,
into: type[dict] = ...,
index: bool = ...,
) -> list[dict]:
...
+ # error: Incompatible default for argument "into" (default has type "type
+ # [dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
@deprecate_nonkeyword_arguments(
version="3.0", allowed_args=["self", "orient"], name="to_dict"
)
@@ -1949,9 +1974,10 @@ def to_dict(
orient: Literal[
"dict", "list", "series", "split", "tight", "records", "index"
] = "dict",
- into: type[dict] = dict,
+ into: type[MutableMappingT]
+ | MutableMappingT = dict, # type: ignore[assignment]
index: bool = True,
- ) -> dict | list[dict]:
+ ) -> MutableMappingT | list[MutableMappingT]:
"""
Convert the DataFrame to a dictionary.
@@ -1979,7 +2005,7 @@ def to_dict(
'tight' as an allowed value for the ``orient`` argument
into : class, default dict
- The collections.abc.Mapping subclass used for all Mappings
+ The collections.abc.MutableMapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
@@ -1993,9 +2019,10 @@ def to_dict(
Returns
-------
- dict, list or collections.abc.Mapping
- Return a collections.abc.Mapping object representing the DataFrame.
- The resulting transformation depends on the `orient` parameter.
+ dict, list or collections.abc.MutableMapping
+ Return a collections.abc.MutableMapping object representing the
+ DataFrame. The resulting transformation depends on the `orient`
+ parameter.
See Also
--------
@@ -2054,7 +2081,7 @@ def to_dict(
"""
from pandas.core.methods.to_dict import to_dict
- return to_dict(self, orient, into, index)
+ return to_dict(self, orient, into=into, index=index)
@deprecate_nonkeyword_arguments(
version="3.0", allowed_args=["self", "destination_table"], name="to_gbq"
diff --git a/pandas/core/methods/to_dict.py b/pandas/core/methods/to_dict.py
index f4e0dcddcd34a..3295c4741c03d 100644
--- a/pandas/core/methods/to_dict.py
+++ b/pandas/core/methods/to_dict.py
@@ -3,6 +3,7 @@
from typing import (
TYPE_CHECKING,
Literal,
+ overload,
)
import warnings
@@ -16,17 +17,66 @@
from pandas.core import common as com
if TYPE_CHECKING:
+ from pandas._typing import MutableMappingT
+
from pandas import DataFrame
+@overload
+def to_dict(
+ df: DataFrame,
+ orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+ *,
+ into: type[MutableMappingT] | MutableMappingT,
+ index: bool = ...,
+) -> MutableMappingT:
+ ...
+
+
+@overload
+def to_dict(
+ df: DataFrame,
+ orient: Literal["records"],
+ *,
+ into: type[MutableMappingT] | MutableMappingT,
+ index: bool = ...,
+) -> list[MutableMappingT]:
+ ...
+
+
+@overload
+def to_dict(
+ df: DataFrame,
+ orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
+ *,
+ into: type[dict] = ...,
+ index: bool = ...,
+) -> dict:
+ ...
+
+
+@overload
+def to_dict(
+ df: DataFrame,
+ orient: Literal["records"],
+ *,
+ into: type[dict] = ...,
+ index: bool = ...,
+) -> list[dict]:
+ ...
+
+
+# error: Incompatible default for argument "into" (default has type "type[dict
+# [Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
def to_dict(
df: DataFrame,
orient: Literal[
"dict", "list", "series", "split", "tight", "records", "index"
] = "dict",
- into: type[dict] = dict,
+ *,
+ into: type[MutableMappingT] | MutableMappingT = dict, # type: ignore[assignment]
index: bool = True,
-) -> dict | list[dict]:
+) -> MutableMappingT | list[MutableMappingT]:
"""
Convert the DataFrame to a dictionary.
@@ -54,7 +104,7 @@ def to_dict(
'tight' as an allowed value for the ``orient`` argument
into : class, default dict
- The collections.abc.Mapping subclass used for all Mappings
+ The collections.abc.MutableMapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
@@ -69,8 +119,8 @@ def to_dict(
Returns
-------
dict, list or collections.abc.Mapping
- Return a collections.abc.Mapping object representing the DataFrame.
- The resulting transformation depends on the `orient` parameter.
+ Return a collections.abc.MutableMapping object representing the
+ DataFrame. The resulting transformation depends on the `orient` parameter.
"""
if not df.columns.is_unique:
warnings.warn(
@@ -103,7 +153,7 @@ def to_dict(
are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes)
if orient == "dict":
- return into_c((k, v.to_dict(into)) for k, v in df.items())
+ return into_c((k, v.to_dict(into=into)) for k, v in df.items())
elif orient == "list":
object_dtype_indices_as_set: set[int] = set(box_native_indices)
diff --git a/pandas/core/series.py b/pandas/core/series.py
index fd50a85f3c2e3..d5785a2171cb3 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -48,6 +48,7 @@
from pandas.util._decorators import (
Appender,
Substitution,
+ deprecate_nonkeyword_arguments,
doc,
)
from pandas.util._exceptions import find_stack_level
@@ -167,6 +168,7 @@
IndexKeyFunc,
IndexLabel,
Level,
+ MutableMappingT,
NaPosition,
NumpySorter,
NumpyValueArrayLike,
@@ -1922,21 +1924,40 @@ def keys(self) -> Index:
"""
return self.index
- def to_dict(self, into: type[dict] = dict) -> dict:
+ @overload
+ def to_dict(
+ self, *, into: type[MutableMappingT] | MutableMappingT
+ ) -> MutableMappingT:
+ ...
+
+ @overload
+ def to_dict(self, *, into: type[dict] = ...) -> dict:
+ ...
+
+ # error: Incompatible default for argument "into" (default has type "type[
+ # dict[Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT")
+ @deprecate_nonkeyword_arguments(
+ version="3.0", allowed_args=["self"], name="to_dict"
+ )
+ def to_dict(
+ self,
+ into: type[MutableMappingT]
+ | MutableMappingT = dict, # type: ignore[assignment]
+ ) -> MutableMappingT:
"""
Convert Series to {label -> value} dict or dict-like object.
Parameters
----------
into : class, default dict
- The collections.abc.Mapping subclass to use as the return
- object. Can be the actual class or an empty
- instance of the mapping type you want. If you want a
- collections.defaultdict, you must pass it initialized.
+ The collections.abc.MutableMapping subclass to use as the return
+ object. Can be the actual class or an empty instance of the mapping
+ type you want. If you want a collections.defaultdict, you must
+ pass it initialized.
Returns
-------
- collections.abc.Mapping
+ collections.abc.MutableMapping
Key-value representation of Series.
Examples
@@ -1945,10 +1966,10 @@ def to_dict(self, into: type[dict] = dict) -> dict:
>>> s.to_dict()
{0: 1, 1: 2, 2: 3, 3: 4}
>>> from collections import OrderedDict, defaultdict
- >>> s.to_dict(OrderedDict)
+ >>> s.to_dict(into=OrderedDict)
OrderedDict([(0, 1), (1, 2), (2, 3), (3, 4)])
>>> dd = defaultdict(list)
- >>> s.to_dict(dd)
+ >>> s.to_dict(into=dd)
defaultdict(<class 'list'>, {0: 1, 1: 2, 2: 3, 3: 4})
"""
# GH16122
diff --git a/pandas/tests/series/methods/test_to_dict.py b/pandas/tests/series/methods/test_to_dict.py
index 4c3d9592eebe3..41c01f4537f23 100644
--- a/pandas/tests/series/methods/test_to_dict.py
+++ b/pandas/tests/series/methods/test_to_dict.py
@@ -13,12 +13,12 @@ class TestSeriesToDict:
)
def test_to_dict(self, mapping, datetime_series):
# GH#16122
- result = Series(datetime_series.to_dict(mapping), name="ts")
+ result = Series(datetime_series.to_dict(into=mapping), name="ts")
expected = datetime_series.copy()
expected.index = expected.index._with_freq(None)
tm.assert_series_equal(result, expected)
- from_method = Series(datetime_series.to_dict(collections.Counter))
+ from_method = Series(datetime_series.to_dict(into=collections.Counter))
from_constructor = Series(collections.Counter(datetime_series.items()))
tm.assert_series_equal(from_method, from_constructor)
| The `into` argument of DataFrame.to_dict and Series.to_dict can be either a class or instance of a class of dict; this is covariant - subclasses of dict can also be used. The argument was annotated as `type[dict]` though, so type checkers marked passing initialized objects (required for collections.defaultdict) as an incorrect argument type.
Fix by annotating `into` to take either a subclass of dict or an initialized instance of a subclass of dict. | https://api.github.com/repos/pandas-dev/pandas/pulls/55130 | 2023-09-13T21:25:06Z | 2023-10-02T16:31:17Z | 2023-10-02T16:31:17Z | 2023-10-02T16:31:24Z |
Use pandasSQL transactions in sql test suite to avoid engine deadlocks | diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index bbdb22955297e..1abe0ad55a864 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -1141,18 +1141,21 @@ def load_types_data(self, types_data):
def _read_sql_iris_parameter(self, sql_strings):
query = sql_strings["read_parameters"][self.flavor]
params = ("Iris-setosa", 5.1)
- iris_frame = self.pandasSQL.read_query(query, params=params)
+ with self.pandasSQL.run_transaction():
+ iris_frame = self.pandasSQL.read_query(query, params=params)
check_iris_frame(iris_frame)
def _read_sql_iris_named_parameter(self, sql_strings):
query = sql_strings["read_named_parameters"][self.flavor]
params = {"name": "Iris-setosa", "length": 5.1}
- iris_frame = self.pandasSQL.read_query(query, params=params)
+ with self.pandasSQL.run_transaction():
+ iris_frame = self.pandasSQL.read_query(query, params=params)
check_iris_frame(iris_frame)
def _read_sql_iris_no_parameter_with_percent(self, sql_strings):
query = sql_strings["read_no_parameters_with_percent"][self.flavor]
- iris_frame = self.pandasSQL.read_query(query, params=None)
+ with self.pandasSQL.run_transaction():
+ iris_frame = self.pandasSQL.read_query(query, params=None)
check_iris_frame(iris_frame)
def _to_sql_empty(self, test_frame1):
@@ -1182,7 +1185,8 @@ def _to_sql_with_sql_engine(self, test_frame1, engine="auto", **engine_kwargs):
def _roundtrip(self, test_frame1):
self.drop_table("test_frame_roundtrip", self.conn)
assert self.pandasSQL.to_sql(test_frame1, "test_frame_roundtrip") == 4
- result = self.pandasSQL.read_query("SELECT * FROM test_frame_roundtrip")
+ with self.pandasSQL.run_transaction():
+ result = self.pandasSQL.read_query("SELECT * FROM test_frame_roundtrip")
result.set_index("level_0", inplace=True)
# result.index.astype(int)
@@ -1232,13 +1236,14 @@ class DummyException(Exception):
except DummyException:
# ignore raised exception
pass
- res = self.pandasSQL.read_query("SELECT * FROM test_trans")
+ with self.pandasSQL.run_transaction():
+ res = self.pandasSQL.read_query("SELECT * FROM test_trans")
assert len(res) == 0
# Make sure when transaction is committed, rows do get inserted
with self.pandasSQL.run_transaction() as trans:
trans.execute(ins_sql)
- res2 = self.pandasSQL.read_query("SELECT * FROM test_trans")
+ res2 = self.pandasSQL.read_query("SELECT * FROM test_trans")
assert len(res2) == 1
| When trying to integrate the engine fixtures into these tests I have been running into a lot of deadlocking issues. From some [research](https://github.com/sqlalchemy/sqlalchemy/issues/5405#issuecomment-646607306) it looks like SQLAlchemy strongly discourages running doing [Connectionless Execution](https://docs.sqlalchemy.org/en/13/core/connections.html#connectionless-execution-implicit-execution)
Note the comment:
> Modern SQLAlchemy usage, especially the ORM, places a heavy stress on working within the context of a transaction at all times; the “implicit execution” concept makes the job of associating statement execution with a particular transaction much more difficult.
We probably need an overhaul of the sql module to more natively account for this, but I think it makes sense to unlock our test suite first so that getting better coverage via fixtures in https://github.com/pandas-dev/pandas/pull/55074 would help us more confidently refactor
Here's an MRE to reproduce this deadlock locally:
```python
import sqlalchemy as sa
from pandas.io.sql import pandasSQL_builder
engine = sa.create_engine("mysql+pymysql://root@localhost:3306/pandas")
pandasSQL = pandasSQL_builder(engine)
with pandasSQL.run_transaction() as trans:
stmt = sa.text("CREATE TABLE test_trans (A INT, B TEXT)")
trans.execute(stmt)
with pandasSQL.run_transaction() as trans:
stmt = sa.text("INSERT INTO test_trans (A,B) VALUES (1, 'blah')")
trans.execute(stmt)
result = pandasSQL.read_query("SELECT * FROM test_trans")
# Assume fixture tries to clean up all tables after function exit
with engine.begin() as conn:
stmt = sa.text("DROP TABLE test_trans")
conn.execute(stmt)
```
If you wrap the read_query call in a transaction there is no deadlock
(tip: if you ran the above locally you will probably want to kill the thread so it doesn't lock up your database. run `show engine innodb status;` inside the db, look for the hanging thread id and run `KILL <thread_id>`)
| https://api.github.com/repos/pandas-dev/pandas/pulls/55129 | 2023-09-13T21:23:21Z | 2023-09-13T23:36:05Z | 2023-09-13T23:36:05Z | 2023-09-13T23:45:40Z |
Backport PR #55072 on branch 2.1.x (BUG: dt.tz with ArrowDtype returned string) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 42af61be26355..6d5da7cdff3b3 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -35,6 +35,7 @@ Bug fixes
~~~~~~~~~
- Fixed bug for :class:`ArrowDtype` raising ``NotImplementedError`` for fixed-size list (:issue:`55000`)
- Fixed bug in :meth:`DataFrame.stack` with ``future_stack=True`` and columns a non-:class:`MultiIndex` consisting of tuples (:issue:`54948`)
+- Fixed bug in :meth:`Series.dt.tz` with :class:`ArrowDtype` where a string was returned instead of a ``tzinfo`` object (:issue:`55003`)
- Fixed bug in :meth:`Series.pct_change` and :meth:`DataFrame.pct_change` showing unnecessary ``FutureWarning`` (:issue:`54981`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 61ee157376656..09c7c6c7706ad 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -18,6 +18,7 @@
from pandas._libs.tslibs import (
Timedelta,
Timestamp,
+ timezones,
)
from pandas.compat import (
pa_version_under7p0,
@@ -2421,7 +2422,7 @@ def _dt_time(self):
@property
def _dt_tz(self):
- return self.dtype.pyarrow_dtype.tz
+ return timezones.maybe_get_tz(self.dtype.pyarrow_dtype.tz)
@property
def _dt_unit(self):
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index d6e75a57f71e7..5017e827c3761 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -31,6 +31,7 @@
import pytest
from pandas._libs import lib
+from pandas._libs.tslibs import timezones
from pandas.compat import (
PY311,
is_ci_environment,
@@ -2477,7 +2478,7 @@ def test_dt_tz(tz):
dtype=ArrowDtype(pa.timestamp("ns", tz=tz)),
)
result = ser.dt.tz
- assert result == tz
+ assert result == timezones.maybe_get_tz(tz)
def test_dt_isocalendar():
| Backport PR #55072: BUG: dt.tz with ArrowDtype returned string | https://api.github.com/repos/pandas-dev/pandas/pulls/55124 | 2023-09-13T17:11:13Z | 2023-09-13T18:59:11Z | 2023-09-13T18:59:11Z | 2023-09-13T18:59:12Z |
BUG: add pyarrow autogenerated prefix | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 0760840f9950a..445b93705cde5 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -314,6 +314,7 @@ MultiIndex
I/O
^^^
- Bug in :func:`read_csv` where ``on_bad_lines="warn"`` would write to ``stderr`` instead of raise a Python warning. This now yields a :class:`.errors.ParserWarning` (:issue:`54296`)
+- Bug in :func:`read_csv` with ``engine="pyarrow"`` where ``usecols`` wasn't working with a csv with no headers (:issue:`54459`)
- Bug in :func:`read_excel`, with ``engine="xlrd"`` (``xls`` files) erroring when file contains NaNs/Infs (:issue:`54564`)
- Bug in :func:`to_excel`, with ``OdsWriter`` (``ods`` files) writing boolean/string value (:issue:`54994`)
diff --git a/pandas/io/parsers/arrow_parser_wrapper.py b/pandas/io/parsers/arrow_parser_wrapper.py
index 765a4ffcd2cb9..35965c90ee7fb 100644
--- a/pandas/io/parsers/arrow_parser_wrapper.py
+++ b/pandas/io/parsers/arrow_parser_wrapper.py
@@ -130,6 +130,12 @@ def handle_warning(invalid_row):
)
}
self.convert_options["strings_can_be_null"] = "" in self.kwds["null_values"]
+ # autogenerated column names are prefixed with 'f' in pyarrow.csv
+ if self.header is None and "include_columns" in self.convert_options:
+ self.convert_options["include_columns"] = [
+ f"f{n}" for n in self.convert_options["include_columns"]
+ ]
+
self.read_options = {
"autogenerate_column_names": self.header is None,
"skip_rows": self.header
diff --git a/pandas/tests/io/parser/test_header.py b/pandas/tests/io/parser/test_header.py
index d72174c40478e..d6eab59074dd6 100644
--- a/pandas/tests/io/parser/test_header.py
+++ b/pandas/tests/io/parser/test_header.py
@@ -684,3 +684,21 @@ def test_header_delim_whitespace(all_parsers):
result = parser.read_csv(StringIO(data), delim_whitespace=True)
expected = DataFrame({"a,b": ["1,2", "3,4"]})
tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_no_header_pyarrow(pyarrow_parser_only):
+ parser = pyarrow_parser_only
+ data = """
+a,i,x
+b,j,y
+"""
+ result = parser.read_csv(
+ StringIO(data),
+ header=None,
+ usecols=[0, 1],
+ dtype="string[pyarrow]",
+ dtype_backend="pyarrow",
+ engine="pyarrow",
+ )
+ expected = DataFrame([["a", "i"], ["b", "j"]], dtype="string[pyarrow]")
+ tm.assert_frame_equal(result, expected)
| - [x] closes #54459
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [X] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [X] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
~Any suggestions where to add a test for this bug (and fix)?~
edit: took a shot and put it in `tests/io/parser/test_header.py` | https://api.github.com/repos/pandas-dev/pandas/pulls/55115 | 2023-09-13T03:56:26Z | 2023-09-27T16:50:46Z | 2023-09-27T16:50:46Z | 2023-09-27T16:50:54Z |
BUG: Index.difference not always returning a unique set of values | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 54e855f61905a..034d67b43087d 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -233,7 +233,7 @@ Interval
Indexing
^^^^^^^^
--
+- Bug in :meth:`Index.difference` not returning a unique set of values when ``other`` is empty or ``other`` is considered non-comparable (:issue:`55113`)
-
Missing
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 8756bb3f3c81b..3adb4dfa227db 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -3598,14 +3598,14 @@ def difference(self, other, sort=None):
if len(other) == 0:
# Note: we do not (yet) sort even if sort=None GH#24959
- result = self.rename(result_name)
+ result = self.unique().rename(result_name)
if sort is True:
return result.sort_values()
return result
if not self._should_compare(other):
# Nothing matches -> difference is everything
- result = self.rename(result_name)
+ result = self.unique().rename(result_name)
if sort is True:
return result.sort_values()
return result
diff --git a/pandas/tests/indexes/test_setops.py b/pandas/tests/indexes/test_setops.py
index a64994efec85a..d6304774b87c4 100644
--- a/pandas/tests/indexes/test_setops.py
+++ b/pandas/tests/indexes/test_setops.py
@@ -796,11 +796,21 @@ def test_difference_name_preservation(self, index, second_name, expected, sort):
assert result.name == expected
def test_difference_empty_arg(self, index, sort):
- first = index[5:20]
+ first = index.copy()
+ first = first[5:20]
first.name = "name"
result = first.difference([], sort)
+ expected = index[5:20].unique()
+ expected.name = "name"
+ tm.assert_index_equal(result, expected)
- tm.assert_index_equal(result, first)
+ def test_difference_should_not_compare(self):
+ # GH 55113
+ left = Index([1, 1])
+ right = Index([True])
+ result = left.difference(right)
+ expected = Index([1])
+ tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("index", ["string"], indirect=True)
def test_difference_identity(self, index, sort):
| - [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [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.
`Index.difference` has two short-circuit paths which currently differ in behavior from the main path in that they do not return a unique set of values when duplicates exist:
```
In [1]: import pandas as pd
In [2]: idx = pd.Index([1, 1])
In [3]: idx.difference([2])
Out[3]: Index([1], dtype='int64') <- main path: unique
In [4]: idx.difference([])
Out[4]: Index([1, 1], dtype='int64') <- short circuit path when right is empty: non-unique
In [5]: idx.difference([True])
Out[5]: Index([1, 1], dtype='int64') <- short circuit path when right is considered "non-comparable": non-unique
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55113 | 2023-09-12T22:02:27Z | 2023-09-18T16:39:40Z | 2023-09-18T16:39:40Z | 2023-11-16T12:56:54Z |
Assorted UBSAN cleanups | diff --git a/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c b/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
index e3e710ce1b876..942bd0b518144 100644
--- a/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
+++ b/pandas/_libs/src/vendored/ujson/lib/ultrajsonenc.c
@@ -44,6 +44,7 @@ Numeric decoder derived from TCL library
#include <float.h>
#include <locale.h>
#include <math.h>
+#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
@@ -763,7 +764,12 @@ void Buffer_AppendIntUnchecked(JSONObjectEncoder *enc, JSINT32 value) {
void Buffer_AppendLongUnchecked(JSONObjectEncoder *enc, JSINT64 value) {
char *wstr;
- JSUINT64 uvalue = (value < 0) ? -value : value;
+ JSUINT64 uvalue;
+ if (value == INT64_MIN) {
+ uvalue = INT64_MAX + UINT64_C(1);
+ } else {
+ uvalue = (value < 0) ? -value : value;
+ }
wstr = enc->offset;
// Conversion. Number is reversed.
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 7b2ee68c73ad2..c3ee68e14a8d4 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -1,4 +1,3 @@
-cimport cython
from cpython.datetime cimport (
PyDateTime_CheckExact,
PyDateTime_DATE_GET_HOUR,
@@ -18,6 +17,7 @@ from cpython.object cimport (
Py_LT,
Py_NE,
)
+from libc.stdint cimport INT64_MAX
import_datetime()
PandasDateTime_IMPORT
@@ -545,7 +545,6 @@ cdef ndarray astype_round_check(
return iresult
-@cython.overflowcheck(True)
cdef int64_t get_conversion_factor(
NPY_DATETIMEUNIT from_unit,
NPY_DATETIMEUNIT to_unit
@@ -553,6 +552,7 @@ cdef int64_t get_conversion_factor(
"""
Find the factor by which we need to multiply to convert from from_unit to to_unit.
"""
+ cdef int64_t value, overflow_limit, factor
if (
from_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
or to_unit == NPY_DATETIMEUNIT.NPY_FR_GENERIC
@@ -565,28 +565,44 @@ cdef int64_t get_conversion_factor(
return 1
if from_unit == NPY_DATETIMEUNIT.NPY_FR_W:
- return 7 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_D, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_D, to_unit)
+ factor = 7
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_D:
- return 24 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_h, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_h, to_unit)
+ factor = 24
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_h:
- return 60 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_m, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_m, to_unit)
+ factor = 60
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_m:
- return 60 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_s, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_s, to_unit)
+ factor = 60
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_s:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ms, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ms, to_unit)
+ factor = 1000
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ms:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_us, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_us, to_unit)
+ factor = 1000
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_us:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ns, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ns, to_unit)
+ factor = 1000
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ns:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ps, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_ps, to_unit)
+ factor = 1000
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_ps:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_fs, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_fs, to_unit)
+ factor = 1000
elif from_unit == NPY_DATETIMEUNIT.NPY_FR_fs:
- return 1000 * get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_as, to_unit)
+ value = get_conversion_factor(NPY_DATETIMEUNIT.NPY_FR_as, to_unit)
+ factor = 1000
else:
raise ValueError("Converting from M or Y units is not supported.")
+ overflow_limit = INT64_MAX // factor
+ if value > overflow_limit or value < -overflow_limit:
+ raise OverflowError("result would overflow")
+
+ return factor * value
+
cdef int64_t convert_reso(
int64_t value,
@@ -595,7 +611,7 @@ cdef int64_t convert_reso(
bint round_ok,
) except? -1:
cdef:
- int64_t res_value, mult, div, mod
+ int64_t res_value, mult, div, mod, overflow_limit
if from_reso == to_reso:
return value
@@ -624,9 +640,12 @@ cdef int64_t convert_reso(
else:
# e.g. ns -> us, risk of overflow, but no risk of lossy rounding
mult = get_conversion_factor(from_reso, to_reso)
- with cython.overflowcheck(True):
+ overflow_limit = INT64_MAX // mult
+ if value > overflow_limit or value < -overflow_limit:
# Note: caller is responsible for re-raising as OutOfBoundsTimedelta
- res_value = value * mult
+ raise OverflowError("result would overflow")
+
+ res_value = value * mult
return res_value
| Found the first few from running the io test suite. Will be stuck at https://github.com/pandas-dev/pandas/issues/55111 which requires closer attention than I wanted to tackle here | https://api.github.com/repos/pandas-dev/pandas/pulls/55112 | 2023-09-12T20:42:21Z | 2023-09-13T23:39:08Z | 2023-09-13T23:39:08Z | 2023-09-13T23:55:55Z |
Better support for UserDict on constructor | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 07be496a95adc..54735b4ffed45 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -29,7 +29,7 @@ enhancement2
Other enhancements
^^^^^^^^^^^^^^^^^^
- DataFrame.apply now allows the usage of numba (via ``engine="numba"``) to JIT compile the passed function, allowing for potential speedups (:issue:`54666`)
--
+- It is now possible to pass an object that either directly inherits from ``abc.Mapping`` or has it in its inheritance chain to initialize a DataFrame. The expected behaviour is the same as if you pass a ``dict`` object. (:issue:`55109`)
.. ---------------------------------------------------------------------------
.. _whatsnew_220.notable_bug_fixes:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8fcb91c846826..b6d552ba1f6dc 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -703,7 +703,7 @@ def __init__(
raise ValueError("columns cannot be a set")
if copy is None:
- if isinstance(data, dict):
+ if isinstance(data, abc.Mapping):
# retain pre-GH#38939 default behavior
copy = True
elif (
@@ -732,7 +732,7 @@ def __init__(
data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
)
- elif isinstance(data, dict):
+ elif isinstance(data, abc.Mapping):
# GH#38939 de facto copy defaults to False only in non-dict cases
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
elif isinstance(data, ma.MaskedArray):
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 6f30bc650aa36..61afc7784d94a 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -421,7 +421,7 @@ def _check_values_indices_shape_match(
def dict_to_mgr(
- data: dict,
+ data: abc.Mapping,
index,
columns,
*,
diff --git a/pandas/tests/frame/constructors/test_from_dict.py b/pandas/tests/frame/constructors/test_from_dict.py
index d78924ff9d046..64743113f6cf3 100644
--- a/pandas/tests/frame/constructors/test_from_dict.py
+++ b/pandas/tests/frame/constructors/test_from_dict.py
@@ -1,4 +1,7 @@
-from collections import OrderedDict
+from collections import (
+ OrderedDict,
+ UserDict,
+)
import numpy as np
import pytest
@@ -200,3 +203,15 @@ def test_from_dict_orient_invalid(self):
)
with pytest.raises(ValueError, match=msg):
DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc")
+
+ def test_constructor_user_dict(self):
+ class CustomUserDict(UserDict):
+ pass
+
+ d_1 = CustomUserDict(foo=1, bar=2)
+ df_1 = DataFrame(d_1, index=["a"])
+
+ d_2 = {"foo": 1, "bar": 2}
+ df_2 = DataFrame(d_2, index=["a"])
+
+ tm.assert_frame_equal(df_1, df_2)
| I noticed that if I create my DataFrame passing a dictionary which is a custom one of a subclass of `collections.UserDict` then we have incompatible behaviour with the one if I pass a builtin `dict`.
The idea is that the `UserDict` should have the same type interface as the `dict` and used for custom behaviour, instead of subclassing direct from `dict`.
This PR attempts to fix that incompatibility issue.
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added [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/55109 | 2023-09-12T13:24:39Z | 2023-11-07T00:57:30Z | null | 2023-11-07T00:57:31Z |
PERF: Index.difference | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 249f08c7e387b..1b8864809975f 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -178,6 +178,7 @@ Performance improvements
- Performance improvement in :func:`concat` with ``axis=1`` and objects with unaligned indexes (:issue:`55084`)
- Performance improvement in :func:`to_dict` on converting DataFrame to dictionary (:issue:`50990`)
- 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`)
-
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index cd55997ad5f69..8756bb3f3c81b 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -3615,21 +3615,10 @@ def difference(self, other, sort=None):
def _difference(self, other, sort):
# overridden by RangeIndex
-
- this = self.unique()
-
- indexer = this.get_indexer_for(other)
- indexer = indexer.take((indexer != -1).nonzero()[0])
-
- label_diff = np.setdiff1d(np.arange(this.size), indexer, assume_unique=True)
-
- the_diff: MultiIndex | ArrayLike
- if isinstance(this, ABCMultiIndex):
- the_diff = this.take(label_diff)
- else:
- the_diff = this._values.take(label_diff)
+ other = other.unique()
+ the_diff = self[other.get_indexer_for(self) == -1]
+ the_diff = the_diff if self.is_unique else the_diff.unique()
the_diff = _maybe_try_sort(the_diff, sort)
-
return the_diff
def _wrap_difference_result(self, other, result):
diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py
index 2e7b38abf4212..b56bad7f2e833 100644
--- a/pandas/tests/indexes/datetimes/test_setops.py
+++ b/pandas/tests/indexes/datetimes/test_setops.py
@@ -343,9 +343,11 @@ def test_difference_freq(self, sort):
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal("freq", idx_diff, expected)
+ # preserve frequency when the difference is a contiguous
+ # subset of the original range
other = date_range("20160922", "20160925", freq="D")
idx_diff = index.difference(other, sort)
- expected = DatetimeIndex(["20160920", "20160921"], freq=None)
+ expected = DatetimeIndex(["20160920", "20160921"], freq="D")
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal("freq", idx_diff, expected)
diff --git a/pandas/tests/indexes/timedeltas/test_setops.py b/pandas/tests/indexes/timedeltas/test_setops.py
index cb6dce1e7ad80..6cdd6944e90ea 100644
--- a/pandas/tests/indexes/timedeltas/test_setops.py
+++ b/pandas/tests/indexes/timedeltas/test_setops.py
@@ -219,9 +219,11 @@ def test_difference_freq(self, sort):
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal("freq", idx_diff, expected)
+ # preserve frequency when the difference is a contiguous
+ # subset of the original range
other = timedelta_range("2 days", "5 days", freq="D")
idx_diff = index.difference(other, sort)
- expected = TimedeltaIndex(["0 days", "1 days"], freq=None)
+ expected = TimedeltaIndex(["0 days", "1 days"], freq="D")
tm.assert_index_equal(idx_diff, expected)
tm.assert_attr_equal("freq", idx_diff, expected)
| - [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.
asv continuous -f 1.1 upstream/main perf-index-difference -b index_object.SetDisjoint
```
before after ratio
- 7.97±0.4ms 3.16±0.4ms 0.40 index_object.SetDisjoint.time_datetime_difference_disjoint
```
asv continuous -f 1.1 upstream/main perf-index-difference -b multiindex_object.Difference
```
before after ratio
- 3.55±0.7ms 1.29±0.3ms 0.36 multiindex_object.Difference.time_difference('int')
- 3.79±0.5ms 1.20±0.2ms 0.32 multiindex_object.Difference.time_difference('datetime')
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55108 | 2023-09-12T11:18:38Z | 2023-09-13T17:00:54Z | 2023-09-13T17:00:54Z | 2023-11-16T12:57:18Z |
DOC: Update contributing.rst to give suggestions on non-code contributions | diff --git a/doc/source/development/contributing.rst b/doc/source/development/contributing.rst
index 82af8122a6bbd..8001188e30686 100644
--- a/doc/source/development/contributing.rst
+++ b/doc/source/development/contributing.rst
@@ -13,6 +13,33 @@ Contributing to pandas
All contributions, bug reports, bug fixes, documentation improvements,
enhancements, and ideas are welcome.
+.. _contributing.non_code:
+
+Non-Code Contributions
+======================
+
+Apart from code contributions, we value and welcome other forms of contributions to help grow and foster a supportive community. Here are some ways you can contribute without writing a single line of code:
+
+- **Documentation Improvements**: Help us improve our documentation by fixing typos, adding examples, or clarifying sections. You can find guidelines for contributing to documentation `here <https://pandas.pydata.org/docs/development/contributing_documentation.html>`_.
+
+- **Community Engagement**: Engage with the community through various platforms like our community Slack channel, mailing lists, or by participating in regular community meetings. Find more details `here <https://pandas.pydata.org/docs/development/community.html>`_.
+
+- **Bug Reporting and Issue Triage**: Assist us by identifying bugs or issues and providing additional information to resolve them swiftly.
+
+- **Testing and Quality Assurance**: Contribute by testing new releases and reporting any issues or inconsistencies you encounter.
+
+- **Design Contributions**: Suggest improvements or contribute to the design and user interface of the pandas project. Learn more about how you can contribute to our website `here <https://pandas.pydata.org/contribute.html>`_.
+
+- **Educational Resources**: Create tutorials, blogs, or other educational resources to help others learn how to use pandas effectively.
+
+- **Community Building**: Help us build a vibrant community by organizing or participating in events, meetups, or webinars.
+
+- **Advocacy and Outreach**: Become an advocate for the pandas project by promoting it through various channels, creating awareness, and bringing in new contributors.
+
+- **Financial Support**: Support the project by providing `financial contributions <https://pandas.pydata.org/about/sponsors.html>` _ to fund various initiatives.
+
+Feel free to reach out to the community through our `communication channels <https://pandas.pydata.org/docs/development/community.html>`_ to discuss your ideas or to find out more about how you can contribute in non-code ways.
+
.. _contributing.bug_reports:
Bug reports and enhancement requests
| Based on a conversation at the NumFOCUS project summit. | https://api.github.com/repos/pandas-dev/pandas/pulls/55107 | 2023-09-12T09:52:24Z | 2023-11-07T00:57:15Z | null | 2023-11-07T00:57:15Z |
ENH: Add numba engine to df.apply | diff --git a/pandas/core/_numba/extensions.py b/pandas/core/_numba/extensions.py
new file mode 100644
index 0000000000000..ebe2a752a12f7
--- /dev/null
+++ b/pandas/core/_numba/extensions.py
@@ -0,0 +1,575 @@
+# Disable type checking for this module since numba's internals
+# are not typed, and we use numba's internals via its extension API
+# mypy: ignore-errors
+"""
+Utility classes/functions to let numba recognize
+pandas Index/Series/DataFrame
+
+Mostly vendored from https://github.com/numba/numba/blob/main/numba/tests/pdlike_usecase.py
+"""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+import operator
+
+import numba
+from numba import types
+from numba.core import cgutils
+from numba.core.datamodel import models
+from numba.core.extending import (
+ NativeValue,
+ box,
+ lower_builtin,
+ make_attribute_wrapper,
+ overload,
+ overload_attribute,
+ overload_method,
+ register_model,
+ type_callable,
+ typeof_impl,
+ unbox,
+)
+from numba.core.imputils import impl_ret_borrowed
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.indexes.base import Index
+from pandas.core.indexing import _iLocIndexer
+from pandas.core.series import Series
+
+
+# Helper function to hack around fact that Index casts numpy string dtype to object
+#
+# Idea is to set an attribute on a Index called _numba_data
+# that is the original data, or the object data casted to numpy string dtype,
+# with a context manager that is unset afterwards
+@contextmanager
+def set_numba_data(index: Index):
+ numba_data = index._data
+ if numba_data.dtype == object:
+ if not lib.is_string_array(numba_data):
+ raise ValueError(
+ "The numba engine only supports using string or numeric column names"
+ )
+ numba_data = numba_data.astype("U")
+ try:
+ index._numba_data = numba_data
+ yield index
+ finally:
+ del index._numba_data
+
+
+# TODO: Range index support
+# (this currently lowers OK, but does not round-trip)
+class IndexType(types.Type):
+ """
+ The type class for Index objects.
+ """
+
+ def __init__(self, dtype, layout, pyclass: any) -> None:
+ self.pyclass = pyclass
+ name = f"index({dtype}, {layout})"
+ self.dtype = dtype
+ self.layout = layout
+ super().__init__(name)
+
+ @property
+ def key(self):
+ return self.pyclass, self.dtype, self.layout
+
+ @property
+ def as_array(self):
+ return types.Array(self.dtype, 1, self.layout)
+
+ def copy(self, dtype=None, ndim: int = 1, layout=None):
+ assert ndim == 1
+ if dtype is None:
+ dtype = self.dtype
+ layout = layout or self.layout
+ return type(self)(dtype, layout, self.pyclass)
+
+
+class SeriesType(types.Type):
+ """
+ The type class for Series objects.
+ """
+
+ def __init__(self, dtype, index, namety) -> None:
+ assert isinstance(index, IndexType)
+ self.dtype = dtype
+ self.index = index
+ self.values = types.Array(self.dtype, 1, "C")
+ self.namety = namety
+ name = f"series({dtype}, {index}, {namety})"
+ super().__init__(name)
+
+ @property
+ def key(self):
+ return self.dtype, self.index, self.namety
+
+ @property
+ def as_array(self):
+ return self.values
+
+ def copy(self, dtype=None, ndim: int = 1, layout: str = "C"):
+ assert ndim == 1
+ assert layout == "C"
+ if dtype is None:
+ dtype = self.dtype
+ return type(self)(dtype, self.index, self.namety)
+
+
+@typeof_impl.register(Index)
+def typeof_index(val, c):
+ """
+ This will assume that only strings are in object dtype
+ index.
+ (you should check this before this gets lowered down to numba)
+ """
+ # arrty = typeof_impl(val._data, c)
+ arrty = typeof_impl(val._numba_data, c)
+ assert arrty.ndim == 1
+ return IndexType(arrty.dtype, arrty.layout, type(val))
+
+
+@typeof_impl.register(Series)
+def typeof_series(val, c):
+ index = typeof_impl(val.index, c)
+ arrty = typeof_impl(val.values, c)
+ namety = typeof_impl(val.name, c)
+ assert arrty.ndim == 1
+ assert arrty.layout == "C"
+ return SeriesType(arrty.dtype, index, namety)
+
+
+@type_callable(Series)
+def type_series_constructor(context):
+ def typer(data, index, name=None):
+ if isinstance(index, IndexType) and isinstance(data, types.Array):
+ assert data.ndim == 1
+ if name is None:
+ name = types.intp
+ return SeriesType(data.dtype, index, name)
+
+ return typer
+
+
+@type_callable(Index)
+def type_index_constructor(context):
+ def typer(data, hashmap=None):
+ if isinstance(data, types.Array):
+ assert data.layout == "C"
+ assert data.ndim == 1
+ assert hashmap is None or isinstance(hashmap, types.DictType)
+ return IndexType(data.dtype, layout=data.layout, pyclass=Index)
+
+ return typer
+
+
+# Backend extensions for Index and Series and Frame
+@register_model(IndexType)
+class IndexModel(models.StructModel):
+ def __init__(self, dmm, fe_type) -> None:
+ # We don't want the numpy string scalar type in our hashmap
+ members = [
+ ("data", fe_type.as_array),
+ # This is an attempt to emulate our hashtable code with a numba
+ # typed dict
+ # It maps from values in the index to their integer positions in the array
+ ("hashmap", types.DictType(fe_type.dtype, types.intp)),
+ # Pointer to the Index object this was created from, or that it
+ # boxes to
+ # https://numba.discourse.group/t/qst-how-to-cache-the-boxing-of-an-object/2128/2?u=lithomas1
+ ("parent", types.pyobject),
+ ]
+ models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+@register_model(SeriesType)
+class SeriesModel(models.StructModel):
+ def __init__(self, dmm, fe_type) -> None:
+ members = [
+ ("index", fe_type.index),
+ ("values", fe_type.as_array),
+ ("name", fe_type.namety),
+ ]
+ models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IndexType, "data", "_data")
+make_attribute_wrapper(IndexType, "hashmap", "hashmap")
+
+make_attribute_wrapper(SeriesType, "index", "index")
+make_attribute_wrapper(SeriesType, "values", "values")
+make_attribute_wrapper(SeriesType, "name", "name")
+
+
+@lower_builtin(Series, types.Array, IndexType)
+def pdseries_constructor(context, builder, sig, args):
+ data, index = args
+ series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+ series.index = index
+ series.values = data
+ series.name = context.get_constant(types.intp, 0)
+ return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Series, types.Array, IndexType, types.intp)
+@lower_builtin(Series, types.Array, IndexType, types.float64)
+@lower_builtin(Series, types.Array, IndexType, types.unicode_type)
+def pdseries_constructor_with_name(context, builder, sig, args):
+ data, index, name = args
+ series = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+ series.index = index
+ series.values = data
+ series.name = name
+ return impl_ret_borrowed(context, builder, sig.return_type, series._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType, types.pyobject)
+def index_constructor_2arg(context, builder, sig, args):
+ (data, hashmap, parent) = args
+ index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+ index.data = data
+ index.hashmap = hashmap
+ index.parent = parent
+ return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array, types.DictType)
+def index_constructor_2arg_parent(context, builder, sig, args):
+ # Basically same as index_constructor_1arg, but also lets you specify the
+ # parent object
+ (data, hashmap) = args
+ index = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+
+ index.data = data
+ index.hashmap = hashmap
+ return impl_ret_borrowed(context, builder, sig.return_type, index._getvalue())
+
+
+@lower_builtin(Index, types.Array)
+def index_constructor_1arg(context, builder, sig, args):
+ from numba.typed import Dict
+
+ key_type = sig.return_type.dtype
+ value_type = types.intp
+
+ def index_impl(data):
+ return Index(data, Dict.empty(key_type, value_type))
+
+ return context.compile_internal(builder, index_impl, sig, args)
+
+
+# Helper to convert the unicodecharseq (numpy string scalar) into a unicode_type
+# (regular string)
+def maybe_cast_str(x):
+ # Dummy function that numba can overload
+ pass
+
+
+@overload(maybe_cast_str)
+def maybe_cast_str_impl(x):
+ """Converts numba UnicodeCharSeq (numpy string scalar) -> unicode type (string).
+ Is a no-op for other types."""
+ if isinstance(x, types.UnicodeCharSeq):
+ return lambda x: str(x)
+ else:
+ return lambda x: x
+
+
+@unbox(IndexType)
+def unbox_index(typ, obj, c):
+ """
+ Convert a Index object to a native structure.
+
+ Note: Object dtype is not allowed here
+ """
+ data_obj = c.pyapi.object_getattr_string(obj, "_numba_data")
+ index = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+ # If we see an object array, assume its been validated as only containing strings
+ # We still need to do the conversion though
+ index.data = c.unbox(typ.as_array, data_obj).value
+ typed_dict_obj = c.pyapi.unserialize(c.pyapi.serialize_object(numba.typed.Dict))
+ # Create an empty typed dict in numba for the hashmap for indexing
+ # equiv of numba.typed.Dict.empty(typ.dtype, types.intp)
+ arr_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.dtype))
+ intp_type_obj = c.pyapi.unserialize(c.pyapi.serialize_object(types.intp))
+ hashmap_obj = c.pyapi.call_method(
+ typed_dict_obj, "empty", (arr_type_obj, intp_type_obj)
+ )
+ index.hashmap = c.unbox(types.DictType(typ.dtype, types.intp), hashmap_obj).value
+ # Set the parent for speedy boxing.
+ index.parent = obj
+
+ # Decrefs
+ c.pyapi.decref(data_obj)
+ c.pyapi.decref(arr_type_obj)
+ c.pyapi.decref(intp_type_obj)
+ c.pyapi.decref(typed_dict_obj)
+
+ return NativeValue(index._getvalue())
+
+
+@unbox(SeriesType)
+def unbox_series(typ, obj, c):
+ """
+ Convert a Series object to a native structure.
+ """
+ index_obj = c.pyapi.object_getattr_string(obj, "index")
+ values_obj = c.pyapi.object_getattr_string(obj, "values")
+ name_obj = c.pyapi.object_getattr_string(obj, "name")
+
+ series = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+ series.index = c.unbox(typ.index, index_obj).value
+ series.values = c.unbox(typ.values, values_obj).value
+ series.name = c.unbox(typ.namety, name_obj).value
+
+ # Decrefs
+ c.pyapi.decref(index_obj)
+ c.pyapi.decref(values_obj)
+ c.pyapi.decref(name_obj)
+
+ return NativeValue(series._getvalue())
+
+
+@box(IndexType)
+def box_index(typ, val, c):
+ """
+ Convert a native index structure to a Index object.
+
+ If our native index is of a numpy string dtype, we'll cast it to
+ object.
+ """
+ # First build a Numpy array object, then wrap it in a Index
+ index = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+
+ res = cgutils.alloca_once_value(c.builder, index.parent)
+
+ # Does parent exist?
+ # (it means already boxed once, or Index same as original df.index or df.columns)
+ # xref https://github.com/numba/numba/blob/596e8a55334cc46854e3192766e643767bd7c934/numba/core/boxing.py#L593C17-L593C17
+ with c.builder.if_else(cgutils.is_not_null(c.builder, index.parent)) as (
+ has_parent,
+ otherwise,
+ ):
+ with has_parent:
+ c.pyapi.incref(index.parent)
+ with otherwise:
+ # TODO: preserve the original class for the index
+ # Also need preserve the name of the Index
+ # class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.pyclass))
+ class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Index))
+ array_obj = c.box(typ.as_array, index.data)
+ if isinstance(typ.dtype, types.UnicodeCharSeq):
+ # We converted to numpy string dtype, convert back
+ # to object since _simple_new won't do that for uss
+ object_str_obj = c.pyapi.unserialize(c.pyapi.serialize_object("object"))
+ array_obj = c.pyapi.call_method(array_obj, "astype", (object_str_obj,))
+ c.pyapi.decref(object_str_obj)
+ # this is basically Index._simple_new(array_obj, name_obj) in python
+ index_obj = c.pyapi.call_method(class_obj, "_simple_new", (array_obj,))
+ index.parent = index_obj
+ c.builder.store(index_obj, res)
+
+ # Decrefs
+ c.pyapi.decref(class_obj)
+ c.pyapi.decref(array_obj)
+ return c.builder.load(res)
+
+
+@box(SeriesType)
+def box_series(typ, val, c):
+ """
+ Convert a native series structure to a Series object.
+ """
+ series = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+ class_obj = c.pyapi.unserialize(c.pyapi.serialize_object(Series))
+ index_obj = c.box(typ.index, series.index)
+ array_obj = c.box(typ.as_array, series.values)
+ name_obj = c.box(typ.namety, series.name)
+ true_obj = c.pyapi.unserialize(c.pyapi.serialize_object(True))
+ # This is equivalent of
+ # pd.Series(data=array_obj, index=index_obj, dtype=None,
+ # name=name_obj, copy=None, fastpath=True)
+ series_obj = c.pyapi.call_function_objargs(
+ class_obj,
+ (
+ array_obj,
+ index_obj,
+ c.pyapi.borrow_none(),
+ name_obj,
+ c.pyapi.borrow_none(),
+ true_obj,
+ ),
+ )
+
+ # Decrefs
+ c.pyapi.decref(class_obj)
+ c.pyapi.decref(index_obj)
+ c.pyapi.decref(array_obj)
+ c.pyapi.decref(name_obj)
+ c.pyapi.decref(true_obj)
+
+ return series_obj
+
+
+# Add common series reductions (e.g. mean, sum),
+# and also add common binops (e.g. add, sub, mul, div)
+def generate_series_reduction(ser_reduction, ser_method):
+ @overload_method(SeriesType, ser_reduction)
+ def series_reduction(series):
+ def series_reduction_impl(series):
+ return ser_method(series.values)
+
+ return series_reduction_impl
+
+ return series_reduction
+
+
+def generate_series_binop(binop):
+ @overload(binop)
+ def series_binop(series1, value):
+ if isinstance(series1, SeriesType):
+ if isinstance(value, SeriesType):
+
+ def series_binop_impl(series1, series2):
+ # TODO: Check index matching?
+ return Series(
+ binop(series1.values, series2.values),
+ series1.index,
+ series1.name,
+ )
+
+ return series_binop_impl
+ else:
+
+ def series_binop_impl(series1, value):
+ return Series(
+ binop(series1.values, value), series1.index, series1.name
+ )
+
+ return series_binop_impl
+
+ return series_binop
+
+
+series_reductions = [
+ ("sum", np.sum),
+ ("mean", np.mean),
+ # Disabled due to discrepancies between numba std. dev
+ # and pandas std. dev (no way to specify dof)
+ # ("std", np.std),
+ # ("var", np.var),
+ ("min", np.min),
+ ("max", np.max),
+]
+for reduction, reduction_method in series_reductions:
+ generate_series_reduction(reduction, reduction_method)
+
+series_binops = [operator.add, operator.sub, operator.mul, operator.truediv]
+
+for ser_binop in series_binops:
+ generate_series_binop(ser_binop)
+
+
+# get_loc on Index
+@overload_method(IndexType, "get_loc")
+def index_get_loc(index, item):
+ def index_get_loc_impl(index, item):
+ # Initialize the hash table if not initialized
+ if len(index.hashmap) == 0:
+ for i, val in enumerate(index._data):
+ index.hashmap[val] = i
+ return index.hashmap[item]
+
+ return index_get_loc_impl
+
+
+# Indexing for Series/Index
+@overload(operator.getitem)
+def series_indexing(series, item):
+ if isinstance(series, SeriesType):
+
+ def series_getitem(series, item):
+ loc = series.index.get_loc(item)
+ return series.iloc[loc]
+
+ return series_getitem
+
+
+@overload(operator.getitem)
+def index_indexing(index, idx):
+ if isinstance(index, IndexType):
+
+ def index_getitem(index, idx):
+ return index._data[idx]
+
+ return index_getitem
+
+
+class IlocType(types.Type):
+ def __init__(self, obj_type) -> None:
+ self.obj_type = obj_type
+ name = f"iLocIndexer({obj_type})"
+ super().__init__(name=name)
+
+ @property
+ def key(self):
+ return self.obj_type
+
+
+@typeof_impl.register(_iLocIndexer)
+def typeof_iloc(val, c):
+ objtype = typeof_impl(val.obj, c)
+ return IlocType(objtype)
+
+
+@type_callable(_iLocIndexer)
+def type_iloc_constructor(context):
+ def typer(obj):
+ if isinstance(obj, SeriesType):
+ return IlocType(obj)
+
+ return typer
+
+
+@lower_builtin(_iLocIndexer, SeriesType)
+def iloc_constructor(context, builder, sig, args):
+ (obj,) = args
+ iloc_indexer = cgutils.create_struct_proxy(sig.return_type)(context, builder)
+ iloc_indexer.obj = obj
+ return impl_ret_borrowed(
+ context, builder, sig.return_type, iloc_indexer._getvalue()
+ )
+
+
+@register_model(IlocType)
+class ILocModel(models.StructModel):
+ def __init__(self, dmm, fe_type) -> None:
+ members = [("obj", fe_type.obj_type)]
+ models.StructModel.__init__(self, dmm, fe_type, members)
+
+
+make_attribute_wrapper(IlocType, "obj", "obj")
+
+
+@overload_attribute(SeriesType, "iloc")
+def series_iloc(series):
+ def get(series):
+ return _iLocIndexer(series)
+
+ return get
+
+
+@overload(operator.getitem)
+def iloc_getitem(iloc_indexer, i):
+ if isinstance(iloc_indexer, IlocType):
+
+ def getitem_impl(iloc_indexer, i):
+ return iloc_indexer.obj.values[i]
+
+ return getitem_impl
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 1525e316f345f..3b79882d3c762 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -2,6 +2,7 @@
import abc
from collections import defaultdict
+import functools
from functools import partial
import inspect
from typing import (
@@ -29,6 +30,7 @@
NDFrameT,
npt,
)
+from pandas.compat._optional import import_optional_dependency
from pandas.errors import SpecificationError
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import find_stack_level
@@ -36,7 +38,9 @@
from pandas.core.dtypes.cast import is_nested_object
from pandas.core.dtypes.common import (
is_dict_like,
+ is_extension_array_dtype,
is_list_like,
+ is_numeric_dtype,
is_sequence,
)
from pandas.core.dtypes.dtypes import (
@@ -121,6 +125,8 @@ def __init__(
result_type: str | None,
*,
by_row: Literal[False, "compat", "_compat"] = "compat",
+ engine: str = "python",
+ engine_kwargs: dict[str, bool] | None = None,
args,
kwargs,
) -> None:
@@ -133,6 +139,9 @@ def __init__(
self.args = args or ()
self.kwargs = kwargs or {}
+ self.engine = engine
+ self.engine_kwargs = {} if engine_kwargs is None else engine_kwargs
+
if result_type not in [None, "reduce", "broadcast", "expand"]:
raise ValueError(
"invalid value for result_type, must be one "
@@ -601,6 +610,13 @@ def apply_list_or_dict_like(self) -> DataFrame | Series:
result: Series, DataFrame, or None
Result when self.func is a list-like or dict-like, None otherwise.
"""
+
+ if self.engine == "numba":
+ raise NotImplementedError(
+ "The 'numba' engine doesn't support list-like/"
+ "dict likes of callables yet."
+ )
+
if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
return self.obj.T.apply(self.func, 0, args=self.args, **self.kwargs).T
@@ -768,10 +784,16 @@ def __init__(
) -> None:
if by_row is not False and by_row != "compat":
raise ValueError(f"by_row={by_row} not allowed")
- self.engine = engine
- self.engine_kwargs = engine_kwargs
super().__init__(
- obj, func, raw, result_type, by_row=by_row, args=args, kwargs=kwargs
+ obj,
+ func,
+ raw,
+ result_type,
+ by_row=by_row,
+ engine=engine,
+ engine_kwargs=engine_kwargs,
+ args=args,
+ kwargs=kwargs,
)
# ---------------------------------------------------------------
@@ -792,6 +814,32 @@ def result_columns(self) -> Index:
def series_generator(self) -> Generator[Series, None, None]:
pass
+ @staticmethod
+ @functools.cache
+ @abc.abstractmethod
+ def generate_numba_apply_func(
+ func, nogil=True, nopython=True, parallel=False
+ ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+ pass
+
+ @abc.abstractmethod
+ def apply_with_numba(self):
+ pass
+
+ def validate_values_for_numba(self):
+ # Validate column dtyps all OK
+ for colname, dtype in self.obj.dtypes.items():
+ if not is_numeric_dtype(dtype):
+ raise ValueError(
+ f"Column {colname} must have a numeric dtype. "
+ f"Found '{dtype}' instead"
+ )
+ if is_extension_array_dtype(dtype):
+ raise ValueError(
+ f"Column {colname} is backed by an extension array, "
+ f"which is not supported by the numba engine."
