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 |
|---|---|---|---|---|---|---|---|
ENH: `Styler.text_gradient`: easy extension alternative to `.background_gradient` | diff --git a/doc/source/_static/style/tg_ax0.png b/doc/source/_static/style/tg_ax0.png
new file mode 100644
index 0000000000000..3460329352282
Binary files /dev/null and b/doc/source/_static/style/tg_ax0.png differ
diff --git a/doc/source/_static/style/tg_axNone.png b/doc/source/_static/style/tg_axNone.png
new file mode 100644
index 0000000000000..00357f7eb016b
Binary files /dev/null and b/doc/source/_static/style/tg_axNone.png differ
diff --git a/doc/source/_static/style/tg_axNone_gmap.png b/doc/source/_static/style/tg_axNone_gmap.png
new file mode 100644
index 0000000000000..d06a4b244a23d
Binary files /dev/null and b/doc/source/_static/style/tg_axNone_gmap.png differ
diff --git a/doc/source/_static/style/tg_axNone_lowhigh.png b/doc/source/_static/style/tg_axNone_lowhigh.png
new file mode 100644
index 0000000000000..bc3fb16ee8e40
Binary files /dev/null and b/doc/source/_static/style/tg_axNone_lowhigh.png differ
diff --git a/doc/source/_static/style/tg_axNone_vminvmax.png b/doc/source/_static/style/tg_axNone_vminvmax.png
new file mode 100644
index 0000000000000..42579c2840fb9
Binary files /dev/null and b/doc/source/_static/style/tg_axNone_vminvmax.png differ
diff --git a/doc/source/_static/style/tg_gmap.png b/doc/source/_static/style/tg_gmap.png
new file mode 100644
index 0000000000000..fb73529544180
Binary files /dev/null and b/doc/source/_static/style/tg_gmap.png differ
diff --git a/doc/source/reference/style.rst b/doc/source/reference/style.rst
index 6a075ad702bde..0d743b5fe8b8b 100644
--- a/doc/source/reference/style.rst
+++ b/doc/source/reference/style.rst
@@ -56,6 +56,7 @@ Builtin styles
Styler.highlight_min
Styler.highlight_between
Styler.background_gradient
+ Styler.text_gradient
Styler.bar
Style export and import
diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 2f2e8aed6fdb8..3fb09f395319f 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -120,8 +120,8 @@ to allow custom CSS highlighting instead of default background coloring (:issue:
Enhancements to other built-in methods include extending the :meth:`.Styler.background_gradient`
method to shade elements based on a given gradient map and not be restricted only to
values in the DataFrame (:issue:`39930` :issue:`22727` :issue:`28901`). Additional
-built-in methods such as :meth:`.Styler.highlight_between` and :meth:`.Styler.highlight_quantile`
-have been added (:issue:`39821` and :issue:`40926`).
+built-in methods such as :meth:`.Styler.highlight_between`, :meth:`.Styler.highlight_quantile`
+and :math:`.Styler.text_gradient` have been added (:issue:`39821`, :issue:`40926`, :issue:`41098`).
The :meth:`.Styler.apply` now consistently allows functions with ``ndarray`` output to
allow more flexible development of UDFs when ``axis`` is ``None`` ``0`` or ``1`` (:issue:`39393`).
diff --git a/pandas/io/formats/style.py b/pandas/io/formats/style.py
index 977a3a24f0844..655a50af9d0f2 100644
--- a/pandas/io/formats/style.py
+++ b/pandas/io/formats/style.py
@@ -1470,6 +1470,13 @@ def hide_columns(self, subset: Subset) -> Styler:
# A collection of "builtin" styles
# -----------------------------------------------------------------------
+ @doc(
+ name="background",
+ alt="text",
+ image_prefix="bg",
+ axis="{0 or 'index', 1 or 'columns', None}",
+ text_threshold="",
+ )
def background_gradient(
self,
cmap="PuBu",
@@ -1483,9 +1490,9 @@ def background_gradient(
gmap: Sequence | None = None,
) -> Styler:
"""
- Color the background in a gradient style.
+ Color the {name} in a gradient style.
- The background color is determined according
+ The {name} color is determined according
to the data in each column, row or frame, or by a given
gradient map. Requires matplotlib.
@@ -1501,7 +1508,7 @@ def background_gradient(
Compress the color range at the high end. This is a multiple of the data
range to extend above the maximum; good values usually in [0, 1],
defaults to 0.
- axis : {0 or 'index', 1 or 'columns', None}, default 0
+ axis : {axis}, default 0
Apply to each column (``axis=0`` or ``'index'``), to each row
(``axis=1`` or ``'columns'``), or to the entire DataFrame at once
with ``axis=None``.
@@ -1510,6 +1517,7 @@ def background_gradient(
or single key, to `DataFrame.loc[:, <subset>]` where the columns are
prioritised, to limit ``data`` to *before* applying the function.
text_color_threshold : float or int
+ {text_threshold}
Luminance threshold for determining text color in [0, 1]. Facilitates text
visibility across varying background colors. All text is dark if 0, and
light if 1, defaults to 0.408.
@@ -1529,7 +1537,7 @@ def background_gradient(
.. versionadded:: 1.0.0
gmap : array-like, optional
- Gradient map for determining the background colors. If not supplied
+ Gradient map for determining the {name} colors. If not supplied
will use the underlying data from rows, columns or frame. If given as an
ndarray or list-like must be an identical shape to the underlying data
considering ``axis`` and ``subset``. If given as DataFrame or Series must
@@ -1543,6 +1551,10 @@ def background_gradient(
-------
self : Styler
+ See Also
+ --------
+ Styler.{alt}_gradient: Color the {alt} in a gradient style.
+
Notes
-----
When using ``low`` and ``high`` the range
@@ -1560,52 +1572,50 @@ def background_gradient(
Examples
--------
- >>> df = pd.DataFrame({
- ... 'City': ['Stockholm', 'Oslo', 'Copenhagen'],
- ... 'Temp (c)': [21.6, 22.4, 24.5],
- ... 'Rain (mm)': [5.0, 13.3, 0.0],
- ... 'Wind (m/s)': [3.2, 3.1, 6.7]
- ... })
+ >>> df = pd.DataFrame(columns=["City", "Temp (c)", "Rain (mm)", "Wind (m/s)"],
+ ... data=[["Stockholm", 21.6, 5.0, 3.2],
+ ... ["Oslo", 22.4, 13.3, 3.1],
+ ... ["Copenhagen", 24.5, 0.0, 6.7]])
Shading the values column-wise, with ``axis=0``, preselecting numeric columns
- >>> df.style.background_gradient(axis=0)
+ >>> df.style.{name}_gradient(axis=0)
- .. figure:: ../../_static/style/bg_ax0.png
+ .. figure:: ../../_static/style/{image_prefix}_ax0.png
Shading all values collectively using ``axis=None``
- >>> df.style.background_gradient(axis=None)
+ >>> df.style.{name}_gradient(axis=None)
- .. figure:: ../../_static/style/bg_axNone.png
+ .. figure:: ../../_static/style/{image_prefix}_axNone.png
Compress the color map from the both ``low`` and ``high`` ends
- >>> df.style.background_gradient(axis=None, low=0.75, high=1.0)
+ >>> df.style.{name}_gradient(axis=None, low=0.75, high=1.0)
- .. figure:: ../../_static/style/bg_axNone_lowhigh.png
+ .. figure:: ../../_static/style/{image_prefix}_axNone_lowhigh.png
Manually setting ``vmin`` and ``vmax`` gradient thresholds
- >>> df.style.background_gradient(axis=None, vmin=6.7, vmax=21.6)
+ >>> df.style.{name}_gradient(axis=None, vmin=6.7, vmax=21.6)
- .. figure:: ../../_static/style/bg_axNone_vminvmax.png
+ .. figure:: ../../_static/style/{image_prefix}_axNone_vminvmax.png
Setting a ``gmap`` and applying to all columns with another ``cmap``
- >>> df.style.background_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
+ >>> df.style.{name}_gradient(axis=0, gmap=df['Temp (c)'], cmap='YlOrRd')
- .. figure:: ../../_static/style/bg_gmap.png
+ .. figure:: ../../_static/style/{image_prefix}_gmap.png
Setting the gradient map for a dataframe (i.e. ``axis=None``), we need to
explicitly state ``subset`` to match the ``gmap`` shape
>>> gmap = np.array([[1,2,3], [2,3,4], [3,4,5]])
- >>> df.style.background_gradient(axis=None, gmap=gmap,
+ >>> df.style.{name}_gradient(axis=None, gmap=gmap,
... cmap='YlOrRd', subset=['Temp (c)', 'Rain (mm)', 'Wind (m/s)']
... )
- .. figure:: ../../_static/style/bg_axNone_gmap.png
+ .. figure:: ../../_static/style/{image_prefix}_axNone_gmap.png
"""
if subset is None and gmap is None:
subset = self.data.select_dtypes(include=np.number).columns
@@ -1624,6 +1634,41 @@ def background_gradient(
)
return self
+ @doc(
+ background_gradient,
+ name="text",
+ alt="background",
+ image_prefix="tg",
+ axis="{0 or 'index', 1 or 'columns', None}",
+ text_threshold="This argument is ignored (only used in `background_gradient`).",
+ )
+ def text_gradient(
+ self,
+ cmap="PuBu",
+ low: float = 0,
+ high: float = 0,
+ axis: Axis | None = 0,
+ subset: Subset | None = None,
+ vmin: float | None = None,
+ vmax: float | None = None,
+ gmap: Sequence | None = None,
+ ) -> Styler:
+ if subset is None and gmap is None:
+ subset = self.data.select_dtypes(include=np.number).columns
+
+ return self.apply(
+ _background_gradient,
+ cmap=cmap,
+ subset=subset,
+ axis=axis,
+ low=low,
+ high=high,
+ vmin=vmin,
+ vmax=vmax,
+ gmap=gmap,
+ text_only=True,
+ )
+
def set_properties(self, subset: Subset | None = None, **kwargs) -> Styler:
"""
Set defined CSS-properties to each ``<td>`` HTML element within the given
@@ -2332,6 +2377,7 @@ def _background_gradient(
vmin: float | None = None,
vmax: float | None = None,
gmap: Sequence | np.ndarray | FrameOrSeries | None = None,
+ text_only: bool = False,
):
"""
Color background in a range according to the data or a gradient map
@@ -2371,16 +2417,19 @@ def relative_luminance(rgba) -> float:
)
return 0.2126 * r + 0.7152 * g + 0.0722 * b
- def css(rgba) -> str:
- dark = relative_luminance(rgba) < text_color_threshold
- text_color = "#f1f1f1" if dark else "#000000"
- return f"background-color: {colors.rgb2hex(rgba)};color: {text_color};"
+ def css(rgba, text_only) -> str:
+ if not text_only:
+ dark = relative_luminance(rgba) < text_color_threshold
+ text_color = "#f1f1f1" if dark else "#000000"
+ return f"background-color: {colors.rgb2hex(rgba)};color: {text_color};"
+ else:
+ return f"color: {colors.rgb2hex(rgba)};"
if data.ndim == 1:
- return [css(rgba) for rgba in rgbas]
+ return [css(rgba, text_only) for rgba in rgbas]
else:
return DataFrame(
- [[css(rgba) for rgba in row] for row in rgbas],
+ [[css(rgba, text_only) for rgba in row] for row in rgbas],
index=data.index,
columns=data.columns,
)
diff --git a/pandas/tests/io/formats/style/test_matplotlib.py b/pandas/tests/io/formats/style/test_matplotlib.py
index 496344c59ec04..029936283327a 100644
--- a/pandas/tests/io/formats/style/test_matplotlib.py
+++ b/pandas/tests/io/formats/style/test_matplotlib.py
@@ -33,17 +33,22 @@ def styler_blank(df_blank):
return Styler(df_blank, uuid_len=0)
-def test_background_gradient(styler):
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_function_gradient(styler, f):
for c_map in [None, "YlOrRd"]:
- result = styler.background_gradient(cmap=c_map)._compute().ctx
+ result = getattr(styler, f)(cmap=c_map)._compute().ctx
assert all("#" in x[0][1] for x in result.values())
assert result[(0, 0)] == result[(0, 1)]
assert result[(1, 0)] == result[(1, 1)]
-def test_background_gradient_color(styler):
- result = styler.background_gradient(subset=IndexSlice[1, "A"])._compute().ctx
- assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")]
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_background_gradient_color(styler, f):
+ result = getattr(styler, f)(subset=IndexSlice[1, "A"])._compute().ctx
+ if f == "background_gradient":
+ assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")]
+ elif f == "text_gradient":
+ assert result[(1, 0)] == [("color", "#fff7fb")]
@pytest.mark.parametrize(
@@ -54,15 +59,23 @@ def test_background_gradient_color(styler):
(None, ["low", "mid", "mid", "high"]),
],
)
-def test_background_gradient_axis(styler, axis, expected):
- bg_colors = {
- "low": [("background-color", "#f7fbff"), ("color", "#000000")],
- "mid": [("background-color", "#abd0e6"), ("color", "#000000")],
- "high": [("background-color", "#08306b"), ("color", "#f1f1f1")],
- }
- result = styler.background_gradient(cmap="Blues", axis=axis)._compute().ctx
+@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"])
+def test_background_gradient_axis(styler, axis, expected, f):
+ if f == "background_gradient":
+ colors = {
+ "low": [("background-color", "#f7fbff"), ("color", "#000000")],
+ "mid": [("background-color", "#abd0e6"), ("color", "#000000")],
+ "high": [("background-color", "#08306b"), ("color", "#f1f1f1")],
+ }
+ elif f == "text_gradient":
+ colors = {
+ "low": [("color", "#f7fbff")],
+ "mid": [("color", "#abd0e6")],
+ "high": [("color", "#08306b")],
+ }
+ result = getattr(styler, f)(cmap="Blues", axis=axis)._compute().ctx
for i, cell in enumerate([(0, 0), (0, 1), (1, 0), (1, 1)]):
- assert result[cell] == bg_colors[expected[i]]
+ assert result[cell] == colors[expected[i]]
@pytest.mark.parametrize(
| This is minor code alteration to the `_background_gradient` function that permits a text-only coloring version, i.e. `Styler.text_gradient`.
This could alternatively be performed with a `text_only=True` keyword addition to `Styler.background_gradient` existing method, but it seemed dissimilar enough to merit its own method (and avoid adding more kwargs).
| https://api.github.com/repos/pandas-dev/pandas/pulls/41098 | 2021-04-22T12:29:41Z | 2021-05-25T09:04:19Z | 2021-05-25T09:04:19Z | 2021-05-25T09:36:23Z |
TYP: Fix typehints for ExtensionDtype | diff --git a/pandas/core/dtypes/base.py b/pandas/core/dtypes/base.py
index 8104b0170fbe2..9671c340a0a92 100644
--- a/pandas/core/dtypes/base.py
+++ b/pandas/core/dtypes/base.py
@@ -7,6 +7,7 @@
from typing import (
TYPE_CHECKING,
Any,
+ TypeVar,
)
import numpy as np
@@ -26,6 +27,9 @@
if TYPE_CHECKING:
from pandas.core.arrays import ExtensionArray
+ # To parameterize on same ExtensionDtype
+ E = TypeVar("E", bound="ExtensionDtype")
+
class ExtensionDtype:
"""
@@ -151,7 +155,7 @@ def na_value(self) -> object:
return np.nan
@property
- def type(self) -> type[Any]:
+ def type(self) -> type_t[Any]:
"""
The scalar type for the array, e.g. ``int``
@@ -364,7 +368,7 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
return None
-def register_extension_dtype(cls: type[ExtensionDtype]) -> type[ExtensionDtype]:
+def register_extension_dtype(cls: type[E]) -> type[E]:
"""
Register an ExtensionType with pandas as class decorator.
| This is a fix for type hints of `ExtensionDtype`. `register_extension_dtype` was confusing pyright autocomplete. It was fixed by parameterizing its return value on its input value.
Example:
```python
import pandas as pd
pd.CategoricalDtype(categories=['a', 'b'])
```
Before this change:
```console
$ pyright --version
pyright 1.1.133
$ pyright --lib pd.py
No configuration file found.
No pyproject.toml file found.
stubPath /home/omer/src/ext/typings is not a valid directory.
Assuming Python platform Linux
Searching for source files
Found 1 source file
/home/omer/src/ext/pd.py
/home/omer/src/ext/pd.py:3:1 - error: Expected no arguments to "ExtensionDtype" constructor (reportGeneralTypeIssues)
1 error, 0 warnings, 0 infos
Completed in 0.72sec
```
After:
```console
$ pyright --lib ../pd.py
No configuration file found.
pyproject.toml file found at /home/omer/src/ext/pandas.
Loading pyproject.toml file at /home/omer/src/ext/pandas/pyproject.toml
Pyproject file "/home/omer/src/ext/pandas/pyproject.toml" is missing "[tool.pyright] section.
stubPath /home/omer/src/ext/pandas/typings is not a valid directory.
Assuming Python platform Linux
Searching for source files
Found 1 source file
0 errors, 0 warnings, 0 infos
Completed in 0.777sec
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/41097 | 2021-04-22T12:23:49Z | 2021-04-26T10:39:44Z | 2021-04-26T10:39:44Z | 2021-04-26T11:40:01Z |
Backport PR #41092: CI/DOC: temporary pin environment to python 3.8 | diff --git a/environment.yml b/environment.yml
index a369f656cb575..61c8351070de9 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
dependencies:
# required
- numpy>=1.16.5
- - python=3
+ - python=3.8
- python-dateutil>=2.7.3
- pytz
| Backport PR #41092 | https://api.github.com/repos/pandas-dev/pandas/pulls/41095 | 2021-04-22T11:18:13Z | 2021-04-22T13:29:31Z | 2021-04-22T13:29:31Z | 2021-04-22T13:29:36Z |
CI/DOC: temporary pin environment to python 3.8 | diff --git a/environment.yml b/environment.yml
index 0d03ad8e0a46a..2e0228a15272e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
dependencies:
# required
- numpy>=1.17.3
- - python=3
+ - python=3.8
- python-dateutil>=2.7.3
- pytz
| The doc build is failing on master (`/home/runner/work/pandas/pandas/doc/source/reference/api/pandas.Series.dt.rst: WARNING: document isn't included in any toctree`), and the build that starts failing seems to have switched from python 3.8 to 3.9 (I don't see any other relevant change in the environment). I suppose the default python changed at the conda(-forge) side. | https://api.github.com/repos/pandas-dev/pandas/pulls/41092 | 2021-04-22T07:07:53Z | 2021-04-22T11:00:17Z | 2021-04-22T11:00:17Z | 2021-04-22T11:22:14Z |
REF: simplify ohlc | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index a059a90aa3a4e..9d6d2d698dfe5 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -363,20 +363,10 @@ def _cython_agg_general(
result = self.grouper._cython_operation(
"aggregate", obj._values, how, axis=0, min_count=min_count
)
-
- if how == "ohlc":
- # e.g. ohlc
- agg_names = ["open", "high", "low", "close"]
- assert len(agg_names) == result.shape[1]
- for result_column, result_name in zip(result.T, agg_names):
- key = base.OutputKey(label=result_name, position=idx)
- output[key] = result_column
- idx += 1
- else:
- assert result.ndim == 1
- key = base.OutputKey(label=name, position=idx)
- output[key] = result
- idx += 1
+ assert result.ndim == 1
+ key = base.OutputKey(label=name, position=idx)
+ output[key] = result
+ idx += 1
if not output:
raise DataError("No numeric types to aggregate")
@@ -942,10 +932,6 @@ def count(self) -> Series:
)
return self._reindex_output(result, fill_value=0)
- def _apply_to_column_groupbys(self, func):
- """ return a pass thru """
- return func(self)
-
def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None):
"""Calculate pct_change of each value to previous entry in group"""
# TODO: Remove this conditional when #23918 is fixed
@@ -1137,6 +1123,7 @@ def _cython_agg_general(
def _cython_agg_manager(
self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
) -> Manager2D:
+ # Note: we never get here with how="ohlc"; that goes through SeriesGroupBy
data: Manager2D = self._get_data_to_aggregate()
@@ -1227,13 +1214,6 @@ def array_func(values: ArrayLike) -> ArrayLike:
# generally if we have numeric_only=False
# and non-applicable functions
# try to python agg
-
- if alt is None:
- # we cannot perform the operation
- # in an alternate way, exclude the block
- assert how == "ohlc"
- raise
-
result = py_fallback(values)
return cast_agg_result(result, values, how)
@@ -1241,7 +1221,6 @@ def array_func(values: ArrayLike) -> ArrayLike:
# TypeError -> we may have an exception in trying to aggregate
# continue and exclude the block
- # NotImplementedError -> "ohlc" with wrong dtype
new_mgr = data.grouped_reduce(array_func, ignore_failures=True)
if not len(new_mgr):
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 9a4f343ab3dc2..f579b04db898e 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1791,7 +1791,25 @@ def ohlc(self) -> DataFrame:
DataFrame
Open, high, low and close values within each group.
"""
- return self._apply_to_column_groupbys(lambda x: x._cython_agg_general("ohlc"))
+ if self.obj.ndim == 1:
+ # self._iterate_slices() yields only self._selected_obj
+ obj = self._selected_obj
+
+ is_numeric = is_numeric_dtype(obj.dtype)
+ if not is_numeric:
+ raise DataError("No numeric types to aggregate")
+
+ res_values = self.grouper._cython_operation(
+ "aggregate", obj._values, "ohlc", axis=0, min_count=-1
+ )
+
+ agg_names = ["open", "high", "low", "close"]
+ result = self.obj._constructor_expanddim(
+ res_values, index=self.grouper.result_index, columns=agg_names
+ )
+ return self._reindex_output(result)
+
+ return self._apply_to_column_groupbys(lambda x: x.ohlc())
@final
@doc(DataFrame.describe)
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 71e6aa38d60e5..bbe9ac6fa8094 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -58,14 +58,16 @@ def test_custom_grouper(index):
g = s.groupby(b)
# check all cython functions work
- funcs = ["add", "mean", "prod", "ohlc", "min", "max", "var"]
+ g.ohlc() # doesn't use _cython_agg_general
+ funcs = ["add", "mean", "prod", "min", "max", "var"]
for f in funcs:
g._cython_agg_general(f)
b = Grouper(freq=Minute(5), closed="right", label="right")
g = s.groupby(b)
# check all cython functions work
- funcs = ["add", "mean", "prod", "ohlc", "min", "max", "var"]
+ g.ohlc() # doesn't use _cython_agg_general
+ funcs = ["add", "mean", "prod", "min", "max", "var"]
for f in funcs:
g._cython_agg_general(f)
@@ -79,7 +81,7 @@ def test_custom_grouper(index):
idx = DatetimeIndex(idx, freq="5T")
expect = Series(arr, index=idx)
- # GH2763 - return in put dtype if we can
+ # GH2763 - return input dtype if we can
result = g.agg(np.sum)
tm.assert_series_equal(result, expect)
| Or more accurately, simplify BaseGroupBy._cython_agg_general by not having ohlc go through it. This will in turn allow us to simplify SeriesGroupBy._wrap_aggregated_output.
This will have a merge conflict with #41066. Doesn't matter which order they are merged in. | https://api.github.com/repos/pandas-dev/pandas/pulls/41091 | 2021-04-22T00:59:22Z | 2021-04-23T01:47:10Z | 2021-04-23T01:47:10Z | 2021-04-23T01:54:53Z |
CLN: annotations, docstrings | diff --git a/pandas/_libs/hashtable_class_helper.pxi.in b/pandas/_libs/hashtable_class_helper.pxi.in
index 4dc5e7516db7e..a25867c4a3b0c 100644
--- a/pandas/_libs/hashtable_class_helper.pxi.in
+++ b/pandas/_libs/hashtable_class_helper.pxi.in
@@ -687,7 +687,10 @@ cdef class {{name}}HashTable(HashTable):
{{if dtype == 'int64'}}
@cython.boundscheck(False)
- def get_labels_groupby(self, const {{dtype}}_t[:] values):
+ def get_labels_groupby(
+ self, const {{dtype}}_t[:] values
+ ) -> tuple[ndarray, ndarray]:
+ # tuple[np.ndarray[np.intp], np.ndarray[{{dtype}}]]
cdef:
Py_ssize_t i, n = len(values)
intp_t[:] labels
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 5a9dd0e89bd65..a9c94b615f49c 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -178,8 +178,8 @@ class PeriodArray(dtl.DatelikeOps):
"days_in_month",
"daysinmonth",
]
- _datetimelike_ops = _field_ops + _object_ops + _bool_ops
- _datetimelike_methods = ["strftime", "to_timestamp", "asfreq"]
+ _datetimelike_ops: list[str] = _field_ops + _object_ops + _bool_ops
+ _datetimelike_methods: list[str] = ["strftime", "to_timestamp", "asfreq"]
# --------------------------------------------------------------------
# Constructors
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 84eede019251b..3d68688c21241 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -48,9 +48,14 @@
)
if TYPE_CHECKING:
+ from datetime import tzinfo
+
import pyarrow
- from pandas import Categorical
+ from pandas import (
+ Categorical,
+ Index,
+ )
from pandas.core.arrays import (
DatetimeArray,
IntervalArray,
@@ -445,8 +450,8 @@ def _hash_categories(self) -> int:
# assumes if any individual category is a tuple, then all our. ATM
# I don't really want to support just some of the categories being
# tuples.
- categories = list(categories) # breaks if a np.array of categories
- cat_array = hash_tuples(categories)
+ cat_list = list(categories) # breaks if a np.array of categories
+ cat_array = hash_tuples(cat_list)
else:
if categories.dtype == "O" and len({type(x) for x in categories}) != 1:
# TODO: hash_array doesn't handle mixed types. It casts
@@ -509,7 +514,7 @@ def validate_ordered(ordered: Ordered) -> None:
raise TypeError("'ordered' must either be 'True' or 'False'")
@staticmethod
- def validate_categories(categories, fastpath: bool = False):
+ def validate_categories(categories, fastpath: bool = False) -> Index:
"""
Validates that we have good categories
@@ -579,7 +584,7 @@ def update_dtype(self, dtype: str_type | CategoricalDtype) -> CategoricalDtype:
return CategoricalDtype(new_categories, new_ordered)
@property
- def categories(self):
+ def categories(self) -> Index:
"""
An ``Index`` containing the unique categories allowed.
"""
@@ -717,7 +722,7 @@ def unit(self) -> str_type:
return self._unit
@property
- def tz(self):
+ def tz(self) -> tzinfo:
"""
The timezone.
"""
@@ -882,7 +887,7 @@ def freq(self):
return self._freq
@classmethod
- def _parse_dtype_strict(cls, freq):
+ def _parse_dtype_strict(cls, freq: str_type) -> BaseOffset:
if isinstance(freq, str):
if freq.startswith("period[") or freq.startswith("Period["):
m = cls._match.search(freq)
@@ -1136,7 +1141,7 @@ def construct_array_type(cls) -> type[IntervalArray]:
return IntervalArray
@classmethod
- def construct_from_string(cls, string):
+ def construct_from_string(cls, string: str_type) -> IntervalDtype:
"""
attempt to construct this type from a string, raise a TypeError
if its not possible
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 0a9c46f6ed069..4f9a71e5af59a 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -281,7 +281,7 @@ class BaseGrouper:
whether this grouper will give sorted result or not
group_keys : bool, default True
mutated : bool, default False
- indexer : intp array, optional
+ indexer : np.ndarray[np.intp], optional
the indexer created by Grouper
some groupers (TimeGrouper) will sort its axis and its
group_info is also sorted, so need the indexer to reorder
diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py
index 58f5ca3de5dce..9b3f2d191831d 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -3029,9 +3029,6 @@ def _union(self, other: Index, sort):
@final
def _wrap_setop_result(self, other: Index, result) -> Index:
- if is_categorical_dtype(self.dtype) and isinstance(result, np.ndarray):
- result = Categorical(result, dtype=self.dtype)
-
name = get_op_result_name(self, other)
if isinstance(result, Index):
if result.name != name:
@@ -4028,7 +4025,7 @@ def join(
return join_index, lindexer, rindexer
@final
- def _join_multi(self, other, how):
+ def _join_multi(self, other: Index, how: str_t):
from pandas.core.indexes.multi import MultiIndex
from pandas.core.reshape.merge import restore_dropped_levels_multijoin
@@ -4273,7 +4270,7 @@ def _get_leaf_sorter(labels: list[np.ndarray]) -> np.ndarray:
return join_index, left_indexer, right_indexer
@final
- def _join_monotonic(self, other: Index, how="left"):
+ def _join_monotonic(self, other: Index, how: str_t = "left"):
# We only get here with matching dtypes
assert other.dtype == self.dtype
@@ -5527,7 +5524,7 @@ def isin(self, values, level=None) -> np.ndarray:
Returns
-------
- is_contained : ndarray[bool]
+ np.ndarray[bool]
NumPy array of boolean values.
See Also
diff --git a/pandas/core/indexing.py b/pandas/core/indexing.py
index 1b68ac9780ee1..04543da167fdd 100644
--- a/pandas/core/indexing.py
+++ b/pandas/core/indexing.py
@@ -36,7 +36,6 @@
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
ABCDataFrame,
- ABCMultiIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import (
@@ -53,7 +52,10 @@
is_list_like_indexer,
length_of_indexer,
)
-from pandas.core.indexes.api import Index
+from pandas.core.indexes.api import (
+ Index,
+ MultiIndex,
+)
if TYPE_CHECKING:
from pandas import (
@@ -642,7 +644,7 @@ def _get_setitem_indexer(self, key):
ax = self.obj._get_axis(0)
- if isinstance(ax, ABCMultiIndex) and self.name != "iloc":
+ if isinstance(ax, MultiIndex) and self.name != "iloc":
with suppress(TypeError, KeyError, InvalidIndexError):
# TypeError e.g. passed a bool
return ax.get_loc(key)
@@ -690,7 +692,7 @@ def _ensure_listlike_indexer(self, key, axis=None, value=None):
if (
axis == column_axis
- and not isinstance(self.obj.columns, ABCMultiIndex)
+ and not isinstance(self.obj.columns, MultiIndex)
and is_list_like_indexer(key)
and not com.is_bool_indexer(key)
and all(is_hashable(k) for k in key)
@@ -756,7 +758,7 @@ def _is_nested_tuple_indexer(self, tup: tuple) -> bool:
-------
bool
"""
- if any(isinstance(ax, ABCMultiIndex) for ax in self.obj.axes):
+ if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
return False
@@ -817,7 +819,7 @@ def _getitem_lowerdim(self, tup: tuple):
ax0 = self.obj._get_axis(0)
# ...but iloc should handle the tuple as simple integer-location
# instead of checking it as multiindex representation (GH 13797)
- if isinstance(ax0, ABCMultiIndex) and self.name != "iloc":
+ if isinstance(ax0, MultiIndex) and self.name != "iloc":
with suppress(IndexingError):
return self._handle_lowerdim_multi_index_axis0(tup)
@@ -996,7 +998,7 @@ def _is_scalar_access(self, key: tuple) -> bool:
return False
ax = self.obj.axes[i]
- if isinstance(ax, ABCMultiIndex):
+ if isinstance(ax, MultiIndex):
return False
if isinstance(k, str) and ax._supports_partial_string_indexing:
@@ -1142,7 +1144,7 @@ def _getitem_axis(self, key, axis: int):
elif is_list_like_indexer(key):
# an iterable multi-selection
- if not (isinstance(key, tuple) and isinstance(labels, ABCMultiIndex)):
+ if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):
if hasattr(key, "ndim") and key.ndim > 1:
raise ValueError("Cannot index with multidimensional key")
@@ -1205,20 +1207,20 @@ def _convert_to_indexer(self, key, axis: int, is_setter: bool = False):
is_int_index = labels.is_integer()
is_int_positional = is_integer(key) and not is_int_index
- if is_scalar(key) or isinstance(labels, ABCMultiIndex):
+ if is_scalar(key) or isinstance(labels, MultiIndex):
# Otherwise get_loc will raise InvalidIndexError
# if we are a label return me
try:
return labels.get_loc(key)
except LookupError:
- if isinstance(key, tuple) and isinstance(labels, ABCMultiIndex):
+ if isinstance(key, tuple) and isinstance(labels, MultiIndex):
if len(key) == labels.nlevels:
return {"key": key}
raise
except InvalidIndexError:
# GH35015, using datetime as column indices raises exception
- if not isinstance(labels, ABCMultiIndex):
+ if not isinstance(labels, MultiIndex):
raise
except TypeError:
pass
@@ -1620,7 +1622,7 @@ def _setitem_with_indexer(self, indexer, value, name="iloc"):
# GH 10360, GH 27841
if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
for i, ax in zip(indexer, self.obj.axes):
- if isinstance(ax, ABCMultiIndex) and not (
+ if isinstance(ax, MultiIndex) and not (
is_integer(i) or com.is_null_slice(i)
):
take_split_path = True
@@ -1819,7 +1821,7 @@ def _setitem_with_indexer_frame_value(self, indexer, value: DataFrame, name: str
sub_indexer = list(indexer)
pi = indexer[0]
- multiindex_indexer = isinstance(self.obj.columns, ABCMultiIndex)
+ multiindex_indexer = isinstance(self.obj.columns, MultiIndex)
unique_cols = value.columns.is_unique
@@ -2163,8 +2165,8 @@ def _align_frame(self, indexer, df: DataFrame):
# we have a multi-index and are trying to align
# with a particular, level GH3738
if (
- isinstance(ax, ABCMultiIndex)
- and isinstance(df.index, ABCMultiIndex)
+ isinstance(ax, MultiIndex)
+ and isinstance(df.index, MultiIndex)
and ax.nlevels != df.index.nlevels
):
raise TypeError(
@@ -2428,7 +2430,7 @@ def is_nested_tuple(tup, labels) -> bool:
for k in tup:
if is_list_like(k) or isinstance(k, slice):
- return isinstance(labels, ABCMultiIndex)
+ return isinstance(labels, MultiIndex)
return False
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 58003c10db9e0..91c77e987654b 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -20,6 +20,7 @@
to_offset,
)
from pandas._typing import (
+ FrameOrSeries,
T,
TimedeltaConvertibleTypes,
TimestampConvertibleTypes,
@@ -1345,9 +1346,15 @@ def _upsample(self, method, limit=None, fill_value=None):
# Get the fill indexer
indexer = memb.get_indexer(new_index, method=method, limit=limit)
- return self._wrap_result(
- _take_new_index(obj, indexer, new_index, axis=self.axis)
+ new_obj = _take_new_index(
+ obj,
+ indexer,
+ # error: Argument 3 to "_take_new_index" has incompatible type
+ # "Optional[Any]"; expected "Index"
+ new_index, # type: ignore[arg-type]
+ axis=self.axis,
)
+ return self._wrap_result(new_obj)
class PeriodIndexResamplerGroupby(_GroupByMixin, PeriodIndexResampler):
@@ -1666,7 +1673,7 @@ def _adjust_bin_edges(self, binner, ax_values):
bin_edges = binner.asi8
return binner, bin_edges
- def _get_time_delta_bins(self, ax):
+ def _get_time_delta_bins(self, ax: TimedeltaIndex):
if not isinstance(ax, TimedeltaIndex):
raise TypeError(
"axis must be a TimedeltaIndex, but got "
@@ -1789,17 +1796,24 @@ def _get_period_bins(self, ax: PeriodIndex):
return binner, bins, labels
-def _take_new_index(obj, indexer, new_index, axis=0):
+def _take_new_index(
+ obj: FrameOrSeries, indexer: np.ndarray, new_index: Index, axis: int = 0
+) -> FrameOrSeries:
+ # indexer: np.ndarray[np.intp]
if isinstance(obj, ABCSeries):
new_values = algos.take_nd(obj._values, indexer)
- return obj._constructor(new_values, index=new_index, name=obj.name)
+ # error: Incompatible return value type (got "Series", expected "FrameOrSeries")
+ return obj._constructor( # type: ignore[return-value]
+ new_values, index=new_index, name=obj.name
+ )
elif isinstance(obj, ABCDataFrame):
if axis == 1:
raise NotImplementedError("axis 1 is not supported")
- return obj._constructor(
- obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
- )
+ new_mgr = obj._mgr.reindex_indexer(new_axis=new_index, indexer=indexer, axis=1)
+ # error: Incompatible return value type
+ # (got "DataFrame", expected "FrameOrSeries")
+ return obj._constructor(new_mgr) # type: ignore[return-value]
else:
raise ValueError("'obj' should be either a Series or a DataFrame")
@@ -1822,7 +1836,7 @@ def _get_timestamp_range_edges(
The ending Timestamp of the range to be adjusted.
freq : pd.DateOffset
The dateoffset to which the Timestamps will be adjusted.
- closed : {'right', 'left'}, default None
+ closed : {'right', 'left'}, default "left"
Which side of bin interval is closed.
origin : {'epoch', 'start', 'start_day'} or Timestamp, default 'start_day'
The timestamp on which to adjust the grouping. The timezone of origin must
@@ -1892,7 +1906,7 @@ def _get_period_range_edges(
The ending Period of the range to be adjusted.
freq : pd.DateOffset
The freq to which the Periods will be adjusted.
- closed : {'right', 'left'}, default None
+ closed : {'right', 'left'}, default "left"
Which side of bin interval is closed.
origin : {'epoch', 'start', 'start_day'}, Timestamp, default 'start_day'
The timestamp on which to adjust the grouping. The timezone of origin must
@@ -2042,7 +2056,7 @@ def asfreq(obj, freq, method=None, how=None, normalize=False, fill_value=None):
return new_obj
-def _asfreq_compat(index, freq):
+def _asfreq_compat(index: DatetimeIndex | PeriodIndex | TimedeltaIndex, freq):
"""
Helper to mimic asfreq on (empty) DatetimeIndex and TimedeltaIndex.
diff --git a/pandas/core/sorting.py b/pandas/core/sorting.py
index 71963ec4a2123..dd7ae904c866c 100644
--- a/pandas/core/sorting.py
+++ b/pandas/core/sorting.py
@@ -182,7 +182,7 @@ def maybe_lift(lab, size):
return out
-def get_compressed_ids(labels, sizes):
+def get_compressed_ids(labels, sizes) -> tuple[np.ndarray, np.ndarray]:
"""
Group_index is offsets into cartesian product of all possible labels. This
space can be huge, so this function compresses it, by computing offsets
@@ -195,7 +195,10 @@ def get_compressed_ids(labels, sizes):
Returns
-------
- tuple of (comp_ids, obs_group_ids)
+ np.ndarray[np.intp]
+ comp_ids
+ np.ndarray[np.int64]
+ obs_group_ids
"""
ids = get_group_index(labels, sizes, sort=True, xnull=False)
return compress_group_index(ids, sort=True)
@@ -254,7 +257,8 @@ def decons_obs_group_ids(comp_ids, obs_ids, shape, labels, xnull: bool):
return [i8copy(lab[i]) for lab in labels]
-def indexer_from_factorized(labels, shape, compress: bool = True):
+def indexer_from_factorized(labels, shape, compress: bool = True) -> np.ndarray:
+ # returned ndarray is np.intp
ids = get_group_index(labels, shape, sort=True, xnull=False)
if not compress:
@@ -268,7 +272,7 @@ def indexer_from_factorized(labels, shape, compress: bool = True):
def lexsort_indexer(
keys, orders=None, na_position: str = "last", key: Callable | None = None
-):
+) -> np.ndarray:
"""
Performs lexical sorting on a set of keys
@@ -288,6 +292,10 @@ def lexsort_indexer(
Callable key function applied to every element in keys before sorting
.. versionadded:: 1.0.0
+
+ Returns
+ -------
+ np.ndarray[np.intp]
"""
from pandas.core.arrays import Categorical
@@ -656,7 +664,20 @@ def compress_group_index(group_index, sort: bool = True):
return ensure_int64(comp_ids), ensure_int64(obs_group_ids)
-def _reorder_by_uniques(uniques, labels):
+def _reorder_by_uniques(
+ uniques: np.ndarray, labels: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+ """
+ Parameters
+ ----------
+ uniques : np.ndarray[np.int64]
+ labels : np.ndarray[np.intp]
+
+ Returns
+ -------
+ np.ndarray[np.int64]
+ np.ndarray[np.intp]
+ """
# sorter is index where elements ought to go
sorter = uniques.argsort()
| Remove a no-longer-needed Categorical kludge in wrap_setop_result
isinstance(obj, ABCMultiIndex) -> isinstance(obj, MultiIndex) in core.indexing | https://api.github.com/repos/pandas-dev/pandas/pulls/41089 | 2021-04-21T21:35:48Z | 2021-04-22T22:27:32Z | 2021-04-22T22:27:32Z | 2021-04-22T22:36:57Z |
Update license year | diff --git a/LICENSE b/LICENSE
index 76954a5a339ab..a0cc369f725b8 100644
--- a/LICENSE
+++ b/LICENSE
@@ -3,7 +3,7 @@ BSD 3-Clause License
Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
All rights reserved.
-Copyright (c) 2011-2020, Open source contributors.
+Copyright (c) 2011-2021, Open source contributors.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
| Saw this today | https://api.github.com/repos/pandas-dev/pandas/pulls/41087 | 2021-04-21T20:42:27Z | 2021-04-22T01:06:47Z | 2021-04-22T01:06:47Z | 2021-04-22T20:01:24Z |
REF: hide ArrayManager implementation details from GroupBy | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 80351a832ec7e..ab03cce0d6476 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -97,7 +97,6 @@
all_indexes_same,
)
import pandas.core.indexes.base as ibase
-from pandas.core.internals import ArrayManager
from pandas.core.series import Series
from pandas.core.util.numba_ import maybe_use_numba
@@ -1103,17 +1102,11 @@ def _cython_agg_general(
if numeric_only:
data = data.get_numeric_data(copy=False)
- using_array_manager = isinstance(data, ArrayManager)
-
def cast_agg_result(result: ArrayLike, values: ArrayLike) -> ArrayLike:
# see if we can cast the values to the desired dtype
# this may not be the original dtype
- if (
- not using_array_manager
- and isinstance(result.dtype, np.dtype)
- and result.ndim == 1
- ):
+ if isinstance(result.dtype, np.dtype) and result.ndim == 1:
# We went through a SeriesGroupByPath and need to reshape
# GH#32223 includes case with IntegerArray values
# We only get here with values.dtype == object
@@ -1794,8 +1787,6 @@ def count(self) -> DataFrame:
ids, _, ngroups = self.grouper.group_info
mask = ids != -1
- using_array_manager = isinstance(data, ArrayManager)
-
def hfunc(bvalues: ArrayLike) -> ArrayLike:
# TODO(2DEA): reshape would not be necessary with 2D EAs
if bvalues.ndim == 1:
@@ -1805,10 +1796,6 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
masked = mask & ~isna(bvalues)
counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups, axis=1)
- if using_array_manager:
- # count_level_2d return (1, N) array for single column
- # -> extract 1D array
- counted = counted[0, :]
return counted
new_mgr = data.grouped_reduce(hfunc)
diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index ff76228646a02..71e6d14e6a716 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -932,12 +932,20 @@ def grouped_reduce(self: T, func: Callable, ignore_failures: bool = False) -> T:
result_indices: list[int] = []
for i, arr in enumerate(self.arrays):
+ # grouped_reduce functions all expect 2D arrays
+ arr = ensure_block_shape(arr, ndim=2)
try:
res = func(arr)
except (TypeError, NotImplementedError):
if not ignore_failures:
raise
continue
+
+ if res.ndim == 2:
+ # reverse of ensure_block_shape
+ assert res.shape[0] == 1
+ res = res[0]
+
result_arrays.append(res)
result_indices.append(i)
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/41086 | 2021-04-21T19:38:42Z | 2021-04-30T18:50:01Z | 2021-04-30T18:50:01Z | 2021-04-30T19:00:20Z |
BUG: GH27721 - test groupby rank with multiindex | diff --git a/pandas/tests/groupby/test_rank.py b/pandas/tests/groupby/test_rank.py
index da88ea5f05107..e07c5f404a02a 100644
--- a/pandas/tests/groupby/test_rank.py
+++ b/pandas/tests/groupby/test_rank.py
@@ -578,3 +578,25 @@ def test_rank_pct_equal_values_on_group_transition(use_nan):
expected = Series([1 / 3, 2 / 3, 1, 1], name="val")
tm.assert_series_equal(result, expected)
+
+
+def test_rank_multiindex():
+ # GH27721
+ df = concat(
+ {
+ "a": DataFrame({"col1": [1, 2], "col2": [3, 4]}),
+ "b": DataFrame({"col3": [5, 6], "col4": [7, 8]}),
+ },
+ axis=1,
+ )
+
+ result = df.groupby(level=0, axis=1).rank(axis=1, ascending=False, method="first")
+ expected = concat(
+ {
+ "a": DataFrame({"col1": [2.0, 2.0], "col2": [1.0, 1.0]}),
+ "b": DataFrame({"col3": [2.0, 2.0], "col4": [1.0, 1.0]}),
+ },
+ axis=1,
+ )
+
+ tm.assert_frame_equal(result, expected)
| - [x] closes #27721
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry | https://api.github.com/repos/pandas-dev/pandas/pulls/41084 | 2021-04-21T19:07:43Z | 2021-04-21T21:55:34Z | 2021-04-21T21:55:33Z | 2021-04-21T22:23:35Z |
CI: ignore ResourceWarning in tm.assert_produces_warning | diff --git a/pandas/_testing/_warnings.py b/pandas/_testing/_warnings.py
index 027a53edb1810..391226b622a01 100644
--- a/pandas/_testing/_warnings.py
+++ b/pandas/_testing/_warnings.py
@@ -142,6 +142,14 @@ def _assert_caught_no_extra_warnings(
for actual_warning in caught_warnings:
if _is_unexpected_warning(actual_warning, expected_warning):
+ unclosed = "unclosed transport <asyncio.sslproto._SSLProtocolTransport"
+ if isinstance(actual_warning, ResourceWarning) and unclosed in str(
+ actual_warning
+ ):
+ # FIXME: kludge because pytest.filterwarnings does not
+ # suppress these, xref GH#38630
+ continue
+
extra_warnings.append(
(
actual_warning.category.__name__,
diff --git a/setup.cfg b/setup.cfg
index 8cdec8ab9feed..610b30e4422a9 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -137,7 +137,6 @@ xfail_strict = True
filterwarnings =
error:Sparse:FutureWarning
error:The SparseArray:FutureWarning
- ignore:unclosed transport <asyncio.sslproto:ResourceWarning
junit_family = xunit2
[codespell]
| reverts a line in setup.cfg that was intended to do this but failed | https://api.github.com/repos/pandas-dev/pandas/pulls/41083 | 2021-04-21T16:40:45Z | 2021-04-22T22:20:05Z | 2021-04-22T22:20:05Z | 2021-04-22T22:22:12Z |
REF: Back DatetimeTZBlock with sometimes-2D DTA | diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index e207dac71752e..593e42f7ed749 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -1413,6 +1413,33 @@ def is_extension_type(arr) -> bool:
return False
+def is_1d_only_ea_obj(obj: Any) -> bool:
+ """
+ ExtensionArray that does not support 2D, or more specifically that does
+ not use HybridBlock.
+ """
+ from pandas.core.arrays import (
+ DatetimeArray,
+ ExtensionArray,
+ TimedeltaArray,
+ )
+
+ return isinstance(obj, ExtensionArray) and not isinstance(
+ obj, (DatetimeArray, TimedeltaArray)
+ )
+
+
+def is_1d_only_ea_dtype(dtype: Optional[DtypeObj]) -> bool:
+ """
+ Analogue to is_extension_array_dtype but excluding DatetimeTZDtype.
+ """
+ # Note: if other EA dtypes are ever held in HybridBlock, exclude those
+ # here too.
+ # NB: need to check DatetimeTZDtype and not is_datetime64tz_dtype
+ # to exclude ArrowTimestampUSDtype
+ return isinstance(dtype, ExtensionDtype) and not isinstance(dtype, DatetimeTZDtype)
+
+
def is_extension_array_dtype(arr_or_dtype) -> bool:
"""
Check if an object is a pandas extension array type.
diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index cfadb3e9f45c5..b0d00775bbed1 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -113,11 +113,15 @@ def is_nonempty(x) -> bool:
to_concat = non_empties
kinds = {obj.dtype.kind for obj in to_concat}
+ contains_datetime = any(kind in ["m", "M"] for kind in kinds)
all_empty = not len(non_empties)
single_dtype = len({x.dtype for x in to_concat}) == 1
any_ea = any(isinstance(x.dtype, ExtensionDtype) for x in to_concat)
+ if contains_datetime:
+ return _concat_datetime(to_concat, axis=axis)
+
if any_ea:
# we ignore axis here, as internally concatting with EAs is always
# for axis=0
@@ -131,9 +135,6 @@ def is_nonempty(x) -> bool:
else:
return np.concatenate(to_concat)
- elif any(kind in ["m", "M"] for kind in kinds):
- return _concat_datetime(to_concat, axis=axis)
-
elif all_empty:
# we have all empties, but may need to coerce the result dtype to
# object if we have non-numeric type operands (numpy would otherwise
@@ -349,14 +350,5 @@ def _concat_datetime(to_concat, axis=0):
# in Timestamp/Timedelta
return _concatenate_2d([x.astype(object) for x in to_concat], axis=axis)
- if axis == 1:
- # TODO(EA2D): kludge not necessary with 2D EAs
- to_concat = [x.reshape(1, -1) if x.ndim == 1 else x for x in to_concat]
-
result = type(to_concat[0])._concat_same_type(to_concat, axis=axis)
-
- if result.ndim == 2 and isinstance(result.dtype, ExtensionDtype):
- # TODO(EA2D): kludge not necessary with 2D EAs
- assert result.shape[0] == 1
- result = result[0]
return result
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 4a7ed2bfc18df..7f970a72cb12c 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -98,6 +98,7 @@
from pandas.core.dtypes.common import (
ensure_platform_int,
infer_dtype_from_object,
+ is_1d_only_ea_dtype,
is_bool_dtype,
is_dataclass,
is_datetime64_any_dtype,
@@ -845,7 +846,9 @@ def _can_fast_transpose(self) -> bool:
if len(blocks) != 1:
return False
- return not self._mgr.any_extension_types
+ dtype = blocks[0].dtype
+ # TODO(EA2D) special case would be unnecessary with 2D EAs
+ return not is_1d_only_ea_dtype(dtype)
# ----------------------------------------------------------------------
# Rendering Methods
diff --git a/pandas/core/internals/api.py b/pandas/core/internals/api.py
index d6b76510c68ab..2f8686fd38929 100644
--- a/pandas/core/internals/api.py
+++ b/pandas/core/internals/api.py
@@ -6,7 +6,7 @@
2) Use only functions exposed here (or in core.internals)
"""
-from typing import Optional
+from __future__ import annotations
import numpy as np
@@ -23,6 +23,7 @@
Block,
DatetimeTZBlock,
check_ndim,
+ ensure_block_shape,
extract_pandas_array,
get_block_type,
maybe_coerce_values,
@@ -30,7 +31,7 @@
def make_block(
- values, placement, klass=None, ndim=None, dtype: Optional[Dtype] = None
+ values, placement, klass=None, ndim=None, dtype: Dtype | None = None
) -> Block:
"""
This is a pseudo-public analogue to blocks.new_block.
@@ -48,6 +49,7 @@ def make_block(
values, dtype = extract_pandas_array(values, dtype, ndim)
+ needs_reshape = False
if klass is None:
dtype = dtype or values.dtype
klass = get_block_type(values, dtype)
@@ -55,17 +57,21 @@ def make_block(
elif klass is DatetimeTZBlock and not is_datetime64tz_dtype(values.dtype):
# pyarrow calls get here
values = DatetimeArray._simple_new(values, dtype=dtype)
+ needs_reshape = True
if not isinstance(placement, BlockPlacement):
placement = BlockPlacement(placement)
ndim = maybe_infer_ndim(values, placement, ndim)
+ if needs_reshape:
+ values = ensure_block_shape(values, ndim)
+
check_ndim(values, placement, ndim)
values = maybe_coerce_values(values)
return klass(values, ndim=ndim, placement=placement)
-def maybe_infer_ndim(values, placement: BlockPlacement, ndim: Optional[int]) -> int:
+def maybe_infer_ndim(values, placement: BlockPlacement, ndim: int | None) -> int:
"""
If `ndim` is not provided, infer it from placment and values.
"""
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 603cc6a6ff1f2..4276aadd8edd6 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -42,6 +42,8 @@
soft_convert_objects,
)
from pandas.core.dtypes.common import (
+ is_1d_only_ea_dtype,
+ is_1d_only_ea_obj,
is_categorical_dtype,
is_dtype_equal,
is_extension_array_dtype,
@@ -224,7 +226,6 @@ def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
# expected "ndarray")
return self.values # type: ignore[return-value]
- @final
def get_block_values_for_json(self) -> np.ndarray:
"""
This is used in the JSON C code.
@@ -415,7 +416,11 @@ def _split_op_result(self, result) -> list[Block]:
# if we get a 2D ExtensionArray, we need to split it into 1D pieces
nbs = []
for i, loc in enumerate(self._mgr_locs):
- vals = result[i]
+ if not is_1d_only_ea_obj(result):
+ vals = result[i : i + 1]
+ else:
+ vals = result[i]
+
block = self.make_block(values=vals, placement=loc)
nbs.append(block)
return nbs
@@ -1670,7 +1675,7 @@ class NumericBlock(NumpyBlock):
is_numeric = True
-class NDArrayBackedExtensionBlock(EABackedBlock):
+class NDArrayBackedExtensionBlock(libinternals.Block, EABackedBlock):
"""
Block backed by an NDArrayBackedExtensionArray
"""
@@ -1683,11 +1688,6 @@ def is_view(self) -> bool:
# check the ndarray values of the DatetimeIndex values
return self.values._ndarray.base is not None
- def iget(self, key):
- # GH#31649 we need to wrap scalars in Timestamp/Timedelta
- # TODO(EA2D): this can be removed if we ever have 2D EA
- return self.values.reshape(self.shape)[key]
-
def setitem(self, indexer, value):
if not self._can_hold_element(value):
# TODO: general case needs casting logic.
@@ -1707,24 +1707,21 @@ def putmask(self, mask, new) -> list[Block]:
if not self._can_hold_element(new):
return self.astype(object).putmask(mask, new)
- # TODO(EA2D): reshape unnecessary with 2D EAs
- arr = self.values.reshape(self.shape)
+ arr = self.values
arr.T.putmask(mask, new)
return [self]
def where(self, other, cond, errors="raise") -> list[Block]:
# TODO(EA2D): reshape unnecessary with 2D EAs
- arr = self.values.reshape(self.shape)
+ arr = self.values
cond = extract_bool_array(cond)
try:
res_values = arr.T.where(cond, other).T
except (ValueError, TypeError):
- return super().where(other, cond, errors=errors)
+ return Block.where(self, other, cond, errors=errors)
- # TODO(EA2D): reshape not needed with 2D EAs
- res_values = res_values.reshape(self.values.shape)
nb = self.make_block_same_class(res_values)
return [nb]
@@ -1748,15 +1745,13 @@ def diff(self, n: int, axis: int = 0) -> list[Block]:
The arguments here are mimicking shift so they are called correctly
by apply.
"""
- # TODO(EA2D): reshape not necessary with 2D EAs
- values = self.values.reshape(self.shape)
+ values = self.values
new_values = values - values.shift(n, axis=axis)
return [self.make_block(new_values)]
def shift(self, periods: int, axis: int = 0, fill_value: Any = None) -> list[Block]:
- # TODO(EA2D) this is unnecessary if these blocks are backed by 2D EAs
- values = self.values.reshape(self.shape)
+ values = self.values
new_values = values.shift(periods, fill_value=fill_value, axis=axis)
return [self.make_block_same_class(new_values)]
@@ -1776,31 +1771,27 @@ def fillna(
return [self.make_block_same_class(values=new_values)]
-class DatetimeLikeBlock(libinternals.Block, NDArrayBackedExtensionBlock):
+class DatetimeLikeBlock(NDArrayBackedExtensionBlock):
"""Block for datetime64[ns], timedelta64[ns]."""
__slots__ = ()
is_numeric = False
values: DatetimeArray | TimedeltaArray
+ def get_block_values_for_json(self):
+ # Not necessary to override, but helps perf
+ return self.values._ndarray
-class DatetimeTZBlock(ExtensionBlock, NDArrayBackedExtensionBlock):
+
+class DatetimeTZBlock(DatetimeLikeBlock):
""" implement a datetime64 block with a tz attribute """
values: DatetimeArray
__slots__ = ()
is_extension = True
- is_numeric = False
-
- diff = NDArrayBackedExtensionBlock.diff
- where = NDArrayBackedExtensionBlock.where
- putmask = NDArrayBackedExtensionBlock.putmask
- fillna = NDArrayBackedExtensionBlock.fillna
-
- get_values = NDArrayBackedExtensionBlock.get_values
-
- is_view = NDArrayBackedExtensionBlock.is_view
+ _validate_ndim = True
+ _can_consolidate = False
class ObjectBlock(NumpyBlock):
@@ -1967,7 +1958,7 @@ def check_ndim(values, placement: BlockPlacement, ndim: int):
f"values.ndim > ndim [{values.ndim} > {ndim}]"
)
- elif isinstance(values.dtype, np.dtype):
+ elif not is_1d_only_ea_dtype(values.dtype):
# TODO(EA2D): special case not needed with 2D EAs
if values.ndim != ndim:
raise ValueError(
@@ -1981,7 +1972,7 @@ def check_ndim(values, placement: BlockPlacement, ndim: int):
)
elif ndim == 2 and len(placement) != 1:
# TODO(EA2D): special case unnecessary with 2D EAs
- raise AssertionError("block.size != values.size")
+ raise ValueError("need to split")
def extract_pandas_array(
@@ -2026,8 +2017,9 @@ def ensure_block_shape(values: ArrayLike, ndim: int = 1) -> ArrayLike:
"""
Reshape if possible to have values.ndim == ndim.
"""
+
if values.ndim < ndim:
- if not is_extension_array_dtype(values.dtype):
+ if not is_1d_only_ea_dtype(values.dtype):
# TODO(EA2D): https://github.com/pandas-dev/pandas/issues/23023
# block.shape is incorrect for "2D" ExtensionArrays
# We can't, and don't need to, reshape.
diff --git a/pandas/core/internals/concat.py b/pandas/core/internals/concat.py
index 0b0013eeb7147..51a381a1b7f4f 100644
--- a/pandas/core/internals/concat.py
+++ b/pandas/core/internals/concat.py
@@ -5,6 +5,7 @@
from typing import (
TYPE_CHECKING,
Sequence,
+ cast,
)
import numpy as np
@@ -23,6 +24,8 @@
find_common_type,
)
from pandas.core.dtypes.common import (
+ is_1d_only_ea_dtype,
+ is_1d_only_ea_obj,
is_datetime64tz_dtype,
is_dtype_equal,
is_extension_array_dtype,
@@ -210,8 +213,8 @@ def concatenate_managers(
values = np.concatenate(vals, axis=blk.ndim - 1)
else:
# TODO(EA2D): special-casing not needed with 2D EAs
- values = concat_compat(vals)
- values = ensure_block_shape(values, ndim=2)
+ values = concat_compat(vals, axis=1)
+ values = ensure_block_shape(values, blk.ndim)
values = ensure_wrapped_if_datetimelike(values)
@@ -412,13 +415,16 @@ def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
fill_value = None
if is_datetime64tz_dtype(empty_dtype):
- # TODO(EA2D): special case unneeded with 2D EAs
- i8values = np.full(self.shape[1], fill_value.value)
+ i8values = np.full(self.shape, fill_value.value)
return DatetimeArray(i8values, dtype=empty_dtype)
+
elif is_extension_array_dtype(blk_dtype):
pass
- elif isinstance(empty_dtype, ExtensionDtype):
+
+ elif is_1d_only_ea_dtype(empty_dtype):
+ empty_dtype = cast(ExtensionDtype, empty_dtype)
cls = empty_dtype.construct_array_type()
+
missing_arr = cls._from_sequence([], dtype=empty_dtype)
ncols, nrows = self.shape
assert ncols == 1, ncols
@@ -429,6 +435,7 @@ def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
else:
# NB: we should never get here with empty_dtype integer or bool;
# if we did, the missing_arr.fill would cast to gibberish
+ empty_dtype = cast(np.dtype, empty_dtype)
missing_arr = np.empty(self.shape, dtype=empty_dtype)
missing_arr.fill(fill_value)
@@ -493,15 +500,17 @@ def _concatenate_join_units(
concat_values = concat_values.copy()
else:
concat_values = concat_values.copy()
- elif any(isinstance(t, ExtensionArray) and t.ndim == 1 for t in to_concat):
+
+ elif any(is_1d_only_ea_obj(t) for t in to_concat):
+ # TODO(EA2D): special case not needed if all EAs used HybridBlocks
+ # NB: we are still assuming here that Hybrid blocks have shape (1, N)
# concatting with at least one EA means we are concatting a single column
# the non-EA values are 2D arrays with shape (1, n)
+
# error: Invalid index type "Tuple[int, slice]" for
# "Union[ExtensionArray, ndarray]"; expected type "Union[int, slice, ndarray]"
to_concat = [
- t
- if (isinstance(t, ExtensionArray) and t.ndim == 1)
- else t[0, :] # type: ignore[index]
+ t if is_1d_only_ea_obj(t) else t[0, :] # type: ignore[index]
for t in to_concat
]
concat_values = concat_compat(to_concat, axis=0, ea_compat_axis=True)
diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py
index 2960fb292818a..83ecdbce5fa80 100644
--- a/pandas/core/internals/construction.py
+++ b/pandas/core/internals/construction.py
@@ -32,6 +32,7 @@
maybe_upcast,
)
from pandas.core.dtypes.common import (
+ is_1d_only_ea_dtype,
is_datetime64tz_dtype,
is_dtype_equal,
is_extension_array_dtype,
@@ -55,7 +56,8 @@
)
from pandas.core.arrays import (
Categorical,
- DatetimeArray,
+ ExtensionArray,
+ TimedeltaArray,
)
from pandas.core.construction import (
extract_array,
@@ -259,7 +261,8 @@ def ndarray_to_mgr(
if not len(values) and columns is not None and len(columns):
values = np.empty((0, 1), dtype=object)
- if is_extension_array_dtype(values) or isinstance(dtype, ExtensionDtype):
+ vdtype = getattr(values, "dtype", None)
+ if is_1d_only_ea_dtype(vdtype) or isinstance(dtype, ExtensionDtype):
# GH#19157
if isinstance(values, np.ndarray) and values.ndim > 1:
@@ -274,9 +277,18 @@ def ndarray_to_mgr(
return arrays_to_mgr(values, columns, index, columns, dtype=dtype, typ=typ)
- # by definition an array here
- # the dtypes will be coerced to a single dtype
- values = _prep_ndarray(values, copy=copy)
+ if is_extension_array_dtype(vdtype) and not is_1d_only_ea_dtype(vdtype):
+ # i.e. Datetime64TZ
+ values = extract_array(values, extract_numpy=True)
+ if copy:
+ values = values.copy()
+ if values.ndim == 1:
+ values = values.reshape(-1, 1)
+
+ else:
+ # by definition an array here
+ # the dtypes will be coerced to a single dtype
+ values = _prep_ndarray(values, copy=copy)
if dtype is not None and not is_dtype_equal(values.dtype, dtype):
shape = values.shape
@@ -320,7 +332,6 @@ def ndarray_to_mgr(
dvals_list = [ensure_block_shape(dval, 2) for dval in dvals_list]
# TODO: What about re-joining object columns?
- dvals_list = [maybe_squeeze_dt64tz(x) for x in dvals_list]
block_values = [
new_block(dvals_list[n], placement=n, ndim=2)
for n in range(len(dvals_list))
@@ -328,12 +339,10 @@ def ndarray_to_mgr(
else:
datelike_vals = maybe_infer_to_datetimelike(values)
- datelike_vals = maybe_squeeze_dt64tz(datelike_vals)
nb = new_block(datelike_vals, placement=slice(len(columns)), ndim=2)
block_values = [nb]
else:
- new_values = maybe_squeeze_dt64tz(values)
- nb = new_block(new_values, placement=slice(len(columns)), ndim=2)
+ nb = new_block(values, placement=slice(len(columns)), ndim=2)
block_values = [nb]
if len(columns) == 0:
@@ -360,20 +369,6 @@ def _check_values_indices_shape_match(
raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
-def maybe_squeeze_dt64tz(dta: ArrayLike) -> ArrayLike:
- """
- If we have a tzaware DatetimeArray with shape (1, N), squeeze to (N,)
- """
- # TODO(EA2D): kludge not needed with 2D EAs
- if isinstance(dta, DatetimeArray) and dta.ndim == 2 and dta.tz is not None:
- assert dta.shape[0] == 1
- # error: Incompatible types in assignment (expression has type
- # "Union[DatetimeLikeArrayMixin, Union[Any, NaTType]]", variable has
- # type "Union[ExtensionArray, ndarray]")
- dta = dta[0] # type: ignore[assignment]
- return dta
-
-
def dict_to_mgr(
data: dict,
index,
@@ -396,7 +391,6 @@ def dict_to_mgr(
arrays = Series(data, index=columns, dtype=object)
data_names = arrays.index
-
missing = arrays.isna()
if index is None:
# GH10856
@@ -481,13 +475,23 @@ def treat_as_nested(data) -> bool:
"""
Check if we should use nested_data_to_arrays.
"""
- return len(data) > 0 and is_list_like(data[0]) and getattr(data[0], "ndim", 1) == 1
+ return (
+ len(data) > 0
+ and is_list_like(data[0])
+ and getattr(data[0], "ndim", 1) == 1
+ and not (isinstance(data, ExtensionArray) and data.ndim == 2)
+ )
# ---------------------------------------------------------------------
def _prep_ndarray(values, copy: bool = True) -> np.ndarray:
+ if isinstance(values, TimedeltaArray):
+ # On older numpy, np.asarray below apparently does not call __array__,
+ # so nanoseconds get dropped.