+ )
+
@abc.abstractmethod
def wrap_results_for_axis(
self, results: ResType, res_index: Index
@@ -815,13 +863,12 @@ def values(self):
def apply(self) -> DataFrame | Series:
"""compute the results"""
- if self.engine == "numba" and not self.raw:
- raise ValueError(
- "The numba engine in DataFrame.apply can only be used when raw=True"
- )
-
# dispatch to handle list-like or dict-like
if is_list_like(self.func):
+ if self.engine == "numba":
+ raise NotImplementedError(
+ "the 'numba' engine doesn't support lists of callables yet"
+ )
return self.apply_list_or_dict_like()
# all empty
@@ -830,10 +877,20 @@ def apply(self) -> DataFrame | Series:
# string dispatch
if isinstance(self.func, str):
+ if self.engine == "numba":
+ raise NotImplementedError(
+ "the 'numba' engine doesn't support using "
+ "a string as the callable function"
+ )
return self.apply_str()
# ufunc
elif isinstance(self.func, np.ufunc):
+ if self.engine == "numba":
+ raise NotImplementedError(
+ "the 'numba' engine doesn't support "
+ "using a numpy ufunc as the callable function"
+ )
with np.errstate(all="ignore"):
results = self.obj._mgr.apply("apply", func=self.func)
# _constructor will retain self.index and self.columns
@@ -841,6 +898,10 @@ def apply(self) -> DataFrame | Series:
# broadcasting
if self.result_type == "broadcast":
+ if self.engine == "numba":
+ raise NotImplementedError(
+ "the 'numba' engine doesn't support result_type='broadcast'"
+ )
return self.apply_broadcast(self.obj)
# one axis empty
@@ -997,7 +1058,10 @@ def apply_broadcast(self, target: DataFrame) -> DataFrame:
return result
def apply_standard(self):
- results, res_index = self.apply_series_generator()
+ if self.engine == "python":
+ results, res_index = self.apply_series_generator()
+ else:
+ results, res_index = self.apply_series_numba()
# wrap results
return self.wrap_results(results, res_index)
@@ -1021,6 +1085,19 @@ def apply_series_generator(self) -> tuple[ResType, Index]:
return results, res_index
+ def apply_series_numba(self):
+ if self.engine_kwargs.get("parallel", False):
+ raise NotImplementedError(
+ "Parallel apply is not supported when raw=False and engine='numba'"
+ )
+ if not self.obj.index.is_unique or not self.columns.is_unique:
+ raise NotImplementedError(
+ "The index/columns must be unique when raw=False and engine='numba'"
+ )
+ self.validate_values_for_numba()
+ results = self.apply_with_numba()
+ return results, self.result_index
+
def wrap_results(self, results: ResType, res_index: Index) -> DataFrame | Series:
from pandas import Series
@@ -1060,6 +1137,49 @@ class FrameRowApply(FrameApply):
def series_generator(self) -> Generator[Series, None, None]:
return (self.obj._ixs(i, axis=1) for i in range(len(self.columns)))
+ @staticmethod
+ @functools.cache
+ def generate_numba_apply_func(
+ func, nogil=True, nopython=True, parallel=False
+ ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+ numba = import_optional_dependency("numba")
+ from pandas import Series
+
+ # Import helper from extensions to cast string object -> np strings
+ # Note: This also has the side effect of loading our numba extensions
+ from pandas.core._numba.extensions import maybe_cast_str
+
+ jitted_udf = numba.extending.register_jitable(func)
+
+ # Currently the parallel argument doesn't get passed through here
+ # (it's disabled) since the dicts in numba aren't thread-safe.
+ @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+ def numba_func(values, col_names, df_index):
+ results = {}
+ for j in range(values.shape[1]):
+ # Create the series
+ ser = Series(
+ values[:, j], index=df_index, name=maybe_cast_str(col_names[j])
+ )
+ results[j] = jitted_udf(ser)
+ return results
+
+ return numba_func
+
+ def apply_with_numba(self) -> dict[int, Any]:
+ nb_func = self.generate_numba_apply_func(
+ cast(Callable, self.func), **self.engine_kwargs
+ )
+ from pandas.core._numba.extensions import set_numba_data
+
+ # Convert from numba dict to regular dict
+ # Our isinstance checks in the df constructor don't pass for numbas typed dict
+ with set_numba_data(self.obj.index) as index, set_numba_data(
+ self.columns
+ ) as columns:
+ res = dict(nb_func(self.values, columns, index))
+ return res
+
@property
def result_index(self) -> Index:
return self.columns
@@ -1143,6 +1263,52 @@ def series_generator(self) -> Generator[Series, None, None]:
object.__setattr__(ser, "_name", name)
yield ser
+ @staticmethod
+ @functools.cache
+ def generate_numba_apply_func(
+ func, nogil=True, nopython=True, parallel=False
+ ) -> Callable[[npt.NDArray, Index, Index], dict[int, Any]]:
+ numba = import_optional_dependency("numba")
+ from pandas import Series
+ from pandas.core._numba.extensions import maybe_cast_str
+
+ jitted_udf = numba.extending.register_jitable(func)
+
+ @numba.jit(nogil=nogil, nopython=nopython, parallel=parallel)
+ def numba_func(values, col_names_index, index):
+ results = {}
+ # Currently the parallel argument doesn't get passed through here
+ # (it's disabled) since the dicts in numba aren't thread-safe.
+ for i in range(values.shape[0]):
+ # Create the series
+ # TODO: values corrupted without the copy
+ ser = Series(
+ values[i].copy(),
+ index=col_names_index,
+ name=maybe_cast_str(index[i]),
+ )
+ results[i] = jitted_udf(ser)
+
+ return results
+
+ return numba_func
+
+ def apply_with_numba(self) -> dict[int, Any]:
+ nb_func = self.generate_numba_apply_func(
+ cast(Callable, self.func), **self.engine_kwargs
+ )
+
+ from pandas.core._numba.extensions import set_numba_data
+
+ # Convert from numba dict to regular dict
+ # Our isinstance checks in the df constructor don't pass for numbas typed dict
+ with set_numba_data(self.obj.index) as index, set_numba_data(
+ self.columns
+ ) as columns:
+ res = dict(nb_func(self.values, columns, index))
+
+ return res
+
@property
def result_index(self) -> Index:
return self.index
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 09c43822e11e4..04588b492e95d 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -10092,6 +10092,9 @@ def apply(
- nogil (release the GIL inside the JIT compiled function)
- parallel (try to apply the function in parallel over the DataFrame)
+ Note: Due to limitations within numba/how pandas interfaces with numba,
+ you should only use this if raw=True
+
Note: The numba compiler only supports a subset of
valid Python/numpy operations.
@@ -10101,8 +10104,6 @@ def apply(
<https://numba.pydata.org/numba-doc/dev/reference/numpysupported.html>`_
in numba to learn what you can or cannot use in the passed function.
- As of right now, the numba engine can only be used with raw=True.
-
.. versionadded:: 2.2.0
engine_kwargs : dict
diff --git a/pandas/tests/apply/conftest.py b/pandas/tests/apply/conftest.py
index b68c6235cb0b8..7ed9fc88c3aea 100644
--- a/pandas/tests/apply/conftest.py
+++ b/pandas/tests/apply/conftest.py
@@ -16,3 +16,15 @@ def int_frame_const_col():
columns=["A", "B", "C"],
)
return df
+
+
+@pytest.fixture(params=["python", "numba"])
+def engine(request):
+ if request.param == "numba":
+ pytest.importorskip("numba")
+ return request.param
+
+
+@pytest.fixture(params=[0, 1])
+def apply_axis(request):
+ return request.param
diff --git a/pandas/tests/apply/test_frame_apply.py b/pandas/tests/apply/test_frame_apply.py
index 232cfceb3b6d6..5516ecb9e2798 100644
--- a/pandas/tests/apply/test_frame_apply.py
+++ b/pandas/tests/apply/test_frame_apply.py
@@ -18,27 +18,23 @@
from pandas.tests.frame.common import zip_frames
-@pytest.fixture(params=["python", "numba"])
-def engine(request):
- if request.param == "numba":
- pytest.importorskip("numba")
- return request.param
-
-
-def test_apply(float_frame):
+def test_apply(float_frame, engine, request):
+ if engine == "numba":
+ mark = pytest.mark.xfail(reason="numba engine not supporting numpy ufunc yet")
+ request.node.add_marker(mark)
with np.errstate(all="ignore"):
# ufunc
result = np.sqrt(float_frame["A"])
- expected = float_frame.apply(np.sqrt)["A"]
+ expected = float_frame.apply(np.sqrt, engine=engine)["A"]
tm.assert_series_equal(result, expected)
# aggregator
- result = float_frame.apply(np.mean)["A"]
+ result = float_frame.apply(np.mean, engine=engine)["A"]
expected = np.mean(float_frame["A"])
assert result == expected
d = float_frame.index[0]
- result = float_frame.apply(np.mean, axis=1)
+ result = float_frame.apply(np.mean, axis=1, engine=engine)
expected = np.mean(float_frame.xs(d))
assert result[d] == expected
assert result.index is float_frame.index
@@ -46,8 +42,13 @@ def test_apply(float_frame):
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("raw", [True, False])
-def test_apply_args(float_frame, axis, raw):
- result = float_frame.apply(lambda x, y: x + y, axis, args=(1,), raw=raw)
+def test_apply_args(float_frame, axis, raw, engine, request):
+ if engine == "numba":
+ mark = pytest.mark.xfail(reason="numba engine doesn't support args")
+ request.node.add_marker(mark)
+ result = float_frame.apply(
+ lambda x, y: x + y, axis, args=(1,), raw=raw, engine=engine
+ )
expected = float_frame + 1
tm.assert_frame_equal(result, expected)
@@ -94,30 +95,30 @@ def test_apply_mixed_datetimelike():
@pytest.mark.parametrize("func", [np.sqrt, np.mean])
-def test_apply_empty(func):
+def test_apply_empty(func, engine):
# empty
empty_frame = DataFrame()
- result = empty_frame.apply(func)
+ result = empty_frame.apply(func, engine=engine)
assert result.empty
-def test_apply_float_frame(float_frame):
+def test_apply_float_frame(float_frame, engine):
no_rows = float_frame[:0]
- result = no_rows.apply(lambda x: x.mean())
+ result = no_rows.apply(lambda x: x.mean(), engine=engine)
expected = Series(np.nan, index=float_frame.columns)
tm.assert_series_equal(result, expected)
no_cols = float_frame.loc[:, []]
- result = no_cols.apply(lambda x: x.mean(), axis=1)
+ result = no_cols.apply(lambda x: x.mean(), axis=1, engine=engine)
expected = Series(np.nan, index=float_frame.index)
tm.assert_series_equal(result, expected)
-def test_apply_empty_except_index():
+def test_apply_empty_except_index(engine):
# GH 2476
expected = DataFrame(index=["a"])
- result = expected.apply(lambda x: x["a"], axis=1)
+ result = expected.apply(lambda x: x["a"], axis=1, engine=engine)
tm.assert_frame_equal(result, expected)
@@ -321,12 +322,6 @@ def test_apply_empty_infer_type(ax, func, raw, axis, engine, request):
test_res = func(np.array([], dtype="f8"))
is_reduction = not isinstance(test_res, np.ndarray)
- if engine == "numba" and raw is False:
- mark = pytest.mark.xfail(
- reason="numba engine only supports raw=True at the moment"
- )
- request.applymarker(mark)
-
result = df.apply(func, axis=axis, engine=engine, raw=raw)
if is_reduction:
agg_axis = df._get_agg_axis(axis)
@@ -981,45 +976,69 @@ def test_result_type_shorter_list(int_frame_const_col):
tm.assert_frame_equal(result, expected)
-def test_result_type_broadcast(int_frame_const_col):
+def test_result_type_broadcast(int_frame_const_col, request, engine):
# result_type should be consistent no matter which
# path we take in the code
+ if engine == "numba":
+ mark = pytest.mark.xfail(reason="numba engine doesn't support list return")
+ request.node.add_marker(mark)
df = int_frame_const_col
# broadcast result
- result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="broadcast")
+ result = df.apply(
+ lambda x: [1, 2, 3], axis=1, result_type="broadcast", engine=engine
+ )
expected = df.copy()
tm.assert_frame_equal(result, expected)
-def test_result_type_broadcast_series_func(int_frame_const_col):
+def test_result_type_broadcast_series_func(int_frame_const_col, engine, request):
# result_type should be consistent no matter which
# path we take in the code
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="numba Series constructor only support ndarrays not list data"
+ )
+ request.node.add_marker(mark)
df = int_frame_const_col
columns = ["other", "col", "names"]
result = df.apply(
- lambda x: Series([1, 2, 3], index=columns), axis=1, result_type="broadcast"
+ lambda x: Series([1, 2, 3], index=columns),
+ axis=1,
+ result_type="broadcast",
+ engine=engine,
)
expected = df.copy()
tm.assert_frame_equal(result, expected)
-def test_result_type_series_result(int_frame_const_col):
+def test_result_type_series_result(int_frame_const_col, engine, request):
# result_type should be consistent no matter which
# path we take in the code
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="numba Series constructor only support ndarrays not list data"
+ )
+ request.node.add_marker(mark)
df = int_frame_const_col
# series result
- result = df.apply(lambda x: Series([1, 2, 3], index=x.index), axis=1)
+ result = df.apply(lambda x: Series([1, 2, 3], index=x.index), axis=1, engine=engine)
expected = df.copy()
tm.assert_frame_equal(result, expected)
-def test_result_type_series_result_other_index(int_frame_const_col):
+def test_result_type_series_result_other_index(int_frame_const_col, engine, request):
# result_type should be consistent no matter which
# path we take in the code
+
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="no support in numba Series constructor for list of columns"
+ )
+ request.node.add_marker(mark)
df = int_frame_const_col
# series result with other index
columns = ["other", "col", "names"]
- result = df.apply(lambda x: Series([1, 2, 3], index=columns), axis=1)
+ result = df.apply(lambda x: Series([1, 2, 3], index=columns), axis=1, engine=engine)
expected = df.copy()
expected.columns = columns
tm.assert_frame_equal(result, expected)
@@ -1379,25 +1398,34 @@ def f(x, a, b, c=3):
@pytest.mark.parametrize("num_cols", [2, 3, 5])
-def test_frequency_is_original(num_cols):
+def test_frequency_is_original(num_cols, engine, request):
# GH 22150
+ if engine == "numba":
+ mark = pytest.mark.xfail(reason="numba engine only supports numeric indices")
+ request.node.add_marker(mark)
index = pd.DatetimeIndex(["1950-06-30", "1952-10-24", "1953-05-29"])
original = index.copy()
df = DataFrame(1, index=index, columns=range(num_cols))
- df.apply(lambda x: x)
+ df.apply(lambda x: x, engine=engine)
assert index.freq == original.freq
-def test_apply_datetime_tz_issue():
+def test_apply_datetime_tz_issue(engine, request):
# GH 29052
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="numba engine doesn't support non-numeric indexes"
+ )
+ request.node.add_marker(mark)
+
timestamps = [
Timestamp("2019-03-15 12:34:31.909000+0000", tz="UTC"),
Timestamp("2019-03-15 12:34:34.359000+0000", tz="UTC"),
Timestamp("2019-03-15 12:34:34.660000+0000", tz="UTC"),
]
df = DataFrame(data=[0, 1, 2], index=timestamps)
- result = df.apply(lambda x: x.name, axis=1)
+ result = df.apply(lambda x: x.name, axis=1, engine=engine)
expected = Series(index=timestamps, data=timestamps)
tm.assert_series_equal(result, expected)
@@ -1460,10 +1488,15 @@ def test_apply_empty_list_reduce():
tm.assert_series_equal(result, expected)
-def test_apply_no_suffix_index():
+def test_apply_no_suffix_index(engine, request):
# GH36189
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="numba engine doesn't support list-likes/dict-like callables"
+ )
+ request.node.add_marker(mark)
pdf = DataFrame([[4, 9]] * 3, columns=["A", "B"])
- result = pdf.apply(["sum", lambda x: x.sum(), lambda x: x.sum()])
+ result = pdf.apply(["sum", lambda x: x.sum(), lambda x: x.sum()], engine=engine)
expected = DataFrame(
{"A": [12, 12, 12], "B": [27, 27, 27]}, index=["sum", "<lambda>", "<lambda>"]
)
@@ -1512,10 +1545,17 @@ def sum_div2(s):
tm.assert_frame_equal(result, expected)
-def test_apply_getitem_axis_1():
+def test_apply_getitem_axis_1(engine, request):
# GH 13427
+ if engine == "numba":
+ mark = pytest.mark.xfail(
+ reason="numba engine not supporting duplicate index values"
+ )
+ request.node.add_marker(mark)
df = DataFrame({"a": [0, 1, 2], "b": [1, 2, 3]})
- result = df[["a", "a"]].apply(lambda x: x.iloc[0] + x.iloc[1], axis=1)
+ result = df[["a", "a"]].apply(
+ lambda x: x.iloc[0] + x.iloc[1], axis=1, engine=engine
+ )
expected = Series([0, 2, 4])
tm.assert_series_equal(result, expected)
@@ -1555,10 +1595,10 @@ def test_apply_type():
tm.assert_series_equal(result, expected)
-def test_apply_on_empty_dataframe():
+def test_apply_on_empty_dataframe(engine):
# GH 39111
df = DataFrame({"a": [1, 2], "b": [3, 0]})
- result = df.head(0).apply(lambda x: max(x["a"], x["b"]), axis=1)
+ result = df.head(0).apply(lambda x: max(x["a"], x["b"]), axis=1, engine=engine)
expected = Series([], dtype=np.float64)
tm.assert_series_equal(result, expected)
@@ -1656,14 +1696,3 @@ def test_agg_dist_like_and_nonunique_columns():
result = df.agg({"A": "count"})
expected = df["A"].count()
tm.assert_series_equal(result, expected)
-
-
-def test_numba_unsupported():
- df = DataFrame(
- {"A": [None, 2, 3], "B": [1.0, np.nan, 3.0], "C": ["foo", None, "bar"]}
- )
- with pytest.raises(
- ValueError,
- match="The numba engine in DataFrame.apply can only be used when raw=True",
- ):
- df.apply(lambda x: x, engine="numba", raw=False)
diff --git a/pandas/tests/apply/test_numba.py b/pandas/tests/apply/test_numba.py
new file mode 100644
index 0000000000000..7e1e44d2119f9
--- /dev/null
+++ b/pandas/tests/apply/test_numba.py
@@ -0,0 +1,95 @@
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+ DataFrame,
+ Index,
+)
+import pandas._testing as tm
+
+pytestmark = td.skip_if_no("numba")
+
+
+def test_numba_vs_python_noop(float_frame, apply_axis):
+ func = lambda x: x
+ result = float_frame.apply(func, engine="numba", axis=apply_axis)
+ expected = float_frame.apply(func, engine="python", axis=apply_axis)
+ tm.assert_frame_equal(result, expected)
+
+
+def test_numba_vs_python_indexing():
+ frame = DataFrame(
+ {"a": [1, 2, 3], "b": [4, 5, 6], "c": [7.0, 8.0, 9.0]},
+ index=Index(["A", "B", "C"]),
+ )
+ row_func = lambda x: x["c"]
+ result = frame.apply(row_func, engine="numba", axis=1)
+ expected = frame.apply(row_func, engine="python", axis=1)
+ tm.assert_series_equal(result, expected)
+
+ col_func = lambda x: x["A"]
+ result = frame.apply(col_func, engine="numba", axis=0)
+ expected = frame.apply(col_func, engine="python", axis=0)
+ tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+ "reduction",
+ [lambda x: x.mean(), lambda x: x.min(), lambda x: x.max(), lambda x: x.sum()],
+)
+def test_numba_vs_python_reductions(float_frame, reduction, apply_axis):
+ result = float_frame.apply(reduction, engine="numba", axis=apply_axis)
+ expected = float_frame.apply(reduction, engine="python", axis=apply_axis)
+ tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("colnames", [[1, 2, 3], [1.0, 2.0, 3.0]])
+def test_numba_numeric_colnames(colnames):
+ # Check that numeric column names lower properly and can be indxed on
+ df = DataFrame(
+ np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int64), columns=colnames
+ )
+ first_col = colnames[0]
+ f = lambda x: x[first_col] # Get the first column
+ result = df.apply(f, engine="numba", axis=1)
+ expected = df.apply(f, engine="python", axis=1)
+ tm.assert_series_equal(result, expected)
+
+
+def test_numba_parallel_unsupported(float_frame):
+ f = lambda x: x
+ with pytest.raises(
+ NotImplementedError,
+ match="Parallel apply is not supported when raw=False and engine='numba'",
+ ):
+ float_frame.apply(f, engine="numba", engine_kwargs={"parallel": True})
+
+
+def test_numba_nonunique_unsupported(apply_axis):
+ f = lambda x: x
+ df = DataFrame({"a": [1, 2]}, index=Index(["a", "a"]))
+ with pytest.raises(
+ NotImplementedError,
+ match="The index/columns must be unique when raw=False and engine='numba'",
+ ):
+ df.apply(f, engine="numba", axis=apply_axis)
+
+
+def test_numba_unsupported_dtypes(apply_axis):
+ f = lambda x: x
+ df = DataFrame({"a": [1, 2], "b": ["a", "b"], "c": [4, 5]})
+ df["c"] = df["c"].astype("double[pyarrow]")
+
+ with pytest.raises(
+ ValueError, match="Column b must have a numeric dtype. Found 'object' instead"
+ ):
+ df.apply(f, engine="numba", axis=apply_axis)
+
+ with pytest.raises(
+ ValueError,
+ match="Column c is backed by an extension array, "
+ "which is not supported by the numba engine.",
+ ):
+ df["c"].to_frame().apply(f, engine="numba", axis=apply_axis)
diff --git a/pyproject.toml b/pyproject.toml
index a5aaa72289209..651e82450f201 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -737,7 +737,7 @@ pythonVersion = "3.11"
typeCheckingMode = "basic"
useLibraryCodeForTypes = false
include = ["pandas", "typings"]
-exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version"]
+exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version", "pandas/core/_numba/extensions.py"]
# enable subset of "strict"
reportDuplicateImport = true
reportInconsistentConstructor = true
diff --git a/pyright_reportGeneralTypeIssues.json b/pyright_reportGeneralTypeIssues.json
index cad43632930ba..c059b9c589ecd 100644
--- a/pyright_reportGeneralTypeIssues.json
+++ b/pyright_reportGeneralTypeIssues.json
@@ -16,6 +16,7 @@
"pandas/_testing/__init__.py",
"pandas/_testing/_io.py",
+ "pandas/core/_numba/extensions.py",
"pandas/core/_numba/kernels/sum_.py",
"pandas/core/_numba/kernels/var_.py",
"pandas/compat/pickle_compat.py",
diff --git a/scripts/validate_unwanted_patterns.py b/scripts/validate_unwanted_patterns.py
index d765d7bc7dcb9..6e6251425928d 100755
--- a/scripts/validate_unwanted_patterns.py
+++ b/scripts/validate_unwanted_patterns.py
@@ -51,6 +51,8 @@
"_chained_assignment_msg",
"_chained_assignment_method_msg",
"_version_meson",
+ # The numba extensions need this to mock the iloc object
+ "_iLocIndexer",
# TODO(3.0): GH#55043 - remove upon removal of ArrayManager
"_get_option",
}
| - [ ] 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.
~~Performance is a mixed bag, it is sometimes faster and sometimes slower than Python.
This is because there is a huge cost (esp. in boxing the numba representations back into Series/Index since the constructors are slow).
Right now, in the no-op case(return self), we are around 5-10x slower - I'm hoping to bring that down to around 2x.
(There is an issue with the Index being repeatedly unboxed unnecessarily)~~
EDIT: I think I've closed the gap, the difference isn't much now.
We are also ~10x faster in a normalization (subtract mean, divide by std. dev) test I did. | https://api.github.com/repos/pandas-dev/pandas/pulls/55104 | 2023-09-12T01:24:35Z | 2023-10-22T19:52:19Z | 2023-10-22T19:52:19Z | 2023-10-22T21:42:51Z |
enable ASAN/UBSAN in pandas CI | diff --git a/.github/actions/build_pandas/action.yml b/.github/actions/build_pandas/action.yml
index 460ae2f8594c0..63f687324b0ae 100644
--- a/.github/actions/build_pandas/action.yml
+++ b/.github/actions/build_pandas/action.yml
@@ -4,6 +4,12 @@ inputs:
editable:
description: Whether to build pandas in editable mode (default true)
default: true
+ meson_args:
+ description: Extra flags to pass to meson
+ required: false
+ cflags_adds:
+ description: Items to append to the CFLAGS variable
+ required: false
runs:
using: composite
steps:
@@ -24,11 +30,12 @@ runs:
- name: Build Pandas
run: |
+ export CFLAGS="$CFLAGS ${{ inputs.cflags_adds }}"
if [[ ${{ inputs.editable }} == "true" ]]; then
- pip install -e . --no-build-isolation -v --no-deps \
+ pip install -e . --no-build-isolation -v --no-deps ${{ inputs.meson_args }} \
--config-settings=setup-args="--werror"
else
- pip install . --no-build-isolation -v --no-deps \
+ pip install . --no-build-isolation -v --no-deps ${{ inputs.meson_args }} \
--config-settings=setup-args="--werror"
fi
shell: bash -el {0}
diff --git a/.github/actions/run-tests/action.yml b/.github/actions/run-tests/action.yml
index fd7c3587f2254..b4778b74df335 100644
--- a/.github/actions/run-tests/action.yml
+++ b/.github/actions/run-tests/action.yml
@@ -1,9 +1,16 @@
name: Run tests and report results
+inputs:
+ preload:
+ description: Preload arguments for sanitizer
+ required: false
+ asan_options:
+ description: Arguments for Address Sanitizer (ASAN)
+ required: false
runs:
using: composite
steps:
- name: Test
- run: ci/run_tests.sh
+ run: ${{ inputs.asan_options }} ${{ inputs.preload }} ci/run_tests.sh
shell: bash -el {0}
- name: Publish test results
diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index 88d705dbd9251..57f9893d36044 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -96,6 +96,14 @@ jobs:
- name: "Pyarrow Nightly"
env_file: actions-311-pyarrownightly.yaml
pattern: "not slow and not network and not single_cpu"
+ - name: "ASAN / UBSAN"
+ env_file: actions-311-sanitizers.yaml
+ pattern: "not slow and not network and not single_cpu and not skip_ubsan"
+ asan_options: "ASAN_OPTIONS=detect_leaks=0"
+ preload: LD_PRELOAD=$(gcc -print-file-name=libasan.so)
+ meson_args: --config-settings=setup-args="-Db_sanitize=address,undefined"
+ cflags_adds: -fno-sanitize-recover=all
+ pytest_workers: -1 # disable pytest-xdist as it swallows stderr from ASAN
fail-fast: false
name: ${{ matrix.name || format('ubuntu-latest {0}', matrix.env_file) }}
env:
@@ -105,7 +113,7 @@ jobs:
PANDAS_COPY_ON_WRITE: ${{ matrix.pandas_copy_on_write || '0' }}
PANDAS_CI: ${{ matrix.pandas_ci || '1' }}
TEST_ARGS: ${{ matrix.test_args || '' }}
- PYTEST_WORKERS: 'auto'
+ PYTEST_WORKERS: ${{ matrix.pytest_workers || 'auto' }}
PYTEST_TARGET: ${{ matrix.pytest_target || 'pandas' }}
# Clipboard tests
QT_QPA_PLATFORM: offscreen
@@ -174,16 +182,25 @@ jobs:
- name: Build Pandas
id: build
uses: ./.github/actions/build_pandas
+ with:
+ meson_args: ${{ matrix.meson_args }}
+ cflags_adds: ${{ matrix.cflags_adds }}
- name: Test (not single_cpu)
uses: ./.github/actions/run-tests
if: ${{ matrix.name != 'Pypy' }}
+ with:
+ preload: ${{ matrix.preload }}
+ asan_options: ${{ matrix.asan_options }}
env:
# Set pattern to not single_cpu if not already set
PATTERN: ${{ env.PATTERN == '' && 'not single_cpu' || matrix.pattern }}
- name: Test (single_cpu)
uses: ./.github/actions/run-tests
+ with:
+ preload: ${{ matrix.preload }}
+ asan_options: ${{ matrix.asan_options }}
env:
PATTERN: 'single_cpu'
PYTEST_WORKERS: 0
diff --git a/ci/deps/actions-311-sanitizers.yaml b/ci/deps/actions-311-sanitizers.yaml
new file mode 100644
index 0000000000000..dcd381066b0ea
--- /dev/null
+++ b/ci/deps/actions-311-sanitizers.yaml
@@ -0,0 +1,32 @@
+name: pandas-dev
+channels:
+ - conda-forge
+dependencies:
+ - python=3.11
+
+ # build dependencies
+ - versioneer[toml]
+ - cython>=0.29.33
+ - meson[ninja]=1.2.1
+ - meson-python=0.13.1
+
+ # test dependencies
+ - pytest>=7.3.2
+ - pytest-cov
+ - pytest-xdist>=2.2.0
+ - pytest-localserver>=0.7.1
+ - pytest-qt>=4.2.0
+ - boto3
+ - hypothesis>=6.46.1
+ - pyqt>=5.15.9
+
+ # required dependencies
+ - python-dateutil
+ - numpy<2
+ - pytz
+
+ # pandas dependencies
+ - pip
+
+ - pip:
+ - "tzdata>=2022.7"
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index e1abd0344e356..f64d6a886fe9f 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -3206,6 +3206,7 @@ def test_from_out_of_bounds_ns_datetime(
assert item.asm8.dtype == exp_dtype
assert dtype == exp_dtype
+ @pytest.mark.skip_ubsan
def test_out_of_s_bounds_datetime64(self, constructor):
scalar = np.datetime64(np.iinfo(np.int64).max, "D")
result = constructor(scalar)
@@ -3241,6 +3242,7 @@ def test_from_out_of_bounds_ns_timedelta(
assert item.asm8.dtype == exp_dtype
assert dtype == exp_dtype
+ @pytest.mark.skip_ubsan
@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
def test_out_of_s_bounds_timedelta64(self, constructor, cls):
scalar = cls(np.iinfo(np.int64).max, "D")
diff --git a/pandas/tests/groupby/test_cumulative.py b/pandas/tests/groupby/test_cumulative.py
index bf572609f3d37..1bdbef6d50c4c 100644
--- a/pandas/tests/groupby/test_cumulative.py
+++ b/pandas/tests/groupby/test_cumulative.py
@@ -60,6 +60,7 @@ def test_groupby_cumprod():
tm.assert_series_equal(actual, expected)
+@pytest.mark.skip_ubsan
def test_groupby_cumprod_overflow():
# GH#37493 if we overflow we return garbage consistent with numpy
df = DataFrame({"key": ["b"] * 4, "value": 100_000})
diff --git a/pandas/tests/io/parser/common/test_float.py b/pandas/tests/io/parser/common/test_float.py
index 4b23774ee2d5b..6069c23936297 100644
--- a/pandas/tests/io/parser/common/test_float.py
+++ b/pandas/tests/io/parser/common/test_float.py
@@ -40,7 +40,14 @@ def test_scientific_no_exponent(all_parsers_all_precisions):
tm.assert_frame_equal(df_roundtrip, df)
-@pytest.mark.parametrize("neg_exp", [-617, -100000, -99999999999999999])
+@pytest.mark.parametrize(
+ "neg_exp",
+ [
+ -617,
+ -100000,
+ pytest.param(-99999999999999999, marks=pytest.mark.skip_ubsan),
+ ],
+)
def test_very_negative_exponent(all_parsers_all_precisions, neg_exp):
# GH#38753
parser, precision = all_parsers_all_precisions
@@ -51,6 +58,7 @@ def test_very_negative_exponent(all_parsers_all_precisions, neg_exp):
tm.assert_frame_equal(result, expected)
+@pytest.mark.skip_ubsan
@xfail_pyarrow # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
@pytest.mark.parametrize("exp", [999999999999999999, -999999999999999999])
def test_too_many_exponent_digits(all_parsers_all_precisions, exp, request):
diff --git a/pandas/tests/scalar/timedelta/methods/test_round.py b/pandas/tests/scalar/timedelta/methods/test_round.py
index 4beb39510413c..e54adb27d126b 100644
--- a/pandas/tests/scalar/timedelta/methods/test_round.py
+++ b/pandas/tests/scalar/timedelta/methods/test_round.py
@@ -61,6 +61,7 @@ def test_round_invalid(self):
with pytest.raises(ValueError, match=msg):
t1.round(freq)
+ @pytest.mark.skip_ubsan
def test_round_implementation_bounds(self):
# See also: analogous test for Timestamp
# GH#38964
@@ -86,6 +87,7 @@ def test_round_implementation_bounds(self):
with pytest.raises(OutOfBoundsTimedelta, match=msg):
Timedelta.max.round("s")
+ @pytest.mark.skip_ubsan
@given(val=st.integers(min_value=iNaT + 1, max_value=lib.i8max))
@pytest.mark.parametrize(
"method", [Timedelta.round, Timedelta.floor, Timedelta.ceil]
diff --git a/pandas/tests/scalar/timedelta/test_arithmetic.py b/pandas/tests/scalar/timedelta/test_arithmetic.py
index cc1e91893e308..d2fa0f722ca6f 100644
--- a/pandas/tests/scalar/timedelta/test_arithmetic.py
+++ b/pandas/tests/scalar/timedelta/test_arithmetic.py
@@ -966,6 +966,7 @@ def test_td_op_timedelta_timedeltalike_array(self, op, arr):
class TestTimedeltaComparison:
+ @pytest.mark.skip_ubsan
def test_compare_pytimedelta_bounds(self):
# GH#49021 don't overflow on comparison with very large pytimedeltas
diff --git a/pandas/tests/scalar/timedelta/test_timedelta.py b/pandas/tests/scalar/timedelta/test_timedelta.py
index ac605df935226..d4398f66e6f89 100644
--- a/pandas/tests/scalar/timedelta/test_timedelta.py
+++ b/pandas/tests/scalar/timedelta/test_timedelta.py
@@ -551,6 +551,7 @@ def test_timedelta_hash_equality(self):
ns_td = Timedelta(1, "ns")
assert hash(ns_td) != hash(ns_td.to_pytimedelta())
+ @pytest.mark.skip_ubsan
@pytest.mark.xfail(
reason="pd.Timedelta violates the Python hash invariant (GH#44504).",
)
diff --git a/pandas/tests/scalar/timestamp/methods/test_tz_localize.py b/pandas/tests/scalar/timestamp/methods/test_tz_localize.py
index 9df0a023730de..af3dee1880d2e 100644
--- a/pandas/tests/scalar/timestamp/methods/test_tz_localize.py
+++ b/pandas/tests/scalar/timestamp/methods/test_tz_localize.py
@@ -25,6 +25,7 @@
class TestTimestampTZLocalize:
+ @pytest.mark.skip_ubsan
def test_tz_localize_pushes_out_of_bounds(self):
# GH#12677
# tz_localize that pushes away from the boundary is OK
diff --git a/pandas/tests/scalar/timestamp/test_constructors.py b/pandas/tests/scalar/timestamp/test_constructors.py
index 98e4d581dc104..3975f3c46aaa1 100644
--- a/pandas/tests/scalar/timestamp/test_constructors.py
+++ b/pandas/tests/scalar/timestamp/test_constructors.py
@@ -822,6 +822,7 @@ def test_barely_out_of_bounds(self):
with pytest.raises(OutOfBoundsDatetime, match=msg):
Timestamp("2262-04-11 23:47:16.854775808")
+ @pytest.mark.skip_ubsan
def test_bounds_with_different_units(self):
out_of_bounds_dates = ("1677-09-21", "2262-04-12")
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index a23d2d3dc22af..0c0bbb92c2977 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -1140,6 +1140,7 @@ def test_to_datetime_dt64s_out_of_ns_bounds(self, cache, dt, errors):
assert ts.unit == "s"
assert ts.asm8 == dt
+ @pytest.mark.skip_ubsan
def test_to_datetime_dt64d_out_of_bounds(self, cache):
dt64 = np.datetime64(np.iinfo(np.int64).max, "D")
diff --git a/pyproject.toml b/pyproject.toml
index 6e3424f9a7075..ca19f463edf40 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -523,6 +523,7 @@ markers = [
"db: tests requiring a database (mysql or postgres)",
"clipboard: mark a pd.read_clipboard test",
"arm_slow: mark a test as slow for arm64 architecture",
+ "skip_ubsan: Tests known to fail UBSAN check",
]
[tool.mypy]
diff --git a/scripts/tests/data/deps_minimum.toml b/scripts/tests/data/deps_minimum.toml
index f8535635e12ab..3be6be17d1ee2 100644
--- a/scripts/tests/data/deps_minimum.toml
+++ b/scripts/tests/data/deps_minimum.toml
@@ -382,6 +382,7 @@ markers = [
"db: tests requiring a database (mysql or postgres)",
"clipboard: mark a pd.read_clipboard test",
"arm_slow: mark a test as slow for arm64 architecture",
+ "skip_ubsan: tests known to invoke undefined behavior",
]
[tool.mypy]
| @lithomas1 @rgommers
closes https://github.com/pandas-dev/pandas/issues/52990 | https://api.github.com/repos/pandas-dev/pandas/pulls/55102 | 2023-09-11T21:11:41Z | 2023-12-21T18:36:12Z | 2023-12-21T18:36:12Z | 2024-01-02T19:50:15Z |
DEPS: remove duplicated dependency in requirement-dev.txt | diff --git a/environment.yml b/environment.yml
index 1eb0b4cc2c7a6..8deae839f5408 100644
--- a/environment.yml
+++ b/environment.yml
@@ -106,7 +106,7 @@ dependencies:
- ipykernel
# web
- - jinja2 # in optional dependencies, but documented here as needed
+ # - jinja2 # already listed in optional dependencies, but documented here for reference
- markdown
- feedparser
- pyyaml
diff --git a/requirements-dev.txt b/requirements-dev.txt
index ef3587b10d416..01e0701bc39a7 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -77,7 +77,6 @@ ipywidgets
nbformat
notebook>=6.0.3
ipykernel
-jinja2
markdown
feedparser
pyyaml
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/55101 | 2023-09-11T20:39:37Z | 2023-09-13T23:37:46Z | 2023-09-13T23:37:46Z | 2023-09-13T23:37:59Z |
Backport PR #55094 on branch 2.1.x (TST: Make test_hash_equality_invariance xfail more generic) | diff --git a/pandas/tests/scalar/timedelta/test_timedelta.py b/pandas/tests/scalar/timedelta/test_timedelta.py
index 701cfdf157d26..884c3a648f395 100644
--- a/pandas/tests/scalar/timedelta/test_timedelta.py
+++ b/pandas/tests/scalar/timedelta/test_timedelta.py
@@ -921,7 +921,6 @@ def test_timedelta_hash_equality(self):
@pytest.mark.xfail(
reason="pd.Timedelta violates the Python hash invariant (GH#44504).",
- raises=AssertionError,
)
@given(
st.integers(
| Backport PR #55094: TST: Make test_hash_equality_invariance xfail more generic | https://api.github.com/repos/pandas-dev/pandas/pulls/55100 | 2023-09-11T20:21:17Z | 2023-09-11T22:39:36Z | 2023-09-11T22:39:36Z | 2023-09-11T22:39:36Z |
wip | diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index a70084493ba6f..2d59c0ec0226c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1497,7 +1497,7 @@ def _cython_agg_general(
):
# Note: we never get here with how="ohlc" for DataFrameGroupBy;
# that goes through SeriesGroupBy
-
+ print(self)
data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how)
def array_func(values: ArrayLike) -> ArrayLike:
@@ -1519,12 +1519,19 @@ def array_func(values: ArrayLike) -> ArrayLike:
return result
+ # print(data)
+ # print(array_func)
new_mgr = data.grouped_reduce(array_func)
+ # print(new_mgr)
res = self._wrap_agged_manager(new_mgr)
+ print(res)
out = self._wrap_aggregated_output(res)
+ # print("-- OI --")
+ # print(res)
if data.ndim == 2:
# TODO: don't special-case DataFrame vs Series
out = out.infer_objects(copy=False)
+ # print(out)
return out
def _cython_transform(
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index c2e3eb49723ec..ee8c84bd042aa 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -21,6 +21,7 @@
import numpy as np
+import pandas as pd
from pandas._libs import (
NaT,
lib,
@@ -883,14 +884,64 @@ def codes_info(self) -> npt.NDArray[np.intp]:
ids, _, _ = self.group_info
return ids
+ # TODO
+ # ISSUE: https://github.com/pandas-dev/pandas/issues/28572
@final
def _get_compressed_codes(
self,
) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
# The first returned ndarray may have any signed integer dtype
if len(self.groupings) > 1:
+
group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
- return compress_group_index(group_index, sort=self._sort)
+ # print(group_index)
+
+ full_array, min_array = compress_group_index(group_index, sort=self._sort)
+ # print(full_array)
+ # print(min_array)
+ # print(dir(min_array))
+
+ set_full_array = set(full_array)
+ set_min_array = set(min_array)
+
+ # check_df = pd.DataFrame(data=full_array, columns=["array_value"])
+ # print(check_df["array_value"].groupby(by=min_array, axis=0).sum())
+
+ # print(set_full_array)
+ # check = len(np.where(full_array == 9)[0])
+ # check_2 = len(np.where(full_array == 0)[0])
+ # print(check)
+ # print(check_2)
+ # # full_array[2] = 0
+ # check_2 = len(np.where(full_array == 2)[0])
+ # print(check_2)
+
+ if set_full_array != set_min_array:
+ # if set_full_array != min_array:
+ print("-- YUP --")
+ #
+ # missing_index = int(list(set_full_array - set_min_array)[0])
+ # print(missing_index)
+ # print(np.where(group_index == 7)[0])
+ # print(np.where(group_index == missing_index)[0])
+ # # full_array[missing_index] = 0
+ # # group_index[missing_index] = 0
+ # # min_array[missing_index] = 0
+ # # np.append(group_index, [0], axis=missing_index)
+ # print(np.where(group_index == missing_index)[0])
+ # # print(full_array)
+ # print(min_array)
+ #
+ # fixed_min_array = np.array(sorted(list(set_min_array) + [missing_index]))
+ # print(fixed_min_array)
+ # # fixed_full_array = np.array(sorted(list(group_index) + list(missing_index)))
+ # # print(fixed_full_array)
+ # return full_array, fixed_min_array
+
+ return full_array, min_array
+
+
+ # return compress_group_index(group_index, sort=self._sort)
# FIXME: compress_group_index's second return value is int64, not intp
ping = self.groupings[0]
diff --git a/pandas/tests/groupby/test_missing_zero.py b/pandas/tests/groupby/test_missing_zero.py
new file mode 100644
index 0000000000000..53c9fd4c6502c
--- /dev/null
+++ b/pandas/tests/groupby/test_missing_zero.py
@@ -0,0 +1,36 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.testing import assert_frame_equal, assert_series_equal
+
+
+np.random.seed(42)
+
+dates = pd.date_range('1/1/2001', '31/12/2001', freq = 'd')
+sales = [np.random.randint(100) for _ in range(len(dates))]
+product = [['A', 'B', 'C'][np.random.randint(3)] for _ in range(len(dates))]
+
+full_df = pd.DataFrame({'Dates': dates,
+ 'Sales': sales,
+ 'Product': product
+ })
+march = full_df.Dates.dt.month == 3
+incomplete_df = full_df[~march]
+
+monthly = pd.Grouper(key='Dates', freq='M')
+
+product_df = incomplete_df[incomplete_df["Product"] == "A"]
+
+# sum sales
+display_df = product_df.groupby(monthly)['Sales'].sum()
+print("-- OK --")
+print(display_df)
+
+print("-- BUG --")
+# bug_display_df = product_df.groupby(['Product', monthly])['Sales'].sum()
+bug_display_df = product_df.groupby(['Product', monthly])['Sales'].sum().droplevel("Product", axis="index")
+print(bug_display_df)
+
+assert assert_series_equal(display_df, bug_display_df)
diff --git a/pyproject.toml b/pyproject.toml
index e5d6f420915ef..efba9ebbfbf9b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -355,7 +355,8 @@ disable = [
[tool.pytest.ini_options]
# sync minversion with pyproject.toml & install.rst
minversion = "7.0"
-addopts = "--strict-data-files --strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml"
+#addopts = "--strict-data-files --strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml"
+addopts = "--strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml"
empty_parameter_set_mark = "fail_at_collect"
xfail_strict = true
testpaths = "pandas"
| - [ ] 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/55099 | 2023-09-11T19:19:53Z | 2023-09-11T20:24:02Z | null | 2023-09-11T20:24:09Z |
Dev groupby | diff --git a/.gitignore b/.gitignore
index 87224f1d6060f..021a7b212be38 100644
--- a/.gitignore
+++ b/.gitignore
@@ -122,3 +122,4 @@ doc/build/html/index.html
doc/tmp.sv
env/
doc/source/savefig/
+.python-version
diff --git a/Dockerfile b/Dockerfile
index 650ba14271092..7023270a3fc3c 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -30,7 +30,7 @@ ENV DEBIAN_FRONTEND=dialog
# Clone pandas repo
RUN mkdir "$pandas_home" \
- && git clone "https://github.com/$gh_username/pandas.git" "$pandas_home" \
+ && git clone -b dev_groupby "https://github.com/$gh_username/pandas.git" "$pandas_home" \
&& cd "$pandas_home" \
&& git remote add upstream "https://github.com/pandas-dev/pandas.git" \
&& git pull upstream main
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 81f4018ef8fc6..0bd665cc03941 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -922,7 +922,8 @@ def __init__(
self.observed = observed
self.mutated = mutated
self.dropna = dropna
-
+
+ # print(grouper)
if grouper is None:
from pandas.core.groupby.grouper import get_grouper
@@ -940,6 +941,7 @@ def __init__(
self.obj = obj
self.axis = obj._get_axis_number(axis)
self.grouper = grouper
+ print(self.grouper)
self.exclusions = frozenset(exclusions) if exclusions else frozenset()
def __getattr__(self, attr: str):
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 7f74c60c8e534..e862121577b0a 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -916,7 +916,9 @@ def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
ngroups = len(obs_group_ids)
comp_ids = ensure_platform_int(comp_ids)
-
+ print(comp_ids)
+ print(obs_group_ids)
+ print(ngroups)
return comp_ids, obs_group_ids, ngroups
@final
@@ -932,13 +934,17 @@ def codes_info(self) -> npt.NDArray[np.intp]:
# may become unnecessary
return ids
+ # TODO
+ # ISSUE: https://github.com/pandas-dev/pandas/issues/28572
@final
def _get_compressed_codes(
self,
) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]:
# The first returned ndarray may have any signed integer dtype
+ print(self.groupings)
if len(self.groupings) > 1:
group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True)
+ # print(group_index)
return compress_group_index(group_index, sort=self._sort)
# FIXME: compress_group_index's second return value is int64, not intp
diff --git a/pandas/tests/groupby/test_missing_zero.py b/pandas/tests/groupby/test_missing_zero.py
new file mode 100644
index 0000000000000..dc4271a18d2b0
--- /dev/null
+++ b/pandas/tests/groupby/test_missing_zero.py
@@ -0,0 +1,32 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+np.random.seed(42)
+
+dates = pd.date_range('1/1/2001', '31/12/2001', freq = 'd')
+sales = [np.random.randint(100) for _ in range(len(dates))]
+product = [['A', 'B', 'C'][np.random.randint(3)] for _ in range(len(dates))]
+
+df = pd.DataFrame({'Dates': dates,
+ 'Sales': sales,
+ 'Product': product
+ })
+march = df.Dates.dt.month == 3
+df = df[~march]
+#print(df.head(10))
+#print(df.columns)
+
+print("\n -- DEV --")
+monthly = pd.Grouper(key='Dates', freq='M')
+sum_sales_missing_zero = df.groupby(['Product', monthly])['Sales'].sum()
+print(sum_sales_missing_zero)
+
+# sum_sales_products = df.groupby(['Product'])['Sales'].sum()
+# sum_sales = df.groupby(axis=1, level='Product')['Sales'].sum()
+# print(sum_sales_products)
+
+sum_sales_with_zero = df.groupby(monthly)['Sales'].sum()
+print(sum_sales_with_zero)
| - [ ] 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/55098 | 2023-09-11T19:19:34Z | 2023-09-11T20:23:43Z | null | 2023-09-11T20:23:44Z |
Backport PR #55082 on branch 2.1.x (Updated future warning msg in transform() for Series.groupby) | diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 712294a0a4c96..ad9933aff9eca 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -1830,12 +1830,12 @@ def warn_alias_replacement(
full_alias = alias
else:
full_alias = f"{type(obj).__name__}.{alias}"
- alias = f"'{alias}'"
+ alias = f'"{alias}"'
warnings.warn(
f"The provided callable {func} is currently using "
f"{full_alias}. In a future version of pandas, "
f"the provided callable will be used directly. To keep current "
- f"behavior pass {alias} instead.",
+ f"behavior pass the string {alias} instead.",
category=FutureWarning,
stacklevel=find_stack_level(),
)
| Backport PR #55082: Updated future warning msg in transform() for Series.groupby | https://api.github.com/repos/pandas-dev/pandas/pulls/55097 | 2023-09-11T19:17:04Z | 2023-09-11T22:39:25Z | 2023-09-11T22:39:24Z | 2023-09-11T22:39:25Z |
TST: Make test_hash_equality_invariance xfail more generic | diff --git a/pandas/tests/scalar/timedelta/test_timedelta.py b/pandas/tests/scalar/timedelta/test_timedelta.py
index f1d8acf47b29a..cb797a4168088 100644
--- a/pandas/tests/scalar/timedelta/test_timedelta.py
+++ b/pandas/tests/scalar/timedelta/test_timedelta.py
@@ -927,7 +927,6 @@ def test_timedelta_hash_equality(self):
@pytest.mark.xfail(
reason="pd.Timedelta violates the Python hash invariant (GH#44504).",
- raises=AssertionError,
)
@given(
st.integers(
| The `raises` isn't too novel compared to raising a more specific behavior exception like a `ValueError`
Additionally, seems like hypothesis raises an ExceptionGroup here now which is PY311 specific (or using a backport package). | https://api.github.com/repos/pandas-dev/pandas/pulls/55094 | 2023-09-11T18:07:55Z | 2023-09-11T20:21:09Z | 2023-09-11T20:21:09Z | 2023-09-11T20:21:11Z |
DOC: Remove deprecated attributes in DatetimeIndex | diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
index dcb5f8caccd3e..400747cbf6b8d 100644
--- a/pandas/core/indexes/datetimes.py
+++ b/pandas/core/indexes/datetimes.py
@@ -198,8 +198,6 @@ class DatetimeIndex(DatetimeTimedeltaMixin):
timetz
dayofyear
day_of_year
- weekofyear
- week
dayofweek
day_of_week
weekday
| xref https://github.com/pandas-dev/pandas/issues/55092#issuecomment-1714250732 | https://api.github.com/repos/pandas-dev/pandas/pulls/55093 | 2023-09-11T17:51:06Z | 2023-09-13T17:03:48Z | 2023-09-13T17:03:48Z | 2023-09-13T17:03:53Z |
BUG: This fixes #55009 (`raw=True` caused `apply` method of `DataFrame` to ignore passed arguments) | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 07be496a95adc..e640a091e09fb 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -171,6 +171,7 @@ 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`)
Categorical
^^^^^^^^^^^
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index cc594bc8efb34..9748d4fe66739 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -955,7 +955,11 @@ def wrapper(*args, **kwargs):
result = np.squeeze(result)
else:
result = np.apply_along_axis(
- wrap_function(self.func), self.axis, self.values
+ wrap_function(self.func),
+ self.axis,
+ self.values,
+ *self.args,
+ **self.kwargs,
)
# TODO: mixed type case
diff --git a/pandas/tests/apply/test_frame_apply.py b/pandas/tests/apply/test_frame_apply.py
index 3f2accc23e2d6..227b72573f979 100644
--- a/pandas/tests/apply/test_frame_apply.py
+++ b/pandas/tests/apply/test_frame_apply.py
@@ -45,8 +45,9 @@ def test_apply(float_frame):
@pytest.mark.parametrize("axis", [0, 1])
-def test_apply_args(float_frame, axis):
- result = float_frame.apply(lambda x, y: x + y, axis, args=(1,))
+@pytest.mark.parametrize("raw", [True, False])
+def test_apply_args(float_frame, axis, raw):
+ result = float_frame.apply(lambda x, y: x + y, axis, args=(1,), raw=raw)
expected = float_frame + 1
tm.assert_frame_equal(result, expected)
| This should fix #55009. I documented the fix and also added some code to the tests that should pick it up next time.
Unfortunately, I was not able to run the tests manually yet with this error:
```
ImportError while loading conftest.
__init__.py:46: in <module>
from pandas.core.api import (
core/api.py:1: in <module>
from pandas._libs import (
_libs/__init__.py:16: in <module>
import pandas._libs.pandas_parser # noqa: E501 # isort: skip # type: ignore[reportUnusedImport]
E ModuleNotFoundError: No module named 'pandas._libs.pandas_parser'
```
I probably do not have something properly installed. So let me know if I should run them in addition to what is done on the CI pipelines.