+ values = values._ndarray
+
if not isinstance(values, (np.ndarray, ABCSeries, Index)):
if len(values) == 0:
return np.empty((0, 0), dtype=object)
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 97d605e2fa2d1..5db6592ba77f9 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -9,6 +9,7 @@
Hashable,
Sequence,
TypeVar,
+ cast,
)
import warnings
@@ -32,6 +33,7 @@
from pandas.core.dtypes.cast import infer_dtype_from_scalar
from pandas.core.dtypes.common import (
ensure_platform_int,
+ is_1d_only_ea_dtype,
is_dtype_equal,
is_extension_array_dtype,
is_list_like,
@@ -47,6 +49,7 @@
)
import pandas.core.algorithms as algos
+from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
from pandas.core.arrays.sparse import SparseDtype
from pandas.core.construction import (
ensure_wrapped_if_datetimelike,
@@ -1048,6 +1051,19 @@ def __init__(
f"Number of Block dimensions ({block.ndim}) must equal "
f"number of axes ({self.ndim})"
)
+ if isinstance(block, DatetimeTZBlock) and block.values.ndim == 1:
+ # TODO: remove once fastparquet no longer needs this
+ # error: Incompatible types in assignment (expression has type
+ # "Union[ExtensionArray, ndarray]", variable has type
+ # "DatetimeArray")
+ block.values = ensure_block_shape( # type: ignore[assignment]
+ block.values, self.ndim
+ )
+ try:
+ block._cache.clear()
+ except AttributeError:
+ # _cache not initialized
+ pass
self._verify_integrity()
@@ -1149,7 +1165,8 @@ def iset(self, loc: int | slice | np.ndarray, value: ArrayLike):
self._rebuild_blknos_and_blklocs()
# Note: we exclude DTA/TDA here
- value_is_extension_type = is_extension_array_dtype(value)
+ vdtype = getattr(value, "dtype", None)
+ value_is_extension_type = is_1d_only_ea_dtype(vdtype)
# categorical/sparse/datetimetz
if value_is_extension_type:
@@ -1780,7 +1797,12 @@ def _form_blocks(
if len(items_dict["DatetimeTZBlock"]):
dttz_blocks = [
- new_block(array, klass=DatetimeTZBlock, placement=i, ndim=2)
+ new_block(
+ ensure_block_shape(extract_array(array), 2),
+ klass=DatetimeTZBlock,
+ placement=i,
+ ndim=2,
+ )
for i, array in items_dict["DatetimeTZBlock"]
]
blocks.extend(dttz_blocks)
@@ -1917,11 +1939,19 @@ def _merge_blocks(
# TODO: optimization potential in case all mgrs contain slices and
# combination of those slices is a slice, too.
new_mgr_locs = np.concatenate([b.mgr_locs.as_array for b in blocks])
- # error: List comprehension has incompatible type List[Union[ndarray,
- # ExtensionArray]]; expected List[Union[complex, generic, Sequence[Union[int,
- # float, complex, str, bytes, generic]], Sequence[Sequence[Any]],
- # _SupportsArray]]
- new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]
+
+ new_values: ArrayLike
+
+ if isinstance(blocks[0].dtype, np.dtype):
+ # error: List comprehension has incompatible type List[Union[ndarray,
+ # ExtensionArray]]; expected List[Union[complex, generic,
+ # Sequence[Union[int, float, complex, str, bytes, generic]],
+ # Sequence[Sequence[Any]], SupportsArray]]
+ new_values = np.vstack([b.values for b in blocks]) # type: ignore[misc]
+ else:
+ bvals = [blk.values for blk in blocks]
+ bvals2 = cast(Sequence[NDArrayBackedExtensionArray], bvals)
+ new_values = bvals2[0]._concat_same_type(bvals2, axis=0)
argsort = np.argsort(new_mgr_locs)
new_values = new_values[argsort]
diff --git a/pandas/core/reshape/reshape.py b/pandas/core/reshape/reshape.py
index d889e84cb9045..1a4d8dbe2885e 100644
--- a/pandas/core/reshape/reshape.py
+++ b/pandas/core/reshape/reshape.py
@@ -16,6 +16,7 @@
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.dtypes.common import (
ensure_platform_int,
+ is_1d_only_ea_dtype,
is_bool_dtype,
is_extension_array_dtype,
is_integer,
@@ -438,7 +439,7 @@ def unstack(obj, level, fill_value=None):
f"index must be a MultiIndex to unstack, {type(obj.index)} was passed"
)
else:
- if is_extension_array_dtype(obj.dtype):
+ if is_1d_only_ea_dtype(obj.dtype):
return _unstack_extension_series(obj, level, fill_value)
unstacker = _Unstacker(
obj.index, level=level, constructor=obj._constructor_expanddim
diff --git a/pandas/tests/arrays/test_datetimes.py b/pandas/tests/arrays/test_datetimes.py
index 8e6c330475e68..b9c1113e7f441 100644
--- a/pandas/tests/arrays/test_datetimes.py
+++ b/pandas/tests/arrays/test_datetimes.py
@@ -226,6 +226,16 @@ def test_fillna_2d(self):
res4 = dta2.fillna(method="backfill")
tm.assert_extension_array_equal(res4, expected2)
+ # test the DataFrame method while we're here
+ df = pd.DataFrame(dta)
+ res = df.fillna(method="pad")
+ expected = pd.DataFrame(expected1)
+ tm.assert_frame_equal(res, expected)
+
+ res = df.fillna(method="backfill")
+ expected = pd.DataFrame(expected2)
+ tm.assert_frame_equal(res, expected)
+
def test_array_interface_tz(self):
tz = "US/Central"
data = DatetimeArray(pd.date_range("2017", periods=2, tz=tz))
diff --git a/pandas/tests/extension/base/constructors.py b/pandas/tests/extension/base/constructors.py
index 56c3f8216f033..6e4ed7b77cad8 100644
--- a/pandas/tests/extension/base/constructors.py
+++ b/pandas/tests/extension/base/constructors.py
@@ -3,7 +3,10 @@
import pandas as pd
from pandas.api.extensions import ExtensionArray
-from pandas.core.internals import ExtensionBlock
+from pandas.core.internals.blocks import (
+ DatetimeTZBlock,
+ ExtensionBlock,
+)
from pandas.tests.extension.base.base import BaseExtensionTests
@@ -26,14 +29,14 @@ def test_series_constructor(self, data):
assert result.dtype == data.dtype
assert len(result) == len(data)
if hasattr(result._mgr, "blocks"):
- assert isinstance(result._mgr.blocks[0], ExtensionBlock)
+ assert isinstance(result._mgr.blocks[0], (ExtensionBlock, DatetimeTZBlock))
assert result._mgr.array is data
# Series[EA] is unboxed / boxed correctly
result2 = pd.Series(result)
assert result2.dtype == data.dtype
if hasattr(result._mgr, "blocks"):
- assert isinstance(result2._mgr.blocks[0], ExtensionBlock)
+ assert isinstance(result2._mgr.blocks[0], (ExtensionBlock, DatetimeTZBlock))
def test_series_constructor_no_data_with_index(self, dtype, na_value):
result = pd.Series(index=[1, 2, 3], dtype=dtype)
@@ -68,7 +71,7 @@ def test_dataframe_constructor_from_dict(self, data, from_series):
assert result.dtypes["A"] == data.dtype
assert result.shape == (len(data), 1)
if hasattr(result._mgr, "blocks"):
- assert isinstance(result._mgr.blocks[0], ExtensionBlock)
+ assert isinstance(result._mgr.blocks[0], (ExtensionBlock, DatetimeTZBlock))
assert isinstance(result._mgr.arrays[0], ExtensionArray)
def test_dataframe_from_series(self, data):
@@ -76,7 +79,7 @@ def test_dataframe_from_series(self, data):
assert result.dtypes[0] == data.dtype
assert result.shape == (len(data), 1)
if hasattr(result._mgr, "blocks"):
- assert isinstance(result._mgr.blocks[0], ExtensionBlock)
+ assert isinstance(result._mgr.blocks[0], (ExtensionBlock, DatetimeTZBlock))
assert isinstance(result._mgr.arrays[0], ExtensionArray)
def test_series_given_mismatched_index_raises(self, data):
diff --git a/pandas/tests/frame/methods/test_set_index.py b/pandas/tests/frame/methods/test_set_index.py
index 430abd9700a23..62dc400f8de9f 100644
--- a/pandas/tests/frame/methods/test_set_index.py
+++ b/pandas/tests/frame/methods/test_set_index.py
@@ -96,15 +96,18 @@ def test_set_index_cast_datetimeindex(self):
idf = df.set_index("A")
assert isinstance(idf.index, DatetimeIndex)
- def test_set_index_dst(self):
+ def test_set_index_dst(self, using_array_manager):
di = date_range("2006-10-29 00:00:00", periods=3, freq="H", tz="US/Pacific")
df = DataFrame(data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=di).reset_index()
# single level
res = df.set_index("index")
exp = DataFrame(
- data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=Index(di, name="index")
+ data={"a": [0, 1, 2], "b": [3, 4, 5]},
+ index=Index(di, name="index"),
)
+ if not using_array_manager:
+ exp.index = exp.index._with_freq(None)
tm.assert_frame_equal(res, exp)
# GH#12920
diff --git a/pandas/tests/frame/test_block_internals.py b/pandas/tests/frame/test_block_internals.py
index 748aa462cddae..ba0acdc4f947b 100644
--- a/pandas/tests/frame/test_block_internals.py
+++ b/pandas/tests/frame/test_block_internals.py
@@ -45,7 +45,7 @@ def test_setitem_invalidates_datetime_index_freq(self):
ts = dti[1]
df = DataFrame({"B": dti})
- assert df["B"]._values.freq == "D"
+ assert df["B"]._values.freq is None
df.iloc[1, 0] = pd.NaT
assert df["B"]._values.freq is None
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index a1c5810ba8bb8..3299503dbc3a4 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -545,7 +545,7 @@ def test_astype(self, t):
mgr = create_mgr("a,b: object; c: bool; d: datetime; e: f4; f: f2; g: f8")
t = np.dtype(t)
- with tm.assert_produces_warning(warn):
+ with tm.assert_produces_warning(warn, check_stacklevel=False):
tmgr = mgr.astype(t, errors="ignore")
assert tmgr.iget(2).dtype.type == t
assert tmgr.iget(4).dtype.type == t
@@ -618,10 +618,10 @@ def _compare(old_mgr, new_mgr):
assert new_mgr.iget(8).dtype == np.float16
def test_invalid_ea_block(self):
- with pytest.raises(AssertionError, match="block.size != values.size"):
+ with pytest.raises(ValueError, match="need to split"):
create_mgr("a: category; b: category")
- with pytest.raises(AssertionError, match="block.size != values.size"):
+ with pytest.raises(ValueError, match="need to split"):
create_mgr("a: category2; b: category2")
def test_interleave(self):
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index 82961a42e4ff0..67649e6e37b35 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -1341,7 +1341,7 @@ def test_constructor_dtype_timedelta64(self):
# td.astype('m8[%s]' % t)
# valid astype
- with tm.assert_produces_warning(FutureWarning):
+ with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
# astype(int64) deprecated
td.astype("int64")
| Same as #40149 but with the perf-improving edits to frame.py reverted | https://api.github.com/repos/pandas-dev/pandas/pulls/41082 | 2021-04-21T16:14:05Z | 2021-04-22T22:43:29Z | 2021-04-22T22:43:29Z | 2021-04-22T22:59:14Z |
REF: remove Block access in the JSON writing code | diff --git a/pandas/_libs/src/ujson/python/objToJSON.c b/pandas/_libs/src/ujson/python/objToJSON.c
index bbcee479aeb5a..31b43cdb28d9d 100644
--- a/pandas/_libs/src/ujson/python/objToJSON.c
+++ b/pandas/_libs/src/ujson/python/objToJSON.c
@@ -83,7 +83,6 @@ typedef struct __PdBlockContext {
int ncols;
int transpose;
- int *cindices; // frame column -> block column map
NpyArrContext **npyCtxts; // NpyArrContext for each column
} PdBlockContext;
@@ -294,7 +293,12 @@ static int is_simple_frame(PyObject *obj) {
if (!mgr) {
return 0;
}
- int ret = (get_attr_length(mgr, "blocks") <= 1);
+ int ret;
+ if (PyObject_HasAttrString(mgr, "blocks")) {
+ ret = (get_attr_length(mgr, "blocks") <= 1);
+ } else {
+ ret = 0;
+ }
Py_DECREF(mgr);
return ret;
@@ -656,16 +660,10 @@ void PdBlockPassThru_iterBegin(JSOBJ Py_UNUSED(obj), JSONTypeContext *tc) {
}
void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) {
- PyObject *obj, *blocks, *block, *values, *tmp;
- PyArrayObject *locs;
+ PyObject *obj, *values, *arrays, *array;
PdBlockContext *blkCtxt;
NpyArrContext *npyarr;
Py_ssize_t i;
- NpyIter *iter;
- NpyIter_IterNextFunc *iternext;
- npy_int64 **dataptr;
- npy_int64 colIdx;
- npy_intp idx;
obj = (PyObject *)_obj;
@@ -687,7 +685,6 @@ void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) {
if (blkCtxt->ncols == 0) {
blkCtxt->npyCtxts = NULL;
- blkCtxt->cindices = NULL;
GET_TC(tc)->iterNext = NpyArr_iterNextNone;
return;
@@ -701,104 +698,45 @@ void PdBlock_iterBegin(JSOBJ _obj, JSONTypeContext *tc) {
return;
}
- blkCtxt->cindices = PyObject_Malloc(sizeof(int) * blkCtxt->ncols);
- if (!blkCtxt->cindices) {
- PyErr_NoMemory();
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- return;
- }
-
- blocks = get_sub_attr(obj, "_mgr", "blocks");
- if (!blocks) {
+ arrays = get_sub_attr(obj, "_mgr", "column_arrays");
+ if (!arrays) {
GET_TC(tc)->iterNext = NpyArr_iterNextNone;
return;
- } else if (!PyTuple_Check(blocks)) {
- PyErr_SetString(PyExc_TypeError, "blocks must be a tuple!");
- goto BLKRET;
}
- // force transpose so each NpyArrContext strides down its column
- GET_TC(tc)->transpose = 1;
-
- for (i = 0; i < PyObject_Length(blocks); i++) {
- block = PyTuple_GET_ITEM(blocks, i);
- if (!block) {
+ for (i = 0; i < PyObject_Length(arrays); i++) {
+ array = PyList_GET_ITEM(arrays, i);
+ if (!array) {
GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto BLKRET;
+ goto ARR_RET;
}
- tmp = PyObject_CallMethod(block, "get_block_values_for_json", NULL);
- if (!tmp) {
+ // 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 BLKRET;
- }
-
- values = PyArray_Transpose((PyArrayObject *)tmp, NULL);
- Py_DECREF(tmp);
- if (!values) {
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto BLKRET;
- }
-
- locs = (PyArrayObject *)get_sub_attr(block, "mgr_locs", "as_array");
- if (!locs) {
- Py_DECREF(values);
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto BLKRET;
+ goto ARR_RET;
}
- iter = NpyIter_New(locs, NPY_ITER_READONLY, NPY_KEEPORDER,
- NPY_NO_CASTING, NULL);
- if (!iter) {
- Py_DECREF(values);
- Py_DECREF(locs);
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto BLKRET;
- }
- iternext = NpyIter_GetIterNext(iter, NULL);
- if (!iternext) {
- NpyIter_Deallocate(iter);
- Py_DECREF(values);
- Py_DECREF(locs);
- GET_TC(tc)->iterNext = NpyArr_iterNextNone;
- goto BLKRET;
- }
- dataptr = (npy_int64 **)NpyIter_GetDataPtrArray(iter);
- do {
- colIdx = **dataptr;
- idx = NpyIter_GetIterIndex(iter);
+ GET_TC(tc)->newObj = values;
- blkCtxt->cindices[colIdx] = idx;
+ // init a dedicated context for this column
+ NpyArr_iterBegin(obj, tc);
+ npyarr = GET_TC(tc)->npyarr;
- // Reference freed in Pdblock_iterend
- Py_INCREF(values);
- GET_TC(tc)->newObj = values;
-
- // init a dedicated context for this column
- NpyArr_iterBegin(obj, tc);
- npyarr = GET_TC(tc)->npyarr;
-
- // set the dataptr to our desired column and initialise
- if (npyarr != NULL) {
- npyarr->dataptr += npyarr->stride * idx;
- NpyArr_iterNext(obj, tc);
- }
- GET_TC(tc)->itemValue = NULL;
- ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = NULL;
-
- blkCtxt->npyCtxts[colIdx] = npyarr;
- GET_TC(tc)->newObj = NULL;
- } while (iternext(iter));
+ GET_TC(tc)->itemValue = NULL;
+ ((PyObjectEncoder *)tc->encoder)->npyCtxtPassthru = NULL;
- NpyIter_Deallocate(iter);
- Py_DECREF(values);
- Py_DECREF(locs);
+ blkCtxt->npyCtxts[i] = npyarr;
+ GET_TC(tc)->newObj = NULL;
}
GET_TC(tc)->npyarr = blkCtxt->npyCtxts[0];
+ goto ARR_RET;
-BLKRET:
- Py_DECREF(blocks);
+ARR_RET:
+ Py_DECREF(arrays);
}
void PdBlock_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
@@ -830,9 +768,6 @@ void PdBlock_iterEnd(JSOBJ obj, JSONTypeContext *tc) {
if (blkCtxt->npyCtxts) {
PyObject_Free(blkCtxt->npyCtxts);
}
- if (blkCtxt->cindices) {
- PyObject_Free(blkCtxt->cindices);
- }
PyObject_Free(blkCtxt);
}
}
diff --git a/pandas/core/internals/array_manager.py b/pandas/core/internals/array_manager.py
index 8c9902d330eee..a25750e7e1eab 100644
--- a/pandas/core/internals/array_manager.py
+++ b/pandas/core/internals/array_manager.py
@@ -919,6 +919,13 @@ def iget_values(self, i: int) -> ArrayLike:
"""
return self.arrays[i]
+ @property
+ def column_arrays(self) -> list[ArrayLike]:
+ """
+ Used in the JSON C code to access column arrays.
+ """
+ return self.arrays
+
def iset(self, loc: int | slice | np.ndarray, value: ArrayLike):
"""
Set new column(s).
diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py
index 4276aadd8edd6..2d7d83d6a2bc3 100644
--- a/pandas/core/internals/blocks.py
+++ b/pandas/core/internals/blocks.py
@@ -226,13 +226,6 @@ def get_values(self, dtype: DtypeObj | None = None) -> np.ndarray:
# expected "ndarray")
return self.values # type: ignore[return-value]
- def get_block_values_for_json(self) -> np.ndarray:
- """
- This is used in the JSON C code.
- """
- # TODO(EA2D): reshape will be unnecessary with 2D EAs
- return np.asarray(self.values).reshape(self.shape)
-
@final
@cache_readonly
def fill_value(self):
@@ -1778,10 +1771,6 @@ class DatetimeLikeBlock(NDArrayBackedExtensionBlock):
is_numeric = False
values: DatetimeArray | TimedeltaArray
- def get_block_values_for_json(self):
- # Not necessary to override, but helps perf
- return self.values._ndarray
-
class DatetimeTZBlock(DatetimeLikeBlock):
""" implement a datetime64 block with a tz attribute """
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 5db6592ba77f9..487047f1a1dbb 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1153,6 +1153,30 @@ def iget_values(self, i: int) -> ArrayLike:
values = block.iget(self.blklocs[i])
return values
+ @property
+ def column_arrays(self) -> list[np.ndarray]:
+ """
+ Used in the JSON C code to access column arrays.
+ This optimizes compared to using `iget_values` by converting each
+ block.values to a np.ndarray only once up front
+ """
+ # special casing datetimetz to avoid conversion through object dtype
+ arrays = [
+ blk.values._ndarray
+ if isinstance(blk, DatetimeTZBlock)
+ else np.asarray(blk.values)
+ for blk in self.blocks
+ ]
+ result = []
+ for i in range(len(self.items)):
+ arr = arrays[self.blknos[i]]
+ if arr.ndim == 2:
+ values = arr[self.blklocs[i]]
+ else:
+ values = arr
+ result.append(values)
+ return result
+
def iset(self, loc: int | slice | np.ndarray, value: ArrayLike):
"""
Set new item in-place. Does not consolidate. Adds new Block if not
diff --git a/pandas/tests/io/json/test_json_table_schema.py b/pandas/tests/io/json/test_json_table_schema.py
index 9d955545aede3..71f1d03ea6d1f 100644
--- a/pandas/tests/io/json/test_json_table_schema.py
+++ b/pandas/tests/io/json/test_json_table_schema.py
@@ -6,8 +6,6 @@
import numpy as np
import pytest
-import pandas.util._test_decorators as td
-
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
@@ -26,8 +24,6 @@
set_default_names,
)
-pytestmark = td.skip_array_manager_not_yet_implemented
-
class TestBuildSchema:
def setup_method(self, method):
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py
index 97d44aafef74b..dc94354728ef6 100644
--- a/pandas/tests/io/json/test_pandas.py
+++ b/pandas/tests/io/json/test_pandas.py
@@ -857,8 +857,6 @@ def test_convert_dates_infer(self, infer_word):
result = read_json(dumps(data))[["id", infer_word]]
tm.assert_frame_equal(result, expected)
- # TODO(ArrayManager) JSON
- @td.skip_array_manager_not_yet_implemented
@pytest.mark.parametrize(
"date,date_unit",
[
| Closes #27164
This is a small refactor of the JSON writing code (`objToJSON.c`) to remove the access of the Blocks. Serializing to JSON was already done column-by-column (also when accessing the blocks), but currently we were iterating over the different "columns" of the 2D array per block in the C code. While with this PR, the C code directly iterates over all the columns (instead of per block).
This gives a less invasive change as the previous attempt of https://github.com/pandas-dev/pandas/pull/27166
To be able to iterate over the column arrays in C, I added the `column_arrays` attribute on the manager, for this.
I ran the JSON ASV benchmarks with this branch:
`$ asv continuous -b json -f 1.01 upstream/master HEAD`
```diff
before after ratio
[692bba57] [4142a532]
<master> <refactor-json-blocks>
+ 162±2ms 192±2ms 1.19 io.json.ToJSON.time_to_json_wide('values', 'df_td_int_ts')
+ 164±2ms 191±2ms 1.16 io.json.ToJSON.time_to_json_wide('split', 'df_td_int_ts')
+ 96.2±0.3ms 111±0.8ms 1.15 io.json.ToJSON.time_to_json('values', 'df')
+ 192±3ms 221±3ms 1.15 io.json.ToJSON.time_to_json_wide('records', 'df_td_int_ts')
+ 106±0.3ms 121±1ms 1.15 io.json.ToJSON.time_to_json('split', 'df')
+ 96.4±2ms 110±2ms 1.15 io.json.ToJSON.time_to_json('values', 'df_date_idx')
+ 192±4ms 219±4ms 1.14 io.json.ToJSON.time_to_json_wide('index', 'df_td_int_ts')
+ 177±4ms 198±3ms 1.12 io.json.ToJSON.time_to_json_wide('columns', 'df_td_int_ts')
+ 112±1ms 124±2ms 1.11 io.json.ToJSON.time_to_json('records', 'df')
+ 129±0.6ms 143±2ms 1.11 io.json.ToJSON.time_to_json('index', 'df')
+ 138±1ms 152±1ms 1.10 io.json.ToJSON.time_to_json('index', 'df_date_idx')
+ 168±0.9ms 182±1ms 1.09 io.json.ToJSONLines.time_floats_with_int_idex_lines
+ 168±2ms 182±1ms 1.08 io.json.ToJSONLines.time_floats_with_dt_index_lines
+ 117±0.9ms 127±1ms 1.08 io.json.ToJSON.time_to_json_wide('split', 'df_date_idx')
+ 157±2ms 165±2ms 1.05 io.json.ToJSON.time_to_json_wide('split', 'df_int_floats')
- 177M 175M 0.99 io.json.ToJSON.peakmem_to_json_wide('split', 'df_int_float_str')
- 185±5ms 172±3ms 0.93 io.json.ToJSON.time_to_json('index', 'df_int_floats')
- 127±8ms 110±0.6ms 0.87 io.json.ToJSON.time_to_json('split', 'df_td_int_ts')
```
</details>
There is a slight slowdown in some of the benchmarks for wide dataframes, but only 20%.
So we can decide to keep the original block-iteration code for BlockManager for now, instead of fully replacing it as I did now, if we don't want this slight slowdown.
This also fixes some remaining cases for ArrayManager (xref https://github.com/pandas-dev/pandas/issues/39146) | https://api.github.com/repos/pandas-dev/pandas/pulls/41081 | 2021-04-21T14:33:46Z | 2021-04-26T07:51:29Z | 2021-04-26T07:51:28Z | 2021-04-26T07:51:32Z |
BUG: GH35865 - int cast to str with colon setitem | diff --git a/pandas/tests/indexing/test_coercion.py b/pandas/tests/indexing/test_coercion.py
index 7642f78076dcb..585dbd338e965 100644
--- a/pandas/tests/indexing/test_coercion.py
+++ b/pandas/tests/indexing/test_coercion.py
@@ -258,6 +258,15 @@ def test_setitem_series_timedelta64(self, val, exp_dtype):
)
self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
+ def test_setitem_series_no_coercion_from_values_list(self):
+ # GH35865 - int casted to str when internally calling np.array(ser.values)
+ ser = pd.Series(["a", 1])
+ ser[:] = list(ser.values)
+
+ expected = pd.Series(["a", 1])
+
+ tm.assert_series_equal(ser, expected)
+
def _assert_setitem_index_conversion(
self, original_series, loc_key, expected_index, expected_dtype
):
| - [x] closes #35865
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry: not sure if it's needed but happy to provide one given some guidelines
| https://api.github.com/repos/pandas-dev/pandas/pulls/41077 | 2021-04-21T13:03:36Z | 2021-04-26T12:21:47Z | 2021-04-26T12:21:47Z | 2021-04-27T12:45:39Z |
BUG: GH41044 - test loc dict assig | diff --git a/pandas/tests/indexing/test_loc.py b/pandas/tests/indexing/test_loc.py
index 97b3412ce626e..11391efde4956 100644
--- a/pandas/tests/indexing/test_loc.py
+++ b/pandas/tests/indexing/test_loc.py
@@ -2700,3 +2700,13 @@ def test_loc_setitem(self, string_series):
string_series.loc[d2] = 6
assert string_series[d1] == 4
assert string_series[d2] == 6
+
+ @pytest.mark.parametrize("dtype", ["object", "string"])
+ def test_loc_assign_dict_to_row(self, dtype):
+ # GH41044
+ df = DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype=dtype)
+ df.loc[0, :] = {"A": "newA", "B": "newB"}
+
+ expected = DataFrame({"A": ["newA", "def"], "B": ["newB", "jkl"]}, dtype=dtype)
+
+ tm.assert_frame_equal(df, expected)
| - [x] closes #41044
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry: not sure if it's needed but happy to provide one given some guidelines | https://api.github.com/repos/pandas-dev/pandas/pulls/41076 | 2021-04-21T12:31:54Z | 2021-04-21T22:34:07Z | 2021-04-21T22:34:07Z | 2021-04-21T22:52:59Z |
[ArrayManager] TST: enable JSON tests (except for table schema) | diff --git a/pandas/tests/io/json/test_compression.py b/pandas/tests/io/json/test_compression.py
index 8c69ffedf1df4..febeb4d690562 100644
--- a/pandas/tests/io/json/test_compression.py
+++ b/pandas/tests/io/json/test_compression.py
@@ -7,8 +7,6 @@
import pandas as pd
import pandas._testing as tm
-pytestmark = td.skip_array_manager_not_yet_implemented
-
def test_compression_roundtrip(compression):
df = pd.DataFrame(
diff --git a/pandas/tests/io/json/test_deprecated_kwargs.py b/pandas/tests/io/json/test_deprecated_kwargs.py
index 7367aaefb1c1e..79245bc9d34a8 100644
--- a/pandas/tests/io/json/test_deprecated_kwargs.py
+++ b/pandas/tests/io/json/test_deprecated_kwargs.py
@@ -2,15 +2,11 @@
Tests for the deprecated keyword arguments for `read_json`.
"""
-import pandas.util._test_decorators as td
-
import pandas as pd
import pandas._testing as tm
from pandas.io.json import read_json
-pytestmark = td.skip_array_manager_not_yet_implemented
-
def test_deprecated_kwargs():
df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
diff --git a/pandas/tests/io/json/test_normalize.py b/pandas/tests/io/json/test_normalize.py
index c01660ab5febe..a428d8c71a793 100644
--- a/pandas/tests/io/json/test_normalize.py
+++ b/pandas/tests/io/json/test_normalize.py
@@ -15,8 +15,6 @@
from pandas.io.json._normalize import nested_to_record
-pytestmark = td.skip_array_manager_not_yet_implemented
-
@pytest.fixture
def deep_nested():
@@ -153,6 +151,8 @@ def test_simple_records(self):
tm.assert_frame_equal(result, expected)
+ # TODO(ArrayManager) sanitize S/U numpy dtypes to object
+ @td.skip_array_manager_not_yet_implemented
def test_simple_normalize(self, state_data):
result = json_normalize(state_data[0], "counties")
expected = DataFrame(state_data[0]["counties"])
@@ -372,6 +372,8 @@ def test_meta_parameter_not_modified(self):
for val in ["metafoo", "metabar", "foo", "bar"]:
assert val in result
+ # TODO(ArrayManager) sanitize S/U numpy dtypes to object
+ @td.skip_array_manager_not_yet_implemented
def test_record_prefix(self, state_data):
result = json_normalize(state_data[0], "counties")
expected = DataFrame(state_data[0]["counties"])
diff --git a/pandas/tests/io/json/test_pandas.py b/pandas/tests/io/json/test_pandas.py
index 3bd78d44a0b04..97d44aafef74b 100644
--- a/pandas/tests/io/json/test_pandas.py
+++ b/pandas/tests/io/json/test_pandas.py
@@ -27,9 +27,6 @@
)
import pandas._testing as tm
-pytestmark = td.skip_array_manager_not_yet_implemented
-
-
_seriesd = tm.getSeriesData()
_frame = DataFrame(_seriesd)
@@ -318,7 +315,13 @@ def test_roundtrip_mixed(self, request, orient, convert_axes, numpy):
'{"columns":["A","B"],'
'"index":["2","3"],'
'"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}',
- r"Shape of passed values is \(3, 2\), indices imply \(2, 2\)",
+ "|".join(
+ [
+ r"Shape of passed values is \(3, 2\), indices imply \(2, 2\)",
+ "Passed arrays should have the same length as the rows Index: "
+ "3 vs 2 rows",
+ ]
+ ),
"split",
),
# too many columns
@@ -854,6 +857,8 @@ def test_convert_dates_infer(self, infer_word):
result = read_json(dumps(data))[["id", infer_word]]
tm.assert_frame_equal(result, expected)
+ # TODO(ArrayManager) JSON
+ @td.skip_array_manager_not_yet_implemented
@pytest.mark.parametrize(
"date,date_unit",
[
diff --git a/pandas/tests/io/json/test_readlines.py b/pandas/tests/io/json/test_readlines.py
index 711addb1ac237..6731c481f8935 100644
--- a/pandas/tests/io/json/test_readlines.py
+++ b/pandas/tests/io/json/test_readlines.py
@@ -3,8 +3,6 @@
import pytest
-import pandas.util._test_decorators as td
-
import pandas as pd
from pandas import (
DataFrame,
@@ -14,8 +12,6 @@
from pandas.io.json._json import JsonReader
-pytestmark = td.skip_array_manager_not_yet_implemented
-
@pytest.fixture
def lines_json_df():
diff --git a/pandas/tests/io/json/test_ujson.py b/pandas/tests/io/json/test_ujson.py
index c0337d1ad3ffe..a44d47470eb5e 100644
--- a/pandas/tests/io/json/test_ujson.py
+++ b/pandas/tests/io/json/test_ujson.py
@@ -18,7 +18,6 @@
IS64,
is_platform_windows,
)
-import pandas.util._test_decorators as td
from pandas import (
DataFrame,
@@ -32,8 +31,6 @@
)
import pandas._testing as tm
-pytestmark = td.skip_array_manager_not_yet_implemented
-
def _clean_dict(d):
"""
| xref https://github.com/pandas-dev/pandas/issues/39146
This already enables most of the JSON tests (it are only the table schema tests that are segfaulting). Those are mostly working because the JSON writer falls back to using `df.values`. This only gives some errors with datetime formatting, for which one test is skipped (and this will then be fixed by letting the JSON code work column-by-column) | https://api.github.com/repos/pandas-dev/pandas/pulls/41075 | 2021-04-21T11:40:19Z | 2021-04-21T17:20:50Z | 2021-04-21T17:20:50Z | 2021-04-21T17:20:53Z |
TYP: Specify specific type for NumericIndex._values | diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index 28144af36d6ea..cd75336ad7bdf 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -48,6 +48,7 @@ class NumericIndex(Index):
This is an abstract class.
"""
+ _values: np.ndarray
_default_dtype: np.dtype
_is_numeric_dtype = True
@@ -253,9 +254,7 @@ def asi8(self) -> np.ndarray:
FutureWarning,
stacklevel=2,
)
- # error: Incompatible return value type (got "Union[ExtensionArray, ndarray]",
- # expected "ndarray")
- return self._values.view(self._default_dtype) # type: ignore[return-value]
+ return self._values.view(self._default_dtype)
class Int64Index(IntegerIndex):
@@ -330,10 +329,7 @@ def astype(self, dtype, copy=True):
elif is_integer_dtype(dtype) and not is_extension_array_dtype(dtype):
# TODO(jreback); this can change once we have an EA Index type
# GH 13149
-
- # error: Argument 1 to "astype_nansafe" has incompatible type
- # "Union[ExtensionArray, ndarray]"; expected "ndarray"
- arr = astype_nansafe(self._values, dtype=dtype) # type: ignore[arg-type]
+ arr = astype_nansafe(self._values, dtype=dtype)
return Int64Index(arr, name=self.name)
return super().astype(dtype, copy=copy)
| Typing clean-up for numeric indexes. | https://api.github.com/repos/pandas-dev/pandas/pulls/41073 | 2021-04-21T10:47:20Z | 2021-04-21T21:54:07Z | 2021-04-21T21:54:07Z | 2021-06-13T10:08:10Z |
REF/TYP: implement NDFrameApply | diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index 86cde647cc798..9a75857c2586d 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -137,14 +137,6 @@ def f(x):
self.orig_f: AggFuncType = func
self.f: AggFuncType = f
- @property
- def index(self) -> Index:
- return self.obj.index
-
- @property
- def agg_axis(self) -> Index:
- return self.obj._get_agg_axis(self.axis)
-
@abc.abstractmethod
def apply(self) -> FrameOrSeriesUnion:
pass
@@ -163,9 +155,8 @@ def agg(self) -> FrameOrSeriesUnion | None:
args = self.args
kwargs = self.kwargs
- result = self.maybe_apply_str()
- if result is not None:
- return result
+ if isinstance(arg, str):
+ return self.maybe_apply_str()
if is_dict_like(arg):
return self.agg_dict_like()
@@ -465,27 +456,20 @@ def agg_dict_like(self) -> FrameOrSeriesUnion:
return result
- def maybe_apply_str(self) -> FrameOrSeriesUnion | None:
+ def maybe_apply_str(self) -> FrameOrSeriesUnion:
"""
Compute apply in case of a string.
Returns
-------
- result: Series, DataFrame, or None
- Result when self.f is a string, None otherwise.
+ result: Series or DataFrame
"""
+ # Caller is responsible for checking isinstance(self.f, str)
f = self.f
- if not isinstance(f, str):
- return None
+ f = cast(str, f)
obj = self.obj
- # TODO: GH 39993 - Avoid special-casing by replacing with lambda
- if f == "size" and isinstance(obj, ABCDataFrame):
- # Special-cased because DataFrame.size returns a single scalar
- value = obj.shape[self.axis]
- return obj._constructor_sliced(value, index=self.agg_axis, name="size")
-
# Support for `frame.transform('method')`
# Some methods (shift, etc.) require the axis argument, others
# don't, so inspect and insert if necessary.
@@ -587,7 +571,22 @@ def _try_aggregate_string_function(self, obj, arg: str, *args, **kwargs):
)
-class FrameApply(Apply):
+class NDFrameApply(Apply):
+ """
+ Methods shared by FrameApply and SeriesApply but
+ not GroupByApply or ResamplerWindowApply
+ """
+
+ @property
+ def index(self) -> Index:
+ return self.obj.index
+
+ @property
+ def agg_axis(self) -> Index:
+ return self.obj._get_agg_axis(self.axis)
+
+
+class FrameApply(NDFrameApply):
obj: DataFrame
# ---------------------------------------------------------------
@@ -644,9 +643,8 @@ def apply(self) -> FrameOrSeriesUnion:
return self.apply_empty_result()
# string dispatch
- result = self.maybe_apply_str()
- if result is not None:
- return result
+ if isinstance(self.f, str):
+ return self.maybe_apply_str()
# ufunc
elif isinstance(self.f, np.ufunc):
@@ -831,6 +829,16 @@ def wrap_results(self, results: ResType, res_index: Index) -> FrameOrSeriesUnion
return result
+ def maybe_apply_str(self) -> FrameOrSeriesUnion:
+ # Caller is responsible for checking isinstance(self.f, str)
+ # TODO: GH#39993 - Avoid special-casing by replacing with lambda
+ if self.f == "size":
+ # Special-cased because DataFrame.size returns a single scalar
+ obj = self.obj
+ value = obj.shape[self.axis]
+ return obj._constructor_sliced(value, index=self.agg_axis, name="size")
+ return super().maybe_apply_str()
+
class FrameRowApply(FrameApply):
axis = 0
@@ -967,7 +975,7 @@ def infer_to_same_shape(self, results: ResType, res_index: Index) -> DataFrame:
return result
-class SeriesApply(Apply):
+class SeriesApply(NDFrameApply):
obj: Series
axis = 0
@@ -1001,10 +1009,9 @@ def apply(self) -> FrameOrSeriesUnion:
if result is not None:
return result
- # if we are a string, try to dispatch
- result = self.maybe_apply_str()
- if result is not None:
- return result
+ if isinstance(self.f, str):
+ # if we are a string, try to dispatch
+ return self.maybe_apply_str()
return self.apply_standard()
| if called from GroupByApply or ResamplerWindowApply, these would raise | https://api.github.com/repos/pandas-dev/pandas/pulls/41067 | 2021-04-21T03:39:56Z | 2021-04-28T15:18:49Z | 2021-04-28T15:18:49Z | 2021-04-28T15:53:23Z |
REF: BaseGroupBy methods belong in GroupBy | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 4a721ae0d4bf6..a059a90aa3a4e 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -345,6 +345,49 @@ def _aggregate_multiple_funcs(self, arg):
)
return self.obj._constructor_expanddim(output, columns=columns)
+ def _cython_agg_general(
+ self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
+ ):
+ output: dict[base.OutputKey, ArrayLike] = {}
+ # Ideally we would be able to enumerate self._iterate_slices and use
+ # the index from enumeration as the key of output, but ohlc in particular
+ # returns a (n x 4) array. Output requires 1D ndarrays as values, so we
+ # need to slice that up into 1D arrays
+ idx = 0
+ for obj in self._iterate_slices():
+ name = obj.name
+ is_numeric = is_numeric_dtype(obj.dtype)
+ if numeric_only and not is_numeric:
+ continue
+
+ result = self.grouper._cython_operation(
+ "aggregate", obj._values, how, axis=0, min_count=min_count
+ )
+
+ if how == "ohlc":
+ # e.g. ohlc
+ agg_names = ["open", "high", "low", "close"]
+ assert len(agg_names) == result.shape[1]
+ for result_column, result_name in zip(result.T, agg_names):
+ key = base.OutputKey(label=result_name, position=idx)
+ output[key] = result_column
+ idx += 1
+ else:
+ assert result.ndim == 1
+ key = base.OutputKey(label=name, position=idx)
+ output[key] = result
+ idx += 1
+
+ if not output:
+ raise DataError("No numeric types to aggregate")
+
+ # error: Argument 1 to "_wrap_aggregated_output" of "BaseGroupBy" has
+ # incompatible type "Dict[OutputKey, Union[ndarray, DatetimeArray]]";
+ # expected "Mapping[OutputKey, ndarray]"
+ return self._wrap_aggregated_output(
+ output, index=self.grouper.result_index # type: ignore[arg-type]
+ )
+
# TODO: index should not be Optional - see GH 35490
def _wrap_series_output(
self,
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index f2fffe4c3741c..9a4f343ab3dc2 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -504,7 +504,7 @@ def f(self):
@contextmanager
-def group_selection_context(groupby: BaseGroupBy) -> Iterator[BaseGroupBy]:
+def group_selection_context(groupby: GroupBy) -> Iterator[GroupBy]:
"""
Set / reset the group_selection_context.
"""
@@ -543,63 +543,9 @@ class BaseGroupBy(PandasObject, SelectionMixin, Generic[FrameOrSeries]):
"squeeze",
}
- def __init__(
- self,
- obj: FrameOrSeries,
- keys: _KeysArgType | None = None,
- axis: int = 0,
- level: IndexLabel | None = None,
- grouper: ops.BaseGrouper | None = None,
- exclusions: set[Hashable] | None = None,
- selection: IndexLabel | None = None,
- as_index: bool = True,
- sort: bool = True,
- group_keys: bool = True,
- squeeze: bool = False,
- observed: bool = False,
- mutated: bool = False,
- dropna: bool = True,
- ):
-
- self._selection = selection
-
- assert isinstance(obj, NDFrame), type(obj)
-
- self.level = level
-
- if not as_index:
- if not isinstance(obj, DataFrame):
- raise TypeError("as_index=False only valid with DataFrame")
- if axis != 0:
- raise ValueError("as_index=False only valid for axis=0")
-
- self.as_index = as_index
- self.keys = keys
- self.sort = sort
- self.group_keys = group_keys
- self.squeeze = squeeze
- self.observed = observed
- self.mutated = mutated
- self.dropna = dropna
-
- if grouper is None:
- from pandas.core.groupby.grouper import get_grouper
-
- grouper, exclusions, obj = get_grouper(
- obj,
- keys,
- axis=axis,
- level=level,
- sort=sort,
- observed=observed,
- mutated=self.mutated,
- dropna=self.dropna,
- )
-
- self.obj = obj
- self.axis = obj._get_axis_number(axis)
- self.grouper = grouper
- self.exclusions = exclusions or set()
+ axis: int
+ grouper: ops.BaseGrouper
+ obj: FrameOrSeries
@final
def __len__(self) -> int:
@@ -711,90 +657,10 @@ def _selected_obj(self):
else:
return self.obj[self._selection]
- @final
- def _reset_group_selection(self) -> None:
- """
- Clear group based selection.
-
- Used for methods needing to return info on each group regardless of
- whether a group selection was previously set.
- """
- if self._group_selection is not None:
- # GH12839 clear cached selection too when changing group selection
- self._group_selection = None
- self._reset_cache("_selected_obj")
-
- @final
- def _set_group_selection(self) -> None:
- """
- Create group based selection.
-
- Used when selection is not passed directly but instead via a grouper.
-
- NOTE: this should be paired with a call to _reset_group_selection
- """
- grp = self.grouper
- if not (
- self.as_index
- and getattr(grp, "groupings", None) is not None
- and self.obj.ndim > 1
- and self._group_selection is None
- ):
- return
-
- groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis]
-
- if len(groupers):
- # GH12839 clear selected obj cache when group selection changes
- ax = self.obj._info_axis
- self._group_selection = ax.difference(Index(groupers), sort=False).tolist()
- self._reset_cache("_selected_obj")
-
- @final
- def _set_result_index_ordered(
- self, result: OutputFrameOrSeries
- ) -> OutputFrameOrSeries:
- # set the result index on the passed values object and
- # return the new object, xref 8046
-
- if self.grouper.is_monotonic:
- # shortcut if we have an already ordered grouper
- result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
- return result
-
- # row order is scrambled => sort the rows by position in original index
- original_positions = Index(
- np.concatenate(self._get_indices(self.grouper.result_index))
- )
- result.set_axis(original_positions, axis=self.axis, inplace=True)
- result = result.sort_index(axis=self.axis)
-
- dropped_rows = len(result.index) < len(self.obj.index)
-
- if dropped_rows:
- # get index by slicing original index according to original positions
- # slice drops attrs => use set_axis when no rows were dropped
- sorted_indexer = result.index
- result.index = self._selected_obj.index[sorted_indexer]
- else:
- result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
-
- return result
-
@final
def _dir_additions(self) -> set[str]:
return self.obj._dir_additions() | self._apply_allowlist
- def __getattr__(self, attr: str):
- if attr in self._internal_names_set:
- return object.__getattribute__(self, attr)
- if attr in self.obj:
- return self[attr]
-
- raise AttributeError(
- f"'{type(self).__name__}' object has no attribute '{attr}'"
- )
-
@Substitution(
klass="GroupBy",
examples=dedent(
@@ -828,44 +694,6 @@ def pipe(
plot = property(GroupByPlot)
- @final
- def _make_wrapper(self, name: str) -> Callable:
- assert name in self._apply_allowlist
-
- with group_selection_context(self):
- # need to setup the selection
- # as are not passed directly but in the grouper
- f = getattr(self._obj_with_exclusions, name)
- if not isinstance(f, types.MethodType):
- return self.apply(lambda self: getattr(self, name))
-
- f = getattr(type(self._obj_with_exclusions), name)
- sig = inspect.signature(f)
-
- def wrapper(*args, **kwargs):
- # a little trickery for aggregation functions that need an axis
- # argument
- if "axis" in sig.parameters:
- if kwargs.get("axis", None) is None:
- kwargs["axis"] = self.axis
-
- def curried(x):
- return f(x, *args, **kwargs)
-
- # preserve the name so we can detect it when calling plot methods,
- # to avoid duplicates
- curried.__name__ = name
-
- # special case otherwise extra plots are created when catching the
- # exception below
- if name in base.plotting_methods:
- return self.apply(curried)
-
- return self._python_apply_general(curried, self._obj_with_exclusions)
-
- wrapper.__name__ = name
- return wrapper
-
@final
def get_group(self, name, obj=None):
"""
@@ -904,145 +732,329 @@ def __iter__(self) -> Iterator[tuple[Hashable, FrameOrSeries]]:
"""
return self.grouper.get_iterator(self.obj, axis=self.axis)
- @Appender(
- _apply_docs["template"].format(
- input="dataframe", examples=_apply_docs["dataframe_examples"]
- )
- )
- def apply(self, func, *args, **kwargs):
-
- func = com.is_builtin_func(func)
- # this is needed so we don't try and wrap strings. If we could
- # resolve functions to their callable functions prior, this
- # wouldn't be needed
- if args or kwargs:
- if callable(func):
+# To track operations that expand dimensions, like ohlc
+OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame)
- @wraps(func)
- def f(g):
- with np.errstate(all="ignore"):
- return func(g, *args, **kwargs)
- elif hasattr(nanops, "nan" + func):
- # TODO: should we wrap this in to e.g. _is_builtin_func?
- f = getattr(nanops, "nan" + func)
+class GroupBy(BaseGroupBy[FrameOrSeries]):
+ """
+ Class for grouping and aggregating relational data.
- else:
- raise ValueError(
- "func must be a callable if args or kwargs are supplied"
- )
- else:
- f = func
+ See aggregate, transform, and apply functions on this object.
- # ignore SettingWithCopy here in case the user mutates
- with option_context("mode.chained_assignment", None):
- try:
- result = self._python_apply_general(f, self._selected_obj)
- except TypeError:
- # gh-20949
- # try again, with .apply acting as a filtering
- # operation, by excluding the grouping column
- # This would normally not be triggered
- # except if the udf is trying an operation that
- # fails on *some* columns, e.g. a numeric operation
- # on a string grouper column
+ It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
- with group_selection_context(self):
- return self._python_apply_general(f, self._selected_obj)
+ ::
- return result
+ grouped = groupby(obj, ...)
- @final
- def _python_apply_general(
- self, f: F, data: FrameOrSeriesUnion
- ) -> FrameOrSeriesUnion:
- """
- Apply function f in python space
+ Parameters
+ ----------
+ obj : pandas object
+ axis : int, default 0
+ level : int, default None
+ Level of MultiIndex
+ groupings : list of Grouping objects
+ Most users should ignore this
+ exclusions : array-like, optional
+ List of columns to exclude
+ name : str
+ Most users should ignore this
- Parameters
- ----------
- f : callable
- Function to apply
- data : Series or DataFrame
- Data to apply f to
+ Returns
+ -------
+ **Attributes**
+ groups : dict
+ {group name -> group labels}
+ len(grouped) : int
+ Number of groups
- Returns
- -------
- Series or DataFrame
- data after applying f
- """
- keys, values, mutated = self.grouper.apply(f, data, self.axis)
+ Notes
+ -----
+ After grouping, see aggregate, apply, and transform functions. Here are
+ some other brief notes about usage. When grouping by multiple groups, the
+ result index will be a MultiIndex (hierarchical) by default.
- return self._wrap_applied_output(
- data, keys, values, not_indexed_same=mutated or self.mutated
+ Iteration produces (key, group) tuples, i.e. chunking the data by group. So
+ you can write code like:
+
+ ::
+
+ grouped = obj.groupby(keys, axis=axis)
+ for key, group in grouped:
+ # do something with the data
+
+ Function calls on GroupBy, if not specially implemented, "dispatch" to the
+ grouped data. So if you group a DataFrame and wish to invoke the std()
+ method on each group, you can simply do:
+
+ ::
+
+ df.groupby(mapper).std()
+
+ rather than
+
+ ::
+
+ df.groupby(mapper).aggregate(np.std)
+
+ You can pass arguments to these "wrapped" functions, too.
+
+ See the online documentation for full exposition on these topics and much
+ more
+ """
+
+ obj: FrameOrSeries
+ grouper: ops.BaseGrouper
+ as_index: bool
+
+ def __init__(
+ self,
+ obj: FrameOrSeries,
+ keys: _KeysArgType | None = None,
+ axis: int = 0,
+ level: IndexLabel | None = None,
+ grouper: ops.BaseGrouper | None = None,
+ exclusions: set[Hashable] | None = None,
+ selection: IndexLabel | None = None,
+ as_index: bool = True,
+ sort: bool = True,
+ group_keys: bool = True,
+ squeeze: bool = False,
+ observed: bool = False,
+ mutated: bool = False,
+ dropna: bool = True,
+ ):
+
+ self._selection = selection
+
+ assert isinstance(obj, NDFrame), type(obj)
+
+ self.level = level
+
+ if not as_index:
+ if not isinstance(obj, DataFrame):
+ raise TypeError("as_index=False only valid with DataFrame")
+ if axis != 0:
+ raise ValueError("as_index=False only valid for axis=0")
+
+ self.as_index = as_index
+ self.keys = keys
+ self.sort = sort
+ self.group_keys = group_keys
+ self.squeeze = squeeze
+ self.observed = observed
+ self.mutated = mutated
+ self.dropna = dropna
+
+ if grouper is None:
+ from pandas.core.groupby.grouper import get_grouper
+
+ grouper, exclusions, obj = get_grouper(
+ obj,
+ keys,
+ axis=axis,
+ level=level,
+ sort=sort,
+ observed=observed,
+ mutated=self.mutated,
+ dropna=self.dropna,
+ )
+
+ self.obj = obj
+ self.axis = obj._get_axis_number(axis)
+ self.grouper = grouper
+ self.exclusions = exclusions or set()
+
+ def __getattr__(self, attr: str):
+ if attr in self._internal_names_set:
+ return object.__getattribute__(self, attr)
+ if attr in self.obj:
+ return self[attr]
+
+ raise AttributeError(
+ f"'{type(self).__name__}' object has no attribute '{attr}'"
)
- def _iterate_slices(self) -> Iterable[Series]:
- raise AbstractMethodError(self)
+ @final
+ def _make_wrapper(self, name: str) -> Callable:
+ assert name in self._apply_allowlist
- def transform(self, func, *args, **kwargs):
- raise AbstractMethodError(self)
+ with group_selection_context(self):
+ # need to setup the selection
+ # as are not passed directly but in the grouper
+ f = getattr(self._obj_with_exclusions, name)
+ if not isinstance(f, types.MethodType):
+ return self.apply(lambda self: getattr(self, name))
+
+ f = getattr(type(self._obj_with_exclusions), name)
+ sig = inspect.signature(f)
+
+ def wrapper(*args, **kwargs):
+ # a little trickery for aggregation functions that need an axis
+ # argument
+ if "axis" in sig.parameters:
+ if kwargs.get("axis", None) is None:
+ kwargs["axis"] = self.axis
+
+ def curried(x):
+ return f(x, *args, **kwargs)
+
+ # preserve the name so we can detect it when calling plot methods,
+ # to avoid duplicates
+ curried.__name__ = name
+
+ # special case otherwise extra plots are created when catching the
+ # exception below
+ if name in base.plotting_methods:
+ return self.apply(curried)
+
+ return self._python_apply_general(curried, self._obj_with_exclusions)
+
+ wrapper.__name__ = name
+ return wrapper
+
+ # -----------------------------------------------------------------
+ # Selection
@final
- def _cumcount_array(self, ascending: bool = True):
+ def _set_group_selection(self) -> None:
"""
- Parameters
- ----------
- ascending : bool, default True
- If False, number in reverse, from length of group - 1 to 0.
+ Create group based selection.
- Notes
- -----
- this is currently implementing sort=False
- (though the default is sort=True) for groupby in general
+ Used when selection is not passed directly but instead via a grouper.
+
+ NOTE: this should be paired with a call to _reset_group_selection
"""
- ids, _, ngroups = self.grouper.group_info
- sorter = get_group_index_sorter(ids, ngroups)
- ids, count = ids[sorter], len(ids)
+ grp = self.grouper
+ if not (
+ self.as_index
+ and getattr(grp, "groupings", None) is not None
+ and self.obj.ndim > 1
+ and self._group_selection is None
+ ):
+ return
- if count == 0:
- return np.empty(0, dtype=np.int64)
+ groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis]
- run = np.r_[True, ids[:-1] != ids[1:]]
- rep = np.diff(np.r_[np.nonzero(run)[0], count])
- out = (~run).cumsum()
+ if len(groupers):
+ # GH12839 clear selected obj cache when group selection changes
+ ax = self.obj._info_axis
+ self._group_selection = ax.difference(Index(groupers), sort=False).tolist()
+ self._reset_cache("_selected_obj")
- if ascending:
- out -= np.repeat(out[run], rep)
- else:
- out = np.repeat(out[np.r_[run[1:], True]], rep) - out
+ @final
+ def _reset_group_selection(self) -> None:
+ """
+ Clear group based selection.
- rev = np.empty(count, dtype=np.intp)
- rev[sorter] = np.arange(count, dtype=np.intp)
- return out[rev].astype(np.int64, copy=False)
+ Used for methods needing to return info on each group regardless of
+ whether a group selection was previously set.
+ """
+ if self._group_selection is not None:
+ # GH12839 clear cached selection too when changing group selection
+ self._group_selection = None
+ self._reset_cache("_selected_obj")
+
+ def _iterate_slices(self) -> Iterable[Series]:
+ raise AbstractMethodError(self)
+
+ # -----------------------------------------------------------------
+ # Dispatch/Wrapping
@final
- def _cython_transform(
- self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
- ):
- output: dict[base.OutputKey, ArrayLike] = {}
+ def _concat_objects(self, keys, values, not_indexed_same: bool = False):
+ from pandas.core.reshape.concat import concat
- for idx, obj in enumerate(self._iterate_slices()):
- name = obj.name
- is_numeric = is_numeric_dtype(obj.dtype)
- if numeric_only and not is_numeric:
- continue
+ def reset_identity(values):
+ # reset the identities of the components
+ # of the values to prevent aliasing
+ for v in com.not_none(*values):
+ ax = v._get_axis(self.axis)
+ ax._reset_identity()
+ return values
- try:
- result = self.grouper._cython_operation(
- "transform", obj._values, how, axis, **kwargs
+ if not not_indexed_same:
+ result = concat(values, axis=self.axis)
+ ax = self.filter(lambda x: True).axes[self.axis]
+
+ # this is a very unfortunate situation
+ # we can't use reindex to restore the original order
+ # when the ax has duplicates
+ # so we resort to this
+ # GH 14776, 30667
+ if ax.has_duplicates and not result.axes[self.axis].equals(ax):
+ indexer, _ = result.index.get_indexer_non_unique(ax._values)
+ indexer = algorithms.unique1d(indexer)
+ result = result.take(indexer, axis=self.axis)
+ else:
+ result = result.reindex(ax, axis=self.axis, copy=False)
+
+ elif self.group_keys:
+
+ values = reset_identity(values)
+ if self.as_index:
+
+ # possible MI return case
+ group_keys = keys
+ group_levels = self.grouper.levels
+ group_names = self.grouper.names
+
+ result = concat(
+ values,
+ axis=self.axis,
+ keys=group_keys,
+ levels=group_levels,
+ names=group_names,
+ sort=False,
)
- except (NotImplementedError, TypeError):
- continue
+ else:
- key = base.OutputKey(label=name, position=idx)
- output[key] = result
+ # GH5610, returns a MI, with the first level being a
+ # range index
+ keys = list(range(len(values)))
+ result = concat(values, axis=self.axis, keys=keys)
+ else:
+ values = reset_identity(values)
+ result = concat(values, axis=self.axis)
- if not output:
- raise DataError("No numeric types to aggregate")
+ if isinstance(result, Series) and self._selection_name is not None:
- return self._wrap_transformed_output(output)
+ result.name = self._selection_name
+
+ return result
+
+ @final
+ def _set_result_index_ordered(
+ self, result: OutputFrameOrSeries
+ ) -> OutputFrameOrSeries:
+ # set the result index on the passed values object and
+ # return the new object, xref 8046
+
+ if self.grouper.is_monotonic:
+ # shortcut if we have an already ordered grouper
+ result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
+ return result
+
+ # row order is scrambled => sort the rows by position in original index
+ original_positions = Index(
+ np.concatenate(self._get_indices(self.grouper.result_index))
+ )
+ result.set_axis(original_positions, axis=self.axis, inplace=True)
+ result = result.sort_index(axis=self.axis)
+
+ dropped_rows = len(result.index) < len(self.obj.index)
+
+ if dropped_rows:
+ # get index by slicing original index according to original positions
+ # slice drops attrs => use set_axis when no rows were dropped
+ sorted_indexer = result.index
+ result.index = self._selected_obj.index[sorted_indexer]
+ else:
+ result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True)
+
+ return result
def _wrap_aggregated_output(
self, output: Mapping[base.OutputKey, np.ndarray], index: Index | None
@@ -1055,84 +1067,8 @@ def _wrap_transformed_output(self, output: Mapping[base.OutputKey, ArrayLike]):
def _wrap_applied_output(self, data, keys, values, not_indexed_same: bool = False):
raise AbstractMethodError(self)
- @final
- def _agg_general(
- self,
- numeric_only: bool = True,
- min_count: int = -1,
- *,
- alias: str,
- npfunc: Callable,
- ):
- with group_selection_context(self):
- # try a cython aggregation if we can
- result = None
- try:
- result = self._cython_agg_general(
- how=alias,
- alt=npfunc,
- numeric_only=numeric_only,
- min_count=min_count,
- )
- except DataError:
- pass
- except NotImplementedError as err:
- if "function is not implemented for this dtype" in str(
- err
- ) or "category dtype not supported" in str(err):
- # raised in _get_cython_function, in some cases can
- # be trimmed by implementing cython funcs for more dtypes
- pass
- else:
- raise
-
- # apply a non-cython aggregation
- if result is None:
- result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
- return result.__finalize__(self.obj, method="groupby")
-
- def _cython_agg_general(
- self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
- ):
- output: dict[base.OutputKey, ArrayLike] = {}
- # Ideally we would be able to enumerate self._iterate_slices and use
- # the index from enumeration as the key of output, but ohlc in particular
- # returns a (n x 4) array. Output requires 1D ndarrays as values, so we
- # need to slice that up into 1D arrays
- idx = 0
- for obj in self._iterate_slices():
- name = obj.name
- is_numeric = is_numeric_dtype(obj.dtype)
- if numeric_only and not is_numeric:
- continue
-
- result = self.grouper._cython_operation(
- "aggregate", obj._values, how, axis=0, min_count=min_count
- )
-
- if how == "ohlc":
- # e.g. ohlc
- agg_names = ["open", "high", "low", "close"]
- assert len(agg_names) == result.shape[1]
- for result_column, result_name in zip(result.T, agg_names):
- key = base.OutputKey(label=result_name, position=idx)
- output[key] = result_column
- idx += 1
- else:
- assert result.ndim == 1
- key = base.OutputKey(label=name, position=idx)
- output[key] = result
- idx += 1
-
- if not output:
- raise DataError("No numeric types to aggregate")
-
- # error: Argument 1 to "_wrap_aggregated_output" of "BaseGroupBy" has
- # incompatible type "Dict[OutputKey, Union[ndarray, DatetimeArray]]";
- # expected "Mapping[OutputKey, ndarray]"
- return self._wrap_aggregated_output(
- output, index=self.grouper.result_index # type: ignore[arg-type]
- )
+ # -----------------------------------------------------------------
+ # numba
@final
def _numba_prep(self, func, data):
@@ -1168,43 +1104,120 @@ def _transform_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs)
sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns)
)
- cache_key = (func, "groupby_transform")
- if cache_key not in NUMBA_FUNC_CACHE:
- NUMBA_FUNC_CACHE[cache_key] = numba_transform_func
+ cache_key = (func, "groupby_transform")
+ if cache_key not in NUMBA_FUNC_CACHE:
+ NUMBA_FUNC_CACHE[cache_key] = numba_transform_func
+
+ # result values needs to be resorted to their original positions since we
+ # evaluated the data sorted by group
+ return result.take(np.argsort(sorted_index), axis=0)
+
+ @final
+ def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs):
+ """
+ Perform groupby aggregation routine with the numba engine.
+
+ This routine mimics the data splitting routine of the DataSplitter class
+ to generate the indices of each group in the sorted data and then passes the
+ data and indices into a Numba jitted function.
+ """
+ starts, ends, sorted_index, sorted_data = self._numba_prep(func, data)
+ group_keys = self.grouper._get_group_keys()
+
+ numba_agg_func = numba_.generate_numba_agg_func(
+ tuple(args), kwargs, func, engine_kwargs
+ )
+ result = numba_agg_func(
+ sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns)
+ )
+
+ cache_key = (func, "groupby_agg")
+ if cache_key not in NUMBA_FUNC_CACHE:
+ NUMBA_FUNC_CACHE[cache_key] = numba_agg_func
+
+ if self.grouper.nkeys > 1:
+ index = MultiIndex.from_tuples(group_keys, names=self.grouper.names)
+ else:
+ index = Index(group_keys, name=self.grouper.names[0])
+ return result, index
+
+ # -----------------------------------------------------------------
+ # apply/agg/transform
+
+ @Appender(
+ _apply_docs["template"].format(
+ input="dataframe", examples=_apply_docs["dataframe_examples"]
+ )
+ )
+ def apply(self, func, *args, **kwargs):
+
+ func = com.is_builtin_func(func)
+
+ # this is needed so we don't try and wrap strings. If we could
+ # resolve functions to their callable functions prior, this
+ # wouldn't be needed
+ if args or kwargs:
+ if callable(func):
+
+ @wraps(func)
+ def f(g):
+ with np.errstate(all="ignore"):
+ return func(g, *args, **kwargs)
+
+ elif hasattr(nanops, "nan" + func):
+ # TODO: should we wrap this in to e.g. _is_builtin_func?
+ f = getattr(nanops, "nan" + func)
+
+ else:
+ raise ValueError(
+ "func must be a callable if args or kwargs are supplied"
+ )
+ else:
+ f = func
+
+ # ignore SettingWithCopy here in case the user mutates
+ with option_context("mode.chained_assignment", None):
+ try:
+ result = self._python_apply_general(f, self._selected_obj)
+ except TypeError:
+ # gh-20949
+ # try again, with .apply acting as a filtering
+ # operation, by excluding the grouping column
+ # This would normally not be triggered
+ # except if the udf is trying an operation that
+ # fails on *some* columns, e.g. a numeric operation
+ # on a string grouper column
+
+ with group_selection_context(self):
+ return self._python_apply_general(f, self._selected_obj)
- # result values needs to be resorted to their original positions since we
- # evaluated the data sorted by group
- return result.take(np.argsort(sorted_index), axis=0)
+ return result
@final
- def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs):
+ def _python_apply_general(
+ self, f: F, data: FrameOrSeriesUnion
+ ) -> FrameOrSeriesUnion:
"""
- Perform groupby aggregation routine with the numba engine.
+ Apply function f in python space
- This routine mimics the data splitting routine of the DataSplitter class
- to generate the indices of each group in the sorted data and then passes the
- data and indices into a Numba jitted function.
+ Parameters
+ ----------
+ f : callable
+ Function to apply
+ data : Series or DataFrame
+ Data to apply f to
+
+ Returns
+ -------
+ Series or DataFrame
+ data after applying f
"""
- starts, ends, sorted_index, sorted_data = self._numba_prep(func, data)
- group_keys = self.grouper._get_group_keys()
+ keys, values, mutated = self.grouper.apply(f, data, self.axis)
- numba_agg_func = numba_.generate_numba_agg_func(
- tuple(args), kwargs, func, engine_kwargs
- )
- result = numba_agg_func(
- sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns)
+ return self._wrap_applied_output(
+ data, keys, values, not_indexed_same=mutated or self.mutated
)
- cache_key = (func, "groupby_agg")
- if cache_key not in NUMBA_FUNC_CACHE:
- NUMBA_FUNC_CACHE[cache_key] = numba_agg_func
-
- if self.grouper.nkeys > 1:
- index = MultiIndex.from_tuples(group_keys, names=self.grouper.names)
- else:
- index = Index(group_keys, name=self.grouper.names[0])
- return result, index
-
@final
def _python_agg_general(self, func, *args, **kwargs):
func = com.is_builtin_func(func)
@@ -1249,66 +1262,78 @@ def _python_agg_general(self, func, *args, **kwargs):
return self._wrap_aggregated_output(output, index=self.grouper.result_index)
@final
- def _concat_objects(self, keys, values, not_indexed_same: bool = False):
- from pandas.core.reshape.concat import concat
-
- def reset_identity(values):
- # reset the identities of the components
- # of the values to prevent aliasing
- for v in com.not_none(*values):
- ax = v._get_axis(self.axis)
- ax._reset_identity()
- return values
-
- if not not_indexed_same:
- result = concat(values, axis=self.axis)
- ax = self.filter(lambda x: True).axes[self.axis]
+ def _agg_general(
+ self,
+ numeric_only: bool = True,
+ min_count: int = -1,
+ *,
+ alias: str,
+ npfunc: Callable,
+ ):
+ with group_selection_context(self):
+ # try a cython aggregation if we can
+ result = None
+ try:
+ result = self._cython_agg_general(
+ how=alias,
+ alt=npfunc,
+ numeric_only=numeric_only,
+ min_count=min_count,
+ )
+ except DataError:
+ pass
+ except NotImplementedError as err:
+ if "function is not implemented for this dtype" in str(
+ err
+ ) or "category dtype not supported" in str(err):
+ # raised in _get_cython_function, in some cases can
+ # be trimmed by implementing cython funcs for more dtypes
+ pass
+ else:
+ raise
- # this is a very unfortunate situation
- # we can't use reindex to restore the original order
- # when the ax has duplicates
- # so we resort to this
- # GH 14776, 30667
- if ax.has_duplicates and not result.axes[self.axis].equals(ax):
- indexer, _ = result.index.get_indexer_non_unique(ax._values)
- indexer = algorithms.unique1d(indexer)
- result = result.take(indexer, axis=self.axis)
- else:
- result = result.reindex(ax, axis=self.axis, copy=False)
+ # apply a non-cython aggregation
+ if result is None:
+ result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
+ return result.__finalize__(self.obj, method="groupby")
- elif self.group_keys:
+ def _cython_agg_general(
+ self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
+ ):
+ raise AbstractMethodError(self)
- values = reset_identity(values)
- if self.as_index:
+ @final
+ def _cython_transform(
+ self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
+ ):
+ output: dict[base.OutputKey, ArrayLike] = {}
- # possible MI return case
- group_keys = keys
- group_levels = self.grouper.levels
- group_names = self.grouper.names
+ for idx, obj in enumerate(self._iterate_slices()):
+ name = obj.name
+ is_numeric = is_numeric_dtype(obj.dtype)
+ if numeric_only and not is_numeric:
+ continue
- result = concat(
- values,
- axis=self.axis,
- keys=group_keys,
- levels=group_levels,
- names=group_names,
- sort=False,
+ try:
+ result = self.grouper._cython_operation(
+ "transform", obj._values, how, axis, **kwargs
)
- else:
+ except (NotImplementedError, TypeError):
+ continue
- # GH5610, returns a MI, with the first level being a
- # range index
- keys = list(range(len(values)))
- result = concat(values, axis=self.axis, keys=keys)
- else:
- values = reset_identity(values)
- result = concat(values, axis=self.axis)
+ key = base.OutputKey(label=name, position=idx)
+ output[key] = result
- if isinstance(result, Series) and self._selection_name is not None:
+ if not output:
+ raise DataError("No numeric types to aggregate")
- result.name = self._selection_name
+ return self._wrap_transformed_output(output)
- return result
+ def transform(self, func, *args, **kwargs):
+ raise AbstractMethodError(self)
+
+ # -----------------------------------------------------------------
+ # Utilities
@final
def _apply_filter(self, indices, dropna):
@@ -1327,78 +1352,40 @@ def _apply_filter(self, indices, dropna):
filtered = self._selected_obj.where(mask) # Fill with NaNs.
return filtered
+ @final
+ def _cumcount_array(self, ascending: bool = True):
+ """
+ Parameters
+ ----------
+ ascending : bool, default True
+ If False, number in reverse, from length of group - 1 to 0.
-# To track operations that expand dimensions, like ohlc
-OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame)
-
-
-class GroupBy(BaseGroupBy[FrameOrSeries]):
- """
- Class for grouping and aggregating relational data.
-
- See aggregate, transform, and apply functions on this object.
-
- It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:
-
- ::
-
- grouped = groupby(obj, ...)
-
- Parameters
- ----------
- obj : pandas object
- axis : int, default 0
- level : int, default None
- Level of MultiIndex
- groupings : list of Grouping objects
- Most users should ignore this
- exclusions : array-like, optional
- List of columns to exclude
- name : str
- Most users should ignore this
-
- Returns
- -------
- **Attributes**
- groups : dict
- {group name -> group labels}
- len(grouped) : int
- Number of groups
-
- Notes
- -----
- After grouping, see aggregate, apply, and transform functions. Here are
- some other brief notes about usage. When grouping by multiple groups, the
- result index will be a MultiIndex (hierarchical) by default.
-
- Iteration produces (key, group) tuples, i.e. chunking the data by group. So
- you can write code like:
-
- ::
-
- grouped = obj.groupby(keys, axis=axis)
- for key, group in grouped:
- # do something with the data
-
- Function calls on GroupBy, if not specially implemented, "dispatch" to the
- grouped data. So if you group a DataFrame and wish to invoke the std()
- method on each group, you can simply do:
-
- ::
-
- df.groupby(mapper).std()
+ Notes
+ -----
+ this is currently implementing sort=False
+ (though the default is sort=True) for groupby in general
+ """
+ ids, _, ngroups = self.grouper.group_info
+ sorter = get_group_index_sorter(ids, ngroups)
+ ids, count = ids[sorter], len(ids)
- rather than
+ if count == 0:
+ return np.empty(0, dtype=np.int64)
- ::
+ run = np.r_[True, ids[:-1] != ids[1:]]
+ rep = np.diff(np.r_[np.nonzero(run)[0], count])
+ out = (~run).cumsum()
- df.groupby(mapper).aggregate(np.std)
+ if ascending:
+ out -= np.repeat(out[run], rep)
+ else:
+ out = np.repeat(out[np.r_[run[1:], True]], rep) - out
- You can pass arguments to these "wrapped" functions, too.
+ rev = np.empty(count, dtype=np.intp)
+ rev[sorter] = np.arange(count, dtype=np.intp)
+ return out[rev].astype(np.int64, copy=False)
- See the online documentation for full exposition on these topics and much
- more
- """
+ # -----------------------------------------------------------------
@final
@property
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 58003c10db9e0..95690146e94a8 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -110,6 +110,11 @@ class Resampler(BaseGroupBy, PandasObject):
After resampling, see aggregate, apply, and transform functions.
"""
+ # error: Incompatible types in assignment (expression has type
+ # "Optional[BinGrouper]", base class "BaseGroupBy" defined the type as
+ # "BaseGrouper")
+ grouper: BinGrouper | None # type: ignore[assignment]
+
# to the groupby descriptor
_attributes = [
"freq",
@@ -134,9 +139,7 @@ def __init__(self, obj, groupby=None, axis=0, kind=None, **kwargs):
self.as_index = True
self.exclusions = set()
self.binner = None
- # error: Incompatible types in assignment (expression has type "None", variable
- # has type "BaseGrouper")
- self.grouper = None # type: ignore[assignment]
+ self.grouper = None
if self.groupby is not None:
self.groupby._set_grouper(self._convert_obj(obj), sort=True)
@@ -1150,10 +1153,10 @@ def _downsample(self, how, **kwargs):
# do we have a regular frequency
- # error: "BaseGrouper" has no attribute "binlabels"
+ # error: Item "None" of "Optional[Any]" has no attribute "binlabels"
if (
(ax.freq is not None or ax.inferred_freq is not None)
- and len(self.grouper.binlabels) > len(ax) # type: ignore[attr-defined]
+ and len(self.grouper.binlabels) > len(ax) # type: ignore[union-attr]
and how is None
):
| BaseGroupBy is also subclassed by Resampler; if Resampler ever accessed these methods they'd get an AttributeError.
Once non-direct move is _cython_agg_general, where the implementation is moved to SeriesGroupBy instead of GroupBy, bc DataFrameGroupBy overrides it. | https://api.github.com/repos/pandas-dev/pandas/pulls/41066 | 2021-04-21T03:06:20Z | 2021-04-22T22:17:16Z | 2021-04-22T22:17:16Z | 2021-04-22T22:19:30Z |
REF: make maybe_cast_result_dtype a WrappedCythonOp method | diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
index e91927d87d318..d739b46620032 100644
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -406,42 +406,6 @@ def maybe_cast_pointwise_result(
return result
-def maybe_cast_result_dtype(dtype: DtypeObj, how: str) -> DtypeObj:
- """
- Get the desired dtype of a result based on the
- input dtype and how it was computed.
-
- Parameters
- ----------
- dtype : DtypeObj
- Input dtype.
- how : str
- How the result was computed.
-
- Returns
- -------
- DtypeObj
- The desired dtype of the result.
- """
- from pandas.core.arrays.boolean import BooleanDtype
- from pandas.core.arrays.floating import Float64Dtype
- from pandas.core.arrays.integer import (
- Int64Dtype,
- _IntegerDtype,
- )
-
- if how in ["add", "cumsum", "sum", "prod"]:
- if dtype == np.dtype(bool):
- return np.dtype(np.int64)
- elif isinstance(dtype, (BooleanDtype, _IntegerDtype)):
- return Int64Dtype()
- elif how in ["mean", "median", "var"] and isinstance(
- dtype, (BooleanDtype, _IntegerDtype)
- ):
- return Float64Dtype()
- return dtype
-
-
def maybe_cast_to_extension_array(
cls: type[ExtensionArray], obj: ArrayLike, dtype: ExtensionDtype | None = None
) -> ArrayLike:
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 4f9a71e5af59a..3ee185d862b01 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -37,7 +37,6 @@
from pandas.core.dtypes.cast import (
maybe_cast_pointwise_result,
- maybe_cast_result_dtype,
maybe_downcast_to_dtype,
)
from pandas.core.dtypes.common import (
@@ -262,6 +261,41 @@ def get_out_dtype(self, dtype: np.dtype) -> np.dtype:
out_dtype = "object"
return np.dtype(out_dtype)
+ def get_result_dtype(self, dtype: DtypeObj) -> DtypeObj:
+ """
+ Get the desired dtype of a result based on the
+ input dtype and how it was computed.
+
+ Parameters
+ ----------
+ dtype : np.dtype or ExtensionDtype
+ Input dtype.
+
+ Returns
+ -------
+ np.dtype or ExtensionDtype
+ The desired dtype of the result.
+ """
+ from pandas.core.arrays.boolean import BooleanDtype
+ from pandas.core.arrays.floating import Float64Dtype
+ from pandas.core.arrays.integer import (
+ Int64Dtype,
+ _IntegerDtype,
+ )
+
+ how = self.how
+
+ if how in ["add", "cumsum", "sum", "prod"]:
+ if dtype == np.dtype(bool):
+ return np.dtype(np.int64)
+ elif isinstance(dtype, (BooleanDtype, _IntegerDtype)):
+ return Int64Dtype()
+ elif how in ["mean", "median", "var"] and isinstance(
+ dtype, (BooleanDtype, _IntegerDtype)
+ ):
+ return Float64Dtype()
+ return dtype
+
def uses_mask(self) -> bool:
return self.how in self._MASKED_CYTHON_FUNCTIONS
@@ -564,7 +598,14 @@ def get_group_levels(self) -> list[Index]:
@final
def _ea_wrap_cython_operation(
- self, kind: str, values, how: str, axis: int, min_count: int = -1, **kwargs
+ self,
+ cy_op: WrappedCythonOp,
+ kind: str,
+ values,
+ how: str,
+ axis: int,
+ min_count: int = -1,
+ **kwargs,
) -> ArrayLike:
"""
If we have an ExtensionArray, unwrap, call _cython_operation, and
@@ -601,7 +642,7 @@ def _ea_wrap_cython_operation(
# other cast_blocklist methods dont go through cython_operation
return res_values
- dtype = maybe_cast_result_dtype(orig_values.dtype, how)
+ dtype = cy_op.get_result_dtype(orig_values.dtype)
# error: Item "dtype[Any]" of "Union[dtype[Any], ExtensionDtype]"
# has no attribute "construct_array_type"
cls = dtype.construct_array_type() # type: ignore[union-attr]
@@ -618,7 +659,7 @@ def _ea_wrap_cython_operation(
# other cast_blocklist methods dont go through cython_operation
return res_values
- dtype = maybe_cast_result_dtype(orig_values.dtype, how)
+ dtype = cy_op.get_result_dtype(orig_values.dtype)
# error: Item "dtype[Any]" of "Union[dtype[Any], ExtensionDtype]"
# has no attribute "construct_array_type"
cls = dtype.construct_array_type() # type: ignore[union-attr]
@@ -631,6 +672,7 @@ def _ea_wrap_cython_operation(
@final
def _masked_ea_wrap_cython_operation(
self,
+ cy_op: WrappedCythonOp,
kind: str,
values: BaseMaskedArray,
how: str,
@@ -651,7 +693,7 @@ def _masked_ea_wrap_cython_operation(
res_values = self._cython_operation(
kind, arr, how, axis, min_count, mask=mask, **kwargs
)
- dtype = maybe_cast_result_dtype(orig_values.dtype, how)
+ dtype = cy_op.get_result_dtype(orig_values.dtype)
assert isinstance(dtype, BaseMaskedDtype)
cls = dtype.construct_array_type()
@@ -694,11 +736,11 @@ def _cython_operation(
if is_extension_array_dtype(dtype):
if isinstance(values, BaseMaskedArray) and func_uses_mask:
return self._masked_ea_wrap_cython_operation(
- kind, values, how, axis, min_count, **kwargs
+ cy_op, kind, values, how, axis, min_count, **kwargs
)
else:
return self._ea_wrap_cython_operation(
- kind, values, how, axis, min_count, **kwargs
+ cy_op, kind, values, how, axis, min_count, **kwargs
)
elif values.ndim == 1:
@@ -797,7 +839,7 @@ def _cython_operation(
if how not in cy_op.cast_blocklist:
# e.g. if we are int64 and need to restore to datetime64/timedelta64
# "rank" is the only member of cast_blocklist we get here
- dtype = maybe_cast_result_dtype(orig_values.dtype, how)
+ dtype = cy_op.get_result_dtype(orig_values.dtype)
op_result = maybe_downcast_to_dtype(result, dtype)
else:
op_result = result
| Abstraction-wise, this is a much better place for it. | https://api.github.com/repos/pandas-dev/pandas/pulls/41065 | 2021-04-21T02:45:29Z | 2021-04-23T01:45:31Z | 2021-04-23T01:45:31Z | 2021-04-23T01:46:28Z |
CLN: Clean-up numeric index dtype validation | diff --git a/pandas/core/indexes/numeric.py b/pandas/core/indexes/numeric.py
index 28144af36d6ea..2182b3647533e 100644
--- a/pandas/core/indexes/numeric.py
+++ b/pandas/core/indexes/numeric.py
@@ -1,6 +1,8 @@
+from __future__ import annotations
+
from typing import (
+ Callable,
Hashable,
- Optional,
)
import warnings
@@ -49,11 +51,12 @@ class NumericIndex(Index):
"""
_default_dtype: np.dtype
+ _dtype_validation_metadata: tuple[Callable[..., bool], str]
_is_numeric_dtype = True
_can_hold_strings = False
- def __new__(cls, data=None, dtype: Optional[Dtype] = None, copy=False, name=None):
+ def __new__(cls, data=None, dtype: Dtype | None = None, copy=False, name=None):
name = maybe_extract_name(name, data, cls)
subarr = cls._ensure_array(data, dtype, copy)
@@ -97,14 +100,8 @@ def _ensure_array(cls, data, dtype, copy: bool):
def _validate_dtype(cls, dtype: Dtype) -> None:
if dtype is None:
return
- validation_metadata = {
- "int64index": (is_signed_integer_dtype, "signed integer"),
- "uint64index": (is_unsigned_integer_dtype, "unsigned integer"),
- "float64index": (is_float_dtype, "float"),
- "rangeindex": (is_signed_integer_dtype, "signed integer"),
- }
-
- validation_func, expected = validation_metadata[cls._typ]
+
+ validation_func, expected = cls._dtype_validation_metadata
if not validation_func(dtype):
raise ValueError(
f"Incorrect `dtype` passed: expected {expected}, received {dtype}"
@@ -264,6 +261,7 @@ class Int64Index(IntegerIndex):
_typ = "int64index"
_engine_type = libindex.Int64Engine
_default_dtype = np.dtype(np.int64)
+ _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
_uint64_descr_args = {
@@ -280,6 +278,7 @@ class UInt64Index(IntegerIndex):
_typ = "uint64index"
_engine_type = libindex.UInt64Engine
_default_dtype = np.dtype(np.uint64)
+ _dtype_validation_metadata = (is_unsigned_integer_dtype, "unsigned integer")
# ----------------------------------------------------------------
# Indexing Methods
@@ -311,6 +310,7 @@ class Float64Index(NumericIndex):
_typ = "float64index"
_engine_type = libindex.Float64Engine
_default_dtype = np.dtype(np.float64)
+ _dtype_validation_metadata = (is_float_dtype, "float")
@property
def inferred_type(self) -> str:
diff --git a/pandas/core/indexes/range.py b/pandas/core/indexes/range.py
index 1e974063bd839..0a2c0820f20a3 100644
--- a/pandas/core/indexes/range.py
+++ b/pandas/core/indexes/range.py
@@ -94,6 +94,7 @@ class RangeIndex(NumericIndex):
_typ = "rangeindex"
_engine_type = libindex.Int64Engine
+ _dtype_validation_metadata = (is_signed_integer_dtype, "signed integer")
_can_hold_na = False
_range: range
| The use of a dict with `_typ` attributes as keys in `NumericIndex._validate_dtype` makes it complicated to subclass numeric indexes and change the subclass' `_typ` attribute.
This PR makes the dtype validation in `NumericIndex._validate_dtype` not dependent on the value of the `_typ` attribute, making subclassing more robust. | https://api.github.com/repos/pandas-dev/pandas/pulls/41063 | 2021-04-20T22:43:48Z | 2021-04-21T12:43:55Z | 2021-04-21T12:43:55Z | 2022-02-06T11:49:39Z |
REF: move only-used-once mixins to resample | diff --git a/pandas/core/groupby/base.py b/pandas/core/groupby/base.py
index 50248d5af8883..7211fc1b71b54 100644
--- a/pandas/core/groupby/base.py
+++ b/pandas/core/groupby/base.py
@@ -7,83 +7,8 @@
import collections
-from pandas._typing import final
-
-from pandas.core.dtypes.common import (
- is_list_like,
- is_scalar,
-)
-
-from pandas.core.base import PandasObject
-
OutputKey = collections.namedtuple("OutputKey", ["label", "position"])
-
-class ShallowMixin(PandasObject):
- _attributes: list[str] = []
-
- @final
- def _shallow_copy(self, obj, **kwargs):
- """
- return a new object with the replacement attributes
- """
- if isinstance(obj, self._constructor):
- obj = obj.obj
- for attr in self._attributes:
- if attr not in kwargs:
- kwargs[attr] = getattr(self, attr)
- return self._constructor(obj, **kwargs)
-
-
-class GotItemMixin(PandasObject):
- """
- Provide the groupby facilities to the mixed object.
- """
-
- _attributes: list[str]
-
- @final
- def _gotitem(self, key, ndim, subset=None):
- """
- Sub-classes to define. Return a sliced object.
-
- Parameters
- ----------
- key : string / list of selections
- ndim : {1, 2}
- requested ndim of result
- subset : object, default None
- subset to act on
- """
- # create a new object to prevent aliasing
- if subset is None:
- # error: "GotItemMixin" has no attribute "obj"
- subset = self.obj # type: ignore[attr-defined]
-
- # we need to make a shallow copy of ourselves
- # with the same groupby
- kwargs = {attr: getattr(self, attr) for attr in self._attributes}
-
- # Try to select from a DataFrame, falling back to a Series
- try:
- # error: "GotItemMixin" has no attribute "_groupby"
- groupby = self._groupby[key] # type: ignore[attr-defined]
- except IndexError:
- # error: "GotItemMixin" has no attribute "_groupby"
- groupby = self._groupby # type: ignore[attr-defined]
-
- # error: Too many arguments for "GotItemMixin"
- # error: Unexpected keyword argument "groupby" for "GotItemMixin"
- # error: Unexpected keyword argument "parent" for "GotItemMixin"
- self = type(self)(
- subset, groupby=groupby, parent=self, **kwargs # type: ignore[call-arg]
- )
- self._reset_cache()
- if subset.ndim == 2 and (is_scalar(key) and key in subset or is_list_like(key)):
- self._selection = key
- return self
-
-
# special case to prevent duplicate plots when catching exceptions when
# forwarding methods from NDFrames
plotting_methods = frozenset(["plot", "hist"])
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index ffe0990a0c8be..58003c10db9e0 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -23,6 +23,7 @@
T,
TimedeltaConvertibleTypes,
TimestampConvertibleTypes,
+ final,
)
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError
@@ -39,16 +40,15 @@
import pandas.core.algorithms as algos
from pandas.core.apply import ResamplerWindowApply
-from pandas.core.base import DataError
+from pandas.core.base import (
+ DataError,
+ PandasObject,
+)
import pandas.core.common as com
from pandas.core.generic import (
NDFrame,
_shared_docs,
)
-from pandas.core.groupby.base import (
- GotItemMixin,
- ShallowMixin,
-)
from pandas.core.groupby.generic import SeriesGroupBy
from pandas.core.groupby.groupby import (
BaseGroupBy,
@@ -86,7 +86,7 @@
_shared_docs_kwargs: dict[str, str] = {}
-class Resampler(BaseGroupBy, ShallowMixin):
+class Resampler(BaseGroupBy, PandasObject):
"""
Class for resampling datetimelike data, a groupby-like operation.
See aggregate, transform, and apply functions on this object.
@@ -141,6 +141,18 @@ def __init__(self, obj, groupby=None, axis=0, kind=None, **kwargs):
if self.groupby is not None:
self.groupby._set_grouper(self._convert_obj(obj), sort=True)
+ @final
+ def _shallow_copy(self, obj, **kwargs):
+ """
+ return a new object with the replacement attributes
+ """
+ if isinstance(obj, self._constructor):
+ obj = obj.obj
+ for attr in self._attributes:
+ if attr not in kwargs:
+ kwargs[attr] = getattr(self, attr)
+ return self._constructor(obj, **kwargs)
+
def __str__(self) -> str:
"""
Provide a nice str repr of our rolling object.
@@ -1018,11 +1030,13 @@ def h(self, _method=method):
setattr(Resampler, method, h)
-class _GroupByMixin(GotItemMixin):
+class _GroupByMixin(PandasObject):
"""
Provide the groupby facilities.
"""
+ _attributes: list[str]
+
def __init__(self, obj, *args, **kwargs):
parent = kwargs.pop("parent", None)
@@ -1064,6 +1078,42 @@ def func(x):
_downsample = _apply
_groupby_and_aggregate = _apply
+ @final
+ def _gotitem(self, key, ndim, subset=None):
+ """
+ Sub-classes to define. Return a sliced object.
+
+ Parameters
+ ----------
+ key : string / list of selections
+ ndim : {1, 2}
+ requested ndim of result
+ subset : object, default None
+ subset to act on
+ """
+ # create a new object to prevent aliasing
+ if subset is None:
+ # error: "GotItemMixin" has no attribute "obj"
+ subset = self.obj # type: ignore[attr-defined]
+
+ # we need to make a shallow copy of ourselves
+ # with the same groupby
+ kwargs = {attr: getattr(self, attr) for attr in self._attributes}
+
+ # Try to select from a DataFrame, falling back to a Series
+ try:
+ groupby = self._groupby[key]
+ except IndexError:
+ groupby = self._groupby
+
+ self = type(self)(subset, groupby=groupby, parent=self, **kwargs)
+ self._reset_cache()
+ if subset.ndim == 2 and (
+ lib.is_scalar(key) and key in subset or lib.is_list_like(key)
+ ):
+ self._selection = key
+ return self
+
class DatetimeIndexResampler(Resampler):
@property
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/41058 | 2021-04-20T17:46:47Z | 2021-04-20T22:54:47Z | 2021-04-20T22:54:47Z | 2021-04-20T23:01:14Z |
DOC: more accurate wording in roadmap | diff --git a/doc/source/development/roadmap.rst b/doc/source/development/roadmap.rst
index 8223edcf6f63a..37e45bf5a42b5 100644
--- a/doc/source/development/roadmap.rst
+++ b/doc/source/development/roadmap.rst
@@ -71,8 +71,8 @@ instead of comparing as False).
Long term, we want to introduce consistent missing data handling for all data
types. This includes consistent behavior in all operations (indexing, arithmetic
-operations, comparisons, etc.). We want to eventually make the new semantics the
-default.
+operations, comparisons, etc.). There has been discussion of eventually making
+the new semantics the default.
This has been discussed at
`github #28095 <https://github.com/pandas-dev/pandas/issues/28095>`__ (and
| This came up the other day in https://github.com/pandas-dev/pandas/pull/40651#issuecomment-820671958. As I [originally](https://github.com/pandas-dev/pandas/pull/35208#issuecomment-656879885) said in the PR that put this inaccurate wording in, this overstates the degree to which there is consensus. | https://api.github.com/repos/pandas-dev/pandas/pulls/41057 | 2021-04-20T17:40:46Z | 2021-04-20T22:55:13Z | 2021-04-20T22:55:13Z | 2021-04-20T22:59:46Z |
[ArrowStringArray] Use utf8_upper and utf8_lower functions from Apache Arrow | diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index ecbb5367febc5..dd09ef4e585ce 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -757,3 +757,9 @@ def _str_map(self, f, na_value=None, dtype: Dtype | None = None):
# or .findall returns a list).
# -> We don't know the result type. E.g. `.get` can return anything.
return lib.map_infer_mask(arr, f, mask.view("uint8"))
+
+ def _str_lower(self):
+ return type(self)(pc.utf8_lower(self._data))
+
+ def _str_upper(self):
+ return type(self)(pc.utf8_upper(self._data))
| ```
data = ["Mouse", "dog", "house and parrot", "23", np.NaN] * 100_000
s = pd.Series(data, dtype="string")
s1 = pd.Series(data, dtype="arrow_string")
%timeit s.str.upper()
# 63.8 ms ± 259 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit s1.str.upper()
# 92 ms ± 152 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
# 4.44 ms ± 35.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
%timeit s.str.lower()
# 49.1 ms ± 273 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit s1.str.lower()
# 78.2 ms ± 814 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
# 4.34 ms ± 32.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/41056 | 2021-04-20T15:01:06Z | 2021-04-20T22:58:18Z | 2021-04-20T22:58:18Z | 2021-07-03T15:10:13Z |
BUG: Handle zero-chunked pyarrow.ChunkedArray in StringArray | diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 85d9acff353be..81f5ad34c53a0 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -700,7 +700,7 @@ Conversion
Strings
^^^^^^^
--
+- Bug in the conversion from ``pyarrow.ChunkedArray`` to :class:`~arrays.StringArray` when the original had zero chunks (:issue:`41040`)
-
Interval
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 0a0bfccc0ea15..6271a13875371 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -126,7 +126,12 @@ def __from_arrow__(
bool_arr = BooleanArray._from_sequence(np.array(arr))
results.append(bool_arr)
- return BooleanArray._concat_same_type(results)
+ if not results:
+ return BooleanArray(
+ np.array([], dtype=np.bool_), np.array([], dtype=np.bool_)
+ )
+ else:
+ return BooleanArray._concat_same_type(results)
def coerce_to_array(
diff --git a/pandas/core/arrays/numeric.py b/pandas/core/arrays/numeric.py
index 4908000a68810..bc467e93c2c2c 100644
--- a/pandas/core/arrays/numeric.py
+++ b/pandas/core/arrays/numeric.py
@@ -66,7 +66,11 @@ def __from_arrow__(
num_arr = array_class(data.copy(), ~mask, copy=False)
results.append(num_arr)
- if len(results) == 1:
+ if not results:
+ return array_class(
+ np.array([], dtype=self.numpy_dtype), np.array([], dtype=np.bool_)
+ )
+ elif len(results) == 1:
# avoid additional copy in _concat_same_type
return results[0]
else:
diff --git a/pandas/core/arrays/string_.py b/pandas/core/arrays/string_.py
index 600aacec9c87a..6954b512c7ad0 100644
--- a/pandas/core/arrays/string_.py
+++ b/pandas/core/arrays/string_.py
@@ -118,7 +118,10 @@ def __from_arrow__(
str_arr = StringArray._from_sequence(np.array(arr))
results.append(str_arr)
- return StringArray._concat_same_type(results)
+ if results:
+ return StringArray._concat_same_type(results)
+ else:
+ return StringArray(np.array([], dtype="object"))
class StringArray(PandasArray):
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index 84eede019251b..e09c24c94992d 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -1005,6 +1005,8 @@ def __from_arrow__(
parr[~mask] = NaT
results.append(parr)
+ if not results:
+ return PeriodArray(np.array([], dtype="int64"), freq=self.freq, copy=False)
return PeriodArray._concat_same_type(results)
@@ -1238,6 +1240,12 @@ def __from_arrow__(
iarr = IntervalArray.from_arrays(left, right, closed=array.type.closed)
results.append(iarr)
+ if not results:
+ return IntervalArray.from_arrays(
+ np.array([], dtype=self.subtype),
+ np.array([], dtype=self.subtype),
+ closed=array.type.closed,
+ )
return IntervalArray._concat_same_type(results)
def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
diff --git a/pandas/tests/arrays/interval/test_interval.py b/pandas/tests/arrays/interval/test_interval.py
index d8fca91c5516a..fde45a1e39bb2 100644
--- a/pandas/tests/arrays/interval/test_interval.py
+++ b/pandas/tests/arrays/interval/test_interval.py
@@ -271,6 +271,13 @@ def test_arrow_table_roundtrip(breaks):
expected = pd.concat([df, df], ignore_index=True)
tm.assert_frame_equal(result, expected)
+ # GH-41040
+ table = pa.table(
+ [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
+ )
+ result = table.to_pandas()
+ tm.assert_frame_equal(result, expected[0:0])
+
@pyarrow_skip
@pytest.mark.parametrize(
diff --git a/pandas/tests/arrays/masked/test_arrow_compat.py b/pandas/tests/arrays/masked/test_arrow_compat.py
index 8bb32dec2cc0e..ec5794a34ac45 100644
--- a/pandas/tests/arrays/masked/test_arrow_compat.py
+++ b/pandas/tests/arrays/masked/test_arrow_compat.py
@@ -41,6 +41,22 @@ def test_arrow_roundtrip(data):
tm.assert_frame_equal(result, df)
+@td.skip_if_no("pyarrow", min_version="0.15.1.dev")
+def test_arrow_load_from_zero_chunks(data):
+ # GH-41040
+ import pyarrow as pa
+
+ df = pd.DataFrame({"a": data[0:0]})
+ table = pa.table(df)
+ assert table.field("a").type == str(data.dtype.numpy_dtype)
+ table = pa.table(
+ [pa.chunked_array([], type=table.field("a").type)], schema=table.schema
+ )
+ result = table.to_pandas()
+ assert result["a"].dtype == data.dtype
+ tm.assert_frame_equal(result, df)
+
+
@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_arrow_from_arrow_uint():
# https://github.com/pandas-dev/pandas/issues/31896
diff --git a/pandas/tests/arrays/period/test_arrow_compat.py b/pandas/tests/arrays/period/test_arrow_compat.py
index f4e803cf4405f..398972a682504 100644
--- a/pandas/tests/arrays/period/test_arrow_compat.py
+++ b/pandas/tests/arrays/period/test_arrow_compat.py
@@ -100,6 +100,26 @@ def test_arrow_table_roundtrip():
tm.assert_frame_equal(result, expected)
+@pyarrow_skip
+def test_arrow_load_from_zero_chunks():
+ # GH-41040
+ import pyarrow as pa
+
+ from pandas.core.arrays._arrow_utils import ArrowPeriodType
+
+ arr = PeriodArray([], freq="D")
+ df = pd.DataFrame({"a": arr})
+
+ table = pa.table(df)
+ assert isinstance(table.field("a").type, ArrowPeriodType)
+ table = pa.table(
+ [pa.chunked_array([], type=table.column(0).type)], schema=table.schema
+ )
+ result = table.to_pandas()
+ assert isinstance(result["a"].dtype, PeriodDtype)
+ tm.assert_frame_equal(result, df)
+
+
@pyarrow_skip
def test_arrow_table_roundtrip_without_metadata():
import pyarrow as pa
diff --git a/pandas/tests/arrays/string_/test_string.py b/pandas/tests/arrays/string_/test_string.py
index 2b2db49c62ba2..e2d8e522abb35 100644
--- a/pandas/tests/arrays/string_/test_string.py
+++ b/pandas/tests/arrays/string_/test_string.py
@@ -476,6 +476,22 @@ def test_arrow_roundtrip(dtype, dtype_object):
assert result.loc[2, "a"] is pd.NA
+@td.skip_if_no("pyarrow", min_version="0.15.1.dev")
+def test_arrow_load_from_zero_chunks(dtype, dtype_object):
+ # GH-41040
+ import pyarrow as pa
+
+ data = pd.array([], dtype=dtype)
+ df = pd.DataFrame({"a": data})
+ table = pa.table(df)
+ assert table.field("a").type == "string"
+ # Instantiate the same table with no chunks at all
+ table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
+ result = table.to_pandas()
+ assert isinstance(result["a"].dtype, dtype_object)
+ tm.assert_frame_equal(result, df)
+
+
def test_value_counts_na(dtype):
arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype)
result = arr.value_counts(dropna=False)
| - [x] closes #41040
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/41052 | 2021-04-20T08:06:20Z | 2021-04-21T19:44:50Z | 2021-04-21T19:44:50Z | 2021-04-22T08:19:28Z |
PERF: optimize conversion from boolean Arrow array to masked BooleanArray | diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 85d9acff353be..74a302922c2aa 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -624,6 +624,7 @@ Performance improvements
- Performance improvement in :class:`Styler` where render times are more than 50% reduced (:issue:`39972` :issue:`39952`)
- Performance improvement in :meth:`core.window.ewm.ExponentialMovingWindow.mean` with ``times`` (:issue:`39784`)
- Performance improvement in :meth:`.GroupBy.apply` when requiring the python fallback implementation (:issue:`40176`)
+- Performance improvement in the conversion of pyarrow boolean array to a pandas nullable boolean array (:issue:`41051`)
- Performance improvement for concatenation of data with type :class:`CategoricalDtype` (:issue:`40193`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/arrays/boolean.py b/pandas/core/arrays/boolean.py
index 0a0bfccc0ea15..0b789693e7f74 100644
--- a/pandas/core/arrays/boolean.py
+++ b/pandas/core/arrays/boolean.py
@@ -114,6 +114,9 @@ def __from_arrow__(
"""
import pyarrow
+ if array.type != pyarrow.bool_():
+ raise TypeError(f"Expected array of boolean type, got {array.type} instead")
+
if isinstance(array, pyarrow.Array):
chunks = [array]
else:
@@ -122,8 +125,19 @@ def __from_arrow__(
results = []
for arr in chunks:
- # TODO should optimize this without going through object array
- bool_arr = BooleanArray._from_sequence(np.array(arr))
+ buflist = arr.buffers()
+ data = pyarrow.BooleanArray.from_buffers(
+ arr.type, len(arr), [None, buflist[1]], offset=arr.offset
+ ).to_numpy(zero_copy_only=False)
+ if arr.null_count != 0:
+ mask = pyarrow.BooleanArray.from_buffers(
+ arr.type, len(arr), [None, buflist[0]], offset=arr.offset
+ ).to_numpy(zero_copy_only=False)
+ mask = ~mask
+ else:
+ mask = np.zeros(len(arr), dtype=bool)
+
+ bool_arr = BooleanArray(data, mask)
results.append(bool_arr)
return BooleanArray._concat_same_type(results)
diff --git a/pandas/tests/arrays/masked/test_arrow_compat.py b/pandas/tests/arrays/masked/test_arrow_compat.py
index 8bb32dec2cc0e..a63f9b195d80c 100644
--- a/pandas/tests/arrays/masked/test_arrow_compat.py
+++ b/pandas/tests/arrays/masked/test_arrow_compat.py
@@ -55,12 +55,39 @@ def test_arrow_from_arrow_uint():
@td.skip_if_no("pyarrow", min_version="0.16.0")
-def test_arrow_sliced():
+def test_arrow_sliced(data):
# https://github.com/pandas-dev/pandas/issues/38525
import pyarrow as pa
- df = pd.DataFrame({"a": pd.array([0, None, 2, 3, None], dtype="Int64")})
+ df = pd.DataFrame({"a": data})
table = pa.table(df)
result = table.slice(2, None).to_pandas()
expected = df.iloc[2:].reset_index(drop=True)
tm.assert_frame_equal(result, expected)
+
+ # no missing values
+ df2 = df.fillna(data[0])
+ table = pa.table(df2)
+ result = table.slice(2, None).to_pandas()
+ expected = df2.iloc[2:].reset_index(drop=True)
+ tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow", min_version="0.16.0")
+def test_from_arrow_type_error(request, data):
+ # ensure that __from_arrow__ returns a TypeError when getting a wrong
+ # array type
+ import pyarrow as pa
+
+ if data.dtype != "boolean":
+ # TODO numeric dtypes cast any incoming array to the correct dtype
+ # instead of erroring
+ request.node.add_marker(
+ pytest.mark.xfail(reason="numeric dtypes don't error but cast")
+ )
+
+ arr = pa.array(data).cast("string")
+ with pytest.raises(TypeError, match=None):
+ # we don't test the exact error message, only the fact that it raises
+ # a TypeError is relevant
+ data.dtype.__from_arrow__(arr)
| For arrays without missing values it doesn't change, but gives a decent speed-up when having missing values:
```
In [1]: arr1 = pa.array([True, False, True, True, False]*100_000)
In [2]: arr2 = pa.array([True, False, None, True, False]*100_000)
In [3]: %timeit pd.BooleanDtype().__from_arrow__(arr1)
9.34 ms ± 27.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
9.31 ms ± 23.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
In [4]: %timeit pd.BooleanDtype().__from_arrow__(arr2)
60.6 ms ± 678 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
18.8 ms ± 99.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
cc @simonjayhawkins | https://api.github.com/repos/pandas-dev/pandas/pulls/41051 | 2021-04-20T07:52:10Z | 2021-04-21T16:09:14Z | 2021-04-21T16:09:14Z | 2021-04-21T16:09:18Z |
DOC: Improve to_datetime() documentation | diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 77bbde62d607e..d4c3760f3e629 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -851,8 +851,19 @@ def to_datetime(
>>> pd.to_datetime([1, 2, 3], unit='D',
... origin=pd.Timestamp('1960-01-01'))
- DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'], \
-dtype='datetime64[ns]', freq=None)
+ DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
+ dtype='datetime64[ns]', freq=None)
+
+ In case input is list-like and the elements of input are of mixed
+ timezones, return will have object type Index if utc=False.
+
+ >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'])
+ Index([2018-10-26 12:00:00-05:30, 2018-10-26 12:00:00-05:00], dtype='object')
+
+ >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
+ ... utc=True)
+ DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
+ dtype='datetime64[ns, UTC]', freq=None)
"""
if arg is None:
return None
| Returns Object Type Index when Mixed Timezones are used in list-like input to pandas.to_datetime()
- [x] closes #40808
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/41049 | 2021-04-20T03:26:33Z | 2021-04-21T16:24:18Z | 2021-04-21T16:24:18Z | 2021-04-21T16:24:23Z |
REF: remove no-op casting in groupby.generic | diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index a381a7bcb33f5..4a721ae0d4bf6 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -46,7 +46,6 @@
from pandas.core.dtypes.cast import (
find_common_type,
- maybe_cast_result_dtype,
maybe_downcast_numeric,
)
from pandas.core.dtypes.common import (
@@ -58,7 +57,6 @@
is_interval_dtype,
is_numeric_dtype,
is_scalar,
- needs_i8_conversion,
)
from pandas.core.dtypes.missing import (
isna,
@@ -1104,13 +1102,11 @@ def _cython_agg_manager(
using_array_manager = isinstance(data, ArrayManager)
- def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
+ def cast_agg_result(
+ result: ArrayLike, values: ArrayLike, how: str
+ ) -> ArrayLike:
# see if we can cast the values to the desired dtype
# this may not be the original dtype
- assert not isinstance(result, DataFrame)
-
- dtype = maybe_cast_result_dtype(values.dtype, how)
- result = maybe_downcast_numeric(result, dtype)
if isinstance(values, Categorical) and isinstance(result, np.ndarray):
# If the Categorical op didn't raise, it is dtype-preserving
@@ -1125,6 +1121,7 @@ def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
):
# We went through a SeriesGroupByPath and need to reshape
# GH#32223 includes case with IntegerArray values
+ # We only get here with values.dtype == object
result = result.reshape(1, -1)
# test_groupby_duplicate_columns gets here with
# result.dtype == int64, values.dtype=object, how="min"
@@ -1140,8 +1137,11 @@ def py_fallback(values: ArrayLike) -> ArrayLike:
# call our grouper again with only this block
if values.ndim == 1:
+ # We only get here with ExtensionArray
+
obj = Series(values)
else:
+ # We only get here with values.dtype == object
# TODO special case not needed with ArrayManager
obj = DataFrame(values.T)
if obj.shape[1] == 1:
@@ -1193,7 +1193,8 @@ def array_func(values: ArrayLike) -> ArrayLike:
result = py_fallback(values)
- return cast_agg_result(result, values, how)
+ return cast_agg_result(result, values, how)
+ return result
# TypeError -> we may have an exception in trying to aggregate
# continue and exclude the block
@@ -1366,11 +1367,7 @@ def _wrap_applied_output_series(
# if we have date/time like in the original, then coerce dates
# as we are stacking can easily have object dtypes here
- so = self._selected_obj
- if so.ndim == 2 and so.dtypes.apply(needs_i8_conversion).any():
- result = result._convert(datetime=True)
- else:
- result = result._convert(datetime=True)
+ result = result._convert(datetime=True)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
@@ -1507,7 +1504,7 @@ def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFram
try:
res_fast = fast_path(group)
except AssertionError:
- raise
+ raise # pragma: no cover
except Exception:
# GH#29631 For user-defined function, we can't predict what may be
# raised; see test_transform.test_transform_fastpath_raises
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 43db889618db6..f2fffe4c3741c 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1033,7 +1033,7 @@ def _cython_transform(
result = self.grouper._cython_operation(
"transform", obj._values, how, axis, **kwargs
)
- except NotImplementedError:
+ except (NotImplementedError, TypeError):
continue
key = base.OutputKey(label=name, position=idx)
diff --git a/pandas/core/groupby/ops.py b/pandas/core/groupby/ops.py
index 26bc9094fdef5..d9bf1adf74a5e 100644
--- a/pandas/core/groupby/ops.py
+++ b/pandas/core/groupby/ops.py
@@ -136,23 +136,17 @@ def _get_cython_function(
# see if there is a fused-type version of function
# only valid for numeric
- f = getattr(libgroupby, ftype, None)
- if f is not None:
- if is_numeric:
- return f
- elif dtype == object:
- if "object" not in f.__signatures__:
- # raise NotImplementedError here rather than TypeError later
- raise NotImplementedError(
- f"function is not implemented for this dtype: "
- f"[how->{how},dtype->{dtype_str}]"
- )
- return f
-
- raise NotImplementedError(
- f"function is not implemented for this dtype: "
- f"[how->{how},dtype->{dtype_str}]"
- )
+ f = getattr(libgroupby, ftype)
+ if is_numeric:
+ return f
+ elif dtype == object:
+ if "object" not in f.__signatures__:
+ # raise NotImplementedError here rather than TypeError later
+ raise NotImplementedError(
+ f"function is not implemented for this dtype: "
+ f"[how->{how},dtype->{dtype_str}]"
+ )
+ return f
def get_cython_func_and_vals(self, values: np.ndarray, is_numeric: bool):
"""
@@ -208,7 +202,14 @@ def disallow_invalid_ops(self, dtype: DtypeObj, is_numeric: bool = False):
# never an invalid op for those dtypes, so return early as fastpath
return
- if is_categorical_dtype(dtype) or is_sparse(dtype):
+ if is_categorical_dtype(dtype):
+ # NotImplementedError for methods that can fall back to a
+ # non-cython implementation.
+ if how in ["add", "prod", "cumsum", "cumprod"]:
+ raise TypeError(f"{dtype} type does not support {how} operations")
+ raise NotImplementedError(f"{dtype} dtype not supported")
+
+ elif is_sparse(dtype):
# categoricals are only 1d, so we
# are not setup for dim transforming
raise NotImplementedError(f"{dtype} dtype not supported")
@@ -216,14 +217,10 @@ def disallow_invalid_ops(self, dtype: DtypeObj, is_numeric: bool = False):
# we raise NotImplemented if this is an invalid operation
# entirely, e.g. adding datetimes
if how in ["add", "prod", "cumsum", "cumprod"]:
- raise NotImplementedError(
- f"datetime64 type does not support {how} operations"
- )
+ raise TypeError(f"datetime64 type does not support {how} operations")
elif is_timedelta64_dtype(dtype):
if how in ["prod", "cumprod"]:
- raise NotImplementedError(
- f"timedelta64 type does not support {how} operations"
- )
+ raise TypeError(f"timedelta64 type does not support {how} operations")
def get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape:
how = self.how
| Simplify _get_cython_function
Raise TypeError instead of NotImplementedError in cases where we shouldn't bother falling back to py_fallback | https://api.github.com/repos/pandas-dev/pandas/pulls/41048 | 2021-04-20T02:11:16Z | 2021-04-20T13:12:04Z | 2021-04-20T13:12:04Z | 2021-04-20T16:02:00Z |
BUG: Slice Arrow buffer before passing it to numpy (#40896) | diff --git a/doc/source/whatsnew/v1.3.0.rst b/doc/source/whatsnew/v1.3.0.rst
index 0e567972e7823..02d64e96e380b 100644
--- a/doc/source/whatsnew/v1.3.0.rst
+++ b/doc/source/whatsnew/v1.3.0.rst
@@ -796,6 +796,7 @@ I/O
- Bug in :func:`read_excel` dropping empty values from single-column spreadsheets (:issue:`39808`)
- Bug in :meth:`DataFrame.to_string` misplacing the truncation column when ``index=False`` (:issue:`40907`)
- Bug in :func:`read_orc` always raising ``AttributeError`` (:issue:`40918`)
+- Bug in the conversion from pyarrow to pandas (e.g. for reading Parquet) with nullable dtypes and a pyarrow array whose data buffer size is not a multiple of dtype size (:issue:`40896`)
Period
^^^^^^
diff --git a/pandas/core/arrays/_arrow_utils.py b/pandas/core/arrays/_arrow_utils.py
index 31f6896b12f98..51e5f36b88c79 100644
--- a/pandas/core/arrays/_arrow_utils.py
+++ b/pandas/core/arrays/_arrow_utils.py
@@ -14,6 +14,8 @@ def pyarrow_array_to_numpy_and_mask(arr, dtype):
Convert a primitive pyarrow.Array to a numpy array and boolean mask based
on the buffers of the Array.
+ At the moment pyarrow.BooleanArray is not supported.
+
Parameters
----------
arr : pyarrow.Array
@@ -25,8 +27,16 @@ def pyarrow_array_to_numpy_and_mask(arr, dtype):
Tuple of two numpy arrays with the raw data (with specified dtype) and
a boolean mask (validity mask, so False means missing)
"""
+ dtype = np.dtype(dtype)
+
buflist = arr.buffers()
- data = np.frombuffer(buflist[1], dtype=dtype)[arr.offset : arr.offset + len(arr)]
+ # Since Arrow buffers might contain padding and the data might be offset,
+ # the buffer gets sliced here before handing it to numpy.
+ # See also https://github.com/pandas-dev/pandas/issues/40896
+ offset = arr.offset * dtype.itemsize
+ length = len(arr) * dtype.itemsize
+ data_buf = buflist[1][offset : offset + length]
+ data = np.frombuffer(data_buf, dtype=dtype)
bitmask = buflist[0]
if bitmask is not None:
mask = pyarrow.BooleanArray.from_buffers(
diff --git a/pandas/tests/arrays/masked/test_arrow_compat.py b/pandas/tests/arrays/masked/test_arrow_compat.py
index 07a02c7152bce..d64dd6fa24d2c 100644
--- a/pandas/tests/arrays/masked/test_arrow_compat.py
+++ b/pandas/tests/arrays/masked/test_arrow_compat.py
@@ -1,3 +1,4 @@
+import numpy as np
import pytest
import pandas.util._test_decorators as td
@@ -5,6 +6,10 @@
import pandas as pd
import pandas._testing as tm
+pa = pytest.importorskip("pyarrow", minversion="0.15.0")
+
+from pandas.core.arrays._arrow_utils import pyarrow_array_to_numpy_and_mask
+
arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_EA_INT_DTYPES]
arrays += [pd.array([0.1, 0.2, 0.3, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES]
arrays += [pd.array([True, False, True, None], dtype="boolean")]
@@ -15,10 +20,8 @@ def data(request):
return request.param
-@td.skip_if_no("pyarrow", min_version="0.15.0")
def test_arrow_array(data):
# protocol added in 0.15.0
- import pyarrow as pa
arr = pa.array(data)
expected = pa.array(
@@ -31,7 +34,6 @@ def test_arrow_array(data):
@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_arrow_roundtrip(data):
# roundtrip possible from arrow 0.16.0
- import pyarrow as pa
df = pd.DataFrame({"a": data})
table = pa.table(df)
@@ -44,7 +46,6 @@ def test_arrow_roundtrip(data):
@td.skip_if_no("pyarrow", min_version="0.15.1.dev")
def test_arrow_load_from_zero_chunks(data):
# GH-41040
- import pyarrow as pa
df = pd.DataFrame({"a": data[0:0]})
table = pa.table(df)
@@ -61,7 +62,6 @@ def test_arrow_load_from_zero_chunks(data):
def test_arrow_from_arrow_uint():
# https://github.com/pandas-dev/pandas/issues/31896
# possible mismatch in types
- import pyarrow as pa
dtype = pd.UInt32Dtype()
result = dtype.__from_arrow__(pa.array([1, 2, 3, 4, None], type="int64"))
@@ -73,7 +73,6 @@ def test_arrow_from_arrow_uint():
@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_arrow_sliced(data):
# https://github.com/pandas-dev/pandas/issues/38525
- import pyarrow as pa
df = pd.DataFrame({"a": data})
table = pa.table(df)
@@ -89,12 +88,87 @@ def test_arrow_sliced(data):
tm.assert_frame_equal(result, expected)
+@pytest.fixture
+def np_dtype_to_arrays(any_real_dtype):
+ np_dtype = np.dtype(any_real_dtype)
+ pa_type = pa.from_numpy_dtype(np_dtype)
+
+ # None ensures the creation of a bitmask buffer.
+ pa_array = pa.array([0, 1, 2, None], type=pa_type)
+ # Since masked Arrow buffer slots are not required to contain a specific
+ # value, assert only the first three values of the created np.array
+ np_expected = np.array([0, 1, 2], dtype=np_dtype)
+ mask_expected = np.array([True, True, True, False])
+ return np_dtype, pa_array, np_expected, mask_expected
+
+
+def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
+ """
+ Test conversion from pyarrow array to numpy array.
+
+ Modifies the pyarrow buffer to contain padding and offset, which are
+ considered valid buffers by pyarrow.
+
+ Also tests empty pyarrow arrays with non empty buffers.
+ See https://github.com/pandas-dev/pandas/issues/40896
+ """
+ np_dtype, pa_array, np_expected, mask_expected = np_dtype_to_arrays
+ data, mask = pyarrow_array_to_numpy_and_mask(pa_array, np_dtype)
+ tm.assert_numpy_array_equal(data[:3], np_expected)
+ tm.assert_numpy_array_equal(mask, mask_expected)
+
+ mask_buffer = pa_array.buffers()[0]
+ data_buffer = pa_array.buffers()[1]
+ data_buffer_bytes = pa_array.buffers()[1].to_pybytes()
+
+ # Add trailing padding to the buffer.
+ data_buffer_trail = pa.py_buffer(data_buffer_bytes + b"\x00")
+ pa_array_trail = pa.Array.from_buffers(
+ type=pa_array.type,
+ length=len(pa_array),
+ buffers=[mask_buffer, data_buffer_trail],
+ offset=pa_array.offset,
+ )
+ pa_array_trail.validate()
+ data, mask = pyarrow_array_to_numpy_and_mask(pa_array_trail, np_dtype)
+ tm.assert_numpy_array_equal(data[:3], np_expected)
+ tm.assert_numpy_array_equal(mask, mask_expected)
+
+ # Add offset to the buffer.
+ offset = b"\x00" * (pa_array.type.bit_width // 8)
+ data_buffer_offset = pa.py_buffer(offset + data_buffer_bytes)
+ mask_buffer_offset = pa.py_buffer(b"\x0E")
+ pa_array_offset = pa.Array.from_buffers(
+ type=pa_array.type,
+ length=len(pa_array),
+ buffers=[mask_buffer_offset, data_buffer_offset],
+ offset=pa_array.offset + 1,
+ )
+ pa_array_offset.validate()
+ data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
+ tm.assert_numpy_array_equal(data[:3], np_expected)
+ tm.assert_numpy_array_equal(mask, mask_expected)
+
+ # Empty array
+ np_expected_empty = np.array([], dtype=np_dtype)
+ mask_expected_empty = np.array([], dtype=np.bool_)
+
+ pa_array_offset = pa.Array.from_buffers(
+ type=pa_array.type,
+ length=0,
+ buffers=[mask_buffer, data_buffer],
+ offset=pa_array.offset,
+ )
+ pa_array_offset.validate()
+ data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype)
+ tm.assert_numpy_array_equal(data[:3], np_expected_empty)
+ tm.assert_numpy_array_equal(mask, mask_expected_empty)
+
+
@td.skip_if_no("pyarrow", min_version="0.16.0")
def test_from_arrow_type_error(request, data):
# ensure that __from_arrow__ returns a TypeError when getting a wrong
# array type
- import pyarrow as pa
-
if data.dtype != "boolean":
# TODO numeric dtypes cast any incoming array to the correct dtype
# instead of erroring
| Add Arrow buffer slicing before handing it over to numpy which is
needed in case the Arrow buffer contains padding or offset.
- [x] closes #40896
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Points I'm not sure about:
- I created a new test file. Would it be better to place the test somewhere else?
- The test only tests int32. Should I add more dtypes?
I'm still new to the internals of pandas and always trying to improve so feedback of any kind is highly appreciated! | https://api.github.com/repos/pandas-dev/pandas/pulls/41046 | 2021-04-19T18:14:00Z | 2021-04-28T13:07:33Z | 2021-04-28T13:07:33Z | 2021-04-28T13:07:44Z |
PERF: implement get_slice in cython | diff --git a/pandas/_libs/internals.pyi b/pandas/_libs/internals.pyi
index a46a1747d1d8d..f3436e9c7afba 100644
--- a/pandas/_libs/internals.pyi
+++ b/pandas/_libs/internals.pyi
@@ -79,3 +79,5 @@ class BlockManager:
_blklocs: np.ndarray
def __init__(self, blocks: tuple[B, ...], axes: list[Index], verify_integrity=True): ...
+
+ def get_slice(self: T, slobj: slice, axis: int=...) -> T: ...
diff --git a/pandas/_libs/internals.pyx b/pandas/_libs/internals.pyx
index 3fd580684a6a2..f3bc70ad8a26b 100644
--- a/pandas/_libs/internals.pyx
+++ b/pandas/_libs/internals.pyx
@@ -515,7 +515,7 @@ cdef class NumpyBlock(SharedBlock):
self.values = values
# @final # not useful in cython, but we _would_ annotate with @final
- def getitem_block_index(self, slicer: slice) -> NumpyBlock:
+ cpdef NumpyBlock getitem_block_index(self, slice slicer):
"""
Perform __getitem__-like specialized to slicing along index.
@@ -610,3 +610,30 @@ cdef class BlockManager:
self._rebuild_blknos_and_blklocs()
# -------------------------------------------------------------------
+ # Indexing
+
+ cdef BlockManager _get_index_slice(self, slobj):
+ cdef:
+ SharedBlock blk, nb
+
+ nbs = []
+ for blk in self.blocks:
+ nb = blk.getitem_block_index(slobj)
+ nbs.append(nb)
+
+ new_axes = [self.axes[0], self.axes[1]._getitem_slice(slobj)]
+ return type(self)(tuple(nbs), new_axes, verify_integrity=False)
+
+ def get_slice(self, slobj: slice, axis: int = 0) -> BlockManager:
+
+ if axis == 0:
+ new_blocks = self._slice_take_blocks_ax0(slobj)
+ elif axis == 1:
+ return self._get_index_slice(slobj)
+ else:
+ raise IndexError("Requested axis not found in manager")
+
+ new_axes = list(self.axes)
+ new_axes[axis] = new_axes[axis]._getitem_slice(slobj)
+
+ return type(self)(tuple(new_blocks), new_axes, verify_integrity=False)
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index 373d3566e1e8a..97d605e2fa2d1 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1116,21 +1116,6 @@ def fast_xs(self, loc: int) -> ArrayLike:
return result
- def get_slice(self, slobj: slice, axis: int = 0) -> BlockManager:
- assert isinstance(slobj, slice), type(slobj)
-
- if axis == 0:
- new_blocks = self._slice_take_blocks_ax0(slobj)
- elif axis == 1:
- new_blocks = [blk.getitem_block_index(slobj) for blk in self.blocks]
- else:
- raise IndexError("Requested axis not found in manager")
-
- new_axes = list(self.axes)
- new_axes[axis] = new_axes[axis]._getitem_slice(slobj)
-
- return type(self)(tuple(new_blocks), new_axes, verify_integrity=False)
-
def iget(self, i: int) -> SingleBlockManager:
"""
Return the data as a SingleBlockManager.
| I'm seeing a 13-19% improvement in the motivating benchmark https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422 | https://api.github.com/repos/pandas-dev/pandas/pulls/41045 | 2021-04-19T17:38:19Z | 2021-04-19T23:21:23Z | 2021-04-19T23:21:23Z | 2021-04-19T23:24:42Z |
ENH: option to export df to Stata dataset with value labels | diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index fee8334940a16..9166b28af4e78 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -101,6 +101,7 @@ Other enhancements
- :meth:`Series.ewm`, :meth:`DataFrame.ewm`, now support a ``method`` argument with a ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`. See :ref:`Window Overview <window.overview>` for performance and functional benefits (:issue:`42273`)
- :meth:`.GroupBy.cummin` and :meth:`.GroupBy.cummax` now support the argument ``skipna`` (:issue:`34047`)
- :meth:`read_table` now supports the argument ``storage_options`` (:issue:`39167`)
+- :meth:`DataFrame.to_stata` and :meth:`StataWriter` now accept the keyword only argument ``value_labels`` to save labels for non-categorical columns
- Methods that relied on hashmap based algos such as :meth:`DataFrameGroupBy.value_counts`, :meth:`DataFrameGroupBy.count` and :func:`factorize` ignored imaginary component for complex numbers (:issue:`17927`)
.. ---------------------------------------------------------------------------
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index db12129a15ef9..c1a3f4d9298b4 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -2382,6 +2382,8 @@ def to_stata(
convert_strl: Sequence[Hashable] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
+ *,
+ value_labels: dict[Hashable, dict[float | int, str]] | None = None,
) -> None:
"""
Export DataFrame object to Stata dta format.
@@ -2463,6 +2465,13 @@ def to_stata(
.. versionadded:: 1.2.0
+ value_labels : dict of dicts
+ Dictionary containing columns as keys and dictionaries of column value
+ to labels as values. Labels for a single variable must be 32,000
+ characters or smaller.
+
+ .. versionadded:: 1.4.0
+
Raises
------
NotImplementedError
@@ -2524,6 +2533,7 @@ def to_stata(
variable_labels=variable_labels,
compression=compression,
storage_options=storage_options,
+ value_labels=value_labels,
**kwargs,
)
writer.write_file()
diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 1deaa634ce3ae..11b9e8f7009c4 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -18,6 +18,7 @@
import struct
import sys
from typing import (
+ TYPE_CHECKING,
Any,
AnyStr,
Hashable,
@@ -46,6 +47,7 @@
ensure_object,
is_categorical_dtype,
is_datetime64_dtype,
+ is_numeric_dtype,
)
from pandas import (
@@ -64,6 +66,9 @@
from pandas.io.common import get_handle
+if TYPE_CHECKING:
+ from typing import Literal
+
_version_error = (
"Version of given Stata file is {version}. pandas supports importing "
"versions 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), "
@@ -658,24 +663,37 @@ def __init__(self, catarray: Series, encoding: str = "latin-1"):
self.labname = catarray.name
self._encoding = encoding
categories = catarray.cat.categories
- self.value_labels = list(zip(np.arange(len(categories)), categories))
+ self.value_labels: list[tuple[int | float, str]] = list(
+ zip(np.arange(len(categories)), categories)
+ )
self.value_labels.sort(key=lambda x: x[0])
+
+ self._prepare_value_labels()
+
+ def _prepare_value_labels(self):
+ """Encode value labels."""
+
self.text_len = 0
self.txt: list[bytes] = []
self.n = 0
+ # Offsets (length of categories), converted to int32
+ self.off = np.array([])
+ # Values, converted to int32
+ self.val = np.array([])
+ self.len = 0
# Compute lengths and setup lists of offsets and labels
offsets: list[int] = []
- values: list[int] = []
+ values: list[int | float] = []
for vl in self.value_labels:
- category = vl[1]
+ category: str | bytes = vl[1]
if not isinstance(category, str):
category = str(category)
warnings.warn(
- value_label_mismatch_doc.format(catarray.name),
+ value_label_mismatch_doc.format(self.labname),
ValueLabelTypeMismatch,
)
- category = category.encode(encoding)
+ category = category.encode(self._encoding)
offsets.append(self.text_len)
self.text_len += len(category) + 1 # +1 for the padding
values.append(vl[0])
@@ -748,6 +766,38 @@ def generate_value_label(self, byteorder: str) -> bytes:
return bio.getvalue()
+class StataNonCatValueLabel(StataValueLabel):
+ """
+ Prepare formatted version of value labels
+
+ Parameters
+ ----------
+ labname : str
+ Value label name
+ value_labels: Dictionary
+ Mapping of values to labels
+ encoding : {"latin-1", "utf-8"}
+ Encoding to use for value labels.
+ """
+
+ def __init__(
+ self,
+ labname: str,
+ value_labels: dict[float | int, str],
+ encoding: Literal["latin-1", "utf-8"] = "latin-1",
+ ):
+
+ if encoding not in ("latin-1", "utf-8"):
+ raise ValueError("Only latin-1 and utf-8 are supported.")
+
+ self.labname = labname
+ self._encoding = encoding
+ self.value_labels: list[tuple[int | float, str]] = sorted(
+ value_labels.items(), key=lambda x: x[0]
+ )
+ self._prepare_value_labels()
+
+
class StataMissingValue:
"""
An observation's missing value.
@@ -2175,6 +2225,13 @@ class StataWriter(StataParser):
.. versionadded:: 1.2.0
+ value_labels : dict of dicts
+ Dictionary containing columns as keys and dictionaries of column value
+ to labels as values. The combined length of all labels for a single
+ variable must be 32,000 characters or smaller.
+
+ .. versionadded:: 1.4.0
+
Returns
-------
writer : StataWriter instance
@@ -2225,15 +2282,22 @@ def __init__(
variable_labels: dict[Hashable, str] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
+ *,
+ value_labels: dict[Hashable, dict[float | int, str]] | None = None,
):
super().__init__()
+ self.data = data
self._convert_dates = {} if convert_dates is None else convert_dates
self._write_index = write_index
self._time_stamp = time_stamp
self._data_label = data_label
self._variable_labels = variable_labels
+ self._non_cat_value_labels = value_labels
+ self._value_labels: list[StataValueLabel] = []
+ self._has_value_labels = np.array([], dtype=bool)
self._compression = compression
self._output_file: Buffer | None = None
+ self._converted_names: dict[Hashable, str] = {}
# attach nobs, nvars, data, varlist, typlist
self._prepare_pandas(data)
self.storage_options = storage_options
@@ -2243,7 +2307,6 @@ def __init__(
self._byteorder = _set_endianness(byteorder)
self._fname = fname
self.type_converters = {253: np.int32, 252: np.int16, 251: np.int8}
- self._converted_names: dict[Hashable, str] = {}
def _write(self, to_write: str) -> None:
"""
@@ -2259,17 +2322,50 @@ def _write_bytes(self, value: bytes) -> None:
"""
self.handles.handle.write(value) # type: ignore[arg-type]
+ def _prepare_non_cat_value_labels(
+ self, data: DataFrame
+ ) -> list[StataNonCatValueLabel]:
+ """
+ Check for value labels provided for non-categorical columns. Value
+ labels
+ """
+ non_cat_value_labels: list[StataNonCatValueLabel] = []
+ if self._non_cat_value_labels is None:
+ return non_cat_value_labels
+
+ for labname, labels in self._non_cat_value_labels.items():
+ if labname in self._converted_names:
+ colname = self._converted_names[labname]
+ elif labname in data.columns:
+ colname = str(labname)
+ else:
+ raise KeyError(
+ f"Can't create value labels for {labname}, it wasn't "
+ "found in the dataset."
+ )
+
+ if not is_numeric_dtype(data[colname].dtype):
+ # Labels should not be passed explicitly for categorical
+ # columns that will be converted to int
+ raise ValueError(
+ f"Can't create value labels for {labname}, value labels "
+ "can only be applied to numeric columns."
+ )
+ svl = StataNonCatValueLabel(colname, labels)
+ non_cat_value_labels.append(svl)
+ return non_cat_value_labels
+
def _prepare_categoricals(self, data: DataFrame) -> DataFrame:
"""
Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int
"""
is_cat = [is_categorical_dtype(data[col].dtype) for col in data]
- self._is_col_cat = is_cat
- self._value_labels: list[StataValueLabel] = []
if not any(is_cat):
return data
+ self._has_value_labels |= np.array(is_cat)
+
get_base_missing_value = StataMissingValue.get_base_missing_value
data_formatted = []
for col, col_is_cat in zip(data, is_cat):
@@ -2449,6 +2545,17 @@ def _prepare_pandas(self, data: DataFrame) -> None:
# Replace NaNs with Stata missing values
data = self._replace_nans(data)
+ # Set all columns to initially unlabelled
+ self._has_value_labels = np.repeat(False, data.shape[1])
+
+ # Create value labels for non-categorical data
+ non_cat_value_labels = self._prepare_non_cat_value_labels(data)
+
+ non_cat_columns = [svl.labname for svl in non_cat_value_labels]
+ has_non_cat_val_labels = data.columns.isin(non_cat_columns)
+ self._has_value_labels |= has_non_cat_val_labels
+ self._value_labels.extend(non_cat_value_labels)
+
# Convert categoricals to int data, and strip labels
data = self._prepare_categoricals(data)
@@ -2688,7 +2795,7 @@ def _write_value_label_names(self) -> None:
# lbllist, 33*nvar, char array
for i in range(self.nvar):
# Use variable name when categorical
- if self._is_col_cat[i]:
+ if self._has_value_labels[i]:
name = self.varlist[i]
name = self._null_terminate_str(name)
name = _pad_bytes(name[:32], 33)
@@ -3059,6 +3166,13 @@ class StataWriter117(StataWriter):
.. versionadded:: 1.1.0
+ value_labels : dict of dicts
+ Dictionary containing columns as keys and dictionaries of column value
+ to labels as values. The combined length of all labels for a single
+ variable must be 32,000 characters or smaller.
+
+ .. versionadded:: 1.4.0
+
Returns
-------
writer : StataWriter117 instance
@@ -3112,6 +3226,8 @@ def __init__(
convert_strl: Sequence[Hashable] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
+ *,
+ value_labels: dict[Hashable, dict[float | int, str]] | None = None,
):
# Copy to new list since convert_strl might be modified later
self._convert_strl: list[Hashable] = []
@@ -3127,6 +3243,7 @@ def __init__(
time_stamp=time_stamp,
data_label=data_label,
variable_labels=variable_labels,
+ value_labels=value_labels,
compression=compression,
storage_options=storage_options,
)
@@ -3272,7 +3389,7 @@ def _write_value_label_names(self) -> None:
for i in range(self.nvar):
# Use variable name when categorical
name = "" # default name
- if self._is_col_cat[i]:
+ if self._has_value_labels[i]:
name = self.varlist[i]
name = self._null_terminate_str(name)
encoded_name = _pad_bytes_new(name[:32].encode(self._encoding), vl_len + 1)
@@ -3449,6 +3566,13 @@ class StataWriterUTF8(StataWriter117):
.. versionadded:: 1.1.0
+ value_labels : dict of dicts
+ Dictionary containing columns as keys and dictionaries of column value
+ to labels as values. The combined length of all labels for a single
+ variable must be 32,000 characters or smaller.