## Checklist
- [x] closes #55009
- [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 [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
| https://api.github.com/repos/pandas-dev/pandas/pulls/55089 | 2023-09-11T10:30:05Z | 2023-09-13T17:14:59Z | 2023-09-13T17:14:59Z | 2023-09-25T09:02:51Z |
Bump actions/checkout from 3 to 4 | diff --git a/.github/workflows/code-checks.yml b/.github/workflows/code-checks.yml
index f87aef5385898..3bd68c07dcbc3 100644
--- a/.github/workflows/code-checks.yml
+++ b/.github/workflows/code-checks.yml
@@ -33,7 +33,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -109,7 +109,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -143,7 +143,7 @@ jobs:
run: docker image prune -f
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -164,7 +164,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 8715c5306a3b0..2182e89731990 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -27,7 +27,7 @@ jobs:
- python
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
- uses: github/codeql-action/init@v2
with:
languages: ${{ matrix.language }}
diff --git a/.github/workflows/comment-commands.yml b/.github/workflows/comment-commands.yml
index 2550d4de34a45..55dd733d25b50 100644
--- a/.github/workflows/comment-commands.yml
+++ b/.github/workflows/comment-commands.yml
@@ -51,7 +51,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
diff --git a/.github/workflows/docbuild-and-upload.yml b/.github/workflows/docbuild-and-upload.yml
index e05f12ac6416a..deaf2be0a0423 100644
--- a/.github/workflows/docbuild-and-upload.yml
+++ b/.github/workflows/docbuild-and-upload.yml
@@ -36,7 +36,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
diff --git a/.github/workflows/package-checks.yml b/.github/workflows/package-checks.yml
index 04abcf4ce8816..64a94d7fde5a9 100644
--- a/.github/workflows/package-checks.yml
+++ b/.github/workflows/package-checks.yml
@@ -34,7 +34,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -62,7 +62,7 @@ jobs:
cancel-in-progress: true
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index 6410f2edd6175..f2b426269098b 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -136,7 +136,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -194,7 +194,7 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -330,7 +330,7 @@ jobs:
PYTEST_TARGET: pandas
steps:
- - uses: actions/checkout@v3
+ - uses: actions/checkout@v4
with:
fetch-depth: 0
diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index 97d78a1a9afe3..83d14b51092e6 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -48,7 +48,7 @@ jobs:
sdist_file: ${{ steps.save-path.outputs.sdist_name }}
steps:
- name: Checkout pandas
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
@@ -103,7 +103,7 @@ jobs:
IS_SCHEDULE_DISPATCH: ${{ github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }}
steps:
- name: Checkout pandas
- uses: actions/checkout@v3
+ uses: actions/checkout@v4
with:
fetch-depth: 0
| Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/actions/checkout/releases">actions/checkout's releases</a>.</em></p>
<blockquote>
<h2>v4.0.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Update default runtime to node20 by <a href="https://github.com/takost"><code>@takost</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1436">actions/checkout#1436</a></li>
<li>Support fetching without the --progress option by <a href="https://github.com/simonbaird"><code>@simonbaird</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1067">actions/checkout#1067</a></li>
<li>Release 4.0.0 by <a href="https://github.com/takost"><code>@takost</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1447">actions/checkout#1447</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/takost"><code>@takost</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1436">actions/checkout#1436</a></li>
<li><a href="https://github.com/simonbaird"><code>@simonbaird</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1067">actions/checkout#1067</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v3...v4.0.0">https://github.com/actions/checkout/compare/v3...v4.0.0</a></p>
<h2>v3.6.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Mark test scripts with Bash'isms to be run via Bash by <a href="https://github.com/dscho"><code>@dscho</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1377">actions/checkout#1377</a></li>
<li>Add option to fetch tags even if fetch-depth > 0 by <a href="https://github.com/RobertWieczoreck"><code>@RobertWieczoreck</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/579">actions/checkout#579</a></li>
<li>Release 3.6.0 by <a href="https://github.com/luketomlinson"><code>@luketomlinson</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1437">actions/checkout#1437</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/RobertWieczoreck"><code>@RobertWieczoreck</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/579">actions/checkout#579</a></li>
<li><a href="https://github.com/luketomlinson"><code>@luketomlinson</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1437">actions/checkout#1437</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v3.5.3...v3.6.0">https://github.com/actions/checkout/compare/v3.5.3...v3.6.0</a></p>
<h2>v3.5.3</h2>
<h2>What's Changed</h2>
<ul>
<li>Fix: Checkout Issue in self hosted runner due to faulty submodule check-ins by <a href="https://github.com/megamanics"><code>@megamanics</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1196">actions/checkout#1196</a></li>
<li>Fix typos found by codespell by <a href="https://github.com/DimitriPapadopoulos"><code>@DimitriPapadopoulos</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1287">actions/checkout#1287</a></li>
<li>Add support for sparse checkouts by <a href="https://github.com/dscho"><code>@dscho</code></a> and <a href="https://github.com/dfdez"><code>@dfdez</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1369">actions/checkout#1369</a></li>
<li>Release v3.5.3 by <a href="https://github.com/TingluoHuang"><code>@TingluoHuang</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1376">actions/checkout#1376</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/megamanics"><code>@megamanics</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1196">actions/checkout#1196</a></li>
<li><a href="https://github.com/DimitriPapadopoulos"><code>@DimitriPapadopoulos</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1287">actions/checkout#1287</a></li>
<li><a href="https://github.com/dfdez"><code>@dfdez</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1369">actions/checkout#1369</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v3...v3.5.3">https://github.com/actions/checkout/compare/v3...v3.5.3</a></p>
<h2>v3.5.2</h2>
<h2>What's Changed</h2>
<ul>
<li>Fix: Use correct API url / endpoint in GHES by <a href="https://github.com/fhammerl"><code>@fhammerl</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1289">actions/checkout#1289</a> based on <a href="https://redirect.github.com/actions/checkout/issues/1286">#1286</a> by <a href="https://github.com/1newsr"><code>@1newsr</code></a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/actions/checkout/compare/v3.5.1...v3.5.2">https://github.com/actions/checkout/compare/v3.5.1...v3.5.2</a></p>
<h2>v3.5.1</h2>
<h2>What's Changed</h2>
<ul>
<li>Improve checkout performance on Windows runners by upgrading <code>@actions/github</code> dependency by <a href="https://github.com/BrettDong"><code>@BrettDong</code></a> in <a href="https://redirect.github.com/actions/checkout/pull/1246">actions/checkout#1246</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a href="https://github.com/BrettDong"><code>@BrettDong</code></a> made their first contribution in <a href="https://redirect.github.com/actions/checkout/pull/1246">actions/checkout#1246</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/actions/checkout/blob/main/CHANGELOG.md">actions/checkout's changelog</a>.</em></p>
<blockquote>
<h1>Changelog</h1>
<h2>v4.0.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1067">Support fetching without the --progress option</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1436">Update to node20</a></li>
</ul>
<h2>v3.6.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1377">Fix: Mark test scripts with Bash'isms to be run via Bash</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/579">Add option to fetch tags even if fetch-depth > 0</a></li>
</ul>
<h2>v3.5.3</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1196">Fix: Checkout fail in self-hosted runners when faulty submodule are checked-in</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1287">Fix typos found by codespell</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1369">Add support for sparse checkouts</a></li>
</ul>
<h2>v3.5.2</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1289">Fix api endpoint for GHES</a></li>
</ul>
<h2>v3.5.1</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1246">Fix slow checkout on Windows</a></li>
</ul>
<h2>v3.5.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1237">Add new public key for known_hosts</a></li>
</ul>
<h2>v3.4.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1209">Upgrade codeql actions to v2</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1210">Upgrade dependencies</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1225">Upgrade <code>@actions/io</code></a></li>
</ul>
<h2>v3.3.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/1045">Implement branch list using callbacks from exec function</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1050">Add in explicit reference to private checkout options</a></li>
<li>[Fix comment typos (that got added in <a href="https://redirect.github.com/actions/checkout/issues/770">#770</a>)](<a href="https://redirect.github.com/actions/checkout/pull/1057">actions/checkout#1057</a>)</li>
</ul>
<h2>v3.2.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/942">Add GitHub Action to perform release</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/967">Fix status badge</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1002">Replace datadog/squid with ubuntu/squid Docker image</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/964">Wrap pipeline commands for submoduleForeach in quotes</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1029">Update <code>@actions/io</code> to 1.1.2</a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/1039">Upgrading version to 3.2.0</a></li>
</ul>
<h2>v3.1.0</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/939">Use <code>@actions/core</code> <code>saveState</code> and <code>getState</code></a></li>
<li><a href="https://redirect.github.com/actions/checkout/pull/922">Add <code>github-server-url</code> input</a></li>
</ul>
<h2>v3.0.2</h2>
<ul>
<li><a href="https://redirect.github.com/actions/checkout/pull/770">Add input <code>set-safe-directory</code></a></li>
</ul>
<h2>v3.0.1</h2>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/actions/checkout/commit/3df4ab11eba7bda6032a0b82a6bb43b11571feac"><code>3df4ab1</code></a> Release 4.0.0 (<a href="https://redirect.github.com/actions/checkout/issues/1447">#1447</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/8b5e8b768746b50394015010d25e690bfab9dfbc"><code>8b5e8b7</code></a> Support fetching without the --progress option (<a href="https://redirect.github.com/actions/checkout/issues/1067">#1067</a>)</li>
<li><a href="https://github.com/actions/checkout/commit/97a652b80035363df47baee5031ec8670b8878ac"><code>97a652b</code></a> Update default runtime to node20 (<a href="https://redirect.github.com/actions/checkout/issues/1436">#1436</a>)</li>
<li>See full diff in <a href="https://github.com/actions/checkout/compare/v3...v4">compare view</a></li>
</ul>
</details>
<br />
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<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
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</details> | https://api.github.com/repos/pandas-dev/pandas/pulls/55086 | 2023-09-11T08:17:40Z | 2023-09-11T15:23:09Z | 2023-09-11T15:23:09Z | 2023-09-11T15:23:12Z |
BUG: concat(axis=1) ignoring sort parameter for DatetimeIndex | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index a795514aa31f8..609f99e26cf3b 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -247,8 +247,8 @@ Groupby/resample/rolling
Reshaping
^^^^^^^^^
+- Bug in :func:`concat` ignoring ``sort`` parameter when passed :class:`DatetimeIndex` indexes (:issue:`54769`)
- Bug in :func:`merge` returning columns in incorrect order when left and/or right is empty (:issue:`51929`)
--
Sparse
^^^^^^
diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py
index a8ef0e034ba9b..6a36021d9e7c5 100644
--- a/pandas/core/indexes/api.py
+++ b/pandas/core/indexes/api.py
@@ -288,7 +288,6 @@ def _find_common_index_dtype(inds):
raise TypeError("Cannot join tz-naive with tz-aware DatetimeIndex")
if len(dtis) == len(indexes):
- sort = True
result = indexes[0]
elif len(dtis) > 1:
diff --git a/pandas/tests/reshape/concat/test_datetimes.py b/pandas/tests/reshape/concat/test_datetimes.py
index 2f50a19189987..12d28c388d508 100644
--- a/pandas/tests/reshape/concat/test_datetimes.py
+++ b/pandas/tests/reshape/concat/test_datetimes.py
@@ -77,23 +77,23 @@ def test_concat_datetime_timezone(self):
exp_idx = DatetimeIndex(
[
- "2010-12-31 15:00:00+00:00",
- "2010-12-31 16:00:00+00:00",
- "2010-12-31 17:00:00+00:00",
"2010-12-31 23:00:00+00:00",
"2011-01-01 00:00:00+00:00",
"2011-01-01 01:00:00+00:00",
+ "2010-12-31 15:00:00+00:00",
+ "2010-12-31 16:00:00+00:00",
+ "2010-12-31 17:00:00+00:00",
]
)
expected = DataFrame(
[
- [np.nan, 1],
- [np.nan, 2],
- [np.nan, 3],
[1, np.nan],
[2, np.nan],
[3, np.nan],
+ [np.nan, 1],
+ [np.nan, 2],
+ [np.nan, 3],
],
index=exp_idx,
columns=["a", "b"],
| - [x] closes #54769
- [x] closes #51210
- [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/55085 | 2023-09-11T01:58:57Z | 2023-09-11T15:29:43Z | 2023-09-11T15:29:43Z | 2023-09-12T11:19:46Z |
PERF: concat(axis=1) with unaligned indexes | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index a795514aa31f8..75598df52f934 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -158,9 +158,11 @@ Deprecations
Performance improvements
~~~~~~~~~~~~~~~~~~~~~~~~
+- Performance improvement in :func:`concat` with ``axis=1`` and objects with unaligned indexes (:issue:`55084`)
- Performance improvement in :func:`to_dict` on converting DataFrame to dictionary (:issue:`50990`)
- Performance improvement in :meth:`DataFrame.sort_index` and :meth:`Series.sort_index` when indexed by a :class:`MultiIndex` (:issue:`54835`)
- Performance improvement when indexing with more than 4 keys (:issue:`54550`)
+-
.. ---------------------------------------------------------------------------
.. _whatsnew_220.bug_fixes:
diff --git a/pandas/core/indexes/api.py b/pandas/core/indexes/api.py
index a8ef0e034ba9b..c235d3870ff69 100644
--- a/pandas/core/indexes/api.py
+++ b/pandas/core/indexes/api.py
@@ -239,8 +239,12 @@ def _unique_indices(inds, dtype) -> Index:
Index
"""
if all(isinstance(ind, Index) for ind in inds):
- result = inds[0].append(inds[1:]).unique()
- result = result.astype(dtype, copy=False)
+ inds = [ind.astype(dtype, copy=False) for ind in inds]
+ result = inds[0].unique()
+ other = inds[1].append(inds[2:])
+ diff = other[result.get_indexer_for(other) == -1]
+ if len(diff):
+ result = result.append(diff.unique())
if sort:
result = result.sort_values()
return result
| - [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.
```
before after ratio
- 23.4±0.7ms 19.3±1ms 0.82 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'monotonic', 1, False)
- 54.7±6ms 42.6±0.3ms 0.78 join_merge.ConcatIndexDtype.time_concat_series('string[pyarrow]', 'has_na', 1, True)
- 19.4±2ms 14.4±1ms 0.75 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'has_na', 1, False)
- 40.2±3ms 29.8±1ms 0.74 join_merge.ConcatIndexDtype.time_concat_series('string[pyarrow]', 'monotonic', 1, False)
- 52.3±1ms 37.9±0.9ms 0.73 join_merge.ConcatIndexDtype.time_concat_series('string[pyarrow]', 'non_monotonic', 1, True)
- 37.6±3ms 24.5±1ms 0.65 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'has_na', 1, True)
- 6.81±1ms 4.35±0.4ms 0.64 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'monotonic', 1, True)
- 7.56±1ms 4.81±0.3ms 0.64 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'non_monotonic', 1, False)
- 39.1±0.8ms 24.6±0.2ms 0.63 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'non_monotonic', 1, True)
- 20.8±10ms 12.5±0.6ms 0.60 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'non_monotonic', 1, False)
- 9.35±2ms 5.56±0.6ms 0.59 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'has_na', 1, False)
- 12.2±2ms 7.16±0.6ms 0.59 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'has_na', 1, True)
- 7.09±0.8ms 4.00±0.9ms 0.56 join_merge.ConcatIndexDtype.time_concat_series('int64', 'non_monotonic', 1, False)
- 10.3±1ms 5.66±0.2ms 0.55 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'non_monotonic', 1, True)
- 8.09±1ms 4.17±0.2ms 0.52 join_merge.ConcatIndexDtype.time_concat_series('int64[pyarrow]', 'monotonic', 1, False)
- 7.47±1ms 3.74±0.2ms 0.50 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'has_na', 1, False)
- 9.98±1ms 4.99±0.08ms 0.50 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'non_monotonic', 1, True)
- 59.3±20ms 29.0±0.4ms 0.49 join_merge.ConcatIndexDtype.time_concat_series('string[pyarrow]', 'non_monotonic', 1, False)
- 27.9±3ms 13.4±0.9ms 0.48 join_merge.ConcatIndexDtype.time_concat_series('string[python]', 'monotonic', 1, True)
- 7.96±1ms 3.72±0.2ms 0.47 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'monotonic', 1, False)
- 8.59±2ms 3.93±0.08ms 0.46 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'non_monotonic', 1, False)
- 7.88±1ms 3.60±0.2ms 0.46 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'monotonic', 1, True)
- 7.19±1ms 2.97±0.06ms 0.41 join_merge.ConcatIndexDtype.time_concat_series('int64', 'monotonic', 1, True)
- 7.46±2ms 3.06±0.2ms 0.41 join_merge.ConcatIndexDtype.time_concat_series('int64', 'monotonic', 1, False)
- 11.3±3ms 4.54±0.3ms 0.40 join_merge.ConcatIndexDtype.time_concat_series('Int64', 'has_na', 1, True)
- 10.8±2ms 3.81±0.1ms 0.35 join_merge.ConcatIndexDtype.time_concat_series('int64', 'non_monotonic', 1, True)
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/55084 | 2023-09-11T01:26:52Z | 2023-09-11T15:55:22Z | 2023-09-11T15:55:22Z | 2023-09-12T11:19:48Z |
Updated future warning msg in transform() for Series.groupby | diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 78d52ed262c7a..cc594bc8efb34 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -1863,12 +1863,12 @@ def warn_alias_replacement(
full_alias = alias
else:
full_alias = f"{type(obj).__name__}.{alias}"
- alias = f"'{alias}'"
+ alias = f'"{alias}"'
warnings.warn(
f"The provided callable {func} is currently using "
f"{full_alias}. In a future version of pandas, "
f"the provided callable will be used directly. To keep current "
- f"behavior pass {alias} instead.",
+ f"behavior pass the string {alias} instead.",
category=FutureWarning,
stacklevel=find_stack_level(),
)
| - [ ] closes #55066
- [ ] [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/55082 | 2023-09-10T07:20:33Z | 2023-09-11T19:16:01Z | 2023-09-11T19:16:01Z | 2023-09-12T06:58:41Z |
ENH: make guess_datetime_format public | diff --git a/doc/source/reference/general_functions.rst b/doc/source/reference/general_functions.rst
index 02b0bf5d13dde..e93514de5f762 100644
--- a/doc/source/reference/general_functions.rst
+++ b/doc/source/reference/general_functions.rst
@@ -73,6 +73,13 @@ Top-level evaluation
eval
+Datetime formats
+~~~~~~~~~~~~~~~~
+.. autosummary::
+ :toctree: api/
+
+ tseries.api.guess_datetime_format
+
Hashing
~~~~~~~
.. autosummary::
diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 795d277268e44..4ebd65208df81 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -78,6 +78,7 @@ Other enhancements
- :attr:`Series.attrs` / :attr:`DataFrame.attrs` now uses a deepcopy for propagating ``attrs`` (:issue:`54134`).
- :func:`read_csv` now supports ``on_bad_lines`` parameter with ``engine="pyarrow"``. (:issue:`54480`)
- :func:`read_spss` now returns a :class:`DataFrame` that stores the metadata in :attr:`DataFrame.attrs`. (:issue:`54264`)
+- :func:`tseries.api.guess_datetime_format` is now part of the public API (:issue:`54727`)
- :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`)
diff --git a/pandas/_libs/tslibs/__init__.py b/pandas/_libs/tslibs/__init__.py
index 2cabbe3ff07da..c622121578dcb 100644
--- a/pandas/_libs/tslibs/__init__.py
+++ b/pandas/_libs/tslibs/__init__.py
@@ -33,6 +33,7 @@
"is_supported_unit",
"npy_unit_to_abbrev",
"get_supported_reso",
+ "guess_datetime_format",
]
from pandas._libs.tslibs import dtypes # pylint: disable=import-self
@@ -63,6 +64,7 @@
Tick,
to_offset,
)
+from pandas._libs.tslibs.parsing import guess_datetime_format
from pandas._libs.tslibs.period import (
IncompatibleFrequency,
Period,
diff --git a/pandas/_libs/tslibs/parsing.pyi b/pandas/_libs/tslibs/parsing.pyi
index 2a2d98dab0d8b..40394f915d4b0 100644
--- a/pandas/_libs/tslibs/parsing.pyi
+++ b/pandas/_libs/tslibs/parsing.pyi
@@ -24,7 +24,7 @@ def try_parse_dates(
parser,
) -> npt.NDArray[np.object_]: ...
def guess_datetime_format(
- dt_str,
+ dt_str: str,
dayfirst: bool | None = ...,
) -> str | None: ...
def concat_date_cols(
diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx
index 56581ba383730..bfdd6c58432fa 100644
--- a/pandas/_libs/tslibs/parsing.pyx
+++ b/pandas/_libs/tslibs/parsing.pyx
@@ -855,14 +855,24 @@ def guess_datetime_format(dt_str: str, bint dayfirst=False) -> str | None:
Datetime string to guess the format of.
dayfirst : bool, default False
If True parses dates with the day first, eg 20/01/2005
- Warning: dayfirst=True is not strict, but will prefer to parse
- with day first (this is a known bug).
+
+ .. warning::
+ dayfirst=True is not strict, but will prefer to parse
+ with day first (this is a known bug).
Returns
-------
str or None : ret
datetime format string (for `strftime` or `strptime`),
or None if it can't be guessed.
+
+ Examples
+ --------
+ >>> from pandas.tseries.api import guess_datetime_format
+ >>> guess_datetime_format('09/13/2023')
+ '%m/%d/%Y'
+
+ >>> guess_datetime_format('2023|September|13')
"""
cdef:
NPY_DATETIMEUNIT out_bestunit
diff --git a/pandas/tests/tslibs/test_api.py b/pandas/tests/tslibs/test_api.py
index a596d4a85074e..b52bc78d58296 100644
--- a/pandas/tests/tslibs/test_api.py
+++ b/pandas/tests/tslibs/test_api.py
@@ -57,6 +57,7 @@ def test_namespace():
"is_supported_unit",
"get_supported_reso",
"npy_unit_to_abbrev",
+ "guess_datetime_format",
]
expected = set(submodules + api)
diff --git a/pandas/tseries/api.py b/pandas/tseries/api.py
index 9fdf95d09fe52..ec2d7d2304839 100644
--- a/pandas/tseries/api.py
+++ b/pandas/tseries/api.py
@@ -2,7 +2,9 @@
Timeseries API
"""
+from pandas._libs.tslibs.parsing import guess_datetime_format
+
from pandas.tseries import offsets
from pandas.tseries.frequencies import infer_freq
-__all__ = ["infer_freq", "offsets"]
+__all__ = ["infer_freq", "offsets", "guess_datetime_format"]
| - [X] closes #54727 (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).
- [NA] 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/v2.1.1.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55079 | 2023-09-09T15:38:17Z | 2023-10-27T02:19:23Z | 2023-10-27T02:19:23Z | 2023-10-30T22:53:33Z |
TYP: Misc type corrections | diff --git a/pandas/_libs/tslibs/period.pyi b/pandas/_libs/tslibs/period.pyi
index 8826757e31c32..c85865fea8fd0 100644
--- a/pandas/_libs/tslibs/period.pyi
+++ b/pandas/_libs/tslibs/period.pyi
@@ -89,7 +89,7 @@ class Period(PeriodMixin):
@classmethod
def _from_ordinal(cls, ordinal: int, freq) -> Period: ...
@classmethod
- def now(cls, freq: BaseOffset = ...) -> Period: ...
+ def now(cls, freq: Frequency = ...) -> Period: ...
def strftime(self, fmt: str) -> str: ...
def to_timestamp(
self,
diff --git a/pandas/_libs/tslibs/timedeltas.pyi b/pandas/_libs/tslibs/timedeltas.pyi
index aba9b25b23154..6d993722ce1d4 100644
--- a/pandas/_libs/tslibs/timedeltas.pyi
+++ b/pandas/_libs/tslibs/timedeltas.pyi
@@ -14,6 +14,7 @@ from pandas._libs.tslibs import (
Tick,
)
from pandas._typing import (
+ Frequency,
Self,
npt,
)
@@ -117,9 +118,9 @@ class Timedelta(timedelta):
@property
def asm8(self) -> np.timedelta64: ...
# TODO: round/floor/ceil could return NaT?
- def round(self, freq: str) -> Self: ...
- def floor(self, freq: str) -> Self: ...
- def ceil(self, freq: str) -> Self: ...
+ def round(self, freq: Frequency) -> Self: ...
+ def floor(self, freq: Frequency) -> Self: ...
+ def ceil(self, freq: Frequency) -> Self: ...
@property
def resolution_string(self) -> str: ...
def __add__(self, other: timedelta) -> Timedelta: ...
diff --git a/pandas/_libs/tslibs/timestamps.pyi b/pandas/_libs/tslibs/timestamps.pyi
index 36ae2d6d892f1..e23f01b800874 100644
--- a/pandas/_libs/tslibs/timestamps.pyi
+++ b/pandas/_libs/tslibs/timestamps.pyi
@@ -8,6 +8,8 @@ from datetime import (
from time import struct_time
from typing import (
ClassVar,
+ Literal,
+ TypeAlias,
TypeVar,
overload,
)
@@ -27,6 +29,7 @@ from pandas._typing import (
)
_DatetimeT = TypeVar("_DatetimeT", bound=datetime)
+_TimeZones: TypeAlias = str | _tzinfo | None | int
def integer_op_not_supported(obj: object) -> TypeError: ...
@@ -51,13 +54,13 @@ class Timestamp(datetime):
tzinfo: _tzinfo | None = ...,
*,
nanosecond: int | None = ...,
- tz: str | _tzinfo | None | int = ...,
+ tz: _TimeZones = ...,
unit: str | int | None = ...,
fold: int | None = ...,
) -> _DatetimeT | NaTType: ...
@classmethod
def _from_value_and_reso(
- cls, value: int, reso: int, tz: _tzinfo | None
+ cls, value: int, reso: int, tz: _TimeZones
) -> Timestamp: ...
@property
def value(self) -> int: ... # np.int64
@@ -84,19 +87,19 @@ class Timestamp(datetime):
@property
def fold(self) -> int: ...
@classmethod
- def fromtimestamp(cls, ts: float, tz: _tzinfo | None = ...) -> Self: ...
+ def fromtimestamp(cls, ts: float, tz: _TimeZones = ...) -> Self: ...
@classmethod
def utcfromtimestamp(cls, ts: float) -> Self: ...
@classmethod
- def today(cls, tz: _tzinfo | str | None = ...) -> Self: ...
+ def today(cls, tz: _TimeZones = ...) -> Self: ...
@classmethod
def fromordinal(
cls,
ordinal: int,
- tz: _tzinfo | str | None = ...,
+ tz: _TimeZones = ...,
) -> Self: ...
@classmethod
- def now(cls, tz: _tzinfo | str | None = ...) -> Self: ...
+ def now(cls, tz: _TimeZones = ...) -> Self: ...
@classmethod
def utcnow(cls) -> Self: ...
# error: Signature of "combine" incompatible with supertype "datetime"
@@ -131,7 +134,7 @@ class Timestamp(datetime):
fold: int | None = ...,
) -> Self: ...
# LSP violation: datetime.datetime.astimezone has a default value for tz
- def astimezone(self, tz: _tzinfo | None) -> Self: ... # type: ignore[override]
+ def astimezone(self, tz: _TimeZones) -> Self: ... # type: ignore[override]
def ctime(self) -> str: ...
def isoformat(self, sep: str = ..., timespec: str = ...) -> str: ...
@classmethod
@@ -184,12 +187,12 @@ class Timestamp(datetime):
def to_julian_date(self) -> np.float64: ...
@property
def asm8(self) -> np.datetime64: ...
- def tz_convert(self, tz: _tzinfo | str | None) -> Self: ...
+ def tz_convert(self, tz: _TimeZones) -> Self: ...
# TODO: could return NaT?
def tz_localize(
self,
- tz: _tzinfo | str | None,
- ambiguous: str = ...,
+ tz: _TimeZones,
+ ambiguous: bool | Literal["raise", "NaT"] = ...,
nonexistent: TimestampNonexistent = ...,
) -> Self: ...
def normalize(self) -> Self: ...
@@ -197,19 +200,19 @@ class Timestamp(datetime):
def round(
self,
freq: str,
- ambiguous: bool | str = ...,
+ ambiguous: bool | Literal["raise", "NaT"] = ...,
nonexistent: TimestampNonexistent = ...,
) -> Self: ...
def floor(
self,
freq: str,
- ambiguous: bool | str = ...,
+ ambiguous: bool | Literal["raise", "NaT"] = ...,
nonexistent: TimestampNonexistent = ...,
) -> Self: ...
def ceil(
self,
freq: str,
- ambiguous: bool | str = ...,
+ ambiguous: bool | Literal["raise", "NaT"] = ...,
nonexistent: TimestampNonexistent = ...,
) -> Self: ...
def day_name(self, locale: str | None = ...) -> str: ...
diff --git a/pandas/_typing.py b/pandas/_typing.py
index 743815b91210d..c2bbebfbe2857 100644
--- a/pandas/_typing.py
+++ b/pandas/_typing.py
@@ -112,7 +112,7 @@
# 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, range]
+ListLike = Union[AnyArrayLike, list, tuple, range]
# scalars
diff --git a/pandas/core/reshape/pivot.py b/pandas/core/reshape/pivot.py
index e8ca520e7b420..79354fdd12a2d 100644
--- a/pandas/core/reshape/pivot.py
+++ b/pandas/core/reshape/pivot.py
@@ -7,6 +7,7 @@
from typing import (
TYPE_CHECKING,
Callable,
+ Literal,
cast,
)
@@ -569,7 +570,7 @@ def crosstab(
margins: bool = False,
margins_name: Hashable = "All",
dropna: bool = True,
- normalize: bool = False,
+ normalize: bool | Literal[0, 1, "all", "index", "columns"] = False,
) -> DataFrame:
"""
Compute a simple cross tabulation of two (or more) factors.
diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py
index e0f171035e89e..e826aad478059 100644
--- a/pandas/io/parsers/readers.py
+++ b/pandas/io/parsers/readers.py
@@ -638,7 +638,10 @@ def read_csv(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -697,7 +700,10 @@ def read_csv(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -757,7 +763,10 @@ def read_csv(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -817,7 +826,10 @@ def read_csv(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -888,7 +900,10 @@ def read_csv(
header: int | Sequence[int] | None | Literal["infer"] = "infer",
names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
index_col: IndexLabel | Literal[False] | None = None,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = None,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = None,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine: CSVEngine | None = None,
@@ -983,7 +998,10 @@ def read_table(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -1040,7 +1058,10 @@ def read_table(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -1097,7 +1118,10 @@ def read_table(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -1154,7 +1178,10 @@ def read_table(
header: int | Sequence[int] | None | Literal["infer"] = ...,
names: Sequence[Hashable] | None | lib.NoDefault = ...,
index_col: IndexLabel | Literal[False] | None = ...,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = ...,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = ...,
dtype: DtypeArg | None = ...,
engine: CSVEngine | None = ...,
converters: Mapping[Hashable, Callable] | None = ...,
@@ -1224,7 +1251,10 @@ def read_table(
header: int | Sequence[int] | None | Literal["infer"] = "infer",
names: Sequence[Hashable] | None | lib.NoDefault = lib.no_default,
index_col: IndexLabel | Literal[False] | None = None,
- usecols: list[HashableT] | Callable[[Hashable], bool] | None = None,
+ usecols: list[HashableT]
+ | tuple[HashableT]
+ | Callable[[Hashable], bool]
+ | None = None,
# General Parsing Configuration
dtype: DtypeArg | None = None,
engine: CSVEngine | None = None,
| Found by validating the pandas annotations on the pandas-stub tests using pyright (there are over 2000 errors left). | https://api.github.com/repos/pandas-dev/pandas/pulls/55078 | 2023-09-09T13:10:03Z | 2023-09-11T16:13:43Z | 2023-09-11T16:13:43Z | 2023-09-19T15:24:48Z |
Revert "BUG: Timestamp origin takes no effect in resample for 'MS' frequency (#53938)" | diff --git a/doc/source/whatsnew/v2.1.2.rst b/doc/source/whatsnew/v2.1.2.rst
index ddd1f95c56aea..569864ba71122 100644
--- a/doc/source/whatsnew/v2.1.2.rst
+++ b/doc/source/whatsnew/v2.1.2.rst
@@ -15,6 +15,7 @@ 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`)
- Fixed regression in :meth:`DataFrame.join` where result has missing values and dtype is arrow backed string (:issue:`55348`)
+- Fixed regression in :meth:`DataFrame.resample` which was extrapolating back to ``origin`` when ``origin`` was outside its bounds (:issue:`55064`)
.. ---------------------------------------------------------------------------
.. _whatsnew_212.bug_fixes:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 6527dcaa2de59..a7183a9d9498a 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -9221,6 +9221,10 @@ def resample(
.. versionadded:: 1.3.0
+ .. note::
+
+ Only takes effect for Tick-frequencies (i.e. fixed frequencies like
+ days, hours, and minutes, rather than months or quarters).
offset : Timedelta or str, default is None
An offset timedelta added to the origin.
@@ -9491,12 +9495,12 @@ def resample(
2000-10-02 00:26:00 24
Freq: 17min, dtype: int64
- >>> ts.resample('17W', origin='2000-01-01').sum()
- 2000-01-02 0
- 2000-04-30 0
- 2000-08-27 0
- 2000-12-24 108
- Freq: 17W-SUN, dtype: int64
+ >>> ts.resample('17min', origin='2000-01-01').sum()
+ 2000-10-01 23:24:00 3
+ 2000-10-01 23:41:00 15
+ 2000-10-01 23:58:00 45
+ 2000-10-02 00:15:00 45
+ Freq: 17min, dtype: int64
If you want to adjust the start of the bins with an `offset` Timedelta, the two
following lines are equivalent:
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index c51c17e04796a..06e6755079a22 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -207,12 +207,12 @@ class Grouper:
2000-10-02 00:26:00 24
Freq: 17min, dtype: int64
- >>> ts.groupby(pd.Grouper(freq='17W', origin='2000-01-01')).sum()
- 2000-01-02 0
- 2000-04-30 0
- 2000-08-27 0
- 2000-12-24 108
- Freq: 17W-SUN, dtype: int64
+ >>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum()
+ 2000-10-01 23:24:00 3
+ 2000-10-01 23:41:00 15
+ 2000-10-01 23:58:00 45
+ 2000-10-02 00:15:00 45
+ Freq: 17min, dtype: int64
If you want to adjust the start of the bins with an `offset` Timedelta, the two
following lines are equivalent:
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index bb5f3ce56b470..59e6a20915c18 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -2528,16 +2528,8 @@ def _get_timestamp_range_edges(
"""
if isinstance(freq, Tick):
index_tz = first.tz
-
- if isinstance(origin, Timestamp) and origin.tz != index_tz:
+ if isinstance(origin, Timestamp) and (origin.tz is None) != (index_tz is None):
raise ValueError("The origin must have the same timezone as the index.")
-
- elif isinstance(origin, Timestamp):
- if origin <= first:
- first = origin
- elif origin >= last:
- last = origin
-
if origin == "epoch":
# set the epoch based on the timezone to have similar bins results when
# resampling on the same kind of indexes on different timezones
@@ -2559,9 +2551,6 @@ def _get_timestamp_range_edges(
first = first.tz_localize(index_tz)
last = last.tz_localize(index_tz)
else:
- if isinstance(origin, Timestamp):
- first = origin
-
first = first.normalize()
last = last.normalize()
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 63f9de2da733d..28d02576156a0 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -796,34 +796,24 @@ def test_resample_offset(unit):
@pytest.mark.parametrize(
- "kwargs, expected",
+ "kwargs",
[
- (
- {"origin": "1999-12-31 23:57:00"},
- ["1999-12-31 23:57:00", "2000-01-01 01:57:00"],
- ),
- (
- {"origin": Timestamp("1970-01-01 00:02:00")},
- ["1970-01-01 00:02:00", "2000-01-01 01:57:00"],
- ),
- (
- {"origin": "epoch", "offset": "2m"},
- ["1999-12-31 23:57:00", "2000-01-01 01:57:00"],
- ),
+ {"origin": "1999-12-31 23:57:00"},
+ {"origin": Timestamp("1970-01-01 00:02:00")},
+ {"origin": "epoch", "offset": "2m"},
# origin of '1999-31-12 12:02:00' should be equivalent for this case
- (
- {"origin": "1999-12-31 12:02:00"},
- ["1999-12-31 12:02:00", "2000-01-01 01:57:00"],
- ),
- ({"offset": "-3m"}, ["1999-12-31 23:57:00", "2000-01-01 01:57:00"]),
+ {"origin": "1999-12-31 12:02:00"},
+ {"offset": "-3m"},
],
)
-def test_resample_origin(kwargs, unit, expected):
+def test_resample_origin(kwargs, unit):
# GH 31809
rng = date_range("2000-01-01 00:00:00", "2000-01-01 02:00", freq="s").as_unit(unit)
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
- exp_rng = date_range(expected[0], expected[1], freq="5min").as_unit(unit)
+ exp_rng = date_range(
+ "1999-12-31 23:57:00", "2000-01-01 01:57", freq="5min"
+ ).as_unit(unit)
resampled = ts.resample("5min", **kwargs).mean()
tm.assert_index_equal(resampled.index, exp_rng)
@@ -853,31 +843,6 @@ def test_resample_bad_offset(offset, unit):
ts.resample("5min", offset=offset)
-def test_resample_monthstart_origin():
- # GH 53662
- df = DataFrame({"ts": [datetime(1999, 12, 31, 0, 0, 0)], "values": [10.0]})
- result = df.resample("2MS", on="ts", origin="1999-11-01")["values"].sum()
- excepted = Series(
- [10.0],
- index=DatetimeIndex(
- ["1999-11-01"], dtype="datetime64[ns]", name="ts", freq="2MS"
- ),
- )
- tm.assert_index_equal(result.index, excepted.index)
-
- df = DataFrame({"ts": [datetime(1999, 12, 31, 20)], "values": [10.0]})
- result = df.resample(
- "3YS", on="ts", closed="left", label="left", origin=datetime(1995, 1, 1)
- )["values"].sum()
- expected = Series(
- [0, 10.0],
- index=DatetimeIndex(
- ["1995-01-01", "1998-01-01"], dtype="datetime64[ns]", name="ts", freq="3YS"
- ),
- )
- tm.assert_index_equal(result.index, expected.index)
-
-
def test_resample_origin_prime_freq(unit):
# GH 31809
start, end = "2000-10-01 23:30:00", "2000-10-02 00:30:00"
@@ -909,7 +874,7 @@ def test_resample_origin_prime_freq(unit):
tm.assert_index_equal(resampled.index, exp_rng)
exp_rng = date_range(
- "2000-01-01 00:00:00", "2000-10-02 00:15:00", freq="17min"
+ "2000-10-01 23:24:00", "2000-10-02 00:15:00", freq="17min"
).as_unit(unit)
resampled = ts.resample("17min", origin="2000-01-01").mean()
tm.assert_index_equal(resampled.index, exp_rng)
@@ -928,12 +893,14 @@ def test_resample_origin_with_tz(unit):
exp_rng = date_range(
"1999-12-31 23:57:00", "2000-01-01 01:57", freq="5min", tz=tz
).as_unit(unit)
- resampled = ts.resample("5min", origin="epoch", offset="2m").mean()
+ resampled = ts.resample("5min", origin="1999-12-31 23:57:00+00:00").mean()
tm.assert_index_equal(resampled.index, exp_rng)
- resampled = ts.resample(
- "5min", origin=Timestamp("1999-12-31 23:57:00", tz=tz)
- ).mean()
+ # origin of '1999-31-12 12:02:00+03:00' should be equivalent for this case
+ resampled = ts.resample("5min", origin="1999-12-31 12:02:00+03:00").mean()
+ tm.assert_index_equal(resampled.index, exp_rng)
+
+ resampled = ts.resample("5min", origin="epoch", offset="2m").mean()
tm.assert_index_equal(resampled.index, exp_rng)
with pytest.raises(ValueError, match=msg):
diff --git a/pandas/tests/resample/test_resampler_grouper.py b/pandas/tests/resample/test_resampler_grouper.py
index 55365d14a68ca..b85ccdc70068f 100644
--- a/pandas/tests/resample/test_resampler_grouper.py
+++ b/pandas/tests/resample/test_resampler_grouper.py
@@ -151,19 +151,6 @@ def test_groupby_with_origin():
start, end = "1/1/2000 00:00:00", "1/31/2000 00:00"
middle = "1/15/2000 00:00:00"
- # test origin on 1970-01-01 00:00:00
- rng = date_range("1970-01-01 00:00:00", end, freq="1231min") # prime number
- ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
- middle_ts = rng[len(rng) // 2]
- ts2 = ts[middle_ts:end]
-
- origin = Timestamp(0)
- adjusted_grouper = pd.Grouper(freq=freq, origin=origin)
- adjusted_count_ts = ts.groupby(adjusted_grouper).agg("count")
- adjusted_count_ts = adjusted_count_ts[middle_ts:end]
- adjusted_count_ts2 = ts2.groupby(adjusted_grouper).agg("count")
- tm.assert_series_equal(adjusted_count_ts, adjusted_count_ts2[middle_ts:end])
-
rng = date_range(start, end, freq="1231min") # prime number
ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
ts2 = ts[middle:end]
@@ -177,19 +164,26 @@ def test_groupby_with_origin():
with pytest.raises(AssertionError, match="Index are different"):
tm.assert_index_equal(count_ts.index, count_ts2.index)
- # test origin on 2049-10-18 20:00:00
+ # test origin on 1970-01-01 00:00:00
+ origin = Timestamp(0)
+ adjusted_grouper = pd.Grouper(freq=freq, origin=origin)
+ adjusted_count_ts = ts.groupby(adjusted_grouper).agg("count")
+ adjusted_count_ts = adjusted_count_ts[middle:end]
+ adjusted_count_ts2 = ts2.groupby(adjusted_grouper).agg("count")
+ tm.assert_series_equal(adjusted_count_ts, adjusted_count_ts2)
- rng = date_range(start, "2049-10-18 20:00:00", freq="1231min") # prime number
- ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng)
- middle_ts = rng[len(rng) // 2]
- ts2 = ts[middle_ts:end]
+ # test origin on 2049-10-18 20:00:00
origin_future = Timestamp(0) + pd.Timedelta("1399min") * 30_000
adjusted_grouper2 = pd.Grouper(freq=freq, origin=origin_future)
adjusted2_count_ts = ts.groupby(adjusted_grouper2).agg("count")
- adjusted2_count_ts = adjusted2_count_ts[middle_ts:end]
+ adjusted2_count_ts = adjusted2_count_ts[middle:end]
adjusted2_count_ts2 = ts2.groupby(adjusted_grouper2).agg("count")
tm.assert_series_equal(adjusted2_count_ts, adjusted2_count_ts2)
+ # both grouper use an adjusted timestamp that is a multiple of 1399 min
+ # they should be equals even if the adjusted_timestamp is in the future
+ tm.assert_series_equal(adjusted_count_ts, adjusted2_count_ts2)
+
def test_nearest():
# GH 17496
| closes #55064
This reverts commit dfc4c3900a181e8a9dff724217d5a14e59ca2e4f.
- [ ] 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.
---
I'm just:
- reverting the commit
- fixing some merge conflicts due to:
- random number generator usage
- 'T' -> 'min' deprecation
- adding a docs note noting that `origin` only takes effect for tick-frequencies
and adding a 2.1.1 whatsnew note | https://api.github.com/repos/pandas-dev/pandas/pulls/55077 | 2023-09-09T08:44:00Z | 2023-10-09T18:33:13Z | 2023-10-09T18:33:13Z | 2023-10-09T18:33:21Z |
TYP/DOC: fix flavor param with incorrect type hint in read_html | diff --git a/pandas/io/html.py b/pandas/io/html.py
index 10701be4f7e0b..68d30fe5ba681 100644
--- a/pandas/io/html.py
+++ b/pandas/io/html.py
@@ -1033,7 +1033,7 @@ def read_html(
io: FilePath | ReadBuffer[str],
*,
match: str | Pattern = ".+",
- flavor: str | None = None,
+ flavor: str | Sequence[str] | None = None,
header: int | Sequence[int] | None = None,
index_col: int | Sequence[int] | None = None,
skiprows: int | Sequence[int] | slice | None = None,
@@ -1074,11 +1074,11 @@ def read_html(
This value is converted to a regular expression so that there is
consistent behavior between Beautiful Soup and lxml.
- flavor : str, optional
- The parsing engine to use. 'bs4' and 'html5lib' are synonymous with
- each other, they are both there for backwards compatibility. The
- default of ``None`` tries to use ``lxml`` to parse and if that fails it
- falls back on ``bs4`` + ``html5lib``.
+ flavor : str or list-like, optional
+ The parsing engine (or list of parsing engines) to use. 'bs4' and
+ 'html5lib' are synonymous with each other, they are both there for
+ backwards compatibility. The default of ``None`` tries to use ``lxml``
+ to parse and if that fails it falls back on ``bs4`` + ``html5lib``.
header : int or list-like, optional
The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
| resolve #55059
| https://api.github.com/repos/pandas-dev/pandas/pulls/55076 | 2023-09-09T05:07:04Z | 2023-09-09T20:44:39Z | 2023-09-09T20:44:39Z | 2023-09-10T11:48:34Z |
Replace classes with pytest in test_sql | diff --git a/pandas/tests/io/test_sql.py b/pandas/tests/io/test_sql.py
index 1abe0ad55a864..f015c9efe7122 100644
--- a/pandas/tests/io/test_sql.py
+++ b/pandas/tests/io/test_sql.py
@@ -397,6 +397,54 @@ def test_frame3():
return DataFrame(data, columns=columns)
+def get_all_views(conn):
+ if isinstance(conn, sqlite3.Connection):
+ c = conn.execute("SELECT name FROM sqlite_master WHERE type='view'")
+ return [view[0] for view in c.fetchall()]
+ else:
+ from sqlalchemy import inspect
+
+ return inspect(conn).get_view_names()
+
+
+def get_all_tables(conn):
+ if isinstance(conn, sqlite3.Connection):
+ c = conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
+ return [table[0] for table in c.fetchall()]
+ else:
+ from sqlalchemy import inspect
+
+ return inspect(conn).get_table_names()
+
+
+def drop_table(
+ table_name: str,
+ conn: sqlite3.Connection | sqlalchemy.engine.Engine | sqlalchemy.engine.Connection,
+):
+ if isinstance(conn, sqlite3.Connection):
+ conn.execute(f"DROP TABLE IF EXISTS {sql._get_valid_sqlite_name(table_name)}")
+ conn.commit()
+ else:
+ with conn.begin() as con:
+ sql.SQLDatabase(con).drop_table(table_name)
+
+
+def drop_view(
+ view_name: str,
+ conn: sqlite3.Connection | sqlalchemy.engine.Engine | sqlalchemy.engine.Connection,
+):
+ if isinstance(conn, sqlite3.Connection):
+ conn.execute(f"DROP VIEW IF EXISTS {sql._get_valid_sqlite_name(view_name)}")
+ conn.commit()
+ else:
+ quoted_view = conn.engine.dialect.identifier_preparer.quote_identifier(
+ view_name
+ )
+ stmt = sqlalchemy.text(f"DROP VIEW IF EXISTS {quoted_view}")
+ with conn.begin() as con:
+ con.execute(stmt) # type: ignore[union-attr]
+
+
@pytest.fixture
def mysql_pymysql_engine(iris_path, types_data):
sqlalchemy = pytest.importorskip("sqlalchemy")
@@ -416,10 +464,10 @@ def mysql_pymysql_engine(iris_path, types_data):
if not insp.has_table("iris_view"):
create_and_load_iris_view(engine)
yield engine
- with engine.connect() as conn:
- with conn.begin():
- stmt = sqlalchemy.text("DROP TABLE IF EXISTS test_frame;")
- conn.execute(stmt)
+ for view in get_all_views(engine):
+ drop_view(view, engine)
+ for tbl in get_all_tables(engine):
+ drop_table(tbl, engine)
engine.dispose()
@@ -445,10 +493,10 @@ def postgresql_psycopg2_engine(iris_path, types_data):
if not insp.has_table("iris_view"):
create_and_load_iris_view(engine)
yield engine
- with engine.connect() as conn:
- with conn.begin():
- stmt = sqlalchemy.text("DROP TABLE IF EXISTS test_frame;")
- conn.execute(stmt)
+ for view in get_all_views(engine):
+ drop_view(view, engine)
+ for tbl in get_all_tables(engine):
+ drop_table(tbl, engine)
engine.dispose()
@@ -481,6 +529,10 @@ def sqlite_engine(sqlite_str, iris_path, types_data):
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)
engine.dispose()
@@ -528,32 +580,60 @@ def sqlite_buildin():
@pytest.fixture
-def sqlite_buildin_iris(sqlite_buildin, iris_path):
+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)
+
+
+@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")
+ types_data = [tuple(entry.values()) for entry in types_data]
+
+ create_and_load_types_sqlite3(sqlite_buildin, types_data)
return sqlite_buildin
mysql_connectable = [
- "mysql_pymysql_engine",
- "mysql_pymysql_conn",
+ pytest.param("mysql_pymysql_engine", marks=pytest.mark.db),
+ pytest.param("mysql_pymysql_conn", marks=pytest.mark.db),
]
postgresql_connectable = [
- "postgresql_psycopg2_engine",
- "postgresql_psycopg2_conn",
+ pytest.param("postgresql_psycopg2_engine", marks=pytest.mark.db),
+ pytest.param("postgresql_psycopg2_conn", marks=pytest.mark.db),
]
sqlite_connectable = [
- "sqlite_engine",
- "sqlite_conn",
- "sqlite_str",
+ 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_iris_connectable = [
- "sqlite_iris_engine",
- "sqlite_iris_conn",
- "sqlite_iris_str",
+ 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),
]
sqlalchemy_connectable = mysql_connectable + postgresql_connectable + sqlite_connectable
@@ -562,12 +642,17 @@ def sqlite_buildin_iris(sqlite_buildin, iris_path):
mysql_connectable + postgresql_connectable + sqlite_iris_connectable
)
-all_connectable = sqlalchemy_connectable + ["sqlite_buildin"]
+all_connectable = sqlalchemy_connectable + [
+ "sqlite_buildin",
+ "sqlite_sqlalchemy_memory_engine",
+]
-all_connectable_iris = sqlalchemy_connectable_iris + ["sqlite_buildin_iris"]
+all_connectable_iris = sqlalchemy_connectable_iris + [
+ "sqlite_buildin_iris",
+ "sqlite_sqlalchemy_memory_engine",
+]
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_dataframe_to_sql(conn, test_frame1, request):
# GH 51086 if conn is sqlite_engine
@@ -575,7 +660,14 @@ def test_dataframe_to_sql(conn, test_frame1, request):
test_frame1.to_sql(name="test", con=conn, if_exists="append", index=False)
-@pytest.mark.db
+@pytest.mark.parametrize("conn", all_connectable)
+def test_dataframe_to_sql_empty(conn, test_frame1, request):
+ # GH 51086 if conn is sqlite_engine
+ conn = request.getfixturevalue(conn)
+ empty_df = test_frame1.iloc[:0]
+ empty_df.to_sql(name="test", con=conn, if_exists="append", index=False)
+
+
@pytest.mark.parametrize("conn", all_connectable)
def test_dataframe_to_sql_arrow_dtypes(conn, request):
# GH 52046
@@ -596,7 +688,6 @@ def test_dataframe_to_sql_arrow_dtypes(conn, request):
df.to_sql(name="test_arrow", con=conn, if_exists="replace", index=False)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_dataframe_to_sql_arrow_dtypes_missing(conn, request, nulls_fixture):
# GH 52046
@@ -612,7 +703,6 @@ def test_dataframe_to_sql_arrow_dtypes_missing(conn, request, nulls_fixture):
df.to_sql(name="test_arrow", con=conn, if_exists="replace", index=False)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
@pytest.mark.parametrize("method", [None, "multi"])
def test_to_sql(conn, method, test_frame1, request):
@@ -623,7 +713,6 @@ def test_to_sql(conn, method, test_frame1, request):
assert count_rows(conn, "test_frame") == len(test_frame1)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
@pytest.mark.parametrize("mode, num_row_coef", [("replace", 1), ("append", 2)])
def test_to_sql_exist(conn, mode, num_row_coef, test_frame1, request):
@@ -635,7 +724,6 @@ def test_to_sql_exist(conn, mode, num_row_coef, test_frame1, request):
assert count_rows(conn, "test_frame") == num_row_coef * len(test_frame1)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_to_sql_exist_fail(conn, test_frame1, request):
conn = request.getfixturevalue(conn)
@@ -648,7 +736,6 @@ def test_to_sql_exist_fail(conn, test_frame1, request):
pandasSQL.to_sql(test_frame1, "test_frame", if_exists="fail")
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_read_iris_query(conn, request):
conn = request.getfixturevalue(conn)
@@ -661,7 +748,6 @@ def test_read_iris_query(conn, request):
assert "SepalWidth" in iris_frame.columns
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_read_iris_query_chunksize(conn, request):
conn = request.getfixturevalue(conn)
@@ -674,7 +760,6 @@ def test_read_iris_query_chunksize(conn, request):
assert "SepalWidth" in iris_frame.columns
-@pytest.mark.db
@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
def test_read_iris_query_expression_with_parameter(conn, request):
conn = request.getfixturevalue(conn)
@@ -696,7 +781,6 @@ def test_read_iris_query_expression_with_parameter(conn, request):
autoload_con.dispose()
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_read_iris_query_string_with_parameter(conn, request, sql_strings):
for db, query in sql_strings["read_parameters"].items():
@@ -709,7 +793,6 @@ def test_read_iris_query_string_with_parameter(conn, request, sql_strings):
check_iris_frame(iris_frame)
-@pytest.mark.db
@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
def test_read_iris_table(conn, request):
# GH 51015 if conn = sqlite_iris_str
@@ -720,7 +803,6 @@ def test_read_iris_table(conn, request):
check_iris_frame(iris_frame)
-@pytest.mark.db
@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
def test_read_iris_table_chunksize(conn, request):
conn = request.getfixturevalue(conn)
@@ -730,7 +812,6 @@ def test_read_iris_table_chunksize(conn, request):
check_iris_frame(iris_frame)
-@pytest.mark.db
@pytest.mark.parametrize("conn", sqlalchemy_connectable)
def test_to_sql_callable(conn, test_frame1, request):
conn = request.getfixturevalue(conn)
@@ -749,26 +830,38 @@ def sample(pd_table, conn, keys, data_iter):
assert count_rows(conn, "test_frame") == len(test_frame1)
-@pytest.mark.db
-@pytest.mark.parametrize("conn", mysql_connectable)
+@pytest.mark.parametrize("conn", all_connectable_iris)
def test_default_type_conversion(conn, request):
+ conn_name = conn
+ if conn_name == "sqlite_buildin_iris":
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="sqlite_buildin connection does not implement read_sql_table"
+ )
+ )
+
conn = request.getfixturevalue(conn)
df = sql.read_sql_table("types", conn)
assert issubclass(df.FloatCol.dtype.type, np.floating)
assert issubclass(df.IntCol.dtype.type, np.integer)
- # MySQL has no real BOOL type (it's an alias for TINYINT)
- assert issubclass(df.BoolCol.dtype.type, np.integer)
+ # MySQL/sqlite has no real BOOL type
+ if "postgresql" in conn_name:
+ assert issubclass(df.BoolCol.dtype.type, np.bool_)
+ else:
+ assert issubclass(df.BoolCol.dtype.type, np.integer)
# Int column with NA values stays as float
assert issubclass(df.IntColWithNull.dtype.type, np.floating)
# Bool column with NA = int column with NA values => becomes float
- assert issubclass(df.BoolColWithNull.dtype.type, np.floating)
+ if "postgresql" in conn_name:
+ assert issubclass(df.BoolColWithNull.dtype.type, object)
+ else:
+ assert issubclass(df.BoolColWithNull.dtype.type, np.floating)
-@pytest.mark.db
@pytest.mark.parametrize("conn", mysql_connectable)
def test_read_procedure(conn, request):
conn = request.getfixturevalue(conn)
@@ -806,7 +899,6 @@ def test_read_procedure(conn, request):
tm.assert_frame_equal(df, res2)
-@pytest.mark.db
@pytest.mark.parametrize("conn", postgresql_connectable)
@pytest.mark.parametrize("expected_count", [2, "Success!"])
def test_copy_from_callable_insertion_method(conn, expected_count, request):
@@ -846,7 +938,6 @@ def psql_insert_copy(table, conn, keys, data_iter):
tm.assert_frame_equal(result, expected)
-@pytest.mark.db
@pytest.mark.parametrize("conn", postgresql_connectable)
def test_insertion_method_on_conflict_do_nothing(conn, request):
# GH 15988: Example in to_sql docstring
@@ -905,7 +996,6 @@ def insert_on_conflict(table, conn, keys, data_iter):
pandasSQL.drop_table("test_insert_conflict")
-@pytest.mark.db
@pytest.mark.parametrize("conn", mysql_connectable)
def test_insertion_method_on_conflict_update(conn, request):
# GH 14553: Example in to_sql docstring
@@ -959,7 +1049,6 @@ def insert_on_conflict(table, conn, keys, data_iter):
pandasSQL.drop_table("test_insert_conflict")
-@pytest.mark.db
@pytest.mark.parametrize("conn", postgresql_connectable)
def test_read_view_postgres(conn, request):
# GH 52969
@@ -1041,217 +1130,65 @@ def test_execute_deprecated(sqlite_buildin_iris):
sql.execute("select * from iris", sqlite_buildin_iris)
-class MixInBase:
- def teardown_method(self):
- # if setup fails, there may not be a connection to close.