+
+ .. versionadded:: 1.4.0
+
Returns
-------
StataWriterUTF8
@@ -3505,6 +3629,8 @@ def __init__(
version: int | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
+ *,
+ value_labels: dict[Hashable, dict[float | int, str]] | None = None,
):
if version is None:
version = 118 if data.shape[1] <= 32767 else 119
@@ -3525,6 +3651,7 @@ def __init__(
time_stamp=time_stamp,
data_label=data_label,
variable_labels=variable_labels,
+ value_labels=value_labels,
convert_strl=convert_strl,
compression=compression,
storage_options=storage_options,
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index 6bf8d23f61937..02cf478c61583 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -29,6 +29,7 @@
PossiblePrecisionLoss,
StataMissingValue,
StataReader,
+ StataWriter,
StataWriterUTF8,
ValueLabelTypeMismatch,
read_stata,
@@ -2048,3 +2049,116 @@ def test_stata_compression(compression_only, read_infer, to_infer):
df.to_stata(path, compression=to_compression)
result = read_stata(path, compression=read_compression, index_col="index")
tm.assert_frame_equal(result, df)
+
+
+def test_non_categorical_value_labels():
+ data = DataFrame(
+ {
+ "fully_labelled": [1, 2, 3, 3, 1],
+ "partially_labelled": [1.0, 2.0, np.nan, 9.0, np.nan],
+ "Y": [7, 7, 9, 8, 10],
+ "Z": pd.Categorical(["j", "k", "l", "k", "j"]),
+ }
+ )
+
+ with tm.ensure_clean() as path:
+ value_labels = {
+ "fully_labelled": {1: "one", 2: "two", 3: "three"},
+ "partially_labelled": {1.0: "one", 2.0: "two"},
+ }
+ expected = {**value_labels, "Z": {0: "j", 1: "k", 2: "l"}}
+
+ writer = StataWriter(path, data, value_labels=value_labels)
+ writer.write_file()
+
+ reader = StataReader(path)
+ reader_value_labels = reader.value_labels()
+ assert reader_value_labels == expected
+
+ msg = "Can't create value labels for notY, it wasn't found in the dataset."
+ with pytest.raises(KeyError, match=msg):
+ value_labels = {"notY": {7: "label1", 8: "label2"}}
+ writer = StataWriter(path, data, value_labels=value_labels)
+
+ msg = (
+ "Can't create value labels for Z, value labels "
+ "can only be applied to numeric columns."
+ )
+ with pytest.raises(ValueError, match=msg):
+ value_labels = {"Z": {1: "a", 2: "k", 3: "j", 4: "i"}}
+ writer = StataWriter(path, data, value_labels=value_labels)
+
+
+def test_non_categorical_value_label_name_conversion():
+ # Check conversion of invalid variable names
+ data = DataFrame(
+ {
+ "invalid~!": [1, 1, 2, 3, 5, 8], # Only alphanumeric and _
+ "6_invalid": [1, 1, 2, 3, 5, 8], # Must start with letter or _
+ "invalid_name_longer_than_32_characters": [8, 8, 9, 9, 8, 8], # Too long
+ "aggregate": [2, 5, 5, 6, 6, 9], # Reserved words
+ (1, 2): [1, 2, 3, 4, 5, 6], # Hashable non-string
+ }
+ )
+
+ value_labels = {
+ "invalid~!": {1: "label1", 2: "label2"},
+ "6_invalid": {1: "label1", 2: "label2"},
+ "invalid_name_longer_than_32_characters": {8: "eight", 9: "nine"},
+ "aggregate": {5: "five"},
+ (1, 2): {3: "three"},
+ }
+
+ expected = {
+ "invalid__": {1: "label1", 2: "label2"},
+ "_6_invalid": {1: "label1", 2: "label2"},
+ "invalid_name_longer_than_32_char": {8: "eight", 9: "nine"},
+ "_aggregate": {5: "five"},
+ "_1__2_": {3: "three"},
+ }
+
+ with tm.ensure_clean() as path:
+ with tm.assert_produces_warning(InvalidColumnName):
+ data.to_stata(path, value_labels=value_labels)
+
+ reader = StataReader(path)
+ reader_value_labels = reader.value_labels()
+ assert reader_value_labels == expected
+
+
+def test_non_categorical_value_label_convert_categoricals_error():
+ # Mapping more than one value to the same label is valid for Stata
+ # labels, but can't be read with convert_categoricals=True
+ value_labels = {
+ "repeated_labels": {10: "Ten", 20: "More than ten", 40: "More than ten"}
+ }
+
+ data = DataFrame(
+ {
+ "repeated_labels": [10, 10, 20, 20, 40, 40],
+ }
+ )
+
+ with tm.ensure_clean() as path:
+ data.to_stata(path, value_labels=value_labels)
+
+ reader = StataReader(path, convert_categoricals=False)
+ reader_value_labels = reader.value_labels()
+ assert reader_value_labels == value_labels
+
+ col = "repeated_labels"
+ repeats = "-" * 80 + "\n" + "\n".join(["More than ten"])
+
+ msg = f"""
+Value labels for column {col} are not unique. These cannot be converted to
+pandas categoricals.
+
+Either read the file with `convert_categoricals` set to False or use the
+low level interface in `StataReader` to separately read the values and the
+value_labels.
+
+The repeated labels are:
+{repeats}
+"""
+ with pytest.raises(ValueError, match=msg):
+ read_stata(path, convert_categoricals=True)
| GH38454
- [x] closes #38454
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry | https://api.github.com/repos/pandas-dev/pandas/pulls/41042 | 2021-04-19T15:17:22Z | 2021-09-10T08:49:20Z | 2021-09-10T08:49:19Z | 2021-09-13T14:00:22Z |
TST: remove __main__ from all test files | diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py
index 410d70c65404f..f925fd792f9ca 100644
--- a/pandas/api/tests/test_api.py
+++ b/pandas/api/tests/test_api.py
@@ -227,8 +227,3 @@ def test_deprecation_access_obj(self):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
pd.datetools.monthEnd
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/computation/tests/test_compat.py b/pandas/computation/tests/test_compat.py
index 8e8924379f153..900dd2c28b4c5 100644
--- a/pandas/computation/tests/test_compat.py
+++ b/pandas/computation/tests/test_compat.py
@@ -61,8 +61,3 @@ def testit():
testit()
else:
testit()
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 3a446bfc36c21..dbac72c619a52 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_eval.py
@@ -1977,8 +1977,3 @@ def test_validate_bool_args(self):
for value in invalid_values:
with self.assertRaises(ValueError):
pd.eval("2+2", inplace=value)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index cf306034001db..cc81c66100a6f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -5762,8 +5762,3 @@ def boxplot(self, column=None, by=None, ax=None, fontsize=None, rot=0,
ops.add_flex_arithmetic_methods(DataFrame, **ops.frame_flex_funcs)
ops.add_special_arithmetic_methods(DataFrame, **ops.frame_special_funcs)
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/formats/format.py b/pandas/formats/format.py
index adfb54c02d926..3bac7d2821760 100644
--- a/pandas/formats/format.py
+++ b/pandas/formats/format.py
@@ -2679,17 +2679,3 @@ def _binify(cols, line_width):
bins.append(len(cols))
return bins
-
-
-if __name__ == '__main__':
- arr = np.array([746.03, 0.00, 5620.00, 1592.36])
- # arr = np.array([11111111.1, 1.55])
- # arr = [314200.0034, 1.4125678]
- arr = np.array(
- [327763.3119, 345040.9076, 364460.9915, 398226.8688, 383800.5172,
- 433442.9262, 539415.0568, 568590.4108, 599502.4276, 620921.8593,
- 620898.5294, 552427.1093, 555221.2193, 519639.7059, 388175.7,
- 379199.5854, 614898.25, 504833.3333, 560600., 941214.2857, 1134250.,
- 1219550., 855736.85, 1042615.4286, 722621.3043, 698167.1818, 803750.])
- fmt = FloatArrayFormatter(arr, digits=7)
- print(fmt.get_result())
diff --git a/pandas/io/tests/json/test_normalize.py b/pandas/io/tests/json/test_normalize.py
index e5aba43648d0c..c60b81ffe504d 100644
--- a/pandas/io/tests/json/test_normalize.py
+++ b/pandas/io/tests/json/test_normalize.py
@@ -1,5 +1,3 @@
-import nose
-
from pandas import DataFrame
import numpy as np
import json
@@ -283,8 +281,3 @@ def test_json_normalize_errors(self):
['general', 'trade_version']],
errors='raise'
)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb',
- '--pdb-failure', '-s'], exit=False)
diff --git a/pandas/io/tests/json/test_pandas.py b/pandas/io/tests/json/test_pandas.py
index 345d181a0e53a..ee5039c38b182 100644
--- a/pandas/io/tests/json/test_pandas.py
+++ b/pandas/io/tests/json/test_pandas.py
@@ -1044,8 +1044,3 @@ def roundtrip(s, encoding='latin-1'):
for s in examples:
roundtrip(s)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb',
- '--pdb-failure', '-s'], exit=False)
diff --git a/pandas/io/tests/json/test_ujson.py b/pandas/io/tests/json/test_ujson.py
index 704023bd847b7..3da61b7696fdc 100644
--- a/pandas/io/tests/json/test_ujson.py
+++ b/pandas/io/tests/json/test_ujson.py
@@ -1611,8 +1611,3 @@ def test_encodeSet(self):
def _clean_dict(d):
return dict((str(k), v) for k, v in compat.iteritems(d))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py
index d84c2ae3beb0c..e06f94c780c8b 100644
--- a/pandas/io/tests/parser/test_network.py
+++ b/pandas/io/tests/parser/test_network.py
@@ -182,7 +182,3 @@ def test_s3_fails(self):
# It's irrelevant here that this isn't actually a table.
with tm.assertRaises(IOError):
read_csv('s3://cant_get_it/')
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/parser/test_parsers.py b/pandas/io/tests/parser/test_parsers.py
index a90f546d37fc8..93b5fdcffed4c 100644
--- a/pandas/io/tests/parser/test_parsers.py
+++ b/pandas/io/tests/parser/test_parsers.py
@@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
import os
-import nose
import pandas.util.testing as tm
@@ -99,7 +98,3 @@ def read_table(self, *args, **kwds):
kwds = kwds.copy()
kwds['engine'] = self.engine
return read_table(*args, **kwds)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/parser/test_textreader.py b/pandas/io/tests/parser/test_textreader.py
index 98cb09cd85480..0e91ca806e8fe 100644
--- a/pandas/io/tests/parser/test_textreader.py
+++ b/pandas/io/tests/parser/test_textreader.py
@@ -10,7 +10,6 @@
import os
import sys
-import nose
from numpy import nan
import numpy as np
@@ -402,7 +401,3 @@ def test_empty_csv_input(self):
def assert_array_dicts_equal(left, right):
for k, v in compat.iteritems(left):
assert(np.array_equal(v, right[k]))
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/parser/test_unsupported.py b/pandas/io/tests/parser/test_unsupported.py
index 4d93df16a0279..e941c9186cd6a 100644
--- a/pandas/io/tests/parser/test_unsupported.py
+++ b/pandas/io/tests/parser/test_unsupported.py
@@ -9,8 +9,6 @@
test suite as new feature support is added to the parsers.
"""
-import nose
-
import pandas.io.parsers as parsers
import pandas.util.testing as tm
@@ -142,7 +140,3 @@ def test_deprecated_args(self):
kwargs = {arg: non_default_val}
read_csv(StringIO(data), engine=engine,
**kwargs)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_date_converters.py b/pandas/io/tests/test_date_converters.py
index 99abbacb604fa..5b54925c65fbd 100644
--- a/pandas/io/tests/test_date_converters.py
+++ b/pandas/io/tests/test_date_converters.py
@@ -1,8 +1,6 @@
from pandas.compat import StringIO
from datetime import date, datetime
-import nose
-
import numpy as np
from pandas import DataFrame, MultiIndex
@@ -150,7 +148,3 @@ def test_parse_date_column_with_empty_string(self):
[621, ' ']]
expected = DataFrame(expected_data, columns=['case', 'opdate'])
assert_frame_equal(result, expected)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index 12aecfd50c3a6..2791e397d5b86 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -2325,8 +2325,3 @@ def check_called(func):
check_called(lambda: panel.to_excel('something.test'))
check_called(lambda: df.to_excel('something.xlsx'))
check_called(lambda: df.to_excel('something.xls', engine='dummy'))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_feather.py b/pandas/io/tests/test_feather.py
index b8b85d7dbbece..dcb057ec30004 100644
--- a/pandas/io/tests/test_feather.py
+++ b/pandas/io/tests/test_feather.py
@@ -116,8 +116,3 @@ def test_write_with_index(self):
df.index = [0, 1, 2]
df.columns = pd.MultiIndex.from_tuples([('a', 1), ('a', 2), ('b', 1)]),
self.check_error_on_write(df, ValueError)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_gbq.py b/pandas/io/tests/test_gbq.py
index 8a414dcd3ba4f..ac481a44de5e8 100644
--- a/pandas/io/tests/test_gbq.py
+++ b/pandas/io/tests/test_gbq.py
@@ -1224,7 +1224,3 @@ def test_upload_data_as_service_account_with_key_contents(self):
project_id=_get_project_id(),
private_key=_get_private_key_contents())
self.assertEqual(result['NUM_ROWS'][0], test_size)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_html.py b/pandas/io/tests/test_html.py
index 9ac8def3a074d..356adb92829c6 100644
--- a/pandas/io/tests/test_html.py
+++ b/pandas/io/tests/test_html.py
@@ -918,7 +918,3 @@ def test_same_ordering():
dfs_lxml = read_html(filename, index_col=0, flavor=['lxml'])
dfs_bs4 = read_html(filename, index_col=0, flavor=['bs4'])
assert_framelist_equal(dfs_lxml, dfs_bs4)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_pickle.py b/pandas/io/tests/test_pickle.py
index a49f50b1bcb9f..b5c316b326b8d 100644
--- a/pandas/io/tests/test_pickle.py
+++ b/pandas/io/tests/test_pickle.py
@@ -283,9 +283,3 @@ def test_pickle_v0_15_2(self):
# with open(pickle_path, 'wb') as f: pickle.dump(cat, f)
#
tm.assert_categorical_equal(cat, pd.read_pickle(pickle_path))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core'],
- exit=False)
diff --git a/pandas/io/tests/test_pytables.py b/pandas/io/tests/test_pytables.py
index 40db10c42d5a7..f4f03856f94e2 100644
--- a/pandas/io/tests/test_pytables.py
+++ b/pandas/io/tests/test_pytables.py
@@ -5516,9 +5516,3 @@ def _test_sort(obj):
return obj.reindex(major=sorted(obj.major_axis))
else:
raise ValueError('type not supported here')
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_s3.py b/pandas/io/tests/test_s3.py
index 8058698a906ea..2983fa647445c 100644
--- a/pandas/io/tests/test_s3.py
+++ b/pandas/io/tests/test_s3.py
@@ -1,14 +1,10 @@
-import nose
from pandas.util import testing as tm
from pandas.io.common import _is_s3_url
class TestS3URL(tm.TestCase):
+
def test_is_s3_url(self):
self.assertTrue(_is_s3_url("s3://pandas/somethingelse.com"))
self.assertFalse(_is_s3_url("s4://pandas/somethingelse.com"))
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_sql.py b/pandas/io/tests/test_sql.py
index 9e639f7ef6057..4bcde764001c1 100644
--- a/pandas/io/tests/test_sql.py
+++ b/pandas/io/tests/test_sql.py
@@ -2658,8 +2658,3 @@ def clean_up(test_table_to_drop):
self.assertEqual(tquery(sql_select, con=self.conn),
[(1, 'A'), (2, 'B'), (3, 'C'), (4, 'D'), (5, 'E')])
clean_up(table_name)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/io/tests/test_stata.py b/pandas/io/tests/test_stata.py
index 8cfd5d98fe05f..fcb935925e61f 100644
--- a/pandas/io/tests/test_stata.py
+++ b/pandas/io/tests/test_stata.py
@@ -1276,8 +1276,3 @@ def test_out_of_range_float(self):
original.to_stata(path)
tm.assertTrue('ColumnTooBig' in cm.exception)
tm.assertTrue('infinity' in cm.exception)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_array.py b/pandas/sparse/tests/test_array.py
index 592926f8e821d..55f292a8a231a 100644
--- a/pandas/sparse/tests/test_array.py
+++ b/pandas/sparse/tests/test_array.py
@@ -810,9 +810,3 @@ def test_ufunc_args(self):
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
result = SparseArray([2, 0, 1, -1], fill_value=1)
tm.assert_sp_array_equal(np.add(sparse, 1), result)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_combine_concat.py b/pandas/sparse/tests/test_combine_concat.py
index fcdc6d9580dd5..5240d592810ad 100644
--- a/pandas/sparse/tests/test_combine_concat.py
+++ b/pandas/sparse/tests/test_combine_concat.py
@@ -1,6 +1,5 @@
# pylint: disable-msg=E1101,W0612
-import nose # noqa
import numpy as np
import pandas as pd
import pandas.util.testing as tm
@@ -356,9 +355,3 @@ def test_concat_sparse_dense(self):
exp = pd.concat([self.dense1, self.dense3], axis=1)
self.assertIsInstance(res, pd.SparseDataFrame)
tm.assert_frame_equal(res, exp)
-
-
-if __name__ == '__main__':
- import nose # noqa
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_frame.py b/pandas/sparse/tests/test_frame.py
index 23bb827974c61..e26c0ed1afe58 100644
--- a/pandas/sparse/tests/test_frame.py
+++ b/pandas/sparse/tests/test_frame.py
@@ -1193,8 +1193,3 @@ def test_numpy_func_call(self):
'std', 'min', 'max']
for func in funcs:
getattr(np, func)(self.frame)
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_libsparse.py b/pandas/sparse/tests/test_libsparse.py
index c289b4a1b204f..b3aa3368e9455 100644
--- a/pandas/sparse/tests/test_libsparse.py
+++ b/pandas/sparse/tests/test_libsparse.py
@@ -1,6 +1,6 @@
from pandas import Series
-import nose # noqa
+import nose
import numpy as np
import operator
import pandas.util.testing as tm
@@ -196,7 +196,7 @@ def _check_correct(a, b, expected):
assert (result.equals(expected))
def _check_length_exc(a, longer):
- nose.tools.assert_raises(Exception, a.intersect, longer)
+ self.assertRaises(Exception, a.intersect, longer)
def _check_case(xloc, xlen, yloc, ylen, eloc, elen):
xindex = BlockIndex(TEST_LENGTH, xloc, xlen)
@@ -585,9 +585,3 @@ def f(self):
g = make_optestf(op)
setattr(TestSparseOperators, g.__name__, g)
del g
-
-
-if __name__ == '__main__':
- import nose # noqa
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_list.py b/pandas/sparse/tests/test_list.py
index b117685b6e968..458681cdc1de0 100644
--- a/pandas/sparse/tests/test_list.py
+++ b/pandas/sparse/tests/test_list.py
@@ -112,9 +112,3 @@ def test_getitem(self):
for i in range(len(arr)):
tm.assert_almost_equal(splist[i], arr[i])
tm.assert_almost_equal(splist[-i], arr[-i])
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py
index 06d76bdd4dd3d..b34f5dd2cee9f 100644
--- a/pandas/sparse/tests/test_series.py
+++ b/pandas/sparse/tests/test_series.py
@@ -1366,9 +1366,3 @@ def test_numpy_func_call(self):
for func in funcs:
for series in ('bseries', 'zbseries'):
getattr(np, func)(getattr(self, series))
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/stats/tests/test_fama_macbeth.py b/pandas/stats/tests/test_fama_macbeth.py
index 706becfa730c4..0c9fcf775ad2d 100644
--- a/pandas/stats/tests/test_fama_macbeth.py
+++ b/pandas/stats/tests/test_fama_macbeth.py
@@ -66,8 +66,3 @@ def _check_stuff_works(self, result):
# does it work?
result.summary
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/stats/tests/test_math.py b/pandas/stats/tests/test_math.py
index bc09f33d2f467..3f89dbcd20065 100644
--- a/pandas/stats/tests/test_math.py
+++ b/pandas/stats/tests/test_math.py
@@ -57,7 +57,3 @@ def test_inv_illformed(self):
rs = pmath.inv(singular)
expected = np.array([[0.1, 0.2], [0.1, 0.2]])
self.assertTrue(np.allclose(rs, expected))
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/stats/tests/test_ols.py b/pandas/stats/tests/test_ols.py
index 2935f986cca9f..09fa21d58ea9d 100644
--- a/pandas/stats/tests/test_ols.py
+++ b/pandas/stats/tests/test_ols.py
@@ -974,9 +974,3 @@ def testFilterWithDictRHS(self):
def tsAssertEqual(self, ts1, ts2, **kwargs):
self.assert_series_equal(ts1, ts2, **kwargs)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/stats/tests/test_var.py b/pandas/stats/tests/test_var.py
index 9f2c95a2d3d5c..04e2019f00a82 100644
--- a/pandas/stats/tests/test_var.py
+++ b/pandas/stats/tests/test_var.py
@@ -6,7 +6,6 @@
from pandas.compat import range
import nose
-import unittest
raise nose.SkipTest('skipping this for now')
@@ -93,7 +92,3 @@ def __init__(self):
self.res1 = VAR2(endog=data).fit(maxlag=2)
from results import results_var
self.res2 = results_var.MacrodataResults()
-
-
-if __name__ == '__main__':
- unittest.main()
diff --git a/pandas/tests/formats/test_format.py b/pandas/tests/formats/test_format.py
index 9eff64b40625d..7a2c5f3b7f7c1 100644
--- a/pandas/tests/formats/test_format.py
+++ b/pandas/tests/formats/test_format.py
@@ -4999,8 +4999,3 @@ def test_format_percentiles():
tm.assertRaises(ValueError, fmt.format_percentiles, [-0.001, 0.1, 0.5])
tm.assertRaises(ValueError, fmt.format_percentiles, [2, 0.1, 0.5])
tm.assertRaises(ValueError, fmt.format_percentiles, [0.1, 0.5, 'a'])
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/formats/test_printing.py b/pandas/tests/formats/test_printing.py
index 3bcceca1f50a7..d1eb1faecc401 100644
--- a/pandas/tests/formats/test_printing.py
+++ b/pandas/tests/formats/test_printing.py
@@ -1,5 +1,4 @@
# -*- coding: utf-8 -*-
-import nose
from pandas import compat
import pandas.formats.printing as printing
import pandas.formats.format as fmt
@@ -135,8 +134,3 @@ def test_ambiguous_width(self):
# result = printing.console_encode(u"\u05d0")
# expected = u"\u05d0".encode('utf-8')
# assert (result == expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_analytics.py b/pandas/tests/frame/test_analytics.py
index 5d51306363053..0dbb78ec89b2e 100644
--- a/pandas/tests/frame/test_analytics.py
+++ b/pandas/tests/frame/test_analytics.py
@@ -2202,7 +2202,3 @@ def test_dot(self):
with tm.assertRaisesRegexp(ValueError, 'aligned'):
df.dot(df2)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_asof.py b/pandas/tests/frame/test_asof.py
index f68219120b48e..323960d54a42c 100644
--- a/pandas/tests/frame/test_asof.py
+++ b/pandas/tests/frame/test_asof.py
@@ -1,7 +1,5 @@
# coding=utf-8
-import nose
-
import numpy as np
from pandas import (DataFrame, date_range, Timestamp, Series,
to_datetime)
@@ -84,7 +82,3 @@ def test_missing(self):
expected = DataFrame(index=to_datetime(['1989-12-31']),
columns=['A', 'B'], dtype='float64')
assert_frame_equal(result, expected)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_constructors.py b/pandas/tests/frame/test_constructors.py
index fe6a12fcca28a..1676c57a274cd 100644
--- a/pandas/tests/frame/test_constructors.py
+++ b/pandas/tests/frame/test_constructors.py
@@ -7,7 +7,6 @@
import itertools
import nose
-
from numpy.random import randn
import numpy as np
@@ -1945,10 +1944,3 @@ def test_frame_timeseries_to_records(self):
result['index'].dtype == 'M8[ns]'
result = df.to_records(index=False)
-
-
-if __name__ == '__main__':
- import nose # noqa
-
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_indexing.py b/pandas/tests/frame/test_indexing.py
index 7d68eac47766e..f0e6ab4c17915 100644
--- a/pandas/tests/frame/test_indexing.py
+++ b/pandas/tests/frame/test_indexing.py
@@ -2954,9 +2954,3 @@ def test_transpose(self):
expected = DataFrame(self.df.values.T)
expected.index = ['A', 'B']
assert_frame_equal(result, expected)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_misc_api.py b/pandas/tests/frame/test_misc_api.py
index f5719fa1d8b85..2fc14d9e4d123 100644
--- a/pandas/tests/frame/test_misc_api.py
+++ b/pandas/tests/frame/test_misc_api.py
@@ -4,7 +4,6 @@
# pylint: disable-msg=W0612,E1101
from copy import deepcopy
import sys
-import nose
from distutils.version import LooseVersion
from pandas.compat import range, lrange
@@ -486,8 +485,3 @@ def _check_f(base, f):
# rename
f = lambda x: x.rename({1: 'foo'}, inplace=True)
_check_f(d.copy(), f)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_missing.py b/pandas/tests/frame/test_missing.py
index eabdb79295c27..8c25f71c00684 100644
--- a/pandas/tests/frame/test_missing.py
+++ b/pandas/tests/frame/test_missing.py
@@ -711,10 +711,3 @@ def test_interp_ignore_all_good(self):
# all good
result = df[['B', 'D']].interpolate(downcast=None)
assert_frame_equal(result, df[['B', 'D']])
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core']
- exit=False)
diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py
index f843a5c08ce05..15f98abe1445d 100644
--- a/pandas/tests/frame/test_operators.py
+++ b/pandas/tests/frame/test_operators.py
@@ -1275,8 +1275,3 @@ def test_alignment_non_pandas(self):
align(df, val, 'index')
with tm.assertRaises(ValueError):
align(df, val, 'columns')
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_query_eval.py b/pandas/tests/frame/test_query_eval.py
index 36ae5dac733a5..a9a90a6f5cd40 100644
--- a/pandas/tests/frame/test_query_eval.py
+++ b/pandas/tests/frame/test_query_eval.py
@@ -1155,8 +1155,3 @@ class TestDataFrameEvalPythonPython(TestDataFrameEvalNumExprPython):
def setUpClass(cls):
super(TestDataFrameEvalPythonPython, cls).tearDownClass()
cls.engine = cls.parser = 'python'
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_timeseries.py b/pandas/tests/frame/test_timeseries.py
index 9a9f0ee67fb89..55848847f2266 100644
--- a/pandas/tests/frame/test_timeseries.py
+++ b/pandas/tests/frame/test_timeseries.py
@@ -575,9 +575,3 @@ def test_frame_to_period(self):
tm.assert_index_equal(pts.columns, exp.columns.asfreq('M'))
self.assertRaises(ValueError, df.to_period, axis=2)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/frame/test_to_csv.py b/pandas/tests/frame/test_to_csv.py
index b585462365606..5c47b0357b4f6 100644
--- a/pandas/tests/frame/test_to_csv.py
+++ b/pandas/tests/frame/test_to_csv.py
@@ -1145,9 +1145,3 @@ def test_to_csv_quoting(self):
df = df.set_index(['a', 'b'])
expected = '"a","b","c"\n"1","3","5"\n"2","4","6"\n'
self.assertEqual(df.to_csv(quoting=csv.QUOTE_ALL), expected)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/groupby/test_aggregate.py b/pandas/tests/groupby/test_aggregate.py
index 6b162b71f79de..5f680a6876873 100644
--- a/pandas/tests/groupby/test_aggregate.py
+++ b/pandas/tests/groupby/test_aggregate.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
-import nose
from datetime import datetime
@@ -487,8 +486,3 @@ def testit(label_list, shape):
shape = (10000, 10000)
label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)]
testit(label_list, shape)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s'
- ], exit=False)
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index 81aa183426be9..82ec1832be961 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -1,9 +1,7 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
-import nose
from numpy import nan
-
from pandas.core.index import Index, MultiIndex, CategoricalIndex
from pandas.core.api import DataFrame, Categorical
@@ -490,8 +488,3 @@ def testit(label_list, shape):
shape = (10000, 10000)
label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)]
testit(label_list, shape)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s'
- ], exit=False)
diff --git a/pandas/tests/groupby/test_filters.py b/pandas/tests/groupby/test_filters.py
index 40d8039f71576..663fbd04e7e5a 100644
--- a/pandas/tests/groupby/test_filters.py
+++ b/pandas/tests/groupby/test_filters.py
@@ -1,7 +1,5 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
-import nose
-
from numpy import nan
@@ -641,8 +639,3 @@ def testit(label_list, shape):
shape = (10000, 10000)
label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)]
testit(label_list, shape)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s'
- ], exit=False)
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index bf61f5ef83859..01c81bd7904bd 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -6057,8 +6057,3 @@ def testit(label_list, shape):
shape = (10000, 10000)
label_list = [np.tile(np.arange(10000), 5), np.tile(np.arange(10000), 5)]
testit(label_list, shape)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s'
- ], exit=False)
diff --git a/pandas/tests/indexes/test_base.py b/pandas/tests/indexes/test_base.py
index a0f2a090c9a06..c574a4a1f01a7 100644
--- a/pandas/tests/indexes/test_base.py
+++ b/pandas/tests/indexes/test_base.py
@@ -11,7 +11,6 @@
import operator
import os
-import nose
import numpy as np
from pandas import (period_range, date_range, Series,
@@ -2078,8 +2077,3 @@ def test_intersect_str_dates(self):
res = i2.intersection(i1)
self.assertEqual(len(res), 0)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/indexes/test_numeric.py b/pandas/tests/indexes/test_numeric.py
index c7acbf51a17e5..4dab7ae76a011 100644
--- a/pandas/tests/indexes/test_numeric.py
+++ b/pandas/tests/indexes/test_numeric.py
@@ -3,7 +3,6 @@
from datetime import datetime
from pandas.compat import range, PY3
-import nose
import numpy as np
from pandas import (date_range, Series, Index, Float64Index,
@@ -1144,8 +1143,3 @@ def test_join_outer(self):
self.assert_index_equal(res, eres)
tm.assert_numpy_array_equal(lidx, elidx)
tm.assert_numpy_array_equal(ridx, eridx)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/indexing/test_callable.py b/pandas/tests/indexing/test_callable.py
index ab225f72934ce..bcadc41b13370 100644
--- a/pandas/tests/indexing/test_callable.py
+++ b/pandas/tests/indexing/test_callable.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
-import nose
import numpy as np
import pandas as pd
@@ -268,8 +267,3 @@ def test_frame_iloc_callable_setitem(self):
exp = df.copy()
exp.iloc[[1, 3], [0]] = [-5, -5]
tm.assert_frame_equal(res, exp)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py
index f7fa07916ca74..a9dfcf2672357 100644
--- a/pandas/tests/indexing/test_indexing.py
+++ b/pandas/tests/indexing/test_indexing.py
@@ -5532,8 +5532,3 @@ def test_boolean_indexing(self):
index=pd.to_timedelta(range(10), unit='s'),
columns=['x'])
tm.assert_frame_equal(expected, result)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py
index 289d48ba6d4cc..f7fd6a8519533 100644
--- a/pandas/tests/plotting/test_boxplot_method.py
+++ b/pandas/tests/plotting/test_boxplot_method.py
@@ -378,8 +378,3 @@ def test_fontsize(self):
df = DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": [0, 0, 0, 1, 1, 1]})
self._check_ticks_props(df.boxplot("a", by="b", fontsize=16),
xlabelsize=16, ylabelsize=16)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_datetimelike.py b/pandas/tests/plotting/test_datetimelike.py
index 6486c8aa21c1b..bcc9c7ceea8b5 100644
--- a/pandas/tests/plotting/test_datetimelike.py
+++ b/pandas/tests/plotting/test_datetimelike.py
@@ -1,3 +1,5 @@
+""" Test cases for time series specific (freq conversion, etc) """
+
from datetime import datetime, timedelta, date, time
import nose
@@ -18,9 +20,6 @@
_skip_if_no_scipy_gaussian_kde)
-""" Test cases for time series specific (freq conversion, etc) """
-
-
@tm.mplskip
class TestTSPlot(TestPlotBase):
@@ -1309,8 +1308,3 @@ def _check_plot_works(f, freq=None, series=None, *args, **kwargs):
plt.savefig(path)
finally:
plt.close(fig)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py
index fba554b03f191..81a54bd38b3f8 100644
--- a/pandas/tests/plotting/test_frame.py
+++ b/pandas/tests/plotting/test_frame.py
@@ -1,5 +1,7 @@
# coding: utf-8
+""" Test cases for DataFrame.plot """
+
import nose
import string
import warnings
@@ -26,9 +28,6 @@
_ok_for_gaussian_kde)
-""" Test cases for DataFrame.plot """
-
-
@tm.mplskip
class TestDataFramePlots(TestPlotBase):
@@ -2726,8 +2725,3 @@ def _generate_4_axes_via_gridspec():
ax_lr = plt.subplot(gs[1, 1])
return gs, [ax_tl, ax_ll, ax_tr, ax_lr]
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_groupby.py b/pandas/tests/plotting/test_groupby.py
index 3c682fbfbb89e..93efb3f994c38 100644
--- a/pandas/tests/plotting/test_groupby.py
+++ b/pandas/tests/plotting/test_groupby.py
@@ -1,6 +1,7 @@
# coding: utf-8
-import nose
+""" Test cases for GroupBy.plot """
+
from pandas import Series, DataFrame
import pandas.util.testing as tm
@@ -10,9 +11,6 @@
from pandas.tests.plotting.common import TestPlotBase
-""" Test cases for GroupBy.plot """
-
-
@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):
@@ -74,8 +72,3 @@ def test_plot_kwargs(self):
res = df.groupby('z').plot.scatter(x='x', y='y')
self.assertEqual(len(res['a'].collections), 1)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/tests/plotting/test_hist_method.py
index bde5544390b85..4f64f66bd3c4d 100644
--- a/pandas/tests/plotting/test_hist_method.py
+++ b/pandas/tests/plotting/test_hist_method.py
@@ -1,6 +1,6 @@
# coding: utf-8
-import nose
+""" Test cases for .hist method """
from pandas import Series, DataFrame
import pandas.util.testing as tm
@@ -13,9 +13,6 @@
from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works)
-""" Test cases for .hist method """
-
-
@tm.mplskip
class TestSeriesPlots(TestPlotBase):
@@ -418,8 +415,3 @@ def test_axis_share_xy(self):
self.assertTrue(ax1._shared_y_axes.joined(ax1, ax2))
self.assertTrue(ax2._shared_y_axes.joined(ax1, ax2))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index 2650ce2879db7..c92287b2bdc42 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -1,6 +1,6 @@
# coding: utf-8
-import nose
+""" Test cases for misc plot functions """
from pandas import Series, DataFrame
from pandas.compat import lmap
@@ -15,8 +15,6 @@
from pandas.tests.plotting.common import (TestPlotBase, _check_plot_works,
_ok_for_gaussian_kde)
-""" Test cases for misc plot functions """
-
@tm.mplskip
class TestSeriesPlots(TestPlotBase):
@@ -298,8 +296,3 @@ def test_subplot_titles(self):
title=title[:-1])
title_list = [ax.get_title() for sublist in plot for ax in sublist]
self.assertEqual(title_list, title[:3] + [''])
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py
index f668c46a15173..8c00d606059a4 100644
--- a/pandas/tests/plotting/test_series.py
+++ b/pandas/tests/plotting/test_series.py
@@ -1,6 +1,8 @@
# coding: utf-8
-import nose
+""" Test cases for Series.plot """
+
+
import itertools
from datetime import datetime
@@ -20,9 +22,6 @@
_ok_for_gaussian_kde)
-""" Test cases for Series.plot """
-
-
@tm.mplskip
class TestSeriesPlots(TestPlotBase):
@@ -811,8 +810,3 @@ def test_custom_business_day_freq(self):
freq=CustomBusinessDay(holidays=['2014-05-26'])))
_check_plot_works(s.plot)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/series/test_asof.py b/pandas/tests/series/test_asof.py
index e2092feab9004..db306d2a742c1 100644
--- a/pandas/tests/series/test_asof.py
+++ b/pandas/tests/series/test_asof.py
@@ -1,9 +1,6 @@
# coding=utf-8
-import nose
-
import numpy as np
-
from pandas import (offsets, Series, notnull,
isnull, date_range, Timestamp)
@@ -152,7 +149,3 @@ def test_errors(self):
s = Series(np.random.randn(N), index=rng)
with self.assertRaises(ValueError):
s.asof(s.index[0], subset='foo')
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/series/test_indexing.py b/pandas/tests/series/test_indexing.py
index bdae11770de65..e0d83d6eeadac 100644
--- a/pandas/tests/series/test_indexing.py
+++ b/pandas/tests/series/test_indexing.py
@@ -2638,9 +2638,3 @@ def test_round_nat(self):
round_method = getattr(s.dt, method)
for freq in ["s", "5s", "min", "5min", "h", "5h"]:
assert_series_equal(round_method(freq), expected)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/series/test_missing.py b/pandas/tests/series/test_missing.py
index 8c877ade6fe98..6821a8b9f4221 100644
--- a/pandas/tests/series/test_missing.py
+++ b/pandas/tests/series/test_missing.py
@@ -1092,10 +1092,3 @@ def test_series_interpolate_intraday(self):
result = ts.reindex(new_index).interpolate(method='time')
self.assert_numpy_array_equal(result.values, exp.values)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core']
- exit=False)
diff --git a/pandas/tests/series/test_timeseries.py b/pandas/tests/series/test_timeseries.py
index 9754a9d3737e3..bd346fb9bb0c8 100644
--- a/pandas/tests/series/test_timeseries.py
+++ b/pandas/tests/series/test_timeseries.py
@@ -927,9 +927,3 @@ def test_get_level_values_box(self):
index = MultiIndex(levels=levels, labels=labels)
self.assertTrue(isinstance(index.get_level_values(0)[0], Timestamp))
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index 99453b9793007..40b277f3f1f8a 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -1372,9 +1372,3 @@ def test_index(self):
idx = Index(['1 day', '1 day', '-1 day', '-1 day 2 min',
'2 min', '2 min'], dtype='timedelta64[ns]')
tm.assert_series_equal(algos.mode(idx), exp)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_base.py b/pandas/tests/test_base.py
index f750936961831..1d1ef1a08859c 100644
--- a/pandas/tests/test_base.py
+++ b/pandas/tests/test_base.py
@@ -4,7 +4,7 @@
import re
import sys
from datetime import datetime, timedelta
-
+import nose
import numpy as np
import pandas as pd
@@ -1105,11 +1105,3 @@ def f():
self.assertRaises(AttributeError, f)
self.assertFalse(hasattr(t, "b"))
-
-
-if __name__ == '__main__':
- import nose
-
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core'],
- exit=False)
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 745914d3e7ef5..be55d6e1976ec 100644
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -4576,10 +4576,3 @@ def test_map(self):
self.assertIsInstance(res, tm.SubclassedCategorical)
exp = Categorical(['A', 'B', 'C'])
tm.assert_categorical_equal(res, exp)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core']
- exit=False)
diff --git a/pandas/tests/test_common.py b/pandas/tests/test_common.py
index 09dd3f7ab517c..0239250129494 100644
--- a/pandas/tests/test_common.py
+++ b/pandas/tests/test_common.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import nose
import numpy as np
from pandas import Series, Timestamp
@@ -196,8 +195,3 @@ def test_dict_compat():
assert (com._dict_compat(data_datetime64) == expected)
assert (com._dict_compat(expected) == expected)
assert (com._dict_compat(data_unchanged) == data_unchanged)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_expressions.py b/pandas/tests/test_expressions.py
index c037f02f20609..18b078d0a677e 100644
--- a/pandas/tests/test_expressions.py
+++ b/pandas/tests/test_expressions.py
@@ -2,12 +2,12 @@
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
-import nose
import re
+import operator
+import nose
from numpy.random import randn
-import operator
import numpy as np
from pandas.core.api import DataFrame, Panel
@@ -439,9 +439,3 @@ def test_bool_ops_warn_on_arithmetic(self):
r = f(df, True)
e = fe(df, True)
tm.assert_frame_equal(r, e)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index 0ca8ba47b8a8f..5bf2eda47ea27 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -2022,7 +2022,3 @@ def test_pipe_panel(self):
with tm.assertRaises(ValueError):
result = wp.pipe((f, 'y'), x=1, y=1)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_internals.py b/pandas/tests/test_internals.py
index 5000d6d4510fb..2bfe31ad4260e 100644
--- a/pandas/tests/test_internals.py
+++ b/pandas/tests/test_internals.py
@@ -1188,8 +1188,3 @@ def assert_add_equals(val, inc, result):
lambda: BlockPlacement([1, 2, 4]).add(-10))
self.assertRaises(ValueError,
lambda: BlockPlacement(slice(2, None, -1)).add(-1))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_join.py b/pandas/tests/test_join.py
index bfdb77f3fb350..0e7dda05a0c27 100644
--- a/pandas/tests/test_join.py
+++ b/pandas/tests/test_join.py
@@ -193,9 +193,3 @@ def test_inner_join_indexer2():
exp_ridx = np.array([0, 1, 2, 3], dtype=np.int64)
assert_almost_equal(ridx, exp_ridx)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_lib.py b/pandas/tests/test_lib.py
index 945f8004687cd..2381c52ef14b6 100644
--- a/pandas/tests/test_lib.py
+++ b/pandas/tests/test_lib.py
@@ -232,10 +232,3 @@ def test_empty_like(self):
expected = np.array([True])
self._check_behavior(arr, expected)
-
-
-if __name__ == '__main__':
- import nose
-
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index 37bfe667b0205..d87ad8d906854 100755
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -2478,8 +2478,3 @@ def test_iloc_mi(self):
for r in range(5)])
assert_frame_equal(result, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index dd3a49de55d73..937c20d009b6b 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -1000,9 +1000,3 @@ def test_nans_skipna(self):
@property
def prng(self):
return np.random.RandomState(1234)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure', '-s'
- ], exit=False)
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index d79081a06dbc0..89e8fb78ad821 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -2538,8 +2538,3 @@ def test_panel_index():
np.repeat([1, 2, 3], 4)],
names=['time', 'panel'])
tm.assert_index_equal(index, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_panel4d.py b/pandas/tests/test_panel4d.py
index 0769b8916a11b..aeca24964222a 100644
--- a/pandas/tests/test_panel4d.py
+++ b/pandas/tests/test_panel4d.py
@@ -949,8 +949,3 @@ def test_rename(self):
def test_get_attr(self):
assert_panel_equal(self.panel4d['l1'], self.panel4d.l1)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_panelnd.py b/pandas/tests/test_panelnd.py
index 92805f3b30ec6..6a578d85d3ee3 100644
--- a/pandas/tests/test_panelnd.py
+++ b/pandas/tests/test_panelnd.py
@@ -1,6 +1,4 @@
# -*- coding: utf-8 -*-
-import nose
-
from pandas.core import panelnd
from pandas.core.panel import Panel
@@ -101,7 +99,3 @@ def test_5d_construction(self):
# test a transpose
# results = p5d.transpose(1,2,3,4,0)
# expected =
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_reshape.py b/pandas/tests/test_reshape.py
index 603674ac01bc0..b5fa945a5bb8f 100644
--- a/pandas/tests/test_reshape.py
+++ b/pandas/tests/test_reshape.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
-import nose
from pandas import DataFrame, Series
from pandas.core.sparse import SparseDataFrame
@@ -914,8 +913,3 @@ def test_multiple_id_columns(self):
exp_frame = exp_frame.set_index(['famid', 'birth', 'age'])[['ht']]
long_frame = wide_to_long(df, 'ht', i=['famid', 'birth'], j='age')
tm.assert_frame_equal(long_frame, exp_frame)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_stats.py b/pandas/tests/test_stats.py
index 41d25b9662b5b..eb8ab02c29548 100644
--- a/pandas/tests/test_stats.py
+++ b/pandas/tests/test_stats.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
from pandas import compat
-import nose
from distutils.version import LooseVersion
from numpy import nan
@@ -185,8 +184,3 @@ def test_rank_object_bug(self):
# smoke tests
Series([np.nan] * 32).astype(object).rank(ascending=True)
Series([np.nan] * 32).astype(object).rank(ascending=False)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_strings.py b/pandas/tests/test_strings.py
index f59127c853ed1..f358946983dce 100644
--- a/pandas/tests/test_strings.py
+++ b/pandas/tests/test_strings.py
@@ -4,8 +4,6 @@
from datetime import datetime, timedelta
import re
-import nose
-
from numpy import nan as NA
import numpy as np
from numpy.random import randint
@@ -2715,8 +2713,3 @@ def test_method_on_bytes(self):
expected = Series(np.array(
['ad', 'be', 'cf'], 'S2').astype(object))
tm.assert_series_equal(result, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_take.py b/pandas/tests/test_take.py
index 98b3b474f785d..bf8a3ab370625 100644
--- a/pandas/tests/test_take.py
+++ b/pandas/tests/test_take.py
@@ -2,7 +2,6 @@
import re
from datetime import datetime
-import nose
import numpy as np
from pandas.compat import long
import pandas.core.algorithms as algos
@@ -448,8 +447,3 @@ def test_2d_datetime64(self):
expected = arr.take(indexer, axis=1)
expected[:, [2, 4]] = datetime(2007, 1, 1)
tm.assert_almost_equal(result, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py
index e2f295a5343bc..5e60efd153ab1 100644
--- a/pandas/tests/test_testing.py
+++ b/pandas/tests/test_testing.py
@@ -802,8 +802,3 @@ def f():
with assertRaises(ValueError):
f()
raise ValueError
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/test_util.py b/pandas/tests/test_util.py
index ed82604035358..e2f6a7f6cc1ed 100644
--- a/pandas/tests/test_util.py
+++ b/pandas/tests/test_util.py
@@ -1,6 +1,4 @@
# -*- coding: utf-8 -*-
-import nose
-
from collections import OrderedDict
import sys
import unittest
@@ -402,8 +400,3 @@ def test_numpy_errstate_is_default():
from pandas.compat import numpy # noqa
# The errstate should be unchanged after that import.
tm.assert_equal(np.geterr(), expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_cast.py b/pandas/tests/types/test_cast.py
index 56a14a51105ca..a8579e89aeb1f 100644
--- a/pandas/tests/types/test_cast.py
+++ b/pandas/tests/types/test_cast.py
@@ -5,8 +5,6 @@
"""
-
-import nose
from datetime import datetime
import numpy as np
@@ -278,8 +276,3 @@ def test_period_dtype(self):
np.dtype('datetime64[ns]'), np.object, np.int64]:
self.assertEqual(_find_common_type([dtype, dtype2]), np.object)
self.assertEqual(_find_common_type([dtype2, dtype]), np.object)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_common.py b/pandas/tests/types/test_common.py
index 4d6f50862c562..7c17c61aec440 100644
--- a/pandas/tests/types/test_common.py
+++ b/pandas/tests/types/test_common.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import nose
import numpy as np
from pandas.types.dtypes import DatetimeTZDtype, PeriodDtype, CategoricalDtype
@@ -55,8 +54,3 @@ def test_dtype_equal():
assert not DatetimeTZDtype.is_dtype(np.int64)
assert not PeriodDtype.is_dtype(np.int64)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_concat.py b/pandas/tests/types/test_concat.py
index 6403dcb5a5350..8acafe0af1792 100644
--- a/pandas/tests/types/test_concat.py
+++ b/pandas/tests/types/test_concat.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import nose
import pandas as pd
import pandas.types.concat as _concat
import pandas.util.testing as tm
@@ -79,8 +78,3 @@ def test_get_dtype_kinds_period(self):
pd.Series([pd.Period('2011-02', freq='D')])]
res = _concat.get_dtype_kinds(to_concat)
self.assertEqual(res, set(['object']))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_dtypes.py b/pandas/tests/types/test_dtypes.py
index f190c85404ff9..68105cfd7c886 100644
--- a/pandas/tests/types/test_dtypes.py
+++ b/pandas/tests/types/test_dtypes.py
@@ -1,7 +1,6 @@
# -*- coding: utf-8 -*-
from itertools import product
-import nose
import numpy as np
import pandas as pd
from pandas import Series, Categorical, date_range
@@ -353,8 +352,3 @@ def test_empty(self):
def test_not_string(self):
# though PeriodDtype has object kind, it cannot be string
self.assertFalse(is_string_dtype(PeriodDtype('D')))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_generic.py b/pandas/tests/types/test_generic.py
index 28600687e8062..2861252bef26a 100644
--- a/pandas/tests/types/test_generic.py
+++ b/pandas/tests/types/test_generic.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import nose
import numpy as np
import pandas as pd
import pandas.util.testing as tm
@@ -41,8 +40,3 @@ def test_abc_types(self):
self.assertIsInstance(self.sparse_array, gt.ABCSparseArray)
self.assertIsInstance(self.categorical, gt.ABCCategorical)
self.assertIsInstance(pd.Period('2012', freq='A-DEC'), gt.ABCPeriod)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py
index 5c35112d0fe19..15f9545f3476c 100644
--- a/pandas/tests/types/test_inference.py
+++ b/pandas/tests/types/test_inference.py
@@ -6,7 +6,6 @@
"""
-import nose
import collections
import re
from datetime import datetime, date, timedelta, time
@@ -968,8 +967,3 @@ def test_ensure_categorical():
values = Categorical(values)
result = _ensure_categorical(values)
tm.assert_categorical_equal(result, values)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_io.py b/pandas/tests/types/test_io.py
index 545edf8f1386c..ce8e23342bf5a 100644
--- a/pandas/tests/types/test_io.py
+++ b/pandas/tests/types/test_io.py
@@ -107,10 +107,3 @@ def test_convert_downcast_int64(self):
expected = np.array([int8_na, 2, 3, 10, 15], dtype=np.int8)
result = lib.downcast_int64(arr, na_values)
self.assert_numpy_array_equal(result, expected)
-
-
-if __name__ == '__main__':
- import nose
-
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tests/types/test_missing.py b/pandas/tests/types/test_missing.py
index fa2bd535bb8d5..2b09cf5ab633d 100644
--- a/pandas/tests/types/test_missing.py
+++ b/pandas/tests/types/test_missing.py
@@ -1,6 +1,5 @@
# -*- coding: utf-8 -*-
-import nose
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
@@ -304,8 +303,3 @@ def test_na_value_for_dtype():
for dtype in ['O']:
assert np.isnan(na_value_for_dtype(np.dtype(dtype)))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index 012d67d29cc3f..ee70515850b25 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -4003,28 +4003,3 @@ def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
if gridsize is not None:
kwds['gridsize'] = gridsize
return self(kind='hexbin', x=x, y=y, C=C, **kwds)
-
-
-if __name__ == '__main__':
- # import pandas.rpy.common as com
- # sales = com.load_data('sanfrancisco.home.sales', package='nutshell')
- # top10 = sales['zip'].value_counts()[:10].index
- # sales2 = sales[sales.zip.isin(top10)]
- # _ = scatter_plot(sales2, 'squarefeet', 'price', by='zip')
-
- # plt.show()
-
- import matplotlib.pyplot as plt
-
- import pandas.tools.plotting as plots
- import pandas.core.frame as fr
- reload(plots) # noqa
- reload(fr) # noqa
- from pandas.core.frame import DataFrame
-
- data = DataFrame([[3, 6, -5], [4, 8, 2], [4, 9, -6],
- [4, 9, -3], [2, 5, -1]],
- columns=['A', 'B', 'C'])
- data.plot(kind='barh', stacked=True)
-
- plt.show()
diff --git a/pandas/tools/tests/test_concat.py b/pandas/tools/tests/test_concat.py
index 2be7e75573d6e..dae24c48b8238 100644
--- a/pandas/tools/tests/test_concat.py
+++ b/pandas/tools/tests/test_concat.py
@@ -1,5 +1,3 @@
-import nose
-
import numpy as np
from numpy.random import randn
@@ -2171,8 +2169,3 @@ def test_concat_multiindex_dfs_with_deepcopy(self):
tm.assert_frame_equal(result_copy, expected)
result_no_copy = pd.concat(example_dict, names=['testname'])
tm.assert_frame_equal(result_no_copy, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py
index 4a2b64d080b4b..605a85026d605 100644
--- a/pandas/tools/tests/test_join.py
+++ b/pandas/tools/tests/test_join.py
@@ -1,7 +1,5 @@
# pylint: disable=E1103
-import nose
-
from numpy.random import randn
import numpy as np
@@ -799,8 +797,3 @@ def _join_by_hand(a, b, how='left'):
for col, s in compat.iteritems(b_re):
a_re[col] = s
return a_re.reindex(columns=result_columns)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_merge.py b/pandas/tools/tests/test_merge.py
index e08074649f7e8..88856a012da6f 100644
--- a/pandas/tools/tests/test_merge.py
+++ b/pandas/tools/tests/test_merge.py
@@ -1,7 +1,5 @@
# pylint: disable=E1103
-import nose
-
from datetime import datetime
from numpy.random import randn
from numpy import nan
@@ -1370,8 +1368,3 @@ def f():
def f():
household.join(log_return, how='outer')
self.assertRaises(NotImplementedError, f)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_merge_asof.py b/pandas/tools/tests/test_merge_asof.py
index ef7b25008e80a..8e7323f72a8f5 100644
--- a/pandas/tools/tests/test_merge_asof.py
+++ b/pandas/tools/tests/test_merge_asof.py
@@ -1,4 +1,3 @@
-import nose
import os
import pytz
@@ -938,8 +937,3 @@ def test_on_float_by_int(self):
columns=['symbol', 'exch', 'price', 'mpv'])
assert_frame_equal(result, expected)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_merge_ordered.py b/pandas/tools/tests/test_merge_ordered.py
index d163468abc88e..e08cc98e50794 100644
--- a/pandas/tools/tests/test_merge_ordered.py
+++ b/pandas/tools/tests/test_merge_ordered.py
@@ -1,5 +1,3 @@
-import nose
-
import pandas as pd
from pandas import DataFrame, merge_ordered
from pandas.util import testing as tm
@@ -92,7 +90,3 @@ def test_empty_sequence_concat(self):
pd.concat([pd.DataFrame()])
pd.concat([None, pd.DataFrame()])
pd.concat([pd.DataFrame(), None])
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py
index 9cc520a7adb05..398e57d4ad0a4 100644
--- a/pandas/tools/tests/test_pivot.py
+++ b/pandas/tools/tests/test_pivot.py
@@ -1321,9 +1321,3 @@ def test_crosstab_with_numpy_size(self):
index=expected_index,
columns=expected_column)
tm.assert_frame_equal(result, expected)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_tile.py b/pandas/tools/tests/test_tile.py
index 5c7cee862ccd3..c5261597cf35d 100644
--- a/pandas/tools/tests/test_tile.py
+++ b/pandas/tools/tests/test_tile.py
@@ -1,5 +1,4 @@
import os
-import nose
import numpy as np
from pandas.compat import zip
@@ -351,8 +350,3 @@ def test_datetime_bin(self):
def curpath():
pth, _ = os.path.split(os.path.abspath(__file__))
return pth
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tools/tests/test_util.py b/pandas/tools/tests/test_util.py
index 8a8960a057926..e1d057eb3c3c0 100644
--- a/pandas/tools/tests/test_util.py
+++ b/pandas/tools/tests/test_util.py
@@ -478,8 +478,3 @@ def test_downcast_limits(self):
for dtype, downcast, min_max in dtype_downcast_min_max:
series = pd.to_numeric(pd.Series(min_max), downcast=downcast)
tm.assert_equal(series.dtype, dtype)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_base.py b/pandas/tseries/tests/test_base.py
index 4f2ac3ff0d87e..2ff06517f175a 100644
--- a/pandas/tseries/tests/test_base.py
+++ b/pandas/tseries/tests/test_base.py
@@ -1860,10 +1860,3 @@ def test_equals(self):
self.assertFalse(idx.asobject.equals(idx3))
self.assertFalse(idx.equals(list(idx3)))
self.assertFalse(idx.equals(pd.Series(idx3)))
-
-
-if __name__ == '__main__':
- import nose
-
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py
index f6cf11c871bba..8caed80f5a45b 100644
--- a/pandas/tseries/tests/test_converter.py
+++ b/pandas/tseries/tests/test_converter.py
@@ -1,7 +1,5 @@
from datetime import datetime, date
-import nose
-
import numpy as np
from pandas import Timestamp, Period, Index
from pandas.compat import u
@@ -12,6 +10,7 @@
try:
import pandas.tseries.converter as converter
except ImportError:
+ import nose
raise nose.SkipTest("no pandas.tseries.converter, skipping")
@@ -199,9 +198,3 @@ def test_integer_passthrough(self):
rs = self.pc.convert([0, 1], None, self.axis)
xp = [0, 1]
self.assertEqual(rs, xp)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_daterange.py b/pandas/tseries/tests/test_daterange.py
index 87f9f55e0189c..209e6e40d5cf0 100644
--- a/pandas/tseries/tests/test_daterange.py
+++ b/pandas/tseries/tests/test_daterange.py
@@ -1,6 +1,5 @@
from datetime import datetime
from pandas.compat import range
-import nose
import numpy as np
from pandas.core.index import Index
@@ -817,8 +816,3 @@ def test_cdaterange_weekmask_and_holidays(self):
holidays=['2013-05-01'])
xp = DatetimeIndex(['2013-05-02', '2013-05-05', '2013-05-06'])
self.assert_index_equal(xp, rng)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_holiday.py b/pandas/tseries/tests/test_holiday.py
index 62446e8e637c6..d4d273347e6e3 100644
--- a/pandas/tseries/tests/test_holiday.py
+++ b/pandas/tseries/tests/test_holiday.py
@@ -15,7 +15,6 @@
USLaborDay, USColumbusDay,
USMartinLutherKingJr, USPresidentsDay)
from pytz import utc
-import nose
class TestCalendar(tm.TestCase):
@@ -385,8 +384,3 @@ def test_both_offset_observance_raises(self):
Holiday("Cyber Monday", month=11, day=1,
offset=[DateOffset(weekday=SA(4))],
observance=next_monday)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_offsets.py b/pandas/tseries/tests/test_offsets.py
index 768e9212e6c42..ac488a3dfdcb2 100644
--- a/pandas/tseries/tests/test_offsets.py
+++ b/pandas/tseries/tests/test_offsets.py
@@ -2,10 +2,11 @@
from distutils.version import LooseVersion
from datetime import date, datetime, timedelta
from dateutil.relativedelta import relativedelta
-from pandas.compat import range, iteritems
-from pandas import compat
+
import nose
from nose.tools import assert_raises
+from pandas.compat import range, iteritems
+from pandas import compat
import numpy as np
@@ -4956,8 +4957,3 @@ def test_all_offset_classes(self):
first = Timestamp(test_values[0], tz='US/Eastern') + offset()
second = Timestamp(test_values[1], tz='US/Eastern')
self.assertEqual(first, second, msg=str(offset))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_period.py b/pandas/tseries/tests/test_period.py
index a707cc3eb74ce..fdc067a827a5b 100644
--- a/pandas/tseries/tests/test_period.py
+++ b/pandas/tseries/tests/test_period.py
@@ -4967,9 +4967,3 @@ def test_get_period_field_raises_on_out_of_range(self):
def test_get_period_field_array_raises_on_out_of_range(self):
self.assertRaises(ValueError, _period.get_period_field_arr, -1,
np.empty(1), 0)
-
-
-if __name__ == '__main__':
- import nose
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_resample.py b/pandas/tseries/tests/test_resample.py
index c40f930fbd094..222ffb735921a 100755
--- a/pandas/tseries/tests/test_resample.py
+++ b/pandas/tseries/tests/test_resample.py
@@ -3,7 +3,6 @@
from datetime import datetime, timedelta
from functools import partial
-import nose
import numpy as np
import pandas as pd
@@ -3188,8 +3187,3 @@ def test_aggregate_with_nat(self):
# if NaT is included, 'var', 'std', 'mean', 'first','last'
# and 'nth' doesn't work yet
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_timedeltas.py b/pandas/tseries/tests/test_timedeltas.py
index 6efa024d81b98..13263259e0b8a 100644
--- a/pandas/tseries/tests/test_timedeltas.py
+++ b/pandas/tseries/tests/test_timedeltas.py
@@ -2,7 +2,6 @@
from __future__ import division
from datetime import timedelta, time
-import nose
from distutils.version import LooseVersion
import numpy as np
@@ -2051,8 +2050,3 @@ def test_add_overflow(self):
result = (to_timedelta([pd.NaT, '5 days', '1 hours']) +
to_timedelta(['7 seconds', pd.NaT, '4 hours']))
tm.assert_index_equal(result, exp)
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_timeseries_legacy.py b/pandas/tseries/tests/test_timeseries_legacy.py
index d8c01c53fb2e5..5395056c93412 100644
--- a/pandas/tseries/tests/test_timeseries_legacy.py
+++ b/pandas/tseries/tests/test_timeseries_legacy.py
@@ -219,8 +219,3 @@ def test_ms_vs_MS(self):
def test_rule_aliases(self):
rule = to_offset('10us')
self.assertEqual(rule, Micro(10))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_timezones.py b/pandas/tseries/tests/test_timezones.py
index 64787b6e4e79a..00b60ba620c4b 100644
--- a/pandas/tseries/tests/test_timezones.py
+++ b/pandas/tseries/tests/test_timezones.py
@@ -1,7 +1,5 @@
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta, tzinfo, date
-import nose
-
import numpy as np
import pytz
from distutils.version import LooseVersion
@@ -1683,8 +1681,3 @@ def test_nat(self):
idx = idx.tz_convert('US/Eastern')
expected = ['2010-12-01 11:00', '2010-12-02 11:00', NaT]
self.assert_index_equal(idx, DatetimeIndex(expected, tz='US/Eastern'))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_tslib.py b/pandas/tseries/tests/test_tslib.py
index cf5dbd671d38c..20e91a6f5bc44 100644
--- a/pandas/tseries/tests/test_tslib.py
+++ b/pandas/tseries/tests/test_tslib.py
@@ -1,4 +1,3 @@
-import nose
import datetime
import numpy as np
from distutils.version import LooseVersion
@@ -690,8 +689,3 @@ def _check_round(freq, expected):
msg = pd.tseries.frequencies._INVALID_FREQ_ERROR
with self.assertRaisesRegexp(ValueError, msg):
stamp.round('foo')
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
diff --git a/pandas/tseries/tests/test_util.py b/pandas/tseries/tests/test_util.py
index 96da32a4a845c..3feffe924c291 100644
--- a/pandas/tseries/tests/test_util.py
+++ b/pandas/tseries/tests/test_util.py
@@ -1,5 +1,4 @@
from pandas.compat import range
-import nose
import numpy as np
@@ -125,8 +124,3 @@ def test_normalize_date():
result = normalize_date(value)
assert (result == datetime(2012, 9, 7))
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
| xref #13856 | https://api.github.com/repos/pandas-dev/pandas/pulls/15330 | 2017-02-07T14:47:12Z | 2017-02-07T16:13:30Z | 2017-02-07T16:13:30Z | 2017-02-07T16:13:49Z |
DOC/CI: ensure correct pandas version (GH15311) | diff --git a/ci/build_docs.sh b/ci/build_docs.sh
index 4dc9a203f1978..5dc649a91c4f7 100755
--- a/ci/build_docs.sh
+++ b/ci/build_docs.sh
@@ -22,8 +22,8 @@ if [ x"$DOC_BUILD" != x"" ]; then
echo "Will build docs"
source activate pandas
- conda install -n pandas -c r r rpy2 --yes
+ # install sudo deps
time sudo apt-get $APT_ARGS install dvipng texlive-latex-base texlive-latex-extra
mv "$TRAVIS_BUILD_DIR"/doc /tmp
diff --git a/ci/requirements-3.5_DOC_BUILD.sh b/ci/requirements-3.5_DOC_BUILD.sh
index ca18ad976d46d..25bc63acc96d1 100644
--- a/ci/requirements-3.5_DOC_BUILD.sh
+++ b/ci/requirements-3.5_DOC_BUILD.sh
@@ -2,6 +2,8 @@
source activate pandas
-echo "install DOC_BUILD"
+echo "[install DOC_BUILD deps]"
conda install -n pandas -c conda-forge feather-format
+
+conda install -n pandas -c r r rpy2 --yes
| closes #15311
| https://api.github.com/repos/pandas-dev/pandas/pulls/15317 | 2017-02-06T14:14:33Z | 2017-02-06T15:55:39Z | 2017-02-06T15:55:39Z | 2017-02-06T15:55:39Z |
Added a tutorial for pandas dataframes | diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst
index c25e734a046b2..401a6446673cf 100644
--- a/doc/source/tutorials.rst
+++ b/doc/source/tutorials.rst
@@ -150,3 +150,4 @@ Various Tutorials
- `Intro to pandas data structures, by Greg Reda <http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/>`_
- `Pandas and Python: Top 10, by Manish Amde <http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
- `Pandas Tutorial, by Mikhail Semeniuk <http://www.bearrelroll.com/2013/05/python-pandas-tutorial>`_
+- `Pandas DataFrames Tutorial, by Karlijn Willems <http://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python>`_
| - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/15295 | 2017-02-03T09:28:24Z | 2017-02-03T13:50:26Z | 2017-02-03T13:50:26Z | 2017-02-03T13:50:29Z |
COMPAT: xarray 0.8.2 test compat w.r.t. CategoricalIndex idempotency | diff --git a/ci/install_travis.sh b/ci/install_travis.sh
index ded428c677f17..52b52d787aade 100755
--- a/ci/install_travis.sh
+++ b/ci/install_travis.sh
@@ -143,7 +143,7 @@ else
echo "[pip installs]"
REQ="ci/requirements-${PYTHON_VERSION}${JOB_TAG}.pip"
if [ -e ${REQ} ]; then
- pip install --upgrade -r $REQ
+ pip install -r $REQ
fi
# may have addtl installation instructions for this build
diff --git a/ci/requirements-2.7.run b/ci/requirements-2.7.run
index 2bfb8a3777fdf..b5fc919297c76 100644
--- a/ci/requirements-2.7.run
+++ b/ci/requirements-2.7.run
@@ -20,4 +20,4 @@ html5lib=1.0b2
beautiful-soup=4.2.1
statsmodels
jinja2=2.8
-xarray
+xarray=0.8.0
diff --git a/ci/requirements-3.5.pip b/ci/requirements-3.5.pip
new file mode 100644
index 0000000000000..0d9e44cf39fa4
--- /dev/null
+++ b/ci/requirements-3.5.pip
@@ -0,0 +1 @@
+xarray==0.9.1
diff --git a/ci/requirements-3.5.run b/ci/requirements-3.5.run
index e15ca6079b4fe..ef354195c8f23 100644
--- a/ci/requirements-3.5.run
+++ b/ci/requirements-3.5.run
@@ -16,6 +16,5 @@ bottleneck
sqlalchemy
pymysql
psycopg2
-xarray
s3fs
beautifulsoup4
diff --git a/pandas/tests/test_generic.py b/pandas/tests/test_generic.py
index f7b7ae8c66382..0ca8ba47b8a8f 100644
--- a/pandas/tests/test_generic.py
+++ b/pandas/tests/test_generic.py
@@ -7,6 +7,7 @@
from numpy import nan
import pandas as pd
+from distutils.version import LooseVersion
from pandas.types.common import is_scalar
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range, Panel4D)
@@ -870,6 +871,7 @@ def test_describe_none(self):
def test_to_xarray(self):
tm._skip_if_no_xarray()
+ import xarray
from xarray import DataArray
s = Series([])
@@ -895,15 +897,16 @@ def testit(index, check_index_type=True, check_categorical=True):
check_index_type=check_index_type,
check_categorical=check_categorical)
- for index in [tm.makeFloatIndex, tm.makeIntIndex,
- tm.makeStringIndex, tm.makeUnicodeIndex,
- tm.makeDateIndex, tm.makePeriodIndex,
- tm.makeTimedeltaIndex]:
- testit(index)
+ l = [tm.makeFloatIndex, tm.makeIntIndex,
+ tm.makeStringIndex, tm.makeUnicodeIndex,
+ tm.makeDateIndex, tm.makePeriodIndex,
+ tm.makeTimedeltaIndex]
+
+ if LooseVersion(xarray.__version__) >= '0.8.0':
+ l.append(tm.makeCategoricalIndex)
- # not idempotent
- testit(tm.makeCategoricalIndex, check_index_type=False,
- check_categorical=False)
+ for index in l:
+ testit(index)
s = Series(range(6))
s.index.name = 'foo'
| closes #15282
| https://api.github.com/repos/pandas-dev/pandas/pulls/15285 | 2017-02-01T19:37:19Z | 2017-02-01T22:55:41Z | 2017-02-01T22:55:41Z | 2017-02-01T22:55:41Z |
CLN: remove build warnings from tslib.pyx, saslib.pyx, some from lib.pyx | diff --git a/pandas/io/sas/saslib.pyx b/pandas/io/sas/saslib.pyx
index a66d62ea41581..4396180da44cb 100644
--- a/pandas/io/sas/saslib.pyx
+++ b/pandas/io/sas/saslib.pyx
@@ -311,7 +311,7 @@ cdef class Parser(object):
self.current_page_subheaders_count =\
self.parser._current_page_subheaders_count
- cdef bint readline(self):
+ cdef readline(self):
cdef:
int offset, bit_offset, align_correction
diff --git a/pandas/lib.pyx b/pandas/lib.pyx
index fce6a3d03287e..b4724bc3dd59b 100644
--- a/pandas/lib.pyx
+++ b/pandas/lib.pyx
@@ -3,6 +3,7 @@ cimport numpy as np
cimport cython
import numpy as np
import sys
+cdef bint PY3 = (sys.version_info[0] >= 3)
from numpy cimport *
@@ -42,7 +43,6 @@ cdef extern from "Python.h":
Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step,
Py_ssize_t *slicelength) except -1
-
cimport cpython
isnan = np.isnan
@@ -1568,62 +1568,65 @@ def get_blkno_indexers(int64_t[:] blknos, bint group=True):
int64_t cur_blkno
Py_ssize_t i, start, stop, n, diff
+ object blkno
list group_order
dict group_slices
int64_t[:] res_view
n = blknos.shape[0]
- if n > 0:
- start = 0
- cur_blkno = blknos[start]
+ if n == 0:
+ return
- if group == False:
- for i in range(1, n):
- if blknos[i] != cur_blkno:
- yield cur_blkno, slice(start, i)
+ start = 0
+ cur_blkno = blknos[start]
- start = i
- cur_blkno = blknos[i]
+ if group == False:
+ for i in range(1, n):
+ if blknos[i] != cur_blkno:
+ yield cur_blkno, slice(start, i)
- yield cur_blkno, slice(start, n)
- else:
- group_order = []
- group_dict = {}
-
- for i in range(1, n):
- if blknos[i] != cur_blkno:
- if cur_blkno not in group_dict:
- group_order.append(cur_blkno)
- group_dict[cur_blkno] = [(start, i)]
- else:
- group_dict[cur_blkno].append((start, i))
-
- start = i
- cur_blkno = blknos[i]
-
- if cur_blkno not in group_dict:
- group_order.append(cur_blkno)
- group_dict[cur_blkno] = [(start, n)]
- else:
- group_dict[cur_blkno].append((start, n))
+ start = i
+ cur_blkno = blknos[i]
- for blkno in group_order:
- slices = group_dict[blkno]
- if len(slices) == 1:
- yield blkno, slice(slices[0][0], slices[0][1])
+ yield cur_blkno, slice(start, n)
+ else:
+ group_order = []
+ group_dict = {}
+
+ for i in range(1, n):
+ if blknos[i] != cur_blkno:
+ if cur_blkno not in group_dict:
+ group_order.append(cur_blkno)
+ group_dict[cur_blkno] = [(start, i)]
else:
- tot_len = sum(stop - start for start, stop in slices)
- result = np.empty(tot_len, dtype=np.int64)
- res_view = result
+ group_dict[cur_blkno].append((start, i))
- i = 0
- for start, stop in slices:
- for diff in range(start, stop):
- res_view[i] = diff
- i += 1
+ start = i
+ cur_blkno = blknos[i]
- yield blkno, result
+ if cur_blkno not in group_dict:
+ group_order.append(cur_blkno)
+ group_dict[cur_blkno] = [(start, n)]
+ else:
+ group_dict[cur_blkno].append((start, n))
+
+ for blkno in group_order:
+ slices = group_dict[blkno]
+ if len(slices) == 1:
+ yield blkno, slice(slices[0][0], slices[0][1])
+ else:
+ tot_len = sum([stop - start for start, stop in slices])
+ result = np.empty(tot_len, dtype=np.int64)
+ res_view = result
+
+ i = 0
+ for start, stop in slices:
+ for diff in range(start, stop):
+ res_view[i] = diff
+ i += 1
+
+ yield blkno, result
@cython.boundscheck(False)
@@ -1670,7 +1673,7 @@ cpdef slice_canonize(slice s):
Convert slice to canonical bounded form.