- if hasattr(self, "conn"):
- self.conn.close()
- # use a fresh connection to ensure we can drop all tables.
- try:
- conn = self.connect()
- except (sqlalchemy.exc.OperationalError, sqlite3.OperationalError):
- pass
- else:
- with conn:
- for view in self._get_all_views(conn):
- self.drop_view(view, conn)
- for tbl in self._get_all_tables(conn):
- self.drop_table(tbl, conn)
-
-
-class SQLiteMixIn(MixInBase):
- def connect(self):
- return sqlite3.connect(":memory:")
-
- def drop_table(self, table_name, conn):
- conn.execute(f"DROP TABLE IF EXISTS {sql._get_valid_sqlite_name(table_name)}")
- conn.commit()
-
- def _get_all_tables(self, conn):
- c = conn.execute("SELECT name FROM sqlite_master WHERE type='table'")
- return [table[0] for table in c.fetchall()]
-
- def drop_view(self, view_name, conn):
- conn.execute(f"DROP VIEW IF EXISTS {sql._get_valid_sqlite_name(view_name)}")
- conn.commit()
-
- def _get_all_views(self, conn):
- c = conn.execute("SELECT name FROM sqlite_master WHERE type='view'")
- return [view[0] for view in c.fetchall()]
-
-
-class SQLAlchemyMixIn(MixInBase):
- @classmethod
- def teardown_class(cls):
- cls.engine.dispose()
-
- def connect(self):
- return self.engine.connect()
-
- def drop_table(self, table_name, conn):
- if conn.in_transaction():
- conn.get_transaction().rollback()
- with conn.begin():
- sql.SQLDatabase(conn).drop_table(table_name)
-
- def _get_all_tables(self, conn):
- from sqlalchemy import inspect
-
- return inspect(conn).get_table_names()
-
- def drop_view(self, view_name, conn):
- quoted_view = conn.engine.dialect.identifier_preparer.quote_identifier(
- view_name
- )
- if conn.in_transaction():
- conn.get_transaction().rollback()
- with conn.begin():
- conn.exec_driver_sql(f"DROP VIEW IF EXISTS {quoted_view}")
-
- def _get_all_views(self, conn):
- from sqlalchemy import inspect
-
- return inspect(conn).get_view_names()
-
-
-class PandasSQLTest:
- """
- Base class with common private methods for SQLAlchemy and fallback cases.
-
- """
-
- def load_iris_data(self, iris_path):
- self.drop_view("iris_view", self.conn)
- self.drop_table("iris", self.conn)
- if isinstance(self.conn, sqlite3.Connection):
- create_and_load_iris_sqlite3(self.conn, iris_path)
- else:
- create_and_load_iris(self.conn, iris_path, self.flavor)
-
- def load_types_data(self, types_data):
- if self.flavor != "postgresql":
- for entry in types_data:
- entry.pop("DateColWithTz")
- if isinstance(self.conn, sqlite3.Connection):
- types_data = [tuple(entry.values()) for entry in types_data]
- create_and_load_types_sqlite3(self.conn, types_data)
- else:
- create_and_load_types(self.conn, types_data, self.flavor)
-
- def _read_sql_iris_parameter(self, sql_strings):
- query = sql_strings["read_parameters"][self.flavor]
- params = ("Iris-setosa", 5.1)
- with self.pandasSQL.run_transaction():
- iris_frame = self.pandasSQL.read_query(query, params=params)
- check_iris_frame(iris_frame)
-
- def _read_sql_iris_named_parameter(self, sql_strings):
- query = sql_strings["read_named_parameters"][self.flavor]
- params = {"name": "Iris-setosa", "length": 5.1}
- with self.pandasSQL.run_transaction():
- iris_frame = self.pandasSQL.read_query(query, params=params)
- check_iris_frame(iris_frame)
-
- def _read_sql_iris_no_parameter_with_percent(self, sql_strings):
- query = sql_strings["read_no_parameters_with_percent"][self.flavor]
- with self.pandasSQL.run_transaction():
- iris_frame = self.pandasSQL.read_query(query, params=None)
- check_iris_frame(iris_frame)
-
- def _to_sql_empty(self, test_frame1):
- self.drop_table("test_frame1", self.conn)
- assert self.pandasSQL.to_sql(test_frame1.iloc[:0], "test_frame1") == 0
-
- def _to_sql_with_sql_engine(self, test_frame1, engine="auto", **engine_kwargs):
- """`to_sql` with the `engine` param"""
- # mostly copied from this class's `_to_sql()` method
- self.drop_table("test_frame1", self.conn)
-
- assert (
- self.pandasSQL.to_sql(
- test_frame1, "test_frame1", engine=engine, **engine_kwargs
- )
- == 4
- )
- assert self.pandasSQL.has_table("test_frame1")
-
- num_entries = len(test_frame1)
- num_rows = count_rows(self.conn, "test_frame1")
- assert num_rows == num_entries
+@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"
- # Nuke table
- self.drop_table("test_frame1", self.conn)
+ raise ValueError(f"unsupported connection: {conn_name}")
- def _roundtrip(self, test_frame1):
- self.drop_table("test_frame_roundtrip", self.conn)
- assert self.pandasSQL.to_sql(test_frame1, "test_frame_roundtrip") == 4
- with self.pandasSQL.run_transaction():
- result = self.pandasSQL.read_query("SELECT * FROM test_frame_roundtrip")
+ return func
- result.set_index("level_0", inplace=True)
- # result.index.astype(int)
- result.index.name = None
+@pytest.mark.parametrize("conn", all_connectable_iris)
+def test_read_sql_iris_parameter(conn, request, sql_strings, flavor):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ query = sql_strings["read_parameters"][flavor(conn_name)]
+ params = ("Iris-setosa", 5.1)
+ pandasSQL = pandasSQL_builder(conn)
- tm.assert_frame_equal(result, test_frame1)
+ with pandasSQL.run_transaction():
+ iris_frame = pandasSQL.read_query(query, params=params)
+ check_iris_frame(iris_frame)
- def _execute_sql(self):
- # drop_sql = "DROP TABLE IF EXISTS test" # should already be done
- iris_results = self.pandasSQL.execute("SELECT * FROM iris")
- row = iris_results.fetchone()
- tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
- def _to_sql_save_index(self):
- df = DataFrame.from_records(
- [(1, 2.1, "line1"), (2, 1.5, "line2")], columns=["A", "B", "C"], index=["A"]
- )
- assert self.pandasSQL.to_sql(df, "test_to_sql_saves_index") == 2
- ix_cols = self._get_index_columns("test_to_sql_saves_index")
- assert ix_cols == [["A"]]
-
- def _transaction_test(self):
- with self.pandasSQL.run_transaction() as trans:
- stmt = "CREATE TABLE test_trans (A INT, B TEXT)"
- if isinstance(self.pandasSQL, SQLiteDatabase):
- trans.execute(stmt)
- else:
- from sqlalchemy import text
+@pytest.mark.parametrize("conn", all_connectable_iris)
+def test_read_sql_iris_named_parameter(conn, request, sql_strings, flavor):
+ 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)
+ check_iris_frame(iris_frame)
- stmt = text(stmt)
- trans.execute(stmt)
- class DummyException(Exception):
- pass
+@pytest.mark.parametrize("conn", all_connectable_iris)
+def test_read_sql_iris_no_parameter_with_percent(conn, request, sql_strings, flavor):
+ if "mysql" in conn or "postgresql" in conn:
+ request.node.add_marker(pytest.mark.xfail(reason="broken test"))
- # Make sure when transaction is rolled back, no rows get inserted
- ins_sql = "INSERT INTO test_trans (A,B) VALUES (1, 'blah')"
- if isinstance(self.pandasSQL, SQLDatabase):
- from sqlalchemy import text
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
- ins_sql = text(ins_sql)
- try:
- with self.pandasSQL.run_transaction() as trans:
- trans.execute(ins_sql)
- raise DummyException("error")
- except DummyException:
- # ignore raised exception
- pass
- with self.pandasSQL.run_transaction():
- res = self.pandasSQL.read_query("SELECT * FROM test_trans")
- assert len(res) == 0
-
- # Make sure when transaction is committed, rows do get inserted
- with self.pandasSQL.run_transaction() as trans:
- trans.execute(ins_sql)
- res2 = self.pandasSQL.read_query("SELECT * FROM test_trans")
- assert len(res2) == 1
+ query = sql_strings["read_no_parameters_with_percent"][flavor(conn_name)]
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ iris_frame = pandasSQL.read_query(query, params=None)
+ check_iris_frame(iris_frame)
# -----------------------------------------------------------------------------
# -- Testing the public API
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_api_read_sql_view(conn, request):
conn = request.getfixturevalue(conn)
@@ -1259,7 +1196,6 @@ def test_api_read_sql_view(conn, request):
check_iris_frame(iris_frame)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_api_read_sql_with_chunksize_no_result(conn, request):
conn = request.getfixturevalue(conn)
@@ -1269,7 +1205,6 @@ def test_api_read_sql_with_chunksize_no_result(conn, request):
tm.assert_frame_equal(concat(with_batch), without_batch)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1281,7 +1216,6 @@ def test_api_to_sql(conn, request, test_frame1):
assert sql.has_table("test_frame1", conn)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_fail(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1297,7 +1231,6 @@ def test_api_to_sql_fail(conn, request, test_frame1):
sql.to_sql(test_frame1, "test_frame2", conn, if_exists="fail")
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_replace(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1316,7 +1249,6 @@ def test_api_to_sql_replace(conn, request, test_frame1):
assert num_rows == num_entries
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_append(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1336,7 +1268,6 @@ def test_api_to_sql_append(conn, request, test_frame1):
assert num_rows == num_entries
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_type_mapping(conn, request, test_frame3):
conn = request.getfixturevalue(conn)
@@ -1350,7 +1281,6 @@ def test_api_to_sql_type_mapping(conn, request, test_frame3):
tm.assert_frame_equal(test_frame3, result)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_series(conn, request):
conn = request.getfixturevalue(conn)
@@ -1364,7 +1294,6 @@ def test_api_to_sql_series(conn, request):
tm.assert_frame_equal(s.to_frame(), s2)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_roundtrip(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1383,7 +1312,6 @@ def test_api_roundtrip(conn, request, test_frame1):
tm.assert_frame_equal(result, test_frame1)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_roundtrip_chunksize(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1402,7 +1330,6 @@ def test_api_roundtrip_chunksize(conn, request, test_frame1):
tm.assert_frame_equal(result, test_frame1)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable_iris)
def test_api_execute_sql(conn, request):
# drop_sql = "DROP TABLE IF EXISTS test" # should already be done
@@ -1413,13 +1340,9 @@ def test_api_execute_sql(conn, request):
tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
-@pytest.mark.db
-@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("conn", all_connectable_iris)
def test_api_date_parsing(conn, request):
conn_name = conn
- if conn_name in {"sqlite_buildin", "sqlite_str"}:
- pytest.skip("types tables not created in sqlite_buildin or sqlite_str fixture")
-
conn = request.getfixturevalue(conn)
# Test date parsing in read_sql
# No Parsing
@@ -1473,8 +1396,7 @@ def test_api_date_parsing(conn, request):
]
-@pytest.mark.db
-@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("conn", all_connectable_iris)
@pytest.mark.parametrize("error", ["ignore", "raise", "coerce"])
@pytest.mark.parametrize(
"read_sql, text, mode",
@@ -1493,10 +1415,11 @@ def test_api_custom_dateparsing_error(
conn, request, read_sql, text, mode, error, types_data_frame
):
conn_name = conn
- if conn_name in {"sqlite_buildin", "sqlite_str"}:
- pytest.skip("types tables not created in sqlite_buildin or sqlite_str fixture")
-
conn = request.getfixturevalue(conn)
+ if text == "types" and conn_name == "sqlite_buildin_iris":
+ request.node.add_marker(
+ pytest.mark.xfail(reason="failing combination of arguments")
+ )
expected = types_data_frame.astype({"DateCol": "datetime64[ns]"})
@@ -1516,14 +1439,9 @@ def test_api_custom_dateparsing_error(
tm.assert_frame_equal(result, expected)
-@pytest.mark.db
-@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("conn", all_connectable_iris)
def test_api_date_and_index(conn, request):
# Test case where same column appears in parse_date and index_col
- conn_name = conn
- if conn_name in {"sqlite_buildin", "sqlite_str"}:
- pytest.skip("types tables not created in sqlite_buildin or sqlite_str fixture")
-
conn = request.getfixturevalue(conn)
df = sql.read_sql_query(
"SELECT * FROM types",
@@ -1536,7 +1454,6 @@ def test_api_date_and_index(conn, request):
assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_timedelta(conn, request):
# see #6921
@@ -1553,7 +1470,6 @@ def test_api_timedelta(conn, request):
tm.assert_series_equal(result["foo"], df["foo"].view("int64"))
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_complex_raises(conn, request):
conn = request.getfixturevalue(conn)
@@ -1563,7 +1479,6 @@ def test_api_complex_raises(conn, request):
assert df.to_sql("test_complex", con=conn) is None
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
@pytest.mark.parametrize(
"index_name,index_label,expected",
@@ -1596,13 +1511,14 @@ def test_api_to_sql_index_label(conn, request, index_name, index_label, expected
assert frame.columns[0] == expected
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_to_sql_index_label_multiindex(conn, request):
conn_name = conn
if "mysql" in conn_name:
request.node.add_marker(
- pytest.mark.xfail(reason="MySQL can fail using TEXT without length as key")
+ pytest.mark.xfail(
+ reason="MySQL can fail using TEXT without length as key", strict=False
+ )
)
conn = request.getfixturevalue(conn)
@@ -1665,7 +1581,6 @@ def test_api_to_sql_index_label_multiindex(conn, request):
)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_multiindex_roundtrip(conn, request):
conn = request.getfixturevalue(conn)
@@ -1686,7 +1601,6 @@ def test_api_multiindex_roundtrip(conn, request):
tm.assert_frame_equal(df, result, check_index_type=True)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
@pytest.mark.parametrize(
"dtype",
@@ -1719,7 +1633,6 @@ def test_api_dtype_argument(conn, request, dtype):
tm.assert_frame_equal(result, expected)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_integer_col_names(conn, request):
conn = request.getfixturevalue(conn)
@@ -1727,7 +1640,6 @@ def test_api_integer_col_names(conn, request):
sql.to_sql(df, "test_frame_integer_col_names", conn, if_exists="replace")
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_get_schema(conn, request, test_frame1):
conn = request.getfixturevalue(conn)
@@ -1735,7 +1647,6 @@ def test_api_get_schema(conn, request, test_frame1):
assert "CREATE" in create_sql
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_get_schema_with_schema(conn, request, test_frame1):
# GH28486
@@ -1744,7 +1655,6 @@ def test_api_get_schema_with_schema(conn, request, test_frame1):
assert "CREATE TABLE pypi." in create_sql
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_get_schema_dtypes(conn, request):
conn_name = conn
@@ -1762,7 +1672,6 @@ def test_api_get_schema_dtypes(conn, request):
assert "INTEGER" in create_sql
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_get_schema_keys(conn, request, test_frame1):
conn_name = conn
@@ -1785,7 +1694,6 @@ def test_api_get_schema_keys(conn, request, test_frame1):
assert constraint_sentence in create_sql
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_chunksize_read(conn, request):
conn_name = conn
@@ -1831,7 +1739,6 @@ def test_api_chunksize_read(conn, request):
tm.assert_frame_equal(res1, res3)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_categorical(conn, request):
# GH8624
@@ -1856,7 +1763,6 @@ def test_api_categorical(conn, request):
tm.assert_frame_equal(res, df)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_unicode_column_name(conn, request):
# GH 11431
@@ -1869,7 +1775,6 @@ def test_api_unicode_column_name(conn, request):
df.to_sql(name="test_unicode", con=conn, index=False)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_escaped_table_name(conn, request):
# GH 13206
@@ -1891,7 +1796,6 @@ def test_api_escaped_table_name(conn, request):
tm.assert_frame_equal(res, df)
-@pytest.mark.db
@pytest.mark.parametrize("conn", all_connectable)
def test_api_read_sql_duplicate_columns(conn, request):
# GH#53117
@@ -1911,1102 +1815,1352 @@ def test_api_read_sql_duplicate_columns(conn, request):
tm.assert_frame_equal(result, expected)
-class _TestSQLApi(PandasSQLTest):
- """
- Base class to test the public API.
-
- From this two classes are derived to run these tests for both the
- sqlalchemy mode (`TestSQLApi`) and the fallback mode
- (`TestSQLiteFallbackApi`). These tests are run with sqlite3. Specific
- tests for the different sql flavours are included in `_TestSQLAlchemy`.
-
- Notes:
- flavor can always be passed even in SQLAlchemy mode,
- should be correctly ignored.
+@pytest.mark.parametrize("conn", all_connectable)
+def test_read_table_columns(conn, request, test_frame1):
+ # test columns argument in read_table
+ conn_name = conn
+ if conn_name == "sqlite_buildin":
+ request.node.add_marker(pytest.mark.xfail(reason="Not Implemented"))
- we don't use drop_table because that isn't part of the public api
+ conn = request.getfixturevalue(conn)
+ sql.to_sql(test_frame1, "test_frame", conn)
- """
+ cols = ["A", "B"]
- flavor = "sqlite"
- mode: str
+ result = sql.read_sql_table("test_frame", conn, columns=cols)
+ assert result.columns.tolist() == cols
- @pytest.fixture(autouse=True)
- def setup_method(self, iris_path, types_data):
- self.conn = self.connect()
- self.load_iris_data(iris_path)
- self.load_types_data(types_data)
- self.load_test_data_and_sql()
- def load_test_data_and_sql(self):
- create_and_load_iris_view(self.conn)
+@pytest.mark.parametrize("conn", all_connectable)
+def test_read_table_index_col(conn, request, test_frame1):
+ # test columns argument in read_table
+ conn_name = conn
+ if conn_name == "sqlite_buildin":
+ request.node.add_marker(pytest.mark.xfail(reason="Not Implemented"))
+ conn = request.getfixturevalue(conn)
+ sql.to_sql(test_frame1, "test_frame", conn)
-@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="SQLAlchemy not installed")
-class TestSQLApi(SQLAlchemyMixIn, _TestSQLApi):
- """
- Test the public API as it would be used directly
+ result = sql.read_sql_table("test_frame", conn, index_col="index")
+ assert result.index.names == ["index"]
- Tests for `read_sql_table` are included here, as this is specific for the
- sqlalchemy mode.
+ result = sql.read_sql_table("test_frame", conn, index_col=["A", "B"])
+ assert result.index.names == ["A", "B"]
- """
+ result = sql.read_sql_table(
+ "test_frame", conn, index_col=["A", "B"], columns=["C", "D"]
+ )
+ assert result.index.names == ["A", "B"]
+ assert result.columns.tolist() == ["C", "D"]
- flavor = "sqlite"
- mode = "sqlalchemy"
- @classmethod
- def setup_class(cls):
- cls.engine = sqlalchemy.create_engine("sqlite:///:memory:")
+@pytest.mark.parametrize("conn", all_connectable_iris)
+def test_read_sql_delegate(conn, request):
+ if conn == "sqlite_buildin_iris":
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason="sqlite_buildin connection does not implement read_sql_table"
+ )
+ )
- def test_read_table_columns(self, test_frame1):
- # test columns argument in read_table
- sql.to_sql(test_frame1, "test_frame", self.conn)
+ conn = request.getfixturevalue(conn)
+ iris_frame1 = sql.read_sql_query("SELECT * FROM iris", conn)
+ iris_frame2 = sql.read_sql("SELECT * FROM iris", conn)
+ tm.assert_frame_equal(iris_frame1, iris_frame2)
- cols = ["A", "B"]
- result = sql.read_sql_table("test_frame", self.conn, columns=cols)
- assert result.columns.tolist() == cols
+ iris_frame1 = sql.read_sql_table("iris", conn)
+ iris_frame2 = sql.read_sql("iris", conn)
+ tm.assert_frame_equal(iris_frame1, iris_frame2)
- def test_read_table_index_col(self, test_frame1):
- # test columns argument in read_table
- sql.to_sql(test_frame1, "test_frame", self.conn)
- result = sql.read_sql_table("test_frame", self.conn, index_col="index")
- assert result.index.names == ["index"]
+def test_not_reflect_all_tables(sqlite_conn):
+ conn = sqlite_conn
+ from sqlalchemy import text
+ from sqlalchemy.engine import Engine
- result = sql.read_sql_table("test_frame", self.conn, index_col=["A", "B"])
- assert result.index.names == ["A", "B"]
+ # create invalid table
+ query_list = [
+ text("CREATE TABLE invalid (x INTEGER, y UNKNOWN);"),
+ text("CREATE TABLE other_table (x INTEGER, y INTEGER);"),
+ ]
- result = sql.read_sql_table(
- "test_frame", self.conn, index_col=["A", "B"], columns=["C", "D"]
- )
- assert result.index.names == ["A", "B"]
- assert result.columns.tolist() == ["C", "D"]
+ for query in query_list:
+ if isinstance(conn, Engine):
+ with conn.connect() as conn:
+ with conn.begin():
+ conn.execute(query)
+ else:
+ with conn.begin():
+ conn.execute(query)
- def test_read_sql_delegate(self):
- iris_frame1 = sql.read_sql_query("SELECT * FROM iris", self.conn)
- iris_frame2 = sql.read_sql("SELECT * FROM iris", self.conn)
- tm.assert_frame_equal(iris_frame1, iris_frame2)
+ with tm.assert_produces_warning(None):
+ sql.read_sql_table("other_table", conn)
+ sql.read_sql_query("SELECT * FROM other_table", conn)
- iris_frame1 = sql.read_sql_table("iris", self.conn)
- iris_frame2 = sql.read_sql("iris", self.conn)
- tm.assert_frame_equal(iris_frame1, iris_frame2)
- def test_not_reflect_all_tables(self):
- from sqlalchemy import text
- from sqlalchemy.engine import Engine
+@pytest.mark.parametrize("conn", all_connectable)
+def test_warning_case_insensitive_table_name(conn, request, test_frame1):
+ conn_name = conn
+ if conn_name == "sqlite_buildin":
+ request.node.add_marker(pytest.mark.xfail(reason="Does not raise warning"))
- # create invalid table
- query_list = [
- text("CREATE TABLE invalid (x INTEGER, y UNKNOWN);"),
- text("CREATE TABLE other_table (x INTEGER, y INTEGER);"),
- ]
- for query in query_list:
- if isinstance(self.conn, Engine):
- with self.conn.connect() as conn:
- with conn.begin():
- conn.execute(query)
- else:
- with self.conn.begin():
- self.conn.execute(query)
+ conn = request.getfixturevalue(conn)
+ # see gh-7815
+ with tm.assert_produces_warning(
+ UserWarning,
+ match=(
+ r"The provided table name 'TABLE1' is not found exactly as such in "
+ r"the database after writing the table, possibly due to case "
+ r"sensitivity issues. Consider using lower case table names."
+ ),
+ ):
+ sql.SQLDatabase(conn).check_case_sensitive("TABLE1", "")
- with tm.assert_produces_warning(None):
- sql.read_sql_table("other_table", self.conn)
- sql.read_sql_query("SELECT * FROM other_table", self.conn)
+ # Test that the warning is certainly NOT triggered in a normal case.
+ with tm.assert_produces_warning(None):
+ test_frame1.to_sql(name="CaseSensitive", con=conn)
- def test_warning_case_insensitive_table_name(self, test_frame1):
- # see gh-7815
- with tm.assert_produces_warning(
- UserWarning,
- match=(
- r"The provided table name 'TABLE1' is not found exactly as such in "
- r"the database after writing the table, possibly due to case "
- r"sensitivity issues. Consider using lower case table names."
- ),
- ):
- sql.SQLDatabase(self.conn).check_case_sensitive("TABLE1", "")
- # Test that the warning is certainly NOT triggered in a normal case.
- with tm.assert_produces_warning(None):
- test_frame1.to_sql(name="CaseSensitive", con=self.conn)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_sqlalchemy_type_mapping(conn, request):
+ conn = request.getfixturevalue(conn)
+ from sqlalchemy import TIMESTAMP
- def _get_index_columns(self, tbl_name):
- from sqlalchemy.engine import reflection
+ # Test Timestamp objects (no datetime64 because of timezone) (GH9085)
+ df = DataFrame(
+ {"time": to_datetime(["2014-12-12 01:54", "2014-12-11 02:54"], utc=True)}
+ )
+ db = sql.SQLDatabase(conn)
+ table = sql.SQLTable("test_type", db, frame=df)
+ # GH 9086: TIMESTAMP is the suggested type for datetimes with timezones
+ assert isinstance(table.table.c["time"].type, TIMESTAMP)
- insp = reflection.Inspector.from_engine(self.conn)
- ixs = insp.get_indexes("test_index_saved")
- ixs = [i["column_names"] for i in ixs]
- return ixs
- def test_sqlalchemy_type_mapping(self):
- from sqlalchemy import TIMESTAMP
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+@pytest.mark.parametrize(
+ "integer, expected",
+ [
+ ("int8", "SMALLINT"),
+ ("Int8", "SMALLINT"),
+ ("uint8", "SMALLINT"),
+ ("UInt8", "SMALLINT"),
+ ("int16", "SMALLINT"),
+ ("Int16", "SMALLINT"),
+ ("uint16", "INTEGER"),
+ ("UInt16", "INTEGER"),
+ ("int32", "INTEGER"),
+ ("Int32", "INTEGER"),
+ ("uint32", "BIGINT"),
+ ("UInt32", "BIGINT"),
+ ("int64", "BIGINT"),
+ ("Int64", "BIGINT"),
+ (int, "BIGINT" if np.dtype(int).name == "int64" else "INTEGER"),
+ ],
+)
+def test_sqlalchemy_integer_mapping(conn, request, integer, expected):
+ # GH35076 Map pandas integer to optimal SQLAlchemy integer type
+ conn = request.getfixturevalue(conn)
+ df = DataFrame([0, 1], columns=["a"], dtype=integer)
+ db = sql.SQLDatabase(conn)
+ table = sql.SQLTable("test_type", db, frame=df)
- # Test Timestamp objects (no datetime64 because of timezone) (GH9085)
- df = DataFrame(
- {"time": to_datetime(["2014-12-12 01:54", "2014-12-11 02:54"], utc=True)}
- )
- db = sql.SQLDatabase(self.conn)
- table = sql.SQLTable("test_type", db, frame=df)
- # GH 9086: TIMESTAMP is the suggested type for datetimes with timezones
- assert isinstance(table.table.c["time"].type, TIMESTAMP)
+ result = str(table.table.c.a.type)
+ assert result == expected
- @pytest.mark.parametrize(
- "integer, expected",
- [
- ("int8", "SMALLINT"),
- ("Int8", "SMALLINT"),
- ("uint8", "SMALLINT"),
- ("UInt8", "SMALLINT"),
- ("int16", "SMALLINT"),
- ("Int16", "SMALLINT"),
- ("uint16", "INTEGER"),
- ("UInt16", "INTEGER"),
- ("int32", "INTEGER"),
- ("Int32", "INTEGER"),
- ("uint32", "BIGINT"),
- ("UInt32", "BIGINT"),
- ("int64", "BIGINT"),
- ("Int64", "BIGINT"),
- (int, "BIGINT" if np.dtype(int).name == "int64" else "INTEGER"),
- ],
- )
- def test_sqlalchemy_integer_mapping(self, integer, expected):
- # GH35076 Map pandas integer to optimal SQLAlchemy integer type
- df = DataFrame([0, 1], columns=["a"], dtype=integer)
- db = sql.SQLDatabase(self.conn)
- table = sql.SQLTable("test_type", db, frame=df)
-
- result = str(table.table.c.a.type)
- assert result == expected
-
- @pytest.mark.parametrize("integer", ["uint64", "UInt64"])
- def test_sqlalchemy_integer_overload_mapping(self, integer):
- # GH35076 Map pandas integer to optimal SQLAlchemy integer type
- df = DataFrame([0, 1], columns=["a"], dtype=integer)
- db = sql.SQLDatabase(self.conn)
- with pytest.raises(
- ValueError, match="Unsigned 64 bit integer datatype is not supported"
- ):
- sql.SQLTable("test_type", db, frame=df)
-
- def test_database_uri_string(self, test_frame1):
- # Test read_sql and .to_sql method with a database URI (GH10654)
- # db_uri = 'sqlite:///:memory:' # raises
- # sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) near
- # "iris": syntax error [SQL: 'iris']
- with tm.ensure_clean() as name:
- db_uri = "sqlite:///" + name
- table = "iris"
- test_frame1.to_sql(name=table, con=db_uri, if_exists="replace", index=False)
- test_frame2 = sql.read_sql(table, db_uri)
- test_frame3 = sql.read_sql_table(table, db_uri)
- query = "SELECT * FROM iris"
- test_frame4 = sql.read_sql_query(query, db_uri)
- tm.assert_frame_equal(test_frame1, test_frame2)
- tm.assert_frame_equal(test_frame1, test_frame3)
- tm.assert_frame_equal(test_frame1, test_frame4)
-
- @td.skip_if_installed("pg8000")
- def test_pg8000_sqlalchemy_passthrough_error(self):
- # using driver that will not be installed on CI to trigger error
- # in sqlalchemy.create_engine -> test passing of this error to user
- db_uri = "postgresql+pg8000://user:pass@host/dbname"
- with pytest.raises(ImportError, match="pg8000"):
- sql.read_sql("select * from table", db_uri)
-
- def test_query_by_text_obj(self):
- # WIP : GH10846
- from sqlalchemy import text
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+@pytest.mark.parametrize("integer", ["uint64", "UInt64"])
+def test_sqlalchemy_integer_overload_mapping(conn, request, integer):
+ conn = request.getfixturevalue(conn)
+ # GH35076 Map pandas integer to optimal SQLAlchemy integer type
+ df = DataFrame([0, 1], columns=["a"], dtype=integer)
+ db = sql.SQLDatabase(conn)
+ with pytest.raises(
+ ValueError, match="Unsigned 64 bit integer datatype is not supported"
+ ):
+ sql.SQLTable("test_type", db, frame=df)
+
+
+@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="fails without SQLAlchemy")
+@pytest.mark.parametrize("conn", all_connectable)
+def test_database_uri_string(conn, request, test_frame1):
+ conn = request.getfixturevalue(conn)
+ # Test read_sql and .to_sql method with a database URI (GH10654)
+ # db_uri = 'sqlite:///:memory:' # raises
+ # sqlalchemy.exc.OperationalError: (sqlite3.OperationalError) near
+ # "iris": syntax error [SQL: 'iris']
+ with tm.ensure_clean() as name:
+ db_uri = "sqlite:///" + name
+ table = "iris"
+ test_frame1.to_sql(name=table, con=db_uri, if_exists="replace", index=False)
+ test_frame2 = sql.read_sql(table, db_uri)
+ test_frame3 = sql.read_sql_table(table, db_uri)
+ query = "SELECT * FROM iris"
+ test_frame4 = sql.read_sql_query(query, db_uri)
+ tm.assert_frame_equal(test_frame1, test_frame2)
+ tm.assert_frame_equal(test_frame1, test_frame3)
+ tm.assert_frame_equal(test_frame1, test_frame4)
+
+
+@td.skip_if_installed("pg8000")
+@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="fails without SQLAlchemy")
+@pytest.mark.parametrize("conn", all_connectable)
+def test_pg8000_sqlalchemy_passthrough_error(conn, request):
+ conn = request.getfixturevalue(conn)
+ # using driver that will not be installed on CI to trigger error
+ # in sqlalchemy.create_engine -> test passing of this error to user
+ db_uri = "postgresql+pg8000://user:pass@host/dbname"
+ with pytest.raises(ImportError, match="pg8000"):
+ sql.read_sql("select * from table", db_uri)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_query_by_text_obj(conn, request):
+ # WIP : GH10846
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ from sqlalchemy import text
+
+ if "postgres" in conn_name:
+ name_text = text('select * from iris where "Name"=:name')
+ else:
name_text = text("select * from iris where name=:name")
- iris_df = sql.read_sql(name_text, self.conn, params={"name": "Iris-versicolor"})
- all_names = set(iris_df["Name"])
- assert all_names == {"Iris-versicolor"}
-
- def test_query_by_select_obj(self):
- # WIP : GH10846
- from sqlalchemy import (
- bindparam,
- select,
- )
+ iris_df = sql.read_sql(name_text, conn, params={"name": "Iris-versicolor"})
+ all_names = set(iris_df["Name"])
+ assert all_names == {"Iris-versicolor"}
- iris = iris_table_metadata(self.flavor)
- name_select = select(iris).where(iris.c.Name == bindparam("name"))
- iris_df = sql.read_sql(name_select, self.conn, params={"name": "Iris-setosa"})
- all_names = set(iris_df["Name"])
- assert all_names == {"Iris-setosa"}
- def test_column_with_percentage(self):
- # GH 37157
- df = DataFrame({"A": [0, 1, 2], "%_variation": [3, 4, 5]})
- df.to_sql(name="test_column_percentage", con=self.conn, index=False)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_query_by_select_obj(conn, request):
+ conn = request.getfixturevalue(conn)
+ # WIP : GH10846
+ from sqlalchemy import (
+ bindparam,
+ select,
+ )
- res = sql.read_sql_table("test_column_percentage", self.conn)
+ iris = iris_table_metadata("postgres")
+ 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"])
+ assert all_names == {"Iris-setosa"}
- tm.assert_frame_equal(res, df)
+@pytest.mark.parametrize("conn", all_connectable)
+def test_column_with_percentage(conn, request):
+ # GH 37157
+ conn_name = conn
+ if conn_name == "sqlite_buildin":
+ request.node.add_marker(pytest.mark.xfail(reason="Not Implemented"))
-class TestSQLiteFallbackApi(SQLiteMixIn, _TestSQLApi):
- """
- Test the public sqlite connection fallback API
+ conn = request.getfixturevalue(conn)
+ df = DataFrame({"A": [0, 1, 2], "%_variation": [3, 4, 5]})
+ df.to_sql(name="test_column_percentage", con=conn, index=False)
- """
+ res = sql.read_sql_table("test_column_percentage", conn)
- flavor = "sqlite"
- mode = "fallback"
+ tm.assert_frame_equal(res, df)
- def connect(self, database=":memory:"):
- return sqlite3.connect(database)
- def test_sql_open_close(self, test_frame3):
- # Test if the IO in the database still work if the connection closed
- # between the writing and reading (as in many real situations).
+def test_sql_open_close(test_frame3):
+ # Test if the IO in the database still work if the connection closed
+ # between the writing and reading (as in many real situations).
- with tm.ensure_clean() as name:
- with closing(self.connect(name)) as conn:
- assert (
- sql.to_sql(test_frame3, "test_frame3_legacy", conn, index=False)
- == 4
- )
+ with tm.ensure_clean() as name:
+ with closing(sqlite3.connect(name)) as conn:
+ assert sql.to_sql(test_frame3, "test_frame3_legacy", conn, index=False) == 4
- with closing(self.connect(name)) as conn:
- result = sql.read_sql_query("SELECT * FROM test_frame3_legacy;", conn)
+ with closing(sqlite3.connect(name)) as conn:
+ result = sql.read_sql_query("SELECT * FROM test_frame3_legacy;", conn)
- tm.assert_frame_equal(test_frame3, result)
+ tm.assert_frame_equal(test_frame3, result)
- @pytest.mark.skipif(SQLALCHEMY_INSTALLED, reason="SQLAlchemy is installed")
- def test_con_string_import_error(self):
- conn = "mysql://root@localhost/pandas"
- msg = "Using URI string without sqlalchemy installed"
- with pytest.raises(ImportError, match=msg):
- sql.read_sql("SELECT * FROM iris", conn)
- @pytest.mark.skipif(SQLALCHEMY_INSTALLED, reason="SQLAlchemy is installed")
- def test_con_unknown_dbapi2_class_does_not_error_without_sql_alchemy_installed(
- self,
- ):
- class MockSqliteConnection:
- def __init__(self, *args, **kwargs) -> None:
- self.conn = sqlite3.Connection(*args, **kwargs)
-
- def __getattr__(self, name):
- return getattr(self.conn, name)
-
- def close(self):
- self.conn.close()
-
- with contextlib.closing(MockSqliteConnection(":memory:")) as conn:
- with tm.assert_produces_warning(UserWarning):
- sql.read_sql("SELECT 1", conn)
-
- def test_read_sql_delegate(self):
- iris_frame1 = sql.read_sql_query("SELECT * FROM iris", self.conn)
- iris_frame2 = sql.read_sql("SELECT * FROM iris", self.conn)
- tm.assert_frame_equal(iris_frame1, iris_frame2)
-
- msg = "Execution failed on sql 'iris': near \"iris\": syntax error"
- with pytest.raises(sql.DatabaseError, match=msg):
- sql.read_sql("iris", self.conn)
-
- def test_get_schema2(self, test_frame1):
- # without providing a connection object (available for backwards comp)
- create_sql = sql.get_schema(test_frame1, "test")
- assert "CREATE" in create_sql
-
- def _get_sqlite_column_type(self, schema, column):
- for col in schema.split("\n"):
- if col.split()[0].strip('"') == column:
- return col.split()[1]
- raise ValueError(f"Column {column} not found")
-
- def test_sqlite_type_mapping(self):
- # Test Timestamp objects (no datetime64 because of timezone) (GH9085)
- df = DataFrame(
- {"time": to_datetime(["2014-12-12 01:54", "2014-12-11 02:54"], utc=True)}
- )
- db = sql.SQLiteDatabase(self.conn)
- table = sql.SQLiteTable("test_type", db, frame=df)
- schema = table.sql_schema()
- assert self._get_sqlite_column_type(schema, "time") == "TIMESTAMP"
+@pytest.mark.skipif(SQLALCHEMY_INSTALLED, reason="SQLAlchemy is installed")
+def test_con_string_import_error():
+ conn = "mysql://root@localhost/pandas"
+ msg = "Using URI string without sqlalchemy installed"
+ with pytest.raises(ImportError, match=msg):
+ sql.read_sql("SELECT * FROM iris", conn)
-# -----------------------------------------------------------------------------
-# -- Database flavor specific tests
+@pytest.mark.skipif(SQLALCHEMY_INSTALLED, reason="SQLAlchemy is installed")
+def test_con_unknown_dbapi2_class_does_not_error_without_sql_alchemy_installed():
+ class MockSqliteConnection:
+ def __init__(self, *args, **kwargs) -> None:
+ self.conn = sqlite3.Connection(*args, **kwargs)
+ def __getattr__(self, name):
+ return getattr(self.conn, name)
-@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="SQLAlchemy not installed")
-class _TestSQLAlchemy(SQLAlchemyMixIn, PandasSQLTest):
- """
- Base class for testing the sqlalchemy backend.
+ def close(self):
+ self.conn.close()
- Subclasses for specific database types are created below. Tests that
- deviate for each flavor are overwritten there.
+ with contextlib.closing(MockSqliteConnection(":memory:")) as conn:
+ with tm.assert_produces_warning(UserWarning):
+ sql.read_sql("SELECT 1", conn)
- """
- flavor: str
+def test_sqlite_read_sql_delegate(sqlite_buildin_iris):
+ conn = sqlite_buildin_iris
+ iris_frame1 = sql.read_sql_query("SELECT * FROM iris", conn)
+ iris_frame2 = sql.read_sql("SELECT * FROM iris", conn)
+ tm.assert_frame_equal(iris_frame1, iris_frame2)
- @classmethod
- def setup_class(cls):
- cls.setup_driver()
- cls.setup_engine()
+ msg = "Execution failed on sql 'iris': near \"iris\": syntax error"
+ with pytest.raises(sql.DatabaseError, match=msg):
+ sql.read_sql("iris", conn)
- @pytest.fixture(autouse=True)
- def setup_method(self, iris_path, types_data):
- try:
- self.conn = self.engine.connect()
- self.pandasSQL = sql.SQLDatabase(self.conn)
- except sqlalchemy.exc.OperationalError:
- pytest.skip(f"Can't connect to {self.flavor} server")
- self.load_iris_data(iris_path)
- self.load_types_data(types_data)
- @classmethod
- def setup_driver(cls):
- raise NotImplementedError()
+def test_get_schema2(test_frame1):
+ # without providing a connection object (available for backwards comp)
+ create_sql = sql.get_schema(test_frame1, "test")
+ assert "CREATE" in create_sql
- @classmethod
- def setup_engine(cls):
- raise NotImplementedError()
- def test_read_sql_parameter(self, sql_strings):
- self._read_sql_iris_parameter(sql_strings)
+def test_sqlite_type_mapping(sqlite_buildin):
+ # Test Timestamp objects (no datetime64 because of timezone) (GH9085)
+ conn = sqlite_buildin
+ df = DataFrame(
+ {"time": to_datetime(["2014-12-12 01:54", "2014-12-11 02:54"], utc=True)}
+ )
+ db = sql.SQLiteDatabase(conn)
+ table = sql.SQLiteTable("test_type", db, frame=df)
+ schema = table.sql_schema()
+ for col in schema.split("\n"):
+ if col.split()[0].strip('"') == "time":
+ assert col.split()[1] == "TIMESTAMP"
- def test_read_sql_named_parameter(self, sql_strings):
- self._read_sql_iris_named_parameter(sql_strings)
- def test_to_sql_empty(self, test_frame1):
- self._to_sql_empty(test_frame1)
+# -----------------------------------------------------------------------------
+# -- Database flavor specific tests
- def test_create_table(self):
- from sqlalchemy import inspect
- temp_conn = self.connect()
- temp_frame = DataFrame(
- {"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
- )
- with sql.SQLDatabase(temp_conn, need_transaction=True) as pandasSQL:
- assert pandasSQL.to_sql(temp_frame, "temp_frame") == 4
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_create_table(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
- insp = inspect(temp_conn)
- assert insp.has_table("temp_frame")
+ conn = request.getfixturevalue(conn)
- # Cleanup
- with sql.SQLDatabase(temp_conn, need_transaction=True) as pandasSQL:
- pandasSQL.drop_table("temp_frame")
+ from sqlalchemy import inspect
- def test_drop_table(self):
- from sqlalchemy import inspect
+ 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, need_transaction=True) as pandasSQL:
+ assert pandasSQL.to_sql(temp_frame, "temp_frame") == 4
- temp_conn = self.connect()
- temp_frame = DataFrame(
- {"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
- )
- pandasSQL = sql.SQLDatabase(temp_conn)
+ insp = inspect(conn)
+ assert insp.has_table("temp_frame")
+
+ # Cleanup
+ with sql.SQLDatabase(conn, need_transaction=True) as pandasSQL:
+ pandasSQL.drop_table("temp_frame")
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_drop_table(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
+
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import inspect
+
+ temp_frame = DataFrame({"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]})
+ pandasSQL = sql.SQLDatabase(conn)
+ with pandasSQL.run_transaction():
assert pandasSQL.to_sql(temp_frame, "temp_frame") == 4
- insp = inspect(temp_conn)
- assert insp.has_table("temp_frame")
+ insp = inspect(conn)
+ assert insp.has_table("temp_frame")
+ with pandasSQL.run_transaction():
pandasSQL.drop_table("temp_frame")
- try:
- insp.clear_cache() # needed with SQLAlchemy 2.0, unavailable prior
- except AttributeError:
- pass
- assert not insp.has_table("temp_frame")
+ try:
+ insp.clear_cache() # needed with SQLAlchemy 2.0, unavailable prior
+ except AttributeError:
+ pass
+ assert not insp.has_table("temp_frame")
- def test_roundtrip(self, test_frame1):
- self._roundtrip(test_frame1)
- def test_execute_sql(self):
- self._execute_sql()
+@pytest.mark.parametrize("conn", all_connectable)
+def test_roundtrip(conn, request, test_frame1):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
- def test_read_table(self):
- iris_frame = sql.read_sql_table("iris", con=self.conn)
- check_iris_frame(iris_frame)
+ conn = request.getfixturevalue(conn)
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(test_frame1, "test_frame_roundtrip") == 4
+ result = pandasSQL.read_query("SELECT * FROM test_frame_roundtrip")
- def test_read_table_columns(self):
- iris_frame = sql.read_sql_table(
- "iris", con=self.conn, columns=["SepalLength", "SepalLength"]
- )
- tm.equalContents(iris_frame.columns.values, ["SepalLength", "SepalLength"])
+ result.set_index("level_0", inplace=True)
+ # result.index.astype(int)
- def test_read_table_absent_raises(self):
- msg = "Table this_doesnt_exist not found"
- with pytest.raises(ValueError, match=msg):
- sql.read_sql_table("this_doesnt_exist", con=self.conn)
+ result.index.name = None
- def test_default_type_conversion(self):
- df = sql.read_sql_table("types", self.conn)
+ tm.assert_frame_equal(result, test_frame1)
- assert issubclass(df.FloatCol.dtype.type, np.floating)
- assert issubclass(df.IntCol.dtype.type, np.integer)
- assert issubclass(df.BoolCol.dtype.type, np.bool_)
- # Int column with NA values stays as float
- assert issubclass(df.IntColWithNull.dtype.type, np.floating)
- # Bool column with NA values becomes object
- assert issubclass(df.BoolColWithNull.dtype.type, object)
+@pytest.mark.parametrize("conn", all_connectable_iris)
+def test_execute_sql(conn, request):
+ conn = request.getfixturevalue(conn)
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ iris_results = pandasSQL.execute("SELECT * FROM iris")
+ row = iris_results.fetchone()
+ tm.equalContents(row, [5.1, 3.5, 1.4, 0.2, "Iris-setosa"])
- def test_bigint(self):
- # int64 should be converted to BigInteger, GH7433
- df = DataFrame(data={"i64": [2**62]})
- assert df.to_sql(name="test_bigint", con=self.conn, index=False) == 1
- result = sql.read_sql_table("test_bigint", self.conn)
-
- tm.assert_frame_equal(df, result)
-
- def test_default_date_load(self):
- df = sql.read_sql_table("types", self.conn)
-
- # IMPORTANT - sqlite has no native date type, so shouldn't parse, but
- # MySQL SHOULD be converted.