"""
cdef:
- Py_ssize_t start, stop, step, length
+ Py_ssize_t start = 0, stop = 0, step = 1, length
if s.step is None:
step = 1
@@ -1727,6 +1730,7 @@ cpdef slice_get_indices_ex(slice slc, Py_ssize_t objlen=PY_SSIZE_T_MAX):
PySlice_GetIndicesEx(<PySliceObject *>slc, objlen,
&start, &stop, &step, &length)
+
return start, stop, step, length
diff --git a/pandas/src/datetime_helper.h b/pandas/src/datetime_helper.h
index 2b24028ff3d8c..bef4b4266c824 100644
--- a/pandas/src/datetime_helper.h
+++ b/pandas/src/datetime_helper.h
@@ -15,10 +15,6 @@ The full license is in the LICENSE file, distributed with this software.
#include "numpy/arrayobject.h"
#include "numpy/arrayscalars.h"
-#if PY_MAJOR_VERSION >= 3
-#define PyInt_AS_LONG PyLong_AsLong
-#endif
-
npy_int64 get_long_attr(PyObject *o, const char *attr) {
npy_int64 long_val;
PyObject *value = PyObject_GetAttrString(o, attr);
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index dd2f5174a516b..fc6e689a35d81 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -8,10 +8,8 @@ from numpy cimport (int8_t, int32_t, int64_t, import_array, ndarray,
from datetime cimport get_datetime64_value, get_timedelta64_value
import numpy as np
-# GH3363
-from sys import version_info
-cdef bint PY2 = version_info[0] == 2
-cdef bint PY3 = not PY2
+import sys
+cdef bint PY3 = (sys.version_info[0] >= 3)
from cpython cimport (
PyTypeObject,
@@ -24,11 +22,8 @@ from cpython cimport (
PyUnicode_AsUTF8String,
)
-
-# Cython < 0.17 doesn't have this in cpython
cdef extern from "Python.h":
cdef PyTypeObject *Py_TYPE(object)
- int PySlice_Check(object)
cdef extern from "datetime_helper.h":
double total_seconds(object)
@@ -2121,8 +2116,8 @@ _DEFAULT_DATETIME = datetime(1, 1, 1).replace(
hour=0, minute=0, second=0, microsecond=0)
_MONTHS = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL',
'AUG', 'SEP', 'OCT', 'NOV', 'DEC']
-_MONTH_NUMBERS = dict((k, i) for i, k in enumerate(_MONTHS))
-_MONTH_ALIASES = dict((k + 1, v) for k, v in enumerate(_MONTHS))
+_MONTH_NUMBERS = {k: i for i, k in enumerate(_MONTHS)}
+_MONTH_ALIASES = {(k + 1): v for k, v in enumerate(_MONTHS)}
cpdef object _get_rule_month(object source, object default='DEC'):
@@ -5529,8 +5524,8 @@ class TimeRE(dict):
'B': self.__seqToRE(self.locale_time.f_month[1:], 'B'),
'b': self.__seqToRE(self.locale_time.a_month[1:], 'b'),
'p': self.__seqToRE(self.locale_time.am_pm, 'p'),
- 'Z': self.__seqToRE((tz for tz_names in self.locale_time.timezone
- for tz in tz_names),
+ 'Z': self.__seqToRE([tz for tz_names in self.locale_time.timezone
+ for tz in tz_names],
'Z'),
'%': '%'})
base.__setitem__('W', base.__getitem__('U').replace('U', 'W'))
@@ -5553,7 +5548,7 @@ class TimeRE(dict):
break
else:
return ''
- regex = '|'.join(re_escape(stuff) for stuff in to_convert)
+ regex = '|'.join([re_escape(stuff) for stuff in to_convert])
regex = '(?P<%s>%s' % (directive, regex)
return '%s)' % regex
diff --git a/setup.py b/setup.py
index 1c9972defabbe..0c4dd33a70482 100755
--- a/setup.py
+++ b/setup.py
@@ -462,6 +462,7 @@ def pxd(name):
tseries_depends = ['pandas/src/datetime/np_datetime.h',
'pandas/src/datetime/np_datetime_strings.h',
+ 'pandas/src/datetime_helper.h',
'pandas/src/period_helper.h',
'pandas/src/datetime.pxd']
@@ -584,6 +585,7 @@ def pxd(name):
ujson_ext = Extension('pandas.json',
depends=['pandas/src/ujson/lib/ultrajson.h',
+ 'pandas/src/datetime_helper.h',
'pandas/src/numpy_helper.h'],
sources=['pandas/src/ujson/python/ujson.c',
'pandas/src/ujson/python/objToJSON.c',
| https://api.github.com/repos/pandas-dev/pandas/pulls/15264 | 2017-01-30T16:11:53Z | 2017-01-30T17:39:51Z | 2017-01-30T17:39:51Z | 2017-01-30T23:27:01Z | |
TST: MultiIndex Slicing maintains first level index (#12697) | diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py
index a6eb3c8a891e7..f7fa07916ca74 100644
--- a/pandas/tests/indexing/test_indexing.py
+++ b/pandas/tests/indexing/test_indexing.py
@@ -5392,6 +5392,23 @@ def test_maybe_numeric_slice(self):
expected = [1]
self.assertEqual(result, expected)
+ def test_multiindex_slice_first_level(self):
+ # GH 12697
+ freq = ['a', 'b', 'c', 'd']
+ idx = pd.MultiIndex.from_product([freq, np.arange(500)])
+ df = pd.DataFrame(list(range(2000)), index=idx, columns=['Test'])
+ df_slice = df.loc[pd.IndexSlice[:, 30:70], :]
+ result = df_slice.loc['a']
+ expected = pd.DataFrame(list(range(30, 71)),
+ columns=['Test'],
+ index=range(30, 71))
+ tm.assert_frame_equal(result, expected)
+ result = df_slice.loc['d']
+ expected = pd.DataFrame(list(range(1530, 1571)),
+ columns=['Test'],
+ index=range(30, 71))
+ tm.assert_frame_equal(result, expected)
+
class TestSeriesNoneCoercion(tm.TestCase):
EXPECTED_RESULTS = [
| - [x] closes #12697
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
The original issue appears to work correctly on master and doesn't seem to have been fixed by a PR from 0.20.0 | https://api.github.com/repos/pandas-dev/pandas/pulls/15255 | 2017-01-29T09:58:31Z | 2017-01-29T21:52:56Z | 2017-01-29T21:52:56Z | 2017-12-20T02:04:06Z |
TST: incorrect locale specification for datetime format | diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 4d286fc62764c..b5daf1ac0ec68 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -1,4 +1,5 @@
# pylint: disable-msg=E1101,W0612
+import locale
import calendar
import operator
import sys
@@ -5369,7 +5370,12 @@ def test_to_datetime_format_integer(self):
assert_series_equal(result, expected)
def test_to_datetime_format_microsecond(self):
- val = '01-Apr-2011 00:00:01.978'
+
+ # these are locale dependent
+ lang, _ = locale.getlocale()
+ month_abbr = calendar.month_abbr[4]
+ val = '01-{}-2011 00:00:01.978'.format(month_abbr)
+
format = '%d-%b-%Y %H:%M:%S.%f'
result = to_datetime(val, format=format)
exp = datetime.strptime(val, format)
| closes #15215
| https://api.github.com/repos/pandas-dev/pandas/pulls/15233 | 2017-01-26T00:21:58Z | 2017-01-26T02:31:04Z | 2017-01-26T02:31:04Z | 2017-01-26T02:31:04Z |
BUG/API: Return a Series from DataFrame.asof with no match | diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 86fa919fec304..07c90b1e64f29 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -327,7 +327,7 @@ Other API Changes
- ``pd.read_csv()`` will now raise a ``ValueError`` for the C engine if the quote character is larger than than one byte (:issue:`11592`)
- ``inplace`` arguments now require a boolean value, else a ``ValueError`` is thrown (:issue:`14189`)
- ``pandas.api.types.is_datetime64_ns_dtype`` will now report ``True`` on a tz-aware dtype, similar to ``pandas.api.types.is_datetime64_any_dtype``
-
+ - ``DataFrame.asof()`` will return a null filled ``Series`` instead the scalar ``NaN`` if a match is not found (:issue:`15118`)
.. _whatsnew_0200.deprecations:
Deprecations
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 2bb492055bcd0..869062bd231fe 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3783,7 +3783,8 @@ def asof(self, where, subset=None):
.. versionadded:: 0.19.0 For DataFrame
- If there is no good value, NaN is returned.
+ If there is no good value, NaN is returned for a Series
+ a Series of NaN values for a DataFrame
Parameters
----------
@@ -3839,6 +3840,9 @@ def asof(self, where, subset=None):
start = start.ordinal
if where < start:
+ if not is_series:
+ from pandas import Series
+ return Series(index=self.columns, name=where)
return np.nan
# It's always much faster to use a *while* loop here for
diff --git a/pandas/tests/frame/test_asof.py b/pandas/tests/frame/test_asof.py
index 47d56c39757db..f68219120b48e 100644
--- a/pandas/tests/frame/test_asof.py
+++ b/pandas/tests/frame/test_asof.py
@@ -3,9 +3,10 @@
import nose
import numpy as np
-from pandas import DataFrame, date_range
+from pandas import (DataFrame, date_range, Timestamp, Series,
+ to_datetime)
-from pandas.util.testing import assert_frame_equal
+from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas.util.testing as tm
from .common import TestData
@@ -67,6 +68,23 @@ def test_subset(self):
assert_frame_equal(result, expected)
+ def test_missing(self):
+ # GH 15118
+ # no match found - `where` value before earliest date in index
+ N = 10
+ rng = date_range('1/1/1990', periods=N, freq='53s')
+ df = DataFrame({'A': np.arange(N), 'B': np.arange(N)},
+ index=rng)
+ result = df.asof('1989-12-31')
+
+ expected = Series(index=['A', 'B'], name=Timestamp('1989-12-31'))
+ assert_series_equal(result, expected)
+
+ result = df.asof(to_datetime(['1989-12-31']))
+ expected = DataFrame(index=to_datetime(['1989-12-31']),
+ columns=['A', 'B'], dtype='float64')
+ assert_frame_equal(result, expected)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
exit=False)
| - [x] closes #15118
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Technically this is an API change, but more of a bug so didn't think there was a need to warn / deprecate?
Also there seems to be room to clean up the whole `asof` implementation, xref #10343, but wanted to keep that separate | https://api.github.com/repos/pandas-dev/pandas/pulls/15232 | 2017-01-26T00:12:31Z | 2017-01-26T12:37:53Z | 2017-01-26T12:37:53Z | 2017-01-26T12:37:55Z |
CLN: remove deprecated google-analytics module (GH11308) | diff --git a/doc/source/remote_data.rst b/doc/source/remote_data.rst
index 019aa82fed1aa..7980133582125 100644
--- a/doc/source/remote_data.rst
+++ b/doc/source/remote_data.rst
@@ -29,63 +29,3 @@ modules to be independently updated to your pandas installation. The API for
.. code-block:: python
from pandas_datareader import data, wb
-
-
-.. _remote_data.ga:
-
-Google Analytics
-----------------
-
-The :mod:`~pandas.io.ga` module provides a wrapper for
-`Google Analytics API <https://developers.google.com/analytics/devguides>`__
-to simplify retrieving traffic data.
-Result sets are parsed into a pandas DataFrame with a shape and data types
-derived from the source table.
-
-Configuring Access to Google Analytics
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The first thing you need to do is to setup accesses to Google Analytics API. Follow the steps below:
-
-#. In the `Google Developers Console <https://console.developers.google.com>`__
- #. enable the Analytics API
- #. create a new project
- #. create a new Client ID for an "Installed Application" (in the "APIs & auth / Credentials section" of the newly created project)
- #. download it (JSON file)
-#. On your machine
- #. rename it to ``client_secrets.json``
- #. move it to the ``pandas/io`` module directory
-
-The first time you use the :func:`read_ga` function, a browser window will open to ask you to authentify to the Google API. Do proceed.
-
-Using the Google Analytics API
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The following will fetch users and pageviews (metrics) data per day of the week, for the first semester of 2014, from a particular property.
-
-.. code-block:: python
-
- import pandas.io.ga as ga
- ga.read_ga(
- account_id = "2360420",
- profile_id = "19462946",
- property_id = "UA-2360420-5",
- metrics = ['users', 'pageviews'],
- dimensions = ['dayOfWeek'],
- start_date = "2014-01-01",
- end_date = "2014-08-01",
- index_col = 0,
- filters = "pagePath=~aboutus;ga:country==France",
- )
-
-The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We strongly recommend that you always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
-
-The ``index_col`` argument indicates which dimension(s) has to be taken as index.
-
-The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page URL has to contain ``aboutus`` AND the visitors country has to be France.
-
-Detailed information in the following:
-
-* `pandas & google analytics, by yhat <http://blog.yhathq.com/posts/pandas-google-analytics.html>`__
-* `Google Analytics integration in pandas, by Chang She <http://quantabee.wordpress.com/2012/12/17/google-analytics-pandas/>`__
-* `Google Analytics Dimensions and Metrics Reference <https://developers.google.com/analytics/devguides/reporting/core/dimsmets>`_
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 3bb472e9361e7..feba3d6224e65 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -163,7 +163,7 @@ Other enhancements:
p.all()
- Added support for ``utcfromtimestamp()``, ``fromtimestamp()``, and ``combine()`` on `Timestamp` class (:issue:`5351`).
-- Added Google Analytics (`pandas.io.ga`) basic documentation (:issue:`8835`). See :ref:`here<remote_data.ga>`.
+- Added Google Analytics (`pandas.io.ga`) basic documentation (:issue:`8835`). See `here<http://pandas.pydata.org/pandas-docs/version/0.15.2/remote_data.html#remote-data-ga>`__.
- ``Timedelta`` arithmetic returns ``NotImplemented`` in unknown cases, allowing extensions by custom classes (:issue:`8813`).
- ``Timedelta`` now supports arithemtic with ``numpy.ndarray`` objects of the appropriate dtype (numpy 1.8 or newer only) (:issue:`8884`).
- Added ``Timedelta.to_timedelta64()`` method to the public API (:issue:`8884`).
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 9895afdc4c7b5..c7027bd57eff4 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -351,6 +351,8 @@ Removal of prior version deprecations/changes
- The ``pandas.rpy`` module is removed. Similar functionality can be accessed
through the `rpy2 <https://rpy2.readthedocs.io/>`__ project.
See the :ref:`R interfacing docs <rpy>` for more details.
+- The ``pandas.io.ga`` module with a ``google-analytics`` interface is removed (:issue:`11308`).
+ Similar functionality can be found in the `Google2Pandas <https://github.com/panalysis/Google2Pandas>`__ package.
- ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`)
diff --git a/pandas/io/ga.py b/pandas/io/ga.py
deleted file mode 100644
index 45424e78ddbe7..0000000000000
--- a/pandas/io/ga.py
+++ /dev/null
@@ -1,463 +0,0 @@
-"""
-1. Goto https://console.developers.google.com/iam-admin/projects
-2. Create new project
-3. Goto APIs and register for OAuth2.0 for installed applications
-4. Download JSON secret file and move into same directory as this file
-"""
-# flake8: noqa
-
-from datetime import datetime
-import re
-from pandas import compat
-import numpy as np
-from pandas import DataFrame
-import pandas as pd
-import pandas.io.parsers as psr
-import pandas.lib as lib
-from pandas.io.date_converters import generic_parser
-import pandas.io.auth as auth
-from pandas.util.decorators import Appender, Substitution
-
-from apiclient.errors import HttpError
-from oauth2client.client import AccessTokenRefreshError
-from pandas.compat import zip, u
-
-# GH11038
-import warnings
-warnings.warn("The pandas.io.ga module is deprecated and will be "
- "removed in a future version.",
- FutureWarning, stacklevel=2)
-
-TYPE_MAP = {u('INTEGER'): int, u('FLOAT'): float, u('TIME'): int}
-
-NO_CALLBACK = auth.OOB_CALLBACK_URN
-DOC_URL = auth.DOC_URL
-
-_QUERY_PARAMS = """metrics : list of str
- Un-prefixed metric names (e.g., 'visitors' and not 'ga:visitors')
-dimensions : list of str
- Un-prefixed dimension variable names
-start_date : str/date/datetime
-end_date : str/date/datetime, optional, default is None but internally set as today
-segment : list of str, optional, default: None
-filters : list of str, optional, default: None
-start_index : int, default 1
-max_results : int, default 10000
- If >10000, must specify chunksize or ValueError will be raised"""
-
-_QUERY_DOC = """
-Construct a google analytics query using given parameters
-Metrics and dimensions do not need the 'ga:' prefix
-
-Parameters
-----------
-profile_id : str
-%s
-""" % _QUERY_PARAMS
-
-_GA_READER_DOC = """Given query parameters, return a DataFrame with all the
-data or an iterator that returns DataFrames containing chunks of the data
-
-Parameters
-----------
-%s
-sort : bool/list, default True
- Sort output by index or list of columns
-chunksize : int, optional
- If max_results >10000, specifies the number of rows per iteration
-index_col : str/list of str/dict, optional, default: None
- If unspecified then dimension variables are set as index
-parse_dates : bool/list/dict, default: True
-keep_date_col : boolean, default: False
-date_parser : optional, default: None
-na_values : optional, default: None
-converters : optional, default: None
-dayfirst : bool, default False
- Informs date parsing
-account_name : str, optional, default: None
-account_id : str, optional, default: None
-property_name : str, optional, default: None
-property_id : str, optional, default: None
-profile_name : str, optional, default: None
-profile_id : str, optional, default: None
-%%(extras)s
-Returns
--------
-data : DataFrame or DataFrame yielding iterator
-""" % _QUERY_PARAMS
-
-_AUTH_PARAMS = """secrets : str, optional
- File path to the secrets file
-scope : str, optional
- Authentication scope
-token_file_name : str, optional
- Path to token storage
-redirect : str, optional
- Local host redirect if unspecified
-"""
-
-
-def reset_token_store():
- """
- Deletes the default token store
- """
- auth.reset_default_token_store()
-
-
-@Substitution(extras=_AUTH_PARAMS)
-@Appender(_GA_READER_DOC)
-def read_ga(metrics, dimensions, start_date, **kwargs):
- lst = ['secrets', 'scope', 'token_file_name', 'redirect']
- reader_kwds = dict((p, kwargs.pop(p)) for p in lst if p in kwargs)
- reader = GAnalytics(**reader_kwds)
- return reader.get_data(metrics=metrics, start_date=start_date,
- dimensions=dimensions, **kwargs)
-
-
-class OAuthDataReader(object):
- """
- Abstract class for handling OAuth2 authentication using the Google
- oauth2client library
- """
- def __init__(self, scope, token_file_name, redirect):
- """
- Parameters
- ----------
- scope : str
- Designates the authentication scope
- token_file_name : str
- Location of cache for authenticated tokens
- redirect : str
- Redirect URL
- """
- self.scope = scope
- self.token_store = auth.make_token_store(token_file_name)
- self.redirect_url = redirect
-
- def authenticate(self, secrets):
- """
- Run the authentication process and return an authorized
- http object
-
- Parameters
- ----------
- secrets : str
- File name for client secrets
-
- Notes
- -----
- See google documention for format of secrets file
- %s
- """ % DOC_URL
- flow = self._create_flow(secrets)
- return auth.authenticate(flow, self.token_store)
-
- def _create_flow(self, secrets):
- """
- Create an authentication flow based on the secrets file
-
- Parameters
- ----------
- secrets : str
- File name for client secrets
-
- Notes
- -----
- See google documentation for format of secrets file
- %s
- """ % DOC_URL
- return auth.get_flow(secrets, self.scope, self.redirect_url)
-
-
-class GDataReader(OAuthDataReader):
- """
- Abstract class for reading data from google APIs using OAuth2
- Subclasses must implement create_query method
- """
- def __init__(self, scope=auth.DEFAULT_SCOPE,
- token_file_name=auth.DEFAULT_TOKEN_FILE,
- redirect=NO_CALLBACK, secrets=auth.DEFAULT_SECRETS):
- super(GDataReader, self).__init__(scope, token_file_name, redirect)
- self._service = self._init_service(secrets)
-
- @property
- def service(self):
- """The authenticated request service object"""
- return self._service
-
- def _init_service(self, secrets):
- """
- Build an authenticated google api request service using the given
- secrets file
- """
- http = self.authenticate(secrets)
- return auth.init_service(http)
-
- def get_account(self, name=None, id=None, **kwargs):
- """ Retrieve an account that matches the name, id, or some account
- attribute specified in **kwargs
-
- Parameters
- ----------
- name : str, optional, default: None
- id : str, optional, default: None
- """
- accounts = self.service.management().accounts().list().execute()
- return _get_match(accounts, name, id, **kwargs)
-
- def get_web_property(self, account_id=None, name=None, id=None, **kwargs):
- """
- Retrieve a web property given and account and property name, id, or
- custom attribute
-
- Parameters
- ----------
- account_id : str, optional, default: None
- name : str, optional, default: None
- id : str, optional, default: None
- """
- prop_store = self.service.management().webproperties()
- kwds = {}
- if account_id is not None:
- kwds['accountId'] = account_id
- prop_for_acct = prop_store.list(**kwds).execute()
- return _get_match(prop_for_acct, name, id, **kwargs)
-
- def get_profile(self, account_id=None, web_property_id=None, name=None,
- id=None, **kwargs):
-
- """
- Retrieve the right profile for the given account, web property, and
- profile attribute (name, id, or arbitrary parameter in kwargs)
-
- Parameters
- ----------
- account_id : str, optional, default: None
- web_property_id : str, optional, default: None
- name : str, optional, default: None
- id : str, optional, default: None
- """
- profile_store = self.service.management().profiles()
- kwds = {}
- if account_id is not None:
- kwds['accountId'] = account_id
- if web_property_id is not None:
- kwds['webPropertyId'] = web_property_id
- profiles = profile_store.list(**kwds).execute()
- return _get_match(profiles, name, id, **kwargs)
-
- def create_query(self, *args, **kwargs):
- raise NotImplementedError()
-
- @Substitution(extras='')
- @Appender(_GA_READER_DOC)
- def get_data(self, metrics, start_date, end_date=None,
- dimensions=None, segment=None, filters=None, start_index=1,
- max_results=10000, index_col=None, parse_dates=True,
- keep_date_col=False, date_parser=None, na_values=None,
- converters=None, sort=True, dayfirst=False,
- account_name=None, account_id=None, property_name=None,
- property_id=None, profile_name=None, profile_id=None,
- chunksize=None):
- if chunksize is None and max_results > 10000:
- raise ValueError('Google API returns maximum of 10,000 rows, '
- 'please set chunksize')
-
- account = self.get_account(account_name, account_id)
- web_property = self.get_web_property(account.get('id'), property_name,
- property_id)
- profile = self.get_profile(account.get('id'), web_property.get('id'),
- profile_name, profile_id)
-
- profile_id = profile.get('id')
-
- if index_col is None and dimensions is not None:
- if isinstance(dimensions, compat.string_types):
- dimensions = [dimensions]
- index_col = _clean_index(list(dimensions), parse_dates)
-
- def _read(start, result_size):
- query = self.create_query(profile_id, metrics, start_date,
- end_date=end_date, dimensions=dimensions,
- segment=segment, filters=filters,
- start_index=start,
- max_results=result_size)
-
- try:
- rs = query.execute()
- rows = rs.get('rows', [])
- col_info = rs.get('columnHeaders', [])
- return self._parse_data(rows, col_info, index_col,
- parse_dates=parse_dates,
- keep_date_col=keep_date_col,
- date_parser=date_parser,
- dayfirst=dayfirst,
- na_values=na_values,
- converters=converters, sort=sort)
- except HttpError as inst:
- raise ValueError('Google API error %s: %s' % (inst.resp.status,
- inst._get_reason()))
-
- if chunksize is None:
- return _read(start_index, max_results)
-
- def iterator():
- curr_start = start_index
-
- while curr_start < max_results:
- yield _read(curr_start, chunksize)
- curr_start += chunksize
- return iterator()
-
- def _parse_data(self, rows, col_info, index_col, parse_dates=True,
- keep_date_col=False, date_parser=None, dayfirst=False,
- na_values=None, converters=None, sort=True):
- # TODO use returned column types
- col_names = _get_col_names(col_info)
- df = psr._read(rows, dict(index_col=index_col, parse_dates=parse_dates,
- date_parser=date_parser, dayfirst=dayfirst,
- na_values=na_values,
- keep_date_col=keep_date_col,
- converters=converters,
- header=None, names=col_names))
-
- if isinstance(sort, bool) and sort:
- return df.sort_index()
- elif isinstance(sort, (compat.string_types, list, tuple, np.ndarray)):
- return df.sort_index(by=sort)
-
- return df
-
-
-class GAnalytics(GDataReader):
-
- @Appender(_QUERY_DOC)
- def create_query(self, profile_id, metrics, start_date, end_date=None,
- dimensions=None, segment=None, filters=None,
- start_index=None, max_results=10000, **kwargs):
- qry = format_query(profile_id, metrics, start_date, end_date=end_date,
- dimensions=dimensions, segment=segment,
- filters=filters, start_index=start_index,
- max_results=max_results, **kwargs)
- try:
- return self.service.data().ga().get(**qry)
- except TypeError as error:
- raise ValueError('Error making query: %s' % error)
-
-
-def format_query(ids, metrics, start_date, end_date=None, dimensions=None,
- segment=None, filters=None, sort=None, start_index=None,
- max_results=10000, **kwargs):
- if isinstance(metrics, compat.string_types):
- metrics = [metrics]
- met = ','.join(['ga:%s' % x for x in metrics])
-
- start_date = pd.to_datetime(start_date).strftime('%Y-%m-%d')
- if end_date is None:
- end_date = datetime.today()
- end_date = pd.to_datetime(end_date).strftime('%Y-%m-%d')
-
- qry = dict(ids='ga:%s' % str(ids),
- metrics=met,
- start_date=start_date,
- end_date=end_date)
- qry.update(kwargs)
-
- names = ['dimensions', 'filters', 'sort']
- lst = [dimensions, filters, sort]
- [_maybe_add_arg(qry, n, d) for n, d in zip(names, lst)]
-
- if isinstance(segment, compat.string_types):
- if re.match("^[a-zA-Z0-9\-\_]+$", segment):
- _maybe_add_arg(qry, 'segment', segment, 'gaid:')
- else:
- _maybe_add_arg(qry, 'segment', segment, 'dynamic::ga')
- elif isinstance(segment, int):
- _maybe_add_arg(qry, 'segment', segment, 'gaid:')
- elif segment:
- raise ValueError("segment must be string for dynamic and int ID")
-
- if start_index is not None:
- qry['start_index'] = str(start_index)
-
- if max_results is not None:
- qry['max_results'] = str(max_results)
-
- return qry
-
-
-def _maybe_add_arg(query, field, data, prefix='ga'):
- if data is not None:
- if isinstance(data, (compat.string_types, int)):
- data = [data]
- data = ','.join(['%s:%s' % (prefix, x) for x in data])
- query[field] = data
-
-
-def _get_match(obj_store, name, id, **kwargs):
- key, val = None, None
- if len(kwargs) > 0:
- key = list(kwargs.keys())[0]
- val = list(kwargs.values())[0]
-
- if name is None and id is None and key is None:
- return obj_store.get('items')[0]
-
- name_ok = lambda item: name is not None and item.get('name') == name
- id_ok = lambda item: id is not None and item.get('id') == id
- key_ok = lambda item: key is not None and item.get(key) == val
-
- match = None
- if obj_store.get('items'):
- # TODO look up gapi for faster lookup
- for item in obj_store.get('items'):
- if name_ok(item) or id_ok(item) or key_ok(item):
- return item
-
-
-def _clean_index(index_dims, parse_dates):
- _should_add = lambda lst: pd.Index(lst).isin(index_dims).all()
- to_remove = []
- to_add = []
-
- if isinstance(parse_dates, (list, tuple, np.ndarray)):
- for lst in parse_dates:
- if isinstance(lst, (list, tuple, np.ndarray)):
- if _should_add(lst):
- to_add.append('_'.join(lst))
- to_remove.extend(lst)
- elif isinstance(parse_dates, dict):
- for name, lst in compat.iteritems(parse_dates):
- if isinstance(lst, (list, tuple, np.ndarray)):
- if _should_add(lst):
- to_add.append(name)
- to_remove.extend(lst)
-
- index_dims = pd.Index(index_dims)
- to_remove = pd.Index(set(to_remove))
- to_add = pd.Index(set(to_add))
-
- return index_dims.difference(to_remove).union(to_add)
-
-
-def _get_col_names(header_info):
- return [x['name'][3:] for x in header_info]
-
-
-def _get_column_types(header_info):
- return [(x['name'][3:], x['columnType']) for x in header_info]
-
-
-def _get_dim_names(header_info):
- return [x['name'][3:] for x in header_info
- if x['columnType'] == u('DIMENSION')]
-
-
-def _get_met_names(header_info):
- return [x['name'][3:] for x in header_info
- if x['columnType'] == u('METRIC')]
-
-
-def _get_data_types(header_info):
- return [(x['name'][3:], TYPE_MAP.get(x['dataType'], object))
- for x in header_info]
diff --git a/pandas/io/tests/test_ga.py b/pandas/io/tests/test_ga.py
deleted file mode 100644
index 469e121f633d7..0000000000000
--- a/pandas/io/tests/test_ga.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# flake8: noqa
-
-import os
-from datetime import datetime
-
-import warnings
-import nose
-import pandas as pd
-from pandas import compat
-from pandas.util.testing import (network, assert_frame_equal,
- with_connectivity_check, slow)
-import pandas.util.testing as tm
-
-if compat.PY3:
- raise nose.SkipTest("python-gflags does not support Python 3 yet")
-
-try:
- import httplib2
- import apiclient
-
- # deprecated
- with warnings.catch_warnings(record=True):
- import pandas.io.ga as ga
-
- from pandas.io.ga import GAnalytics, read_ga
- from pandas.io.auth import AuthenticationConfigError, reset_default_token_store
- from pandas.io import auth
-except ImportError:
- raise nose.SkipTest("need httplib2 and auth libs")
-
-
-class TestGoogle(tm.TestCase):
-
- _multiprocess_can_split_ = True
-
- def test_remove_token_store(self):
- auth.DEFAULT_TOKEN_FILE = 'test.dat'
- with open(auth.DEFAULT_TOKEN_FILE, 'w') as fh:
- fh.write('test')
-
- reset_default_token_store()
- self.assertFalse(os.path.exists(auth.DEFAULT_TOKEN_FILE))
-
- @with_connectivity_check("http://www.google.com")
- def test_getdata(self):
- try:
- end_date = datetime.now()
- start_date = end_date - pd.offsets.Day() * 5
- end_date = end_date.strftime('%Y-%m-%d')
- start_date = start_date.strftime('%Y-%m-%d')
-
- reader = GAnalytics()
- df = reader.get_data(
- metrics=['avgTimeOnSite', 'visitors', 'newVisits',
- 'pageviewsPerVisit'],
- start_date=start_date,
- end_date=end_date,
- dimensions=['date', 'hour'],
- parse_dates={'ts': ['date', 'hour']},
- index_col=0)
-
- self.assertIsInstance(df, pd.DataFrame)
- self.assertIsInstance(df.index, pd.DatetimeIndex)
- self.assertGreater(len(df), 1)
- self.assertTrue('date' not in df)
- self.assertTrue('hour' not in df)
- self.assertEqual(df.index.name, 'ts')
- self.assertTrue('avgTimeOnSite' in df)
- self.assertTrue('visitors' in df)
- self.assertTrue('newVisits' in df)
- self.assertTrue('pageviewsPerVisit' in df)
-
- df2 = read_ga(
- metrics=['avgTimeOnSite', 'visitors', 'newVisits',
- 'pageviewsPerVisit'],
- start_date=start_date,
- end_date=end_date,
- dimensions=['date', 'hour'],
- parse_dates={'ts': ['date', 'hour']},
- index_col=0)
-
- assert_frame_equal(df, df2)
-
- except AuthenticationConfigError:
- raise nose.SkipTest("authentication error")
-
- @with_connectivity_check("http://www.google.com")
- def test_iterator(self):
- try:
- reader = GAnalytics()
-
- it = reader.get_data(
- metrics='visitors',
- start_date='2005-1-1',
- dimensions='date',
- max_results=10, chunksize=5,
- index_col=0)
-
- df1 = next(it)
- df2 = next(it)
-
- for df in [df1, df2]:
- self.assertIsInstance(df, pd.DataFrame)
- self.assertIsInstance(df.index, pd.DatetimeIndex)
- self.assertEqual(len(df), 5)
- self.assertTrue('date' not in df)
- self.assertEqual(df.index.name, 'date')
- self.assertTrue('visitors' in df)
-
- self.assertTrue((df2.index > df1.index).all())
-
- except AuthenticationConfigError:
- raise nose.SkipTest("authentication error")
-
- def test_v2_advanced_segment_format(self):
- advanced_segment_id = 1234567
- query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id)
- self.assertEqual(query['segment'], 'gaid::' + str(advanced_segment_id), "An integer value should be formatted as an advanced segment.")
-
- def test_v2_dynamic_segment_format(self):
- dynamic_segment_id = 'medium==referral'
- query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=dynamic_segment_id)
- self.assertEqual(query['segment'], 'dynamic::ga:' + str(dynamic_segment_id), "A string value with more than just letters and numbers should be formatted as a dynamic segment.")
-
- def test_v3_advanced_segment_common_format(self):
- advanced_segment_id = 'aZwqR234'
- query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id)
- self.assertEqual(query['segment'], 'gaid::' + str(advanced_segment_id), "A string value with just letters and numbers should be formatted as an advanced segment.")
-
- def test_v3_advanced_segment_weird_format(self):
- advanced_segment_id = '_aZwqR234-s1'
- query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id)
- self.assertEqual(query['segment'], 'gaid::' + str(advanced_segment_id), "A string value with just letters, numbers, and hyphens should be formatted as an advanced segment.")
-
- def test_v3_advanced_segment_with_underscore_format(self):
- advanced_segment_id = 'aZwqR234_s1'
- query = ga.format_query('google_profile_id', ['visits'], '2013-09-01', segment=advanced_segment_id)
- self.assertEqual(query['segment'], 'gaid::' + str(advanced_segment_id), "A string value with just letters, numbers, and underscores should be formatted as an advanced segment.")
-
- @with_connectivity_check("http://www.google.com")
- def test_segment(self):
- try:
- end_date = datetime.now()
- start_date = end_date - pd.offsets.Day() * 5
- end_date = end_date.strftime('%Y-%m-%d')
- start_date = start_date.strftime('%Y-%m-%d')
-
- reader = GAnalytics()
- df = reader.get_data(
- metrics=['avgTimeOnSite', 'visitors', 'newVisits',
- 'pageviewsPerVisit'],
- start_date=start_date,
- end_date=end_date,
- segment=-2,
- dimensions=['date', 'hour'],
- parse_dates={'ts': ['date', 'hour']},
- index_col=0)
-
- self.assertIsInstance(df, pd.DataFrame)
- self.assertIsInstance(df.index, pd.DatetimeIndex)
- self.assertGreater(len(df), 1)
- self.assertTrue('date' not in df)
- self.assertTrue('hour' not in df)
- self.assertEqual(df.index.name, 'ts')
- self.assertTrue('avgTimeOnSite' in df)
- self.assertTrue('visitors' in df)
- self.assertTrue('newVisits' in df)
- self.assertTrue('pageviewsPerVisit' in df)
-
- # dynamic
- df = read_ga(
- metrics=['avgTimeOnSite', 'visitors', 'newVisits',
- 'pageviewsPerVisit'],
- start_date=start_date,
- end_date=end_date,
- segment="source=~twitter",
- dimensions=['date', 'hour'],
- parse_dates={'ts': ['date', 'hour']},
- index_col=0)
-
- assert isinstance(df, pd.DataFrame)
- assert isinstance(df.index, pd.DatetimeIndex)
- self.assertGreater(len(df), 1)
- self.assertTrue('date' not in df)
- self.assertTrue('hour' not in df)
- self.assertEqual(df.index.name, 'ts')
- self.assertTrue('avgTimeOnSite' in df)
- self.assertTrue('visitors' in df)
- self.assertTrue('newVisits' in df)
- self.assertTrue('pageviewsPerVisit' in df)
-
- except AuthenticationConfigError:
- raise nose.SkipTest("authentication error")
-
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- exit=False)
| https://api.github.com/repos/pandas-dev/pandas/pulls/15223 | 2017-01-25T11:09:43Z | 2017-01-25T15:43:14Z | 2017-01-25T15:43:14Z | 2017-01-25T15:43:14Z | |
BLD: hashtable.pxd is a pxd depends for _join, hashtable, and index | diff --git a/setup.py b/setup.py
index 8cdd72b2f4682..1c9972defabbe 100755
--- a/setup.py
+++ b/setup.py
@@ -490,13 +490,13 @@ def pxd(name):
index={'pyxfile': 'index',
'sources': ['pandas/src/datetime/np_datetime.c',
'pandas/src/datetime/np_datetime_strings.c'],
- 'pxdfiles': ['src/util'],
+ 'pxdfiles': ['src/util', 'hashtable'],
'depends': _pxi_dep['index']},
algos={'pyxfile': 'algos',
- 'pxdfiles': ['src/util'],
+ 'pxdfiles': ['src/util', 'hashtable'],
'depends': _pxi_dep['algos']},
_join={'pyxfile': 'src/join',
- 'pxdfiles': ['src/util'],
+ 'pxdfiles': ['src/util', 'hashtable'],
'depends': _pxi_dep['_join']},
_window={'pyxfile': 'window',
'pxdfiles': ['src/skiplist', 'src/util'],
| https://api.github.com/repos/pandas-dev/pandas/pulls/15218 | 2017-01-25T01:51:36Z | 2017-01-25T12:01:34Z | 2017-01-25T12:01:34Z | 2017-01-25T12:01:34Z | |
BLD: remove more build warnings | diff --git a/pandas/src/numpy_helper.h b/pandas/src/numpy_helper.h
index 17d5ec12f4f79..809edb2e99fa2 100644
--- a/pandas/src/numpy_helper.h
+++ b/pandas/src/numpy_helper.h
@@ -125,7 +125,7 @@ PyObject* sarr_from_data(PyArray_Descr* descr, int length, void* data) {
void transfer_object_column(char* dst, char* src, size_t stride,
size_t length) {
- int i;
+ size_t i;
size_t sz = sizeof(PyObject*);
for (i = 0; i < length; ++i) {
diff --git a/pandas/tslib.pyx b/pandas/tslib.pyx
index 15b279d20021a..7c65ab5422309 100644
--- a/pandas/tslib.pyx
+++ b/pandas/tslib.pyx
@@ -1945,7 +1945,7 @@ cdef inline object _parse_dateabbr_string(object date_string, object default,
object freq):
cdef:
object ret
- int year, quarter, month, mnum, date_len
+ int year, quarter = -1, month, mnum, date_len
# special handling for possibilities eg, 2Q2005, 2Q05, 2005Q1, 05Q1
assert util.is_string_object(date_string)
| https://api.github.com/repos/pandas-dev/pandas/pulls/15191 | 2017-01-22T17:36:03Z | 2017-01-22T21:42:57Z | 2017-01-22T21:42:57Z | 2017-01-22T21:42:57Z | |
CI: add matplotlib, xlwt to build for 3.6 | diff --git a/ci/requirements-3.6.pip b/ci/requirements-3.6.pip
index 688866a18542f..e69de29bb2d1d 100644
--- a/ci/requirements-3.6.pip
+++ b/ci/requirements-3.6.pip
@@ -1 +0,0 @@
-xlwt
diff --git a/ci/requirements-3.6.run b/ci/requirements-3.6.run
index daf07be2d1785..5d9cb05a7b402 100644
--- a/ci/requirements-3.6.run
+++ b/ci/requirements-3.6.run
@@ -5,10 +5,10 @@ scipy
openpyxl
xlsxwriter
xlrd
-# xlwt (installed via pip)
+xlwt
numexpr
pytables
-# matplotlib (not avail on defaults ATM)
+matplotlib
lxml
html5lib
jinja2
| https://api.github.com/repos/pandas-dev/pandas/pulls/15189 | 2017-01-22T16:08:25Z | 2017-01-22T16:20:58Z | 2017-01-22T16:20:58Z | 2017-01-23T20:01:16Z | |
CLN: remove build warnings in join_asof indexers | diff --git a/pandas/src/algos_common_helper.pxi.in b/pandas/src/algos_common_helper.pxi.in
index a579a5020f6e7..5e87528943005 100644
--- a/pandas/src/algos_common_helper.pxi.in
+++ b/pandas/src/algos_common_helper.pxi.in
@@ -374,7 +374,7 @@ def is_monotonic_{{name}}(ndarray[{{c_type}}] arr, bint timelike):
n = len(arr)
if n == 1:
- if arr[0] != arr[0] or (timelike and arr[0] == iNaT):
+ if arr[0] != arr[0] or (timelike and <int64_t>arr[0] == iNaT):
# single value is NaN
return False, False, True
else:
@@ -382,14 +382,14 @@ def is_monotonic_{{name}}(ndarray[{{c_type}}] arr, bint timelike):
elif n < 2:
return True, True, True
- if timelike and arr[0] == iNaT:
+ if timelike and <int64_t>arr[0] == iNaT:
return False, False, True
{{nogil_str}}
{{tab}}prev = arr[0]
{{tab}}for i in range(1, n):
{{tab}} cur = arr[i]
- {{tab}} if timelike and cur == iNaT:
+ {{tab}} if timelike and <int64_t>cur == iNaT:
{{tab}} is_monotonic_inc = 0
{{tab}} is_monotonic_dec = 0
{{tab}} break
diff --git a/pandas/src/algos_groupby_helper.pxi.in b/pandas/src/algos_groupby_helper.pxi.in
index 70862e198edba..4b031afed3d43 100644
--- a/pandas/src/algos_groupby_helper.pxi.in
+++ b/pandas/src/algos_groupby_helper.pxi.in
@@ -580,7 +580,7 @@ def group_cummin_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
"""
cdef:
Py_ssize_t i, j, N, K, size
- {{dest_type2}} val, min_val
+ {{dest_type2}} val, min_val = 0
int64_t lab
N, K = (<object> values).shape
@@ -615,7 +615,7 @@ def group_cummax_{{name}}(ndarray[{{dest_type2}}, ndim=2] out,
"""
cdef:
Py_ssize_t i, j, N, K, size
- {{dest_type2}} val, max_val
+ {{dest_type2}} val, max_val = 0
int64_t lab
N, K = (<object> values).shape
diff --git a/pandas/src/joins_func_helper.pxi.in b/pandas/src/joins_func_helper.pxi.in
index 486c65368602b..9cca9bba2a197 100644
--- a/pandas/src/joins_func_helper.pxi.in
+++ b/pandas/src/joins_func_helper.pxi.in
@@ -41,8 +41,8 @@ def asof_join_backward_{{on_dtype}}_by_{{by_dtype}}(
Py_ssize_t left_pos, right_pos, left_size, right_size, found_right_pos
ndarray[int64_t] left_indexer, right_indexer
bint has_tolerance = 0
- {{on_dtype}} tolerance_
- {{on_dtype}} diff
+ {{on_dtype}} tolerance_ = 0
+ {{on_dtype}} diff = 0
{{table_type}} hash_table
{{by_dtype}} by_value
@@ -106,8 +106,8 @@ def asof_join_forward_{{on_dtype}}_by_{{by_dtype}}(
Py_ssize_t left_pos, right_pos, left_size, right_size, found_right_pos
ndarray[int64_t] left_indexer, right_indexer
bint has_tolerance = 0
- {{on_dtype}} tolerance_
- {{on_dtype}} diff
+ {{on_dtype}} tolerance_ = 0
+ {{on_dtype}} diff = 0
{{table_type}} hash_table
{{by_dtype}} by_value
@@ -236,8 +236,8 @@ def asof_join_backward_{{on_dtype}}(
Py_ssize_t left_pos, right_pos, left_size, right_size
ndarray[int64_t] left_indexer, right_indexer
bint has_tolerance = 0
- {{on_dtype}} tolerance_
- {{on_dtype}} diff
+ {{on_dtype}} tolerance_ = 0
+ {{on_dtype}} diff = 0
# if we are using tolerance, set our objects
if tolerance is not None:
@@ -290,8 +290,8 @@ def asof_join_forward_{{on_dtype}}(
Py_ssize_t left_pos, right_pos, left_size, right_size
ndarray[int64_t] left_indexer, right_indexer
bint has_tolerance = 0
- {{on_dtype}} tolerance_
- {{on_dtype}} diff
+ {{on_dtype}} tolerance_ = 0
+ {{on_dtype}} diff = 0
# if we are using tolerance, set our objects
if tolerance is not None:
@@ -371,4 +371,3 @@ def asof_join_nearest_{{on_dtype}}(
return left_indexer, right_indexer
{{endfor}}
-
| https://api.github.com/repos/pandas-dev/pandas/pulls/15188 | 2017-01-22T16:08:02Z | 2017-01-22T17:20:19Z | 2017-01-22T17:20:19Z | 2017-01-22T17:20:19Z | |
DEPR: remove openpyxl deprecation warnings | diff --git a/pandas/io/excel.py b/pandas/io/excel.py
index 6b7c597ecfcdc..f34ba65cf7b51 100644
--- a/pandas/io/excel.py
+++ b/pandas/io/excel.py
@@ -788,9 +788,15 @@ def __init__(self, path, engine=None, **engine_kwargs):
# Create workbook object with default optimized_write=True.
self.book = Workbook()
+
# Openpyxl 1.6.1 adds a dummy sheet. We remove it.
if self.book.worksheets:
- self.book.remove_sheet(self.book.worksheets[0])
+ try:
+ self.book.remove(self.book.worksheets[0])
+ except AttributeError:
+
+ # compat
+ self.book.remove_sheet(self.book.worksheets[0])
def save(self):
"""
@@ -911,7 +917,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0):
for cell in cells:
colletter = get_column_letter(startcol + cell.col + 1)
- xcell = wks.cell("%s%s" % (colletter, startrow + cell.row + 1))
+ xcell = wks["%s%s" % (colletter, startrow + cell.row + 1)]
xcell.value = _conv_value(cell.val)
style_kwargs = {}
@@ -952,7 +958,7 @@ def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0):
# Ignore first cell. It is already handled.
continue
colletter = get_column_letter(col)
- xcell = wks.cell("%s%s" % (colletter, row))
+ xcell = wks["%s%s" % (colletter, row)]
xcell.style = xcell.style.copy(**style_kwargs)
@classmethod
diff --git a/pandas/io/tests/test_excel.py b/pandas/io/tests/test_excel.py
index 9dd2739608b7a..a2162320d9d1b 100644
--- a/pandas/io/tests/test_excel.py
+++ b/pandas/io/tests/test_excel.py
@@ -2006,8 +2006,8 @@ def test_write_cells_merge_styled(self):
writer.write_cells(merge_cells, sheet_name=sheet_name)
wks = writer.sheets[sheet_name]
- xcell_b1 = wks.cell('B1')
- xcell_a2 = wks.cell('A2')
+ xcell_b1 = wks['B1']
+ xcell_a2 = wks['A2']
self.assertEqual(xcell_b1.style, openpyxl_sty_merged)
self.assertEqual(xcell_a2.style, openpyxl_sty_merged)
@@ -2118,8 +2118,8 @@ def test_write_cells_merge_styled(self):
writer.write_cells(merge_cells, sheet_name=sheet_name)
wks = writer.sheets[sheet_name]
- xcell_b1 = wks.cell('B1')
- xcell_a2 = wks.cell('A2')
+ xcell_b1 = wks['B1']
+ xcell_a2 = wks['A2']
self.assertEqual(xcell_b1.font, openpyxl_sty_merged)
self.assertEqual(xcell_a2.font, openpyxl_sty_merged)
@@ -2213,11 +2213,18 @@ def test_column_format(self):
writer.save()
read_workbook = openpyxl.load_workbook(path)
- read_worksheet = read_workbook.get_sheet_by_name(name='Sheet1')
+ try:
+ read_worksheet = read_workbook['Sheet1']
+ except TypeError:
+ # compat
+ read_worksheet = read_workbook.get_sheet_by_name(name='Sheet1')
- # Get the number format from the cell. This method is backward
- # compatible with older versions of openpyxl.
- cell = read_worksheet.cell('B2')
+ # Get the number format from the cell.
+ try:
+ cell = read_worksheet['B2']
+ except TypeError:
+ # compat
+ cell = read_worksheet.cell('B2')
try:
read_num_format = cell.number_format
| closes #15090
| https://api.github.com/repos/pandas-dev/pandas/pulls/15185 | 2017-01-21T16:31:05Z | 2017-01-21T16:49:22Z | 2017-01-21T16:49:22Z | 2017-01-21T16:49:22Z |
BLD: Remove csv dialect warning in io.rst | diff --git a/doc/source/io.rst b/doc/source/io.rst
index c53083238e098..6152f83675867 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -419,6 +419,7 @@ finds the closing double quote.
We can get around this using ``dialect``
.. ipython:: python
+ :okwarning:
dia = csv.excel()
dia.quoting = csv.QUOTE_NONE
| Title is self-explanatory.
xref <a href="https://github.com/pandas-dev/pandas/pull/14911#issuecomment-273896473">#14911 (comment)</a>. | https://api.github.com/repos/pandas-dev/pandas/pulls/15177 | 2017-01-20T10:29:53Z | 2017-01-20T20:53:11Z | 2017-01-20T20:53:11Z | 2017-01-21T08:40:15Z |
DOC: add subsequent_indent to wrapped text | diff --git a/pandas/io/parsers.py b/pandas/io/parsers.py
index fdf26fdef6b25..f8905dfa315c4 100755
--- a/pandas/io/parsers.py
+++ b/pandas/io/parsers.py
@@ -148,7 +148,8 @@
na_values : scalar, str, list-like, or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific
per-column NA values. By default the following values are interpreted as
- NaN: '""" + fill("', '".join(sorted(_NA_VALUES)), 70) + """'`.
+ NaN: '""" + fill("', '".join(sorted(_NA_VALUES)),
+ 70, subsequent_indent=" ") + """'`.
keep_default_na : bool, default True
If na_values are specified and keep_default_na is False the default NaN
values are overridden, otherwise they're appended to.
| Hey guys,
This should fix the parsing error in the [read_csv documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv) :
`fill` wraps the line to 70 characters (what's left after the beginning of the line) but it wasn't indenting the subsequent line, so the parser was marking it as a new parameter (in bold).
This PR adds the missing indent, which should fix the parsing. | https://api.github.com/repos/pandas-dev/pandas/pulls/15173 | 2017-01-19T22:09:04Z | 2017-01-22T11:35:13Z | 2017-01-22T11:35:13Z | 2017-01-22T11:35:24Z |
CI: have 34_SLOW build use matplotlib latest from conda-forge | diff --git a/ci/requirements-3.4_SLOW.build b/ci/requirements-3.4_SLOW.build
index de36b1afb9fa4..c05a68a14b402 100644
--- a/ci/requirements-3.4_SLOW.build
+++ b/ci/requirements-3.4_SLOW.build
@@ -1,4 +1,4 @@
python-dateutil
pytz
-numpy=1.9.3
+numpy=1.10*
cython
diff --git a/ci/requirements-3.4_SLOW.run b/ci/requirements-3.4_SLOW.run
index f9f226e3f1465..215f840381ada 100644
--- a/ci/requirements-3.4_SLOW.run
+++ b/ci/requirements-3.4_SLOW.run
@@ -1,6 +1,6 @@
python-dateutil
pytz
-numpy=1.9.3
+numpy=1.10*
openpyxl
xlsxwriter
xlrd
diff --git a/ci/requirements-3.4_SLOW.sh b/ci/requirements-3.4_SLOW.sh
index bc8fb79147d2c..24f1e042ed69e 100644
--- a/ci/requirements-3.4_SLOW.sh
+++ b/ci/requirements-3.4_SLOW.sh
@@ -4,4 +4,4 @@ source activate pandas
echo "install 34_slow"
-conda install -n pandas -c conda-forge/label/rc -c conda-forge matplotlib
+conda install -n pandas -c conda-forge matplotlib
| CI: change 34_SLOW to use numpy 1.10* as mpl 2.0 requires anyhow
| https://api.github.com/repos/pandas-dev/pandas/pulls/15170 | 2017-01-19T20:28:37Z | 2017-01-19T21:53:38Z | 2017-01-19T21:53:38Z | 2017-01-19T21:53:38Z |
DOC: Clarified wording of coerce_float | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 504554d6410f9..ef8b7a0d16bdc 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -962,7 +962,7 @@ def from_records(cls, data, index=None, exclude=None, columns=None,
in the result (any names not found in the data will become all-NA
columns)
coerce_float : boolean, default False
- Attempt to convert values to non-string, non-numeric objects (like
+ Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
Returns
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index c9f8d32e1b504..9fa01c413aca8 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -214,7 +214,7 @@ def read_sql_table(table_name, con, schema=None, index_col=None,
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex)
coerce_float : boolean, default True
- Attempt to convert values to non-string, non-numeric objects (like
+ Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point. Can result in loss of Precision.
parse_dates : list or dict, default: None
- List of column names to parse as dates
@@ -289,7 +289,7 @@ def read_sql_query(sql, con, index_col=None, coerce_float=True, params=None,
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex)
coerce_float : boolean, default True
- Attempt to convert values to non-string, non-numeric objects (like
+ Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
params : list, tuple or dict, optional, default: None
List of parameters to pass to execute method. The syntax used
@@ -348,7 +348,7 @@ def read_sql(sql, con, index_col=None, coerce_float=True, params=None,
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex)
coerce_float : boolean, default True
- Attempt to convert values to non-string, non-numeric objects (like
+ Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
params : list, tuple or dict, optional, default: None
List of parameters to pass to execute method. The syntax used
@@ -986,7 +986,7 @@ def read_table(self, table_name, index_col=None, coerce_float=True,
index_col : string, optional, default: None
Column to set as index
coerce_float : boolean, default True
- Attempt to convert values to non-string, non-numeric objects
+ Attempt to convert values of non-string, non-numeric objects
(like decimal.Decimal) to floating point. This can result in
loss of precision.
parse_dates : list or dict, default: None
@@ -1048,7 +1048,7 @@ def read_query(self, sql, index_col=None, coerce_float=True,
index_col : string, optional, default: None
Column name to use as index for the returned DataFrame object.
coerce_float : boolean, default True
- Attempt to convert values to non-string, non-numeric objects (like
+ Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets
params : list, tuple or dict, optional, default: None
List of parameters to pass to execute method. The syntax used
| I think this is the intended meaning (and it matches with use). | https://api.github.com/repos/pandas-dev/pandas/pulls/15164 | 2017-01-19T12:58:06Z | 2017-01-19T13:57:19Z | 2017-01-19T13:57:19Z | 2017-01-19T13:57:21Z |
Apply fontsize to both x- and y-axis tick labels | diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index f3d60fa8cc05f..798151971363e 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -439,6 +439,9 @@ Bug Fixes
- Bug in ``pd.read_csv()`` for the C engine where ``usecols`` were being indexed incorrectly with ``parse_dates`` (:issue:`14792`)
- Bug in ``Series.dt.round`` inconsistent behaviour on NAT's with different arguments (:issue:`14940`)
+
- Bug in ``.read_json()`` for Python 2 where ``lines=True`` and contents contain non-ascii unicode characters (:issue:`15132`)
- Bug in ``pd.read_csv()`` with ``float_precision='round_trip'`` which caused a segfault when a text entry is parsed (:issue:`15140`)
+
+- Bug in ``DataFrame.boxplot`` where ``fontsize`` was not applied to the tick labels on both axes (:issue:`15108`)
diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py
index f76cdb70c0240..289d48ba6d4cc 100644
--- a/pandas/tests/plotting/test_boxplot_method.py
+++ b/pandas/tests/plotting/test_boxplot_method.py
@@ -158,6 +158,11 @@ def test_boxplot_empty_column(self):
df.loc[:, 0] = np.nan
_check_plot_works(df.boxplot, return_type='axes')
+ def test_fontsize(self):
+ df = DataFrame({"a": [1, 2, 3, 4, 5, 6]})
+ self._check_ticks_props(df.boxplot("a", fontsize=16),
+ xlabelsize=16, ylabelsize=16)
+
@tm.mplskip
class TestDataFrameGroupByPlots(TestPlotBase):
@@ -369,6 +374,11 @@ def test_grouped_box_multiple_axes(self):
with tm.assert_produces_warning(UserWarning):
axes = df.groupby('classroom').boxplot(ax=axes)
+ def test_fontsize(self):
+ df = DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": [0, 0, 0, 1, 1, 1]})
+ self._check_ticks_props(df.boxplot("a", by="b", fontsize=16),
+ xlabelsize=16, ylabelsize=16)
+
if __name__ == '__main__':
nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
diff --git a/pandas/tools/plotting.py b/pandas/tools/plotting.py
index bd9933b12b580..012d67d29cc3f 100644
--- a/pandas/tools/plotting.py
+++ b/pandas/tools/plotting.py
@@ -2768,10 +2768,12 @@ def plot_group(keys, values, ax):
keys = [pprint_thing(x) for x in keys]
values = [remove_na(v) for v in values]
bp = ax.boxplot(values, **kwds)
+ if fontsize is not None:
+ ax.tick_params(axis='both', labelsize=fontsize)
if kwds.get('vert', 1):
- ax.set_xticklabels(keys, rotation=rot, fontsize=fontsize)
+ ax.set_xticklabels(keys, rotation=rot)
else:
- ax.set_yticklabels(keys, rotation=rot, fontsize=fontsize)
+ ax.set_yticklabels(keys, rotation=rot)
maybe_color_bp(bp)
# Return axes in multiplot case, maybe revisit later # 985
| - [x] closes #15108
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/15161 | 2017-01-19T08:16:39Z | 2017-01-20T17:12:58Z | 2017-01-20T17:12:58Z | 2017-01-20T17:13:03Z |
CI: add .pxi.in to build caching | diff --git a/ci/prep_cython_cache.sh b/ci/prep_cython_cache.sh
index cadc356b641f9..e091bb00ccedc 100755
--- a/ci/prep_cython_cache.sh
+++ b/ci/prep_cython_cache.sh
@@ -3,8 +3,8 @@
ls "$HOME/.cache/"
PYX_CACHE_DIR="$HOME/.cache/pyxfiles"
-pyx_file_list=`find ${TRAVIS_BUILD_DIR} -name "*.pyx" -o -name "*.pxd"`
-pyx_cache_file_list=`find ${PYX_CACHE_DIR} -name "*.pyx" -o -name "*.pxd"`
+pyx_file_list=`find ${TRAVIS_BUILD_DIR} -name "*.pyx" -o -name "*.pxd" -o -name "*.pxi.in"`
+pyx_cache_file_list=`find ${PYX_CACHE_DIR} -name "*.pyx" -o -name "*.pxd" -o -name "*.pxi.in"`
CACHE_File="$HOME/.cache/cython_files.tar"
| closes #15154
| https://api.github.com/repos/pandas-dev/pandas/pulls/15159 | 2017-01-19T02:20:20Z | 2017-01-19T02:22:15Z | 2017-01-19T02:22:14Z | 2017-01-19T02:22:15Z |
DOC: fix doc-build for .ix deprecations in whatsnew | diff --git a/doc/source/indexing.rst b/doc/source/indexing.rst
index 9c653f6d9eeff..e2916c2d969d6 100644
--- a/doc/source/indexing.rst
+++ b/doc/source/indexing.rst
@@ -586,7 +586,7 @@ Using ``.loc``. Here we will select the appropriate indexes from the index, then
.. ipython:: python
- dfd.loc[df.index[[0, 2]], 'A']
+ dfd.loc[dfd.index[[0, 2]], 'A']
This can also be expressed using ``.iloc``, by explicitly getting locations on the indexers, and using
*positional* indexing to select things.
diff --git a/doc/source/whatsnew/v0.10.1.txt b/doc/source/whatsnew/v0.10.1.txt
index b61d31932c60d..edc628fe85027 100644
--- a/doc/source/whatsnew/v0.10.1.txt
+++ b/doc/source/whatsnew/v0.10.1.txt
@@ -51,8 +51,8 @@ perform queries on a table, by passing a list to ``data_columns``
df = DataFrame(randn(8, 3), index=date_range('1/1/2000', periods=8),
columns=['A', 'B', 'C'])
df['string'] = 'foo'
- df.ix[4:6,'string'] = np.nan
- df.ix[7:9,'string'] = 'bar'
+ df.loc[df.index[4:6], 'string'] = np.nan
+ df.loc[df.index[7:9], 'string'] = 'bar'
df['string2'] = 'cool'
df
@@ -78,7 +78,7 @@ You can now store ``datetime64`` in data columns
df_mixed = df.copy()
df_mixed['datetime64'] = Timestamp('20010102')
- df_mixed.ix[3:4,['A','B']] = np.nan
+ df_mixed.loc[df_mixed.index[3:4], ['A','B']] = np.nan
store.append('df_mixed', df_mixed)
df_mixed1 = store.select('df_mixed')
@@ -208,4 +208,3 @@ combined result, by using ``where`` on a selector table.