- assert issubclass(df.DateCol.dtype.type, np.datetime64)
-
- def test_datetime_with_timezone(self, request):
- # edge case that converts postgresql datetime with time zone types
- # to datetime64[ns,psycopg2.tz.FixedOffsetTimezone..], which is ok
- # but should be more natural, so coerce to datetime64[ns] for now
-
- 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}"
- )
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_sqlalchemy_read_table(conn, request):
+ conn = request.getfixturevalue(conn)
+ iris_frame = sql.read_sql_table("iris", con=conn)
+ check_iris_frame(iris_frame)
- # GH11216
- df = read_sql_query("select * from types", self.conn)
- if not hasattr(df, "DateColWithTz"):
- request.node.add_marker(
- pytest.mark.xfail(reason="no column with datetime with time zone")
- )
- # this is parsed on Travis (linux), but not on macosx for some reason
- # even with the same versions of psycopg2 & sqlalchemy, possibly a
- # Postgresql server version difference
- col = df.DateColWithTz
- assert isinstance(col.dtype, DatetimeTZDtype)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_sqlalchemy_read_table_columns(conn, request):
+ conn = request.getfixturevalue(conn)
+ iris_frame = sql.read_sql_table(
+ "iris", con=conn, columns=["SepalLength", "SepalLength"]
+ )
+ tm.equalContents(iris_frame.columns.values, ["SepalLength", "SepalLength"])
- df = read_sql_query(
- "select * from types", self.conn, parse_dates=["DateColWithTz"]
- )
- if not hasattr(df, "DateColWithTz"):
- request.node.add_marker(
- 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)
-
- df = concat(
- list(read_sql_query("select * from types", self.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", self.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", self.conn)
- check(df.DateColWithTz)
-
- def test_datetime_with_timezone_roundtrip(self):
- # GH 9086
- # Write datetimetz data to a db and read it back
- # For dbs that support timestamps with timezones, should get back UTC
- # otherwise naive data should be returned
- expected = DataFrame(
- {"A": date_range("2013-01-01 09:00:00", periods=3, tz="US/Pacific")}
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_read_table_absent_raises(conn, request):
+ conn = request.getfixturevalue(conn)
+ msg = "Table this_doesnt_exist not found"
+ with pytest.raises(ValueError, match=msg):
+ sql.read_sql_table("this_doesnt_exist", con=conn)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_sqlalchemy_default_type_conversion(conn, request):
+ conn_name = conn
+ if conn_name == "sqlite_str":
+ pytest.skip("types tables not created in sqlite_str fixture")
+ elif "mysql" in conn_name or "sqlite" in conn_name:
+ request.node.add_marker(
+ pytest.mark.xfail(reason="boolean dtype not inferred properly")
)
- assert expected.to_sql(name="test_datetime_tz", con=self.conn, index=False) == 3
- if self.flavor == "postgresql":
- # SQLAlchemy "timezones" (i.e. offsets) are coerced to UTC
- expected["A"] = expected["A"].dt.tz_convert("UTC")
- else:
- # Otherwise, timestamps are returned as local, naive
- expected["A"] = expected["A"].dt.tz_localize(None)
+ conn = request.getfixturevalue(conn)
+ df = sql.read_sql_table("types", conn)
- result = sql.read_sql_table("test_datetime_tz", self.conn)
- tm.assert_frame_equal(result, expected)
+ assert issubclass(df.FloatCol.dtype.type, np.floating)
+ assert issubclass(df.IntCol.dtype.type, np.integer)
+ assert issubclass(df.BoolCol.dtype.type, np.bool_)
- result = sql.read_sql_query("SELECT * FROM test_datetime_tz", self.conn)
- if self.flavor == "sqlite":
- # read_sql_query does not return datetime type like read_sql_table
- assert isinstance(result.loc[0, "A"], str)
- result["A"] = to_datetime(result["A"])
- tm.assert_frame_equal(result, expected)
+ # Int column with NA values stays as float
+ assert issubclass(df.IntColWithNull.dtype.type, np.floating)
+ # Bool column with NA values becomes object
+ assert issubclass(df.BoolColWithNull.dtype.type, object)
- def test_out_of_bounds_datetime(self):
- # GH 26761
- data = DataFrame({"date": datetime(9999, 1, 1)}, index=[0])
- assert data.to_sql(name="test_datetime_obb", con=self.conn, index=False) == 1
- result = sql.read_sql_table("test_datetime_obb", self.conn)
- expected = DataFrame([pd.NaT], columns=["date"])
- tm.assert_frame_equal(result, expected)
- def test_naive_datetimeindex_roundtrip(self):
- # GH 23510
- # Ensure that a naive DatetimeIndex isn't converted to UTC
- dates = date_range("2018-01-01", periods=5, freq="6H")._with_freq(None)
- expected = DataFrame({"nums": range(5)}, index=dates)
- assert (
- expected.to_sql(name="foo_table", con=self.conn, index_label="info_date")
- == 5
- )
- result = sql.read_sql_table("foo_table", self.conn, index_col="info_date")
- # result index with gain a name from a set_index operation; expected
- tm.assert_frame_equal(result, expected, check_names=False)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_bigint(conn, request):
+ # int64 should be converted to BigInteger, GH7433
+ conn = request.getfixturevalue(conn)
+ df = DataFrame(data={"i64": [2**62]})
+ assert df.to_sql(name="test_bigint", con=conn, index=False) == 1
+ result = sql.read_sql_table("test_bigint", conn)
- def test_date_parsing(self):
- # No Parsing
- df = sql.read_sql_table("types", self.conn)
- expected_type = object if self.flavor == "sqlite" else np.datetime64
- assert issubclass(df.DateCol.dtype.type, expected_type)
+ tm.assert_frame_equal(df, result)
- df = sql.read_sql_table("types", self.conn, parse_dates=["DateCol"])
- assert issubclass(df.DateCol.dtype.type, np.datetime64)
- df = sql.read_sql_table(
- "types", self.conn, parse_dates={"DateCol": "%Y-%m-%d %H:%M:%S"}
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_default_date_load(conn, request):
+ conn_name = conn
+ if conn_name == "sqlite_str":
+ pytest.skip("types tables not created in sqlite_str fixture")
+ elif "sqlite" in conn_name:
+ request.node.add_marker(
+ pytest.mark.xfail(reason="sqlite does not read date properly")
)
- assert issubclass(df.DateCol.dtype.type, np.datetime64)
- df = sql.read_sql_table(
- "types",
- self.conn,
- parse_dates={"DateCol": {"format": "%Y-%m-%d %H:%M:%S"}},
- )
- assert issubclass(df.DateCol.dtype.type, np.datetime64)
+ conn = request.getfixturevalue(conn)
+ df = sql.read_sql_table("types", conn)
+
+ assert issubclass(df.DateCol.dtype.type, np.datetime64)
- df = sql.read_sql_table("types", self.conn, parse_dates=["IntDateCol"])
- assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
- df = sql.read_sql_table("types", self.conn, parse_dates={"IntDateCol": "s"})
- assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_datetime_with_timezone(conn, request):
+ # edge case that converts postgresql datetime with time zone types
+ # to datetime64[ns,psycopg2.tz.FixedOffsetTimezone..], which is ok
+ # but should be more natural, so coerce to datetime64[ns] for now
+
+ 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)
- df = sql.read_sql_table(
- "types", self.conn, parse_dates={"IntDateCol": {"unit": "s"}}
+ 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.node.add_marker(
+ pytest.mark.xfail(reason="no column with datetime with time zone")
)
- assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
- def test_datetime(self):
- df = DataFrame(
- {"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
+ # 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
+ col = df.DateColWithTz
+ assert isinstance(col.dtype, DatetimeTZDtype)
+
+ df = read_sql_query("select * from types", conn, parse_dates=["DateColWithTz"])
+ if not hasattr(df, "DateColWithTz"):
+ request.node.add_marker(
+ pytest.mark.xfail(reason="no column with datetime with time zone")
)
- assert df.to_sql(name="test_datetime", con=self.conn) == 3
+ col = df.DateColWithTz
+ assert isinstance(col.dtype, DatetimeTZDtype)
+ assert str(col.dt.tz) == "UTC"
+ check(df.DateColWithTz)
+
+ df = concat(
+ list(read_sql_query("select * from types", 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)
- # with read_table -> type information from schema used
- result = sql.read_sql_table("test_datetime", self.conn)
- result = result.drop("index", axis=1)
- tm.assert_frame_equal(result, df)
- # with read_sql -> no type information -> sqlite has no native
- result = sql.read_sql_query("SELECT * FROM test_datetime", self.conn)
- result = result.drop("index", axis=1)
- if self.flavor == "sqlite":
- assert isinstance(result.loc[0, "A"], str)
- result["A"] = to_datetime(result["A"])
- tm.assert_frame_equal(result, df)
- else:
- tm.assert_frame_equal(result, df)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_datetime_with_timezone_roundtrip(conn, request):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ # GH 9086
+ # Write datetimetz data to a db and read it back
+ # For dbs that support timestamps with timezones, should get back UTC
+ # otherwise naive data should be returned
+ expected = DataFrame(
+ {"A": date_range("2013-01-01 09:00:00", periods=3, tz="US/Pacific")}
+ )
+ assert expected.to_sql(name="test_datetime_tz", con=conn, index=False) == 3
- def test_datetime_NaT(self):
- df = DataFrame(
- {"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
- )
- df.loc[1, "A"] = np.nan
- assert df.to_sql(name="test_datetime", con=self.conn, index=False) == 3
+ if "postgresql" in conn_name:
+ # SQLAlchemy "timezones" (i.e. offsets) are coerced to UTC
+ expected["A"] = expected["A"].dt.tz_convert("UTC")
+ else:
+ # Otherwise, timestamps are returned as local, naive
+ expected["A"] = expected["A"].dt.tz_localize(None)
- # with read_table -> type information from schema used
- result = sql.read_sql_table("test_datetime", self.conn)
- tm.assert_frame_equal(result, df)
+ result = sql.read_sql_table("test_datetime_tz", conn)
+ tm.assert_frame_equal(result, expected)
- # with read_sql -> no type information -> sqlite has no native
- result = sql.read_sql_query("SELECT * FROM test_datetime", self.conn)
- if self.flavor == "sqlite":
- assert isinstance(result.loc[0, "A"], str)
- result["A"] = to_datetime(result["A"], errors="coerce")
- tm.assert_frame_equal(result, df)
- else:
- tm.assert_frame_equal(result, df)
-
- def test_datetime_date(self):
- # test support for datetime.date
- df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
- assert df.to_sql(name="test_date", con=self.conn, index=False) == 2
- res = read_sql_table("test_date", self.conn)
- result = res["a"]
- expected = to_datetime(df["a"])
- # comes back as datetime64
- tm.assert_series_equal(result, expected)
-
- def test_datetime_time(self, sqlite_buildin):
- # test support for datetime.time
- df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"])
- assert df.to_sql(name="test_time", con=self.conn, index=False) == 2
- res = read_sql_table("test_time", self.conn)
- tm.assert_frame_equal(res, df)
-
- # GH8341
- # first, use the fallback to have the sqlite adapter put in place
- sqlite_conn = sqlite_buildin
- assert sql.to_sql(df, "test_time2", sqlite_conn, index=False) == 2
- res = sql.read_sql_query("SELECT * FROM test_time2", sqlite_conn)
- ref = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
- tm.assert_frame_equal(ref, res) # check if adapter is in place
- # then test if sqlalchemy is unaffected by the sqlite adapter
- assert sql.to_sql(df, "test_time3", self.conn, index=False) == 2
- if self.flavor == "sqlite":
- res = sql.read_sql_query("SELECT * FROM test_time3", self.conn)
- ref = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
- tm.assert_frame_equal(ref, res)
- res = sql.read_sql_table("test_time3", self.conn)
- tm.assert_frame_equal(df, res)
-
- def test_mixed_dtype_insert(self):
- # see GH6509
- s1 = Series(2**25 + 1, dtype=np.int32)
- s2 = Series(0.0, dtype=np.float32)
- df = DataFrame({"s1": s1, "s2": s2})
-
- # write and read again
- assert df.to_sql(name="test_read_write", con=self.conn, index=False) == 1
- df2 = sql.read_sql_table("test_read_write", self.conn)
-
- tm.assert_frame_equal(df, df2, check_dtype=False, check_exact=True)
-
- def test_nan_numeric(self):
- # NaNs in numeric float column
- df = DataFrame({"A": [0, 1, 2], "B": [0.2, np.nan, 5.6]})
- assert df.to_sql(name="test_nan", con=self.conn, index=False) == 3
-
- # with read_table
- result = sql.read_sql_table("test_nan", self.conn)
- tm.assert_frame_equal(result, df)
+ result = sql.read_sql_query("SELECT * FROM test_datetime_tz", conn)
+ if "sqlite" in conn_name:
+ # read_sql_query does not return datetime type like read_sql_table
+ assert isinstance(result.loc[0, "A"], str)
+ result["A"] = to_datetime(result["A"])
+ tm.assert_frame_equal(result, expected)
- # with read_sql
- result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
- tm.assert_frame_equal(result, df)
- def test_nan_fullcolumn(self):
- # full NaN column (numeric float column)
- df = DataFrame({"A": [0, 1, 2], "B": [np.nan, np.nan, np.nan]})
- assert df.to_sql(name="test_nan", con=self.conn, index=False) == 3
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_out_of_bounds_datetime(conn, request):
+ # GH 26761
+ conn = request.getfixturevalue(conn)
+ data = DataFrame({"date": datetime(9999, 1, 1)}, index=[0])
+ assert data.to_sql(name="test_datetime_obb", con=conn, index=False) == 1
+ result = sql.read_sql_table("test_datetime_obb", conn)
+ expected = DataFrame([pd.NaT], columns=["date"])
+ tm.assert_frame_equal(result, expected)
- # with read_table
- result = sql.read_sql_table("test_nan", self.conn)
- tm.assert_frame_equal(result, df)
- # with read_sql -> not type info from table -> stays None
- df["B"] = df["B"].astype("object")
- df["B"] = None
- result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
- tm.assert_frame_equal(result, df)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_naive_datetimeindex_roundtrip(conn, request):
+ # GH 23510
+ # Ensure that a naive DatetimeIndex isn't converted to UTC
+ conn = request.getfixturevalue(conn)
+ dates = date_range("2018-01-01", periods=5, freq="6H")._with_freq(None)
+ expected = DataFrame({"nums": range(5)}, index=dates)
+ assert expected.to_sql(name="foo_table", con=conn, index_label="info_date") == 5
+ result = sql.read_sql_table("foo_table", conn, index_col="info_date")
+ # result index with gain a name from a set_index operation; expected
+ tm.assert_frame_equal(result, expected, check_names=False)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable_iris)
+def test_date_parsing(conn, request):
+ # No Parsing
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ df = sql.read_sql_table("types", conn)
+ expected_type = object if "sqlite" in conn_name else np.datetime64
+ assert issubclass(df.DateCol.dtype.type, expected_type)
+
+ df = sql.read_sql_table("types", conn, parse_dates=["DateCol"])
+ assert issubclass(df.DateCol.dtype.type, np.datetime64)
+
+ df = sql.read_sql_table("types", conn, parse_dates={"DateCol": "%Y-%m-%d %H:%M:%S"})
+ assert issubclass(df.DateCol.dtype.type, np.datetime64)
- def test_nan_string(self):
- # NaNs in string column
- df = DataFrame({"A": [0, 1, 2], "B": ["a", "b", np.nan]})
- assert df.to_sql(name="test_nan", con=self.conn, index=False) == 3
+ df = sql.read_sql_table(
+ "types",
+ conn,
+ parse_dates={"DateCol": {"format": "%Y-%m-%d %H:%M:%S"}},
+ )
+ assert issubclass(df.DateCol.dtype.type, np.datetime64)
+
+ df = sql.read_sql_table("types", conn, parse_dates=["IntDateCol"])
+ assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
+
+ df = sql.read_sql_table("types", conn, parse_dates={"IntDateCol": "s"})
+ assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
+
+ df = sql.read_sql_table("types", conn, parse_dates={"IntDateCol": {"unit": "s"}})
+ assert issubclass(df.IntDateCol.dtype.type, np.datetime64)
- # NaNs are coming back as None
- df.loc[2, "B"] = None
- # with read_table
- result = sql.read_sql_table("test_nan", self.conn)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_datetime(conn, request):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ df = DataFrame(
+ {"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
+ )
+ assert df.to_sql(name="test_datetime", con=conn) == 3
+
+ # with read_table -> type information from schema used
+ result = sql.read_sql_table("test_datetime", conn)
+ result = result.drop("index", axis=1)
+ tm.assert_frame_equal(result, df)
+
+ # with read_sql -> no type information -> sqlite has no native
+ result = sql.read_sql_query("SELECT * FROM test_datetime", conn)
+ result = result.drop("index", axis=1)
+ if "sqlite" in conn_name:
+ assert isinstance(result.loc[0, "A"], str)
+ result["A"] = to_datetime(result["A"])
+ tm.assert_frame_equal(result, df)
+ else:
tm.assert_frame_equal(result, df)
- # with read_sql
- result = sql.read_sql_query("SELECT * FROM test_nan", self.conn)
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_datetime_NaT(conn, request):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ df = DataFrame(
+ {"A": date_range("2013-01-01 09:00:00", periods=3), "B": np.arange(3.0)}
+ )
+ df.loc[1, "A"] = np.nan
+ assert df.to_sql(name="test_datetime", con=conn, index=False) == 3
+
+ # with read_table -> type information from schema used
+ result = sql.read_sql_table("test_datetime", conn)
+ tm.assert_frame_equal(result, df)
+
+ # with read_sql -> no type information -> sqlite has no native
+ result = sql.read_sql_query("SELECT * FROM test_datetime", conn)
+ if "sqlite" in conn_name:
+ assert isinstance(result.loc[0, "A"], str)
+ result["A"] = to_datetime(result["A"], errors="coerce")
+ tm.assert_frame_equal(result, df)
+ else:
tm.assert_frame_equal(result, df)
- def _get_index_columns(self, tbl_name):
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_datetime_date(conn, request):
+ # test support for datetime.date
+ conn = request.getfixturevalue(conn)
+ df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
+ assert df.to_sql(name="test_date", con=conn, index=False) == 2
+ res = read_sql_table("test_date", conn)
+ result = res["a"]
+ expected = to_datetime(df["a"])
+ # comes back as datetime64
+ tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_datetime_time(conn, request, sqlite_buildin):
+ # test support for datetime.time
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ df = DataFrame([time(9, 0, 0), time(9, 1, 30)], columns=["a"])
+ assert df.to_sql(name="test_time", con=conn, index=False) == 2
+ res = read_sql_table("test_time", conn)
+ tm.assert_frame_equal(res, df)
+
+ # GH8341
+ # first, use the fallback to have the sqlite adapter put in place
+ sqlite_conn = sqlite_buildin
+ assert sql.to_sql(df, "test_time2", sqlite_conn, index=False) == 2
+ res = sql.read_sql_query("SELECT * FROM test_time2", sqlite_conn)
+ ref = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(ref, res) # check if adapter is in place
+ # then test if sqlalchemy is unaffected by the sqlite adapter
+ assert sql.to_sql(df, "test_time3", conn, index=False) == 2
+ if "sqlite" in conn_name:
+ res = sql.read_sql_query("SELECT * FROM test_time3", conn)
+ ref = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(ref, res)
+ res = sql.read_sql_table("test_time3", conn)
+ tm.assert_frame_equal(df, res)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_mixed_dtype_insert(conn, request):
+ # see GH6509
+ conn = request.getfixturevalue(conn)
+ s1 = Series(2**25 + 1, dtype=np.int32)
+ s2 = Series(0.0, dtype=np.float32)
+ df = DataFrame({"s1": s1, "s2": s2})
+
+ # write and read again
+ assert df.to_sql(name="test_read_write", con=conn, index=False) == 1
+ df2 = sql.read_sql_table("test_read_write", conn)
+
+ tm.assert_frame_equal(df, df2, check_dtype=False, check_exact=True)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_nan_numeric(conn, request):
+ # NaNs in numeric float column
+ conn = request.getfixturevalue(conn)
+ df = DataFrame({"A": [0, 1, 2], "B": [0.2, np.nan, 5.6]})
+ assert df.to_sql(name="test_nan", con=conn, index=False) == 3
+
+ # with read_table
+ result = sql.read_sql_table("test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+ # with read_sql
+ result = sql.read_sql_query("SELECT * FROM test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_nan_fullcolumn(conn, request):
+ # full NaN column (numeric float column)
+ conn = request.getfixturevalue(conn)
+ df = DataFrame({"A": [0, 1, 2], "B": [np.nan, np.nan, np.nan]})
+ assert df.to_sql(name="test_nan", con=conn, index=False) == 3
+
+ # with read_table
+ result = sql.read_sql_table("test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+ # with read_sql -> not type info from table -> stays None
+ df["B"] = df["B"].astype("object")
+ df["B"] = None
+ result = sql.read_sql_query("SELECT * FROM test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_nan_string(conn, request):
+ # NaNs in string column
+ conn = request.getfixturevalue(conn)
+ df = DataFrame({"A": [0, 1, 2], "B": ["a", "b", np.nan]})
+ assert df.to_sql(name="test_nan", con=conn, index=False) == 3
+
+ # NaNs are coming back as None
+ df.loc[2, "B"] = None
+
+ # with read_table
+ result = sql.read_sql_table("test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+ # with read_sql
+ result = sql.read_sql_query("SELECT * FROM test_nan", conn)
+ tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+def test_to_sql_save_index(conn, request):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ df = DataFrame.from_records(
+ [(1, 2.1, "line1"), (2, 1.5, "line2")], columns=["A", "B", "C"], index=["A"]
+ )
+
+ pandasSQL = pandasSQL_builder(conn)
+ tbl_name = "test_to_sql_saves_index"
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(df, tbl_name) == 2
+
+ if conn_name in {"sqlite_buildin", "sqlite_str"}:
+ ixs = sql.read_sql_query(
+ "SELECT * FROM sqlite_master WHERE type = 'index' "
+ f"AND tbl_name = '{tbl_name}'",
+ conn,
+ )
+ ix_cols = []
+ for ix_name in ixs.name:
+ ix_info = sql.read_sql_query(f"PRAGMA index_info({ix_name})", conn)
+ ix_cols.append(ix_info.name.tolist())
+ else:
from sqlalchemy import inspect
- insp = inspect(self.conn)
+ insp = inspect(conn)
ixs = insp.get_indexes(tbl_name)
- ixs = [i["column_names"] for i in ixs]
- return ixs
+ ix_cols = [i["column_names"] for i in ixs]
+
+ assert ix_cols == [["A"]]
- def test_to_sql_save_index(self):
- self._to_sql_save_index()
- def test_transactions(self):
- self._transaction_test()
+@pytest.mark.parametrize("conn", all_connectable)
+def test_transactions(conn, request):
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
- def test_get_schema_create_table(self, test_frame3):
- # Use a dataframe without a bool column, since MySQL converts bool to
- # TINYINT (which read_sql_table returns as an int and causes a dtype
- # mismatch)
+ stmt = "CREATE TABLE test_trans (A INT, B TEXT)"
+ pandasSQL = pandasSQL_builder(conn)
+ if conn_name != "sqlite_buildin":
from sqlalchemy import text
- from sqlalchemy.engine import Engine
- tbl = "test_get_schema_create_table"
- create_sql = sql.get_schema(test_frame3, tbl, con=self.conn)
- blank_test_df = test_frame3.iloc[:0]
+ stmt = text(stmt)
- self.drop_table(tbl, self.conn)
- create_sql = text(create_sql)
- if isinstance(self.conn, Engine):
- with self.conn.connect() as conn:
- with conn.begin():
- conn.execute(create_sql)
+ with pandasSQL.run_transaction() as trans:
+ trans.execute(stmt)
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+def test_transaction_rollback(conn, request):
+ conn = request.getfixturevalue(conn)
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction() as trans:
+ stmt = "CREATE TABLE test_trans (A INT, B TEXT)"
+ if isinstance(pandasSQL, SQLiteDatabase):
+ trans.execute(stmt)
else:
- with self.conn.begin():
- self.conn.execute(create_sql)
- returned_df = sql.read_sql_table(tbl, self.conn)
- tm.assert_frame_equal(returned_df, blank_test_df, check_index_type=False)
- self.drop_table(tbl, self.conn)
-
- def test_dtype(self):
- from sqlalchemy import (
- TEXT,
- String,
- )
- from sqlalchemy.schema import MetaData
-
- cols = ["A", "B"]
- data = [(0.8, True), (0.9, None)]
- df = DataFrame(data, columns=cols)
- assert df.to_sql(name="dtype_test", con=self.conn) == 2
- assert df.to_sql(name="dtype_test2", con=self.conn, dtype={"B": TEXT}) == 2
- meta = MetaData()
- meta.reflect(bind=self.conn)
- sqltype = meta.tables["dtype_test2"].columns["B"].type
- assert isinstance(sqltype, TEXT)
- msg = "The type of B is not a SQLAlchemy type"
- with pytest.raises(ValueError, match=msg):
- df.to_sql(name="error", con=self.conn, dtype={"B": str})
+ from sqlalchemy import text
- # GH9083
- assert (
- df.to_sql(name="dtype_test3", con=self.conn, dtype={"B": String(10)}) == 2
- )
- meta.reflect(bind=self.conn)
- sqltype = meta.tables["dtype_test3"].columns["B"].type
- assert isinstance(sqltype, String)
- assert sqltype.length == 10
-
- # single dtype
- assert df.to_sql(name="single_dtype_test", con=self.conn, dtype=TEXT) == 2
- meta.reflect(bind=self.conn)
- sqltypea = meta.tables["single_dtype_test"].columns["A"].type
- sqltypeb = meta.tables["single_dtype_test"].columns["B"].type
- assert isinstance(sqltypea, TEXT)
- assert isinstance(sqltypeb, TEXT)
-
- def test_notna_dtype(self):
- from sqlalchemy import (
- Boolean,
- DateTime,
- Float,
- Integer,
+ stmt = text(stmt)
+ trans.execute(stmt)
+
+ class DummyException(Exception):
+ pass
+
+ # Make sure when transaction is rolled back, no rows get inserted
+ ins_sql = "INSERT INTO test_trans (A,B) VALUES (1, 'blah')"
+ if isinstance(pandasSQL, SQLDatabase):
+ from sqlalchemy import text
+
+ ins_sql = text(ins_sql)
+ try:
+ with pandasSQL.run_transaction() as trans:
+ trans.execute(ins_sql)
+ raise DummyException("error")
+ except DummyException:
+ # ignore raised exception
+ pass
+ with pandasSQL.run_transaction():
+ res = pandasSQL.read_query("SELECT * FROM test_trans")
+ assert len(res) == 0
+
+ # Make sure when transaction is committed, rows do get inserted
+ with pandasSQL.run_transaction() as trans:
+ trans.execute(ins_sql)
+ res2 = pandasSQL.read_query("SELECT * FROM test_trans")
+ assert len(res2) == 1
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_get_schema_create_table(conn, request, test_frame3):
+ # Use a dataframe without a bool column, since MySQL converts bool to
+ # TINYINT (which read_sql_table returns as an int and causes a dtype
+ # mismatch)
+ if conn == "sqlite_str":
+ request.node.add_marker(
+ pytest.mark.xfail(reason="test does not support sqlite_str fixture")
)
- from sqlalchemy.schema import MetaData
- cols = {
- "Bool": Series([True, None]),
- "Date": Series([datetime(2012, 5, 1), None]),
- "Int": Series([1, None], dtype="object"),
- "Float": Series([1.1, None]),
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import text
+ from sqlalchemy.engine import Engine
+
+ tbl = "test_get_schema_create_table"
+ create_sql = sql.get_schema(test_frame3, tbl, con=conn)
+ blank_test_df = test_frame3.iloc[:0]
+
+ create_sql = text(create_sql)
+ if isinstance(conn, Engine):
+ with conn.connect() as newcon:
+ with newcon.begin():
+ newcon.execute(create_sql)
+ else:
+ conn.execute(create_sql)
+ returned_df = sql.read_sql_table(tbl, conn)
+ tm.assert_frame_equal(returned_df, blank_test_df, check_index_type=False)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_dtype(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
+
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import (
+ TEXT,
+ String,
+ )
+ from sqlalchemy.schema import MetaData
+
+ cols = ["A", "B"]
+ data = [(0.8, True), (0.9, None)]
+ df = DataFrame(data, columns=cols)
+ assert df.to_sql(name="dtype_test", con=conn) == 2
+ assert df.to_sql(name="dtype_test2", con=conn, dtype={"B": TEXT}) == 2
+ meta = MetaData()
+ meta.reflect(bind=conn)
+ sqltype = meta.tables["dtype_test2"].columns["B"].type
+ assert isinstance(sqltype, TEXT)
+ msg = "The type of B is not a SQLAlchemy type"
+ with pytest.raises(ValueError, match=msg):
+ df.to_sql(name="error", con=conn, dtype={"B": str})
+
+ # GH9083
+ assert df.to_sql(name="dtype_test3", con=conn, dtype={"B": String(10)}) == 2
+ meta.reflect(bind=conn)
+ sqltype = meta.tables["dtype_test3"].columns["B"].type
+ assert isinstance(sqltype, String)
+ assert sqltype.length == 10
+
+ # single dtype
+ assert df.to_sql(name="single_dtype_test", con=conn, dtype=TEXT) == 2
+ meta.reflect(bind=conn)
+ sqltypea = meta.tables["single_dtype_test"].columns["A"].type
+ sqltypeb = meta.tables["single_dtype_test"].columns["B"].type
+ assert isinstance(sqltypea, TEXT)
+ assert isinstance(sqltypeb, TEXT)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_notna_dtype(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
+
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import (
+ Boolean,
+ DateTime,
+ Float,
+ Integer,
+ )
+ from sqlalchemy.schema import MetaData
+
+ cols = {
+ "Bool": Series([True, None]),
+ "Date": Series([datetime(2012, 5, 1), None]),
+ "Int": Series([1, None], dtype="object"),
+ "Float": Series([1.1, None]),
+ }
+ df = DataFrame(cols)
+
+ tbl = "notna_dtype_test"
+ assert df.to_sql(name=tbl, con=conn) == 2
+ _ = sql.read_sql_table(tbl, conn)
+ meta = MetaData()
+ meta.reflect(bind=conn)
+ my_type = Integer if "mysql" in conn_name else Boolean
+ col_dict = meta.tables[tbl].columns
+ assert isinstance(col_dict["Bool"].type, my_type)
+ assert isinstance(col_dict["Date"].type, DateTime)
+ assert isinstance(col_dict["Int"].type, Integer)
+ assert isinstance(col_dict["Float"].type, Float)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_double_precision(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("sqlite_str has no inspection system")
+
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import (
+ BigInteger,
+ Float,
+ Integer,
+ )
+ from sqlalchemy.schema import MetaData
+
+ V = 1.23456789101112131415
+
+ df = DataFrame(
+ {
+ "f32": Series([V], dtype="float32"),
+ "f64": Series([V], dtype="float64"),
+ "f64_as_f32": Series([V], dtype="float64"),
+ "i32": Series([5], dtype="int32"),
+ "i64": Series([5], dtype="int64"),
}
- df = DataFrame(cols)
-
- tbl = "notna_dtype_test"
- assert df.to_sql(name=tbl, con=self.conn) == 2
- _ = sql.read_sql_table(tbl, self.conn)
- meta = MetaData()
- meta.reflect(bind=self.conn)
- my_type = Integer if self.flavor == "mysql" else Boolean
- col_dict = meta.tables[tbl].columns
- assert isinstance(col_dict["Bool"].type, my_type)
- assert isinstance(col_dict["Date"].type, DateTime)
- assert isinstance(col_dict["Int"].type, Integer)
- assert isinstance(col_dict["Float"].type, Float)
-
- def test_double_precision(self):
- from sqlalchemy import (
- BigInteger,
- Float,
- Integer,
+ )
+
+ assert (
+ df.to_sql(
+ name="test_dtypes",
+ con=conn,
+ index=False,
+ if_exists="replace",
+ dtype={"f64_as_f32": Float(precision=23)},
)
- from sqlalchemy.schema import MetaData
+ == 1
+ )
+ res = sql.read_sql_table("test_dtypes", conn)
- V = 1.23456789101112131415
+ # check precision of float64
+ assert np.round(df["f64"].iloc[0], 14) == np.round(res["f64"].iloc[0], 14)
- df = DataFrame(
- {
- "f32": Series([V], dtype="float32"),
- "f64": Series([V], dtype="float64"),
- "f64_as_f32": Series([V], dtype="float64"),
- "i32": Series([5], dtype="int32"),
- "i64": Series([5], dtype="int64"),
- }
- )
+ # check sql types
+ meta = MetaData()
+ meta.reflect(bind=conn)
+ col_dict = meta.tables["test_dtypes"].columns
+ assert str(col_dict["f32"].type) == str(col_dict["f64_as_f32"].type)
+ assert isinstance(col_dict["f32"].type, Float)
+ assert isinstance(col_dict["f64"].type, Float)
+ assert isinstance(col_dict["i32"].type, Integer)
+ assert isinstance(col_dict["i64"].type, BigInteger)
- assert (
- df.to_sql(
- name="test_dtypes",
- con=self.conn,
- index=False,
- if_exists="replace",
- dtype={"f64_as_f32": Float(precision=23)},
- )
- == 1
- )
- res = sql.read_sql_table("test_dtypes", self.conn)
-
- # check precision of float64
- assert np.round(df["f64"].iloc[0], 14) == np.round(res["f64"].iloc[0], 14)
-
- # check sql types
- meta = MetaData()
- meta.reflect(bind=self.conn)
- col_dict = meta.tables["test_dtypes"].columns
- assert str(col_dict["f32"].type) == str(col_dict["f64_as_f32"].type)
- assert isinstance(col_dict["f32"].type, Float)
- assert isinstance(col_dict["f64"].type, Float)
- assert isinstance(col_dict["i32"].type, Integer)
- assert isinstance(col_dict["i64"].type, BigInteger)
-
- def test_connectable_issue_example(self):
- # This tests the example raised in issue
- # https://github.com/pandas-dev/pandas/issues/10104
- from sqlalchemy.engine import Engine
- def test_select(connection):
- query = "SELECT test_foo_data FROM test_foo_data"
- return sql.read_sql_query(query, con=connection)
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_connectable_issue_example(conn, request):
+ conn = request.getfixturevalue(conn)
- def test_append(connection, data):
- data.to_sql(name="test_foo_data", con=connection, if_exists="append")
+ # This tests the example raised in issue
+ # https://github.com/pandas-dev/pandas/issues/10104
+ from sqlalchemy.engine import Engine
- def test_connectable(conn):
- # https://github.com/sqlalchemy/sqlalchemy/commit/
- # 00b5c10846e800304caa86549ab9da373b42fa5d#r48323973
- foo_data = test_select(conn)
- test_append(conn, foo_data)
+ def test_select(connection):
+ query = "SELECT test_foo_data FROM test_foo_data"
+ return sql.read_sql_query(query, con=connection)
- def main(connectable):
- if isinstance(connectable, Engine):
- with connectable.connect() as conn:
- with conn.begin():
- test_connectable(conn)
- else:
- test_connectable(connectable)
+ def test_append(connection, data):
+ data.to_sql(name="test_foo_data", con=connection, if_exists="append")
- assert (
- DataFrame({"test_foo_data": [0, 1, 2]}).to_sql(
- name="test_foo_data", con=self.conn
- )
- == 3
- )
- main(self.conn)
-
- @pytest.mark.parametrize(
- "input",
- [{"foo": [np.inf]}, {"foo": [-np.inf]}, {"foo": [-np.inf], "infe0": ["bar"]}],
- )
- def test_to_sql_with_negative_npinf(self, input, request):
- # GH 34431
-
- df = DataFrame(input)
-
- if self.flavor == "mysql":
- # GH 36465
- # The input {"foo": [-np.inf], "infe0": ["bar"]} does not raise any error
- # for pymysql version >= 0.10
- # TODO(GH#36465): remove this version check after GH 36465 is fixed
- pymysql = pytest.importorskip("pymysql")
-
- if (
- Version(pymysql.__version__) < Version("1.0.3")
- and "infe0" in df.columns
- ):
- mark = pytest.mark.xfail(reason="GH 36465")
- request.node.add_marker(mark)
-
- msg = "inf cannot be used with MySQL"
- with pytest.raises(ValueError, match=msg):
- df.to_sql(name="foobar", con=self.conn, index=False)
+ def test_connectable(conn):
+ # https://github.com/sqlalchemy/sqlalchemy/commit/
+ # 00b5c10846e800304caa86549ab9da373b42fa5d#r48323973
+ foo_data = test_select(conn)
+ test_append(conn, foo_data)
+
+ def main(connectable):
+ if isinstance(connectable, Engine):
+ with connectable.connect() as conn:
+ with conn.begin():
+ test_connectable(conn)
else:
- assert df.to_sql(name="foobar", con=self.conn, index=False) == 1
- res = sql.read_sql_table("foobar", self.conn)
- tm.assert_equal(df, res)
-
- def test_temporary_table(self):
- from sqlalchemy import (
- Column,
- Integer,
- Unicode,
- select,
- )
- from sqlalchemy.orm import (
- Session,
- declarative_base,
- )
+ test_connectable(connectable)
+
+ assert (
+ DataFrame({"test_foo_data": [0, 1, 2]}).to_sql(name="test_foo_data", con=conn)
+ == 3
+ )
+ main(conn)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+@pytest.mark.parametrize(
+ "input",
+ [{"foo": [np.inf]}, {"foo": [-np.inf]}, {"foo": [-np.inf], "infe0": ["bar"]}],
+)
+def test_to_sql_with_negative_npinf(conn, request, input):
+ # GH 34431
+
+ df = DataFrame(input)
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+
+ if "mysql" in conn_name:
+ # GH 36465
+ # The input {"foo": [-np.inf], "infe0": ["bar"]} does not raise any error
+ # for pymysql version >= 0.10
+ # TODO(GH#36465): remove this version check after GH 36465 is fixed
+ pymysql = pytest.importorskip("pymysql")
- test_data = "Hello, World!"
- expected = DataFrame({"spam": [test_data]})
- Base = declarative_base()
-
- class Temporary(Base):
- __tablename__ = "temp_test"
- __table_args__ = {"prefixes": ["TEMPORARY"]}
- id = Column(Integer, primary_key=True)
- spam = Column(Unicode(30), nullable=False)
-
- with Session(self.conn) as session:
- with session.begin():
- conn = session.connection()
- Temporary.__table__.create(conn)
- session.add(Temporary(spam=test_data))
- session.flush()
- df = sql.read_sql_query(sql=select(Temporary.spam), con=conn)
- tm.assert_frame_equal(df, expected)
-
- # -- SQL Engine tests (in the base class for now)
- def test_invalid_engine(self, test_frame1):
- msg = "engine must be one of 'auto', 'sqlalchemy'"
+ if Version(pymysql.__version__) < Version("1.0.3") and "infe0" in df.columns:
+ mark = pytest.mark.xfail(reason="GH 36465")
+ request.node.add_marker(mark)
+
+ msg = "inf cannot be used with MySQL"
with pytest.raises(ValueError, match=msg):
- self._to_sql_with_sql_engine(test_frame1, "bad_engine")
+ df.to_sql(name="foobar", con=conn, index=False)
+ else:
+ assert df.to_sql(name="foobar", con=conn, index=False) == 1
+ res = sql.read_sql_table("foobar", conn)
+ tm.assert_equal(df, res)
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_temporary_table(conn, request):
+ if conn == "sqlite_str":
+ pytest.skip("test does not work with str connection")
+
+ conn = request.getfixturevalue(conn)
+
+ from sqlalchemy import (
+ Column,
+ Integer,
+ Unicode,
+ select,
+ )
+ from sqlalchemy.orm import (
+ Session,
+ declarative_base,
+ )
+
+ test_data = "Hello, World!"
+ expected = DataFrame({"spam": [test_data]})
+ Base = declarative_base()
+
+ class Temporary(Base):
+ __tablename__ = "temp_test"
+ __table_args__ = {"prefixes": ["TEMPORARY"]}
+ id = Column(Integer, primary_key=True)
+ spam = Column(Unicode(30), nullable=False)
+
+ with Session(conn) as session:
+ with session.begin():
+ conn = session.connection()
+ Temporary.__table__.create(conn)
+ session.add(Temporary(spam=test_data))
+ session.flush()
+ df = sql.read_sql_query(sql=select(Temporary.spam), con=conn)
+ tm.assert_frame_equal(df, expected)
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+def test_invalid_engine(conn, request, test_frame1):
+ if conn == "sqlite_buildin":
+ request.node.add_marker(
+ pytest.mark.xfail(reason="SQLiteDatabase does not raise for bad engine")
+ )
+
+ conn = request.getfixturevalue(conn)
+ msg = "engine must be one of 'auto', 'sqlalchemy'"
+ pandasSQL = pandasSQL_builder(conn)
+ with pytest.raises(ValueError, match=msg):
+ pandasSQL.to_sql(test_frame1, "test_frame1", engine="bad_engine")
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+def test_to_sql_with_sql_engine(conn, request, test_frame1):
+ """`to_sql` with the `engine` param"""
+ # mostly copied from this class's `_to_sql()` method
+ conn = request.getfixturevalue(conn)
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(test_frame1, "test_frame1", engine="auto") == 4
+ assert pandasSQL.has_table("test_frame1")
- def test_options_sqlalchemy(self, test_frame1):
- # use the set option
- with pd.option_context("io.sql.engine", "sqlalchemy"):
- self._to_sql_with_sql_engine(test_frame1)
+ num_entries = len(test_frame1)
+ num_rows = count_rows(conn, "test_frame1")
+ assert num_rows == num_entries
+
+
+@pytest.mark.parametrize("conn", sqlalchemy_connectable)
+def test_options_sqlalchemy(conn, request, test_frame1):
+ # use the set option
+ conn = request.getfixturevalue(conn)
+ with pd.option_context("io.sql.engine", "sqlalchemy"):
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(test_frame1, "test_frame1") == 4
+ assert pandasSQL.has_table("test_frame1")
- def test_options_auto(self, test_frame1):
- # use the set option
- with pd.option_context("io.sql.engine", "auto"):
- self._to_sql_with_sql_engine(test_frame1)
+ num_entries = len(test_frame1)
+ num_rows = count_rows(conn, "test_frame1")
+ assert num_rows == num_entries
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+def test_options_auto(conn, request, test_frame1):
+ # use the set option
+ conn = request.getfixturevalue(conn)
+ with pd.option_context("io.sql.engine", "auto"):
+ pandasSQL = pandasSQL_builder(conn)
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(test_frame1, "test_frame1") == 4
+ assert pandasSQL.has_table("test_frame1")
+
+ num_entries = len(test_frame1)
+ num_rows = count_rows(conn, "test_frame1")
+ assert num_rows == num_entries
- def test_options_get_engine(self):
+
+@pytest.mark.skipif(not SQLALCHEMY_INSTALLED, reason="fails without SQLAlchemy")
+def test_options_get_engine():
+ assert isinstance(get_engine("sqlalchemy"), SQLAlchemyEngine)
+
+ with pd.option_context("io.sql.engine", "sqlalchemy"):
+ assert isinstance(get_engine("auto"), SQLAlchemyEngine)
assert isinstance(get_engine("sqlalchemy"), SQLAlchemyEngine)
- with pd.option_context("io.sql.engine", "sqlalchemy"):
- assert isinstance(get_engine("auto"), SQLAlchemyEngine)
- assert isinstance(get_engine("sqlalchemy"), SQLAlchemyEngine)
+ with pd.option_context("io.sql.engine", "auto"):
+ assert isinstance(get_engine("auto"), SQLAlchemyEngine)
+ assert isinstance(get_engine("sqlalchemy"), SQLAlchemyEngine)
- with pd.option_context("io.sql.engine", "auto"):
- assert isinstance(get_engine("auto"), SQLAlchemyEngine)
- assert isinstance(get_engine("sqlalchemy"), SQLAlchemyEngine)
- def test_get_engine_auto_error_message(self):
- # Expect different error messages from get_engine(engine="auto")
- # if engines aren't installed vs. are installed but bad version
- pass
- # TODO(GH#36893) fill this in when we add more engines
-
- @pytest.mark.parametrize("func", ["read_sql", "read_sql_query"])
- def test_read_sql_dtype_backend(self, string_storage, func, dtype_backend):
- # GH#50048
- table = "test"
- df = self.dtype_backend_data()
- df.to_sql(name=table, con=self.conn, index=False, if_exists="replace")
-
- with pd.option_context("mode.string_storage", string_storage):
- result = getattr(pd, func)(
- f"Select * from {table}", self.conn, dtype_backend=dtype_backend
+def test_get_engine_auto_error_message():
+ # Expect different error messages from get_engine(engine="auto")
+ # if engines aren't installed vs. are installed but bad version
+ pass
+ # TODO(GH#36893) fill this in when we add more engines
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("func", ["read_sql", "read_sql_query"])
+def test_read_sql_dtype_backend(
+ conn,
+ request,
+ string_storage,
+ func,
+ dtype_backend,
+ dtype_backend_data,
+ dtype_backend_expected,
+):
+ # GH#50048
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ table = "test"
+ df = dtype_backend_data
+ df.to_sql(name=table, con=conn, index=False, if_exists="replace")
+
+ with pd.option_context("mode.string_storage", string_storage):
+ result = getattr(pd, func)(
+ f"Select * from {table}", conn, dtype_backend=dtype_backend
+ )
+ expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+ tm.assert_frame_equal(result, expected)
+
+ with pd.option_context("mode.string_storage", string_storage):
+ iterator = getattr(pd, func)(
+ f"Select * from {table}",
+ con=conn,
+ dtype_backend=dtype_backend,
+ chunksize=3,
+ )
+ expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+ for result in iterator:
+ tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("func", ["read_sql", "read_sql_table"])
+def test_read_sql_dtype_backend_table(
+ conn,
+ request,
+ string_storage,
+ func,
+ dtype_backend,
+ dtype_backend_data,
+ dtype_backend_expected,
+):
+ if "sqlite" in conn:
+ request.node.add_marker(
+ pytest.mark.xfail(
+ reason=(
+ "SQLite actually returns proper boolean values via "
+ "read_sql_table, but before pytest refactor was skipped"
+ )
)
- expected = self.dtype_backend_expected(string_storage, dtype_backend)
- tm.assert_frame_equal(result, expected)
+ )
+ # GH#50048
+ conn_name = conn
+ conn = request.getfixturevalue(conn)
+ table = "test"
+ df = dtype_backend_data
+ df.to_sql(name=table, con=conn, index=False, if_exists="replace")
+
+ with pd.option_context("mode.string_storage", string_storage):
+ result = getattr(pd, func)(table, conn, dtype_backend=dtype_backend)
+ expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+ tm.assert_frame_equal(result, expected)
+
+ with pd.option_context("mode.string_storage", string_storage):
+ iterator = getattr(pd, func)(
+ table,
+ conn,
+ dtype_backend=dtype_backend,
+ chunksize=3,
+ )
+ expected = dtype_backend_expected(string_storage, dtype_backend, conn_name)
+ for result in iterator:
+ tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("func", ["read_sql", "read_sql_table", "read_sql_query"])
+def test_read_sql_invalid_dtype_backend_table(conn, request, func, dtype_backend_data):
+ conn = request.getfixturevalue(conn)
+ table = "test"
+ df = dtype_backend_data
+ df.to_sql(name=table, con=conn, index=False, if_exists="replace")
+
+ msg = (
+ "dtype_backend numpy is invalid, only 'numpy_nullable' and "
+ "'pyarrow' are allowed."
+ )
+ with pytest.raises(ValueError, match=msg):
+ getattr(pd, func)(table, conn, dtype_backend="numpy")
- with pd.option_context("mode.string_storage", string_storage):
- iterator = getattr(pd, func)(
- f"Select * from {table}",
- con=self.conn,
- dtype_backend=dtype_backend,
- chunksize=3,
- )
- expected = self.dtype_backend_expected(string_storage, dtype_backend)
- for result in iterator:
- tm.assert_frame_equal(result, expected)
-
- @pytest.mark.parametrize("func", ["read_sql", "read_sql_table"])
- def test_read_sql_dtype_backend_table(self, string_storage, func, dtype_backend):
- # GH#50048
- table = "test"
- df = self.dtype_backend_data()
- df.to_sql(name=table, con=self.conn, index=False, if_exists="replace")
-
- with pd.option_context("mode.string_storage", string_storage):
- result = getattr(pd, func)(table, self.conn, dtype_backend=dtype_backend)
- expected = self.dtype_backend_expected(string_storage, dtype_backend)
- tm.assert_frame_equal(result, expected)
- with pd.option_context("mode.string_storage", string_storage):
- iterator = getattr(pd, func)(
- table,
- self.conn,
- dtype_backend=dtype_backend,
- chunksize=3,
- )
- expected = self.dtype_backend_expected(string_storage, dtype_backend)
- for result in iterator:
- tm.assert_frame_equal(result, expected)
-
- @pytest.mark.parametrize("func", ["read_sql", "read_sql_table", "read_sql_query"])
- def test_read_sql_invalid_dtype_backend_table(self, func):
- table = "test"
- df = self.dtype_backend_data()
- df.to_sql(name=table, con=self.conn, index=False, if_exists="replace")
-
- msg = (
- "dtype_backend numpy is invalid, only 'numpy_nullable' and "
- "'pyarrow' are allowed."
- )
- with pytest.raises(ValueError, match=msg):
- getattr(pd, func)(table, self.conn, dtype_backend="numpy")
+@pytest.fixture
+def dtype_backend_data() -> DataFrame:
+ return DataFrame(
+ {
+ "a": Series([1, np.nan, 3], dtype="Int64"),
+ "b": Series([1, 2, 3], dtype="Int64"),
+ "c": Series([1.5, np.nan, 2.5], dtype="Float64"),
+ "d": Series([1.5, 2.0, 2.5], dtype="Float64"),
+ "e": [True, False, None],
+ "f": [True, False, True],
+ "g": ["a", "b", "c"],
+ "h": ["a", "b", None],
+ }
+ )
- def dtype_backend_data(self) -> DataFrame:
- return DataFrame(
- {
- "a": Series([1, np.nan, 3], dtype="Int64"),
- "b": Series([1, 2, 3], dtype="Int64"),
- "c": Series([1.5, np.nan, 2.5], dtype="Float64"),
- "d": Series([1.5, 2.0, 2.5], dtype="Float64"),
- "e": [True, False, None],
- "f": [True, False, True],
- "g": ["a", "b", "c"],
- "h": ["a", "b", None],
- }
- )
- def dtype_backend_expected(self, storage, dtype_backend) -> 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":
@@ -3041,557 +3195,396 @@ def dtype_backend_expected(self, storage, dtype_backend) -> DataFrame:
for col in df.columns
}
)
- return df
-
- def test_chunksize_empty_dtypes(self):
- # GH#50245
- dtypes = {"a": "int64", "b": "object"}
- df = DataFrame(columns=["a", "b"]).astype(dtypes)
- expected = df.copy()
- df.to_sql(name="test", con=self.conn, index=False, if_exists="replace")
-
- for result in read_sql_query(
- "SELECT * FROM test",
- self.conn,
- dtype=dtypes,
- chunksize=1,
- ):
- tm.assert_frame_equal(result, expected)
-
- @pytest.mark.parametrize("dtype_backend", [lib.no_default, "numpy_nullable"])
- @pytest.mark.parametrize("func", ["read_sql", "read_sql_query"])
- def test_read_sql_dtype(self, func, dtype_backend):
- # GH#50797
- table = "test"
- df = DataFrame({"a": [1, 2, 3], "b": 5})
- df.to_sql(name=table, con=self.conn, index=False, if_exists="replace")
-
- result = getattr(pd, func)(
- f"Select * from {table}",
- self.conn,
- dtype={"a": np.float64},
- dtype_backend=dtype_backend,
- )
- expected = DataFrame(
- {
- "a": Series([1, 2, 3], dtype=np.float64),
- "b": Series(
- [5, 5, 5],
- dtype="int64" if not dtype_backend == "numpy_nullable" else "Int64",
- ),
- }
- )
- tm.assert_frame_equal(result, expected)
-
-class TestSQLiteAlchemy(_TestSQLAlchemy):
- """
- Test the sqlalchemy backend against an in-memory sqlite database.
-
- """
-
- flavor = "sqlite"
-
- @classmethod
- def setup_engine(cls):
- cls.engine = sqlalchemy.create_engine("sqlite:///:memory:")
-
- @classmethod
- def setup_driver(cls):
- # sqlite3 is built-in
- cls.driver = None
+ if "mysql" in conn_name or "sqlite" in conn_name:
+ if dtype_backend == "numpy_nullable":
+ df = df.astype({"e": "Int64", "f": "Int64"})
+ else:
+ df = df.astype({"e": "int64[pyarrow]", "f": "int64[pyarrow]"})
- def test_keyword_deprecation(self):
- # GH 54397
- msg = (
- "Starting with pandas version 3.0 all arguments of to_sql except for the "
- "arguments 'name' and 'con' will be keyword-only."
- )
- df = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 1, "B": 2, "C": 3}])
- df.to_sql("example", self.conn)
+ return df
- with tm.assert_produces_warning(FutureWarning, match=msg):
- df.to_sql("example", self.conn, None, if_exists="replace")
+ return func
- def test_default_type_conversion(self):
- df = sql.read_sql_table("types", self.conn)
- assert issubclass(df.FloatCol.dtype.type, np.floating)
- assert issubclass(df.IntCol.dtype.type, np.integer)
+@pytest.mark.parametrize("conn", all_connectable)
+def test_chunksize_empty_dtypes(conn, request):
+ # GH#50245
+ conn = request.getfixturevalue(conn)
+ dtypes = {"a": "int64", "b": "object"}
+ df = DataFrame(columns=["a", "b"]).astype(dtypes)
+ expected = df.copy()
+ df.to_sql(name="test", con=conn, index=False, if_exists="replace")
- # sqlite has no boolean type, so integer type is returned
- assert issubclass(df.BoolCol.dtype.type, np.integer)
+ for result in read_sql_query(
+ "SELECT * FROM test",
+ conn,
+ dtype=dtypes,
+ chunksize=1,
+ ):
+ tm.assert_frame_equal(result, expected)
- # Int column with NA values stays as float
- assert issubclass(df.IntColWithNull.dtype.type, np.floating)
- # Non-native Bool column with NA values stays as float
- assert issubclass(df.BoolColWithNull.dtype.type, np.floating)
+@pytest.mark.parametrize("conn", all_connectable)
+@pytest.mark.parametrize("dtype_backend", [lib.no_default, "numpy_nullable"])
+@pytest.mark.parametrize("func", ["read_sql", "read_sql_query"])
+def test_read_sql_dtype(conn, request, func, dtype_backend):
+ # GH#50797
+ conn = request.getfixturevalue(conn)
+ table = "test"
+ df = DataFrame({"a": [1, 2, 3], "b": 5})
+ df.to_sql(name=table, con=conn, index=False, if_exists="replace")
- def test_default_date_load(self):
- df = sql.read_sql_table("types", self.conn)
+ result = getattr(pd, func)(
+ f"Select * from {table}",
+ conn,
+ dtype={"a": np.float64},
+ dtype_backend=dtype_backend,
+ )
+ expected = DataFrame(
+ {
+ "a": Series([1, 2, 3], dtype=np.float64),
+ "b": Series(
+ [5, 5, 5],
+ dtype="int64" if not dtype_backend == "numpy_nullable" else "Int64",
+ ),
+ }
+ )
+ tm.assert_frame_equal(result, expected)
- # IMPORTANT - sqlite has no native date type, so shouldn't parse, but
- assert not issubclass(df.DateCol.dtype.type, np.datetime64)
- def test_bigint_warning(self):
- # 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=self.conn, index=False) == 2
+def test_keyword_deprecation(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_engine
+ # GH 54397
+ msg = (
+ "Starting with pandas version 3.0 all arguments of to_sql except for the "
+ "arguments 'name' and 'con' will be keyword-only."
+ )
+ df = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 1, "B": 2, "C": 3}])
+ df.to_sql("example", conn)
- with tm.assert_produces_warning(None):
- sql.read_sql_table("test_bigintwarning", self.conn)
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ df.to_sql("example", conn, None, if_exists="replace")
- def test_valueerror_exception(self):
- df = DataFrame({"col1": [1, 2], "col2": [3, 4]})
- with pytest.raises(ValueError, match="Empty table name specified"):
- df.to_sql(name="", con=self.conn, if_exists="replace", index=False)
- def test_row_object_is_named_tuple(self):
- # GH 40682
- # Test for the is_named_tuple() function
- # Placed here due to its usage of sqlalchemy
+def test_bigint_warning(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_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
- from sqlalchemy import (
- Column,
- Integer,
- String,
- )
- from sqlalchemy.orm import (
- declarative_base,
- sessionmaker,
- )
+ with tm.assert_produces_warning(None):
+ sql.read_sql_table("test_bigintwarning", conn)
- BaseModel = declarative_base()
-
- class Test(BaseModel):
- __tablename__ = "test_frame"
- id = Column(Integer, primary_key=True)
- string_column = Column(String(50))
-
- with self.conn.begin():
- BaseModel.metadata.create_all(self.conn)
- Session = sessionmaker(bind=self.conn)
- with Session() as session:
- df = DataFrame({"id": [0, 1], "string_column": ["hello", "world"]})
- assert (
- df.to_sql(
- name="test_frame", con=self.conn, index=False, if_exists="replace"
- )
- == 2
- )
- session.commit()
- test_query = session.query(Test.id, Test.string_column)
- df = DataFrame(test_query)
- assert list(df.columns) == ["id", "string_column"]
+def test_valueerror_exception(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_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 dtype_backend_expected(self, storage, dtype_backend) -> DataFrame:
- df = super().dtype_backend_expected(storage, dtype_backend)
- if dtype_backend == "numpy_nullable":
- df = df.astype({"e": "Int64", "f": "Int64"})
- else:
- df = df.astype({"e": "int64[pyarrow]", "f": "int64[pyarrow]"})
- return df
+def test_row_object_is_named_tuple(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_engine
+ # GH 40682
+ # Test for the is_named_tuple() function
+ # Placed here due to its usage of sqlalchemy
- @pytest.mark.parametrize("func", ["read_sql", "read_sql_table"])
- def test_read_sql_dtype_backend_table(self, string_storage, func):
- # GH#50048 Not supported for sqlite
- pass
+ from sqlalchemy import (
+ Column,
+ Integer,
+ String,
+ )
+ from sqlalchemy.orm import (
+ declarative_base,
+ sessionmaker,
+ )
- def test_read_sql_string_inference(self):
- # GH#54430
- pytest.importorskip("pyarrow")
- table = "test"
- df = DataFrame({"a": ["x", "y"]})
- df.to_sql(table, con=self.conn, index=False, if_exists="replace")
+ BaseModel = declarative_base()
- with pd.option_context("future.infer_string", True):
- result = read_sql_table(table, self.conn)
+ class Test(BaseModel):
+ __tablename__ = "test_frame"
+ id = Column(Integer, primary_key=True)
+ string_column = Column(String(50))
- dtype = "string[pyarrow_numpy]"
- expected = DataFrame(
- {"a": ["x", "y"]}, dtype=dtype, columns=Index(["a"], dtype=dtype)
+ with conn.begin():
+ BaseModel.metadata.create_all(conn)
+ Session = sessionmaker(bind=conn)
+ with Session() as session:
+ df = DataFrame({"id": [0, 1], "string_column": ["hello", "world"]})
+ assert (
+ df.to_sql(name="test_frame", con=conn, index=False, if_exists="replace")
+ == 2
)
+ session.commit()
+ test_query = session.query(Test.id, Test.string_column)
+ df = DataFrame(test_query)
- tm.assert_frame_equal(result, expected)
-
- def test_roundtripping_datetimes(self):
- # GH#54877
- df = DataFrame({"t": [datetime(2020, 12, 31, 12)]}, dtype="datetime64[ns]")
- df.to_sql("test", self.conn, if_exists="replace", index=False)
- result = pd.read_sql("select * from test", self.conn).iloc[0, 0]
- assert result == "2020-12-31 12:00:00.000000"
-
-
-@pytest.mark.db
-class TestMySQLAlchemy(_TestSQLAlchemy):
- """
- Test the sqlalchemy backend against an MySQL database.
+ assert list(df.columns) == ["id", "string_column"]
- """
- flavor = "mysql"
- port = 3306
+def test_read_sql_string_inference(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_engine
+ # GH#54430
+ pytest.importorskip("pyarrow")
+ table = "test"
+ df = DataFrame({"a": ["x", "y"]})
+ df.to_sql(table, con=conn, index=False, if_exists="replace")
- @classmethod
- def setup_engine(cls):
- cls.engine = sqlalchemy.create_engine(
- f"mysql+{cls.driver}://root@localhost:{cls.port}/pandas",
- connect_args=cls.connect_args,
- )
+ with pd.option_context("future.infer_string", True):
+ result = read_sql_table(table, conn)
- @classmethod
- def setup_driver(cls):
- pymysql = pytest.importorskip("pymysql")
- cls.driver = "pymysql"
- cls.connect_args = {"client_flag": pymysql.constants.CLIENT.MULTI_STATEMENTS}
+ dtype = "string[pyarrow_numpy]"
+ expected = DataFrame(
+ {"a": ["x", "y"]}, dtype=dtype, columns=Index(["a"], dtype=dtype)
+ )
- def test_default_type_conversion(self):
- pass
+ tm.assert_frame_equal(result, expected)
- def dtype_backend_expected(self, storage, dtype_backend) -> DataFrame:
- df = super().dtype_backend_expected(storage, dtype_backend)
- if dtype_backend == "numpy_nullable":
- df = df.astype({"e": "Int64", "f": "Int64"})
- else:
- df = df.astype({"e": "int64[pyarrow]", "f": "int64[pyarrow]"})
- return df
+def test_roundtripping_datetimes(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_engine
+ # GH#54877
+ df = DataFrame({"t": [datetime(2020, 12, 31, 12)]}, dtype="datetime64[ns]")
+ df.to_sql("test", conn, if_exists="replace", index=False)
+ result = pd.read_sql("select * from test", conn).iloc[0, 0]
+ assert result == "2020-12-31 12:00:00.000000"
@pytest.mark.db
-class TestPostgreSQLAlchemy(_TestSQLAlchemy):
- """
- Test the sqlalchemy backend against an PostgreSQL database.