See the :ref:`full release notes
<release>` or issue tracker
on GitHub for a complete list.
-
diff --git a/doc/source/whatsnew/v0.11.0.txt b/doc/source/whatsnew/v0.11.0.txt
index 50b74fc5af090..ea149595e681f 100644
--- a/doc/source/whatsnew/v0.11.0.txt
+++ b/doc/source/whatsnew/v0.11.0.txt
@@ -190,7 +190,7 @@ Furthermore ``datetime64[ns]`` columns are created by default, when passed datet
df.get_dtype_counts()
# use the traditional nan, which is mapped to NaT internally
- df.ix[2:4,['A','timestamp']] = np.nan
+ df.loc[df.index[2:4], ['A','timestamp']] = np.nan
df
Astype conversion on ``datetime64[ns]`` to ``object``, implicity converts ``NaT`` to ``np.nan``
diff --git a/doc/source/whatsnew/v0.13.0.txt b/doc/source/whatsnew/v0.13.0.txt
index 6ecd4b487c798..118632cc2c0ee 100644
--- a/doc/source/whatsnew/v0.13.0.txt
+++ b/doc/source/whatsnew/v0.13.0.txt
@@ -274,7 +274,6 @@ Float64Index API Change
.. ipython:: python
s[3]
- s.ix[3]
s.loc[3]
The only positional indexing is via ``iloc``
@@ -290,7 +289,6 @@ Float64Index API Change
.. ipython:: python
s[2:4]
- s.ix[2:4]
s.loc[2:4]
s.iloc[2:4]
diff --git a/doc/source/whatsnew/v0.13.1.txt b/doc/source/whatsnew/v0.13.1.txt
index 349acf508bbf3..d5d54ba43b622 100644
--- a/doc/source/whatsnew/v0.13.1.txt
+++ b/doc/source/whatsnew/v0.13.1.txt
@@ -36,7 +36,7 @@ Highlights include:
.. ipython:: python
df = DataFrame(dict(A = np.array(['foo','bar','bah','foo','bar'])))
- df.ix[0,'A'] = np.nan
+ df.loc[0,'A'] = np.nan
df
Output Formatting Enhancements
@@ -121,7 +121,7 @@ API changes
.. ipython:: python
:okwarning:
-
+
df = DataFrame({'col':['foo', 0, np.nan]})
df2 = DataFrame({'col':[np.nan, 0, 'foo']}, index=[2,1,0])
df.equals(df2)
diff --git a/doc/source/whatsnew/v0.14.0.txt b/doc/source/whatsnew/v0.14.0.txt
index 78f96e3c0e049..f1feab4b909dc 100644
--- a/doc/source/whatsnew/v0.14.0.txt
+++ b/doc/source/whatsnew/v0.14.0.txt
@@ -516,7 +516,7 @@ See also issues (:issue:`6134`, :issue:`4036`, :issue:`3057`, :issue:`2598`, :is
names=['lvl0', 'lvl1'])
df = DataFrame(np.arange(len(index)*len(columns)).reshape((len(index),len(columns))),
index=index,
- columns=columns).sortlevel().sortlevel(axis=1)
+ columns=columns).sort_index().sort_index(axis=1)
df
Basic multi-index slicing using slices, lists, and labels.
diff --git a/doc/source/whatsnew/v0.15.0.txt b/doc/source/whatsnew/v0.15.0.txt
index df1171fb34486..aff8ec9092cdc 100644
--- a/doc/source/whatsnew/v0.15.0.txt
+++ b/doc/source/whatsnew/v0.15.0.txt
@@ -705,7 +705,7 @@ Other notable API changes:
s = Series(np.arange(3,dtype='int64'),
index=MultiIndex.from_product([['A'],['foo','bar','baz']],
names=['one','two'])
- ).sortlevel()
+ ).sort_index()
s
try:
s.loc[['D']]
diff --git a/doc/source/whatsnew/v0.15.2.txt b/doc/source/whatsnew/v0.15.2.txt
index 3a62ac38f7260..3bb472e9361e7 100644
--- a/doc/source/whatsnew/v0.15.2.txt
+++ b/doc/source/whatsnew/v0.15.2.txt
@@ -35,7 +35,7 @@ API changes
df.loc[(1, 'z')]
# lexically sorting
- df2 = df.sortlevel()
+ df2 = df.sort_index()
df2
df2.index.lexsort_depth
df2.loc[(1,'z')]
diff --git a/doc/source/whatsnew/v0.16.0.txt b/doc/source/whatsnew/v0.16.0.txt
index 4255f4839bca0..4d43660960597 100644
--- a/doc/source/whatsnew/v0.16.0.txt
+++ b/doc/source/whatsnew/v0.16.0.txt
@@ -300,9 +300,14 @@ The behavior of a small sub-set of edge cases for using ``.loc`` have changed (:
New Behavior
- .. ipython:: python
+ .. code-block:: python
- s.ix[-1.0:2]
+ In [2]: s.ix[-1.0:2]
+ Out[2]:
+ -1 1
+ 1 2
+ 2 3
+ dtype: int64
- Provide a useful exception for indexing with an invalid type for that index when using ``.loc``. For example trying to use ``.loc`` on an index of type ``DatetimeIndex`` or ``PeriodIndex`` or ``TimedeltaIndex``, with an integer (or a float).
diff --git a/doc/source/whatsnew/v0.18.0.txt b/doc/source/whatsnew/v0.18.0.txt
index 7418cd0e6baa3..893922b719b34 100644
--- a/doc/source/whatsnew/v0.18.0.txt
+++ b/doc/source/whatsnew/v0.18.0.txt
@@ -448,7 +448,7 @@ New Behavior:
columns=list('abc'),
index=[[4,4,8], [8,10,12]])
df
- df.ix[4, 'c'] = np.array([0., 1.])
+ df.loc[4, 'c'] = np.array([0., 1.])
df
.. _whatsnew_0180.enhancements.xarray:
@@ -1115,7 +1115,6 @@ Other indexers will coerce to a like integer for both getting and setting. The `
s[5.0]
s.loc[5.0]
- s.ix[5.0]
and setting
@@ -1127,30 +1126,31 @@ and setting
s_copy = s.copy()
s_copy.loc[5.0] = 10
s_copy
- s_copy = s.copy()
- s_copy.ix[5.0] = 10
- s_copy
Positional setting with ``.ix`` and a float indexer will ADD this value to the index, rather than previously setting the value by position.
-.. ipython:: python
+.. code-block:: python
- s2.ix[1.0] = 10
- s2
+ In [3]: s2.ix[1.0] = 10
+ In [4]: s2
+ Out[4]:
+ a 1
+ b 2
+ c 3
+ 1.0 10
+ dtype: int64
Slicing will also coerce integer-like floats to integers for a non-``Float64Index``.
.. ipython:: python
s.loc[5.0:6]
- s.ix[5.0:6]
Note that for floats that are NOT coercible to ints, the label based bounds will be excluded
.. ipython:: python
s.loc[5.1:6]
- s.ix[5.1:6]
Float indexing on a ``Float64Index`` is unchanged.
diff --git a/doc/source/whatsnew/v0.7.0.txt b/doc/source/whatsnew/v0.7.0.txt
index cfba2ad3d05b6..21d91950e7b78 100644
--- a/doc/source/whatsnew/v0.7.0.txt
+++ b/doc/source/whatsnew/v0.7.0.txt
@@ -169,16 +169,30 @@ API tweaks regarding label-based slicing
Label-based slicing using ``ix`` now requires that the index be sorted
(monotonic) **unless** both the start and endpoint are contained in the index:
-.. ipython:: python
+.. code-block:: python
- s = Series(randn(6), index=list('gmkaec'))
- s
+ In [1]: s = Series(randn(6), index=list('gmkaec'))
+
+ In [2]: s
+ Out[2]:
+ g -1.182230
+ m -0.276183
+ k -0.243550
+ a 1.628992
+ e 0.073308
+ c -0.539890
+ dtype: float64
Then this is OK:
-.. ipython:: python
+.. code-block:: python
- s.ix['k':'e']
+ In [3]: s.ix['k':'e']
+ Out[3]:
+ k -0.243550
+ a 1.628992
+ e 0.073308
+ dtype: float64
But this is not:
@@ -189,11 +203,26 @@ But this is not:
If the index had been sorted, the "range selection" would have been possible:
-.. ipython:: python
+.. code-block:: python
+
+ In [4]: s2 = s.sort_index()
- s2 = s.sort_index()
- s2
- s2.ix['b':'h']
+ In [5]: s2
+ Out[5]:
+ a 1.628992
+ c -0.539890
+ e 0.073308
+ g -1.182230
+ k -0.243550
+ m -0.276183
+ dtype: float64
+
+ In [6]: s2.ix['b':'h']
+ Out[6]:
+ c -0.539890
+ e 0.073308
+ g -1.182230
+ dtype: float64
Changes to Series ``[]`` operator
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -269,4 +298,3 @@ Performance improvements
``level`` parameter passed (:issue:`545`)
- Ported skiplist data structure to C to speed up ``rolling_median`` by about
5-10x in most typical use cases (:issue:`374`)
-
diff --git a/doc/source/whatsnew/v0.9.1.txt b/doc/source/whatsnew/v0.9.1.txt
index 51788f77a6f0f..9dd29a5fe7bf7 100644
--- a/doc/source/whatsnew/v0.9.1.txt
+++ b/doc/source/whatsnew/v0.9.1.txt
@@ -36,7 +36,7 @@ New features
df = DataFrame(np.random.randn(6, 3), columns=['A', 'B', 'C'])
- df.ix[2:4] = np.nan
+ df.loc[2:4] = np.nan
df.rank()
| https://api.github.com/repos/pandas-dev/pandas/pulls/15158 | 2017-01-19T01:59:44Z | 2017-01-19T02:01:45Z | 2017-01-19T02:01:45Z | 2017-01-19T02:01:46Z | |
CLN/DEPR: remove deprecated pandas.rpy module (GH9602) | diff --git a/ci/lint.sh b/ci/lint.sh
index 5c4613f88649e..2cbfdadf486b8 100755
--- a/ci/lint.sh
+++ b/ci/lint.sh
@@ -8,10 +8,9 @@ RET=0
if [ "$LINT" ]; then
- # pandas/rpy is deprecated and will be removed.
# pandas/src is C code, so no need to search there.
echo "Linting *.py"
- flake8 pandas --filename=*.py --exclude pandas/rpy,pandas/src
+ flake8 pandas --filename=*.py --exclude pandas/src
if [ $? -ne "0" ]; then
RET=1
fi
diff --git a/doc/source/r_interface.rst b/doc/source/r_interface.rst
index f2a8668bbda91..b5d699cad69d5 100644
--- a/doc/source/r_interface.rst
+++ b/doc/source/r_interface.rst
@@ -1,5 +1,3 @@
-.. currentmodule:: pandas.rpy
-
.. _rpy:
.. ipython:: python
@@ -15,132 +13,55 @@ rpy2 / R interface
.. warning::
- In v0.16.0, the ``pandas.rpy`` interface has been **deprecated and will be
- removed in a future version**. Similar functionality can be accessed
- through the `rpy2 <https://rpy2.readthedocs.io/>`__ project.
- See the :ref:`updating <rpy.updating>` section for a guide to port your
- code from the ``pandas.rpy`` to ``rpy2`` functions.
-
-
-.. _rpy.updating:
-
-Updating your code to use rpy2 functions
-----------------------------------------
-
-In v0.16.0, the ``pandas.rpy`` module has been **deprecated** and users are
-pointed to the similar functionality in ``rpy2`` itself (rpy2 >= 2.4).
+ Up to pandas 0.19, a ``pandas.rpy`` module existed with functionality to
+ convert between pandas and ``rpy2`` objects. This functionality now lives in
+ the `rpy2 <https://rpy2.readthedocs.io/>`__ project itself.
+ See the `updating section <http://pandas.pydata.org/pandas-docs/version/0.19.0/r_interface.html#updating-your-code-to-use-rpy2-functions>`__
+ of the previous documentation for a guide to port your code from the
+ removed ``pandas.rpy`` to ``rpy2`` functions.
-Instead of importing ``import pandas.rpy.common as com``, the following imports
-should be done to activate the pandas conversion support in rpy2::
-
- from rpy2.robjects import pandas2ri
- pandas2ri.activate()
+`rpy2 <http://rpy2.bitbucket.org/>`__ is an interface to R running embedded in a Python process, and also includes functionality to deal with pandas DataFrames.
Converting data frames back and forth between rpy2 and pandas should be largely
automated (no need to convert explicitly, it will be done on the fly in most
rpy2 functions).
-
To convert explicitly, the functions are ``pandas2ri.py2ri()`` and
-``pandas2ri.ri2py()``. So these functions can be used to replace the existing
-functions in pandas:
-
-- ``com.convert_to_r_dataframe(df)`` should be replaced with ``pandas2ri.py2ri(df)``
-- ``com.convert_robj(rdf)`` should be replaced with ``pandas2ri.ri2py(rdf)``
-
-Note: these functions are for the latest version (rpy2 2.5.x) and were called
-``pandas2ri.pandas2ri()`` and ``pandas2ri.ri2pandas()`` previously.
-
-Some of the other functionality in `pandas.rpy` can be replaced easily as well.
-For example to load R data as done with the ``load_data`` function, the
-current method::
-
- df_iris = com.load_data('iris')
-
-can be replaced with::
-
- from rpy2.robjects import r
- r.data('iris')
- df_iris = pandas2ri.ri2py(r[name])
-
-The ``convert_to_r_matrix`` function can be replaced by the normal
-``pandas2ri.py2ri`` to convert dataframes, with a subsequent call to R
-``as.matrix`` function.
-
-.. warning::
-
- Not all conversion functions in rpy2 are working exactly the same as the
- current methods in pandas. If you experience problems or limitations in
- comparison to the ones in pandas, please report this at the
- `issue tracker <https://github.com/pandas-dev/pandas/issues>`_.
-
-See also the documentation of the `rpy2 <http://rpy2.bitbucket.org/>`__ project.
-
+``pandas2ri.ri2py()``.
-R interface with rpy2
----------------------
-If your computer has R and rpy2 (> 2.2) installed (which will be left to the
-reader), you will be able to leverage the below functionality. On Windows,
-doing this is quite an ordeal at the moment, but users on Unix-like systems
-should find it quite easy. rpy2 evolves in time, and is currently reaching
-its release 2.3, while the current interface is
-designed for the 2.2.x series. We recommend to use 2.2.x over other series
-unless you are prepared to fix parts of the code, yet the rpy2-2.3.0
-introduces improvements such as a better R-Python bridge memory management
-layer so it might be a good idea to bite the bullet and submit patches for
-the few minor differences that need to be fixed.
+See also the documentation of the `rpy2 <http://rpy2.bitbucket.org/>`__ project: https://rpy2.readthedocs.io.
+In the remainder of this page, a few examples of explicit conversion is given. The pandas conversion of rpy2 needs first to be activated:
-::
-
- # if installing for the first time
- hg clone http://bitbucket.org/lgautier/rpy2
-
- cd rpy2
- hg pull
- hg update version_2.2.x
- sudo python setup.py install
-
-.. note::
-
- To use R packages with this interface, you will need to install
- them inside R yourself. At the moment it cannot install them for
- you.
+.. ipython:: python
-Once you have done installed R and rpy2, you should be able to import
-``pandas.rpy.common`` without a hitch.
+ from rpy2.robjects import r, pandas2ri
+ pandas2ri.activate()
Transferring R data sets into Python
------------------------------------
-The **load_data** function retrieves an R data set and converts it to the
+The ``pandas2ri.ri2py`` function retrieves an R data set and converts it to the
appropriate pandas object (most likely a DataFrame):
-
.. ipython:: python
- :okwarning:
-
- import pandas.rpy.common as com
- infert = com.load_data('infert')
- infert.head()
+ r.data('iris')
+ df_iris = pandas2ri.ri2py(r['iris'])
+ df_iris.head()
Converting DataFrames into R objects
------------------------------------
-.. versionadded:: 0.8
-
-Starting from pandas 0.8, there is **experimental** support to convert
+The ``pandas2ri.py2ri`` function support the reverse operation to convert
DataFrames into the equivalent R object (that is, **data.frame**):
.. ipython:: python
- import pandas.rpy.common as com
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]},
index=["one", "two", "three"])
- r_dataframe = com.convert_to_r_dataframe(df)
-
+ r_dataframe = pandas2ri.py2ri(df)
print(type(r_dataframe))
print(r_dataframe)
@@ -148,24 +69,7 @@ DataFrames into the equivalent R object (that is, **data.frame**):
The DataFrame's index is stored as the ``rownames`` attribute of the
data.frame instance.
-You can also use **convert_to_r_matrix** to obtain a ``Matrix`` instance, but
-bear in mind that it will only work with homogeneously-typed DataFrames (as
-R matrices bear no information on the data type):
-
-.. ipython:: python
-
- import pandas.rpy.common as com
- r_matrix = com.convert_to_r_matrix(df)
-
- print(type(r_matrix))
- print(r_matrix)
-
-
-Calling R functions with pandas objects
----------------------------------------
-
-
-
-High-level interface to R estimators
-------------------------------------
+..
+ Calling R functions with pandas objects
+ High-level interface to R estimators
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index 2a825edd0e98a..c93f8b310ebc0 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -271,6 +271,9 @@ Deprecations
Removal of prior version deprecations/changes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+- The ``pandas.rpy`` module is removed. Similar functionality can be accessed
+ through the `rpy2 <https://rpy2.readthedocs.io/>`__ project.
+ See the :ref:`R interfacing docs <rpy>` for more details.
- ``pd.to_datetime`` and ``pd.to_timedelta`` have dropped the ``coerce`` parameter in favor of ``errors`` (:issue:`13602`)
diff --git a/pandas/api/tests/test_api.py b/pandas/api/tests/test_api.py
index b13b4d7de60ca..f6b765fbf7418 100644
--- a/pandas/api/tests/test_api.py
+++ b/pandas/api/tests/test_api.py
@@ -30,7 +30,7 @@ class TestPDApi(Base, tm.TestCase):
# these are optionally imported based on testing
# & need to be ignored
- ignored = ['tests', 'rpy', 'locale']
+ ignored = ['tests', 'locale']
# top-level sub-packages
lib = ['api', 'compat', 'computation', 'core',
diff --git a/pandas/rpy/__init__.py b/pandas/rpy/__init__.py
deleted file mode 100644
index b771a3d8374a3..0000000000000
--- a/pandas/rpy/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-
-# GH9602
-# deprecate rpy to instead directly use rpy2
-
-# flake8: noqa
-
-import warnings
-warnings.warn("The pandas.rpy module is deprecated and will be "
- "removed in a future version. We refer to external packages "
- "like rpy2. "
- "\nSee here for a guide on how to port your code to rpy2: "
- "http://pandas.pydata.org/pandas-docs/stable/r_interface.html",
- FutureWarning, stacklevel=2)
-
-try:
- from .common import importr, r, load_data
-except ImportError:
- pass
diff --git a/pandas/rpy/base.py b/pandas/rpy/base.py
deleted file mode 100644
index ac339dd366b0b..0000000000000
--- a/pandas/rpy/base.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# flake8: noqa
-
-import pandas.rpy.util as util
-
-
-class lm(object):
- """
- Examples
- --------
- >>> model = lm('x ~ y + z', data)
- >>> model.coef
- """
- def __init__(self, formula, data):
- pass
diff --git a/pandas/rpy/common.py b/pandas/rpy/common.py
deleted file mode 100644
index 95a072154dc68..0000000000000
--- a/pandas/rpy/common.py
+++ /dev/null
@@ -1,372 +0,0 @@
-"""
-Utilities for making working with rpy2 more user- and
-developer-friendly.
-"""
-
-# flake8: noqa
-
-from __future__ import print_function
-
-from distutils.version import LooseVersion
-from pandas.compat import zip, range
-import numpy as np
-
-import pandas as pd
-import pandas.core.common as com
-import pandas.util.testing as _test
-
-from rpy2.robjects.packages import importr
-from rpy2.robjects import r
-import rpy2.robjects as robj
-
-import itertools as IT
-
-
-__all__ = ['convert_robj', 'load_data', 'convert_to_r_dataframe',
- 'convert_to_r_matrix']
-
-
-def load_data(name, package=None, convert=True):
- if package:
- importr(package)
-
- r.data(name)
-
- robj = r[name]
-
- if convert:
- return convert_robj(robj)
- else:
- return robj
-
-
-def _rclass(obj):
- """
- Return R class name for input object
- """
- return r['class'](obj)[0]
-
-
-def _is_null(obj):
- return _rclass(obj) == 'NULL'
-
-
-def _convert_list(obj):
- """
- Convert named Vector to dict, factors to list
- """
- try:
- values = [convert_robj(x) for x in obj]
- keys = r['names'](obj)
- return dict(zip(keys, values))
- except TypeError:
- # For state.division and state.region
- factors = list(r['factor'](obj))
- level = list(r['levels'](obj))
- result = [level[index-1] for index in factors]
- return result
-
-
-def _convert_array(obj):
- """
- Convert Array to DataFrame
- """
- def _list(item):
- try:
- return list(item)
- except TypeError:
- return []
-
- # For iris3, HairEyeColor, UCBAdmissions, Titanic
- dim = list(obj.dim)
- values = np.array(list(obj))
- names = r['dimnames'](obj)
- try:
- columns = list(r['names'](names))[::-1]
- except TypeError:
- columns = ['X{:d}'.format(i) for i in range(len(names))][::-1]
- columns.append('value')
- name_list = [(_list(x) or range(d)) for x, d in zip(names, dim)][::-1]
- arr = np.array(list(IT.product(*name_list)))
- arr = np.column_stack([arr,values])
- df = pd.DataFrame(arr, columns=columns)
- return df
-
-
-def _convert_vector(obj):
- if isinstance(obj, robj.IntVector):
- return _convert_int_vector(obj)
- elif isinstance(obj, robj.StrVector):
- return _convert_str_vector(obj)
- # Check if the vector has extra information attached to it that can be used
- # as an index
- try:
- attributes = set(r['attributes'](obj).names)
- except AttributeError:
- return list(obj)
- if 'names' in attributes:
- return pd.Series(list(obj), index=r['names'](obj))
- elif 'tsp' in attributes:
- return pd.Series(list(obj), index=r['time'](obj))
- elif 'labels' in attributes:
- return pd.Series(list(obj), index=r['labels'](obj))
- if _rclass(obj) == 'dist':
- # For 'eurodist'. WARNING: This results in a DataFrame, not a Series or list.
- matrix = r['as.matrix'](obj)
- return convert_robj(matrix)
- else:
- return list(obj)
-
-NA_INTEGER = -2147483648
-
-
-def _convert_int_vector(obj):
- arr = np.asarray(obj)
- mask = arr == NA_INTEGER
- if mask.any():
- arr = arr.astype(float)
- arr[mask] = np.nan
- return arr
-
-
-def _convert_str_vector(obj):
- arr = np.asarray(obj, dtype=object)
- mask = arr == robj.NA_Character
- if mask.any():
- arr[mask] = np.nan
- return arr
-
-
-def _convert_DataFrame(rdf):
- columns = list(rdf.colnames)
- rows = np.array(rdf.rownames)
-
- data = {}
- for i, col in enumerate(columns):
- vec = rdf.rx2(i + 1)
- values = _convert_vector(vec)
-
- if isinstance(vec, robj.FactorVector):
- levels = np.asarray(vec.levels)
- if com.is_float_dtype(values):
- mask = np.isnan(values)
- notmask = -mask
- result = np.empty(len(values), dtype=object)
- result[mask] = np.nan
-
- locs = (values[notmask] - 1).astype(np.int_)
- result[notmask] = levels.take(locs)
- values = result
- else:
- values = np.asarray(vec.levels).take(values - 1)
-
- data[col] = values
-
- return pd.DataFrame(data, index=_check_int(rows), columns=columns)
-
-
-def _convert_Matrix(mat):
- columns = mat.colnames
- rows = mat.rownames
-
- columns = None if _is_null(columns) else list(columns)
- index = r['time'](mat) if _is_null(rows) else list(rows)
- return pd.DataFrame(np.array(mat), index=_check_int(index),
- columns=columns)
-
-
-def _check_int(vec):
- try:
- # R observation numbers come through as strings
- vec = vec.astype(int)
- except Exception:
- pass
-
- return vec
-
-_pandas_converters = [
- (robj.DataFrame, _convert_DataFrame),
- (robj.Matrix, _convert_Matrix),
- (robj.StrVector, _convert_vector),
- (robj.FloatVector, _convert_vector),
- (robj.Array, _convert_array),
- (robj.Vector, _convert_list),
-]
-
-_converters = [
- (robj.DataFrame, lambda x: _convert_DataFrame(x).toRecords(index=False)),
- (robj.Matrix, lambda x: _convert_Matrix(x).toRecords(index=False)),
- (robj.IntVector, _convert_vector),
- (robj.StrVector, _convert_vector),
- (robj.FloatVector, _convert_vector),
- (robj.Array, _convert_array),
- (robj.Vector, _convert_list),
-]
-
-
-def convert_robj(obj, use_pandas=True):
- """
- Convert rpy2 object to a pandas-friendly form
-
- Parameters
- ----------
- obj : rpy2 object
-
- Returns
- -------
- Non-rpy data structure, mix of NumPy and pandas objects
- """
- if not isinstance(obj, robj.RObjectMixin):
- return obj
-
- converters = _pandas_converters if use_pandas else _converters
-
- for rpy_type, converter in converters:
- if isinstance(obj, rpy_type):
- return converter(obj)
-
- raise TypeError('Do not know what to do with %s object' % type(obj))
-
-
-def convert_to_r_posixct(obj):
- """
- Convert DatetimeIndex or np.datetime array to R POSIXct using
- m8[s] format.
-
- Parameters
- ----------
- obj : source pandas object (one of [DatetimeIndex, np.datetime])
-
- Returns
- -------
- An R POSIXct vector (rpy2.robjects.vectors.POSIXct)
-
- """
- import time
- from rpy2.rinterface import StrSexpVector
-
- # convert m8[ns] to m8[s]
- vals = robj.vectors.FloatSexpVector(obj.values.view('i8') / 1E9)
- as_posixct = robj.baseenv.get('as.POSIXct')
- origin = StrSexpVector([time.strftime("%Y-%m-%d",
- time.gmtime(0)), ])
-
- # We will be sending ints as UTC
- tz = obj.tz.zone if hasattr(
- obj, 'tz') and hasattr(obj.tz, 'zone') else 'UTC'
- tz = StrSexpVector([tz])
- utc_tz = StrSexpVector(['UTC'])
-
- posixct = as_posixct(vals, origin=origin, tz=utc_tz)
- posixct.do_slot_assign('tzone', tz)
- return posixct
-
-
-VECTOR_TYPES = {np.float64: robj.FloatVector,
- np.float32: robj.FloatVector,
- np.float: robj.FloatVector,
- np.int: robj.IntVector,
- np.int32: robj.IntVector,
- np.int64: robj.IntVector,
- np.object_: robj.StrVector,
- np.str: robj.StrVector,
- np.bool: robj.BoolVector}
-
-
-NA_TYPES = {np.float64: robj.NA_Real,
- np.float32: robj.NA_Real,
- np.float: robj.NA_Real,
- np.int: robj.NA_Integer,
- np.int32: robj.NA_Integer,
- np.int64: robj.NA_Integer,
- np.object_: robj.NA_Character,
- np.str: robj.NA_Character,
- np.bool: robj.NA_Logical}
-
-
-if LooseVersion(np.__version__) >= LooseVersion('1.8'):
- for dict_ in (VECTOR_TYPES, NA_TYPES):
- dict_.update({
- np.bool_: dict_[np.bool],
- np.int_: dict_[np.int],
- np.float_: dict_[np.float],
- np.string_: dict_[np.str]
- })
-
-
-def convert_to_r_dataframe(df, strings_as_factors=False):
- """
- Convert a pandas DataFrame to a R data.frame.
-
- Parameters
- ----------
- df: The DataFrame being converted
- strings_as_factors: Whether to turn strings into R factors (default: False)
-
- Returns
- -------
- A R data.frame
-
- """
-
- import rpy2.rlike.container as rlc
-
- columns = rlc.OrdDict()
-
- # FIXME: This doesn't handle MultiIndex
-
- for column in df:
- value = df[column]
- value_type = value.dtype.type
-
- if value_type == np.datetime64:
- value = convert_to_r_posixct(value)
- else:
- value = [item if pd.notnull(item) else NA_TYPES[value_type]
- for item in value]
-
- value = VECTOR_TYPES[value_type](value)
-
- if not strings_as_factors:
- I = robj.baseenv.get("I")
- value = I(value)
-
- columns[column] = value
-
- r_dataframe = robj.DataFrame(columns)
-
- del columns
-
- r_dataframe.rownames = robj.StrVector(df.index)
-
- return r_dataframe
-
-
-def convert_to_r_matrix(df, strings_as_factors=False):
-
- """
- Convert a pandas DataFrame to a R matrix.
-
- Parameters
- ----------
- df: The DataFrame being converted
- strings_as_factors: Whether to turn strings into R factors (default: False)
-
- Returns
- -------
- A R matrix
-
- """
-
- if df._is_mixed_type:
- raise TypeError("Conversion to matrix only possible with non-mixed "
- "type DataFrames")
-
- r_dataframe = convert_to_r_dataframe(df, strings_as_factors)
- as_matrix = robj.baseenv.get("as.matrix")
- r_matrix = as_matrix(r_dataframe)
-
- return r_matrix
-
-if __name__ == '__main__':
- pass
diff --git a/pandas/rpy/mass.py b/pandas/rpy/mass.py
deleted file mode 100644
index 12fbbdfa4dc98..0000000000000
--- a/pandas/rpy/mass.py
+++ /dev/null
@@ -1,2 +0,0 @@
-class rlm(object):
- pass
diff --git a/pandas/rpy/tests/__init__.py b/pandas/rpy/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d..0000000000000
diff --git a/pandas/rpy/tests/test_common.py b/pandas/rpy/tests/test_common.py
deleted file mode 100644
index c3f09e21b1545..0000000000000
--- a/pandas/rpy/tests/test_common.py
+++ /dev/null
@@ -1,216 +0,0 @@
-"""
-Testing that functions from rpy work as expected
-"""
-
-# flake8: noqa
-
-import pandas as pd
-import numpy as np
-import unittest
-import nose
-import warnings
-import pandas.util.testing as tm
-
-try:
- import pandas.rpy.common as com
- from rpy2.robjects import r
- import rpy2.robjects as robj
-except ImportError:
- raise nose.SkipTest('R not installed')
-
-
-class TestCommon(unittest.TestCase):
- def test_convert_list(self):
- obj = r('list(a=1, b=2, c=3)')
-
- converted = com.convert_robj(obj)
- expected = {'a': [1], 'b': [2], 'c': [3]}
-
- tm.assert_dict_equal(converted, expected)
-
- def test_convert_nested_list(self):
- obj = r('list(a=list(foo=1, bar=2))')
-
- converted = com.convert_robj(obj)
- expected = {'a': {'foo': [1], 'bar': [2]}}
-
- tm.assert_dict_equal(converted, expected)
-
- def test_convert_frame(self):
- # built-in dataset
- df = r['faithful']
-
- converted = com.convert_robj(df)
-
- assert np.array_equal(converted.columns, ['eruptions', 'waiting'])
- assert np.array_equal(converted.index, np.arange(1, 273))
-
- def _test_matrix(self):
- r('mat <- matrix(rnorm(9), ncol=3)')
- r('colnames(mat) <- c("one", "two", "three")')
- r('rownames(mat) <- c("a", "b", "c")')
-
- return r['mat']
-
- def test_convert_matrix(self):
- mat = self._test_matrix()
-
- converted = com.convert_robj(mat)
-
- assert np.array_equal(converted.index, ['a', 'b', 'c'])
- assert np.array_equal(converted.columns, ['one', 'two', 'three'])
-
- def test_convert_r_dataframe(self):
-
- is_na = robj.baseenv.get("is.na")
-
- seriesd = tm.getSeriesData()
- frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A'])
-
- # Null data
- frame["E"] = [np.nan for item in frame["A"]]
- # Some mixed type data
- frame["F"] = ["text" if item %
- 2 == 0 else np.nan for item in range(30)]
-
- r_dataframe = com.convert_to_r_dataframe(frame)
-
- assert np.array_equal(
- com.convert_robj(r_dataframe.rownames), frame.index)
- assert np.array_equal(
- com.convert_robj(r_dataframe.colnames), frame.columns)
- assert all(is_na(item) for item in r_dataframe.rx2("E"))
-
- for column in frame[["A", "B", "C", "D"]]:
- coldata = r_dataframe.rx2(column)
- original_data = frame[column]
- assert np.array_equal(com.convert_robj(coldata), original_data)
-
- for column in frame[["D", "E"]]:
- for original, converted in zip(frame[column],
- r_dataframe.rx2(column)):
-
- if pd.isnull(original):
- assert is_na(converted)
- else:
- assert original == converted
-
- def test_convert_r_matrix(self):
-
- is_na = robj.baseenv.get("is.na")
-
- seriesd = tm.getSeriesData()
- frame = pd.DataFrame(seriesd, columns=['D', 'C', 'B', 'A'])
- # Null data
- frame["E"] = [np.nan for item in frame["A"]]
-
- r_dataframe = com.convert_to_r_matrix(frame)
-
- assert np.array_equal(
- com.convert_robj(r_dataframe.rownames), frame.index)
- assert np.array_equal(
- com.convert_robj(r_dataframe.colnames), frame.columns)
- assert all(is_na(item) for item in r_dataframe.rx(True, "E"))
-
- for column in frame[["A", "B", "C", "D"]]:
- coldata = r_dataframe.rx(True, column)
- original_data = frame[column]
- assert np.array_equal(com.convert_robj(coldata),
- original_data)
-
- # Pandas bug 1282
- frame["F"] = ["text" if item %
- 2 == 0 else np.nan for item in range(30)]
-
- try:
- wrong_matrix = com.convert_to_r_matrix(frame)
- except TypeError:
- pass
- except Exception:
- raise
-
- def test_dist(self):
- for name in ('eurodist',):
- df = com.load_data(name)
- dist = r[name]
- labels = r['labels'](dist)
- assert np.array_equal(df.index, labels)
- assert np.array_equal(df.columns, labels)
-
- def test_timeseries(self):
- """
- Test that the series has an informative index.
- Unfortunately the code currently does not build a DateTimeIndex
- """
- for name in (
- 'austres', 'co2', 'fdeaths', 'freeny.y', 'JohnsonJohnson',
- 'ldeaths', 'mdeaths', 'nottem', 'presidents', 'sunspot.month', 'sunspots',
- 'UKDriverDeaths', 'UKgas', 'USAccDeaths',
- 'airmiles', 'discoveries', 'EuStockMarkets',
- 'LakeHuron', 'lh', 'lynx', 'nhtemp', 'Nile',
- 'Seatbelts', 'sunspot.year', 'treering', 'uspop'):
- series = com.load_data(name)
- ts = r[name]
- assert np.array_equal(series.index, r['time'](ts))
-
- def test_numeric(self):
- for name in ('euro', 'islands', 'precip'):
- series = com.load_data(name)
- numeric = r[name]
- names = numeric.names
- assert np.array_equal(series.index, names)
-
- def test_table(self):
- iris3 = pd.DataFrame({'X0': {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'},
- 'X1': {0: 'Sepal L.',
- 1: 'Sepal L.',
- 2: 'Sepal L.',
- 3: 'Sepal L.',
- 4: 'Sepal L.'},
- 'X2': {0: 'Setosa',
- 1: 'Setosa',
- 2: 'Setosa',
- 3: 'Setosa',
- 4: 'Setosa'},
- 'value': {0: '5.1', 1: '4.9', 2: '4.7', 3: '4.6', 4: '5.0'}})
- hec = pd.DataFrame(
- {
- 'Eye': {0: 'Brown', 1: 'Brown', 2: 'Brown', 3: 'Brown', 4: 'Blue'},
- 'Hair': {0: 'Black', 1: 'Brown', 2: 'Red', 3: 'Blond', 4: 'Black'},
- 'Sex': {0: 'Male', 1: 'Male', 2: 'Male', 3: 'Male', 4: 'Male'},
- 'value': {0: '32.0', 1: '53.0', 2: '10.0', 3: '3.0', 4: '11.0'}})
- titanic = pd.DataFrame(
- {
- 'Age': {0: 'Child', 1: 'Child', 2: 'Child', 3: 'Child', 4: 'Child'},
- 'Class': {0: '1st', 1: '2nd', 2: '3rd', 3: 'Crew', 4: '1st'},
- 'Sex': {0: 'Male', 1: 'Male', 2: 'Male', 3: 'Male', 4: 'Female'},
- 'Survived': {0: 'No', 1: 'No', 2: 'No', 3: 'No', 4: 'No'},
- 'value': {0: '0.0', 1: '0.0', 2: '35.0', 3: '0.0', 4: '0.0'}})
- for name, expected in zip(('HairEyeColor', 'Titanic', 'iris3'),
- (hec, titanic, iris3)):
- df = com.load_data(name)
- table = r[name]
- names = r['dimnames'](table)
- try:
- columns = list(r['names'](names))[::-1]
- except TypeError:
- columns = ['X{:d}'.format(i) for i in range(len(names))][::-1]
- columns.append('value')
- assert np.array_equal(df.columns, columns)
- result = df.head()
- cond = ((result.sort(axis=1) == expected.sort(axis=1))).values
- assert np.all(cond)
-
- def test_factor(self):
- for name in ('state.division', 'state.region'):
- vector = r[name]
- factors = list(r['factor'](vector))
- level = list(r['levels'](vector))
- factors = [level[index - 1] for index in factors]
- result = com.load_data(name)
- assert np.equal(result, factors)
-
-if __name__ == '__main__':
- nose.runmodule(argv=[__file__, '-vvs', '-x', '--pdb', '--pdb-failure'],
- # '--with-coverage', '--cover-package=pandas.core'],
- exit=False)
diff --git a/pandas/rpy/vars.py b/pandas/rpy/vars.py
deleted file mode 100644
index 2073b47483141..0000000000000
--- a/pandas/rpy/vars.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# flake8: noqa
-
-import pandas.rpy.util as util
-
-
-class VAR(object):
- """
-
- Parameters
- ----------
- y :
- p :
- type : {"const", "trend", "both", "none"}
- season :
- exogen :
- lag_max :
- ic : {"AIC", "HQ", "SC", "FPE"}
- Information criterion to use, if lag_max is not None
- """
- def __init__(y, p=1, type="none", season=None, exogen=None,
- lag_max=None, ic=None):
- pass
diff --git a/setup.py b/setup.py
index 0a84cf527bfb1..d8d9968c51576 100755
--- a/setup.py
+++ b/setup.py
@@ -630,7 +630,6 @@ def pxd(name):
'pandas.io',
'pandas.io.sas',
'pandas.formats',
- 'pandas.rpy',
'pandas.sparse',
'pandas.sparse.tests',
'pandas.stats',
| Closes #9602 | https://api.github.com/repos/pandas-dev/pandas/pulls/15142 | 2017-01-16T15:47:42Z | 2017-01-22T12:15:10Z | 2017-01-22T12:15:10Z | 2018-04-10T11:35:47Z |
MAINT: remove shebang from test modules | diff --git a/pandas/computation/tests/test_compat.py b/pandas/computation/tests/test_compat.py
index 80b415739c647..8e8924379f153 100644
--- a/pandas/computation/tests/test_compat.py
+++ b/pandas/computation/tests/test_compat.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# flake8: noqa
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 1b577a574350d..3a446bfc36c21 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_eval.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# flake8: noqa
diff --git a/pandas/tests/plotting/test_boxplot_method.py b/pandas/tests/plotting/test_boxplot_method.py
index 0916693ade2ce..f76cdb70c0240 100644
--- a/pandas/tests/plotting/test_boxplot_method.py
+++ b/pandas/tests/plotting/test_boxplot_method.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/plotting/test_frame.py b/pandas/tests/plotting/test_frame.py
index 87cf89ebf0a9d..f4c64c3d7a3f8 100644
--- a/pandas/tests/plotting/test_frame.py
+++ b/pandas/tests/plotting/test_frame.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/plotting/test_groupby.py b/pandas/tests/plotting/test_groupby.py
index 101a6556c61bf..3c682fbfbb89e 100644
--- a/pandas/tests/plotting/test_groupby.py
+++ b/pandas/tests/plotting/test_groupby.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/plotting/test_hist_method.py b/pandas/tests/plotting/test_hist_method.py
index c7bff5a31fc02..bde5544390b85 100644
--- a/pandas/tests/plotting/test_hist_method.py
+++ b/pandas/tests/plotting/test_hist_method.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/plotting/test_misc.py b/pandas/tests/plotting/test_misc.py
index 6c313f5937602..2650ce2879db7 100644
--- a/pandas/tests/plotting/test_misc.py
+++ b/pandas/tests/plotting/test_misc.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/plotting/test_series.py b/pandas/tests/plotting/test_series.py
index 6878ca0e1bc06..f668c46a15173 100644
--- a/pandas/tests/plotting/test_series.py
+++ b/pandas/tests/plotting/test_series.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import nose
diff --git a/pandas/tests/test_config.py b/pandas/tests/test_config.py
index ea226851c9101..ed8c37fd6dd20 100644
--- a/pandas/tests/test_config.py
+++ b/pandas/tests/test_config.py
@@ -1,4 +1,3 @@
-#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import unittest
diff --git a/pandas/tests/test_msgpack/test_buffer.py b/pandas/tests/test_msgpack/test_buffer.py
index 43f5e64012885..caaa22bfd08fc 100644
--- a/pandas/tests/test_msgpack/test_buffer.py
+++ b/pandas/tests/test_msgpack/test_buffer.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
from pandas.msgpack import packb, unpackb
diff --git a/pandas/tests/test_msgpack/test_case.py b/pandas/tests/test_msgpack/test_case.py
index 5e8bbff390d07..a8a45b5b37eb0 100644
--- a/pandas/tests/test_msgpack/test_case.py
+++ b/pandas/tests/test_msgpack/test_case.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
from pandas.msgpack import packb, unpackb
diff --git a/pandas/tests/test_msgpack/test_except.py b/pandas/tests/test_msgpack/test_except.py
index 79290ebb891fd..76b91bb375bbc 100644
--- a/pandas/tests/test_msgpack/test_except.py
+++ b/pandas/tests/test_msgpack/test_except.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import unittest
diff --git a/pandas/tests/test_msgpack/test_format.py b/pandas/tests/test_msgpack/test_format.py
index 203726ae6a5f9..a4b309ebb657d 100644
--- a/pandas/tests/test_msgpack/test_format.py
+++ b/pandas/tests/test_msgpack/test_format.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
from pandas.msgpack import unpackb
diff --git a/pandas/tests/test_msgpack/test_limits.py b/pandas/tests/test_msgpack/test_limits.py
index 2cf52aae65f2a..9c08f328b90dd 100644
--- a/pandas/tests/test_msgpack/test_limits.py
+++ b/pandas/tests/test_msgpack/test_limits.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
from __future__ import (absolute_import, division, print_function,
unicode_literals)
diff --git a/pandas/tests/test_msgpack/test_pack.py b/pandas/tests/test_msgpack/test_pack.py
index 99c7453212b8b..005352691d908 100644
--- a/pandas/tests/test_msgpack/test_pack.py
+++ b/pandas/tests/test_msgpack/test_pack.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import unittest
diff --git a/pandas/tests/test_msgpack/test_seq.py b/pandas/tests/test_msgpack/test_seq.py
index 76a21b98f22da..927c2622419a6 100644
--- a/pandas/tests/test_msgpack/test_seq.py
+++ b/pandas/tests/test_msgpack/test_seq.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import io
diff --git a/pandas/tests/test_msgpack/test_sequnpack.py b/pandas/tests/test_msgpack/test_sequnpack.py
index 2f496b9fbbafa..fe089ccda1c7f 100644
--- a/pandas/tests/test_msgpack/test_sequnpack.py
+++ b/pandas/tests/test_msgpack/test_sequnpack.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
import unittest
diff --git a/pandas/tests/test_msgpack/test_subtype.py b/pandas/tests/test_msgpack/test_subtype.py
index c89b36717a159..d6dd72c4d9850 100644
--- a/pandas/tests/test_msgpack/test_subtype.py
+++ b/pandas/tests/test_msgpack/test_subtype.py
@@ -1,4 +1,3 @@
-#!/usr/bin/env python
# coding: utf-8
from pandas.msgpack import packb
diff --git a/pandas/tests/test_testing.py b/pandas/tests/test_testing.py
index 7a217ed9dbd86..e2f295a5343bc 100644
--- a/pandas/tests/test_testing.py
+++ b/pandas/tests/test_testing.py
@@ -1,4 +1,3 @@
-#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import unittest
| - [x] closes #15131 | https://api.github.com/repos/pandas-dev/pandas/pulls/15134 | 2017-01-15T12:51:43Z | 2017-01-15T16:09:03Z | 2017-01-15T16:09:02Z | 2017-07-11T10:06:15Z |
TST: tests.groupby needs to be a package to run tests | diff --git a/pandas/tests/groupby/__init__.py b/pandas/tests/groupby/__init__.py
new file mode 100644
index 0000000000000..e69de29bb2d1d
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index 8e61fa3a5fb66..873c63ca257c4 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -5808,9 +5808,12 @@ def test_cummin_cummax(self):
df.loc[[2, 6], 'B'] = min_val
expected.loc[[2, 3, 6, 7], 'B'] = min_val
result = df.groupby('A').cummin()
- tm.assert_frame_equal(result, expected)
+
+ # TODO: GH 15019
+ # overwriting NaNs
+ # tm.assert_frame_equal(result, expected)
expected = df.groupby('A').B.apply(lambda x: x.cummin()).to_frame()
- tm.assert_frame_equal(result, expected)
+ # tm.assert_frame_equal(result, expected)
# cummax
expected = pd.DataFrame({'B': expected_maxs}).astype(dtype)
@@ -5823,9 +5826,13 @@ def test_cummin_cummax(self):
df.loc[[2, 6], 'B'] = max_val
expected.loc[[2, 3, 6, 7], 'B'] = max_val
result = df.groupby('A').cummax()
- tm.assert_frame_equal(result, expected)
+
+ # TODO: GH 15019
+ # overwriting NaNs
+ # tm.assert_frame_equal(result, expected)
+
expected = df.groupby('A').B.apply(lambda x: x.cummax()).to_frame()
- tm.assert_frame_equal(result, expected)
+ # tm.assert_frame_equal(result, expected)
# Test nan in some values
base_df.loc[[0, 2, 4, 6], 'B'] = np.nan
| skip parts of cummin test, xref #15109
| https://api.github.com/repos/pandas-dev/pandas/pulls/15127 | 2017-01-13T19:34:21Z | 2017-01-13T20:21:22Z | 2017-01-13T20:21:22Z | 2017-01-13T20:21:22Z |
Fix typo in docstring | diff --git a/pandas/util/testing.py b/pandas/util/testing.py
index 05517bf6cf53a..d96f57f2810e3 100644
--- a/pandas/util/testing.py
+++ b/pandas/util/testing.py
@@ -1220,7 +1220,7 @@ def assert_frame_equal(left, right, check_dtype=True,
are identical.
check_frame_type : bool, default False
Whether to check the DataFrame class is identical.
- check_less_precise : bool or it, default False
+ check_less_precise : bool or int, default False
Specify comparison precision. Only used when check_exact is False.
5 digits (False) or 3 digits (True) after decimal points are compared.
If int, then specify the digits to compare
| it -> int | https://api.github.com/repos/pandas-dev/pandas/pulls/15121 | 2017-01-12T21:53:39Z | 2017-01-12T22:10:59Z | 2017-01-12T22:10:59Z | 2017-01-12T22:11:11Z |
TST: Add another test for uneven CSV rows | diff --git a/pandas/io/tests/parser/usecols.py b/pandas/io/tests/parser/usecols.py
index 96790e872abc3..4875282067fb3 100644
--- a/pandas/io/tests/parser/usecols.py
+++ b/pandas/io/tests/parser/usecols.py
@@ -465,3 +465,13 @@ def test_uneven_length_cols(self):
[10, 20, 30]])
df = self.read_csv(StringIO(data), header=None, usecols=usecols)
tm.assert_frame_equal(df, expected)
+
+ # see gh-9549
+ usecols = ['A', 'B', 'C']
+ data = ('A,B,C\n1,2,3\n3,4,5\n1,2,4,5,1,6\n'
+ '1,2,3,,,1,\n1,2,3\n5,6,7')
+ expected = DataFrame({'A': [1, 3, 1, 1, 1, 5],
+ 'B': [2, 4, 2, 2, 2, 6],
+ 'C': [3, 5, 4, 3, 3, 7]})
+ df = self.read_csv(StringIO(data), usecols=usecols)
+ tm.assert_frame_equal(df, expected)
| Title is self-explanatory.
xref #15066.
Closes #9549.
| https://api.github.com/repos/pandas-dev/pandas/pulls/15114 | 2017-01-12T05:30:30Z | 2017-01-12T22:33:49Z | 2017-01-12T22:33:49Z | 2017-01-12T22:38:24Z |
TST: add network decorator on parser network tests for compressed url in parser | diff --git a/pandas/io/tests/parser/test_network.py b/pandas/io/tests/parser/test_network.py
index 8e71cf1cc7e4c..d84c2ae3beb0c 100644
--- a/pandas/io/tests/parser/test_network.py
+++ b/pandas/io/tests/parser/test_network.py
@@ -30,8 +30,9 @@ def __init__(self):
self.base_url = ('https://github.com/pandas-dev/pandas/raw/master/'
'pandas/io/tests/parser/data/salaries.csv')
+ @tm.network
def test_compressed_urls(self):
- """Test reading compressed tables from URL."""
+ # Test reading compressed tables from URL.
msg = ('Test reading {}-compressed tables from URL: '
'compression="{}", engine="{}"')
| https://api.github.com/repos/pandas-dev/pandas/pulls/15102 | 2017-01-10T21:09:15Z | 2017-01-10T21:46:55Z | 2017-01-10T21:46:55Z | 2017-01-10T21:46:55Z | |
CLN: check NULL and have similar __dealloc__ code for all hashtables | diff --git a/pandas/src/hashtable_class_helper.pxi.in b/pandas/src/hashtable_class_helper.pxi.in
index b26839599ef38..1d3c4b2cb5889 100644
--- a/pandas/src/hashtable_class_helper.pxi.in
+++ b/pandas/src/hashtable_class_helper.pxi.in
@@ -85,7 +85,9 @@ cdef class {{name}}Vector:
self.data.data = <{{arg}}*> self.ao.data
def __dealloc__(self):
- PyMem_Free(self.data)
+ if self.data is not NULL:
+ PyMem_Free(self.data)
+ self.data = NULL
def __len__(self):
return self.data.n
@@ -133,8 +135,11 @@ cdef class StringVector:
self.data.data[i] = orig_data[i]
def __dealloc__(self):
- free(self.data.data)
- PyMem_Free(self.data)
+ if self.data is not NULL:
+ if self.data.data is not NULL:
+ free(self.data.data)
+ PyMem_Free(self.data)
+ self.data = NULL
def __len__(self):
return self.data.n
@@ -223,7 +228,9 @@ cdef class {{name}}HashTable(HashTable):
return self.table.size
def __dealloc__(self):
- kh_destroy_{{dtype}}(self.table)
+ if self.table is not NULL:
+ kh_destroy_{{dtype}}(self.table)
+ self.table = NULL
def __contains__(self, object key):
cdef khiter_t k
@@ -453,7 +460,9 @@ cdef class StringHashTable(HashTable):
kh_resize_str(self.table, size_hint)
def __dealloc__(self):
- kh_destroy_str(self.table)
+ if self.table is not NULL:
+ kh_destroy_str(self.table)
+ self.table = NULL
cpdef get_item(self, object val):
cdef:
@@ -691,7 +700,8 @@ cdef class PyObjectHashTable(HashTable):
def __dealloc__(self):
if self.table is not NULL:
- self.destroy()
+ kh_destroy_pymap(self.table)
+ self.table = NULL
def __len__(self):
return self.table.size
@@ -704,10 +714,6 @@ cdef class PyObjectHashTable(HashTable):
k = kh_get_pymap(self.table, <PyObject*>key)
return k != self.table.n_buckets
- def destroy(self):
- kh_destroy_pymap(self.table)
- self.table = NULL
-
cpdef get_item(self, object val):
cdef khiter_t k
if val != val or val is None:
| https://api.github.com/repos/pandas-dev/pandas/pulls/15101 | 2017-01-10T18:16:48Z | 2017-01-10T21:11:27Z | 2017-01-10T21:11:27Z | 2017-01-10T21:11:27Z | |
TST: close sas read handles properly in tests | diff --git a/pandas/io/tests/sas/test_sas7bdat.py b/pandas/io/tests/sas/test_sas7bdat.py
index e20ea48247119..69073a90e9669 100644
--- a/pandas/io/tests/sas/test_sas7bdat.py
+++ b/pandas/io/tests/sas/test_sas7bdat.py
@@ -51,6 +51,7 @@ def test_from_buffer(self):
iterator=True, encoding='utf-8')
df = rdr.read()
tm.assert_frame_equal(df, df0, check_exact=False)
+ rdr.close()
def test_from_iterator(self):
for j in 0, 1:
@@ -62,6 +63,7 @@ def test_from_iterator(self):
tm.assert_frame_equal(df, df0.iloc[0:2, :])
df = rdr.read(3)
tm.assert_frame_equal(df, df0.iloc[2:5, :])
+ rdr.close()
def test_iterator_loop(self):
# github #13654
@@ -74,6 +76,7 @@ def test_iterator_loop(self):
for x in rdr:
y += x.shape[0]
self.assertTrue(y == rdr.row_count)
+ rdr.close()
def test_iterator_read_too_much(self):
# github #14734
@@ -82,9 +85,12 @@ def test_iterator_read_too_much(self):
rdr = pd.read_sas(fname, format="sas7bdat",
iterator=True, encoding='utf-8')
d1 = rdr.read(rdr.row_count + 20)
+ rdr.close()
+
rdr = pd.read_sas(fname, iterator=True, encoding="utf-8")
d2 = rdr.read(rdr.row_count + 20)
tm.assert_frame_equal(d1, d2)
+ rdr.close()
def test_encoding_options():
| https://api.github.com/repos/pandas-dev/pandas/pulls/15100 | 2017-01-10T18:15:51Z | 2017-01-10T21:10:05Z | 2017-01-10T21:10:05Z | 2017-01-10T21:10:05Z | |
DEPR: deprecate sortlevel in favor of sort_index | diff --git a/doc/source/api.rst b/doc/source/api.rst
index 272dfe72eafe7..b7a1b8a005d89 100644
--- a/doc/source/api.rst
+++ b/doc/source/api.rst
@@ -418,7 +418,6 @@ Reshaping, sorting
Series.reorder_levels
Series.sort_values
Series.sort_index
- Series.sortlevel
Series.swaplevel
Series.unstack
Series.searchsorted
@@ -931,7 +930,6 @@ Reshaping, sorting, transposing
DataFrame.reorder_levels
DataFrame.sort_values
DataFrame.sort_index
- DataFrame.sortlevel
DataFrame.nlargest
DataFrame.nsmallest
DataFrame.swaplevel
diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index c9ea7b427b3f2..72fa428869e8f 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -261,7 +261,7 @@ Deprecations
- ``Categorical.searchsorted()`` and ``Series.searchsorted()`` have deprecated the ``v`` parameter in favor of ``value`` (:issue:`12662`)
- ``TimedeltaIndex.searchsorted()``, ``DatetimeIndex.searchsorted()``, and ``PeriodIndex.searchsorted()`` have deprecated the ``key`` parameter in favor of ``value`` (:issue:`12662`)
- ``DataFrame.astype()`` has deprecated the ``raise_on_error`` parameter in favor of ``errors`` (:issue:`14878`)
-
+- ``Series.sortlevel`` and ``DataFrame.sortlevel`` have been deprecated in favor of ``Series.sort_index`` and ``DataFrame.sort_index`` (:issue:`15099`)
.. _whatsnew_0200.prior_deprecations:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index b9290c0ce3457..4288e03c2cc49 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1274,7 +1274,7 @@ def to_panel(self):
# minor axis must be sorted
if self.index.lexsort_depth < 2:
- selfsorted = self.sortlevel(0)
+ selfsorted = self.sort_index(level=0)
else:
selfsorted = self
@@ -3337,6 +3337,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
def sortlevel(self, level=0, axis=0, ascending=True, inplace=False,
sort_remaining=True):
"""
+ DEPRECATED: use :meth:`DataFrame.sort_index`
+
Sort multilevel index by chosen axis and primary level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order)
@@ -3360,6 +3362,8 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False,
DataFrame.sort_index(level=...)
"""
+ warnings.warn("sortlevel is deprecated, use sort_index(level= ...)",
+ FutureWarning, stacklevel=2)
return self.sort_index(level=level, axis=axis, ascending=ascending,
inplace=inplace, sort_remaining=sort_remaining)
diff --git a/pandas/core/reshape.py b/pandas/core/reshape.py
index b359c54535b28..0831568e8c955 100644
--- a/pandas/core/reshape.py
+++ b/pandas/core/reshape.py
@@ -375,7 +375,7 @@ def pivot_simple(index, columns, values):
hindex = MultiIndex.from_arrays([index, columns])
series = Series(values.ravel(), index=hindex)
- series = series.sortlevel(0)
+ series = series.sort_index(level=0)
return series.unstack()
@@ -596,7 +596,7 @@ def _convert_level_number(level_num, columns):
# which interferes with trying to sort based on the first
# level
level_to_sort = _convert_level_number(0, this.columns)
- this = this.sortlevel(level_to_sort, axis=1)
+ this = this.sort_index(level=level_to_sort, axis=1)
# tuple list excluding level for grouping columns
if len(frame.columns.levels) > 2:
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 0b29e8c93a12d..ab1498c617467 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1988,6 +1988,8 @@ def nsmallest(self, n=5, keep='first'):
def sortlevel(self, level=0, ascending=True, sort_remaining=True):
"""
+ DEPRECATED: use :meth:`Series.sort_index`
+
Sort Series with MultiIndex by chosen level. Data will be
lexicographically sorted by the chosen level followed by the other
levels (in order)
@@ -2006,6 +2008,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True):
Series.sort_index(level=...)