-
- """
-
- flavor = "postgresql"
- port = 5432
-
- @classmethod
- def setup_engine(cls):
- cls.engine = sqlalchemy.create_engine(
- f"postgresql+{cls.driver}://postgres:postgres@localhost:{cls.port}/pandas"
- )
-
- @classmethod
- def setup_driver(cls):
- pytest.importorskip("psycopg2")
- cls.driver = "psycopg2"
-
- def test_schema_support(self):
- from sqlalchemy.engine import Engine
-
- # only test this for postgresql (schema's not supported in
- # mysql/sqlite)
- df = DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]})
-
- # create a schema
- with self.conn.begin():
- self.conn.exec_driver_sql("DROP SCHEMA IF EXISTS other CASCADE;")
- self.conn.exec_driver_sql("CREATE SCHEMA other;")
-
- # write dataframe to different schema's
- assert df.to_sql(name="test_schema_public", con=self.conn, index=False) == 2
- assert (
- df.to_sql(
- name="test_schema_public_explicit",
- con=self.conn,
- index=False,
- schema="public",
- )
- == 2
- )
- assert (
- df.to_sql(
- name="test_schema_other", con=self.conn, index=False, schema="other"
- )
- == 2
+def test_psycopg2_schema_support(postgresql_psycopg2_engine):
+ conn = postgresql_psycopg2_engine
+
+ # only test this for postgresql (schema's not supported in
+ # mysql/sqlite)
+ df = DataFrame({"col1": [1, 2], "col2": [0.1, 0.2], "col3": ["a", "n"]})
+
+ # create a schema
+ with conn.connect() as con:
+ with con.begin():
+ con.exec_driver_sql("DROP SCHEMA IF EXISTS other CASCADE;")
+ con.exec_driver_sql("CREATE SCHEMA other;")
+
+ # write dataframe to different schema's
+ assert df.to_sql(name="test_schema_public", con=conn, index=False) == 2
+ assert (
+ df.to_sql(
+ name="test_schema_public_explicit",
+ con=conn,
+ index=False,
+ schema="public",
)
+ == 2
+ )
+ assert (
+ df.to_sql(name="test_schema_other", con=conn, index=False, schema="other") == 2
+ )
- # read dataframes back in
- res1 = sql.read_sql_table("test_schema_public", self.conn)
- tm.assert_frame_equal(df, res1)
- res2 = sql.read_sql_table("test_schema_public_explicit", self.conn)
- tm.assert_frame_equal(df, res2)
- res3 = sql.read_sql_table(
- "test_schema_public_explicit", self.conn, schema="public"
- )
- tm.assert_frame_equal(df, res3)
- res4 = sql.read_sql_table("test_schema_other", self.conn, schema="other")
- tm.assert_frame_equal(df, res4)
- msg = "Table test_schema_other not found"
- with pytest.raises(ValueError, match=msg):
- sql.read_sql_table("test_schema_other", self.conn, schema="public")
+ # read dataframes back in
+ res1 = sql.read_sql_table("test_schema_public", conn)
+ tm.assert_frame_equal(df, res1)
+ res2 = sql.read_sql_table("test_schema_public_explicit", conn)
+ tm.assert_frame_equal(df, res2)
+ res3 = sql.read_sql_table("test_schema_public_explicit", conn, schema="public")
+ tm.assert_frame_equal(df, res3)
+ res4 = sql.read_sql_table("test_schema_other", conn, schema="other")
+ tm.assert_frame_equal(df, res4)
+ msg = "Table test_schema_other not found"
+ with pytest.raises(ValueError, match=msg):
+ sql.read_sql_table("test_schema_other", conn, schema="public")
- # different if_exists options
+ # different if_exists options
- # create a schema
- with self.conn.begin():
- self.conn.exec_driver_sql("DROP SCHEMA IF EXISTS other CASCADE;")
- self.conn.exec_driver_sql("CREATE SCHEMA other;")
+ # create a schema
+ with conn.connect() as con:
+ with con.begin():
+ con.exec_driver_sql("DROP SCHEMA IF EXISTS other CASCADE;")
+ con.exec_driver_sql("CREATE SCHEMA other;")
- # write dataframe with different if_exists options
- assert (
- df.to_sql(
- name="test_schema_other", con=self.conn, schema="other", index=False
- )
- == 2
- )
+ # write dataframe with different if_exists options
+ assert (
+ df.to_sql(name="test_schema_other", con=conn, schema="other", index=False) == 2
+ )
+ df.to_sql(
+ name="test_schema_other",
+ con=conn,
+ schema="other",
+ index=False,
+ if_exists="replace",
+ )
+ assert (
df.to_sql(
name="test_schema_other",
- con=self.conn,
+ con=conn,
schema="other",
index=False,
- if_exists="replace",
- )
- assert (
- df.to_sql(
- name="test_schema_other",
- con=self.conn,
- schema="other",
- index=False,
- if_exists="append",
- )
- == 2
+ if_exists="append",
)
- res = sql.read_sql_table("test_schema_other", self.conn, schema="other")
- tm.assert_frame_equal(concat([df, df], ignore_index=True), res)
-
- # specifying schema in user-provided meta
-
- # The schema won't be applied on another Connection
- # because of transactional schemas
- if isinstance(self.conn, Engine):
- engine2 = self.connect()
- pdsql = sql.SQLDatabase(engine2, schema="other")
- assert pdsql.to_sql(df, "test_schema_other2", index=False) == 2
- assert (
- pdsql.to_sql(df, "test_schema_other2", index=False, if_exists="replace")
- == 2
- )
- assert (
- pdsql.to_sql(df, "test_schema_other2", index=False, if_exists="append")
- == 2
- )
- res1 = sql.read_sql_table("test_schema_other2", self.conn, schema="other")
- res2 = pdsql.read_table("test_schema_other2")
- tm.assert_frame_equal(res1, res2)
+ == 2
+ )
+ res = sql.read_sql_table("test_schema_other", conn, schema="other")
+ tm.assert_frame_equal(concat([df, df], ignore_index=True), res)
- def test_self_join_date_columns(self):
- # GH 44421
- from sqlalchemy.engine import Engine
- from sqlalchemy.sql import text
- create_table = text(
- """
- CREATE TABLE person
- (
- id serial constraint person_pkey primary key,
- created_dt timestamp with time zone
- );
+@pytest.mark.db
+def test_self_join_date_columns(postgresql_psycopg2_engine):
+ # GH 44421
+ conn = postgresql_psycopg2_engine
+ from sqlalchemy.sql import text
- INSERT INTO person
- VALUES (1, '2021-01-01T00:00:00Z');
+ create_table = text(
"""
- )
- if isinstance(self.conn, Engine):
- with self.conn.connect() as con:
- with con.begin():
- con.execute(create_table)
- else:
- with self.conn.begin():
- self.conn.execute(create_table)
+ CREATE TABLE person
+ (
+ id serial constraint person_pkey primary key,
+ created_dt timestamp with time zone
+ );
- sql_query = (
- 'SELECT * FROM "person" AS p1 INNER JOIN "person" AS p2 ON p1.id = p2.id;'
- )
- result = pd.read_sql(sql_query, self.conn)
- expected = DataFrame(
- [[1, Timestamp("2021", tz="UTC")] * 2], columns=["id", "created_dt"] * 2
- )
- tm.assert_frame_equal(result, expected)
+ INSERT INTO person
+ VALUES (1, '2021-01-01T00:00:00Z');
+ """
+ )
+ with conn.connect() as con:
+ with con.begin():
+ con.execute(create_table)
- # Cleanup
- with sql.SQLDatabase(self.conn, need_transaction=True) as pandasSQL:
- pandasSQL.drop_table("person")
+ sql_query = (
+ 'SELECT * FROM "person" AS p1 INNER JOIN "person" AS p2 ON p1.id = p2.id;'
+ )
+ result = pd.read_sql(sql_query, conn)
+ expected = DataFrame(
+ [[1, Timestamp("2021", tz="UTC")] * 2], columns=["id", "created_dt"] * 2
+ )
+ tm.assert_frame_equal(result, expected)
+ # Cleanup
+ with sql.SQLDatabase(conn, need_transaction=True) as pandasSQL:
+ pandasSQL.drop_table("person")
-# -----------------------------------------------------------------------------
-# -- Test Sqlite / MySQL fallback
+def test_create_and_drop_table(sqlite_sqlalchemy_memory_engine):
+ conn = sqlite_sqlalchemy_memory_engine
+ temp_frame = DataFrame({"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]})
+ pandasSQL = sql.SQLDatabase(conn)
-class TestSQLiteFallback(SQLiteMixIn, PandasSQLTest):
- """
- Test the fallback mode against an in-memory sqlite database.
+ with pandasSQL.run_transaction():
+ assert pandasSQL.to_sql(temp_frame, "drop_test_frame") == 4
- """
+ assert pandasSQL.has_table("drop_test_frame")
- flavor = "sqlite"
+ with pandasSQL.run_transaction():
+ pandasSQL.drop_table("drop_test_frame")
- @pytest.fixture(autouse=True)
- def setup_method(self, iris_path, types_data):
- self.conn = self.connect()
- self.load_iris_data(iris_path)
- self.load_types_data(types_data)
- self.pandasSQL = sql.SQLiteDatabase(self.conn)
+ assert not pandasSQL.has_table("drop_test_frame")
- def test_read_sql_parameter(self, sql_strings):
- self._read_sql_iris_parameter(sql_strings)
- def test_read_sql_named_parameter(self, sql_strings):
- self._read_sql_iris_named_parameter(sql_strings)
+def test_sqlite_datetime_date(sqlite_buildin):
+ conn = sqlite_buildin
+ df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
+ assert df.to_sql(name="test_date", con=conn, index=False) == 2
+ res = read_sql_query("SELECT * FROM test_date", conn)
+ # comes back as strings
+ tm.assert_frame_equal(res, df.astype(str))
- def test_to_sql_empty(self, test_frame1):
- self._to_sql_empty(test_frame1)
- def test_create_and_drop_table(self):
- temp_frame = DataFrame(
- {"one": [1.0, 2.0, 3.0, 4.0], "two": [4.0, 3.0, 2.0, 1.0]}
- )
+@pytest.mark.parametrize("tz_aware", [False, True])
+def test_sqlite_datetime_time(tz_aware, sqlite_buildin):
+ conn = sqlite_buildin
+ # test support for datetime.time, GH #8341
+ if not tz_aware:
+ tz_times = [time(9, 0, 0), time(9, 1, 30)]
+ else:
+ tz_dt = date_range("2013-01-01 09:00:00", periods=2, tz="US/Pacific")
+ tz_times = Series(tz_dt.to_pydatetime()).map(lambda dt: dt.timetz())
- assert self.pandasSQL.to_sql(temp_frame, "drop_test_frame") == 4
+ df = DataFrame(tz_times, columns=["a"])
- assert self.pandasSQL.has_table("drop_test_frame")
+ assert df.to_sql(name="test_time", con=conn, index=False) == 2
+ res = read_sql_query("SELECT * FROM test_time", conn)
+ # comes back as strings
+ expected = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
+ tm.assert_frame_equal(res, expected)
- self.pandasSQL.drop_table("drop_test_frame")
- assert not self.pandasSQL.has_table("drop_test_frame")
+def get_sqlite_column_type(conn, table, column):
+ recs = conn.execute(f"PRAGMA table_info({table})")
+ for cid, name, ctype, not_null, default, pk in recs:
+ if name == column:
+ return ctype
+ raise ValueError(f"Table {table}, column {column} not found")
- def test_roundtrip(self, test_frame1):
- self._roundtrip(test_frame1)
- def test_execute_sql(self):
- self._execute_sql()
+def test_sqlite_test_dtype(sqlite_buildin):
+ conn = sqlite_buildin
+ cols = ["A", "B"]
+ data = [(0.8, True), (0.9, None)]
+ df = DataFrame(data, columns=cols)
+ assert df.to_sql(name="dtype_test", con=conn) == 2
+ assert df.to_sql(name="dtype_test2", con=conn, dtype={"B": "STRING"}) == 2
- def test_datetime_date(self):
- # test support for datetime.date
- df = DataFrame([date(2014, 1, 1), date(2014, 1, 2)], columns=["a"])
- assert df.to_sql(name="test_date", con=self.conn, index=False) == 2
- res = read_sql_query("SELECT * FROM test_date", self.conn)
- if self.flavor == "sqlite":
- # comes back as strings
- tm.assert_frame_equal(res, df.astype(str))
- elif self.flavor == "mysql":
- tm.assert_frame_equal(res, df)
+ # sqlite stores Boolean values as INTEGER
+ assert get_sqlite_column_type(conn, "dtype_test", "B") == "INTEGER"
- @pytest.mark.parametrize("tz_aware", [False, True])
- def test_datetime_time(self, tz_aware):
- # test support for datetime.time, GH #8341
- if not tz_aware:
- tz_times = [time(9, 0, 0), time(9, 1, 30)]
- else:
- tz_dt = date_range("2013-01-01 09:00:00", periods=2, tz="US/Pacific")
- tz_times = Series(tz_dt.to_pydatetime()).map(lambda dt: dt.timetz())
+ assert get_sqlite_column_type(conn, "dtype_test2", "B") == "STRING"
+ msg = r"B \(<class 'bool'>\) not a string"
+ with pytest.raises(ValueError, match=msg):
+ df.to_sql(name="error", con=conn, dtype={"B": bool})
- df = DataFrame(tz_times, columns=["a"])
+ # single dtype
+ assert df.to_sql(name="single_dtype_test", con=conn, dtype="STRING") == 2
+ assert get_sqlite_column_type(conn, "single_dtype_test", "A") == "STRING"
+ assert get_sqlite_column_type(conn, "single_dtype_test", "B") == "STRING"
- assert df.to_sql(name="test_time", con=self.conn, index=False) == 2
- res = read_sql_query("SELECT * FROM test_time", self.conn)
- if self.flavor == "sqlite":
- # comes back as strings
- expected = df.map(lambda _: _.strftime("%H:%M:%S.%f"))
- tm.assert_frame_equal(res, expected)
- def _get_index_columns(self, tbl_name):
- ixs = sql.read_sql_query(
- "SELECT * FROM sqlite_master WHERE type = 'index' "
- f"AND tbl_name = '{tbl_name}'",
- self.conn,
- )
- ix_cols = []
- for ix_name in ixs.name:
- ix_info = sql.read_sql_query(f"PRAGMA index_info({ix_name})", self.conn)
- ix_cols.append(ix_info.name.tolist())
- return ix_cols
-
- def test_to_sql_save_index(self):
- self._to_sql_save_index()
-
- def test_transactions(self):
- self._transaction_test()
-
- def _get_sqlite_column_type(self, table, column):
- recs = self.conn.execute(f"PRAGMA table_info({table})")
- for cid, name, ctype, not_null, default, pk in recs:
- if name == column:
- return ctype
- raise ValueError(f"Table {table}, column {column} not found")
-
- def test_dtype(self):
- if self.flavor == "mysql":
- pytest.skip("Not applicable to MySQL legacy")
- cols = ["A", "B"]
- data = [(0.8, True), (0.9, None)]
- df = DataFrame(data, columns=cols)
- assert df.to_sql(name="dtype_test", con=self.conn) == 2
- assert df.to_sql(name="dtype_test2", con=self.conn, dtype={"B": "STRING"}) == 2
-
- # sqlite stores Boolean values as INTEGER
- assert self._get_sqlite_column_type("dtype_test", "B") == "INTEGER"
-
- assert self._get_sqlite_column_type("dtype_test2", "B") == "STRING"
- msg = r"B \(<class 'bool'>\) not a string"
- with pytest.raises(ValueError, match=msg):
- df.to_sql(name="error", con=self.conn, dtype={"B": bool})
-
- # single dtype
- assert df.to_sql(name="single_dtype_test", con=self.conn, dtype="STRING") == 2
- assert self._get_sqlite_column_type("single_dtype_test", "A") == "STRING"
- assert self._get_sqlite_column_type("single_dtype_test", "B") == "STRING"
-
- def test_notna_dtype(self):
- if self.flavor == "mysql":
- pytest.skip("Not applicable to MySQL legacy")
-
- cols = {
- "Bool": Series([True, None]),
- "Date": Series([datetime(2012, 5, 1), None]),
- "Int": Series([1, None], dtype="object"),
- "Float": Series([1.1, None]),
- }
- df = DataFrame(cols)
+def test_sqlite_notna_dtype(sqlite_buildin):
+ conn = sqlite_buildin
+ cols = {
+ "Bool": Series([True, None]),
+ "Date": Series([datetime(2012, 5, 1), None]),
+ "Int": Series([1, None], dtype="object"),
+ "Float": Series([1.1, None]),
+ }
+ df = DataFrame(cols)
- tbl = "notna_dtype_test"
- assert df.to_sql(name=tbl, con=self.conn) == 2
+ tbl = "notna_dtype_test"
+ assert df.to_sql(name=tbl, con=conn) == 2
- assert self._get_sqlite_column_type(tbl, "Bool") == "INTEGER"
- assert self._get_sqlite_column_type(tbl, "Date") == "TIMESTAMP"
- assert self._get_sqlite_column_type(tbl, "Int") == "INTEGER"
- assert self._get_sqlite_column_type(tbl, "Float") == "REAL"
+ assert get_sqlite_column_type(conn, tbl, "Bool") == "INTEGER"
+ assert get_sqlite_column_type(conn, tbl, "Date") == "TIMESTAMP"
+ assert get_sqlite_column_type(conn, tbl, "Int") == "INTEGER"
+ assert get_sqlite_column_type(conn, tbl, "Float") == "REAL"
- def test_illegal_names(self):
- # For sqlite, these should work fine
- df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
- msg = "Empty table or column name specified"
- with pytest.raises(ValueError, match=msg):
- df.to_sql(name="", con=self.conn)
-
- for ndx, weird_name in enumerate(
- [
- "test_weird_name]",
- "test_weird_name[",
- "test_weird_name`",
- 'test_weird_name"',
- "test_weird_name'",
- "_b.test_weird_name_01-30",
- '"_b.test_weird_name_01-30"',
- "99beginswithnumber",
- "12345",
- "\xe9",
- ]
- ):
- assert df.to_sql(name=weird_name, con=self.conn) == 2
- sql.table_exists(weird_name, self.conn)
+def test_sqlite_illegal_names(sqlite_buildin):
+ # For sqlite, these should work fine
+ conn = sqlite_buildin
+ df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])
+
+ msg = "Empty table or column name specified"
+ with pytest.raises(ValueError, match=msg):
+ df.to_sql(name="", con=conn)
+
+ for ndx, weird_name in enumerate(
+ [
+ "test_weird_name]",
+ "test_weird_name[",
+ "test_weird_name`",
+ 'test_weird_name"',
+ "test_weird_name'",
+ "_b.test_weird_name_01-30",
+ '"_b.test_weird_name_01-30"',
+ "99beginswithnumber",
+ "12345",
+ "\xe9",
+ ]
+ ):
+ assert df.to_sql(name=weird_name, con=conn) == 2
+ sql.table_exists(weird_name, conn)
- df2 = DataFrame([[1, 2], [3, 4]], columns=["a", weird_name])
- c_tbl = f"test_weird_col_name{ndx:d}"
- assert df2.to_sql(name=c_tbl, con=self.conn) == 2
- sql.table_exists(c_tbl, self.conn)
+ df2 = DataFrame([[1, 2], [3, 4]], columns=["a", weird_name])
+ c_tbl = f"test_weird_col_name{ndx:d}"
+ assert df2.to_sql(name=c_tbl, con=conn) == 2
+ sql.table_exists(c_tbl, conn)
# -----------------------------------------------------------------------------
@@ -3630,227 +3623,222 @@ def tquery(query, con=None):
return None if res is None else list(res)
-class TestXSQLite:
- def drop_table(self, table_name, conn):
- cur = conn.cursor()
- cur.execute(f"DROP TABLE IF EXISTS {sql._get_valid_sqlite_name(table_name)}")
- conn.commit()
+def test_xsqlite_basic(sqlite_buildin):
+ frame = tm.makeTimeDataFrame()
+ assert sql.to_sql(frame, name="test_table", con=sqlite_buildin, index=False) == 30
+ result = sql.read_sql("select * from test_table", sqlite_buildin)
- def test_basic(self, sqlite_buildin):
- frame = tm.makeTimeDataFrame()
- assert (
- sql.to_sql(frame, name="test_table", con=sqlite_buildin, index=False) == 30
- )
- result = sql.read_sql("select * from test_table", sqlite_buildin)
+ # HACK! Change this once indexes are handled properly.
+ result.index = frame.index
- # HACK! Change this once indexes are handled properly.
- result.index = frame.index
+ expected = frame
+ tm.assert_frame_equal(result, frame)
- expected = frame
- tm.assert_frame_equal(result, frame)
+ frame["txt"] = ["a"] * len(frame)
+ frame2 = frame.copy()
+ new_idx = Index(np.arange(len(frame2)), dtype=np.int64) + 10
+ frame2["Idx"] = new_idx.copy()
+ assert sql.to_sql(frame2, name="test_table2", con=sqlite_buildin, index=False) == 30
+ result = sql.read_sql("select * from test_table2", sqlite_buildin, index_col="Idx")
+ expected = frame.copy()
+ expected.index = new_idx
+ expected.index.name = "Idx"
+ tm.assert_frame_equal(expected, result)
- frame["txt"] = ["a"] * len(frame)
- frame2 = frame.copy()
- new_idx = Index(np.arange(len(frame2)), dtype=np.int64) + 10
- frame2["Idx"] = new_idx.copy()
- assert (
- sql.to_sql(frame2, name="test_table2", con=sqlite_buildin, index=False)
- == 30
- )
- result = sql.read_sql(
- "select * from test_table2", sqlite_buildin, index_col="Idx"
- )
- expected = frame.copy()
- expected.index = new_idx
- expected.index.name = "Idx"
- tm.assert_frame_equal(expected, result)
-
- def test_write_row_by_row(self, sqlite_buildin):
- frame = tm.makeTimeDataFrame()
- frame.iloc[0, 0] = np.nan
- create_sql = sql.get_schema(frame, "test")
- cur = sqlite_buildin.cursor()
- cur.execute(create_sql)
- ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
- for _, row in frame.iterrows():
- fmt_sql = format_query(ins, *row)
- tquery(fmt_sql, con=sqlite_buildin)
+def test_xsqlite_write_row_by_row(sqlite_buildin):
+ frame = tm.makeTimeDataFrame()
+ frame.iloc[0, 0] = np.nan
+ create_sql = sql.get_schema(frame, "test")
+ cur = sqlite_buildin.cursor()
+ cur.execute(create_sql)
- sqlite_buildin.commit()
+ ins = "INSERT INTO test VALUES (%s, %s, %s, %s)"
+ for _, row in frame.iterrows():
+ fmt_sql = format_query(ins, *row)
+ tquery(fmt_sql, con=sqlite_buildin)
- result = sql.read_sql("select * from test", con=sqlite_buildin)
- result.index = frame.index
- tm.assert_frame_equal(result, frame, rtol=1e-3)
+ sqlite_buildin.commit()
- def test_execute(self, sqlite_buildin):
- frame = tm.makeTimeDataFrame()
- create_sql = sql.get_schema(frame, "test")
- cur = sqlite_buildin.cursor()
- cur.execute(create_sql)
- ins = "INSERT INTO test VALUES (?, ?, ?, ?)"
-
- row = frame.iloc[0]
- with sql.pandasSQL_builder(sqlite_buildin) as pandas_sql:
- pandas_sql.execute(ins, tuple(row))
- sqlite_buildin.commit()
-
- result = sql.read_sql("select * from test", sqlite_buildin)
- result.index = frame.index[:1]
- tm.assert_frame_equal(result, frame[:1])
-
- def test_schema(self, sqlite_buildin):
- frame = tm.makeTimeDataFrame()
- create_sql = sql.get_schema(frame, "test")
- lines = create_sql.splitlines()
- for line in lines:
- tokens = line.split(" ")
- if len(tokens) == 2 and tokens[0] == "A":
- assert tokens[1] == "DATETIME"
-
- create_sql = sql.get_schema(frame, "test", keys=["A", "B"])
- lines = create_sql.splitlines()
- assert 'PRIMARY KEY ("A", "B")' in create_sql
- cur = sqlite_buildin.cursor()
- cur.execute(create_sql)
+ result = sql.read_sql("select * from test", con=sqlite_buildin)
+ result.index = frame.index
+ tm.assert_frame_equal(result, frame, rtol=1e-3)
- def test_execute_fail(self, sqlite_buildin):
- create_sql = """
- CREATE TABLE test
- (
- a TEXT,
- b TEXT,
- c REAL,
- PRIMARY KEY (a, b)
- );
- """
- cur = sqlite_buildin.cursor()
+
+def test_xsqlite_execute(sqlite_buildin):
+ frame = tm.makeTimeDataFrame()
+ create_sql = sql.get_schema(frame, "test")
+ cur = sqlite_buildin.cursor()
+ cur.execute(create_sql)
+ ins = "INSERT INTO test VALUES (?, ?, ?, ?)"
+
+ row = frame.iloc[0]
+ with sql.pandasSQL_builder(sqlite_buildin) as pandas_sql:
+ pandas_sql.execute(ins, tuple(row))
+ sqlite_buildin.commit()
+
+ result = sql.read_sql("select * from test", sqlite_buildin)
+ result.index = frame.index[:1]
+ tm.assert_frame_equal(result, frame[:1])
+
+
+def test_xsqlite_schema(sqlite_buildin):
+ frame = tm.makeTimeDataFrame()
+ create_sql = sql.get_schema(frame, "test")
+ lines = create_sql.splitlines()
+ for line in lines:
+ tokens = line.split(" ")
+ if len(tokens) == 2 and tokens[0] == "A":
+ assert tokens[1] == "DATETIME"
+
+ create_sql = sql.get_schema(frame, "test", keys=["A", "B"])
+ lines = create_sql.splitlines()
+ assert 'PRIMARY KEY ("A", "B")' in create_sql
+ cur = sqlite_buildin.cursor()
+ cur.execute(create_sql)
+
+
+def test_xsqlite_execute_fail(sqlite_buildin):
+ create_sql = """
+ CREATE TABLE test
+ (
+ a TEXT,
+ b TEXT,
+ c REAL,
+ PRIMARY KEY (a, b)
+ );
+ """
+ cur = sqlite_buildin.cursor()
+ cur.execute(create_sql)
+
+ with sql.pandasSQL_builder(sqlite_buildin) as pandas_sql:
+ pandas_sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)')
+ pandas_sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)')
+
+ with pytest.raises(sql.DatabaseError, match="Execution failed on sql"):
+ pandas_sql.execute('INSERT INTO test VALUES("foo", "bar", 7)')
+
+
+def test_xsqlite_execute_closed_connection():
+ create_sql = """
+ CREATE TABLE test
+ (
+ a TEXT,
+ b TEXT,
+ c REAL,
+ PRIMARY KEY (a, b)
+ );
+ """
+ with contextlib.closing(sqlite3.connect(":memory:")) as conn:
+ cur = conn.cursor()
cur.execute(create_sql)
- with sql.pandasSQL_builder(sqlite_buildin) as pandas_sql:
+ with sql.pandasSQL_builder(conn) as pandas_sql:
pandas_sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)')
- pandas_sql.execute('INSERT INTO test VALUES("foo", "baz", 2.567)')
- with pytest.raises(sql.DatabaseError, match="Execution failed on sql"):
- pandas_sql.execute('INSERT INTO test VALUES("foo", "bar", 7)')
+ msg = "Cannot operate on a closed database."
+ with pytest.raises(sqlite3.ProgrammingError, match=msg):
+ tquery("select * from test", con=conn)
- def test_execute_closed_connection(self):
- create_sql = """
- CREATE TABLE test
- (
- a TEXT,
- b TEXT,
- c REAL,
- PRIMARY KEY (a, b)
- );
- """
- with contextlib.closing(sqlite3.connect(":memory:")) as conn:
- cur = conn.cursor()
- cur.execute(create_sql)
-
- with sql.pandasSQL_builder(conn) as pandas_sql:
- pandas_sql.execute('INSERT INTO test VALUES("foo", "bar", 1.234)')
-
- msg = "Cannot operate on a closed database."
- with pytest.raises(sqlite3.ProgrammingError, match=msg):
- tquery("select * from test", con=conn)
-
- def test_keyword_as_column_names(self, sqlite_buildin):
- df = DataFrame({"From": np.ones(5)})
- assert sql.to_sql(df, con=sqlite_buildin, name="testkeywords", index=False) == 5
-
- def test_onecolumn_of_integer(self, sqlite_buildin):
- # GH 3628
- # a column_of_integers dataframe should transfer well to sql
-
- mono_df = DataFrame([1, 2], columns=["c0"])
- assert sql.to_sql(mono_df, con=sqlite_buildin, name="mono_df", index=False) == 2
- # computing the sum via sql
- con_x = sqlite_buildin
- the_sum = sum(my_c0[0] for my_c0 in con_x.execute("select * from mono_df"))
- # it should not fail, and gives 3 ( Issue #3628 )
- assert the_sum == 3
-
- result = sql.read_sql("select * from mono_df", con_x)
- tm.assert_frame_equal(result, mono_df)
-
- def test_if_exists(self, sqlite_buildin):
- df_if_exists_1 = DataFrame({"col1": [1, 2], "col2": ["A", "B"]})
- df_if_exists_2 = DataFrame({"col1": [3, 4, 5], "col2": ["C", "D", "E"]})
- table_name = "table_if_exists"
- sql_select = f"SELECT * FROM {table_name}"
-
- msg = "'notvalidvalue' is not valid for if_exists"
- with pytest.raises(ValueError, match=msg):
- sql.to_sql(
- frame=df_if_exists_1,
- con=sqlite_buildin,
- name=table_name,
- if_exists="notvalidvalue",
- )
- self.drop_table(table_name, sqlite_buildin)
- # test if_exists='fail'
+def test_xsqlite_keyword_as_column_names(sqlite_buildin):
+ df = DataFrame({"From": np.ones(5)})
+ assert sql.to_sql(df, con=sqlite_buildin, name="testkeywords", index=False) == 5
+
+
+def test_xsqlite_onecolumn_of_integer(sqlite_buildin):
+ # GH 3628
+ # a column_of_integers dataframe should transfer well to sql
+
+ mono_df = DataFrame([1, 2], columns=["c0"])
+ assert sql.to_sql(mono_df, con=sqlite_buildin, name="mono_df", index=False) == 2
+ # computing the sum via sql
+ con_x = sqlite_buildin
+ the_sum = sum(my_c0[0] for my_c0 in con_x.execute("select * from mono_df"))
+ # it should not fail, and gives 3 ( Issue #3628 )
+ assert the_sum == 3
+
+ result = sql.read_sql("select * from mono_df", con_x)
+ tm.assert_frame_equal(result, mono_df)
+
+
+def test_xsqlite_if_exists(sqlite_buildin):
+ df_if_exists_1 = DataFrame({"col1": [1, 2], "col2": ["A", "B"]})
+ df_if_exists_2 = DataFrame({"col1": [3, 4, 5], "col2": ["C", "D", "E"]})
+ table_name = "table_if_exists"
+ sql_select = f"SELECT * FROM {table_name}"
+
+ msg = "'notvalidvalue' is not valid for if_exists"
+ with pytest.raises(ValueError, match=msg):
sql.to_sql(
- frame=df_if_exists_1, con=sqlite_buildin, name=table_name, if_exists="fail"
+ frame=df_if_exists_1,
+ con=sqlite_buildin,
+ name=table_name,
+ if_exists="notvalidvalue",
)
- msg = "Table 'table_if_exists' already exists"
- with pytest.raises(ValueError, match=msg):
- sql.to_sql(
- frame=df_if_exists_1,
- con=sqlite_buildin,
- name=table_name,
- if_exists="fail",
- )
- # test if_exists='replace'
+ drop_table(table_name, sqlite_buildin)
+
+ # test if_exists='fail'
+ sql.to_sql(
+ frame=df_if_exists_1, con=sqlite_buildin, name=table_name, if_exists="fail"
+ )
+ msg = "Table 'table_if_exists' already exists"
+ with pytest.raises(ValueError, match=msg):
sql.to_sql(
frame=df_if_exists_1,
con=sqlite_buildin,
name=table_name,
+ if_exists="fail",
+ )
+ # test if_exists='replace'
+ sql.to_sql(
+ frame=df_if_exists_1,
+ con=sqlite_buildin,
+ name=table_name,
+ if_exists="replace",
+ index=False,
+ )
+ assert tquery(sql_select, con=sqlite_buildin) == [(1, "A"), (2, "B")]
+ assert (
+ sql.to_sql(
+ frame=df_if_exists_2,
+ con=sqlite_buildin,
+ name=table_name,
if_exists="replace",
index=False,
)
- assert tquery(sql_select, con=sqlite_buildin) == [(1, "A"), (2, "B")]
- assert (
- sql.to_sql(
- frame=df_if_exists_2,
- con=sqlite_buildin,
- name=table_name,
- if_exists="replace",
- index=False,
- )
- == 3
- )
- assert tquery(sql_select, con=sqlite_buildin) == [(3, "C"), (4, "D"), (5, "E")]
- self.drop_table(table_name, sqlite_buildin)
+ == 3
+ )
+ assert tquery(sql_select, con=sqlite_buildin) == [(3, "C"), (4, "D"), (5, "E")]
+ drop_table(table_name, sqlite_buildin)
- # test if_exists='append'
- assert (
- sql.to_sql(
- frame=df_if_exists_1,
- con=sqlite_buildin,
- name=table_name,
- if_exists="fail",
- index=False,
- )
- == 2
+ # test if_exists='append'
+ assert (
+ sql.to_sql(
+ frame=df_if_exists_1,
+ con=sqlite_buildin,
+ name=table_name,
+ if_exists="fail",
+ index=False,
)
- assert tquery(sql_select, con=sqlite_buildin) == [(1, "A"), (2, "B")]
- assert (
- sql.to_sql(
- frame=df_if_exists_2,
- con=sqlite_buildin,
- name=table_name,
- if_exists="append",
- index=False,
- )
- == 3
+ == 2
+ )
+ assert tquery(sql_select, con=sqlite_buildin) == [(1, "A"), (2, "B")]
+ assert (
+ sql.to_sql(
+ frame=df_if_exists_2,
+ con=sqlite_buildin,
+ name=table_name,
+ if_exists="append",
+ index=False,
)
- assert tquery(sql_select, con=sqlite_buildin) == [
- (1, "A"),
- (2, "B"),
- (3, "C"),
- (4, "D"),
- (5, "E"),
- ]
- self.drop_table(table_name, sqlite_buildin)
+ == 3
+ )
+ assert tquery(sql_select, con=sqlite_buildin) == [
+ (1, "A"),
+ (2, "B"),
+ (3, "C"),
+ (4, "D"),
+ (5, "E"),
+ ]
+ drop_table(table_name, sqlite_buildin)
| A lot of this is very intertwined so hard to break up into small diffs. | https://api.github.com/repos/pandas-dev/pandas/pulls/55074 | 2023-09-08T21:17:25Z | 2023-09-18T23:47:10Z | 2023-09-18T23:47:10Z | 2023-09-18T23:47:29Z |
Backport PR #55048 on branch 2.1.x (COMPAT: bump pyarrow min version for div on duration) | diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index be0a762642e46..684e9dccdc0f9 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -30,6 +30,7 @@
pa_version_under9p0,
pa_version_under11p0,
pa_version_under13p0,
+ pa_version_under14p0,
)
if TYPE_CHECKING:
@@ -186,6 +187,7 @@ def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
"pa_version_under9p0",
"pa_version_under11p0",
"pa_version_under13p0",
+ "pa_version_under14p0",
"IS64",
"ISMUSL",
"PY310",
diff --git a/pandas/compat/pyarrow.py b/pandas/compat/pyarrow.py
index 049ce50920e28..12f58be109d98 100644
--- a/pandas/compat/pyarrow.py
+++ b/pandas/compat/pyarrow.py
@@ -15,6 +15,7 @@
pa_version_under11p0 = _palv < Version("11.0.0")
pa_version_under12p0 = _palv < Version("12.0.0")
pa_version_under13p0 = _palv < Version("13.0.0")
+ pa_version_under14p0 = _palv < Version("14.0.0")
except ImportError:
pa_version_under7p0 = True
pa_version_under8p0 = True
@@ -23,3 +24,4 @@
pa_version_under11p0 = True
pa_version_under12p0 = True
pa_version_under13p0 = True
+ pa_version_under14p0 = True
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index ec2ca494b2aa1..d6e75a57f71e7 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -40,6 +40,7 @@
pa_version_under9p0,
pa_version_under11p0,
pa_version_under13p0,
+ pa_version_under14p0,
)
from pandas.core.dtypes.dtypes import (
@@ -951,7 +952,7 @@ def _is_temporal_supported(self, opname, pa_dtype):
or (
opname
in ("__truediv__", "__rtruediv__", "__floordiv__", "__rfloordiv__")
- and not pa_version_under13p0
+ and not pa_version_under14p0
)
)
and pa.types.is_duration(pa_dtype)
| Backport PR #55048: COMPAT: bump pyarrow min version for div on duration | https://api.github.com/repos/pandas-dev/pandas/pulls/55073 | 2023-09-08T20:33:55Z | 2023-09-08T23:53:33Z | 2023-09-08T23:53:33Z | 2023-09-08T23:53:33Z |
BUG: dt.tz with ArrowDtype returned string | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 42af61be26355..6d5da7cdff3b3 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -35,6 +35,7 @@ Bug fixes
~~~~~~~~~
- Fixed bug for :class:`ArrowDtype` raising ``NotImplementedError`` for fixed-size list (:issue:`55000`)
- Fixed bug in :meth:`DataFrame.stack` with ``future_stack=True`` and columns a non-:class:`MultiIndex` consisting of tuples (:issue:`54948`)
+- Fixed bug in :meth:`Series.dt.tz` with :class:`ArrowDtype` where a string was returned instead of a ``tzinfo`` object (:issue:`55003`)
- Fixed bug in :meth:`Series.pct_change` and :meth:`DataFrame.pct_change` showing unnecessary ``FutureWarning`` (:issue:`54981`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 83ed54c42a23c..2b2e0c843564f 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -18,6 +18,7 @@
from pandas._libs.tslibs import (
Timedelta,
Timestamp,
+ timezones,
)
from pandas.compat import (
pa_version_under7p0,
@@ -2425,7 +2426,7 @@ def _dt_time(self):
@property
def _dt_tz(self):
- return self.dtype.pyarrow_dtype.tz
+ return timezones.maybe_get_tz(self.dtype.pyarrow_dtype.tz)
@property
def _dt_unit(self):
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index fa6e85ba204d2..90e35e3677306 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -31,6 +31,7 @@
import pytest
from pandas._libs import lib
+from pandas._libs.tslibs import timezones
from pandas.compat import (
PY311,
is_ci_environment,
@@ -2431,7 +2432,7 @@ def test_dt_tz(tz):
dtype=ArrowDtype(pa.timestamp("ns", tz=tz)),
)
result = ser.dt.tz
- assert result == tz
+ assert result == timezones.maybe_get_tz(tz)
def test_dt_isocalendar():
| - [x] closes #55003 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [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/55072 | 2023-09-08T19:03:19Z | 2023-09-13T17:10:10Z | 2023-09-13T17:10:10Z | 2023-09-13T17:10:16Z |
BUG: read_csv(on_bad_lines='warn') did not raise a Python warning | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 7bb4aaec0dd7c..a795514aa31f8 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -227,6 +227,7 @@ MultiIndex
I/O
^^^
+- Bug in :func:`read_csv` where ``on_bad_lines="warn"`` would write to ``stderr`` instead of raise a Python warning. This now yields a :class:`.errors.ParserWarning` (:issue:`54296`)
- Bug in :func:`read_excel`, with ``engine="xlrd"`` (``xls`` files) erroring when file contains NaNs/Infs (:issue:`54564`)
Period
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index 6d66e21ce49f5..5f51f48b43ca9 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -6,7 +6,6 @@ from csv import (
QUOTE_NONE,
QUOTE_NONNUMERIC,
)
-import sys
import time
import warnings
@@ -880,9 +879,15 @@ cdef class TextReader:
cdef _check_tokenize_status(self, int status):
if self.parser.warn_msg != NULL:
- print(PyUnicode_DecodeUTF8(
- self.parser.warn_msg, strlen(self.parser.warn_msg),
- self.encoding_errors), file=sys.stderr)
+ warnings.warn(
+ PyUnicode_DecodeUTF8(
+ self.parser.warn_msg,
+ strlen(self.parser.warn_msg),
+ self.encoding_errors
+ ),
+ ParserWarning,
+ stacklevel=find_stack_level()
+ )
free(self.parser.warn_msg)
self.parser.warn_msg = NULL
diff --git a/pandas/io/parsers/python_parser.py b/pandas/io/parsers/python_parser.py
index 6846ea2b196b8..43fb4ec3b55fc 100644
--- a/pandas/io/parsers/python_parser.py
+++ b/pandas/io/parsers/python_parser.py
@@ -13,7 +13,6 @@
import csv
from io import StringIO
import re
-import sys
from typing import (
IO,
TYPE_CHECKING,
@@ -21,6 +20,7 @@
Literal,
cast,
)
+import warnings
import numpy as np
@@ -28,8 +28,10 @@
from pandas.errors import (
EmptyDataError,
ParserError,
+ ParserWarning,
)
from pandas.util._decorators import cache_readonly
+from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
is_bool_dtype,
@@ -778,8 +780,11 @@ def _alert_malformed(self, msg: str, row_num: int) -> None:
if self.on_bad_lines == self.BadLineHandleMethod.ERROR:
raise ParserError(msg)
if self.on_bad_lines == self.BadLineHandleMethod.WARN:
- base = f"Skipping line {row_num}: "
- sys.stderr.write(base + msg + "\n")
+ warnings.warn(
+ f"Skipping line {row_num}: {msg}\n",
+ ParserWarning,
+ stacklevel=find_stack_level(),
+ )
def _next_iter_line(self, row_num: int) -> list[Scalar] | None:
"""
diff --git a/pandas/tests/io/parser/common/test_read_errors.py b/pandas/tests/io/parser/common/test_read_errors.py
index 492b4d5ec058e..0c5a2e0d04e5a 100644
--- a/pandas/tests/io/parser/common/test_read_errors.py
+++ b/pandas/tests/io/parser/common/test_read_errors.py
@@ -15,6 +15,7 @@
from pandas.errors import (
EmptyDataError,
ParserError,
+ ParserWarning,
)
from pandas import DataFrame
@@ -129,18 +130,16 @@ def test_unexpected_keyword_parameter_exception(all_parsers):
parser.read_table("foo.tsv", foo=1)
-def test_suppress_error_output(all_parsers, capsys):
+def test_suppress_error_output(all_parsers):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
- result = parser.read_csv(StringIO(data), on_bad_lines="skip")
+ with tm.assert_produces_warning(None):
+ result = parser.read_csv(StringIO(data), on_bad_lines="skip")
tm.assert_frame_equal(result, expected)
- captured = capsys.readouterr()
- assert captured.err == ""
-
def test_error_bad_lines(all_parsers):
# see gh-15925
@@ -152,19 +151,18 @@ def test_error_bad_lines(all_parsers):
parser.read_csv(StringIO(data), on_bad_lines="error")
-def test_warn_bad_lines(all_parsers, capsys):
+def test_warn_bad_lines(all_parsers):
# see gh-15925
parser = all_parsers
data = "a\n1\n1,2,3\n4\n5,6,7"
expected = DataFrame({"a": [1, 4]})
- result = parser.read_csv(StringIO(data), on_bad_lines="warn")
+ with tm.assert_produces_warning(
+ ParserWarning, match="Skipping line", check_stacklevel=False
+ ):
+ result = parser.read_csv(StringIO(data), on_bad_lines="warn")
tm.assert_frame_equal(result, expected)
- captured = capsys.readouterr()
- assert "Skipping line 3" in captured.err
- assert "Skipping line 5" in captured.err
-
def test_read_csv_wrong_num_columns(all_parsers):
# Too few columns.
@@ -245,7 +243,7 @@ def test_bad_header_uniform_error(all_parsers):
parser.read_csv(StringIO(data), index_col=0, on_bad_lines="error")
-def test_on_bad_lines_warn_correct_formatting(all_parsers, capsys):
+def test_on_bad_lines_warn_correct_formatting(all_parsers):
# see gh-15925
parser = all_parsers
data = """1,2
@@ -256,17 +254,8 @@ def test_on_bad_lines_warn_correct_formatting(all_parsers, capsys):
"""
expected = DataFrame({"1": "a", "2": ["b"] * 2})
- result = parser.read_csv(StringIO(data), on_bad_lines="warn")
+ with tm.assert_produces_warning(
+ ParserWarning, match="Skipping line", check_stacklevel=False
+ ):
+ result = parser.read_csv(StringIO(data), on_bad_lines="warn")
tm.assert_frame_equal(result, expected)
-
- captured = capsys.readouterr()
- if parser.engine == "c":
- warn = """Skipping line 3: expected 2 fields, saw 3
-Skipping line 4: expected 2 fields, saw 3
-
-"""
- else:
- warn = """Skipping line 3: Expected 2 fields in line 3, saw 3
-Skipping line 4: Expected 2 fields in line 4, saw 3
-"""
- assert captured.err == warn
diff --git a/pandas/tests/io/parser/test_c_parser_only.py b/pandas/tests/io/parser/test_c_parser_only.py
index 32a010b3aeb34..18eee01f87621 100644
--- a/pandas/tests/io/parser/test_c_parser_only.py
+++ b/pandas/tests/io/parser/test_c_parser_only.py
@@ -19,7 +19,10 @@
from pandas.compat import is_ci_environment
from pandas.compat.numpy import np_version_gte1p24
-from pandas.errors import ParserError
+from pandas.errors import (
+ ParserError,
+ ParserWarning,
+)
import pandas.util._test_decorators as td
from pandas import (
@@ -461,7 +464,7 @@ def test_data_after_quote(c_parser_only):
tm.assert_frame_equal(result, expected)
-def test_comment_whitespace_delimited(c_parser_only, capsys):
+def test_comment_whitespace_delimited(c_parser_only):
parser = c_parser_only
test_input = """\
1 2
@@ -474,18 +477,17 @@ def test_comment_whitespace_delimited(c_parser_only, capsys):
8# 1 field, NaN
9 2 3 # skipped line
# comment"""
- df = parser.read_csv(
- StringIO(test_input),
- comment="#",
- header=None,
- delimiter="\\s+",
- skiprows=0,
- on_bad_lines="warn",
- )
- captured = capsys.readouterr()
- # skipped lines 2, 3, 4, 9
- for line_num in (2, 3, 4, 9):
- assert f"Skipping line {line_num}" in captured.err
+ with tm.assert_produces_warning(
+ ParserWarning, match="Skipping line", check_stacklevel=False
+ ):
+ df = parser.read_csv(
+ StringIO(test_input),
+ comment="#",
+ header=None,
+ delimiter="\\s+",
+ skiprows=0,
+ on_bad_lines="warn",
+ )
expected = DataFrame([[1, 2], [5, 2], [6, 2], [7, np.nan], [8, np.nan]])
tm.assert_frame_equal(df, expected)
diff --git a/pandas/tests/io/parser/test_python_parser_only.py b/pandas/tests/io/parser/test_python_parser_only.py
index 959b988e208c1..dbd474c6ae0b9 100644
--- a/pandas/tests/io/parser/test_python_parser_only.py
+++ b/pandas/tests/io/parser/test_python_parser_only.py
@@ -274,7 +274,7 @@ def test_multi_char_sep_quotes(python_parser_only, quoting):
parser.read_csv(StringIO(data), quoting=quoting, **kwargs)
-def test_none_delimiter(python_parser_only, capsys):
+def test_none_delimiter(python_parser_only):
# see gh-13374 and gh-17465
parser = python_parser_only
data = "a,b,c\n0,1,2\n3,4,5,6\n7,8,9"
@@ -283,12 +283,14 @@ def test_none_delimiter(python_parser_only, capsys):
# We expect the third line in the data to be
# skipped because it is malformed, but we do
# not expect any errors to occur.
- result = parser.read_csv(StringIO(data), header=0, sep=None, on_bad_lines="warn")
+ with tm.assert_produces_warning(
+ ParserWarning, match="Skipping line 3", check_stacklevel=False
+ ):
+ result = parser.read_csv(
+ StringIO(data), header=0, sep=None, on_bad_lines="warn"
+ )
tm.assert_frame_equal(result, expected)
- captured = capsys.readouterr()
- assert "Skipping line 3" in captured.err
-
@pytest.mark.parametrize("data", ['a\n1\n"b"a', 'a,b,c\ncat,foo,bar\ndog,foo,"baz'])
@pytest.mark.parametrize("skipfooter", [0, 1])
diff --git a/pandas/tests/io/parser/test_textreader.py b/pandas/tests/io/parser/test_textreader.py
index f150ed3903443..e2d785a38eb51 100644
--- a/pandas/tests/io/parser/test_textreader.py
+++ b/pandas/tests/io/parser/test_textreader.py
@@ -12,6 +12,7 @@
import pandas._libs.parsers as parser
from pandas._libs.parsers import TextReader
+from pandas.errors import ParserWarning
from pandas import DataFrame
import pandas._testing as tm
@@ -125,7 +126,7 @@ def test_integer_thousands_alt(self):
expected = DataFrame([123456, 12500])
tm.assert_frame_equal(result, expected)
- def test_skip_bad_lines(self, capsys):
+ def test_skip_bad_lines(self):
# too many lines, see #2430 for why
data = "a:b:c\nd:e:f\ng:h:i\nj:k:l:m\nl:m:n\no:p:q:r"
@@ -145,14 +146,11 @@ def test_skip_bad_lines(self, capsys):
}
assert_array_dicts_equal(result, expected)
- reader = TextReader(
- StringIO(data), delimiter=":", header=None, on_bad_lines=1 # Warn
- )
- reader.read()
- captured = capsys.readouterr()
-
- assert "Skipping line 4" in captured.err
- assert "Skipping line 6" in captured.err
+ with tm.assert_produces_warning(ParserWarning, match="Skipping line"):
+ reader = TextReader(
+ StringIO(data), delimiter=":", header=None, on_bad_lines=1 # Warn
+ )
+ reader.read()
def test_header_not_enough_lines(self):
data = "skip this\nskip this\na,b,c\n1,2,3\n4,5,6"
| - [x] closes #54296 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55071 | 2023-09-08T17:57:03Z | 2023-09-08T20:32:21Z | 2023-09-08T20:32:21Z | 2023-09-08T20:32:24Z |
ENH: Allow empty groupby | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 7bb4aaec0dd7c..a20ba6225fd8c 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -16,7 +16,7 @@ Enhancements
.. _whatsnew_220.enhancements.enhancement1:
-enhancement1
+:class:`GroupBy` now accepts an empty list of ``keys`` and applies any aggregation over all the data (:issue:`35366`)
^^^^^^^^^^^^
.. _whatsnew_220.enhancements.enhancement2:
diff --git a/doc/titanic.xlsx b/doc/titanic.xlsx
new file mode 100644
index 0000000000000..8173c090c5290
Binary files /dev/null and b/doc/titanic.xlsx differ
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index e6dd6a990d285..c1591390eca7f 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1289,6 +1289,10 @@ def __init__(
dropna: bool = True,
) -> None:
self._selection = selection
+ # to resolve case when passed empty list
+ # didn't change self.keys directly to avoid side effects
+ # e.g. the use of get_grouper
+ keys = [np.int64(0)] * len(obj) if (iter(keys) and len(keys) == 0) else keys
assert isinstance(obj, NDFrame), type(obj)
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index fdd959f0e8754..9802edee61bd9 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -33,6 +33,62 @@
pytestmark = pytest.mark.filterwarnings("ignore:Mean of empty slice:RuntimeWarning")
+def test_groupby_empty_list_keys():
+ # sample case of a series
+ s = Series([1, 2, 3])
+ # result = s.groupby([]).agg("min")
+ result = s.groupby([]).agg("min")
+ expected = Series([1])
+ tm.assert_series_equal(result, expected)
+ # sample case one column
+ df = DataFrame(
+ {
+ "group": ["A", "B", "A"],
+ "col": np.arange(3),
+ }
+ )
+ result_df = df.groupby([]).agg(col_min=("col", "min"))
+ expected_df = DataFrame([{"col_min": 0}])
+ tm.assert_frame_equal(result_df, expected_df)
+
+ # two columns in a datafram with other columns
+ df = DataFrame(
+ {
+ "group": ["A", "B", "A"],
+ "col1": np.arange(3),
+ "col2": np.arange(3, 6),
+ "col3": np.arange(6, 9),
+ }
+ )
+ result_df = df.groupby([]).agg(
+ col1_min=("col1", "min"),
+ col2_min=("col2", "sum"),
+ )
+ expected_df = DataFrame(
+ [{"col1_min": 0, "col2_min": 12}],
+ )
+ tm.assert_frame_equal(result_df, expected_df)
+
+ # std
+ result_df = df.groupby([]).agg(
+ col1_std=("col1", "std"),
+ )
+ expected_df = DataFrame(
+ [{"col1_std": 1.0}],
+ )
+ tm.assert_frame_equal(result_df, expected_df)
+
+ # different type of iterables
+ for key_iter in [np.array([]), (), set()]:
+ result_df = df.groupby(key_iter).agg(
+ col1_std=("col1", "std"),
+ )
+ expected_df = DataFrame(
+ [{"col1_std": 1.0}],
+ )
+ tm.assert_frame_equal(result_df, expected_df)
+
+
def test_repr():
# GH18203
result = repr(Grouper(key="A", level="B"))
diff --git a/pandas/tests/groupby/test_grouping.py b/pandas/tests/groupby/test_grouping.py
index d05b60fd56b5f..c71c199e5f7b5 100644
--- a/pandas/tests/groupby/test_grouping.py
+++ b/pandas/tests/groupby/test_grouping.py
@@ -393,11 +393,6 @@ def test_grouper_getting_correct_binner(self):
def test_grouper_iter(self, df):
assert sorted(df.groupby("A").grouper) == ["bar", "foo"]
- def test_empty_groups(self, df):
- # see gh-1048
- with pytest.raises(ValueError, match="No group keys passed!"):
- df.groupby([])
-
def test_groupby_grouper(self, df):
grouped = df.groupby("A")
| - [x] closes #35366
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [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.
I defined the keys as a dummy list in case the user passes an empty list `[]`. I added tests with empty groupings and tested the code running the command `pytest -k "groupby and not arrow`. Please, let me know if it works.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55068 | 2023-09-08T16:10:21Z | 2023-09-09T21:24:50Z | null | 2023-09-09T21:24:50Z |
Backport PR #54949 on branch 2.1.x (BLD: improvements to meson.build files) | diff --git a/generate_version.py b/generate_version.py
index 46e9f52bfc5de..06e38ce0fd978 100644
--- a/generate_version.py
+++ b/generate_version.py
@@ -1,3 +1,5 @@
+#!/usr/bin/env python3
+
# Note: This file has to live next to setup.py or versioneer will not work
import argparse
import os
diff --git a/meson.build b/meson.build
index 09a1494135af4..e0e533ffade97 100644
--- a/meson.build
+++ b/meson.build
@@ -2,19 +2,17 @@
project(
'pandas',
'c', 'cpp', 'cython',
- version: run_command(['python', 'generate_version.py', '--print'], check: true).stdout().strip(),
+ version: run_command(['generate_version.py', '--print'], check: true).stdout().strip(),
license: 'BSD-3',
meson_version: '>=1.0.1',
default_options: [
'buildtype=release',
- # TODO: Reactivate werror, some warnings on Windows
- #'werror=true',
'c_std=c99'
]
)
fs = import('fs')
-py = import('python').find_installation()
+py = import('python').find_installation(pure: false)
tempita = files('generate_pxi.py')
versioneer = files('generate_version.py')
@@ -30,7 +28,7 @@ add_project_arguments('-DNPY_TARGET_VERSION=NPY_1_21_API_VERSION', language : 'c
if fs.exists('_version_meson.py')
- py.install_sources('_version_meson.py', pure: false, subdir: 'pandas')
+ py.install_sources('_version_meson.py', subdir: 'pandas')
else
custom_target('write_version_file',
output: '_version_meson.py',
@@ -40,11 +38,15 @@ else
build_by_default: true,
build_always_stale: true,
install: true,
- install_dir: py.get_install_dir(pure: false) / 'pandas'
+ install_dir: py.get_install_dir() / 'pandas'
)
meson.add_dist_script(py, versioneer, '-o', '_version_meson.py')
endif
# Needed by pandas.test() when it looks for the pytest ini options
-py.install_sources('pyproject.toml', pure: false, subdir: 'pandas')
+py.install_sources(
+ 'pyproject.toml',
+ subdir: 'pandas'
+)
+
subdir('pandas')
diff --git a/pandas/_libs/meson.build b/pandas/_libs/meson.build
index c0a9d1ad8ee4a..1cf2c4343d844 100644
--- a/pandas/_libs/meson.build
+++ b/pandas/_libs/meson.build
@@ -114,8 +114,9 @@ foreach ext_name, ext_dict : libs_sources
)
endforeach
-py.install_sources('__init__.py',
- pure: false,
- subdir: 'pandas/_libs')
+py.install_sources(
+ '__init__.py',
+ subdir: 'pandas/_libs'
+)
subdir('window')
diff --git a/pandas/_libs/tslibs/meson.build b/pandas/_libs/tslibs/meson.build
index 14d2eef46da20..167695b84514c 100644
--- a/pandas/_libs/tslibs/meson.build
+++ b/pandas/_libs/tslibs/meson.build
@@ -31,6 +31,7 @@ foreach ext_name, ext_dict : tslibs_sources
)
endforeach
-py.install_sources('__init__.py',
- pure: false,
- subdir: 'pandas/_libs/tslibs')
+py.install_sources(
+ '__init__.py',
+ subdir: 'pandas/_libs/tslibs'
+)
diff --git a/pandas/meson.build b/pandas/meson.build
index 1dc9955aa4ff6..f02258c98d46a 100644
--- a/pandas/meson.build
+++ b/pandas/meson.build
@@ -40,8 +40,9 @@ subdirs_list = [
'util'
]
foreach subdir: subdirs_list
- install_subdir(subdir, install_dir: py.get_install_dir(pure: false) / 'pandas')
+ install_subdir(subdir, install_dir: py.get_install_dir() / 'pandas')
endforeach
+
top_level_py_list = [
'__init__.py',
'_typing.py',
@@ -49,8 +50,4 @@ top_level_py_list = [
'conftest.py',
'testing.py'
]
-foreach file: top_level_py_list
- py.install_sources(file,
- pure: false,
- subdir: 'pandas')
-endforeach
+py.install_sources(top_level_py_list, subdir: 'pandas')
| Backport PR #54949: BLD: improvements to meson.build files | https://api.github.com/repos/pandas-dev/pandas/pulls/55065 | 2023-09-08T14:32:16Z | 2023-09-08T17:01:50Z | 2023-09-08T17:01:50Z | 2023-09-08T17:01:50Z |
Let isocalendar() return int32 (no NaT) and float64 (in presence of NaT) | diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index dd2d7c0060392..e511fc6bd290a 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -1475,17 +1475,18 @@ def isocalendar(self) -> DataFrame:
2019-12-30 1
2019-12-31 1
2020-01-01 1
- Freq: D, Name: week, dtype: UInt32
+ Freq: D, Name: week, dtype: int32
"""
from pandas import DataFrame
values = self._local_timestamps()
sarray = fields.build_isocalendar_sarray(values, reso=self._creso)
+ dtype = np.dtype([("year", "float64"), ("week", "float64"), ("day", "float64")])
+ sarray = self._maybe_mask_results(sarray, fill_value=None, convert=dtype)
+ dtype = None if sarray.dtype == dtype else "int32"
iso_calendar_df = DataFrame(
- sarray, columns=["year", "week", "day"], dtype="UInt32"
+ sarray, columns=["year", "week", "day"], dtype=dtype
)
- if self._hasna:
- iso_calendar_df.iloc[self._isnan] = None
return iso_calendar_df
year = _field_accessor(
| - [x] closes #54657
- [ ] [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/55062 | 2023-09-08T06:24:43Z | 2023-11-07T00:56:46Z | null | 2023-11-07T00:56:47Z |
Convert to compatible NumPy dtype for MaskedArray to_numpy | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 8cb4b3f24d435..3b363d38e18cb 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -105,6 +105,37 @@ For a full list of ADBC drivers and their development status, see the `ADBC Driv
Implementation Status <https://arrow.apache.org/adbc/current/driver/status.html>`_
documentation.
+.. _whatsnew_220.enhancements.to_numpy_ea:
+
+ExtensionArray.to_numpy converts to suitable NumPy dtype
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:meth:`ExtensionArray.to_numpy`` will now convert to a suitable NumPy dtype instead
+of ``object`` dtype for nullable extension dtypes.
+
+*Old behavior:*
+
+.. code-block:: ipython
+
+ In [1]: ser = pd.Series([1, 2, 3], dtype="Int64")
+ In [2]: ser.to_numpy()
+ Out[2]: array([1, 2, 3], dtype=object)
+
+*New behavior:*
+
+.. ipython:: python
+
+ ser = pd.Series([1, 2, 3], dtype="Int64")
+ ser.to_numpy()
+
+The default NumPy dtype (without any arguments) is determined as follows:
+
+- float dtypes are cast to NumPy floats
+- integer dtypes without missing values are cast to NumPy integer dtypes
+- integer dtypes with missing values are cast to NumPy float dtypes and ``NaN`` is used as missing value indicator
+- boolean dtypes without missing values are cast to NumPy bool dtype
+- boolean dtypes with missing values keep object dtype
+
.. _whatsnew_220.enhancements.struct_accessor:
Series.struct accessor to with PyArrow structured data
diff --git a/pandas/_libs/parsers.pyx b/pandas/_libs/parsers.pyx
index ab28b34be58f2..0f78080a3daf4 100644
--- a/pandas/_libs/parsers.pyx
+++ b/pandas/_libs/parsers.pyx
@@ -1477,7 +1477,7 @@ def _maybe_upcast(
import pyarrow as pa
if isinstance(arr, IntegerArray) and arr.isna().all():
# use null instead of int64 in pyarrow
- arr = arr.to_numpy()
+ arr = arr.to_numpy(na_value=None)
arr = ArrowExtensionArray(pa.array(arr, from_pandas=True))
return arr
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 58909643ed46a..201ce44ed0163 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -38,11 +38,15 @@
IS64,
is_platform_windows,
)
-from pandas.errors import AbstractMethodError
+from pandas.errors import (
+ AbstractMethodError,
+ LossySetitemError,
+)
from pandas.util._decorators import doc
from pandas.util._validators import validate_fillna_kwargs
from pandas.core.dtypes.base import ExtensionDtype
+from pandas.core.dtypes.cast import np_can_hold_element
from pandas.core.dtypes.common import (
is_bool,
is_integer_dtype,
@@ -69,6 +73,7 @@
from pandas.core.algorithms import (
factorize_array,
isin,
+ map_array,
mode,
take,
)
@@ -473,13 +478,35 @@ def to_numpy(
>>> a.to_numpy(dtype="bool", na_value=False)
array([ True, False, False])
"""
- if na_value is lib.no_default:
- na_value = libmissing.NA
+ hasna = self._hasna
+
if dtype is None:
- dtype = object
+ dtype_given = False
+ if hasna:
+ if self.dtype.kind == "b":
+ dtype = object
+ else:
+ if self.dtype.kind in "iu":
+ dtype = np.dtype(np.float64)
+ else:
+ dtype = self.dtype.numpy_dtype
+ if na_value is lib.no_default:
+ na_value = np.nan
+ else:
+ dtype = self.dtype.numpy_dtype
else:
dtype = np.dtype(dtype)
- if self._hasna:
+ dtype_given = True
+ if na_value is lib.no_default:
+ na_value = libmissing.NA
+
+ if not dtype_given and hasna:
+ try:
+ np_can_hold_element(dtype, na_value) # type: ignore[arg-type]
+ except LossySetitemError:
+ dtype = object
+
+ if hasna:
if (
dtype != object
and not is_string_dtype(dtype)
@@ -506,7 +533,7 @@ def tolist(self):
if self.ndim > 1:
return [x.tolist() for x in self]
dtype = None if self._hasna else self._data.dtype
- return self.to_numpy(dtype=dtype).tolist()
+ return self.to_numpy(dtype=dtype, na_value=libmissing.NA).tolist()
@overload
def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
@@ -1300,6 +1327,9 @@ def max(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
)
return self._wrap_reduction_result("max", result, skipna=skipna, axis=axis)
+ def map(self, mapper, na_action=None):
+ return map_array(self.to_numpy(), mapper, na_action=None)
+
def any(self, *, skipna: bool = True, axis: AxisInt | None = 0, **kwargs):
"""
Return whether any element is truthy.
diff --git a/pandas/core/methods/to_dict.py b/pandas/core/methods/to_dict.py
index 3295c4741c03d..7bd4851425c3b 100644
--- a/pandas/core/methods/to_dict.py
+++ b/pandas/core/methods/to_dict.py
@@ -9,10 +9,17 @@
import numpy as np
+from pandas._libs import (
+ lib,
+ missing as libmissing,
+)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.cast import maybe_box_native
-from pandas.core.dtypes.dtypes import ExtensionDtype
+from pandas.core.dtypes.dtypes import (
+ BaseMaskedDtype,
+ ExtensionDtype,
+)
from pandas.core import common as com
@@ -150,6 +157,10 @@ def to_dict(
for i, col_dtype in enumerate(df.dtypes.values)
if col_dtype == np.dtype(object) or isinstance(col_dtype, ExtensionDtype)
]
+ box_na_values = [
+ lib.no_default if not isinstance(col_dtype, BaseMaskedDtype) else libmissing.NA
+ for i, col_dtype in enumerate(df.dtypes.values)
+ ]
are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes)
if orient == "dict":
@@ -160,7 +171,11 @@ def to_dict(
return into_c(
(
k,
- list(map(maybe_box_native, v.to_numpy().tolist()))
+ list(
+ map(
+ maybe_box_native, v.to_numpy(na_value=box_na_values[i]).tolist()
+ )
+ )
if i in object_dtype_indices_as_set
else v.to_numpy().tolist(),
)
diff --git a/pandas/io/formats/format.py b/pandas/io/formats/format.py
index bb2fd06d98e1d..ecb09aa6cacf1 100644
--- a/pandas/io/formats/format.py
+++ b/pandas/io/formats/format.py
@@ -61,6 +61,7 @@
)
from pandas.core.arrays import (
+ BaseMaskedArray,
Categorical,
DatetimeArray,
ExtensionArray,
@@ -1527,6 +1528,8 @@ def _format_strings(self) -> list[str]:
if isinstance(values, Categorical):
# Categorical is special for now, so that we can preserve tzinfo
array = values._internal_get_values()
+ elif isinstance(values, BaseMaskedArray):
+ array = values.to_numpy(dtype=object)
else:
array = np.asarray(values)
diff --git a/pandas/io/parsers/base_parser.py b/pandas/io/parsers/base_parser.py
index 4fb97779c690e..3ceb798a7f5ca 100644
--- a/pandas/io/parsers/base_parser.py
+++ b/pandas/io/parsers/base_parser.py
@@ -63,6 +63,7 @@
from pandas.core import algorithms
from pandas.core.arrays import (
ArrowExtensionArray,
+ BaseMaskedArray,
BooleanArray,
Categorical,
ExtensionArray,
@@ -762,8 +763,15 @@ def _infer_types(
pa = import_optional_dependency("pyarrow")
if isinstance(result, np.ndarray):
result = ArrowExtensionArray(pa.array(result, from_pandas=True))
+ elif isinstance(result, BaseMaskedArray):
+ if result._mask.all():
+ # We want an arrow null array here
+ result = ArrowExtensionArray(pa.array([None] * len(result)))
+ else:
+ result = ArrowExtensionArray(
+ pa.array(result._data, mask=result._mask)
+ )
else:
- # ExtensionArray
result = ArrowExtensionArray(
pa.array(result.to_numpy(), from_pandas=True)
)
diff --git a/pandas/tests/arrays/boolean/test_construction.py b/pandas/tests/arrays/boolean/test_construction.py
index d26eea19c06e9..6459b315c684d 100644
--- a/pandas/tests/arrays/boolean/test_construction.py
+++ b/pandas/tests/arrays/boolean/test_construction.py
@@ -223,7 +223,7 @@ def test_coerce_to_numpy_array():
# also with no missing values -> object dtype
arr = pd.array([True, False, True], dtype="boolean")
result = np.array(arr)
- expected = np.array([True, False, True], dtype="object")
+ expected = np.array([True, False, True], dtype="bool")
tm.assert_numpy_array_equal(result, expected)
# force bool dtype
@@ -263,7 +263,7 @@ def test_to_numpy(box):
# default (with or without missing values) -> object dtype
arr = con([True, False, True], dtype="boolean")
result = arr.to_numpy()
- expected = np.array([True, False, True], dtype="object")
+ expected = np.array([True, False, True], dtype="bool")
tm.assert_numpy_array_equal(result, expected)
arr = con([True, False, None], dtype="boolean")
diff --git a/pandas/tests/arrays/floating/test_to_numpy.py b/pandas/tests/arrays/floating/test_to_numpy.py
index 2ed52439adf53..a25ac40cb3e7c 100644
--- a/pandas/tests/arrays/floating/test_to_numpy.py
+++ b/pandas/tests/arrays/floating/test_to_numpy.py
@@ -13,12 +13,12 @@ def test_to_numpy(box):
# default (with or without missing values) -> object dtype
arr = con([0.1, 0.2, 0.3], dtype="Float64")
result = arr.to_numpy()
- expected = np.array([0.1, 0.2, 0.3], dtype="object")
+ expected = np.array([0.1, 0.2, 0.3], dtype="float64")
tm.assert_numpy_array_equal(result, expected)
arr = con([0.1, 0.2, None], dtype="Float64")
result = arr.to_numpy()
- expected = np.array([0.1, 0.2, pd.NA], dtype="object")
+ expected = np.array([0.1, 0.2, np.nan], dtype="float64")
tm.assert_numpy_array_equal(result, expected)
diff --git a/pandas/tests/arrays/integer/test_dtypes.py b/pandas/tests/arrays/integer/test_dtypes.py
index fd38a8df7fa22..e3848cdfe3aa9 100644
--- a/pandas/tests/arrays/integer/test_dtypes.py
+++ b/pandas/tests/arrays/integer/test_dtypes.py
@@ -141,7 +141,7 @@ def test_astype(all_data):
# coerce to object
s = pd.Series(mixed)
result = s.astype("object")
- expected = pd.Series(np.asarray(mixed))
+ expected = pd.Series(np.asarray(mixed, dtype=object))
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/base/test_conversion.py b/pandas/tests/base/test_conversion.py
index 4f3e4d3365179..3955e0e88e776 100644
--- a/pandas/tests/base/test_conversion.py
+++ b/pandas/tests/base/test_conversion.py
@@ -295,7 +295,7 @@ def test_array_multiindex_raises():
pd.core.arrays.period_array(["2000", "2001"], freq="D"),
np.array([pd.Period("2000", freq="D"), pd.Period("2001", freq="D")]),
),
- (pd.array([0, np.nan], dtype="Int64"), np.array([0, pd.NA], dtype=object)),
+ (pd.array([0, np.nan], dtype="Int64"), np.array([0, np.nan])),
(
IntervalArray.from_breaks([0, 1, 2]),
np.array([pd.Interval(0, 1), pd.Interval(1, 2)], dtype=object),
@@ -346,10 +346,6 @@ def test_to_numpy(arr, expected, index_or_series_or_array, request):
with tm.assert_produces_warning(None):
thing = box(arr)
- if arr.dtype.name == "int64" and box is pd.array:
- mark = pytest.mark.xfail(reason="thing is Int64 and to_numpy() returns object")
- request.applymarker(mark)
-
result = thing.to_numpy()
tm.assert_numpy_array_equal(result, expected)
diff --git a/pandas/tests/copy_view/test_array.py b/pandas/tests/copy_view/test_array.py
index e5d0aafb80b35..9a3f83e0293f5 100644
--- a/pandas/tests/copy_view/test_array.py
+++ b/pandas/tests/copy_view/test_array.py
@@ -133,21 +133,25 @@ def test_series_array_ea_dtypes(using_copy_on_write):
assert arr.flags.writeable is True
arr = np.asarray(ser)
- assert not np.shares_memory(arr, get_array(ser))
- assert arr.flags.writeable is True
+ assert np.shares_memory(arr, get_array(ser))
+ if using_copy_on_write:
+ assert arr.flags.writeable is False
+ else:
+ assert arr.flags.writeable is True
def test_dataframe_array_ea_dtypes(using_copy_on_write):
df = DataFrame({"a": [1, 2, 3]}, dtype="Int64")
arr = np.asarray(df, dtype="int64")
- # TODO: This should be able to share memory, but we are roundtripping
- # through object
- assert not np.shares_memory(arr, get_array(df, "a"))
- assert arr.flags.writeable is True
+ assert np.shares_memory(arr, get_array(df, "a"))
+ if using_copy_on_write:
+ assert arr.flags.writeable is False
+ else:
+ assert arr.flags.writeable is True
arr = np.asarray(df)
+ assert np.shares_memory(arr, get_array(df, "a"))
if using_copy_on_write:
- # TODO(CoW): This should be True
assert arr.flags.writeable is False
else:
assert arr.flags.writeable is True
diff --git a/pandas/tests/extension/test_masked.py b/pandas/tests/extension/test_masked.py
index be4077d921a9e..3efc561d6a125 100644
--- a/pandas/tests/extension/test_masked.py
+++ b/pandas/tests/extension/test_masked.py
@@ -169,6 +169,16 @@ def data_for_grouping(dtype):
class TestMaskedArrays(base.ExtensionTests):
+ @pytest.mark.parametrize("na_action", [None, "ignore"])
+ def test_map(self, data_missing, na_action):
+ result = data_missing.map(lambda x: x, na_action=na_action)
+ if data_missing.dtype == Float32Dtype():
+ # map roundtrips through objects, which converts to float64
+ expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
+ else:
+ expected = data_missing.to_numpy()
+ tm.assert_numpy_array_equal(result, expected)
+
def _get_expected_exception(self, op_name, obj, other):
try:
dtype = tm.get_dtype(obj)
diff --git a/pandas/tests/frame/indexing/test_where.py b/pandas/tests/frame/indexing/test_where.py
index 6b2bf211ab748..103ec67951a01 100644
--- a/pandas/tests/frame/indexing/test_where.py
+++ b/pandas/tests/frame/indexing/test_where.py
@@ -717,15 +717,12 @@ def test_where_ea_other(self):
# TODO: ideally we would get Int64 instead of object
result = df.where(mask, ser, axis=0)
- expected = DataFrame({"A": [1, pd.NA, 3], "B": [4, pd.NA, 6]}).astype(object)
+ expected = DataFrame({"A": [1, np.nan, 3], "B": [4, np.nan, 6]})
tm.assert_frame_equal(result, expected)
ser2 = Series(arr[:2], index=["A", "B"])
- expected = DataFrame({"A": [1, 7, 3], "B": [4, pd.NA, 6]})
- expected["B"] = expected["B"].astype(object)
- msg = "Downcasting behavior in Series and DataFrame methods 'where'"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.where(mask, ser2, axis=1)
+ expected = DataFrame({"A": [1, 7, 3], "B": [4, np.nan, 6]})
+ result = df.where(mask, ser2, axis=1)
tm.assert_frame_equal(result, expected)
def test_where_interval_noop(self):
diff --git a/pandas/tests/frame/test_repr.py b/pandas/tests/frame/test_repr.py
index eed48b9db116b..6184e791cab5d 100644
--- a/pandas/tests/frame/test_repr.py
+++ b/pandas/tests/frame/test_repr.py
@@ -456,6 +456,20 @@ def test_to_records_with_inf_record(self):
result = repr(df)
assert result == expected
+ def test_masked_ea_with_formatter(self):
+ # GH#39336
+ df = DataFrame(
+ {
+ "a": Series([0.123456789, 1.123456789], dtype="Float64"),
+ "b": Series([1, 2], dtype="Int64"),
+ }
+ )
+ result = df.to_string(formatters=["{:.2f}".format, "{:.2f}".format])
+ expected = """ a b
+0 0.12 1.00
+1 1.12 2.00"""
+ assert result == expected
+
def test_repr_ea_columns(self, any_string_dtype):
# GH#54797
pytest.importorskip("pyarrow")
| - [ ] closes #48891 (Replace xxxx with the GitHub issue number)
- [ ] closes #40630
- [ ] closes #37460
- [ ] [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.
This still needs a whatsnew | https://api.github.com/repos/pandas-dev/pandas/pulls/55058 | 2023-09-07T20:53:31Z | 2023-12-01T19:34:48Z | 2023-12-01T19:34:48Z | 2023-12-01T19:36:29Z |
DOC: fix an example which raises an Error in whatsnew/v0.10.0.rst | diff --git a/doc/source/whatsnew/v0.10.0.rst b/doc/source/whatsnew/v0.10.0.rst
index 3425986a37743..422efc1b36946 100644
--- a/doc/source/whatsnew/v0.10.0.rst
+++ b/doc/source/whatsnew/v0.10.0.rst
@@ -180,19 +180,36 @@ labeled the aggregated group with the end of the interval: the next day).
DataFrame constructor with no columns specified. The v0.9.0 behavior (names
``X0``, ``X1``, ...) can be reproduced by specifying ``prefix='X'``:
-.. ipython:: python
- :okexcept:
-
- import io
-
- data = """
- a,b,c
- 1,Yes,2
- 3,No,4
- """
- print(data)
- pd.read_csv(io.StringIO(data), header=None)
- pd.read_csv(io.StringIO(data), header=None, prefix="X")
+.. code-block:: ipython
+
+ In [6]: import io
+
+ In [7]: data = """
+ ...: a,b,c
+ ...: 1,Yes,2
+ ...: 3,No,4
+ ...: """
+ ...:
+
+ In [8]: print(data)
+
+ a,b,c
+ 1,Yes,2
+ 3,No,4
+
+ In [9]: pd.read_csv(io.StringIO(data), header=None)
+ Out[9]:
+ 0 1 2
+ 0 a b c
+ 1 1 Yes 2
+ 2 3 No 4
+
+ In [10]: pd.read_csv(io.StringIO(data), header=None, prefix="X")
+ Out[10]:
+ X0 X1 X2
+ 0 a b c
+ 1 1 Yes 2
+ 2 3 No 4
- Values like ``'Yes'`` and ``'No'`` are not interpreted as boolean by default,
though this can be controlled by new ``true_values`` and ``false_values``
| Fix an example in doc/source/whatsnew/v0.10.0.rst, which raises an IndentationError (see
https://pandas.pydata.org/docs/dev/whatsnew/v0.10.0.html) | https://api.github.com/repos/pandas-dev/pandas/pulls/55057 | 2023-09-07T19:40:51Z | 2023-09-11T16:14:59Z | 2023-09-11T16:14:59Z | 2023-09-11T16:15:06Z |
Backport PR #55042 on branch 2.1.x (REGR: DataFrameGroupBy.agg with duplicate column names and a dict) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index fe511b5cdec67..42af61be26355 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -21,6 +21,7 @@ Fixed regressions
- Fixed regression in :meth:`DataFrame.__setitem__` raising ``AssertionError`` when setting a :class:`Series` with a partial :class:`MultiIndex` (:issue:`54875`)
- Fixed regression in :meth:`DataFrame.filter` not respecting the order of elements for ``filter`` (:issue:`54980`)
- Fixed regression in :meth:`DataFrame.to_sql` not roundtripping datetime columns correctly for sqlite (:issue:`54877`)
+- Fixed regression in :meth:`DataFrameGroupBy.agg` when aggregating a DataFrame with duplicate column names using a dictionary (:issue:`55006`)
- Fixed regression in :meth:`MultiIndex.append` raising when appending overlapping :class:`IntervalIndex` levels (:issue:`54934`)
- Fixed regression in :meth:`Series.drop_duplicates` for PyArrow strings (:issue:`54904`)
- Fixed regression in :meth:`Series.interpolate` raising when ``fill_value`` was given (:issue:`54920`)
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 6ab2f958b8730..712294a0a4c96 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -434,7 +434,13 @@ def compute_dict_like(
Data for result. When aggregating with a Series, this can contain any
Python object.
"""
+ from pandas.core.groupby.generic import (
+ DataFrameGroupBy,
+ SeriesGroupBy,
+ )
+
obj = self.obj
+ is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
func = cast(AggFuncTypeDict, self.func)
func = self.normalize_dictlike_arg(op_name, selected_obj, func)
@@ -448,7 +454,7 @@ def compute_dict_like(
colg = obj._gotitem(selection, ndim=1)
results = [getattr(colg, op_name)(how, **kwargs) for _, how in func.items()]
keys = list(func.keys())
- elif is_non_unique_col:
+ elif not is_groupby and is_non_unique_col:
# key used for column selection and output
# GH#51099
results = []
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index cdfa80c8c7cb5..406f4b9b127e7 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -513,6 +513,18 @@ def test_groupby_agg_dict_with_getitem():
tm.assert_frame_equal(result, expected)
+def test_groupby_agg_dict_dup_columns():
+ # GH#55006
+ df = DataFrame(
+ [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]],
+ columns=["a", "b", "c", "c"],
+ )
+ gb = df.groupby("a")
+ result = gb.agg({"b": "sum"})
+ expected = DataFrame({"b": [5, 4]}, index=Index([1, 2], name="a"))
+ tm.assert_frame_equal(result, expected)
+
+
@pytest.mark.parametrize(
"op",
[
| Backport PR #55042: REGR: DataFrameGroupBy.agg with duplicate column names and a dict | https://api.github.com/repos/pandas-dev/pandas/pulls/55056 | 2023-09-07T16:33:02Z | 2023-09-07T18:28:37Z | 2023-09-07T18:28:37Z | 2023-09-07T18:28:37Z |
Backport PR #55010 on branch 2.1.x (BLD: Build wheels for Python 3.12) | diff --git a/.circleci/config.yml b/.circleci/config.yml
index 50f6a116a6630..ba124533e953a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -48,7 +48,7 @@ jobs:
name: Build aarch64 wheels
no_output_timeout: 30m # Sometimes the tests won't generate any output, make sure the job doesn't get killed by that
command: |
- pip3 install cibuildwheel==2.14.1
+ pip3 install cibuildwheel==2.15.0
cibuildwheel --prerelease-pythons --output-dir wheelhouse
environment:
CIBW_BUILD: << parameters.cibw-build >>
@@ -92,5 +92,4 @@ workflows:
only: /^v.*/
matrix:
parameters:
- # TODO: Enable Python 3.12 wheels when numpy releases a version that supports Python 3.12
- cibw-build: ["cp39-manylinux_aarch64", "cp310-manylinux_aarch64", "cp311-manylinux_aarch64"]#, "cp312-manylinux_aarch64"]
+ cibw-build: ["cp39-manylinux_aarch64", "cp310-manylinux_aarch64", "cp311-manylinux_aarch64", "cp312-manylinux_aarch64"]
diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index 5f541f1bae1fd..97d78a1a9afe3 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -97,8 +97,7 @@ jobs:
- [macos-12, macosx_*]
- [windows-2022, win_amd64]
# TODO: support PyPy?
- # TODO: Enable Python 3.12 wheels when numpy releases a version that supports Python 3.12
- python: [["cp39", "3.9"], ["cp310", "3.10"], ["cp311", "3.11"]]#, ["cp312", "3.12"]]
+ python: [["cp39", "3.9"], ["cp310", "3.10"], ["cp311", "3.11"], ["cp312", "3.12"]]
env:
IS_PUSH: ${{ github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v') }}
IS_SCHEDULE_DISPATCH: ${{ github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }}
@@ -150,8 +149,10 @@ jobs:
uses: mamba-org/setup-micromamba@v1
with:
environment-name: wheel-env
+ # Use a fixed Python, since we might have an unreleased Python not
+ # yet present on conda-forge
create-args: >-
- python=${{ matrix.python[1] }}
+ python=3.11
anaconda-client
wheel
cache-downloads: true
@@ -167,12 +168,13 @@ jobs:
shell: pwsh
run: |
$TST_CMD = @"
- python -m pip install pytz six numpy python-dateutil tzdata>=2022.1 hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17;
- python -m pip install --find-links=pandas\wheelhouse --no-index pandas;
+ python -m pip install hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-asyncio>=0.17;
+ python -m pip install `$(Get-Item pandas\wheelhouse\*.whl);
python -c `'import pandas as pd; pd.test(extra_args=[\"`\"--no-strict-data-files`\"\", \"`\"-m not clipboard and not single_cpu and not slow and not network and not db`\"\"])`';
"@
- docker pull python:${{ matrix.python[1] }}-windowsservercore
- docker run --env PANDAS_CI='1' -v ${PWD}:C:\pandas python:${{ matrix.python[1] }}-windowsservercore powershell -Command $TST_CMD
+ # add rc to the end of the image name if the Python version is unreleased
+ docker pull python:${{ matrix.python[1] == '3.12' && '3.12-rc' || format('{0}-windowsservercore', matrix.python[1]) }}
+ docker run --env PANDAS_CI='1' -v ${PWD}:C:\pandas python:${{ matrix.python[1] == '3.12' && '3.12-rc' || format('{0}-windowsservercore', matrix.python[1]) }} powershell -Command $TST_CMD
- uses: actions/upload-artifact@v3
with:
diff --git a/pyproject.toml b/pyproject.toml
index d2fb6a8d854d7..752ee846eed33 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,8 @@ requires = [
# we don't want to force users to compile with 1.25 though
# (Ideally, in the future, though, oldest-supported-numpy can be dropped when our min numpy is 1.25.x)
"oldest-supported-numpy>=2022.8.16; python_version<'3.12'",
- "numpy>=1.22.4; python_version>='3.12'",
+ # TODO: This needs to be updated when the official numpy 1.26 comes out
+ "numpy>=1.26.0b1; python_version>='3.12'",
"versioneer[toml]"
]
@@ -30,7 +31,9 @@ license = {file = 'LICENSE'}
requires-python = '>=3.9'
dependencies = [
"numpy>=1.22.4; python_version<'3.11'",
- "numpy>=1.23.2; python_version>='3.11'",
+ "numpy>=1.23.2; python_version=='3.11'",
+ # TODO: This needs to be updated when the official numpy 1.26 comes out
+ "numpy>=1.26.0b1; python_version>='3.12'",
"python-dateutil>=2.8.2",
"pytz>=2020.1",
"tzdata>=2022.1"
| Backport PR #55010: BLD: Build wheels for Python 3.12 | https://api.github.com/repos/pandas-dev/pandas/pulls/55055 | 2023-09-07T16:28:54Z | 2023-09-07T20:23:21Z | 2023-09-07T20:23:21Z | 2023-09-07T20:23:21Z |
Backport PR #55051 on branch 2.1.x (Fix pickle roundtrip for new arrow string dtype) | diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index cae975c96d61f..0b8d3d6b13f60 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -466,7 +466,10 @@ def _result_converter(cls, values, na=None):
def __getattribute__(self, item):
# ArrowStringArray and we both inherit from ArrowExtensionArray, which
# creates inheritance problems (Diamond inheritance)
- if item in ArrowStringArrayMixin.__dict__ and item != "_pa_array":
+ if item in ArrowStringArrayMixin.__dict__ and item not in (
+ "_pa_array",
+ "__dict__",
+ ):
return partial(getattr(ArrowStringArrayMixin, item), self)
return super().__getattribute__(item)
diff --git a/pandas/tests/arrays/string_/test_string_arrow.py b/pandas/tests/arrays/string_/test_string_arrow.py
index 09f9f788dc3e4..b611e19f939ad 100644
--- a/pandas/tests/arrays/string_/test_string_arrow.py
+++ b/pandas/tests/arrays/string_/test_string_arrow.py
@@ -241,9 +241,10 @@ def test_setitem_invalid_indexer_raises():
@skip_if_no_pyarrow
-def test_pickle_roundtrip():
+@pytest.mark.parametrize("dtype", ["string[pyarrow]", "string[pyarrow_numpy]"])
+def test_pickle_roundtrip(dtype):
# GH 42600
- expected = pd.Series(range(10), dtype="string[pyarrow]")
+ expected = pd.Series(range(10), dtype=dtype)
expected_sliced = expected.head(2)
full_pickled = pickle.dumps(expected)
sliced_pickled = pickle.dumps(expected_sliced)
| Backport PR #55051: Fix pickle roundtrip for new arrow string dtype | https://api.github.com/repos/pandas-dev/pandas/pulls/55054 | 2023-09-07T16:05:17Z | 2023-09-07T18:13:00Z | 2023-09-07T18:13:00Z | 2023-09-07T18:13:01Z |
Backport PR #55052 on branch 2.1.x (Improve error message for StringDtype with invalid storage) | diff --git a/pandas/core/arrays/string_.py b/pandas/core/arrays/string_.py
index 84a85b9e56aff..58c638e7f59b5 100644
--- a/pandas/core/arrays/string_.py
+++ b/pandas/core/arrays/string_.py
@@ -119,7 +119,8 @@ def __init__(self, storage=None) -> None:
storage = get_option("mode.string_storage")
if storage not in {"python", "pyarrow", "pyarrow_numpy"}:
raise ValueError(
- f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
+ f"Storage must be 'python', 'pyarrow' or 'pyarrow_numpy'. "
+ f"Got {storage} instead."
)
if storage in ("pyarrow", "pyarrow_numpy") and pa_version_under7p0:
raise ImportError(
diff --git a/pandas/tests/arrays/string_/test_string_arrow.py b/pandas/tests/arrays/string_/test_string_arrow.py
index 09f9f788dc3e4..fb6c338b8f8ea 100644
--- a/pandas/tests/arrays/string_/test_string_arrow.py
+++ b/pandas/tests/arrays/string_/test_string_arrow.py
@@ -255,3 +255,11 @@ def test_pickle_roundtrip():
result_sliced = pickle.loads(sliced_pickled)
tm.assert_series_equal(result_sliced, expected_sliced)
+
+
+@skip_if_no_pyarrow
+def test_string_dtype_error_message():
+ # GH#55051
+ msg = "Storage must be 'python', 'pyarrow' or 'pyarrow_numpy'."
+ with pytest.raises(ValueError, match=msg):
+ StringDtype("bla")
| Backport PR #55052: Improve error message for StringDtype with invalid storage | https://api.github.com/repos/pandas-dev/pandas/pulls/55053 | 2023-09-07T16:02:03Z | 2023-09-07T18:12:46Z | 2023-09-07T18:12:46Z | 2023-09-07T18:12:46Z |
Improve error message for StringDtype with invalid storage | diff --git a/pandas/core/arrays/string_.py b/pandas/core/arrays/string_.py
index c90127c0e9812..693ebad0ca16f 100644
--- a/pandas/core/arrays/string_.py
+++ b/pandas/core/arrays/string_.py
@@ -123,7 +123,8 @@ def __init__(self, storage=None) -> None:
storage = get_option("mode.string_storage")
if storage not in {"python", "pyarrow", "pyarrow_numpy"}:
raise ValueError(
- f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
+ f"Storage must be 'python', 'pyarrow' or 'pyarrow_numpy'. "
+ f"Got {storage} instead."
)
if storage in ("pyarrow", "pyarrow_numpy") and pa_version_under7p0:
raise ImportError(
diff --git a/pandas/tests/arrays/string_/test_string_arrow.py b/pandas/tests/arrays/string_/test_string_arrow.py
index 09f9f788dc3e4..fb6c338b8f8ea 100644
--- a/pandas/tests/arrays/string_/test_string_arrow.py
+++ b/pandas/tests/arrays/string_/test_string_arrow.py
@@ -255,3 +255,11 @@ def test_pickle_roundtrip():
result_sliced = pickle.loads(sliced_pickled)
tm.assert_series_equal(result_sliced, expected_sliced)
+
+
+@skip_if_no_pyarrow
+def test_string_dtype_error_message():
+ # GH#55051
+ msg = "Storage must be 'python', 'pyarrow' or 'pyarrow_numpy'."
+ with pytest.raises(ValueError, match=msg):
+ StringDtype("bla")
| - [ ] 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/55052 | 2023-09-07T13:58:35Z | 2023-09-07T16:00:41Z | 2023-09-07T16:00:41Z | 2023-09-07T16:05:43Z |
Fix pickle roundtrip for new arrow string dtype | diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index a6838fbc73be9..6262055827428 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -523,7 +523,10 @@ def _result_converter(cls, values, na=None):
def __getattribute__(self, item):
# ArrowStringArray and we both inherit from ArrowExtensionArray, which
# creates inheritance problems (Diamond inheritance)
- if item in ArrowStringArrayMixin.__dict__ and item != "_pa_array":
+ if item in ArrowStringArrayMixin.__dict__ and item not in (
+ "_pa_array",
+ "__dict__",
+ ):
return partial(getattr(ArrowStringArrayMixin, item), self)
return super().__getattribute__(item)
diff --git a/pandas/tests/arrays/string_/test_string_arrow.py b/pandas/tests/arrays/string_/test_string_arrow.py
index 09f9f788dc3e4..b611e19f939ad 100644
--- a/pandas/tests/arrays/string_/test_string_arrow.py
+++ b/pandas/tests/arrays/string_/test_string_arrow.py
@@ -241,9 +241,10 @@ def test_setitem_invalid_indexer_raises():
@skip_if_no_pyarrow
-def test_pickle_roundtrip():
+@pytest.mark.parametrize("dtype", ["string[pyarrow]", "string[pyarrow_numpy]"])
+def test_pickle_roundtrip(dtype):
# GH 42600
- expected = pd.Series(range(10), dtype="string[pyarrow]")
+ expected = pd.Series(range(10), dtype=dtype)
expected_sliced = expected.head(2)
full_pickled = pickle.dumps(expected)
sliced_pickled = pickle.dumps(expected_sliced)
| - [ ] 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/55051 | 2023-09-07T13:58:15Z | 2023-09-07T16:05:09Z | 2023-09-07T16:05:09Z | 2023-09-07T16:05:39Z |
Fix docstring of Index.join in base.py | diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 6a397862712de..cd55997ad5f69 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -4557,7 +4557,7 @@ def join(
-------
join_index, (left_indexer, right_indexer)
- Examples
+ Examples
--------
>>> idx1 = pd.Index([1, 2, 3])
>>> idx2 = pd.Index([4, 5, 6])
| - [ ] 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/55050 | 2023-09-07T13:43:26Z | 2023-09-07T16:07:11Z | 2023-09-07T16:07:11Z | 2023-09-20T11:00:16Z |
COMPAT: bump pyarrow min version for div on duration | diff --git a/pandas/compat/__init__.py b/pandas/compat/__init__.py
index be0a762642e46..684e9dccdc0f9 100644
--- a/pandas/compat/__init__.py
+++ b/pandas/compat/__init__.py
@@ -30,6 +30,7 @@
pa_version_under9p0,
pa_version_under11p0,
pa_version_under13p0,
+ pa_version_under14p0,
)
if TYPE_CHECKING:
@@ -186,6 +187,7 @@ def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
"pa_version_under9p0",
"pa_version_under11p0",
"pa_version_under13p0",
+ "pa_version_under14p0",
"IS64",
"ISMUSL",
"PY310",
diff --git a/pandas/compat/pyarrow.py b/pandas/compat/pyarrow.py
index 049ce50920e28..12f58be109d98 100644
--- a/pandas/compat/pyarrow.py
+++ b/pandas/compat/pyarrow.py
@@ -15,6 +15,7 @@
pa_version_under11p0 = _palv < Version("11.0.0")
pa_version_under12p0 = _palv < Version("12.0.0")
pa_version_under13p0 = _palv < Version("13.0.0")
+ pa_version_under14p0 = _palv < Version("14.0.0")
except ImportError:
pa_version_under7p0 = True
pa_version_under8p0 = True
@@ -23,3 +24,4 @@
pa_version_under11p0 = True
pa_version_under12p0 = True
pa_version_under13p0 = True
+ pa_version_under14p0 = True
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index fa6e85ba204d2..2e98eea3cac8a 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -40,6 +40,7 @@
pa_version_under9p0,
pa_version_under11p0,
pa_version_under13p0,
+ pa_version_under14p0,
)
from pandas.core.dtypes.dtypes import (
@@ -917,7 +918,7 @@ def _is_temporal_supported(self, opname, pa_dtype):
or (
opname
in ("__truediv__", "__rtruediv__", "__floordiv__", "__rfloordiv__")
- and not pa_version_under13p0
+ and not pa_version_under14p0
)
)
and pa.types.is_duration(pa_dtype)
| - [x] closes #55020
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55048 | 2023-09-07T12:58:48Z | 2023-09-08T20:33:47Z | 2023-09-08T20:33:47Z | 2023-09-08T20:33:54Z |
DEPR: ArrayManager | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 55a3419e95703..4b3ed10f22a22 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -198,6 +198,7 @@ Deprecations
- 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`)
- Deprecated the extension test classes ``BaseNoReduceTests``, ``BaseBooleanReduceTests``, and ``BaseNumericReduceTests``, use ``BaseReduceTests`` instead (:issue:`54663`)
+- Deprecated the option ``mode.data_manager`` and the ``ArrayManager``; only the ``BlockManager`` will be available in future versions (:issue:`55043`)
- Deprecating downcasting the results of :meth:`DataFrame.fillna`, :meth:`Series.fillna`, :meth:`DataFrame.ffill`, :meth:`Series.ffill`, :meth:`DataFrame.bfill`, :meth:`Series.bfill` in object-dtype cases. To opt in to the future version, use ``pd.set_option("future.no_silent_downcasting", True)`` (:issue:`54261`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/__init__.py b/pandas/__init__.py
index d11a429987ac4..41e34309232ee 100644
--- a/pandas/__init__.py
+++ b/pandas/__init__.py
@@ -1,5 +1,8 @@
from __future__ import annotations
+import os
+import warnings
+
__docformat__ = "restructuredtext"
# Let users know if they're missing any of our hard dependencies
@@ -190,6 +193,17 @@
__git_version__ = v.get("full-revisionid")
del get_versions, v
+# GH#55043 - deprecation of the data_manager option
+if "PANDAS_DATA_MANAGER" in os.environ:
+ warnings.warn(
+ "The env variable PANDAS_DATA_MANAGER is set. The data_manager option is "
+ "deprecated and will be removed in a future version. Only the BlockManager "
+ "will be available. Unset this environment variable to silence this warning.",
+ FutureWarning,
+ stacklevel=2,
+ )
+# Don't allow users to use pandas.os or pandas.warnings
+del os, warnings
# module level doc-string
__doc__ = """
diff --git a/pandas/conftest.py b/pandas/conftest.py
index ac0275bf695d4..62f22921f0482 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -49,6 +49,8 @@
utc,
)
+from pandas._config.config import _get_option
+
import pandas.util._test_decorators as td
from pandas.core.dtypes.dtypes import (
@@ -1983,7 +1985,7 @@ def using_array_manager() -> bool:
"""
Fixture to check if the array manager is being used.
"""
- return pd.options.mode.data_manager == "array"
+ return _get_option("mode.data_manager", silent=True) == "array"
@pytest.fixture
@@ -1991,7 +1993,10 @@ def using_copy_on_write() -> bool:
"""
Fixture to check if Copy-on-Write is enabled.
"""
- return pd.options.mode.copy_on_write and pd.options.mode.data_manager == "block"
+ return (
+ pd.options.mode.copy_on_write
+ and _get_option("mode.data_manager", silent=True) == "block"
+ )
warsaws = ["Europe/Warsaw", "dateutil/Europe/Warsaw"]
diff --git a/pandas/core/config_init.py b/pandas/core/config_init.py
index 750b374043193..4652acdcae287 100644
--- a/pandas/core/config_init.py
+++ b/pandas/core/config_init.py
@@ -454,6 +454,13 @@ def use_inf_as_na_cb(key) -> None:
validator=is_one_of_factory(["block", "array"]),
)
+cf.deprecate_option(
+ # GH#55043
+ "mode.data_manager",
+ "data_manager option is deprecated and will be removed in a future "
+ "version. Only the BlockManager will be available.",
+)
+
# TODO better name?
copy_on_write_doc = """
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 4e87e90278e7b..3e32a6d93b023 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -43,6 +43,7 @@
get_option,
using_copy_on_write,
)
+from pandas._config.config import _get_option
from pandas._libs import (
algos as libalgos,
@@ -694,7 +695,7 @@ def __init__(
NDFrame.__init__(self, data)
return
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
# GH47215
if isinstance(index, set):
@@ -2411,7 +2412,7 @@ def maybe_reorder(
columns = columns.drop(exclude)
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
mgr = arrays_to_mgr(arrays, columns, result_index, typ=manager)
return cls(mgr)
@@ -2612,7 +2613,7 @@ def _from_arrays(
if dtype is not None:
dtype = pandas_dtype(dtype)
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
columns = ensure_index(columns)
if len(columns) != len(arrays):
raise ValueError("len(columns) must match len(arrays)")
diff --git a/pandas/core/series.py b/pandas/core/series.py
index e0e27581ef7e2..8f64c0ecf5d73 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -26,10 +26,8 @@
import numpy as np
-from pandas._config import (
- get_option,
- using_copy_on_write,
-)
+from pandas._config import using_copy_on_write
+from pandas._config.config import _get_option
from pandas._libs import (
lib,
@@ -407,7 +405,7 @@ def __init__(
if fastpath:
# data is a ndarray, index is defined
if not isinstance(data, (SingleBlockManager, SingleArrayManager)):
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
if manager == "block":
data = SingleBlockManager.from_array(data, index)
elif manager == "array":
@@ -513,7 +511,7 @@ def __init__(
else:
data = sanitize_array(data, index, dtype, copy)
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
if manager == "block":
data = SingleBlockManager.from_array(data, index, refs=refs)
elif manager == "array":
diff --git a/pandas/io/parquet.py b/pandas/io/parquet.py
index f51b98a929440..ed254191d2736 100644
--- a/pandas/io/parquet.py
+++ b/pandas/io/parquet.py
@@ -13,6 +13,7 @@
from warnings import catch_warnings
from pandas._config import using_pyarrow_string_dtype
+from pandas._config.config import _get_option
from pandas._libs import lib
from pandas.compat._optional import import_optional_dependency
@@ -258,7 +259,7 @@ def read(
elif using_pyarrow_string_dtype():
to_pandas_kwargs["types_mapper"] = arrow_string_types_mapper()
- manager = get_option("mode.data_manager")
+ manager = _get_option("mode.data_manager", silent=True)
if manager == "array":
to_pandas_kwargs["split_blocks"] = True # type: ignore[assignment]
diff --git a/pandas/tests/extension/conftest.py b/pandas/tests/extension/conftest.py
index eb60aea7cc8c2..a94f7de283d01 100644
--- a/pandas/tests/extension/conftest.py
+++ b/pandas/tests/extension/conftest.py
@@ -2,6 +2,8 @@
import pytest
+from pandas._config.config import _get_option
+
from pandas import (
Series,
options,
@@ -212,4 +214,7 @@ def using_copy_on_write() -> bool:
"""
Fixture to check if Copy-on-Write is enabled.
"""
- return options.mode.copy_on_write and options.mode.data_manager == "block"
+ return (
+ options.mode.copy_on_write
+ and _get_option("mode.data_manager", silent=True) == "block"
+ )
diff --git a/pandas/tests/internals/test_managers.py b/pandas/tests/internals/test_managers.py
index 75aa901fce910..f40362c299717 100644
--- a/pandas/tests/internals/test_managers.py
+++ b/pandas/tests/internals/test_managers.py
@@ -1,6 +1,12 @@
"""
Testing interaction between the different managers (BlockManager, ArrayManager)
"""
+import os
+import subprocess
+import sys
+
+import pytest
+
from pandas.core.dtypes.missing import array_equivalent
import pandas as pd
@@ -14,12 +20,19 @@
def test_dataframe_creation():
- with pd.option_context("mode.data_manager", "block"):
- df_block = pd.DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]})
+ msg = "data_manager option is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ with pd.option_context("mode.data_manager", "block"):
+ df_block = pd.DataFrame(
+ {"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]}
+ )
assert isinstance(df_block._mgr, BlockManager)
- with pd.option_context("mode.data_manager", "array"):
- df_array = pd.DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]})
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ with pd.option_context("mode.data_manager", "array"):
+ df_array = pd.DataFrame(
+ {"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]}
+ )
assert isinstance(df_array._mgr, ArrayManager)
# also ensure both are seen as equal
@@ -45,12 +58,15 @@ def test_dataframe_creation():
def test_series_creation():
- with pd.option_context("mode.data_manager", "block"):
- s_block = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
+ msg = "data_manager option is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ with pd.option_context("mode.data_manager", "block"):
+ s_block = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
assert isinstance(s_block._mgr, SingleBlockManager)
- with pd.option_context("mode.data_manager", "array"):
- s_array = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ with pd.option_context("mode.data_manager", "array"):
+ s_array = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"])
assert isinstance(s_array._mgr, SingleArrayManager)
# also ensure both are seen as equal
@@ -68,3 +84,20 @@ def test_series_creation():
result = s_array._as_manager("block")
assert isinstance(result._mgr, SingleBlockManager)
tm.assert_series_equal(result, s_array)
+
+
+@pytest.mark.single_cpu
+@pytest.mark.parametrize("manager", ["block", "array"])
+def test_array_manager_depr_env_var(manager):
+ # GH#55043
+ test_env = os.environ.copy()
+ test_env["PANDAS_DATA_MANAGER"] = manager
+ response = subprocess.run(
+ [sys.executable, "-c", "import pandas"],
+ capture_output=True,
+ env=test_env,
+ check=True,
+ )
+ msg = "FutureWarning: The env variable PANDAS_DATA_MANAGER is set"
+ stderr_msg = response.stderr.decode("utf-8")
+ assert msg in stderr_msg, stderr_msg
diff --git a/pandas/tests/io/test_parquet.py b/pandas/tests/io/test_parquet.py
index 55445e44b9366..b043f9fab23ae 100644
--- a/pandas/tests/io/test_parquet.py
+++ b/pandas/tests/io/test_parquet.py
@@ -8,10 +8,8 @@
import numpy as np
import pytest
-from pandas._config import (
- get_option,
- using_copy_on_write,
-)
+from pandas._config import using_copy_on_write
+from pandas._config.config import _get_option
from pandas.compat import is_platform_windows
from pandas.compat.pyarrow import (
@@ -61,7 +59,8 @@
pytest.param(
"fastparquet",
marks=pytest.mark.skipif(
- not _HAVE_FASTPARQUET or get_option("mode.data_manager") == "array",
+ not _HAVE_FASTPARQUET
+ or _get_option("mode.data_manager", silent=True) == "array",
reason="fastparquet is not installed or ArrayManager is used",
),
),
@@ -88,7 +87,7 @@ def pa():
def fp():
if not _HAVE_FASTPARQUET:
pytest.skip("fastparquet is not installed")
- elif get_option("mode.data_manager") == "array":
+ elif _get_option("mode.data_manager", silent=True) == "array":
pytest.skip("ArrayManager is not supported with fastparquet")
return "fastparquet"
diff --git a/pandas/util/_test_decorators.py b/pandas/util/_test_decorators.py
index 03011a1ffe622..9be0c3edaa998 100644
--- a/pandas/util/_test_decorators.py
+++ b/pandas/util/_test_decorators.py
@@ -38,6 +38,9 @@ def test_foo():
if TYPE_CHECKING:
from pandas._typing import F
+
+from pandas._config.config import _get_option
+
from pandas.compat import (
IS64,
is_platform_windows,
@@ -230,12 +233,12 @@ def mark_array_manager_not_yet_implemented(request) -> None:
skip_array_manager_not_yet_implemented = pytest.mark.xfail(
- get_option("mode.data_manager") == "array",
+ _get_option("mode.data_manager", silent=True) == "array",
reason="Not yet implemented for ArrayManager",
)
skip_array_manager_invalid_test = pytest.mark.skipif(
- get_option("mode.data_manager") == "array",
+ _get_option("mode.data_manager", silent=True) == "array",
reason="Test that relies on BlockManager internals or specific behaviour",
)
diff --git a/scripts/validate_unwanted_patterns.py b/scripts/validate_unwanted_patterns.py
index 0931dd209ee05..d765d7bc7dcb9 100755
--- a/scripts/validate_unwanted_patterns.py
+++ b/scripts/validate_unwanted_patterns.py
@@ -51,6 +51,8 @@
"_chained_assignment_msg",
"_chained_assignment_method_msg",
"_version_meson",
+ # TODO(3.0): GH#55043 - remove upon removal of ArrayManager
+ "_get_option",
}
| - [x] closes #55043 (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/55044 | 2023-09-07T02:49:01Z | 2023-09-20T01:53:51Z | 2023-09-20T01:53:51Z | 2023-09-20T02:21:32Z |
REGR: DataFrameGroupBy.agg with duplicate column names and a dict | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 11b19b1508a71..3262f576b4e6b 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -19,6 +19,7 @@ Fixed regressions
- Fixed regression in :meth:`.GroupBy.get_group` raising for ``axis=1`` (:issue:`54858`)
- Fixed regression in :meth:`DataFrame.__setitem__` raising ``AssertionError`` when setting a :class:`Series` with a partial :class:`MultiIndex` (:issue:`54875`)
- Fixed regression in :meth:`DataFrame.to_sql` not roundtripping datetime columns correctly for sqlite (:issue:`54877`)
+- Fixed regression in :meth:`DataFrameGroupBy.agg` when aggregating a DataFrame with duplicate column names using a dictionary (:issue:`55006`)
- Fixed regression in :meth:`MultiIndex.append` raising when appending overlapping :class:`IntervalIndex` levels (:issue:`54934`)
- Fixed regression in :meth:`Series.drop_duplicates` for PyArrow strings (:issue:`54904`)
- Fixed regression in :meth:`Series.value_counts` raising for numeric data if ``bins`` was specified (:issue:`54857`)
diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 4d6dd8f4fd577..26467a4a982fa 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -436,7 +436,13 @@ def compute_dict_like(
Data for result. When aggregating with a Series, this can contain any
Python object.