"""
+ warnings.warn("sortlevel is deprecated, use sort_index(level=...)",
+ FutureWarning, stacklevel=2)
return self.sort_index(level=level, ascending=ascending,
sort_remaining=sort_remaining)
diff --git a/pandas/sparse/frame.py b/pandas/sparse/frame.py
index 4cdcbc6fca32c..1fc93a967bdbb 100644
--- a/pandas/sparse/frame.py
+++ b/pandas/sparse/frame.py
@@ -816,7 +816,7 @@ def stack_sparse_frame(frame):
lp = DataFrame(stacked_values.reshape((nobs, 1)), index=index,
columns=['foo'])
- return lp.sortlevel(level=0)
+ return lp.sort_index(level=0)
def homogenize(series_dict):
diff --git a/pandas/sparse/tests/test_series.py b/pandas/sparse/tests/test_series.py
index 14339ab388a5d..b5ca3a730ed27 100644
--- a/pandas/sparse/tests/test_series.py
+++ b/pandas/sparse/tests/test_series.py
@@ -947,7 +947,7 @@ def setUp(self):
micol = pd.MultiIndex.from_product(
[['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar'])
dense_multiindex_frame = pd.DataFrame(
- index=miindex, columns=micol).sortlevel().sortlevel(axis=1)
+ index=miindex, columns=micol).sort_index().sort_index(axis=1)
self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14)
def test_to_sparse_preserve_multiindex_names_columns(self):
diff --git a/pandas/stats/plm.py b/pandas/stats/plm.py
index 099c45d5ec60b..806dc289f843a 100644
--- a/pandas/stats/plm.py
+++ b/pandas/stats/plm.py
@@ -792,8 +792,8 @@ def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis,
resid = DataFrame(yv[:, None] - Xb, index=y.index, columns=['resid'])
if cluster_axis == 1:
- x = x.swaplevel(0, 1).sortlevel(0)
- resid = resid.swaplevel(0, 1).sortlevel(0)
+ x = x.swaplevel(0, 1).sort_index(level=0)
+ resid = resid.swaplevel(0, 1).sort_index(level=0)
m = _group_agg(x.values * resid.values, x.index._bounds,
lambda x: np.sum(x, axis=0))
diff --git a/pandas/tests/frame/test_misc_api.py b/pandas/tests/frame/test_misc_api.py
index bc750727493a3..b739d087b8e93 100644
--- a/pandas/tests/frame/test_misc_api.py
+++ b/pandas/tests/frame/test_misc_api.py
@@ -456,10 +456,6 @@ def _check_f(base, f):
f = lambda x: x.sort_index(inplace=True)
_check_f(data.copy(), f)
- # sortlevel
- f = lambda x: x.sortlevel(0, inplace=True)
- _check_f(data.set_index(['a', 'b']), f)
-
# fillna
f = lambda x: x.fillna(0, inplace=True)
_check_f(data.copy(), f)
diff --git a/pandas/tests/frame/test_operators.py b/pandas/tests/frame/test_operators.py
index 8462d5cd9bcf6..f7e821c0434aa 100644
--- a/pandas/tests/frame/test_operators.py
+++ b/pandas/tests/frame/test_operators.py
@@ -467,7 +467,7 @@ def test_binary_ops_align(self):
df = DataFrame(np.arange(27 * 3).reshape(27, 3),
index=index,
- columns=['value1', 'value2', 'value3']).sortlevel()
+ columns=['value1', 'value2', 'value3']).sort_index()
idx = pd.IndexSlice
for op in ['add', 'sub', 'mul', 'div', 'truediv']:
@@ -479,7 +479,7 @@ def test_binary_ops_align(self):
result = getattr(df, op)(x, level='third', axis=0)
expected = pd.concat([opa(df.loc[idx[:, :, i], :], v)
- for i, v in x.iteritems()]).sortlevel()
+ for i, v in x.iteritems()]).sort_index()
assert_frame_equal(result, expected)
x = Series([1.0, 10.0], ['two', 'three'])
@@ -487,7 +487,7 @@ def test_binary_ops_align(self):
expected = (pd.concat([opa(df.loc[idx[:, i], :], v)
for i, v in x.iteritems()])
- .reindex_like(df).sortlevel())
+ .reindex_like(df).sort_index())
assert_frame_equal(result, expected)
# GH9463 (alignment level of dataframe with series)
diff --git a/pandas/tests/frame/test_sorting.py b/pandas/tests/frame/test_sorting.py
index 579a4bf5d54d5..2abef59df284d 100644
--- a/pandas/tests/frame/test_sorting.py
+++ b/pandas/tests/frame/test_sorting.py
@@ -53,16 +53,6 @@ def test_sort_index_multiindex(self):
mi = MultiIndex.from_tuples([[2, 1, 3], [1, 1, 1]], names=list('ABC'))
df = DataFrame([[1, 2], [3, 4]], mi)
- result = df.sort_index(level='A', sort_remaining=False)
- expected = df.sortlevel('A', sort_remaining=False)
- assert_frame_equal(result, expected)
-
- # sort columns by specified level of multi-index
- df = df.T
- result = df.sort_index(level='A', axis=1, sort_remaining=False)
- expected = df.sortlevel('A', axis=1, sort_remaining=False)
- assert_frame_equal(result, expected)
-
# MI sort, but no level: sort_level has no effect
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
df = DataFrame([[1, 2], [3, 4]], mi)
@@ -79,6 +69,8 @@ def test_sort(self):
frame.sort(columns='A')
with tm.assert_produces_warning(FutureWarning):
frame.sort()
+ with tm.assert_produces_warning(FutureWarning):
+ frame.sortlevel()
def test_sort_values(self):
frame = DataFrame([[1, 1, 2], [3, 1, 0], [4, 5, 6]],
@@ -453,13 +445,13 @@ def test_sort_index_duplicates(self):
result = df.sort_values(by=('a', 1))
assert_frame_equal(result, expected)
- def test_sortlevel(self):
+ def test_sort_index_level(self):
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
df = DataFrame([[1, 2], [3, 4]], mi)
- res = df.sortlevel('A', sort_remaining=False)
+ res = df.sort_index(level='A', sort_remaining=False)
assert_frame_equal(df, res)
- res = df.sortlevel(['A', 'B'], sort_remaining=False)
+ res = df.sort_index(level=['A', 'B'], sort_remaining=False)
assert_frame_equal(df, res)
def test_sort_datetimes(self):
diff --git a/pandas/tests/groupby/test_groupby.py b/pandas/tests/groupby/test_groupby.py
index e87b5d04271e8..dbeb36c894971 100644
--- a/pandas/tests/groupby/test_groupby.py
+++ b/pandas/tests/groupby/test_groupby.py
@@ -3427,7 +3427,7 @@ def test_int64_overflow(self):
left = lg.sum()['values']
right = rg.sum()['values']
- exp_index, _ = left.index.sortlevel(0)
+ exp_index, _ = left.index.sortlevel()
self.assert_index_equal(left.index, exp_index)
exp_index, _ = right.index.sortlevel(0)
@@ -3708,7 +3708,7 @@ def test_more_flexible_frame_multi_function(self):
exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))
expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
- expected = expected.swaplevel(0, 1, axis=1).sortlevel(0, axis=1)
+ expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)
d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
result = grouped.aggregate(d)
@@ -4711,7 +4711,7 @@ def test_timegrouper_with_reg_groups(self):
expected = df.groupby('user_id')[
'whole_cost'].resample(
freq).sum().dropna().reorder_levels(
- ['date', 'user_id']).sortlevel().astype('int64')
+ ['date', 'user_id']).sort_index().astype('int64')
expected.name = 'whole_cost'
result1 = df.sort_index().groupby([pd.TimeGrouper(freq=freq),
diff --git a/pandas/tests/indexes/test_multi.py b/pandas/tests/indexes/test_multi.py
index 7da3cb377e63d..16831219e0930 100644
--- a/pandas/tests/indexes/test_multi.py
+++ b/pandas/tests/indexes/test_multi.py
@@ -440,7 +440,7 @@ def test_set_value_keeps_names(self):
np.random.randn(6, 4),
columns=['one', 'two', 'three', 'four'],
index=idx)
- df = df.sortlevel()
+ df = df.sort_index()
self.assertIsNone(df.is_copy)
self.assertEqual(df.index.names, ('Name', 'Number'))
df = df.set_value(('grethe', '4'), 'one', 99.34)
diff --git a/pandas/tests/indexing/test_indexing.py b/pandas/tests/indexing/test_indexing.py
index 4e5558309bad5..6fc24e41ee914 100644
--- a/pandas/tests/indexing/test_indexing.py
+++ b/pandas/tests/indexing/test_indexing.py
@@ -2104,7 +2104,7 @@ def test_ix_general(self):
tm.assert_frame_equal(df.ix[key], df.iloc[2:])
# this is ok
- df.sortlevel(inplace=True)
+ df.sort_index(inplace=True)
res = df.ix[key]
# col has float dtype, result should be Float64Index
index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3],
@@ -2137,7 +2137,7 @@ def test_xs_multiindex(self):
[('a', 'foo'), ('a', 'bar'), ('b', 'hello'),
('b', 'world')], names=['lvl0', 'lvl1'])
df = DataFrame(np.random.randn(4, 4), columns=columns)
- df.sortlevel(axis=1, inplace=True)
+ df.sort_index(axis=1, inplace=True)
result = df.xs('a', level='lvl0', axis=1)
expected = df.iloc[:, 0:2].loc[:, 'a']
tm.assert_frame_equal(result, expected)
@@ -2180,7 +2180,7 @@ def test_per_axis_per_level_getitem(self):
df = DataFrame(
np.arange(16, dtype='int64').reshape(
4, 4), index=index, columns=columns)
- df = df.sortlevel(axis=0).sortlevel(axis=1)
+ df = df.sort_index(axis=0).sort_index(axis=1)
# identity
result = df.loc[(slice(None), slice(None)), :]
@@ -2249,7 +2249,7 @@ def f():
# not lexsorted
self.assertEqual(df.index.lexsort_depth, 2)
- df = df.sortlevel(level=1, axis=0)
+ df = df.sort_index(level=1, axis=0)
self.assertEqual(df.index.lexsort_depth, 0)
with tm.assertRaisesRegexp(
UnsortedIndexError,
@@ -2265,11 +2265,11 @@ def test_multiindex_slicers_non_unique(self):
B=['a', 'a', 'a', 'a'],
C=[1, 2, 1, 3],
D=[1, 2, 3, 4]))
- .set_index(['A', 'B', 'C']).sortlevel())
+ .set_index(['A', 'B', 'C']).sort_index())
self.assertFalse(df.index.is_unique)
expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
C=[1, 1], D=[1, 3]))
- .set_index(['A', 'B', 'C']).sortlevel())
+ .set_index(['A', 'B', 'C']).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
tm.assert_frame_equal(result, expected)
@@ -2281,11 +2281,11 @@ def test_multiindex_slicers_non_unique(self):
B=['a', 'a', 'a', 'a'],
C=[1, 2, 1, 2],
D=[1, 2, 3, 4]))
- .set_index(['A', 'B', 'C']).sortlevel())
+ .set_index(['A', 'B', 'C']).sort_index())
self.assertFalse(df.index.is_unique)
expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
C=[1, 1], D=[1, 3]))
- .set_index(['A', 'B', 'C']).sortlevel())
+ .set_index(['A', 'B', 'C']).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
self.assertFalse(result.index.is_unique)
tm.assert_frame_equal(result, expected)
@@ -2357,7 +2357,7 @@ def test_multiindex_slicers_edges(self):
df['DATE'] = pd.to_datetime(df['DATE'])
df1 = df.set_index(['A', 'B', 'DATE'])
- df1 = df1.sortlevel()
+ df1 = df1.sort_index()
# A1 - Get all values under "A0" and "A1"
result = df1.loc[(slice('A1')), :]
@@ -2440,7 +2440,7 @@ def f():
df.loc['A1', (slice(None), 'foo')]
self.assertRaises(UnsortedIndexError, f)
- df = df.sortlevel(axis=1)
+ df = df.sort_index(axis=1)
# slicing
df.loc['A1', (slice(None), 'foo')]
@@ -2459,7 +2459,7 @@ def test_loc_axis_arguments(self):
df = DataFrame(np.arange(len(index) * len(columns), dtype='int64')
.reshape((len(index), len(columns))),
index=index,
- columns=columns).sortlevel().sortlevel(axis=1)
+ columns=columns).sort_index().sort_index(axis=1)
# axis 0
result = df.loc(axis=0)['A1':'A3', :, ['C1', 'C3']]
@@ -2551,7 +2551,7 @@ def test_per_axis_per_level_setitem(self):
df_orig = DataFrame(
np.arange(16, dtype='int64').reshape(
4, 4), index=index, columns=columns)
- df_orig = df_orig.sortlevel(axis=0).sortlevel(axis=1)
+ df_orig = df_orig.sort_index(axis=0).sort_index(axis=1)
# identity
df = df_orig.copy()
@@ -2764,12 +2764,12 @@ def f():
idx = pd.MultiIndex.from_product([['A', 'B', 'C'],
['foo', 'bar', 'baz']],
names=['one', 'two'])
- s = pd.Series(np.arange(9, dtype='int64'), index=idx).sortlevel()
+ s = pd.Series(np.arange(9, dtype='int64'), index=idx).sort_index()
exp_idx = pd.MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
names=['one', 'two'])
expected = pd.Series(np.arange(3, dtype='int64'),
- index=exp_idx).sortlevel()
+ index=exp_idx).sort_index()
result = s.loc[['A']]
tm.assert_series_equal(result, expected)
@@ -2786,7 +2786,7 @@ def f():
idx = pd.IndexSlice
expected = pd.Series([0, 3, 6], index=pd.MultiIndex.from_product(
- [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sortlevel()
+ [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
result = s.loc[idx[:, ['foo']]]
tm.assert_series_equal(result, expected)
@@ -2799,7 +2799,7 @@ def f():
['alpha', 'beta']))
df = DataFrame(
np.random.randn(5, 6), index=range(5), columns=multi_index)
- df = df.sortlevel(0, axis=1)
+ df = df.sort_index(level=0, axis=1)
expected = DataFrame(index=range(5),
columns=multi_index.reindex([])[0])
diff --git a/pandas/tests/series/test_analytics.py b/pandas/tests/series/test_analytics.py
index 3896e255f0c2f..e66e34156b6e9 100644
--- a/pandas/tests/series/test_analytics.py
+++ b/pandas/tests/series/test_analytics.py
@@ -1595,21 +1595,21 @@ def test_nsmallest_nlargest(self):
expected = s.sort_values().head(3)
assert_series_equal(result, expected)
- def test_sortlevel(self):
+ def test_sort_index_level(self):
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
s = Series([1, 2], mi)
backwards = s.iloc[[1, 0]]
- res = s.sortlevel('A')
+ res = s.sort_index(level='A')
assert_series_equal(backwards, res)
- res = s.sortlevel(['A', 'B'])
+ res = s.sort_index(level=['A', 'B'])
assert_series_equal(backwards, res)
- res = s.sortlevel('A', sort_remaining=False)
+ res = s.sort_index(level='A', sort_remaining=False)
assert_series_equal(s, res)
- res = s.sortlevel(['A', 'B'], sort_remaining=False)
+ res = s.sort_index(level=['A', 'B'], sort_remaining=False)
assert_series_equal(s, res)
def test_apply_categorical(self):
@@ -1738,7 +1738,8 @@ def test_unstack(self):
s = Series(np.random.randn(6), index=index)
exp_index = MultiIndex(levels=[['one', 'two', 'three'], [0, 1]],
labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]])
- expected = DataFrame({'bar': s.values}, index=exp_index).sortlevel(0)
+ expected = DataFrame({'bar': s.values},
+ index=exp_index).sort_index(level=0)
unstacked = s.unstack(0)
assert_frame_equal(unstacked, expected)
diff --git a/pandas/tests/series/test_sorting.py b/pandas/tests/series/test_sorting.py
index 69e70c15cae50..fb3817eb84acd 100644
--- a/pandas/tests/series/test_sorting.py
+++ b/pandas/tests/series/test_sorting.py
@@ -23,6 +23,8 @@ def test_sort(self):
with tm.assert_produces_warning(FutureWarning):
ts.sort() # sorts inplace
self.assert_series_equal(ts, self.ts.sort_values())
+ with tm.assert_produces_warning(FutureWarning):
+ ts.sortlevel()
def test_order(self):
diff --git a/pandas/tests/test_categorical.py b/pandas/tests/test_categorical.py
index 382f1dd1decfb..d159e1a20d069 100644
--- a/pandas/tests/test_categorical.py
+++ b/pandas/tests/test_categorical.py
@@ -3100,7 +3100,7 @@ def test_groupby(self):
['foo', 'bar']],
names=['A', 'B', 'C'])
expected = DataFrame({'values': Series(
- np.nan, index=exp_index)}).sortlevel()
+ np.nan, index=exp_index)}).sort_index()
expected.iloc[[1, 2, 7, 8], 0] = [1, 2, 3, 4]
result = gb.sum()
tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/test_multilevel.py b/pandas/tests/test_multilevel.py
index 4e7ace4173227..59d9e1e094d9d 100755
--- a/pandas/tests/test_multilevel.py
+++ b/pandas/tests/test_multilevel.py
@@ -691,7 +691,7 @@ def test_getitem_partial(self):
assert_frame_equal(result, expected)
def test_getitem_slice_not_sorted(self):
- df = self.frame.sortlevel(1).T
+ df = self.frame.sort_index(level=1).T
# buglet with int typechecking
result = df.ix[:, :np.int32(3)]
@@ -744,31 +744,31 @@ def test_getitem_partial_column_select(self):
self.assertRaises(KeyError, df.ix.__getitem__,
(('a', 'foo'), slice(None, None)))
- def test_sortlevel(self):
+ def test_sort_index_level(self):
df = self.frame.copy()
df.index = np.arange(len(df))
# axis=1
# series
- a_sorted = self.frame['A'].sortlevel(0)
+ a_sorted = self.frame['A'].sort_index(level=0)
# preserve names
self.assertEqual(a_sorted.index.names, self.frame.index.names)
# inplace
rs = self.frame.copy()
- rs.sortlevel(0, inplace=True)
- assert_frame_equal(rs, self.frame.sortlevel(0))
+ rs.sort_index(level=0, inplace=True)
+ assert_frame_equal(rs, self.frame.sort_index(level=0))
- def test_sortlevel_large_cardinality(self):
+ def test_sort_index_level_large_cardinality(self):
# #2684 (int64)
index = MultiIndex.from_arrays([np.arange(4000)] * 3)
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)
# it works!
- result = df.sortlevel(0)
+ result = df.sort_index(level=0)
self.assertTrue(result.index.lexsort_depth == 3)
# #2684 (int32)
@@ -776,7 +776,7 @@ def test_sortlevel_large_cardinality(self):
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)
# it works!
- result = df.sortlevel(0)
+ result = df.sort_index(level=0)
self.assertTrue((result.dtypes.values == df.dtypes.values).all())
self.assertTrue(result.index.lexsort_depth == 3)
@@ -803,25 +803,25 @@ def test_reset_index_with_drop(self):
deleveled = self.series.reset_index(drop=True)
tm.assertIsInstance(deleveled, Series)
- def test_sortlevel_by_name(self):
+ def test_sort_index_level_by_name(self):
self.frame.index.names = ['first', 'second']
- result = self.frame.sortlevel(level='second')
- expected = self.frame.sortlevel(level=1)
+ result = self.frame.sort_index(level='second')
+ expected = self.frame.sort_index(level=1)
assert_frame_equal(result, expected)
- def test_sortlevel_mixed(self):
- sorted_before = self.frame.sortlevel(1)
+ def test_sort_index_level_mixed(self):
+ sorted_before = self.frame.sort_index(level=1)
df = self.frame.copy()
df['foo'] = 'bar'
- sorted_after = df.sortlevel(1)
+ sorted_after = df.sort_index(level=1)
assert_frame_equal(sorted_before, sorted_after.drop(['foo'], axis=1))
dft = self.frame.T
- sorted_before = dft.sortlevel(1, axis=1)
+ sorted_before = dft.sort_index(level=1, axis=1)
dft['foo', 'three'] = 'bar'
- sorted_after = dft.sortlevel(1, axis=1)
+ sorted_after = dft.sort_index(level=1, axis=1)
assert_frame_equal(sorted_before.drop([('foo', 'three')], axis=1),
sorted_after.drop([('foo', 'three')], axis=1))
@@ -915,21 +915,21 @@ def test_stack(self):
restacked = unstacked.stack()
assert_frame_equal(restacked, self.ymd)
- unlexsorted = self.ymd.sortlevel(2)
+ unlexsorted = self.ymd.sort_index(level=2)
unstacked = unlexsorted.unstack(2)
restacked = unstacked.stack()
- assert_frame_equal(restacked.sortlevel(0), self.ymd)
+ assert_frame_equal(restacked.sort_index(level=0), self.ymd)
unlexsorted = unlexsorted[::-1]
unstacked = unlexsorted.unstack(1)
restacked = unstacked.stack().swaplevel(1, 2)
- assert_frame_equal(restacked.sortlevel(0), self.ymd)
+ assert_frame_equal(restacked.sort_index(level=0), self.ymd)
unlexsorted = unlexsorted.swaplevel(0, 1)
unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1)
restacked = unstacked.stack(0).swaplevel(1, 2)
- assert_frame_equal(restacked.sortlevel(0), self.ymd)
+ assert_frame_equal(restacked.sort_index(level=0), self.ymd)
# columns unsorted
unstacked = self.ymd.unstack()
@@ -1025,7 +1025,7 @@ def test_unstack_odd_failure(self):
def test_stack_mixed_dtype(self):
df = self.frame.T
df['foo', 'four'] = 'foo'
- df = df.sortlevel(1, axis=1)
+ df = df.sort_index(level=1, axis=1)
stacked = df.stack()
result = df['foo'].stack()
@@ -1084,7 +1084,7 @@ def test_stack_unstack_multiple(self):
restacked = unstacked.stack(['year', 'month'])
restacked = restacked.swaplevel(0, 1).swaplevel(1, 2)
- restacked = restacked.sortlevel(0)
+ restacked = restacked.sort_index(level=0)
assert_frame_equal(restacked, self.ymd)
self.assertEqual(restacked.index.names, self.ymd.index.names)
@@ -1421,7 +1421,7 @@ def test_frame_getitem_view(self):
# but not if it's mixed-type
df['foo', 'four'] = 'foo'
- df = df.sortlevel(0, axis=1)
+ df = df.sort_index(level=0, axis=1)
# this will work, but will raise/warn as its chained assignment
def f():
@@ -1698,7 +1698,7 @@ def test_getitem_lowerdim_corner(self):
# in theory should be inserting in a sorted space????
self.frame.ix[('bar', 'three'), 'B'] = 0
- self.assertEqual(self.frame.sortlevel().ix[('bar', 'three'), 'B'], 0)
+ self.assertEqual(self.frame.sort_index().ix[('bar', 'three'), 'B'], 0)
# ---------------------------------------------------------------------
# AMBIGUOUS CASES!
@@ -2147,7 +2147,7 @@ def test_duplicate_mi(self):
['bah', 'bam', 6.0, 6]],
columns=list('ABCD'))
df = df.set_index(['A', 'B'])
- df = df.sortlevel(0)
+ df = df.sort_index(level=0)
expected = DataFrame([['foo', 'bar', 1.0, 1], ['foo', 'bar', 2.0, 2],
['foo', 'bar', 5.0, 5]],
columns=list('ABCD')).set_index(['A', 'B'])
diff --git a/pandas/tests/test_panel.py b/pandas/tests/test_panel.py
index 9cb2dd5a40ac4..78edd27877783 100644
--- a/pandas/tests/test_panel.py
+++ b/pandas/tests/test_panel.py
@@ -2370,10 +2370,10 @@ def test_sort(self):
def is_sorted(arr):
return (arr[1:] > arr[:-1]).any()
- sorted_minor = self.panel.sortlevel(level=1)
+ sorted_minor = self.panel.sort_index(level=1)
self.assertTrue(is_sorted(sorted_minor.index.labels[1]))
- sorted_major = sorted_minor.sortlevel(level=0)
+ sorted_major = sorted_minor.sort_index(level=0)
self.assertTrue(is_sorted(sorted_major.index.labels[0]))
def test_to_string(self):
diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py
index 0f56b0b076897..01eefe5f07173 100644
--- a/pandas/tools/pivot.py
+++ b/pandas/tools/pivot.py
@@ -158,10 +158,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
pass # it's a single level or a series
if isinstance(table, DataFrame):
- if isinstance(table.columns, MultiIndex):
- table = table.sortlevel(axis=1)
- else:
- table = table.sort_index(axis=1)
+ table = table.sort_index(axis=1)
if fill_value is not None:
table = table.fillna(value=fill_value, downcast='infer')
diff --git a/pandas/tools/tests/test_join.py b/pandas/tools/tests/test_join.py
index f33d5f16cd439..ecf0592c66eff 100644
--- a/pandas/tools/tests/test_join.py
+++ b/pandas/tools/tests/test_join.py
@@ -369,8 +369,8 @@ def test_join_multiindex(self):
df2 = DataFrame(data=np.random.randn(6), index=index2,
columns=['var Y'])
- df1 = df1.sortlevel(0)
- df2 = df2.sortlevel(0)
+ df1 = df1.sort_index(level=0)
+ df2 = df2.sort_index(level=0)
joined = df1.join(df2, how='outer')
ex_index = index1._tuple_index.union(index2._tuple_index)
@@ -379,10 +379,10 @@ def test_join_multiindex(self):
assert_frame_equal(joined, expected)
self.assertEqual(joined.index.names, index1.names)
- df1 = df1.sortlevel(1)
- df2 = df2.sortlevel(1)
+ df1 = df1.sort_index(level=1)
+ df2 = df2.sort_index(level=1)
- joined = df1.join(df2, how='outer').sortlevel(0)
+ joined = df1.join(df2, how='outer').sort_index(level=0)
ex_index = index1._tuple_index.union(index2._tuple_index)
expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
expected.index.names = index1.names
diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py
index e63cfcc8c0590..b88c6167f6670 100644
--- a/pandas/tools/tests/test_pivot.py
+++ b/pandas/tools/tests/test_pivot.py
@@ -307,7 +307,7 @@ def _check_output(result, values_col, index=['A', 'B'],
check_names=False)
self.assertEqual(col_margins.name, margins_col)
- result = result.sortlevel()
+ result = result.sort_index()
index_margins = result.ix[(margins_col, '')].iloc[:-1]
expected_ix_margins = self.data.groupby(columns)[values_col].mean()
tm.assert_series_equal(index_margins, expected_ix_margins,
| closes #14279
| https://api.github.com/repos/pandas-dev/pandas/pulls/15099 | 2017-01-10T15:38:41Z | 2017-01-11T08:02:34Z | 2017-01-11T08:02:34Z | 2017-01-11T08:02:34Z |
fix misspelled word | diff --git a/pandas/formats/style.py b/pandas/formats/style.py
index 4d5e72a38bb98..b3e0f0f6c7462 100644
--- a/pandas/formats/style.py
+++ b/pandas/formats/style.py
@@ -781,7 +781,7 @@ def background_gradient(self, cmap='PuBu', low=0, high=0, axis=0,
low, high: float
compress the range by these values.
axis: int or str
- 1 or 'columns' for colunwise, 0 or 'index' for rowwise
+ 1 or 'columns' for columnwise, 0 or 'index' for rowwise
subset: IndexSlice
a valid slice for ``data`` to limit the style application to
| `colunwise` should be `columnwise`
| https://api.github.com/repos/pandas-dev/pandas/pulls/15097 | 2017-01-10T15:28:36Z | 2017-01-10T15:37:15Z | 2017-01-10T15:37:15Z | 2017-01-10T15:37:18Z |
BUG: use more generic type inference for fast plotting | diff --git a/doc/source/whatsnew/v0.20.0.txt b/doc/source/whatsnew/v0.20.0.txt
index c82dc370e3e71..a13b97266811a 100644
--- a/doc/source/whatsnew/v0.20.0.txt
+++ b/doc/source/whatsnew/v0.20.0.txt
@@ -248,6 +248,7 @@ Other API Changes
- ``pd.read_csv()`` will now issue a ``ParserWarning`` whenever there are conflicting values provided by the ``dialect`` parameter and the user (:issue:`14898`)
- ``pd.read_csv()`` will now raise a ``ValueError`` for the C engine if the quote character is larger than than one byte (:issue:`11592`)
- ``inplace`` arguments now require a boolean value, else a ``ValueError`` is thrown (:issue:`14189`)
+- ``pandas.api.types.is_datetime64_ns_dtype`` will now report ``True`` on a tz-aware dtype, similar to ``pandas.api.types.is_datetime64_any_dtype``
.. _whatsnew_0200.deprecations:
diff --git a/pandas/tests/types/test_dtypes.py b/pandas/tests/types/test_dtypes.py
index a2b0a9ebfa6cc..f190c85404ff9 100644
--- a/pandas/tests/types/test_dtypes.py
+++ b/pandas/tests/types/test_dtypes.py
@@ -11,7 +11,8 @@
is_datetime64tz_dtype, is_datetimetz,
is_period_dtype, is_period,
is_dtype_equal, is_datetime64_ns_dtype,
- is_datetime64_dtype, is_string_dtype,
+ is_datetime64_dtype,
+ is_datetime64_any_dtype, is_string_dtype,
_coerce_to_dtype)
import pandas.util.testing as tm
@@ -132,8 +133,12 @@ def test_coerce_to_dtype(self):
DatetimeTZDtype('ns', 'Asia/Tokyo'))
def test_compat(self):
- self.assertFalse(is_datetime64_ns_dtype(self.dtype))
- self.assertFalse(is_datetime64_ns_dtype('datetime64[ns, US/Eastern]'))
+ self.assertTrue(is_datetime64tz_dtype(self.dtype))
+ self.assertTrue(is_datetime64tz_dtype('datetime64[ns, US/Eastern]'))
+ self.assertTrue(is_datetime64_any_dtype(self.dtype))
+ self.assertTrue(is_datetime64_any_dtype('datetime64[ns, US/Eastern]'))
+ self.assertTrue(is_datetime64_ns_dtype(self.dtype))
+ self.assertTrue(is_datetime64_ns_dtype('datetime64[ns, US/Eastern]'))
self.assertFalse(is_datetime64_dtype(self.dtype))
self.assertFalse(is_datetime64_dtype('datetime64[ns, US/Eastern]'))
diff --git a/pandas/tests/types/test_inference.py b/pandas/tests/types/test_inference.py
index 8180757d9e706..5c35112d0fe19 100644
--- a/pandas/tests/types/test_inference.py
+++ b/pandas/tests/types/test_inference.py
@@ -22,6 +22,10 @@
from pandas.types import inference
from pandas.types.common import (is_timedelta64_dtype,
is_timedelta64_ns_dtype,
+ is_datetime64_dtype,
+ is_datetime64_ns_dtype,
+ is_datetime64_any_dtype,
+ is_datetime64tz_dtype,
is_number,
is_integer,
is_float,
@@ -805,6 +809,38 @@ def test_is_float(self):
self.assertFalse(is_float(np.timedelta64(1, 'D')))
self.assertFalse(is_float(Timedelta('1 days')))
+ def test_is_datetime_dtypes(self):
+
+ ts = pd.date_range('20130101', periods=3)
+ tsa = pd.date_range('20130101', periods=3, tz='US/Eastern')
+
+ self.assertTrue(is_datetime64_dtype('datetime64'))
+ self.assertTrue(is_datetime64_dtype('datetime64[ns]'))
+ self.assertTrue(is_datetime64_dtype(ts))
+ self.assertFalse(is_datetime64_dtype(tsa))
+
+ self.assertFalse(is_datetime64_ns_dtype('datetime64'))
+ self.assertTrue(is_datetime64_ns_dtype('datetime64[ns]'))
+ self.assertTrue(is_datetime64_ns_dtype(ts))
+ self.assertTrue(is_datetime64_ns_dtype(tsa))
+
+ self.assertTrue(is_datetime64_any_dtype('datetime64'))
+ self.assertTrue(is_datetime64_any_dtype('datetime64[ns]'))
+ self.assertTrue(is_datetime64_any_dtype(ts))
+ self.assertTrue(is_datetime64_any_dtype(tsa))
+
+ self.assertFalse(is_datetime64tz_dtype('datetime64'))
+ self.assertFalse(is_datetime64tz_dtype('datetime64[ns]'))
+ self.assertFalse(is_datetime64tz_dtype(ts))
+ self.assertTrue(is_datetime64tz_dtype(tsa))
+
+ for tz in ['US/Eastern', 'UTC']:
+ dtype = 'datetime64[ns, {}]'.format(tz)
+ self.assertFalse(is_datetime64_dtype(dtype))
+ self.assertTrue(is_datetime64tz_dtype(dtype))
+ self.assertTrue(is_datetime64_ns_dtype(dtype))
+ self.assertTrue(is_datetime64_any_dtype(dtype))
+
def test_is_timedelta(self):
self.assertTrue(is_timedelta64_dtype('timedelta64'))
self.assertTrue(is_timedelta64_dtype('timedelta64[ns]'))
diff --git a/pandas/tseries/tests/test_converter.py b/pandas/tseries/tests/test_converter.py
index 37d9c35639c32..f6cf11c871bba 100644
--- a/pandas/tseries/tests/test_converter.py
+++ b/pandas/tseries/tests/test_converter.py
@@ -3,10 +3,10 @@
import nose
import numpy as np
-from pandas import Timestamp, Period
+from pandas import Timestamp, Period, Index
from pandas.compat import u
import pandas.util.testing as tm
-from pandas.tseries.offsets import Second, Milli, Micro
+from pandas.tseries.offsets import Second, Milli, Micro, Day
from pandas.compat.numpy import np_datetime64_compat
try:
@@ -62,6 +62,25 @@ def test_conversion(self):
np_datetime64_compat('2012-01-02 00:00:00+0000')]), None, None)
self.assertEqual(rs[0], xp)
+ # we have a tz-aware date (constructed to that when we turn to utc it
+ # is the same as our sample)
+ ts = (Timestamp('2012-01-01')
+ .tz_localize('UTC')
+ .tz_convert('US/Eastern')
+ )
+ rs = self.dtc.convert(ts, None, None)
+ self.assertEqual(rs, xp)
+
+ rs = self.dtc.convert(ts.to_pydatetime(), None, None)
+ self.assertEqual(rs, xp)
+
+ rs = self.dtc.convert(Index([ts - Day(1), ts]), None, None)
+ self.assertEqual(rs[1], xp)
+
+ rs = self.dtc.convert(Index([ts - Day(1), ts]).to_pydatetime(),
+ None, None)
+ self.assertEqual(rs[1], xp)
+
def test_conversion_float(self):
decimals = 9
diff --git a/pandas/tseries/tests/test_timeseries.py b/pandas/tseries/tests/test_timeseries.py
index 1834c56e59bb9..b7e77c2eb770a 100644
--- a/pandas/tseries/tests/test_timeseries.py
+++ b/pandas/tseries/tests/test_timeseries.py
@@ -2404,7 +2404,7 @@ def test_to_datetime_tz_psycopg2(self):
i = pd.DatetimeIndex([
'2000-01-01 08:00:00+00:00'
], tz=psycopg2.tz.FixedOffsetTimezone(offset=-300, name=None))
- self.assertFalse(is_datetime64_ns_dtype(i))
+ self.assertTrue(is_datetime64_ns_dtype(i))
# tz coerceion
result = pd.to_datetime(i, errors='coerce')
diff --git a/pandas/tseries/tools.py b/pandas/tseries/tools.py
index 21e1c9744aa88..f746409aadfc9 100644
--- a/pandas/tseries/tools.py
+++ b/pandas/tseries/tools.py
@@ -309,7 +309,14 @@ def _convert_listlike(arg, box, format, name=None, tz=tz):
arg = np.array(arg, dtype='O')
# these are shortcutable
- if is_datetime64_ns_dtype(arg):
+ if is_datetime64tz_dtype(arg):
+ if not isinstance(arg, DatetimeIndex):
+ return DatetimeIndex(arg, tz=tz, name=name)
+ if utc:
+ arg = arg.tz_convert(None).tz_localize('UTC')
+ return arg
+
+ elif is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
try:
return DatetimeIndex(arg, tz=tz, name=name)
@@ -318,13 +325,6 @@ def _convert_listlike(arg, box, format, name=None, tz=tz):
return arg
- elif is_datetime64tz_dtype(arg):
- if not isinstance(arg, DatetimeIndex):
- return DatetimeIndex(arg, tz=tz, name=name)
- if utc:
- arg = arg.tz_convert(None).tz_localize('UTC')
- return arg
-
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
diff --git a/pandas/types/common.py b/pandas/types/common.py
index 96eb6d6968bfb..e58e0826ea49a 100644
--- a/pandas/types/common.py
+++ b/pandas/types/common.py
@@ -187,8 +187,11 @@ def is_datetime64_ns_dtype(arr_or_dtype):
try:
tipo = _get_dtype(arr_or_dtype)
except TypeError:
- return False
- return tipo == _NS_DTYPE
+ if is_datetime64tz_dtype(arr_or_dtype):
+ tipo = _get_dtype(arr_or_dtype.dtype)
+ else:
+ return False
+ return tipo == _NS_DTYPE or getattr(tipo, 'base', None) == _NS_DTYPE
def is_timedelta64_ns_dtype(arr_or_dtype):
| xref #15073
https://travis-ci.org/pandas-dev/pandas/jobs/190356982
was failing on tz-aware dtypes. these routines are the same in that they require datetime64ns (but any *also* accepts tz-aware). | https://api.github.com/repos/pandas-dev/pandas/pulls/15094 | 2017-01-10T00:42:26Z | 2017-01-12T23:33:39Z | 2017-01-12T23:33:39Z | 2017-01-12T23:33:39Z |
BUG: Inconsistent return type for downsampling on resample of empty DataFrame | diff --git a/doc/source/whatsnew/v0.21.0.txt b/doc/source/whatsnew/v0.21.0.txt
index 36ca79e8b8714..56275cda80e57 100644
--- a/doc/source/whatsnew/v0.21.0.txt
+++ b/doc/source/whatsnew/v0.21.0.txt
@@ -112,6 +112,7 @@ Plotting
Groupby/Resample/Rolling
^^^^^^^^^^^^^^^^^^^^^^^^
+- Bug in ``DataFrame.resample().size()`` where an empty DataFrame did not return a Series (:issue:`14962`)
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 2bb825541e23b..a8a48624fb885 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -17,6 +17,7 @@
from pandas.core.indexes.period import PeriodIndex, period_range
import pandas.core.common as com
import pandas.core.algorithms as algos
+from pandas.core.dtypes.generic import ABCDataFrame
import pandas.compat as compat
from pandas.compat.numpy import function as nv
@@ -549,6 +550,15 @@ def var(self, ddof=1, *args, **kwargs):
nv.validate_resampler_func('var', args, kwargs)
return self._downsample('var', ddof=ddof)
+ @Appender(GroupBy.size.__doc__)
+ def size(self):
+ # It's a special case as higher level does return
+ # a copy of 0-len objects. GH14962
+ result = self._downsample('size')
+ if not len(self.ax) and isinstance(self._selected_obj, ABCDataFrame):
+ result = pd.Series([], index=result.index, dtype='int64')
+ return result
+
Resampler._deprecated_valids += dir(Resampler)
@@ -563,8 +573,7 @@ def f(self, _method=method, *args, **kwargs):
setattr(Resampler, method, f)
# groupby & aggregate methods
-for method in ['count', 'size']:
-
+for method in ['count']:
def f(self, _method=method):
return self._downsample(_method)
f.__doc__ = getattr(GroupBy, method).__doc__
diff --git a/pandas/tests/test_resample.py b/pandas/tests/test_resample.py
index 959e3d2f459ce..15bbd7a9ef5e9 100644
--- a/pandas/tests/test_resample.py
+++ b/pandas/tests/test_resample.py
@@ -783,15 +783,19 @@ def test_resample_empty_dataframe(self):
for freq in ['M', 'D', 'H']:
# count retains dimensions too
- methods = downsample_methods + ['count']
+ methods = downsample_methods + upsample_methods
for method in methods:
result = getattr(f.resample(freq), method)()
+ if method != 'size':
+ expected = f.copy()
+ else:
+ # GH14962
+ expected = Series([])
- expected = f.copy()
expected.index = f.index._shallow_copy(freq=freq)
assert_index_equal(result.index, expected.index)
assert result.index.freq == expected.index.freq
- assert_frame_equal(result, expected, check_dtype=False)
+ assert_almost_equal(result, expected, check_dtype=False)
# test size for GH13212 (currently stays as df)
| - [x] closes #14962
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
| https://api.github.com/repos/pandas-dev/pandas/pulls/15093 | 2017-01-09T21:58:41Z | 2017-06-13T23:16:03Z | 2017-06-13T23:16:03Z | 2017-06-13T23:16:07Z |
BLD: add deps for 3.6 build | diff --git a/ci/requirements-3.6.pip b/ci/requirements-3.6.pip
new file mode 100644
index 0000000000000..688866a18542f
--- /dev/null
+++ b/ci/requirements-3.6.pip
@@ -0,0 +1 @@
+xlwt
diff --git a/ci/requirements-3.6.run b/ci/requirements-3.6.run
index 684dab333648a..daf07be2d1785 100644
--- a/ci/requirements-3.6.run
+++ b/ci/requirements-3.6.run
@@ -2,3 +2,19 @@ python-dateutil
pytz
numpy
scipy
+openpyxl
+xlsxwriter
+xlrd
+# xlwt (installed via pip)
+numexpr
+pytables
+# matplotlib (not avail on defaults ATM)
+lxml
+html5lib
+jinja2
+sqlalchemy
+pymysql
+# psycopg2 (not avail on defaults ATM)
+beautifulsoup4
+s3fs
+xarray
diff --git a/ci/requirements-3.6.sh b/ci/requirements-3.6.sh
new file mode 100644
index 0000000000000..7d88ede751ec8
--- /dev/null
+++ b/ci/requirements-3.6.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+source activate pandas
+
+echo "install 36"
+
+conda install -n pandas -c conda-forge feather-format
| https://api.github.com/repos/pandas-dev/pandas/pulls/15089 | 2017-01-09T18:48:48Z | 2017-01-09T18:48:53Z | 2017-01-09T18:48:53Z | 2017-01-09T18:48:54Z | |
TYP: EA.isin | diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 5abdfe69e52c0..d8b074fe61322 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -1023,7 +1023,7 @@ def fillna(
return super().fillna(value=value, method=method, limit=limit, copy=copy)
- def isin(self, values) -> npt.NDArray[np.bool_]:
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
# short-circuit to return all False array.
if not len(values):
return np.zeros(len(self), dtype=bool)
diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index e61e374009163..3272a594f4cf4 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -1355,7 +1355,7 @@ def equals(self, other: object) -> bool:
equal_na = self.isna() & other.isna() # type: ignore[operator]
return bool((equal_values | equal_na).all())
- def isin(self, values) -> npt.NDArray[np.bool_]:
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
"""
Pointwise comparison for set containment in the given values.
@@ -1363,7 +1363,7 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
Parameters
----------
- values : Sequence
+ values : np.ndarray or ExtensionArray
Returns
-------
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index eec833c600177..96a868178c4b0 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2570,7 +2570,7 @@ def describe(self) -> DataFrame:
return result
- def isin(self, values) -> npt.NDArray[np.bool_]:
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
"""
Check whether `values` are contained in Categorical.
@@ -2580,7 +2580,7 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
Parameters
----------
- values : set or list-like
+ values : np.ndarray or ExtensionArray
The sequence of values to test. Passing in a single string will
raise a ``TypeError``. Instead, turn a single string into a
list of one element.
@@ -2611,13 +2611,6 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
>>> s.isin(['lama'])
array([ True, False, True, False, True, False])
"""
- if not is_list_like(values):
- values_type = type(values).__name__
- raise TypeError(
- "only list-like objects are allowed to be passed "
- f"to isin(), you passed a `{values_type}`"
- )
- values = sanitize_array(values, None, None)
null_mask = np.asarray(isna(values))
code_values = self.categories.get_indexer_for(values)
code_values = code_values[null_mask | (code_values >= 0)]
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index eb1c2ecc0b0fe..2a6a45ad18421 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -734,22 +734,19 @@ def map(self, mapper, na_action=None):
else:
return result.array
- def isin(self, values) -> npt.NDArray[np.bool_]:
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
"""
Compute boolean array of whether each value is found in the
passed set of values.
Parameters
----------
- values : set or sequence of values
+ values : np.ndarray or ExtensionArray
Returns
-------
ndarray[bool]
"""
- if not hasattr(values, "dtype"):
- values = np.asarray(values)
-
if values.dtype.kind in "fiuc":
# TODO: de-duplicate with equals, validate_comparison_value
return np.zeros(self.shape, dtype=bool)
@@ -781,15 +778,22 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
if self.dtype.kind in "mM":
self = cast("DatetimeArray | TimedeltaArray", self)
- values = values.as_unit(self.unit)
+ # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+ # has no attribute "as_unit"
+ values = values.as_unit(self.unit) # type: ignore[union-attr]
try:
- self._check_compatible_with(values)
+ # error: Argument 1 to "_check_compatible_with" of "DatetimeLikeArrayMixin"
+ # has incompatible type "ExtensionArray | ndarray[Any, Any]"; expected
+ # "Period | Timestamp | Timedelta | NaTType"
+ self._check_compatible_with(values) # type: ignore[arg-type]
except (TypeError, ValueError):
# Includes tzawareness mismatch and IncompatibleFrequencyError
return np.zeros(self.shape, dtype=bool)
- return isin(self.asi8, values.asi8)
+ # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
+ # has no attribute "asi8"
+ return isin(self.asi8, values.asi8) # type: ignore[union-attr]
# ------------------------------------------------------------------
# Null Handling
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 126484ed4a2a0..383f8a49fd02c 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1789,12 +1789,8 @@ def contains(self, other):
other < self._right if self.open_right else other <= self._right
)
- def isin(self, values) -> npt.NDArray[np.bool_]:
- if not hasattr(values, "dtype"):
- values = np.array(values)
- values = extract_array(values, extract_numpy=True)
-
- if isinstance(values.dtype, IntervalDtype):
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
+ if isinstance(values, IntervalArray):
if self.closed != values.closed:
# not comparable -> no overlap
return np.zeros(self.shape, dtype=bool)
diff --git a/pandas/core/arrays/masked.py b/pandas/core/arrays/masked.py
index 201ce44ed0163..2f0cf7a67c1cc 100644
--- a/pandas/core/arrays/masked.py
+++ b/pandas/core/arrays/masked.py
@@ -955,7 +955,7 @@ def take(
# error: Return type "BooleanArray" of "isin" incompatible with return type
# "ndarray" in supertype "ExtensionArray"
- def isin(self, values) -> BooleanArray: # type: ignore[override]
+ def isin(self, values: ArrayLike) -> BooleanArray: # type: ignore[override]
from pandas.core.arrays import BooleanArray
# algorithms.isin will eventually convert values to an ndarray, so no extra
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index 976a8d3c32b23..21fe7cd8180ad 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -54,6 +54,7 @@
from collections.abc import Sequence
from pandas._typing import (
+ ArrayLike,
AxisInt,
Dtype,
Scalar,
@@ -212,7 +213,7 @@ def _maybe_convert_setitem_value(self, value):
raise TypeError("Scalar must be NA or str")
return super()._maybe_convert_setitem_value(value)
- def isin(self, values) -> npt.NDArray[np.bool_]:
+ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
value_set = [
pa_scalar.as_py()
for pa_scalar in [pa.scalar(value, from_pandas=True) for value in values]
| - [ ] 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/56423 | 2023-12-09T03:15:16Z | 2023-12-09T18:29:50Z | 2023-12-09T18:29:50Z | 2023-12-09T18:30:15Z |
DEPR: make_block | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 8a1906d20c243..c455aef452ecc 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -431,6 +431,7 @@ Other Deprecations
^^^^^^^^^^^^^^^^^^
- Changed :meth:`Timedelta.resolution_string` to return ``h``, ``min``, ``s``, ``ms``, ``us``, and ``ns`` instead of ``H``, ``T``, ``S``, ``L``, ``U``, and ``N``, for compatibility with respective deprecations in frequency aliases (:issue:`52536`)
- Deprecated :func:`pandas.api.types.is_interval` and :func:`pandas.api.types.is_period`, use ``isinstance(obj, pd.Interval)`` and ``isinstance(obj, pd.Period)`` instead (:issue:`55264`)
+- Deprecated :func:`pd.core.internals.api.make_block`, use public APIs instead (:issue:`40226`)
- 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`)
@@ -479,7 +480,6 @@ Other Deprecations
- 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`)
- Deprecated the previous implementation of :class:`DataFrame.stack`; specify ``future_stack=True`` to adopt the future version (:issue:`53515`)
--
.. ---------------------------------------------------------------------------
.. _whatsnew_220.performance:
diff --git a/pandas/core/internals/api.py b/pandas/core/internals/api.py
index b0b3937ca47ea..e5ef44d07061e 100644
--- a/pandas/core/internals/api.py
+++ b/pandas/core/internals/api.py
@@ -9,10 +9,12 @@
from __future__ import annotations
from typing import TYPE_CHECKING
+import warnings
import numpy as np
from pandas._libs.internals import BlockPlacement
+from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import pandas_dtype
from pandas.core.dtypes.dtypes import (
@@ -50,6 +52,14 @@ def make_block(
- Block.make_block_same_class
- Block.__init__
"""
+ warnings.warn(
+ # GH#40226
+ "make_block is deprecated and will be removed in a future version. "
+ "Use public APIs instead.",
+ DeprecationWarning,
+ stacklevel=find_stack_level(),
+ )
+
if dtype is not None:
dtype = pandas_dtype(dtype)
@@ -113,7 +123,6 @@ def maybe_infer_ndim(values, placement: BlockPlacement, ndim: int | None) -> int
def __getattr__(name: str):
# GH#55139
- import warnings
if name in [
"Block",
diff --git a/pandas/tests/internals/test_api.py b/pandas/tests/internals/test_api.py
index 1251a6ae97a1c..f816cef38b9ab 100644
--- a/pandas/tests/internals/test_api.py
+++ b/pandas/tests/internals/test_api.py
@@ -68,7 +68,9 @@ def test_deprecations(name):
def test_make_block_2d_with_dti():
# GH#41168
dti = pd.date_range("2012", periods=3, tz="UTC")
- blk = api.make_block(dti, placement=[0])
+ msg = "make_block is deprecated"
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
+ blk = api.make_block(dti, placement=[0])
assert blk.shape == (1, 3)
assert blk.values.shape == (1, 3)
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index a9dbdb21f59fb..2fc7166761f61 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -1415,9 +1415,11 @@ def test_validate_ndim():
values = np.array([1.0, 2.0])
placement = BlockPlacement(slice(2))
msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]"
+ depr_msg = "make_block is deprecated"
with pytest.raises(ValueError, match=msg):
- make_block(values, placement, ndim=2)
+ with tm.assert_produces_warning(DeprecationWarning, match=depr_msg):
+ make_block(values, placement, ndim=2)
def test_block_shape():
@@ -1432,8 +1434,12 @@ def test_make_block_no_pandas_array(block_maker):
# https://github.com/pandas-dev/pandas/pull/24866
arr = pd.arrays.NumpyExtensionArray(np.array([1, 2]))
+ warn = None if block_maker is not make_block else DeprecationWarning
+ msg = "make_block is deprecated and will be removed in a future version"
+
# NumpyExtensionArray, no dtype
- result = block_maker(arr, BlockPlacement(slice(len(arr))), ndim=arr.ndim)
+ with tm.assert_produces_warning(warn, match=msg):
+ result = block_maker(arr, BlockPlacement(slice(len(arr))), ndim=arr.ndim)
assert result.dtype.kind in ["i", "u"]
if block_maker is make_block:
@@ -1441,14 +1447,16 @@ def test_make_block_no_pandas_array(block_maker):
assert result.is_extension is False
# NumpyExtensionArray, NumpyEADtype
- result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim)
+ with tm.assert_produces_warning(warn, match=msg):
+ result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim)
assert result.dtype.kind in ["i", "u"]
assert result.is_extension is False
# new_block no longer taked dtype keyword
# ndarray, NumpyEADtype
- result = block_maker(
- arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim
- )
+ with tm.assert_produces_warning(warn, match=msg):
+ result = block_maker(
+ arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim
+ )
assert result.dtype.kind in ["i", "u"]
assert result.is_extension is False
diff --git a/pandas/tests/io/parser/common/test_chunksize.py b/pandas/tests/io/parser/common/test_chunksize.py
index baed74fc212e4..5e47bcc1c5b0e 100644
--- a/pandas/tests/io/parser/common/test_chunksize.py
+++ b/pandas/tests/io/parser/common/test_chunksize.py
@@ -233,6 +233,7 @@ def test_chunks_have_consistent_numerical_type(all_parsers, monkeypatch):
assert result.a.dtype == float
+@pytest.mark.filterwarnings("ignore:make_block is deprecated:FutureWarning")
def test_warn_if_chunks_have_mismatched_type(all_parsers):
warning_type = None
parser = all_parsers
diff --git a/pandas/tests/io/parser/test_parse_dates.py b/pandas/tests/io/parser/test_parse_dates.py
index 113402cda1b9a..d9c1101fbbf68 100644
--- a/pandas/tests/io/parser/test_parse_dates.py
+++ b/pandas/tests/io/parser/test_parse_dates.py
@@ -36,9 +36,12 @@
from pandas.io.parsers import read_csv
-pytestmark = pytest.mark.filterwarnings(
- "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
-)
+pytestmark = [
+ pytest.mark.filterwarnings(
+ "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+ ),
+ pytest.mark.filterwarnings("ignore:make_block is deprecated:DeprecationWarning"),
+]
xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
| xref #40226 | https://api.github.com/repos/pandas-dev/pandas/pulls/56422 | 2023-12-09T02:46:51Z | 2023-12-11T17:43:41Z | 2023-12-11T17:43:41Z | 2024-01-08T21:24:50Z |
DEPR: type argument in Index.view | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 8a1906d20c243..410dfa3fd5b4a 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -438,6 +438,7 @@ Other Deprecations
- Deprecated :meth:`Series.view`, use :meth:`Series.astype` instead to change the dtype (:issue:`20251`)
- 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 accepting a type as an argument in :meth:`Index.view`, call without any arguments instead (:issue:`55709`)
- Deprecated allowing non-integer ``periods`` argument in :func:`date_range`, :func:`timedelta_range`, :func:`period_range`, and :func:`interval_range` (:issue:`56036`)
- 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/indexes/base.py b/pandas/core/indexes/base.py
index 3abe77b97fe58..678d7578a65c4 100644
--- a/pandas/core/indexes/base.py
+++ b/pandas/core/indexes/base.py
@@ -1012,6 +1012,16 @@ def view(self, cls=None):
result = self._data.view(cls)
else:
+ if cls is not None:
+ warnings.warn(
+ # GH#55709
+ f"Passing a type in {type(self).__name__}.view is deprecated "
+ "and will raise in a future version. "
+ "Call view without any argument to retain the old behavior.",
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
+
result = self._view()
if isinstance(result, Index):
result._id = self._id
diff --git a/pandas/tests/indexes/datetimes/test_setops.py b/pandas/tests/indexes/datetimes/test_setops.py
index 3ed7fcc027a06..fc3a1d4721841 100644
--- a/pandas/tests/indexes/datetimes/test_setops.py
+++ b/pandas/tests/indexes/datetimes/test_setops.py
@@ -535,7 +535,7 @@ def test_intersection(self):
assert isinstance(the_int, DatetimeIndex)
assert the_int.freq == rng.freq
- the_int = rng1.intersection(rng2.view(DatetimeIndex))
+ the_int = rng1.intersection(rng2)
tm.assert_index_equal(the_int, expected)
# non-overlapping
diff --git a/pandas/tests/indexes/numeric/test_numeric.py b/pandas/tests/indexes/numeric/test_numeric.py
index 944e215ee17bd..7ce55db6c0bbc 100644
--- a/pandas/tests/indexes/numeric/test_numeric.py
+++ b/pandas/tests/indexes/numeric/test_numeric.py
@@ -318,7 +318,9 @@ def test_cant_or_shouldnt_cast(self, dtype):
def test_view_index(self, simple_index):
index = simple_index
- index.view(Index)
+ msg = "Passing a type in .*Index.view is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ index.view(Index)
def test_prevent_casting(self, simple_index):
index = simple_index
diff --git a/pandas/tests/indexes/ranges/test_range.py b/pandas/tests/indexes/ranges/test_range.py
index ffb2dac840198..06e19eeca6766 100644
--- a/pandas/tests/indexes/ranges/test_range.py
+++ b/pandas/tests/indexes/ranges/test_range.py
@@ -199,7 +199,9 @@ def test_view(self):
i_view = i.view("i8")
tm.assert_numpy_array_equal(i.values, i_view)
- i_view = i.view(RangeIndex)
+ msg = "Passing a type in RangeIndex.view is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ i_view = i.view(RangeIndex)
tm.assert_index_equal(i, i_view)
def test_dtype(self, simple_index):
@@ -382,7 +384,9 @@ def test_cant_or_shouldnt_cast(self, start, stop, step):
def test_view_index(self, simple_index):
index = simple_index
- index.view(Index)
+ msg = "Passing a type in RangeIndex.view is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ index.view(Index)
def test_prevent_casting(self, simple_index):
index = simple_index
diff --git a/pandas/tests/indexes/test_datetimelike.py b/pandas/tests/indexes/test_datetimelike.py
index 27e01427006ec..21a686e8bc05b 100644
--- a/pandas/tests/indexes/test_datetimelike.py
+++ b/pandas/tests/indexes/test_datetimelike.py
@@ -89,7 +89,9 @@ def test_view(self, simple_index):
result = type(simple_index)(idx)
tm.assert_index_equal(result, idx)
- idx_view = idx.view(type(simple_index))
+ msg = "Passing a type in .*Index.view is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ idx_view = idx.view(type(simple_index))
result = type(simple_index)(idx)
tm.assert_index_equal(result, idx_view)
diff --git a/pandas/tests/indexes/test_old_base.py b/pandas/tests/indexes/test_old_base.py
index 0fff6abcfc6a5..b467e93b75e96 100644
--- a/pandas/tests/indexes/test_old_base.py
+++ b/pandas/tests/indexes/test_old_base.py
@@ -960,7 +960,9 @@ def test_view(self, simple_index):
idx_view = idx.view(dtype)
tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
- idx_view = idx.view(index_cls)
+ msg = "Passing a type in .*Index.view is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ idx_view = idx.view(index_cls)
tm.assert_index_equal(idx, index_cls(idx_view, name="Foo"), exact=True)
def test_format(self, simple_index):
| - [x] closes #55709 (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/56421 | 2023-12-09T02:32:03Z | 2023-12-09T18:57:27Z | 2023-12-09T18:57:27Z | 2023-12-11T20:49:43Z |
CLN: Remove unnecessary copy keyword | diff --git a/pandas/_testing/asserters.py b/pandas/_testing/asserters.py
index 6cad71b3dfd18..d9db2bc5cddb4 100644
--- a/pandas/_testing/asserters.py
+++ b/pandas/_testing/asserters.py
@@ -1206,8 +1206,8 @@ def assert_frame_equal(
# compare by blocks
if by_blocks:
- rblocks = right._to_dict_of_blocks(copy=False)
- lblocks = left._to_dict_of_blocks(copy=False)
+ rblocks = right._to_dict_of_blocks()
+ lblocks = left._to_dict_of_blocks()
for dtype in list(set(list(lblocks.keys()) + list(rblocks.keys()))):
assert dtype in lblocks
assert dtype in rblocks
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 179279cc08bab..24b7951e3bb85 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -12484,7 +12484,7 @@ def isin_(x):
# ----------------------------------------------------------------------
# Internal Interface Methods
- def _to_dict_of_blocks(self, copy: bool = True):
+ def _to_dict_of_blocks(self):
"""
Return a dict of dtype -> Constructor Types that
each is a homogeneous dtype.
@@ -12496,7 +12496,7 @@ def _to_dict_of_blocks(self, copy: bool = True):
mgr = cast(BlockManager, mgr_to_mgr(mgr, "block"))
return {
k: self._constructor_from_mgr(v, axes=v.axes).__finalize__(self)
- for k, v, in mgr.to_dict(copy=copy).items()
+ for k, v, in mgr.to_dict().items()
}
@property
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 5a2f8d8454526..204083ac6c04e 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -2012,7 +2012,7 @@ def _get_data_to_aggregate(
mgr = obj._mgr
if numeric_only:
- mgr = mgr.get_numeric_data(copy=False)
+ mgr = mgr.get_numeric_data()
return mgr
def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index cc88312d5b58f..809baaff66a1e 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -495,17 +495,12 @@ def is_view(self) -> bool:
def _get_data_subset(self, predicate: Callable) -> Self:
blocks = [blk for blk in self.blocks if predicate(blk.values)]
- return self._combine(blocks, copy=False)
+ return self._combine(blocks)
- def get_bool_data(self, copy: bool = False) -> Self:
+ def get_bool_data(self) -> Self:
"""
Select blocks that are bool-dtype and columns from object-dtype blocks
that are all-bool.
-
- Parameters
- ----------
- copy : bool, default False
- Whether to copy the blocks
"""
new_blocks = []
@@ -518,26 +513,16 @@ def get_bool_data(self, copy: bool = False) -> Self:
nbs = blk._split()
new_blocks.extend(nb for nb in nbs if nb.is_bool)
- return self._combine(new_blocks, copy)
+ return self._combine(new_blocks)
- def get_numeric_data(self, copy: bool = False) -> Self:
- """
- Parameters
- ----------
- copy : bool, default False
- Whether to copy the blocks
- """
+ def get_numeric_data(self) -> Self:
numeric_blocks = [blk for blk in self.blocks if blk.is_numeric]
if len(numeric_blocks) == len(self.blocks):
# Avoid somewhat expensive _combine
- if copy:
- return self.copy(deep=True)
return self
- return self._combine(numeric_blocks, copy)
+ return self._combine(numeric_blocks)
- def _combine(
- self, blocks: list[Block], copy: bool = True, index: Index | None = None
- ) -> Self:
+ def _combine(self, blocks: list[Block], index: Index | None = None) -> Self:
"""return a new manager with the blocks"""
if len(blocks) == 0:
if self.ndim == 2:
@@ -554,11 +539,8 @@ def _combine(
inv_indexer = lib.get_reverse_indexer(indexer, self.shape[0])
new_blocks: list[Block] = []
- # TODO(CoW) we could optimize here if we know that the passed blocks
- # are fully "owned" (eg created from an operation, not coming from
- # an existing manager)
for b in blocks:
- nb = b.copy(deep=copy)
+ nb = b.copy(deep=False)
nb.mgr_locs = BlockPlacement(inv_indexer[nb.mgr_locs.indexer])
new_blocks.append(nb)
@@ -1630,14 +1612,10 @@ def unstack(self, unstacker, fill_value) -> BlockManager:
bm = BlockManager(new_blocks, [new_columns, new_index], verify_integrity=False)
return bm
- def to_dict(self, copy: bool = True) -> dict[str, Self]:
+ def to_dict(self) -> dict[str, Self]:
"""
Return a dict of str(dtype) -> BlockManager
- Parameters
- ----------
- copy : bool, default True
-
Returns
-------
values : a dict of dtype -> BlockManager
@@ -1648,7 +1626,7 @@ def to_dict(self, copy: bool = True) -> dict[str, Self]:
bd.setdefault(str(b.dtype), []).append(b)
# TODO(EA2D): the combine will be unnecessary with 2D EAs
- return {dtype: self._combine(blocks, copy=copy) for dtype, blocks in bd.items()}
+ return {dtype: self._combine(blocks) for dtype, blocks in bd.items()}
def as_array(
self,
@@ -2028,9 +2006,9 @@ def array_values(self) -> ExtensionArray:
"""The array that Series.array returns"""
return self._block.array_values
- def get_numeric_data(self, copy: bool = False) -> Self:
+ def get_numeric_data(self) -> Self:
if self._block.is_numeric:
- return self.copy(deep=copy)
+ return self.copy(deep=False)
return self.make_empty()
@property
diff --git a/pandas/tests/frame/constructors/test_from_records.py b/pandas/tests/frame/constructors/test_from_records.py
index bcf4e8fb0e64a..edb21fb92f6a2 100644
--- a/pandas/tests/frame/constructors/test_from_records.py
+++ b/pandas/tests/frame/constructors/test_from_records.py
@@ -80,7 +80,7 @@ def test_from_records_sequencelike(self):
# this is actually tricky to create the recordlike arrays and
# have the dtypes be intact
- blocks = df._to_dict_of_blocks(copy=False)
+ blocks = df._to_dict_of_blocks()
tuples = []
columns = []
dtypes = []
@@ -169,7 +169,7 @@ def test_from_records_dictlike(self):
# columns is in a different order here than the actual items iterated
# from the dict
- blocks = df._to_dict_of_blocks(copy=False)
+ blocks = df._to_dict_of_blocks()
columns = []
for b in blocks.values():
columns.extend(b.columns)
diff --git a/pandas/tests/frame/methods/test_to_dict_of_blocks.py b/pandas/tests/frame/methods/test_to_dict_of_blocks.py
index 28973fe0d7900..f7d9dc914a2ee 100644
--- a/pandas/tests/frame/methods/test_to_dict_of_blocks.py
+++ b/pandas/tests/frame/methods/test_to_dict_of_blocks.py
@@ -14,22 +14,6 @@
class TestToDictOfBlocks:
- def test_copy_blocks(self, float_frame):
- # GH#9607
- df = DataFrame(float_frame, copy=True)
- column = df.columns[0]
-
- # use the default copy=True, change a column
- _last_df = None
- blocks = df._to_dict_of_blocks(copy=True)
- for _df in blocks.values():
- _last_df = _df
- if column in _df:
- _df.loc[:, column] = _df[column] + 1
-
- # make sure we did not change the original DataFrame
- assert _last_df is not None and not _last_df[column].equals(df[column])
-
@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
def test_no_copy_blocks(self, float_frame, using_copy_on_write):
# GH#9607
@@ -38,7 +22,7 @@ def test_no_copy_blocks(self, float_frame, using_copy_on_write):
_last_df = None
# use the copy=False, change a column
- blocks = df._to_dict_of_blocks(copy=False)
+ blocks = df._to_dict_of_blocks()
for _df in blocks.values():
_last_df = _df
if column in _df:
diff --git a/pandas/tests/internals/test_internals.py b/pandas/tests/internals/test_internals.py
index a9dbdb21f59fb..ce88bae6e02f2 100644
--- a/pandas/tests/internals/test_internals.py
+++ b/pandas/tests/internals/test_internals.py
@@ -790,24 +790,6 @@ def test_get_numeric_data(self, using_copy_on_write):
np.array([100.0, 200.0, 300.0]),
)
- numeric2 = mgr.get_numeric_data(copy=True)
- tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"]))
- numeric2.iset(
- numeric2.items.get_loc("float"),
- np.array([1000.0, 2000.0, 3000.0]),
- inplace=True,
- )
- if using_copy_on_write:
- tm.assert_almost_equal(
- mgr.iget(mgr.items.get_loc("float")).internal_values(),
- np.array([1.0, 1.0, 1.0]),
- )
- else:
- tm.assert_almost_equal(
- mgr.iget(mgr.items.get_loc("float")).internal_values(),
- np.array([100.0, 200.0, 300.0]),
- )
-
def test_get_bool_data(self, using_copy_on_write):
mgr = create_mgr(
"int: int; float: float; complex: complex;"
@@ -835,20 +817,6 @@ def test_get_bool_data(self, using_copy_on_write):
np.array([True, False, True]),
)
- # Check sharing
- bools2 = mgr.get_bool_data(copy=True)
- bools2.iset(0, np.array([False, True, False]))
- if using_copy_on_write:
- tm.assert_numpy_array_equal(
- mgr.iget(mgr.items.get_loc("bool")).internal_values(),
- np.array([True, True, True]),
- )
- else:
- tm.assert_numpy_array_equal(
- mgr.iget(mgr.items.get_loc("bool")).internal_values(),
- np.array([True, False, True]),
- )
-
def test_unicode_repr_doesnt_raise(self):
repr(create_mgr("b,\u05d0: object"))
| The copy keyword here is really not needed | https://api.github.com/repos/pandas-dev/pandas/pulls/56420 | 2023-12-08T23:08:29Z | 2023-12-09T01:00:55Z | 2023-12-09T01:00:55Z | 2023-12-09T08:11:45Z |
CoW: Update test with read-only array | diff --git a/pandas/tests/frame/methods/test_to_dict_of_blocks.py b/pandas/tests/frame/methods/test_to_dict_of_blocks.py
index 28973fe0d7900..7d08efe916a76 100644
--- a/pandas/tests/frame/methods/test_to_dict_of_blocks.py
+++ b/pandas/tests/frame/methods/test_to_dict_of_blocks.py
@@ -51,9 +51,7 @@ def test_no_copy_blocks(self, float_frame, using_copy_on_write):
assert _last_df is not None and not _last_df[column].equals(df[column])
-def test_to_dict_of_blocks_item_cache(request, using_copy_on_write, warn_copy_on_write):
- if using_copy_on_write:
- request.applymarker(pytest.mark.xfail(reason="CoW - not yet implemented"))
+def test_to_dict_of_blocks_item_cache(using_copy_on_write, warn_copy_on_write):
# Calling to_dict_of_blocks should not poison item_cache
df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]})
df["c"] = NumpyExtensionArray(np.array([1, 2, None, 3], dtype=object))
@@ -65,10 +63,8 @@ def test_to_dict_of_blocks_item_cache(request, using_copy_on_write, warn_copy_on
df._to_dict_of_blocks()
if using_copy_on_write:
- # TODO(CoW) we should disallow this, so `df` doesn't get updated,
- # this currently still updates df, so this test fails
- ser.values[0] = "foo"
- assert df.loc[0, "b"] == "a"
+ with pytest.raises(ValueError, match="read-only"):
+ ser.values[0] = "foo"
elif warn_copy_on_write:
ser.values[0] = "foo"
assert df.loc[0, "b"] == "foo"
| - [ ] 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/56418 | 2023-12-08T22:52:59Z | 2023-12-11T13:06:00Z | 2023-12-11T13:06:00Z | 2023-12-11T13:08:39Z |
ENH: unify warning message in to_offset() | diff --git a/pandas/_libs/tslibs/offsets.pyx b/pandas/_libs/tslibs/offsets.pyx
index 3ea8fbf69b28b..b893939969c68 100644
--- a/pandas/_libs/tslibs/offsets.pyx
+++ b/pandas/_libs/tslibs/offsets.pyx
@@ -4786,7 +4786,8 @@ cpdef to_offset(freq, bint is_period=False):
for n, (sep, stride, name) in enumerate(tups):
if is_period is False and name in c_OFFSET_DEPR_FREQSTR:
warnings.warn(
- f"\'{name}\' is deprecated, please use "
+ f"\'{name}\' is deprecated and will be removed "
+ f"in a future version, please use "
f"\'{c_OFFSET_DEPR_FREQSTR.get(name)}\' instead.",
FutureWarning,
stacklevel=find_stack_level(),
@@ -4829,9 +4830,8 @@ cpdef to_offset(freq, bint is_period=False):
if prefix in c_DEPR_ABBREVS:
warnings.warn(
f"\'{prefix}\' is deprecated and will be removed "
- f"in a future version. Please use "
- f"\'{c_DEPR_ABBREVS.get(prefix)}\' "
- f"instead of \'{prefix}\'.",
+ f"in a future version, please use "
+ f"\'{c_DEPR_ABBREVS.get(prefix)}\' instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
diff --git a/pandas/tests/arrays/test_datetimes.py b/pandas/tests/arrays/test_datetimes.py
index 1fa55f13af2a8..c0e72c01a2ce9 100644
--- a/pandas/tests/arrays/test_datetimes.py
+++ b/pandas/tests/arrays/test_datetimes.py
@@ -772,7 +772,8 @@ def test_iter_zoneinfo_fold(self, tz):
)
def test_date_range_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
# GH#9586, GH#54275
- depr_msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
expected = pd.date_range("1/1/2000", periods=4, freq=freq)
with tm.assert_produces_warning(FutureWarning, match=depr_msg):
diff --git a/pandas/tests/frame/methods/test_asfreq.py b/pandas/tests/frame/methods/test_asfreq.py
index 6926917a09602..ef72ca1ac86b9 100644
--- a/pandas/tests/frame/methods/test_asfreq.py
+++ b/pandas/tests/frame/methods/test_asfreq.py
@@ -252,7 +252,8 @@ def test_asfreq_2ME(self, freq, freq_half):
)
def test_asfreq_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
# GH#9586, #55978
- depr_msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
index = date_range("1/1/2000", periods=4, freq=f"{freq[1:]}")
df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)})
diff --git a/pandas/tests/indexes/datetimes/methods/test_to_period.py b/pandas/tests/indexes/datetimes/methods/test_to_period.py
index 2c68ddd3d3d15..42a3f3b0f7b42 100644
--- a/pandas/tests/indexes/datetimes/methods/test_to_period.py
+++ b/pandas/tests/indexes/datetimes/methods/test_to_period.py
@@ -103,7 +103,8 @@ def test_dti_to_period_2monthish(self, freq_offset, freq_period):
)
def test_to_period_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
# GH#9586
- msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
rng = date_range("01-Jan-2012", periods=8, freq=freq)
prng = rng.to_period()
@@ -119,7 +120,8 @@ def test_to_period_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr):
)
def test_to_period_frequency_BQ_BY_deprecated(self, freq, freq_depr):
# GH#9586
- msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
rng = date_range("01-Jan-2012", periods=8, freq=freq)
prng = rng.to_period()
diff --git a/pandas/tests/indexes/datetimes/test_date_range.py b/pandas/tests/indexes/datetimes/test_date_range.py
index dfad4d7ad01ea..44dd64e162413 100644
--- a/pandas/tests/indexes/datetimes/test_date_range.py
+++ b/pandas/tests/indexes/datetimes/test_date_range.py
@@ -157,7 +157,8 @@ def test_date_range_fractional_period(self):
)
def test_date_range_frequency_M_SM_BQ_BY_deprecated(self, freq, freq_depr):
# GH#52064
- depr_msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
expected = date_range("1/1/2000", periods=4, freq=freq)
with tm.assert_produces_warning(FutureWarning, match=depr_msg):
@@ -788,8 +789,12 @@ def test_freq_dateoffset_with_relateivedelta_nanos(self):
)
def test_frequencies_H_T_S_L_U_N_deprecated(self, freq, freq_depr):
# GH#52536
- freq_msg = re.split("[0-9]*", freq_depr, maxsplit=1)[1]
- msg = f"'{freq_msg}' is deprecated and will be removed in a future version."
+ freq_msg = re.split("[0-9]*", freq, maxsplit=1)[1]
+ freq_depr_msg = re.split("[0-9]*", freq_depr, maxsplit=1)[1]
+ msg = (
+ f"'{freq_depr_msg}' is deprecated and will be removed in a future version, "
+ )
+ f"please use '{freq_msg}' instead"
expected = date_range("1/1/2000", periods=2, freq=freq)
with tm.assert_produces_warning(FutureWarning, match=msg):
@@ -809,7 +814,8 @@ def test_frequencies_A_deprecated_Y_renamed(self, freq, freq_depr):
# GH#9586, GH#54275
freq_msg = re.split("[0-9]*", freq, maxsplit=1)[1]
freq_depr_msg = re.split("[0-9]*", freq_depr, maxsplit=1)[1]
- msg = f"'{freq_depr_msg}' is deprecated, please use '{freq_msg}' instead."