"""
+ from pandas.core.groupby.generic import (
+ DataFrameGroupBy,
+ SeriesGroupBy,
+ )
+
obj = self.obj
+ is_groupby = isinstance(obj, (DataFrameGroupBy, SeriesGroupBy))
func = cast(AggFuncTypeDict, self.func)
func = self.normalize_dictlike_arg(op_name, selected_obj, func)
@@ -450,7 +456,7 @@ def compute_dict_like(
colg = obj._gotitem(selection, ndim=1)
results = [getattr(colg, op_name)(how, **kwargs) for _, how in func.items()]
keys = list(func.keys())
- elif is_non_unique_col:
+ elif not is_groupby and is_non_unique_col:
# key used for column selection and output
# GH#51099
results = []
diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py
index c01ca4922a84b..882f42ff18bdd 100644
--- a/pandas/tests/groupby/aggregate/test_aggregate.py
+++ b/pandas/tests/groupby/aggregate/test_aggregate.py
@@ -515,6 +515,18 @@ def test_groupby_agg_dict_with_getitem():
tm.assert_frame_equal(result, expected)
+def test_groupby_agg_dict_dup_columns():
+ # GH#55006
+ df = DataFrame(
+ [[1, 2, 3, 4], [1, 3, 4, 5], [2, 4, 5, 6]],
+ columns=["a", "b", "c", "c"],
+ )
+ gb = df.groupby("a")
+ result = gb.agg({"b": "sum"})
+ expected = DataFrame({"b": [5, 4]}, index=Index([1, 2], name="a"))
+ tm.assert_frame_equal(result, expected)
+
+
@pytest.mark.parametrize(
"op",
[
| - [x] closes #55006 (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.
Unlike what I reported in #55006, the core issue is not how we treat kwargs. Rather, I believe the issue occurred in #52115 where logic specific for duplicate columns was added for Series/DataFrame but also impacted groupby.
This PR fixes the regression, but groupby still does not work with duplicate columns. I've confirmed groupby.agg does not work with duplicate columns in this case on 2.0.x, and believe it hasn't for a long time (possibly forever). For this, I've opened #55041. | https://api.github.com/repos/pandas-dev/pandas/pulls/55042 | 2023-09-07T00:37:26Z | 2023-09-07T16:32:53Z | 2023-09-07T16:32:53Z | 2023-09-07T20:22:42Z |
Backport PR #54983 on branch 2.1.x (BUG: pct_change showing unnecessary FutureWarning) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index b9bdb36fe0ed3..fe511b5cdec67 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -34,6 +34,7 @@ Bug fixes
~~~~~~~~~
- Fixed bug for :class:`ArrowDtype` raising ``NotImplementedError`` for fixed-size list (:issue:`55000`)
- Fixed bug in :meth:`DataFrame.stack` with ``future_stack=True`` and columns a non-:class:`MultiIndex` consisting of tuples (:issue:`54948`)
+- Fixed bug in :meth:`Series.pct_change` and :meth:`DataFrame.pct_change` showing unnecessary ``FutureWarning`` (:issue:`54981`)
.. ---------------------------------------------------------------------------
.. _whatsnew_211.other:
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f94e7e0bbab13..08d1bf6a0fabc 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -11692,15 +11692,21 @@ def pct_change(
stacklevel=find_stack_level(),
)
if fill_method is lib.no_default:
- if self.isna().values.any():
- warnings.warn(
- "The default fill_method='pad' in "
- f"{type(self).__name__}.pct_change is deprecated and will be "
- "removed in a future version. Call ffill before calling "
- "pct_change to retain current behavior and silence this warning.",
- FutureWarning,
- stacklevel=find_stack_level(),
- )
+ cols = self.items() if self.ndim == 2 else [(None, self)]
+ for _, col in cols:
+ mask = col.isna().values
+ mask = mask[np.argmax(~mask) :]
+ if mask.any():
+ warnings.warn(
+ "The default fill_method='pad' in "
+ f"{type(self).__name__}.pct_change is deprecated and will be "
+ "removed in a future version. Call ffill before calling "
+ "pct_change to retain current behavior and silence this "
+ "warning.",
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
+ break
fill_method = "pad"
if limit is lib.no_default:
limit = None
diff --git a/pandas/tests/frame/methods/test_pct_change.py b/pandas/tests/frame/methods/test_pct_change.py
index d0153da038a75..ede212ae18ae9 100644
--- a/pandas/tests/frame/methods/test_pct_change.py
+++ b/pandas/tests/frame/methods/test_pct_change.py
@@ -160,3 +160,21 @@ def test_pct_change_with_duplicated_indices(fill_method):
index=["a", "b"] * 3,
)
tm.assert_frame_equal(result, expected)
+
+
+def test_pct_change_none_beginning_no_warning():
+ # GH#54481
+ df = DataFrame(
+ [
+ [1, None],
+ [2, 1],
+ [3, 2],
+ [4, 3],
+ [5, 4],
+ ]
+ )
+ result = df.pct_change()
+ expected = DataFrame(
+ {0: [np.nan, 1, 0.5, 1 / 3, 0.25], 1: [np.nan, np.nan, 1, 0.5, 1 / 3]}
+ )
+ tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/series/methods/test_pct_change.py b/pandas/tests/series/methods/test_pct_change.py
index 4dabf7b87e2cd..6740b8756853e 100644
--- a/pandas/tests/series/methods/test_pct_change.py
+++ b/pandas/tests/series/methods/test_pct_change.py
@@ -107,3 +107,11 @@ def test_pct_change_with_duplicated_indices(fill_method):
expected = Series([np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], index=["a", "b"] * 3)
tm.assert_series_equal(result, expected)
+
+
+def test_pct_change_no_warning_na_beginning():
+ # GH#54981
+ ser = Series([None, None, 1, 2, 3])
+ result = ser.pct_change()
+ expected = Series([np.nan, np.nan, np.nan, 1, 0.5])
+ tm.assert_series_equal(result, expected)
| Backport PR #54983: BUG: pct_change showing unnecessary FutureWarning | https://api.github.com/repos/pandas-dev/pandas/pulls/55040 | 2023-09-06T22:41:28Z | 2023-09-07T00:20:29Z | 2023-09-07T00:20:29Z | 2023-09-07T00:20:29Z |
Add TODO note to BlockManager.fast_xs for EA dtypes | diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 4cb7b610074ba..b1db2d2e708e8 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -969,6 +969,10 @@ def fast_xs(self, loc: int) -> SingleBlockManager:
n = len(self)
if isinstance(dtype, ExtensionDtype):
+ # TODO: use object dtype as workaround for non-performant
+ # EA.__setitem__ methods. (primarily ArrowExtensionArray.__setitem__
+ # when iteratively setting individual values)
+ # https://github.com/pandas-dev/pandas/pull/54508#issuecomment-1675827918
result = np.empty(n, dtype=object)
else:
result = np.empty(n, dtype=dtype)
| cc @jbrockmendel
re: https://github.com/pandas-dev/pandas/pull/54508#issuecomment-1708623215
| https://api.github.com/repos/pandas-dev/pandas/pulls/55039 | 2023-09-06T21:44:30Z | 2023-09-08T16:41:42Z | 2023-09-08T16:41:42Z | 2023-09-11T01:29:45Z |
Cythonize is_null_slice | diff --git a/pandas/_libs/lib.pyi b/pandas/_libs/lib.pyi
index 15bd5a7379105..946cbb7dbf6f1 100644
--- a/pandas/_libs/lib.pyi
+++ b/pandas/_libs/lib.pyi
@@ -44,6 +44,7 @@ def is_iterator(obj: object) -> bool: ...
def is_scalar(val: object) -> bool: ...
def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ...
def is_pyarrow_array(obj: object) -> bool: ...
+def is_null_slice(obj: object) -> bool: ...
def is_period(val: object) -> TypeGuard[Period]: ...
def is_interval(val: object) -> TypeGuard[Interval]: ...
def is_decimal(val: object) -> TypeGuard[Decimal]: ...
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 0c0610f72044e..a9af158556f3e 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -29,6 +29,7 @@ from cpython.object cimport (
)
from cpython.ref cimport Py_INCREF
from cpython.sequence cimport PySequence_Check
+from cpython.slice cimport PySlice_Unpack
from cpython.tuple cimport (
PyTuple_New,
PyTuple_SET_ITEM,
@@ -71,6 +72,7 @@ cdef extern from "Python.h":
# Note: importing extern-style allows us to declare these as nogil
# functions, whereas `from cpython cimport` does not.
bint PyObject_TypeCheck(object obj, PyTypeObject* type) nogil
+ cdef Py_ssize_t PY_SSIZE_T_MAX
cdef extern from "numpy/arrayobject.h":
# cython's numpy.dtype specification is incorrect, which leads to
@@ -1250,6 +1252,20 @@ def is_pyarrow_array(obj):
return False
+def is_null_slice(obj):
+ cdef Py_ssize_t start, stop, step
+ if isinstance(obj, slice):
+ try:
+ PySlice_Unpack(obj, &start, &stop, &step)
+ except TypeError:
+ return False
+
+ if start == 0 and stop == PY_SSIZE_T_MAX and step == 1:
+ return True
+
+ return False
+
+
_TYPE_MAP = {
"categorical": "categorical",
"category": "categorical",
diff --git a/pandas/core/common.py b/pandas/core/common.py
index 8fd8b10c6fc32..2b243ec21818d 100644
--- a/pandas/core/common.py
+++ b/pandas/core/common.py
@@ -307,12 +307,7 @@ def is_null_slice(obj) -> bool:
"""
We have a null slice.
"""
- return (
- isinstance(obj, slice)
- and obj.start is None
- and obj.stop is None
- and obj.step is None
- )
+ return lib.is_null_slice(obj)
def is_empty_slice(obj) -> bool:
| This makes the diff in #54506 smaller | https://api.github.com/repos/pandas-dev/pandas/pulls/55037 | 2023-09-06T20:32:11Z | 2023-09-14T21:18:40Z | null | 2023-09-14T21:18:40Z |
BUG: Interval with Timestamp with tz shows tz | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 4f38d420a53b4..bfdfaf12a764f 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -207,7 +207,7 @@ Strings
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`)
-
Indexing
diff --git a/pandas/_libs/interval.pyx b/pandas/_libs/interval.pyx
index e07d80dd04b31..82f69c1dedd53 100644
--- a/pandas/_libs/interval.pyx
+++ b/pandas/_libs/interval.pyx
@@ -478,31 +478,16 @@ cdef class Interval(IntervalMixin):
args = (self.left, self.right, self.closed)
return (type(self), args)
- def _repr_base(self):
- left = self.left
- right = self.right
-
- # TODO: need more general formatting methodology here
- if isinstance(left, _Timestamp) and isinstance(right, _Timestamp):
- left = left._short_repr
- right = right._short_repr
-
- return left, right
-
def __repr__(self) -> str:
-
- left, right = self._repr_base()
- disp = str if isinstance(left, np.generic) else repr
+ disp = str if isinstance(self.left, (np.generic, _Timestamp)) else repr
name = type(self).__name__
- repr_str = f"{name}({disp(left)}, {disp(right)}, closed={repr(self.closed)})"
+ repr_str = f"{name}({disp(self.left)}, {disp(self.right)}, closed={repr(self.closed)})" # noqa: E501
return repr_str
def __str__(self) -> str:
-
- left, right = self._repr_base()
start_symbol = "[" if self.closed_left else "("
end_symbol = "]" if self.closed_right else ")"
- return f"{start_symbol}{left}, {right}{end_symbol}"
+ return f"{start_symbol}{self.left}, {self.right}{end_symbol}"
def __add__(self, y):
if (
diff --git a/pandas/_libs/tslibs/timestamps.pyx b/pandas/_libs/tslibs/timestamps.pyx
index 944a2b0e97382..65d0d454ac817 100644
--- a/pandas/_libs/tslibs/timestamps.pyx
+++ b/pandas/_libs/tslibs/timestamps.pyx
@@ -1078,18 +1078,6 @@ cdef class _Timestamp(ABCTimestamp):
return result
- @property
- def _short_repr(self) -> str:
- # format a Timestamp with only _date_repr if possible
- # otherwise _repr_base
- if (self.hour == 0 and
- self.minute == 0 and
- self.second == 0 and
- self.microsecond == 0 and
- self.nanosecond == 0):
- return self._date_repr
- return self._repr_base
-
# -----------------------------------------------------------------
# Conversion Methods
diff --git a/pandas/core/indexes/interval.py b/pandas/core/indexes/interval.py
index b36672df32e61..eb8d25bcea592 100644
--- a/pandas/core/indexes/interval.py
+++ b/pandas/core/indexes/interval.py
@@ -1039,8 +1039,9 @@ def interval_range(
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... end=pd.Timestamp('2017-01-04'))
- IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
- (2017-01-03, 2017-01-04]],
+ IntervalIndex([(2017-01-01 00:00:00, 2017-01-02 00:00:00],
+ (2017-01-02 00:00:00, 2017-01-03 00:00:00],
+ (2017-01-03 00:00:00, 2017-01-04 00:00:00]],
dtype='interval[datetime64[ns], right]')
The ``freq`` parameter specifies the frequency between the left and right.
@@ -1056,8 +1057,9 @@ def interval_range(
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... periods=3, freq='MS')
- IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
- (2017-03-01, 2017-04-01]],
+ IntervalIndex([(2017-01-01 00:00:00, 2017-02-01 00:00:00],
+ (2017-02-01 00:00:00, 2017-03-01 00:00:00],
+ (2017-03-01 00:00:00, 2017-04-01 00:00:00]],
dtype='interval[datetime64[ns], right]')
Specify ``start``, ``end``, and ``periods``; the frequency is generated
diff --git a/pandas/tests/frame/methods/test_to_csv.py b/pandas/tests/frame/methods/test_to_csv.py
index 294da02e259b7..9f45347c31165 100644
--- a/pandas/tests/frame/methods/test_to_csv.py
+++ b/pandas/tests/frame/methods/test_to_csv.py
@@ -1326,6 +1326,6 @@ def test_to_csv_categorical_and_interval(self):
)
df["a"] = df["a"].astype("category")
result = df.to_csv()
- expected_rows = [",a", '0,"[2020-01-01, 2020-01-02]"']
+ expected_rows = [",a", '0,"[2020-01-01 00:00:00, 2020-01-02 00:00:00]"']
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert result == expected
diff --git a/pandas/tests/indexes/interval/test_formats.py b/pandas/tests/indexes/interval/test_formats.py
index 4d6f3a62d4dd0..f003211abd857 100644
--- a/pandas/tests/indexes/interval/test_formats.py
+++ b/pandas/tests/indexes/interval/test_formats.py
@@ -80,7 +80,11 @@ def test_repr_floats(self):
((Timestamp("20180102"), Timestamp("20180103"))),
],
"both",
- ["[2018-01-01, 2018-01-02]", "NaN", "[2018-01-02, 2018-01-03]"],
+ [
+ "[2018-01-01 00:00:00, 2018-01-02 00:00:00]",
+ "NaN",
+ "[2018-01-02 00:00:00, 2018-01-03 00:00:00]",
+ ],
),
(
[
@@ -103,3 +107,19 @@ def test_to_native_types(self, tuples, closed, expected_data):
result = index._format_native_types()
expected = np.array(expected_data)
tm.assert_numpy_array_equal(result, expected)
+
+ def test_timestamp_with_timezone(self):
+ # GH 55035
+ index = IntervalIndex(
+ [
+ Interval(
+ Timestamp("2020-01-01", tz="UTC"), Timestamp("2020-01-02", tz="UTC")
+ )
+ ]
+ )
+ result = repr(index)
+ expected = (
+ "IntervalIndex([(2020-01-01 00:00:00+00:00, 2020-01-02 00:00:00+00:00]], "
+ "dtype='interval[datetime64[ns, UTC], right]')"
+ )
+ assert result == expected
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 9cbb29605a1ec..3944599b167c1 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -307,10 +307,10 @@ def test_multiindex_interval_datetimes(self, ext):
[
range(4),
[
- "(2020-01-31, 2020-07-31]",
- "(2020-07-31, 2021-01-31]",
- "(2021-01-31, 2021-07-31]",
- "(2021-07-31, 2022-01-31]",
+ "(2020-01-31 00:00:00, 2020-07-31 00:00:00]",
+ "(2020-07-31 00:00:00, 2021-01-31 00:00:00]",
+ "(2021-01-31 00:00:00, 2021-07-31 00:00:00]",
+ "(2021-07-31 00:00:00, 2022-01-31 00:00:00]",
],
]
),
| - [x] closes #55015 (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55035 | 2023-09-06T18:12:17Z | 2023-09-18T16:54:13Z | 2023-09-18T16:54:13Z | 2023-09-18T16:54:16Z |
DOC: Add missing word to `IndexOpsMixin.array` docstring | diff --git a/pandas/core/base.py b/pandas/core/base.py
index d973f8f5fe35a..3026189e747bb 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -485,8 +485,8 @@ def array(self) -> ExtensionArray:
types, this is the actual array. For NumPy native types, this
is a thin (no copy) wrapper around :class:`numpy.ndarray`.
- ``.array`` differs ``.values`` which may require converting the
- data to a different form.
+ ``.array`` differs from ``.values``, which may require converting
+ the data to a different form.
See Also
--------
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/55034 | 2023-09-06T17:58:20Z | 2023-09-06T20:06:14Z | 2023-09-06T20:06:14Z | 2023-09-06T22:55:08Z |
TST: Use more explicit object names | diff --git a/pandas/tests/frame/methods/test_reindex.py b/pandas/tests/frame/methods/test_reindex.py
index 0858e33a989b7..56bdd2fc664cc 100644
--- a/pandas/tests/frame/methods/test_reindex.py
+++ b/pandas/tests/frame/methods/test_reindex.py
@@ -26,7 +26,7 @@
isna,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
+from pandas.api.types import CategoricalDtype
class TestReindexSetIndex:
@@ -1082,7 +1082,9 @@ def test_reindex_with_categoricalindex(self):
{
"A": np.arange(3, dtype="int64"),
},
- index=CategoricalIndex(list("abc"), dtype=CDT(list("cabe")), name="B"),
+ index=CategoricalIndex(
+ list("abc"), dtype=CategoricalDtype(list("cabe")), name="B"
+ ),
)
# reindexing
@@ -1111,13 +1113,13 @@ def test_reindex_with_categoricalindex(self):
result = df.reindex(Categorical(["a", "e"], categories=cats))
expected = DataFrame(
- {"A": [0, np.nan], "B": Series(list("ae")).astype(CDT(cats))}
+ {"A": [0, np.nan], "B": Series(list("ae")).astype(CategoricalDtype(cats))}
).set_index("B")
tm.assert_frame_equal(result, expected, check_index_type=True)
result = df.reindex(Categorical(["a"], categories=cats))
expected = DataFrame(
- {"A": [0], "B": Series(list("a")).astype(CDT(cats))}
+ {"A": [0], "B": Series(list("a")).astype(CategoricalDtype(cats))}
).set_index("B")
tm.assert_frame_equal(result, expected, check_index_type=True)
@@ -1138,13 +1140,19 @@ def test_reindex_with_categoricalindex(self):
# give back the type of categorical that we received
result = df.reindex(Categorical(["a", "e"], categories=cats, ordered=True))
expected = DataFrame(
- {"A": [0, np.nan], "B": Series(list("ae")).astype(CDT(cats, ordered=True))}
+ {
+ "A": [0, np.nan],
+ "B": Series(list("ae")).astype(CategoricalDtype(cats, ordered=True)),
+ }
).set_index("B")
tm.assert_frame_equal(result, expected, check_index_type=True)
result = df.reindex(Categorical(["a", "d"], categories=["a", "d"]))
expected = DataFrame(
- {"A": [0, np.nan], "B": Series(list("ad")).astype(CDT(["a", "d"]))}
+ {
+ "A": [0, np.nan],
+ "B": Series(list("ad")).astype(CategoricalDtype(["a", "d"])),
+ }
).set_index("B")
tm.assert_frame_equal(result, expected, check_index_type=True)
@@ -1152,7 +1160,9 @@ def test_reindex_with_categoricalindex(self):
{
"A": np.arange(6, dtype="int64"),
},
- index=CategoricalIndex(list("aabbca"), dtype=CDT(list("cabe")), name="B"),
+ index=CategoricalIndex(
+ list("aabbca"), dtype=CategoricalDtype(list("cabe")), name="B"
+ ),
)
# passed duplicate indexers are not allowed
msg = "cannot reindex on an axis with duplicate labels"
diff --git a/pandas/tests/indexes/ranges/test_range.py b/pandas/tests/indexes/ranges/test_range.py
index 5f137df281fa3..132704434829e 100644
--- a/pandas/tests/indexes/ranges/test_range.py
+++ b/pandas/tests/indexes/ranges/test_range.py
@@ -10,9 +10,6 @@
)
import pandas._testing as tm
-# aliases to make some tests easier to read
-RI = RangeIndex
-
class TestRangeIndex:
@pytest.fixture
@@ -507,25 +504,31 @@ def test_len_specialised(self, step):
@pytest.mark.parametrize(
"indices, expected",
[
- ([RI(1, 12, 5)], RI(1, 12, 5)),
- ([RI(0, 6, 4)], RI(0, 6, 4)),
- ([RI(1, 3), RI(3, 7)], RI(1, 7)),
- ([RI(1, 5, 2), RI(5, 6)], RI(1, 6, 2)),
- ([RI(1, 3, 2), RI(4, 7, 3)], RI(1, 7, 3)),
- ([RI(-4, 3, 2), RI(4, 7, 2)], RI(-4, 7, 2)),
- ([RI(-4, -8), RI(-8, -12)], RI(0, 0)),
- ([RI(-4, -8), RI(3, -4)], RI(0, 0)),
- ([RI(-4, -8), RI(3, 5)], RI(3, 5)),
- ([RI(-4, -2), RI(3, 5)], Index([-4, -3, 3, 4])),
- ([RI(-2), RI(3, 5)], RI(3, 5)),
- ([RI(2), RI(2)], Index([0, 1, 0, 1])),
- ([RI(2), RI(2, 5), RI(5, 8, 4)], RI(0, 6)),
- ([RI(2), RI(3, 5), RI(5, 8, 4)], Index([0, 1, 3, 4, 5])),
- ([RI(-2, 2), RI(2, 5), RI(5, 8, 4)], RI(-2, 6)),
- ([RI(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])),
- ([RI(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])),
- ([RI(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])),
- ([RI(3, 1), Index(["a", None, 14])], Index(["a", None, 14])),
+ ([RangeIndex(1, 12, 5)], RangeIndex(1, 12, 5)),
+ ([RangeIndex(0, 6, 4)], RangeIndex(0, 6, 4)),
+ ([RangeIndex(1, 3), RangeIndex(3, 7)], RangeIndex(1, 7)),
+ ([RangeIndex(1, 5, 2), RangeIndex(5, 6)], RangeIndex(1, 6, 2)),
+ ([RangeIndex(1, 3, 2), RangeIndex(4, 7, 3)], RangeIndex(1, 7, 3)),
+ ([RangeIndex(-4, 3, 2), RangeIndex(4, 7, 2)], RangeIndex(-4, 7, 2)),
+ ([RangeIndex(-4, -8), RangeIndex(-8, -12)], RangeIndex(0, 0)),
+ ([RangeIndex(-4, -8), RangeIndex(3, -4)], RangeIndex(0, 0)),
+ ([RangeIndex(-4, -8), RangeIndex(3, 5)], RangeIndex(3, 5)),
+ ([RangeIndex(-4, -2), RangeIndex(3, 5)], Index([-4, -3, 3, 4])),
+ ([RangeIndex(-2), RangeIndex(3, 5)], RangeIndex(3, 5)),
+ ([RangeIndex(2), RangeIndex(2)], Index([0, 1, 0, 1])),
+ ([RangeIndex(2), RangeIndex(2, 5), RangeIndex(5, 8, 4)], RangeIndex(0, 6)),
+ (
+ [RangeIndex(2), RangeIndex(3, 5), RangeIndex(5, 8, 4)],
+ Index([0, 1, 3, 4, 5]),
+ ),
+ (
+ [RangeIndex(-2, 2), RangeIndex(2, 5), RangeIndex(5, 8, 4)],
+ RangeIndex(-2, 6),
+ ),
+ ([RangeIndex(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])),
+ ([RangeIndex(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])),
+ ([RangeIndex(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])),
+ ([RangeIndex(3, 1), Index(["a", None, 14])], Index(["a", None, 14])),
],
)
def test_append(self, indices, expected):
@@ -567,7 +570,7 @@ def test_format_empty(self):
assert empty_idx.format(name=True) == [""]
@pytest.mark.parametrize(
- "RI",
+ "ri",
[
RangeIndex(0, -1, -1),
RangeIndex(0, 1, 1),
@@ -576,10 +579,10 @@ def test_format_empty(self):
RangeIndex(-3, -5, -2),
],
)
- def test_append_len_one(self, RI):
+ def test_append_len_one(self, ri):
# GH39401
- result = RI.append([])
- tm.assert_index_equal(result, RI, exact=True)
+ result = ri.append([])
+ tm.assert_index_equal(result, ri, exact=True)
@pytest.mark.parametrize("base", [RangeIndex(0, 2), Index([0, 1])])
def test_isin_range(self, base):
diff --git a/pandas/tests/indexing/test_categorical.py b/pandas/tests/indexing/test_categorical.py
index b45d197af332e..d3a6d4bf7cebf 100644
--- a/pandas/tests/indexing/test_categorical.py
+++ b/pandas/tests/indexing/test_categorical.py
@@ -16,7 +16,6 @@
Timestamp,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
@pytest.fixture
@@ -25,7 +24,9 @@ def df():
{
"A": np.arange(6, dtype="int64"),
},
- index=CategoricalIndex(list("aabbca"), dtype=CDT(list("cab")), name="B"),
+ index=CategoricalIndex(
+ list("aabbca"), dtype=CategoricalDtype(list("cab")), name="B"
+ ),
)
@@ -35,13 +36,15 @@ def df2():
{
"A": np.arange(6, dtype="int64"),
},
- index=CategoricalIndex(list("aabbca"), dtype=CDT(list("cabe")), name="B"),
+ index=CategoricalIndex(
+ list("aabbca"), dtype=CategoricalDtype(list("cabe")), name="B"
+ ),
)
class TestCategoricalIndex:
def test_loc_scalar(self, df):
- dtype = CDT(list("cab"))
+ dtype = CategoricalDtype(list("cab"))
result = df.loc["a"]
bidx = Series(list("aaa"), name="B").astype(dtype)
assert bidx.dtype == dtype
diff --git a/pandas/tests/indexing/test_chaining_and_caching.py b/pandas/tests/indexing/test_chaining_and_caching.py
index f36fdf0d36ea9..7353b5ef76ba3 100644
--- a/pandas/tests/indexing/test_chaining_and_caching.py
+++ b/pandas/tests/indexing/test_chaining_and_caching.py
@@ -1,4 +1,4 @@
-from string import ascii_letters as letters
+from string import ascii_letters
import numpy as np
import pytest
@@ -24,9 +24,9 @@
def random_text(nobs=100):
# Construct a DataFrame where each row is a random slice from 'letters'
- idxs = np.random.default_rng(2).integers(len(letters), size=(nobs, 2))
+ idxs = np.random.default_rng(2).integers(len(ascii_letters), size=(nobs, 2))
idxs.sort(axis=1)
- strings = [letters[x[0] : x[1]] for x in idxs]
+ strings = [ascii_letters[x[0] : x[1]] for x in idxs]
return DataFrame(strings, columns=["letters"])
diff --git a/pandas/tests/io/formats/test_info.py b/pandas/tests/io/formats/test_info.py
index 73de2b068b699..6c3bf01cb1857 100644
--- a/pandas/tests/io/formats/test_info.py
+++ b/pandas/tests/io/formats/test_info.py
@@ -1,6 +1,6 @@
from io import StringIO
import re
-from string import ascii_uppercase as uppercase
+from string import ascii_uppercase
import sys
import textwrap
@@ -452,9 +452,9 @@ def memory_usage(f):
return f.memory_usage(deep=True).sum()
N = 100
- M = len(uppercase)
+ M = len(ascii_uppercase)
index = MultiIndex.from_product(
- [list(uppercase), date_range("20160101", periods=N)],
+ [list(ascii_uppercase), date_range("20160101", periods=N)],
names=["id", "date"],
)
df = DataFrame(
diff --git a/pandas/tests/io/formats/test_series_info.py b/pandas/tests/io/formats/test_series_info.py
index 02827ee25042a..29dd704f6efa9 100644
--- a/pandas/tests/io/formats/test_series_info.py
+++ b/pandas/tests/io/formats/test_series_info.py
@@ -1,5 +1,5 @@
from io import StringIO
-from string import ascii_uppercase as uppercase
+from string import ascii_uppercase
import textwrap
import numpy as np
@@ -165,9 +165,9 @@ def test_info_memory_usage_bug_on_multiindex():
# GH 14308
# memory usage introspection should not materialize .values
N = 100
- M = len(uppercase)
+ M = len(ascii_uppercase)
index = MultiIndex.from_product(
- [list(uppercase), date_range("20160101", periods=N)],
+ [list(ascii_uppercase), date_range("20160101", periods=N)],
names=["id", "date"],
)
s = Series(np.random.default_rng(2).standard_normal(N * M), index=index)
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index 37ccfddfc82cd..d889ae2e4806b 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -26,7 +26,6 @@
TimedeltaIndex,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import (
MergeError,
@@ -1842,7 +1841,7 @@ def left():
{
"X": Series(
np.random.default_rng(2).choice(["foo", "bar"], size=(10,))
- ).astype(CDT(["foo", "bar"])),
+ ).astype(CategoricalDtype(["foo", "bar"])),
"Y": np.random.default_rng(2).choice(["one", "two", "three"], size=(10,)),
}
)
@@ -1851,7 +1850,10 @@ def left():
@pytest.fixture
def right():
return DataFrame(
- {"X": Series(["foo", "bar"]).astype(CDT(["foo", "bar"])), "Z": [1, 2]}
+ {
+ "X": Series(["foo", "bar"]).astype(CategoricalDtype(["foo", "bar"])),
+ "Z": [1, 2],
+ }
)
@@ -2002,8 +2004,8 @@ def test_other_columns(self, left, right):
"change",
[
lambda x: x,
- lambda x: x.astype(CDT(["foo", "bar", "bah"])),
- lambda x: x.astype(CDT(ordered=True)),
+ lambda x: x.astype(CategoricalDtype(["foo", "bar", "bah"])),
+ lambda x: x.astype(CategoricalDtype(ordered=True)),
],
)
def test_dtype_on_merged_different(self, change, join_type, left, right):
@@ -2110,11 +2112,13 @@ def test_merging_with_bool_or_int_cateorical_column(
# GH 17187
# merging with a boolean/int categorical column
df1 = DataFrame({"id": [1, 2, 3, 4], "cat": category_column})
- df1["cat"] = df1["cat"].astype(CDT(categories, ordered=ordered))
+ df1["cat"] = df1["cat"].astype(CategoricalDtype(categories, ordered=ordered))
df2 = DataFrame({"id": [2, 4], "num": [1, 9]})
result = df1.merge(df2)
expected = DataFrame({"id": [2, 4], "cat": expected_categories, "num": [1, 9]})
- expected["cat"] = expected["cat"].astype(CDT(categories, ordered=ordered))
+ expected["cat"] = expected["cat"].astype(
+ CategoricalDtype(categories, ordered=ordered)
+ )
tm.assert_frame_equal(expected, result)
def test_merge_on_int_array(self):
diff --git a/pandas/tests/reshape/test_cut.py b/pandas/tests/reshape/test_cut.py
index 81b466b059702..3a284f7732ac1 100644
--- a/pandas/tests/reshape/test_cut.py
+++ b/pandas/tests/reshape/test_cut.py
@@ -21,7 +21,7 @@
to_datetime,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
+from pandas.api.types import CategoricalDtype
import pandas.core.reshape.tile as tmod
@@ -359,7 +359,7 @@ def test_cut_return_intervals():
IntervalIndex.from_breaks(exp_bins, closed="right").take(
[0, 0, 0, 1, 1, 1, 2, 2, 2]
)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
tm.assert_series_equal(result, expected)
@@ -370,7 +370,7 @@ def test_series_ret_bins():
expected = Series(
IntervalIndex.from_breaks([-0.003, 1.5, 3], closed="right").repeat(2)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
tm.assert_series_equal(result, expected)
@@ -445,7 +445,7 @@ def test_datetime_bin(conv):
Interval(Timestamp(bin_data[1]), Timestamp(bin_data[2])),
]
)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
bins = [conv(v) for v in bin_data]
result = Series(cut(data, bins=bins))
@@ -491,7 +491,7 @@ def test_datetime_cut(data):
),
]
)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
tm.assert_series_equal(Series(result), expected)
@@ -534,7 +534,7 @@ def test_datetime_tz_cut(bins, box):
),
]
)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/reshape/test_pivot.py b/pandas/tests/reshape/test_pivot.py
index c43fd05fd5501..28ad133a0c8d6 100644
--- a/pandas/tests/reshape/test_pivot.py
+++ b/pandas/tests/reshape/test_pivot.py
@@ -23,7 +23,7 @@
date_range,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
+from pandas.api.types import CategoricalDtype
from pandas.core.reshape import reshape as reshape_lib
from pandas.core.reshape.pivot import pivot_table
@@ -219,10 +219,12 @@ def test_pivot_table_dropna_categoricals(self, dropna):
}
)
- df["A"] = df["A"].astype(CDT(categories, ordered=False))
+ df["A"] = df["A"].astype(CategoricalDtype(categories, ordered=False))
result = df.pivot_table(index="B", columns="A", values="C", dropna=dropna)
expected_columns = Series(["a", "b", "c"], name="A")
- expected_columns = expected_columns.astype(CDT(categories, ordered=False))
+ expected_columns = expected_columns.astype(
+ CategoricalDtype(categories, ordered=False)
+ )
expected_index = Series([1, 2, 3], name="B")
expected = DataFrame(
[[0.0, 3.0, 6.0], [1.0, 4.0, 7.0], [2.0, 5.0, 8.0]],
diff --git a/pandas/tests/reshape/test_qcut.py b/pandas/tests/reshape/test_qcut.py
index 907eeca6e9b5e..bcfbe5ed1aa20 100644
--- a/pandas/tests/reshape/test_qcut.py
+++ b/pandas/tests/reshape/test_qcut.py
@@ -20,7 +20,7 @@
timedelta_range,
)
import pandas._testing as tm
-from pandas.api.types import CategoricalDtype as CDT
+from pandas.api.types import CategoricalDtype
from pandas.tseries.offsets import (
Day,
@@ -129,7 +129,9 @@ def test_qcut_return_intervals():
exp_levels = np.array(
[Interval(-0.001, 2.664), Interval(2.664, 5.328), Interval(5.328, 8)]
)
- exp = Series(exp_levels.take([0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(CDT(ordered=True))
+ exp = Series(exp_levels.take([0, 0, 0, 1, 1, 1, 2, 2, 2])).astype(
+ CategoricalDtype(ordered=True)
+ )
tm.assert_series_equal(res, exp)
@@ -199,7 +201,7 @@ def test_single_quantile(data, start, end, length, labels):
if labels is None:
intervals = IntervalIndex([Interval(start, end)] * length, closed="right")
- expected = Series(intervals).astype(CDT(ordered=True))
+ expected = Series(intervals).astype(CategoricalDtype(ordered=True))
else:
expected = Series([0] * length, dtype=np.intp)
@@ -249,7 +251,7 @@ def test_datetime_tz_qcut(bins):
),
]
)
- ).astype(CDT(ordered=True))
+ ).astype(CategoricalDtype(ordered=True))
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index cb703d3439d44..661290fb00d13 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -17,7 +17,7 @@
is_integer_dtype,
is_object_dtype,
)
-from pandas.core.dtypes.dtypes import CategoricalDtype as CDT
+from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
@@ -1182,7 +1182,7 @@ def test_value_counts(self):
with tm.assert_produces_warning(FutureWarning, match=msg):
result = algos.value_counts(factor)
breaks = [-1.606, -1.018, -0.431, 0.155, 0.741]
- index = IntervalIndex.from_breaks(breaks).astype(CDT(ordered=True))
+ index = IntervalIndex.from_breaks(breaks).astype(CategoricalDtype(ordered=True))
expected = Series([1, 0, 2, 1], index=index, name="count")
tm.assert_series_equal(result.sort_index(), expected.sort_index())
| When going over some tests, the `import obj as o` patterns have not been the most clear so making some of these usages more explicit | https://api.github.com/repos/pandas-dev/pandas/pulls/55033 | 2023-09-06T17:39:13Z | 2023-09-06T20:07:07Z | 2023-09-06T20:07:07Z | 2023-09-06T20:12:43Z |
Doc: lines added | diff --git a/doc/source/getting_started/comparison/comparison_with_r.rst b/doc/source/getting_started/comparison/comparison_with_r.rst
index a6cfcd4614984..9d970209ec8e7 100644
--- a/doc/source/getting_started/comparison/comparison_with_r.rst
+++ b/doc/source/getting_started/comparison/comparison_with_r.rst
@@ -6,8 +6,8 @@ Comparison with R / R libraries
*******************************
Since pandas aims to provide a lot of the data manipulation and analysis
-functionality that people use `R <https://www.r-project.org/>`__ for, this page
-was started to provide a more detailed look at the `R language
+functionality that people use `R <https://www.r-project.org/>`__ for Data analysis and statistics, this page
+was started to provide a more detailed look at the `R language
<https://en.wikipedia.org/wiki/R_(programming_language)>`__ and its many third
party libraries as they relate to pandas. In comparisons with R and CRAN
libraries, we care about the following things:
diff --git a/pandas/__init__.py b/pandas/__init__.py
deleted file mode 100644
index d11a429987ac4..0000000000000
--- a/pandas/__init__.py
+++ /dev/null
@@ -1,353 +0,0 @@
-from __future__ import annotations
-
-__docformat__ = "restructuredtext"
-
-# Let users know if they're missing any of our hard dependencies
-_hard_dependencies = ("numpy", "pytz", "dateutil")
-_missing_dependencies = []
-
-for _dependency in _hard_dependencies:
- try:
- __import__(_dependency)
- except ImportError as _e: # pragma: no cover
- _missing_dependencies.append(f"{_dependency}: {_e}")
-
-if _missing_dependencies: # pragma: no cover
- raise ImportError(
- "Unable to import required dependencies:\n" + "\n".join(_missing_dependencies)
- )
-del _hard_dependencies, _dependency, _missing_dependencies
-
-try:
- # numpy compat
- from pandas.compat import (
- is_numpy_dev as _is_numpy_dev, # pyright: ignore[reportUnusedImport] # noqa: F401,E501
- )
-except ImportError as _err: # pragma: no cover
- _module = _err.name
- raise ImportError(
- f"C extension: {_module} not built. If you want to import "
- "pandas from the source directory, you may need to run "
- "'python setup.py build_ext' to build the C extensions first."
- ) from _err
-
-from pandas._config import (
- get_option,
- set_option,
- reset_option,
- describe_option,
- option_context,
- options,
-)
-
-# let init-time option registration happen
-import pandas.core.config_init # pyright: ignore[reportUnusedImport] # noqa: F401
-
-from pandas.core.api import (
- # dtype
- ArrowDtype,
- Int8Dtype,
- Int16Dtype,
- Int32Dtype,
- Int64Dtype,
- UInt8Dtype,
- UInt16Dtype,
- UInt32Dtype,
- UInt64Dtype,
- Float32Dtype,
- Float64Dtype,
- CategoricalDtype,
- PeriodDtype,
- IntervalDtype,
- DatetimeTZDtype,
- StringDtype,
- BooleanDtype,
- # missing
- NA,
- isna,
- isnull,
- notna,
- notnull,
- # indexes
- Index,
- CategoricalIndex,
- RangeIndex,
- MultiIndex,
- IntervalIndex,
- TimedeltaIndex,
- DatetimeIndex,
- PeriodIndex,
- IndexSlice,
- # tseries
- NaT,
- Period,
- period_range,
- Timedelta,
- timedelta_range,
- Timestamp,
- date_range,
- bdate_range,
- Interval,
- interval_range,
- DateOffset,
- # conversion
- to_numeric,
- to_datetime,
- to_timedelta,
- # misc
- Flags,
- Grouper,
- factorize,
- unique,
- value_counts,
- NamedAgg,
- array,
- Categorical,
- set_eng_float_format,
- Series,
- DataFrame,
-)
-
-from pandas.core.dtypes.dtypes import SparseDtype
-
-from pandas.tseries.api import infer_freq
-from pandas.tseries import offsets
-
-from pandas.core.computation.api import eval
-
-from pandas.core.reshape.api import (
- concat,
- lreshape,
- melt,
- wide_to_long,
- merge,
- merge_asof,
- merge_ordered,
- crosstab,
- pivot,
- pivot_table,
- get_dummies,
- from_dummies,
- cut,
- qcut,
-)
-
-from pandas import api, arrays, errors, io, plotting, tseries
-from pandas import testing
-from pandas.util._print_versions import show_versions
-
-from pandas.io.api import (
- # excel
- ExcelFile,
- ExcelWriter,
- read_excel,
- # parsers
- read_csv,
- read_fwf,
- read_table,
- # pickle
- read_pickle,
- to_pickle,
- # pytables
- HDFStore,
- read_hdf,
- # sql
- read_sql,
- read_sql_query,
- read_sql_table,
- # misc
- read_clipboard,
- read_parquet,
- read_orc,
- read_feather,
- read_gbq,
- read_html,
- read_xml,
- read_json,
- read_stata,
- read_sas,
- read_spss,
-)
-
-from pandas.io.json._normalize import json_normalize
-
-from pandas.util._tester import test
-
-# use the closest tagged version if possible
-_built_with_meson = False
-try:
- from pandas._version_meson import ( # pyright: ignore [reportMissingImports]
- __version__,
- __git_version__,
- )
-
- _built_with_meson = True
-except ImportError:
- from pandas._version import get_versions
-
- v = get_versions()
- __version__ = v.get("closest-tag", v["version"])
- __git_version__ = v.get("full-revisionid")
- del get_versions, v
-
-
-# module level doc-string
-__doc__ = """
-pandas - a powerful data analysis and manipulation library for Python
-=====================================================================
-
-**pandas** is a Python package providing fast, flexible, and expressive data
-structures designed to make working with "relational" or "labeled" data both
-easy and intuitive. It aims to be the fundamental high-level building block for
-doing practical, **real world** data analysis in Python. Additionally, it has
-the broader goal of becoming **the most powerful and flexible open source data
-analysis / manipulation tool available in any language**. It is already well on
-its way toward this goal.
-
-Main Features
--------------
-Here are just a few of the things that pandas does well:
-
- - Easy handling of missing data in floating point as well as non-floating
- point data.
- - Size mutability: columns can be inserted and deleted from DataFrame and
- higher dimensional objects
- - Automatic and explicit data alignment: objects can be explicitly aligned
- to a set of labels, or the user can simply ignore the labels and let
- `Series`, `DataFrame`, etc. automatically align the data for you in
- computations.
- - Powerful, flexible group by functionality to perform split-apply-combine
- operations on data sets, for both aggregating and transforming data.
- - Make it easy to convert ragged, differently-indexed data in other Python
- and NumPy data structures into DataFrame objects.
- - Intelligent label-based slicing, fancy indexing, and subsetting of large
- data sets.
- - Intuitive merging and joining data sets.
- - Flexible reshaping and pivoting of data sets.
- - Hierarchical labeling of axes (possible to have multiple labels per tick).
- - Robust IO tools for loading data from flat files (CSV and delimited),
- Excel files, databases, and saving/loading data from the ultrafast HDF5
- format.
- - Time series-specific functionality: date range generation and frequency
- conversion, moving window statistics, date shifting and lagging.
-"""
-
-# Use __all__ to let type checkers know what is part of the public API.
-# Pandas is not (yet) a py.typed library: the public API is determined
-# based on the documentation.
-__all__ = [
- "ArrowDtype",
- "BooleanDtype",
- "Categorical",
- "CategoricalDtype",
- "CategoricalIndex",
- "DataFrame",
- "DateOffset",
- "DatetimeIndex",
- "DatetimeTZDtype",
- "ExcelFile",
- "ExcelWriter",
- "Flags",
- "Float32Dtype",
- "Float64Dtype",
- "Grouper",
- "HDFStore",
- "Index",
- "IndexSlice",
- "Int16Dtype",
- "Int32Dtype",
- "Int64Dtype",
- "Int8Dtype",
- "Interval",
- "IntervalDtype",
- "IntervalIndex",
- "MultiIndex",
- "NA",
- "NaT",
- "NamedAgg",
- "Period",
- "PeriodDtype",
- "PeriodIndex",
- "RangeIndex",
- "Series",
- "SparseDtype",
- "StringDtype",
- "Timedelta",
- "TimedeltaIndex",
- "Timestamp",
- "UInt16Dtype",
- "UInt32Dtype",
- "UInt64Dtype",
- "UInt8Dtype",
- "api",
- "array",
- "arrays",
- "bdate_range",
- "concat",
- "crosstab",
- "cut",
- "date_range",
- "describe_option",
- "errors",
- "eval",
- "factorize",
- "get_dummies",
- "from_dummies",
- "get_option",
- "infer_freq",
- "interval_range",
- "io",
- "isna",
- "isnull",
- "json_normalize",
- "lreshape",
- "melt",
- "merge",
- "merge_asof",
- "merge_ordered",
- "notna",
- "notnull",
- "offsets",
- "option_context",
- "options",
- "period_range",
- "pivot",
- "pivot_table",
- "plotting",
- "qcut",
- "read_clipboard",
- "read_csv",
- "read_excel",
- "read_feather",
- "read_fwf",
- "read_gbq",
- "read_hdf",
- "read_html",
- "read_json",
- "read_orc",
- "read_parquet",
- "read_pickle",
- "read_sas",
- "read_spss",
- "read_sql",
- "read_sql_query",
- "read_sql_table",
- "read_stata",
- "read_table",
- "read_xml",
- "reset_option",
- "set_eng_float_format",
- "set_option",
- "show_versions",
- "test",
- "testing",
- "timedelta_range",
- "to_datetime",
- "to_numeric",
- "to_pickle",
- "to_timedelta",
- "tseries",
- "unique",
- "value_counts",
- "wide_to_long",
-]
|
DOC:
- [ ] [Tests added and passed](doc/source/getting_started/comparison/comparison_with_r.rst)
- This addition helps readers understand the purpose of the page and the context of the comparison between pandas and the R language.
- Provide additional clarity to the sentence, making it explicit that the comparison is related to these specific aspects.
| https://api.github.com/repos/pandas-dev/pandas/pulls/55029 | 2023-09-06T11:37:14Z | 2023-09-06T17:06:35Z | null | 2023-09-07T07:53:52Z |
BUG: merge with left and/or right empty returning mis-ordered columns | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index bd15d5fa085e9..4f38d420a53b4 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -246,7 +246,7 @@ Groupby/resample/rolling
Reshaping
^^^^^^^^^
--
+- Bug in :func:`merge` returning columns in incorrect order when left and/or right is empty (:issue:`51929`)
-
Sparse
diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index 5b07a0010acdd..6d1ff07e07c76 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -1272,12 +1272,7 @@ def _get_merge_keys(
# work-around for merge_asof(right_index=True)
right_keys.append(right.index._values)
if lk is not None and lk == rk: # FIXME: what about other NAs?
- # avoid key upcast in corner case (length-0)
- lk = cast(Hashable, lk)
- if len(left) > 0:
- right_drop.append(rk)
- else:
- left_drop.append(lk)
+ right_drop.append(rk)
else:
rk = cast(ArrayLike, rk)
right_keys.append(rk)
diff --git a/pandas/tests/reshape/merge/test_merge.py b/pandas/tests/reshape/merge/test_merge.py
index 4659c16909ed7..37ccfddfc82cd 100644
--- a/pandas/tests/reshape/merge/test_merge.py
+++ b/pandas/tests/reshape/merge/test_merge.py
@@ -582,11 +582,11 @@ def test_merge_empty_frame(self, series_of_dtype, series_of_dtype2):
df_empty = df[:0]
expected = DataFrame(
{
- "value_x": Series(dtype=df.dtypes["value"]),
"key": Series(dtype=df.dtypes["key"]),
+ "value_x": Series(dtype=df.dtypes["value"]),
"value_y": Series(dtype=df.dtypes["value"]),
},
- columns=["value_x", "key", "value_y"],
+ columns=["key", "value_x", "value_y"],
)
actual = df_empty.merge(df, on="key")
tm.assert_frame_equal(actual, expected)
@@ -889,13 +889,13 @@ def test_merge_on_datetime64tz_empty(self):
result = left.merge(right, on="date")
expected = DataFrame(
{
+ "date": Series(dtype=dtz),
"value_x": Series(dtype=float),
"date2_x": Series(dtype=dtz),
- "date": Series(dtype=dtz),
"value_y": Series(dtype=float),
"date2_y": Series(dtype=dtz),
},
- columns=["value_x", "date2_x", "date", "value_y", "date2_y"],
+ columns=["date", "value_x", "date2_x", "value_y", "date2_y"],
)
tm.assert_frame_equal(result, expected)
@@ -1827,11 +1827,9 @@ def test_merge_empty(self, left_empty, how, exp):
if exp == "left":
expected = DataFrame({"A": [2, 1], "B": [3, 4], "C": [np.nan, np.nan]})
elif exp == "right":
- expected = DataFrame({"B": [np.nan], "A": [1], "C": [5]})
+ expected = DataFrame({"A": [1], "B": [np.nan], "C": [5]})
elif exp == "empty":
expected = DataFrame(columns=["A", "B", "C"], dtype="int64")
- if left_empty:
- expected = expected[["B", "A", "C"]]
elif exp == "empty_cross":
expected = DataFrame(columns=["A_x", "B", "A_y", "C"], dtype="int64")
@@ -2481,14 +2479,12 @@ def test_merge_multiindex_columns():
result = frame_x.merge(frame_y, on="id", suffixes=((l_suf, r_suf)))
# Constructing the expected results
- expected_labels = [letter + l_suf for letter in letters] + [
- letter + r_suf for letter in letters
- ]
- expected_index = MultiIndex.from_product(
- [expected_labels, numbers], names=["outer", "inner"]
- )
+ tuples = [(letter + l_suf, num) for letter in letters for num in numbers]
+ tuples += [("id", "")]
+ tuples += [(letter + r_suf, num) for letter in letters for num in numbers]
+
+ expected_index = MultiIndex.from_tuples(tuples, names=["outer", "inner"])
expected = DataFrame(columns=expected_index)
- expected["id"] = ""
tm.assert_frame_equal(result, expected)
@@ -2959,3 +2955,26 @@ def test_merge_arrow_string_index(any_string_dtype):
{"a": Series(["a", "b"], dtype=any_string_dtype), "b": [1, np.nan]}
)
tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("left_empty", [True, False])
+@pytest.mark.parametrize("right_empty", [True, False])
+def test_merge_empty_frames_column_order(left_empty, right_empty):
+ # GH 51929
+ df1 = DataFrame(1, index=[0], columns=["A", "B"])
+ df2 = DataFrame(1, index=[0], columns=["A", "C", "D"])
+
+ if left_empty:
+ df1 = df1.iloc[:0]
+ if right_empty:
+ df2 = df2.iloc[:0]
+
+ result = merge(df1, df2, on=["A"], how="outer")
+ expected = DataFrame(1, index=[0], columns=["A", "B", "C", "D"])
+ if left_empty and right_empty:
+ expected = expected.iloc[:0]
+ elif left_empty:
+ expected.loc[:, "B"] = np.nan
+ elif right_empty:
+ expected.loc[:, ["C", "D"]] = np.nan
+ tm.assert_frame_equal(result, expected)
| - [x] closes #51929
- [x] closes #43087
- [x] closes #9937
- [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/55028 | 2023-09-06T11:11:07Z | 2023-09-06T17:12:29Z | 2023-09-06T17:12:29Z | 2023-09-11T01:29:49Z |
Backport PR #54982 on branch 2.1.x (REG: filter not respecting the order of labels) | diff --git a/doc/source/whatsnew/v2.1.1.rst b/doc/source/whatsnew/v2.1.1.rst
index 11b19b1508a71..4f1ecf3a6e666 100644
--- a/doc/source/whatsnew/v2.1.1.rst
+++ b/doc/source/whatsnew/v2.1.1.rst
@@ -18,6 +18,7 @@ Fixed regressions
- Fixed regression in :func:`read_csv` when ``delim_whitespace`` is True (:issue:`54918`, :issue:`54931`)
- Fixed regression in :meth:`.GroupBy.get_group` raising for ``axis=1`` (:issue:`54858`)
- Fixed regression in :meth:`DataFrame.__setitem__` raising ``AssertionError`` when setting a :class:`Series` with a partial :class:`MultiIndex` (:issue:`54875`)
+- Fixed regression in :meth:`DataFrame.filter` not respecting the order of elements for ``filter`` (:issue:`54980`)
- Fixed regression in :meth:`DataFrame.to_sql` not roundtripping datetime columns correctly for sqlite (:issue:`54877`)
- Fixed regression in :meth:`MultiIndex.append` raising when appending overlapping :class:`IntervalIndex` levels (:issue:`54934`)
- Fixed regression in :meth:`Series.drop_duplicates` for PyArrow strings (:issue:`54904`)
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index f6bf51f65049a..cea2f77c44e85 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -5701,10 +5701,12 @@ def filter(
if items is not None:
name = self._get_axis_name(axis)
+ items = Index(items).intersection(labels)
+ if len(items) == 0:
+ # Keep the dtype of labels when we are empty
+ items = items.astype(labels.dtype)
# error: Keywords must be strings
- return self.reindex( # type: ignore[misc]
- **{name: labels.intersection(items)}
- )
+ return self.reindex(**{name: items}) # type: ignore[misc]
elif like:
def f(x) -> bool_t:
diff --git a/pandas/tests/frame/methods/test_filter.py b/pandas/tests/frame/methods/test_filter.py
index 1a2fbf8a65a55..9d5e6876bb08c 100644
--- a/pandas/tests/frame/methods/test_filter.py
+++ b/pandas/tests/frame/methods/test_filter.py
@@ -137,3 +137,17 @@ def test_filter_regex_non_string(self):
result = df.filter(regex="STRING")
expected = df[["STRING"]]
tm.assert_frame_equal(result, expected)
+
+ def test_filter_keep_order(self):
+ # GH#54980
+ df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
+ result = df.filter(items=["B", "A"])
+ expected = df[["B", "A"]]
+ tm.assert_frame_equal(result, expected)
+
+ def test_filter_different_dtype(self):
+ # GH#54980
+ df = DataFrame({1: [1, 2, 3], 2: [4, 5, 6]})
+ result = df.filter(items=["B", "A"])
+ expected = df[[]]
+ tm.assert_frame_equal(result, expected)
| Backport PR #54982: REG: filter not respecting the order of labels | https://api.github.com/repos/pandas-dev/pandas/pulls/55022 | 2023-09-05T22:42:00Z | 2023-09-06T01:01:49Z | 2023-09-06T01:01:49Z | 2023-09-06T01:01:49Z |
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