+ msg = f"'{freq_depr_msg}' is deprecated and will be removed "
+ f"in a future version, please use '{freq_msg}' instead."
expected = date_range("1/1/2000", periods=2, freq=freq)
with tm.assert_produces_warning(FutureWarning, match=msg):
diff --git a/pandas/tests/indexes/datetimes/test_datetime.py b/pandas/tests/indexes/datetimes/test_datetime.py
index d2d627c27312a..f7fc64d4b0163 100644
--- a/pandas/tests/indexes/datetimes/test_datetime.py
+++ b/pandas/tests/indexes/datetimes/test_datetime.py
@@ -202,7 +202,8 @@ def test_AS_BAS_deprecated(self, freq_depr, expected_values, expected_freq):
)
def test_BM_BQ_BY_deprecated(self, freq, expected_values, freq_depr):
# GH#52064
- msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
with tm.assert_produces_warning(FutureWarning, match=msg):
expected = date_range(start="2016-02-21", end="2016-08-21", freq=freq_depr)
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 8a725c6e51e3f..c4513087b4529 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -2100,7 +2100,8 @@ def test_resample_empty_series_with_tz():
)
def test_resample_M_Q_Y_A_deprecated(freq, freq_depr):
# GH#9586
- depr_msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
s = Series(range(10), index=date_range("20130101", freq="d", periods=10))
expected = s.resample(freq).mean()
@@ -2119,7 +2120,8 @@ def test_resample_M_Q_Y_A_deprecated(freq, freq_depr):
)
def test_resample_BM_BQ_deprecated(freq, freq_depr):
# GH#52064
- depr_msg = f"'{freq_depr[1:]}' is deprecated, please use '{freq[1:]}' instead."
+ depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed "
+ f"in a future version, please use '{freq[1:]}' instead."
s = Series(range(10), index=date_range("20130101", freq="d", periods=10))
expected = s.resample(freq).mean()
| xref #56346
unified warning message for deprecated frequencies in to_offset in order to make test parametrization more clear | https://api.github.com/repos/pandas-dev/pandas/pulls/56417 | 2023-12-08T22:45:45Z | 2023-12-12T16:46:41Z | 2023-12-12T16:46:41Z | 2023-12-12T16:57:38Z |
CLN: generate_range | diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index a88f40013b3f6..eb1c2ecc0b0fe 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -2103,7 +2103,9 @@ def _validate_frequency(cls, index, freq: BaseOffset, **kwargs):
) from err
@classmethod
- def _generate_range(cls, start, end, periods, freq, *args, **kwargs) -> Self:
+ def _generate_range(
+ cls, start, end, periods: int | None, freq, *args, **kwargs
+ ) -> Self:
raise AbstractMethodError(cls)
# --------------------------------------------------------------
diff --git a/pandas/core/arrays/datetimes.py b/pandas/core/arrays/datetimes.py
index 496a6987c3264..64f08adcd48c4 100644
--- a/pandas/core/arrays/datetimes.py
+++ b/pandas/core/arrays/datetimes.py
@@ -405,7 +405,7 @@ def _generate_range( # type: ignore[override]
cls,
start,
end,
- periods,
+ periods: int | None,
freq,
tz=None,
normalize: bool = False,
@@ -441,9 +441,9 @@ def _generate_range( # type: ignore[override]
else:
unit = "ns"
- if start is not None and unit is not None:
+ if start is not None:
start = start.as_unit(unit, round_ok=False)
- if end is not None and unit is not None:
+ if end is not None:
end = end.as_unit(unit, round_ok=False)
left_inclusive, right_inclusive = validate_inclusive(inclusive)
@@ -452,14 +452,8 @@ def _generate_range( # type: ignore[override]
if tz is not None:
# Localize the start and end arguments
- start_tz = None if start is None else start.tz
- end_tz = None if end is None else end.tz
- start = _maybe_localize_point(
- start, start_tz, start, freq, tz, ambiguous, nonexistent
- )
- end = _maybe_localize_point(
- end, end_tz, end, freq, tz, ambiguous, nonexistent
- )
+ start = _maybe_localize_point(start, freq, tz, ambiguous, nonexistent)
+ end = _maybe_localize_point(end, freq, tz, ambiguous, nonexistent)
if freq is not None:
# We break Day arithmetic (fixed 24 hour) here and opt for
@@ -505,6 +499,7 @@ def _generate_range( # type: ignore[override]
# Nanosecond-granularity timestamps aren't always correctly
# representable with doubles, so we limit the range that we
# pass to np.linspace as much as possible
+ periods = cast(int, periods)
i8values = (
np.linspace(0, end._value - start._value, periods, dtype="int64")
+ start._value
@@ -2688,7 +2683,9 @@ def _maybe_normalize_endpoints(
return start, end
-def _maybe_localize_point(ts, is_none, is_not_none, freq, tz, ambiguous, nonexistent):
+def _maybe_localize_point(
+ ts: Timestamp | None, freq, tz, ambiguous, nonexistent
+) -> Timestamp | None:
"""
Localize a start or end Timestamp to the timezone of the corresponding
start or end Timestamp
@@ -2696,8 +2693,6 @@ def _maybe_localize_point(ts, is_none, is_not_none, freq, tz, ambiguous, nonexis
Parameters
----------
ts : start or end Timestamp to potentially localize
- is_none : argument that should be None
- is_not_none : argument that should not be None
freq : Tick, DateOffset, or None
tz : str, timezone object or None
ambiguous: str, localization behavior for ambiguous times
@@ -2710,7 +2705,7 @@ def _maybe_localize_point(ts, is_none, is_not_none, freq, tz, ambiguous, nonexis
# Make sure start and end are timezone localized if:
# 1) freq = a Timedelta-like frequency (Tick)
# 2) freq = None i.e. generating a linspaced range
- if is_none is None and is_not_none is not None:
+ if ts is not None and ts.tzinfo is None:
# Note: We can't ambiguous='infer' a singular ambiguous time; however,
# we have historically defaulted ambiguous=False
ambiguous = ambiguous if ambiguous != "infer" else False
| - [ ] 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/56416 | 2023-12-08T21:49:50Z | 2023-12-08T23:25:25Z | 2023-12-08T23:25:25Z | 2023-12-08T23:29:53Z |
BUG: fillna with mixed-resolution dt64/td64 | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 8a1906d20c243..88a4f3aacc174 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -534,6 +534,7 @@ Datetimelike
- Bug in :meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` always caching :meth:`Index.is_unique` as ``True`` when first value in index is ``NaT`` (:issue:`55755`)
- Bug in :meth:`Index.view` to a datetime64 dtype with non-supported resolution incorrectly raising (:issue:`55710`)
- Bug in :meth:`Series.dt.round` with non-nanosecond resolution and ``NaT`` entries incorrectly raising ``OverflowError`` (:issue:`56158`)
+- Bug in :meth:`Series.fillna` with non-nanosecond resolution dtypes and higher-resolution vector values returning incorrect (internally-corrupted) results (:issue:`56410`)
- Bug in :meth:`Tick.delta` with very large ticks raising ``OverflowError`` instead of ``OutOfBoundsTimedelta`` (:issue:`55503`)
- Bug in :meth:`Timestamp.unit` being inferred incorrectly from an ISO8601 format string with minute or hour resolution and a timezone offset (:issue:`56208`)
- Bug in ``.astype`` converting from a higher-resolution ``datetime64`` dtype to a lower-resolution ``datetime64`` dtype (e.g. ``datetime64[us]->datetim64[ms]``) silently overflowing with values near the lower implementation bound (:issue:`55979`)
@@ -547,7 +548,6 @@ Datetimelike
- Bug in parsing datetime strings with nanosecond resolution with non-ISO8601 formats incorrectly truncating sub-microsecond components (:issue:`56051`)
- Bug in parsing datetime strings with sub-second resolution and trailing zeros incorrectly inferring second or millisecond resolution (:issue:`55737`)
- Bug in the results of :func:`to_datetime` with an floating-dtype argument with ``unit`` not matching the pointwise results of :class:`Timestamp` (:issue:`56037`)
--
Timedelta
^^^^^^^^^
diff --git a/pandas/core/arrays/_mixins.py b/pandas/core/arrays/_mixins.py
index d6f4dbfe7f549..8d1f5262e7911 100644
--- a/pandas/core/arrays/_mixins.py
+++ b/pandas/core/arrays/_mixins.py
@@ -430,6 +430,12 @@ def _where(self: Self, mask: npt.NDArray[np.bool_], value) -> Self:
value = self._validate_setitem_value(value)
res_values = np.where(mask, self._ndarray, value)
+ if res_values.dtype != self._ndarray.dtype:
+ raise AssertionError(
+ # GH#56410
+ "Something has gone wrong, please report a bug at "
+ "github.com/pandas-dev/pandas/"
+ )
return self._from_backing_data(res_values)
# ------------------------------------------------------------------------
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index eb1c2ecc0b0fe..ffdcc7f51dd3f 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -646,6 +646,9 @@ def _validation_error_message(self, value, allow_listlike: bool = False) -> str:
def _validate_listlike(self, value, allow_object: bool = False):
if isinstance(value, type(self)):
+ if self.dtype.kind in "mM" and not allow_object:
+ # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+ value = value.as_unit(self.unit, round_ok=False) # type: ignore[attr-defined]
return value
if isinstance(value, list) and len(value) == 0:
@@ -694,6 +697,9 @@ def _validate_listlike(self, value, allow_object: bool = False):
msg = self._validation_error_message(value, True)
raise TypeError(msg)
+ if self.dtype.kind in "mM" and not allow_object:
+ # error: "DatetimeLikeArrayMixin" has no attribute "as_unit"
+ value = value.as_unit(self.unit, round_ok=False) # type: ignore[attr-defined]
return value
def _validate_setitem_value(self, value):
@@ -2121,12 +2127,12 @@ def unit(self) -> str:
# "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
return dtype_to_unit(self.dtype) # type: ignore[arg-type]
- def as_unit(self, unit: str) -> Self:
+ def as_unit(self, unit: str, round_ok: bool = True) -> Self:
if unit not in ["s", "ms", "us", "ns"]:
raise ValueError("Supported units are 's', 'ms', 'us', 'ns'")
dtype = np.dtype(f"{self.dtype.kind}8[{unit}]")
- new_values = astype_overflowsafe(self._ndarray, dtype, round_ok=True)
+ new_values = astype_overflowsafe(self._ndarray, dtype, round_ok=round_ok)
if isinstance(self.dtype, np.dtype):
new_dtype = new_values.dtype
diff --git a/pandas/tests/series/methods/test_fillna.py b/pandas/tests/series/methods/test_fillna.py
index a5170898b1720..acc5805578f22 100644
--- a/pandas/tests/series/methods/test_fillna.py
+++ b/pandas/tests/series/methods/test_fillna.py
@@ -19,6 +19,7 @@
Timestamp,
date_range,
isna,
+ timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays import period_array
@@ -239,7 +240,7 @@ def test_fillna_downcast_infer_objects_to_numeric(self):
expected = Series([0, 1, 2.5, 4, 4], dtype=np.float64)
tm.assert_series_equal(res, expected)
- def test_timedelta_fillna(self, frame_or_series):
+ def test_timedelta_fillna(self, frame_or_series, unit):
# GH#3371
ser = Series(
[
@@ -247,7 +248,8 @@ def test_timedelta_fillna(self, frame_or_series):
Timestamp("20130101"),
Timestamp("20130102"),
Timestamp("20130103 9:01:01"),
- ]
+ ],
+ dtype=f"M8[{unit}]",
)
td = ser.diff()
obj = frame_or_series(td).copy()
@@ -260,7 +262,8 @@ def test_timedelta_fillna(self, frame_or_series):
timedelta(0),
timedelta(1),
timedelta(days=1, seconds=9 * 3600 + 60 + 1),
- ]
+ ],
+ dtype=f"m8[{unit}]",
)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@@ -279,7 +282,8 @@ def test_timedelta_fillna(self, frame_or_series):
timedelta(0),
timedelta(1),
timedelta(days=1, seconds=9 * 3600 + 60 + 1),
- ]
+ ],
+ dtype=f"m8[{unit}]",
)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@@ -291,7 +295,8 @@ def test_timedelta_fillna(self, frame_or_series):
timedelta(0),
timedelta(1),
timedelta(days=1, seconds=9 * 3600 + 60 + 1),
- ]
+ ],
+ dtype=f"m8[{unit}]",
)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@@ -303,7 +308,8 @@ def test_timedelta_fillna(self, frame_or_series):
timedelta(0),
timedelta(1),
timedelta(days=1, seconds=9 * 3600 + 60 + 1),
- ]
+ ],
+ dtype=f"m8[{unit}]",
)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@@ -316,7 +322,7 @@ def test_timedelta_fillna(self, frame_or_series):
timedelta(1),
timedelta(days=1, seconds=9 * 3600 + 60 + 1),
],
- dtype="m8[ns]",
+ dtype=f"m8[{unit}]",
)
expected = frame_or_series(expected)
tm.assert_equal(result, expected)
@@ -375,6 +381,72 @@ def test_datetime64_fillna(self):
)
tm.assert_series_equal(result, expected)
+ @pytest.mark.parametrize(
+ "scalar",
+ [
+ False,
+ pytest.param(
+ True,
+ marks=pytest.mark.xfail(
+ reason="GH#56410 scalar case not yet addressed"
+ ),
+ ),
+ ],
+ )
+ @pytest.mark.parametrize("tz", [None, "UTC"])
+ def test_datetime64_fillna_mismatched_reso_no_rounding(self, tz, scalar):
+ # GH#56410
+ dti = date_range("2016-01-01", periods=3, unit="s", tz=tz)
+ item = Timestamp("2016-02-03 04:05:06.789", tz=tz)
+ vec = date_range(item, periods=3, unit="ms")
+
+ exp_dtype = "M8[ms]" if tz is None else "M8[ms, UTC]"
+ expected = Series([item, dti[1], dti[2]], dtype=exp_dtype)
+
+ ser = Series(dti)
+ ser[0] = NaT
+ ser2 = ser.copy()
+
+ res = ser.fillna(item)
+ res2 = ser2.fillna(Series(vec))
+
+ if scalar:
+ tm.assert_series_equal(res, expected)
+ else:
+ tm.assert_series_equal(res2, expected)
+
+ @pytest.mark.parametrize(
+ "scalar",
+ [
+ False,
+ pytest.param(
+ True,
+ marks=pytest.mark.xfail(
+ reason="GH#56410 scalar case not yet addressed"
+ ),
+ ),
+ ],
+ )
+ def test_timedelta64_fillna_mismatched_reso_no_rounding(self, scalar):
+ # GH#56410
+ tdi = date_range("2016-01-01", periods=3, unit="s") - Timestamp("1970-01-01")
+ item = Timestamp("2016-02-03 04:05:06.789") - Timestamp("1970-01-01")
+ vec = timedelta_range(item, periods=3, unit="ms")
+
+ expected = Series([item, tdi[1], tdi[2]], dtype="m8[ms]")
+
+ ser = Series(tdi)
+ ser[0] = NaT
+ ser2 = ser.copy()
+
+ res = ser.fillna(item)
+ res2 = ser2.fillna(Series(vec))
+
+ if scalar:
+ tm.assert_series_equal(res, expected)
+ else:
+ tm.assert_series_equal(res2, expected)
+
def test_datetime64_fillna_backfill(self):
# GH#6587
# make sure that we are treating as integer when filling
@@ -392,7 +464,7 @@ def test_datetime64_fillna_backfill(self):
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("tz", ["US/Eastern", "Asia/Tokyo"])
- def test_datetime64_tz_fillna(self, tz):
+ def test_datetime64_tz_fillna(self, tz, unit):
# DatetimeLikeBlock
ser = Series(
[
@@ -400,7 +472,8 @@ def test_datetime64_tz_fillna(self, tz):
NaT,
Timestamp("2011-01-03 10:00"),
NaT,
- ]
+ ],
+ dtype=f"M8[{unit}]",
)
null_loc = Series([False, True, False, True])
@@ -411,7 +484,8 @@ def test_datetime64_tz_fillna(self, tz):
Timestamp("2011-01-02 10:00"),
Timestamp("2011-01-03 10:00"),
Timestamp("2011-01-02 10:00"),
- ]
+ ],
+ dtype=f"M8[{unit}]",
)
tm.assert_series_equal(expected, result)
# check s is not changed
@@ -468,15 +542,18 @@ def test_datetime64_tz_fillna(self, tz):
Timestamp("2011-01-02 10:00"),
Timestamp("2011-01-03 10:00"),
Timestamp("2011-01-04 10:00"),
- ]
+ ],
+ dtype=f"M8[{unit}]",
)
tm.assert_series_equal(expected, result)
tm.assert_series_equal(isna(ser), null_loc)
# DatetimeTZBlock
- idx = DatetimeIndex(["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz=tz)
+ idx = DatetimeIndex(
+ ["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz=tz
+ ).as_unit(unit)
ser = Series(idx)
- assert ser.dtype == f"datetime64[ns, {tz}]"
+ assert ser.dtype == f"datetime64[{unit}, {tz}]"
tm.assert_series_equal(isna(ser), null_loc)
result = ser.fillna(Timestamp("2011-01-02 10:00"))
@@ -500,7 +577,7 @@ def test_datetime64_tz_fillna(self, tz):
"2011-01-02 10:00",
],
tz=tz,
- )
+ ).as_unit(unit)
expected = Series(idx)
tm.assert_series_equal(expected, result)
tm.assert_series_equal(isna(ser), null_loc)
@@ -514,7 +591,7 @@ def test_datetime64_tz_fillna(self, tz):
"2011-01-02 10:00",
],
tz=tz,
- )
+ ).as_unit(unit)
expected = Series(idx)
tm.assert_series_equal(expected, result)
tm.assert_series_equal(isna(ser), null_loc)
@@ -562,7 +639,7 @@ def test_datetime64_tz_fillna(self, tz):
Timestamp("2011-01-03 10:00", tz=tz),
Timestamp("2011-01-04 10:00", tz=tz),
]
- )
+ ).dt.as_unit(unit)
tm.assert_series_equal(expected, result)
tm.assert_series_equal(isna(ser), null_loc)
@@ -589,7 +666,7 @@ def test_datetime64_tz_fillna(self, tz):
Timestamp("2011-01-03 10:00", tz=tz),
Timestamp("2013-01-01", tz="US/Pacific").tz_convert(tz),
]
- )
+ ).dt.as_unit(unit)
tm.assert_series_equal(expected, result)
tm.assert_series_equal(isna(ser), null_loc)
| - [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [x] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] 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.
xref (but does not close) #56410
Started as a diff-trimmer for #55901, then identified some bugs | https://api.github.com/repos/pandas-dev/pandas/pulls/56413 | 2023-12-08T21:43:03Z | 2023-12-09T18:38:17Z | 2023-12-09T18:38:17Z | 2023-12-09T18:40:16Z |
Series.str.find fix for arrow strings when start < 0 | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 919ac8b03f936..1544b3d953d71 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -577,6 +577,7 @@ Strings
- Bug in :func:`pandas.api.types.is_string_dtype` while checking object array with no elements is of the string dtype (:issue:`54661`)
- Bug in :meth:`DataFrame.apply` failing when ``engine="numba"`` and columns or index have ``StringDtype`` (:issue:`56189`)
- Bug in :meth:`Series.__mul__` for :class:`ArrowDtype` with ``pyarrow.string`` dtype and ``string[pyarrow]`` for the pyarrow backend (:issue:`51970`)
+- Bug in :meth:`Series.str.find` when ``start < 0`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56411`)
- Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56404`)
- Bug in :meth:`Series.str.startswith` and :meth:`Series.str.endswith` with arguments of type ``tuple[str, ...]`` for ``string[pyarrow]`` (:issue:`54942`)
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index ae6942db11fae..5abdfe69e52c0 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -2193,7 +2193,8 @@ def _str_find(self, sub: str, start: int = 0, end: int | None = None):
slices = pc.utf8_slice_codeunits(self._pa_array, start, stop=end)
result = pc.find_substring(slices, sub)
not_found = pc.equal(result, -1)
- offset_result = pc.add(result, end - start)
+ start_offset = max(0, start)
+ offset_result = pc.add(result, start_offset)
result = pc.if_else(not_found, result, offset_result)
elif start == 0 and end is None:
slices = self._pa_array
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 1941e359299b6..75e5fb00586e6 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -1835,7 +1835,7 @@ def test_str_fullmatch(pat, case, na, exp):
@pytest.mark.parametrize(
"sub, start, end, exp, exp_typ",
- [["ab", 0, None, [0, None], pa.int32()], ["bc", 1, 3, [2, None], pa.int64()]],
+ [["ab", 0, None, [0, None], pa.int32()], ["bc", 1, 3, [1, None], pa.int64()]],
)
def test_str_find(sub, start, end, exp, exp_typ):
ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
@@ -1844,6 +1844,14 @@ def test_str_find(sub, start, end, exp, exp_typ):
tm.assert_series_equal(result, expected)
+def test_str_find_negative_start():
+ # GH 56411
+ ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
+ result = ser.str.find(sub="b", start=-1000, end=3)
+ expected = pd.Series([1, None], dtype=ArrowDtype(pa.int64()))
+ tm.assert_series_equal(result, expected)
+
+
def test_str_find_notimplemented():
ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
with pytest.raises(NotImplementedError, match="find not implemented"):
| - [x] closes #56411(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/56412 | 2023-12-08T21:01:52Z | 2023-12-08T23:41:57Z | 2023-12-08T23:41:57Z | 2023-12-08T23:53:41Z |
STY: Fix doctest and docstring formatting errors | diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index e91629744463f..e41f625e583c0 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -14,6 +14,8 @@
# $ ./ci/code_checks.sh single-docs # check single-page docs build warning-free
# $ ./ci/code_checks.sh notebooks # check execution of documentation notebooks
+set -uo pipefail
+
[[ -z "$1" || "$1" == "code" || "$1" == "doctests" || "$1" == "docstrings" || "$1" == "single-docs" || "$1" == "notebooks" ]] || \
{ echo "Unknown command $1. Usage: $0 [code|doctests|docstrings|single-docs|notebooks]"; exit 9999; }
diff --git a/doc/make.py b/doc/make.py
index dfa8ae6c1e34c..c4b7ab124f68f 100755
--- a/doc/make.py
+++ b/doc/make.py
@@ -236,8 +236,9 @@ def html(self):
os.remove(zip_fname)
if ret_code == 0:
- if self.single_doc_html is not None and not self.no_browser:
- self._open_browser(self.single_doc_html)
+ if self.single_doc_html is not None:
+ if not self.no_browser:
+ self._open_browser(self.single_doc_html)
else:
self._add_redirects()
if self.whatsnew and not self.no_browser:
diff --git a/pandas/core/arrays/sparse/accessor.py b/pandas/core/arrays/sparse/accessor.py
index 6eb1387c63a0a..c98fbb836cc6d 100644
--- a/pandas/core/arrays/sparse/accessor.py
+++ b/pandas/core/arrays/sparse/accessor.py
@@ -270,7 +270,7 @@ def from_spmatrix(cls, data, index=None, columns=None) -> DataFrame:
Examples
--------
>>> import scipy.sparse
- >>> mat = scipy.sparse.eye(3)
+ >>> mat = scipy.sparse.eye(3, dtype=float)
>>> pd.DataFrame.sparse.from_spmatrix(mat)
0 1 2
0 1.0 0.0 0.0
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 90946b8d9b5f4..ef10958ac1153 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -3955,7 +3955,7 @@ def to_csv(
>>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']}})
- >>> df.to_csv('out.csv', index=False) # doctest: +SKIP
+ >>> df.to_csv('out.csv', index=False) # doctest: +SKIP
Create 'out.zip' containing 'out.csv'
@@ -8972,7 +8972,7 @@ def clip(
Clips using specific lower and upper thresholds per column:
- >>> df.clip([-2, -1], [4,5])
+ >>> df.clip([-2, -1], [4, 5])
col_0 col_1
0 4 -1
1 -2 -1
diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 5a2f8d8454526..3f2696fd59334 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -470,10 +470,9 @@ def _aggregate_named(self, func, *args, **kwargs):
__examples_series_doc = dedent(
"""
- >>> ser = pd.Series(
- ... [390.0, 350.0, 30.0, 20.0],
- ... index=["Falcon", "Falcon", "Parrot", "Parrot"],
- ... name="Max Speed")
+ >>> ser = pd.Series([390.0, 350.0, 30.0, 20.0],
+ ... index=["Falcon", "Falcon", "Parrot", "Parrot"],
+ ... name="Max Speed")
>>> grouped = ser.groupby([1, 1, 2, 2])
>>> grouped.transform(lambda x: (x - x.mean()) / x.std())
Falcon 0.707107
@@ -1331,14 +1330,10 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
"""
Examples
--------
- >>> df = pd.DataFrame(
- ... {
- ... "A": [1, 1, 2, 2],
+ >>> data = {"A": [1, 1, 2, 2],
... "B": [1, 2, 3, 4],
- ... "C": [0.362838, 0.227877, 1.267767, -0.562860],
- ... }
- ... )
-
+ ... "C": [0.362838, 0.227877, 1.267767, -0.562860]}
+ >>> df = pd.DataFrame(data)
>>> df
A B C
0 1 1 0.362838
@@ -1393,7 +1388,8 @@ class DataFrameGroupBy(GroupBy[DataFrame]):
>>> df.groupby("A").agg(
... b_min=pd.NamedAgg(column="B", aggfunc="min"),
- ... c_sum=pd.NamedAgg(column="C", aggfunc="sum"))
+ ... c_sum=pd.NamedAgg(column="C", aggfunc="sum")
+ ... )
b_min c_sum
A
1 1 0.590715
@@ -2154,7 +2150,7 @@ def idxmax(
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
... 'co2_emissions': [37.2, 19.66, 1712]},
- ... index=['Pork', 'Wheat Products', 'Beef'])
+ ... index=['Pork', 'Wheat Products', 'Beef'])
>>> df
consumption co2_emissions
@@ -2236,7 +2232,7 @@ def idxmin(
>>> df = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],
... 'co2_emissions': [37.2, 19.66, 1712]},
- ... index=['Pork', 'Wheat Products', 'Beef'])
+ ... index=['Pork', 'Wheat Products', 'Beef'])
>>> df
consumption co2_emissions
@@ -2319,9 +2315,9 @@ def value_counts(
Examples
--------
>>> df = pd.DataFrame({
- ... 'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
- ... 'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
- ... 'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
+ ... 'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
+ ... 'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
+ ... 'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
... })
>>> df
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 7d284db4eba2c..e51983f0aabb7 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -232,8 +232,8 @@ class providing the base-class of operations.
""",
"dataframe_examples": """
>>> df = pd.DataFrame({'A': 'a a b'.split(),
- ... 'B': [1,2,3],
- ... 'C': [4,6,5]})
+ ... 'B': [1, 2, 3],
+ ... 'C': [4, 6, 5]})
>>> g1 = df.groupby('A', group_keys=False)
>>> g2 = df.groupby('A', group_keys=True)
@@ -313,7 +313,7 @@ class providing the base-class of operations.
The resulting dtype will reflect the return value of the passed ``func``.
- >>> g1.apply(lambda x: x*2 if x.name == 'a' else x/2)
+ >>> g1.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
a 0.0
a 2.0
b 1.0
@@ -322,7 +322,7 @@ class providing the base-class of operations.
In the above, the groups are not part of the index. We can have them included
by using ``g2`` where ``group_keys=True``:
- >>> g2.apply(lambda x: x*2 if x.name == 'a' else x/2)
+ >>> g2.apply(lambda x: x * 2 if x.name == 'a' else x / 2)
a a 0.0
a 2.0
b b 1.0
@@ -421,14 +421,18 @@ class providing the base-class of operations.
functions that expect Series, DataFrames, GroupBy or Resampler objects.
Instead of writing
->>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) # doctest: +SKIP
+>>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3
+>>> g = lambda x, arg1: x * 5 / arg1
+>>> f = lambda x: x ** 4
+>>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"])
+>>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3) # doctest: +SKIP
You can write
>>> (df.groupby('group')
... .pipe(f)
-... .pipe(g, arg1=a)
-... .pipe(h, arg2=b, arg3=c)) # doctest: +SKIP
+... .pipe(g, arg1=1)
+... .pipe(h, arg2=2, arg3=3)) # doctest: +SKIP
which is much more readable.
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 86693f241ddb1..46343b84afb43 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -862,7 +862,8 @@ def levels(self) -> FrozenList:
Examples
--------
>>> index = pd.MultiIndex.from_product([['mammal'],
- ... ('goat', 'human', 'cat', 'dog')], names=['Category', 'Animals'])
+ ... ('goat', 'human', 'cat', 'dog')],
+ ... names=['Category', 'Animals'])
>>> leg_num = pd.DataFrame(data=(4, 2, 4, 4), index=index, columns=['Legs'])
>>> leg_num
Legs
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 31e41acbf1774..254c6dcbc6bf6 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -854,7 +854,7 @@ def fillna(self, method, limit: int | None = None):
Missing values present before the upsampling are not affected.
>>> sm = pd.Series([1, None, 3],
- ... index=pd.date_range('20180101', periods=3, freq='h'))
+ ... index=pd.date_range('20180101', periods=3, freq='h'))
>>> sm
2018-01-01 00:00:00 1.0
2018-01-01 01:00:00 NaN
@@ -1023,13 +1023,8 @@ def interpolate(
Examples
--------
- >>> import datetime as dt
- >>> timesteps = [
- ... dt.datetime(2023, 3, 1, 7, 0, 0),
- ... dt.datetime(2023, 3, 1, 7, 0, 1),
- ... dt.datetime(2023, 3, 1, 7, 0, 2),
- ... dt.datetime(2023, 3, 1, 7, 0, 3),
- ... dt.datetime(2023, 3, 1, 7, 0, 4)]
+ >>> start = "2023-03-01T07:00:00"
+ >>> timesteps = pd.date_range(start, periods=5, freq="s")
>>> series = pd.Series(data=[1, -1, 2, 1, 3], index=timesteps)
>>> series
2023-03-01 07:00:00 1
@@ -1037,7 +1032,7 @@ def interpolate(
2023-03-01 07:00:02 2
2023-03-01 07:00:03 1
2023-03-01 07:00:04 3
- dtype: int64
+ Freq: s, dtype: int64
Upsample the dataframe to 0.5Hz by providing the period time of 2s.
diff --git a/pandas/core/shared_docs.py b/pandas/core/shared_docs.py
index fdb8ef1cc5dad..25f7e7e9f832b 100644
--- a/pandas/core/shared_docs.py
+++ b/pandas/core/shared_docs.py
@@ -797,7 +797,7 @@
... 'B': ['a', 'b', 'c', 'd', 'e'],
... 'C': ['f', 'g', 'h', 'i', 'j']}})
- >>> df.replace(to_replace='^[a-g]', value = 'e', regex=True)
+ >>> df.replace(to_replace='^[a-g]', value='e', regex=True)
A B C
0 0 e e
1 1 e e
@@ -808,7 +808,7 @@
If ``value`` is not ``None`` and `to_replace` is a dictionary, the dictionary
keys will be the DataFrame columns that the replacement will be applied.
- >>> df.replace(to_replace={{'B': '^[a-c]', 'C': '^[h-j]'}}, value = 'e', regex=True)
+ >>> df.replace(to_replace={{'B': '^[a-c]', 'C': '^[h-j]'}}, value='e', regex=True)
A B C
0 0 e f
1 1 e g
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index f90863a8ea1ef..f268d36d7fdc4 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -2439,14 +2439,14 @@ def var(
create_section_header("Examples"),
dedent(
"""\
- >>> ser = pd.Series([1, 5, 2, 7, 12, 6])
+ >>> ser = pd.Series([1, 5, 2, 7, 15, 6])
>>> ser.rolling(3).skew().round(6)
0 NaN
1 NaN
2 1.293343
3 -0.585583
- 4 0.000000
- 5 1.545393
+ 4 0.670284
+ 5 1.652317
dtype: float64
"""
),
@@ -2794,12 +2794,12 @@ def cov(
>>> v1 = [3, 3, 3, 5, 8]
>>> v2 = [3, 4, 4, 4, 8]
- >>> # numpy returns a 2X2 array, the correlation coefficient
- >>> # is the number at entry [0][1]
- >>> print(f"{{np.corrcoef(v1[:-1], v2[:-1])[0][1]:.6f}}")
- 0.333333
- >>> print(f"{{np.corrcoef(v1[1:], v2[1:])[0][1]:.6f}}")
- 0.916949
+ >>> np.corrcoef(v1[:-1], v2[:-1])
+ array([[1. , 0.33333333],
+ [0.33333333, 1. ]])
+ >>> np.corrcoef(v1[1:], v2[1:])
+ array([[1. , 0.9169493],
+ [0.9169493, 1. ]])
>>> s1 = pd.Series(v1)
>>> s2 = pd.Series(v2)
>>> s1.rolling(4).corr(s2)
@@ -2813,15 +2813,18 @@ def cov(
The below example shows a similar rolling calculation on a
DataFrame using the pairwise option.
- >>> matrix = np.array([[51., 35.], [49., 30.], [47., 32.],\
- [46., 31.], [50., 36.]])
- >>> print(np.corrcoef(matrix[:-1,0], matrix[:-1,1]).round(7))
- [[1. 0.6263001]
- [0.6263001 1. ]]
- >>> print(np.corrcoef(matrix[1:,0], matrix[1:,1]).round(7))
- [[1. 0.5553681]
- [0.5553681 1. ]]
- >>> df = pd.DataFrame(matrix, columns=['X','Y'])
+ >>> matrix = np.array([[51., 35.],
+ ... [49., 30.],
+ ... [47., 32.],
+ ... [46., 31.],
+ ... [50., 36.]])
+ >>> np.corrcoef(matrix[:-1, 0], matrix[:-1, 1])
+ array([[1. , 0.6263001],
+ [0.6263001, 1. ]])
+ >>> np.corrcoef(matrix[1:, 0], matrix[1:, 1])
+ array([[1. , 0.55536811],
+ [0.55536811, 1. ]])
+ >>> df = pd.DataFrame(matrix, columns=['X', 'Y'])
>>> df
X Y
0 51.0 35.0
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index d4b6602d9f0eb..a83c2bf241450 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -680,7 +680,7 @@ def read_sql(
pandas now supports reading via ADBC drivers
- >>> from adbc_driver_postgresql import dbapi
+ >>> from adbc_driver_postgresql import dbapi # doctest:+SKIP
>>> with dbapi.connect('postgres:///db_name') as conn: # doctest:+SKIP
... pd.read_sql('SELECT int_column FROM test_data', conn)
int_column
diff --git a/pandas/plotting/_core.py b/pandas/plotting/_core.py
index a017787f2dc2d..bd04e812e0be1 100644
--- a/pandas/plotting/_core.py
+++ b/pandas/plotting/_core.py
@@ -241,10 +241,10 @@ def hist_frame(
.. plot::
:context: close-figs
- >>> df = pd.DataFrame({
- ... 'length': [1.5, 0.5, 1.2, 0.9, 3],
- ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]
- ... }, index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
+ >>> data = {'length': [1.5, 0.5, 1.2, 0.9, 3],
+ ... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]}
+ >>> index = ['pig', 'rabbit', 'duck', 'chicken', 'horse']
+ >>> df = pd.DataFrame(data, index=index)
>>> hist = df.hist(bins=3)
"""
plot_backend = _get_plot_backend(backend)
@@ -607,10 +607,10 @@ def boxplot_frame_groupby(
>>> import itertools
>>> tuples = [t for t in itertools.product(range(1000), range(4))]
>>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
- >>> data = np.random.randn(len(index),4)
+ >>> data = np.random.randn(len(index), 4)
>>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
>>> grouped = df.groupby(level='lvl1')
- >>> grouped.boxplot(rot=45, fontsize=12, figsize=(8,10)) # doctest: +SKIP
+ >>> grouped.boxplot(rot=45, fontsize=12, figsize=(8, 10)) # doctest: +SKIP
The ``subplots=False`` option shows the boxplots in a single figure.
@@ -1400,9 +1400,7 @@ def hist(
.. plot::
:context: close-figs
- >>> df = pd.DataFrame(
- ... np.random.randint(1, 7, 6000),
- ... columns = ['one'])
+ >>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
>>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)
diff --git a/pandas/plotting/_misc.py b/pandas/plotting/_misc.py
index 5e5a55b4d0a98..18db460d388a4 100644
--- a/pandas/plotting/_misc.py
+++ b/pandas/plotting/_misc.py
@@ -439,7 +439,7 @@ def bootstrap_plot(
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
- >>> pd.plotting.bootstrap_plot(s)
+ >>> pd.plotting.bootstrap_plot(s) # doctest: +SKIP
<Figure size 640x480 with 6 Axes>
"""
plot_backend = _get_plot_backend("matplotlib")
diff --git a/scripts/validate_docstrings.py b/scripts/validate_docstrings.py
index 0a6a852bb0f85..98b55f8d690cf 100755
--- a/scripts/validate_docstrings.py
+++ b/scripts/validate_docstrings.py
@@ -228,11 +228,12 @@ def validate_pep8(self):
file.name,
]
response = subprocess.run(cmd, capture_output=True, check=False, text=True)
- stdout = response.stdout
- stdout = stdout.replace(file.name, "")
- messages = stdout.strip("\n").splitlines()
- if messages:
- error_messages.extend(messages)
+ for output in ("stdout", "stderr"):
+ out = getattr(response, output)
+ out = out.replace(file.name, "")
+ messages = out.strip("\n").splitlines()
+ if messages:
+ error_messages.extend(messages)
finally:
file.close()
os.unlink(file.name)
| Looks like there's some hiding formatting error in docstrings that I found in https://github.com/pandas-dev/pandas/actions/runs/7144701820/job/19458890003?pr=56003 (which appears unrelated) | https://api.github.com/repos/pandas-dev/pandas/pulls/56408 | 2023-12-08T19:28:12Z | 2023-12-09T01:43:35Z | 2023-12-09T01:43:34Z | 2023-12-09T01:43:37Z |
DOC: Fix typo in resample.py, "as_freq" | diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 8af81cd43d62e..31e41acbf1774 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -1061,7 +1061,7 @@ def interpolate(
2023-03-01 07:00:04.000 3.0
Freq: 500ms, dtype: float64
- Internal reindexing with ``as_freq()`` prior to interpolation leads to
+ Internal reindexing with ``asfreq()`` prior to interpolation leads to
an interpolated timeseries on the basis the reindexed timestamps (anchors).
Since not all datapoints from original series become anchors,
it can lead to misleading interpolation results as in the following example:
| null | https://api.github.com/repos/pandas-dev/pandas/pulls/56407 | 2023-12-08T19:17:45Z | 2023-12-08T23:24:24Z | 2023-12-08T23:24:24Z | 2023-12-09T23:08:02Z |
fix negative n for str.replace with arrow string | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index c878fd2664dc4..919ac8b03f936 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -577,6 +577,7 @@ Strings
- Bug in :func:`pandas.api.types.is_string_dtype` while checking object array with no elements is of the string dtype (:issue:`54661`)
- Bug in :meth:`DataFrame.apply` failing when ``engine="numba"`` and columns or index have ``StringDtype`` (:issue:`56189`)
- Bug in :meth:`Series.__mul__` for :class:`ArrowDtype` with ``pyarrow.string`` dtype and ``string[pyarrow]`` for the pyarrow backend (:issue:`51970`)
+- Bug in :meth:`Series.str.replace` when ``n < 0`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56404`)
- Bug in :meth:`Series.str.startswith` and :meth:`Series.str.endswith` with arguments of type ``tuple[str, ...]`` for ``string[pyarrow]`` (:issue:`54942`)
Interval
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index e7a50dbba9935..ae6942db11fae 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -2155,7 +2155,15 @@ def _str_replace(
)
func = pc.replace_substring_regex if regex else pc.replace_substring
- result = func(self._pa_array, pattern=pat, replacement=repl, max_replacements=n)
+ # https://github.com/apache/arrow/issues/39149
+ # GH 56404, unexpected behavior with negative max_replacements with pyarrow.
+ pa_max_replacements = None if n < 0 else n
+ result = func(
+ self._pa_array,
+ pattern=pat,
+ replacement=repl,
+ max_replacements=pa_max_replacements,
+ )
return type(self)(result)
def _str_repeat(self, repeats: int | Sequence[int]):
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 3ce3cee9714e4..1941e359299b6 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -1776,6 +1776,14 @@ def test_str_replace(pat, repl, n, regex, exp):
tm.assert_series_equal(result, expected)
+def test_str_replace_negative_n():
+ # GH 56404
+ ser = pd.Series(["abc", "aaaaaa"], dtype=ArrowDtype(pa.string()))
+ actual = ser.str.replace("a", "", -3, True)
+ expected = pd.Series(["bc", ""], dtype=ArrowDtype(pa.string()))
+ tm.assert_series_equal(expected, actual)
+
+
def test_str_repeat_unsupported():
ser = pd.Series(["abc", None], dtype=ArrowDtype(pa.string()))
with pytest.raises(NotImplementedError, match="repeat is not"):
| - [ ] closes #56404 (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/56406 | 2023-12-08T19:01:28Z | 2023-12-08T20:06:05Z | 2023-12-08T20:06:04Z | 2023-12-08T20:06:14Z |
DOC: add deprecation of chained assignment to 2.2 whatsnew | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 03d6025b4ef93..991daf6e65c8e 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -451,6 +451,70 @@ Other API changes
Deprecations
~~~~~~~~~~~~
+Chained assignment
+^^^^^^^^^^^^^^^^^^
+
+In preparation of larger upcoming changes to the copy / view behaviour in pandas 3.0
+(:ref:`copy_on_write`, PDEP-7), we started deprecating *chained assignment*.
+
+Chained assignment occurs when you try to update a pandas DataFrame or Series through
+two subsequent indexing operations. Depending on the type and order of those operations
+this currently does or does not work.
+
+A typical example is as follows:
+
+.. code-block:: python
+
+ df = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
+
+ # first selecting rows with a mask, then assigning values to a column
+ # -> this has never worked and raises a SettingWithCopyWarning
+ df[df["bar"] > 5]["foo"] = 100
+
+ # first selecting the column, and then assigning to a subset of that column
+ # -> this currently works
+ df["foo"][df["bar"] > 5] = 100
+
+This second example of chained assignment currently works to update the original ``df``.
+This will no longer work in pandas 3.0, and therefore we started deprecating this:
+
+.. code-block:: python
+
+ >>> df["foo"][df["bar"] > 5] = 100
+ FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
+ You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
+ A typical example is when you are setting values in a column of a DataFrame, like:
+
+ df["col"][row_indexer] = value
+
+ Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.
+
+ See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
+
+You can fix this warning and ensure your code is ready for pandas 3.0 by removing
+the usage of chained assignment. Typically, this can be done by doing the assignment
+in a single step using for example ``.loc``. For the example above, we can do:
+
+.. code-block:: python
+
+ df.loc[df["bar"] > 5, "foo"] = 100
+
+The same deprecation applies to inplace methods that are done in a chained manner, such as:
+
+.. code-block:: python
+
+ >>> df["foo"].fillna(0, inplace=True)
+ FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
+ The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
+
+ For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
+
+When the goal is to update the column in the DataFrame ``df``, the alternative here is
+to call the method on ``df`` itself, such as ``df.fillna({"foo": 0}, inplace=True)``.
+
+See more details in the :ref:`migration guide <copy_on_write.migration_guide>`.
+
+
Deprecate aliases ``M``, ``Q``, ``Y``, etc. in favour of ``ME``, ``QE``, ``YE``, etc. for offsets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| Part of the warnings we have been adding in https://github.com/pandas-dev/pandas/issues/56019, specifically the ones for chained assignment / chained inplace methods, are already being raised in the default mode, because this is a part of the changes with CoW we can clearly deprecate in advance.
Therefore adding a whatsnew section about this.
| https://api.github.com/repos/pandas-dev/pandas/pulls/56403 | 2023-12-08T15:56:58Z | 2023-12-21T22:46:07Z | 2023-12-21T22:46:07Z | 2023-12-22T08:48:10Z |
TST/CoW: expand test for chained inplace methods | diff --git a/pandas/tests/copy_view/test_chained_assignment_deprecation.py b/pandas/tests/copy_view/test_chained_assignment_deprecation.py
index 80e38380ed27c..0a37f6b813e55 100644
--- a/pandas/tests/copy_view/test_chained_assignment_deprecation.py
+++ b/pandas/tests/copy_view/test_chained_assignment_deprecation.py
@@ -1,6 +1,7 @@
import numpy as np
import pytest
+from pandas.compat import PY311
from pandas.errors import (
ChainedAssignmentError,
SettingWithCopyWarning,
@@ -42,7 +43,9 @@ def test_methods_iloc_warn(using_copy_on_write):
("ffill", ()),
],
)
-def test_methods_iloc_getitem_item_cache(func, args, using_copy_on_write):
+def test_methods_iloc_getitem_item_cache(
+ func, args, using_copy_on_write, warn_copy_on_write
+):
# ensure we don't incorrectly raise chained assignment warning because
# of the item cache / iloc not setting the item cache
df_orig = DataFrame({"a": [1, 2, 3], "b": 1})
@@ -66,14 +69,74 @@ def test_methods_iloc_getitem_item_cache(func, args, using_copy_on_write):
ser = df["a"]
getattr(ser, func)(*args, inplace=True)
+ df = df_orig.copy()
+ df["a"] # populate the item_cache
+ # TODO(CoW-warn) because of the usage of *args, this doesn't warn on Py3.11+
+ if using_copy_on_write:
+ with tm.raises_chained_assignment_error(not PY311):
+ getattr(df["a"], func)(*args, inplace=True)
+ else:
+ with tm.assert_cow_warning(not PY311, match="A value"):
+ getattr(df["a"], func)(*args, inplace=True)
+
+ df = df_orig.copy()
+ ser = df["a"] # populate the item_cache and keep ref
+ if using_copy_on_write:
+ with tm.raises_chained_assignment_error(not PY311):
+ getattr(df["a"], func)(*args, inplace=True)
+ else:
+ # ideally also warns on the default mode, but the ser' _cacher
+ # messes up the refcount + even in warning mode this doesn't trigger
+ # the warning of Py3.1+ (see above)
+ with tm.assert_cow_warning(warn_copy_on_write and not PY311, match="A value"):
+ getattr(df["a"], func)(*args, inplace=True)
+
+
+def test_methods_iloc_getitem_item_cache_fillna(
+ using_copy_on_write, warn_copy_on_write
+):
+ # ensure we don't incorrectly raise chained assignment warning because
+ # of the item cache / iloc not setting the item cache
+ df_orig = DataFrame({"a": [1, 2, 3], "b": 1})
+
+ df = df_orig.copy()
+ ser = df.iloc[:, 0]
+ ser.fillna(1, inplace=True)
+
+ # parent that holds item_cache is dead, so don't increase ref count
+ df = df_orig.copy()
+ ser = df.copy()["a"]
+ ser.fillna(1, inplace=True)
+
+ df = df_orig.copy()
+ df["a"] # populate the item_cache
+ ser = df.iloc[:, 0] # iloc creates a new object
+ ser.fillna(1, inplace=True)
+
+ df = df_orig.copy()
+ df["a"] # populate the item_cache
+ ser = df["a"]
+ ser.fillna(1, inplace=True)
+
df = df_orig.copy()
df["a"] # populate the item_cache
if using_copy_on_write:
with tm.raises_chained_assignment_error():
- df["a"].fillna(0, inplace=True)
+ df["a"].fillna(1, inplace=True)
else:
with tm.assert_cow_warning(match="A value"):
- df["a"].fillna(0, inplace=True)
+ df["a"].fillna(1, inplace=True)
+
+ df = df_orig.copy()
+ ser = df["a"] # populate the item_cache and keep ref
+ if using_copy_on_write:
+ with tm.raises_chained_assignment_error():
+ df["a"].fillna(1, inplace=True)
+ else:
+ # TODO(CoW-warn) ideally also warns on the default mode, but the ser' _cacher
+ # messes up the refcount
+ with tm.assert_cow_warning(warn_copy_on_write, match="A value"):
+ df["a"].fillna(1, inplace=True)
# TODO(CoW-warn) expand the cases
| Another follow-up on https://github.com/pandas-dev/pandas/pull/56400 | https://api.github.com/repos/pandas-dev/pandas/pulls/56402 | 2023-12-08T14:52:22Z | 2024-01-08T23:27:50Z | 2024-01-08T23:27:50Z | 2024-01-08T23:27:57Z |
TST: clean CoW chained assignment warning iloc test case | diff --git a/pandas/tests/copy_view/test_chained_assignment_deprecation.py b/pandas/tests/copy_view/test_chained_assignment_deprecation.py
index 25935d9489730..80e38380ed27c 100644
--- a/pandas/tests/copy_view/test_chained_assignment_deprecation.py
+++ b/pandas/tests/copy_view/test_chained_assignment_deprecation.py
@@ -35,36 +35,38 @@ def test_methods_iloc_warn(using_copy_on_write):
@pytest.mark.parametrize(
"func, args",
[
- ("replace", (1, 5)),
+ ("replace", (4, 5)),
("fillna", (1,)),
("interpolate", ()),
("bfill", ()),
("ffill", ()),
],
)
-def test_methods_iloc_getitem_item_cache(
- func, args, using_copy_on_write, warn_copy_on_write
-):
- df = DataFrame({"a": [1, 2, 3], "b": 1})
+def test_methods_iloc_getitem_item_cache(func, args, using_copy_on_write):
+ # ensure we don't incorrectly raise chained assignment warning because
+ # of the item cache / iloc not setting the item cache
+ df_orig = DataFrame({"a": [1, 2, 3], "b": 1})
+
+ df = df_orig.copy()
ser = df.iloc[:, 0]
- with tm.assert_cow_warning(warn_copy_on_write and func == "replace"):
- getattr(ser, func)(*args, inplace=True)
+ getattr(ser, func)(*args, inplace=True)
# parent that holds item_cache is dead, so don't increase ref count
+ df = df_orig.copy()
ser = df.copy()["a"]
getattr(ser, func)(*args, inplace=True)
- df = df.copy()
-
+ df = df_orig.copy()
df["a"] # populate the item_cache
ser = df.iloc[:, 0] # iloc creates a new object
- ser.fillna(0, inplace=True)
+ getattr(ser, func)(*args, inplace=True)
+ df = df_orig.copy()
df["a"] # populate the item_cache
ser = df["a"]
- ser.fillna(0, inplace=True)
+ getattr(ser, func)(*args, inplace=True)
- df = df.copy()
+ df = df_orig.copy()
df["a"] # populate the item_cache
if using_copy_on_write:
with tm.raises_chained_assignment_error():
| Cleaning up the test added in https://github.com/pandas-dev/pandas/pull/56166, now https://github.com/pandas-dev/pandas/pull/56297 and https://github.com/pandas-dev/pandas/pull/56289 are merged | https://api.github.com/repos/pandas-dev/pandas/pulls/56400 | 2023-12-08T11:07:48Z | 2023-12-08T13:55:30Z | 2023-12-08T13:55:30Z | 2023-12-08T14:49:39Z |
Backport PR #56393 on branch 2.1.x (DOC: Add release date for 2.1.4) | diff --git a/doc/source/whatsnew/v2.1.4.rst b/doc/source/whatsnew/v2.1.4.rst
index 13df35a1477d0..fdbe119268e50 100644
--- a/doc/source/whatsnew/v2.1.4.rst
+++ b/doc/source/whatsnew/v2.1.4.rst
@@ -1,6 +1,6 @@
.. _whatsnew_214:
-What's new in 2.1.4 (December ??, 2023)
+What's new in 2.1.4 (December 8, 2023)
---------------------------------------
These are the changes in pandas 2.1.4. See :ref:`release` for a full changelog
@@ -14,7 +14,6 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression when trying to read a pickled pandas :class:`DataFrame` from pandas 1.3 (:issue:`55137`)
--
.. ---------------------------------------------------------------------------
.. _whatsnew_214.bug_fixes:
@@ -37,14 +36,6 @@ Bug fixes
- Fixed bug in :meth:`Series.str.translate` losing object dtype when string option is set (:issue:`56152`)
-
-.. ---------------------------------------------------------------------------
-.. _whatsnew_214.other:
-
-Other
-~~~~~
--
--
-
.. ---------------------------------------------------------------------------
.. _whatsnew_214.contributors:
| Backport PR #56393: DOC: Add release date for 2.1.4 | https://api.github.com/repos/pandas-dev/pandas/pulls/56395 | 2023-12-08T03:10:34Z | 2023-12-08T04:14:20Z | 2023-12-08T04:14:20Z | 2023-12-08T04:14:20Z |
DOC: Add release date for 2.1.4 | diff --git a/doc/source/whatsnew/v2.1.4.rst b/doc/source/whatsnew/v2.1.4.rst
index 723c33280a679..6edd33614c63c 100644
--- a/doc/source/whatsnew/v2.1.4.rst
+++ b/doc/source/whatsnew/v2.1.4.rst
@@ -1,6 +1,6 @@
.. _whatsnew_214:
-What's new in 2.1.4 (December ??, 2023)
+What's new in 2.1.4 (December 8, 2023)
---------------------------------------
These are the changes in pandas 2.1.4. See :ref:`release` for a full changelog
@@ -14,7 +14,6 @@ including other versions of pandas.
Fixed regressions
~~~~~~~~~~~~~~~~~
- Fixed regression when trying to read a pickled pandas :class:`DataFrame` from pandas 1.3 (:issue:`55137`)
--
.. ---------------------------------------------------------------------------
.. _whatsnew_214.bug_fixes:
@@ -33,14 +32,6 @@ Bug fixes
- Fixed bug in :meth:`Series.str.split` and :meth:`Series.str.rsplit` when ``pat=None`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56271`)
- Fixed bug in :meth:`Series.str.translate` losing object dtype when string option is set (:issue:`56152`)
-.. ---------------------------------------------------------------------------
-.. _whatsnew_214.other:
-
-Other
-~~~~~
--
--
-
.. ---------------------------------------------------------------------------
.. _whatsnew_214.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/56393 | 2023-12-08T00:25:09Z | 2023-12-08T03:09:32Z | 2023-12-08T03:09:32Z | 2023-12-08T03:09:33Z |
CoW: Avoid warnings in stata code | diff --git a/pandas/io/stata.py b/pandas/io/stata.py
index 1eb8f531dc62a..218afb734d629 100644
--- a/pandas/io/stata.py
+++ b/pandas/io/stata.py
@@ -345,10 +345,7 @@ def convert_delta_safe(base, deltas, unit) -> Series:
has_bad_values = False
if bad_locs.any():
has_bad_values = True
- # reset cache to avoid SettingWithCopy checks (we own the DataFrame and the
- # `dates` Series is used to overwrite itself in the DataFramae)
- dates._reset_cacher()
- dates[bad_locs] = 1.0 # Replace with NaT
+ dates._values[bad_locs] = 1.0 # Replace with NaT
dates = dates.astype(np.int64)
if fmt.startswith(("%tc", "tc")): # Delta ms relative to base
@@ -465,11 +462,10 @@ def g(x: datetime) -> int:
bad_loc = isna(dates)
index = dates.index
if bad_loc.any():
- dates = Series(dates)
if lib.is_np_dtype(dates.dtype, "M"):
- dates[bad_loc] = to_datetime(stata_epoch)
+ dates._values[bad_loc] = to_datetime(stata_epoch)
else:
- dates[bad_loc] = stata_epoch
+ dates._values[bad_loc] = stata_epoch
if fmt in ["%tc", "tc"]:
d = parse_dates_safe(dates, delta=True)
@@ -599,9 +595,8 @@ def _cast_to_stata_types(data: DataFrame) -> DataFrame:
for col in data:
# Cast from unsupported types to supported types
is_nullable_int = isinstance(data[col].dtype, (IntegerDtype, BooleanDtype))
- orig = data[col]
# We need to find orig_missing before altering data below
- orig_missing = orig.isna()
+ orig_missing = data[col].isna()
if is_nullable_int:
missing_loc = data[col].isna()
if missing_loc.any():
@@ -1783,15 +1778,15 @@ def read(
for idx in valid_dtypes:
dtype = data.iloc[:, idx].dtype
if dtype not in (object_type, self._dtyplist[idx]):
- data.iloc[:, idx] = data.iloc[:, idx].astype(dtype)
+ data.isetitem(idx, data.iloc[:, idx].astype(dtype))
data = self._do_convert_missing(data, convert_missing)
if convert_dates:
for i, fmt in enumerate(self._fmtlist):
if any(fmt.startswith(date_fmt) for date_fmt in _date_formats):
- data.iloc[:, i] = _stata_elapsed_date_to_datetime_vec(
- data.iloc[:, i], fmt
+ data.isetitem(
+ i, _stata_elapsed_date_to_datetime_vec(data.iloc[:, i], fmt)
)
if convert_categoricals and self._format_version > 108:
@@ -1866,7 +1861,7 @@ def _do_convert_missing(self, data: DataFrame, convert_missing: bool) -> DataFra
replacements[i] = replacement
if replacements:
for idx, value in replacements.items():
- data.iloc[:, idx] = value
+ data.isetitem(idx, value)
return data
def _insert_strls(self, data: DataFrame) -> DataFrame:
@@ -1876,7 +1871,7 @@ def _insert_strls(self, data: DataFrame) -> DataFrame:
if typ != "Q":
continue
# Wrap v_o in a string to allow uint64 values as keys on 32bit OS
- data.iloc[:, i] = [self.GSO[str(k)] for k in data.iloc[:, i]]
+ data.isetitem(i, [self.GSO[str(k)] for k in data.iloc[:, i]])
return data
def _do_select_columns(self, data: DataFrame, columns: Sequence[str]) -> DataFrame:
diff --git a/pandas/tests/io/test_common.py b/pandas/tests/io/test_common.py
index 718f967f2f3d8..074033868635a 100644
--- a/pandas/tests/io/test_common.py
+++ b/pandas/tests/io/test_common.py
@@ -289,8 +289,6 @@ def test_read_expands_user_home_dir(
):
reader(path)
- # TODO(CoW-warn) avoid warnings in the stata reader code
- @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
@pytest.mark.parametrize(
"reader, module, path",
[
diff --git a/pandas/tests/io/test_stata.py b/pandas/tests/io/test_stata.py
index 19d81d50f5774..6e76c8fef6bc3 100644
--- a/pandas/tests/io/test_stata.py
+++ b/pandas/tests/io/test_stata.py
@@ -32,11 +32,6 @@
read_stata,
)
-# TODO(CoW-warn) avoid warnings in the stata reader code
-pytestmark = pytest.mark.filterwarnings(
- "ignore:Setting a value on a view:FutureWarning"
-)
-
@pytest.fixture
def mixed_frame():
@@ -140,7 +135,6 @@ def test_read_dta1(self, file, datapath):
tm.assert_frame_equal(parsed, expected)
- @pytest.mark.filterwarnings("always")
def test_read_dta2(self, datapath):
expected = DataFrame.from_records(
[
@@ -183,13 +177,11 @@ def test_read_dta2(self, datapath):
path2 = datapath("io", "data", "stata", "stata2_115.dta")
path3 = datapath("io", "data", "stata", "stata2_117.dta")
- # TODO(CoW-warn) avoid warnings in the stata reader code
- # once fixed -> remove `raise_on_extra_warnings=False` again
- with tm.assert_produces_warning(UserWarning, raise_on_extra_warnings=False):
+ with tm.assert_produces_warning(UserWarning):
parsed_114 = self.read_dta(path1)
- with tm.assert_produces_warning(UserWarning, raise_on_extra_warnings=False):
+ with tm.assert_produces_warning(UserWarning):
parsed_115 = self.read_dta(path2)
- with tm.assert_produces_warning(UserWarning, raise_on_extra_warnings=False):
+ with tm.assert_produces_warning(UserWarning):
parsed_117 = self.read_dta(path3)
# FIXME: don't leave commented-out
# 113 is buggy due to limits of date format support in Stata
| xref https://github.com/pandas-dev/pandas/issues/56019 | https://api.github.com/repos/pandas-dev/pandas/pulls/56392 | 2023-12-07T23:56:12Z | 2023-12-15T22:47:27Z | 2023-12-15T22:47:27Z | 2023-12-15T22:47:31Z |
CoW: Avoid warning in case of expansion | diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py
index a221d02b75bb2..cc88312d5b58f 100644
--- a/pandas/core/internals/managers.py
+++ b/pandas/core/internals/managers.py
@@ -1325,16 +1325,16 @@ def column_setitem(
This is a method on the BlockManager level, to avoid creating an
intermediate Series at the DataFrame level (`s = df[loc]; s[idx] = value`)
"""
+ needs_to_warn = False
if warn_copy_on_write() and not self._has_no_reference(loc):
if not isinstance(
self.blocks[self.blknos[loc]].values,
(ArrowExtensionArray, ArrowStringArray),
):
- warnings.warn(
- COW_WARNING_GENERAL_MSG,
- FutureWarning,
- stacklevel=find_stack_level(),
- )
+ # We might raise if we are in an expansion case, so defer
+ # warning till we actually updated
+ needs_to_warn = True
+
elif using_copy_on_write() and not self._has_no_reference(loc):
blkno = self.blknos[loc]
# Split blocks to only copy the column we want to modify
@@ -1358,6 +1358,13 @@ def column_setitem(
new_mgr = col_mgr.setitem((idx,), value)
self.iset(loc, new_mgr._block.values, inplace=True)
+ if needs_to_warn:
+ warnings.warn(
+ COW_WARNING_GENERAL_MSG,
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
+
def insert(self, loc: int, item: Hashable, value: ArrayLike, refs=None) -> None:
"""
Insert item at selected position.
diff --git a/pandas/tests/copy_view/test_indexing.py b/pandas/tests/copy_view/test_indexing.py
index 33b8ce218f029..6f3850ab64daa 100644
--- a/pandas/tests/copy_view/test_indexing.py
+++ b/pandas/tests/copy_view/test_indexing.py
@@ -1145,13 +1145,12 @@ def test_set_value_copy_only_necessary_column(
view = df[:]
if val == "a" and indexer[0] != slice(None):
- # TODO(CoW-warn) assert the FutureWarning for CoW is also raised
with tm.assert_produces_warning(
FutureWarning, match="Setting an item of incompatible dtype is deprecated"
):
indexer_func(df)[indexer] = val
else:
- with tm.assert_cow_warning(warn_copy_on_write):
+ with tm.assert_cow_warning(warn_copy_on_write and val == 100):
indexer_func(df)[indexer] = val
if using_copy_on_write:
diff --git a/pandas/tests/frame/indexing/test_indexing.py b/pandas/tests/frame/indexing/test_indexing.py
index dfb4a3092789a..40c6b8e180c5b 100644
--- a/pandas/tests/frame/indexing/test_indexing.py
+++ b/pandas/tests/frame/indexing/test_indexing.py
@@ -1397,9 +1397,7 @@ def test_iloc_setitem_enlarge_no_warning(self, warn_copy_on_write):
df = DataFrame(columns=["a", "b"])
expected = df.copy()
view = df[:]
- # TODO(CoW-warn) false positive: shouldn't warn in case of enlargement?
- with tm.assert_produces_warning(FutureWarning if warn_copy_on_write else None):
- df.iloc[:, 0] = np.array([1, 2], dtype=np.float64)
+ df.iloc[:, 0] = np.array([1, 2], dtype=np.float64)
tm.assert_frame_equal(view, expected)
def test_loc_internals_not_updated_correctly(self):
| xref https://github.com/pandas-dev/pandas/issues/56019 | https://api.github.com/repos/pandas-dev/pandas/pulls/56391 | 2023-12-07T23:27:49Z | 2023-12-08T09:38:38Z | 2023-12-08T09:38:38Z | 2023-12-08T09:40:19Z |
CoW: Remove filterwarnings and todo | diff --git a/pandas/tests/frame/test_subclass.py b/pandas/tests/frame/test_subclass.py
index f19e31002c877..ef78ae62cb4d6 100644
--- a/pandas/tests/frame/test_subclass.py
+++ b/pandas/tests/frame/test_subclass.py
@@ -524,8 +524,6 @@ def test_subclassed_wide_to_long(self):
tm.assert_frame_equal(long_frame, expected)
- # TODO(CoW-warn) should not need to warn
- @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning")
def test_subclassed_apply(self):
# GH 19822
| Looks like we fixed this already | https://api.github.com/repos/pandas-dev/pandas/pulls/56390 | 2023-12-07T23:22:45Z | 2023-12-08T08:32:46Z | 2023-12-08T08:32:46Z | 2023-12-08T08:56:15Z |
TYP: tighter typing in _maybe_convert_freq, _from_ordinal | diff --git a/pandas/_libs/tslibs/period.pyi b/pandas/_libs/tslibs/period.pyi
index df6ce675b07fc..22f3bdbe668de 100644
--- a/pandas/_libs/tslibs/period.pyi
+++ b/pandas/_libs/tslibs/period.pyi
@@ -87,7 +87,7 @@ class Period(PeriodMixin):
@classmethod
def _maybe_convert_freq(cls, freq) -> BaseOffset: ...
@classmethod
- def _from_ordinal(cls, ordinal: int, freq) -> Period: ...
+ def _from_ordinal(cls, ordinal: int, freq: BaseOffset) -> Period: ...
@classmethod
def now(cls, freq: Frequency) -> Period: ...
def strftime(self, fmt: str | None) -> str: ...
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 6b105f5974f9b..eeaf472c23b60 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -1756,21 +1756,12 @@ cdef class _Period(PeriodMixin):
@classmethod
def _maybe_convert_freq(cls, object freq) -> BaseOffset:
"""
- Internally we allow integer and tuple representations (for now) that
- are not recognized by to_offset, so we convert them here. Also, a
- Period's freq attribute must have `freq.n > 0`, which we check for here.
+ A Period's freq attribute must have `freq.n > 0`, which we check for here.
Returns
-------
DateOffset
"""
- if isinstance(freq, int):
- # We already have a dtype code
- dtype = PeriodDtypeBase(freq, 1)
- freq = dtype._freqstr
- elif isinstance(freq, PeriodDtypeBase):
- freq = freq._freqstr
-
freq = to_offset(freq, is_period=True)
if freq.n <= 0:
@@ -1780,7 +1771,7 @@ cdef class _Period(PeriodMixin):
return freq
@classmethod
- def _from_ordinal(cls, ordinal: int64_t, freq) -> "Period":
+ def _from_ordinal(cls, ordinal: int64_t, freq: BaseOffset) -> "Period":
"""
Fast creation from an ordinal and freq that are already validated!
"""
@@ -1988,8 +1979,10 @@ cdef class _Period(PeriodMixin):
return endpoint - np.timedelta64(1, "ns")
if freq is None:
- freq = self._dtype._get_to_timestamp_base()
- base = freq
+ freq_code = self._dtype._get_to_timestamp_base()
+ dtype = PeriodDtypeBase(freq_code, 1)
+ freq = dtype._freqstr
+ base = freq_code
else:
freq = self._maybe_convert_freq(freq)
base = freq._period_dtype_code
@@ -2836,7 +2829,8 @@ class Period(_Period):
FutureWarning,
stacklevel=find_stack_level(),
)
-
+ if ordinal == NPY_NAT:
+ return NaT
return cls._from_ordinal(ordinal, freq)
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 5f267a9f816e7..1ff3896eea798 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -35,6 +35,7 @@
)
from pandas._libs.tslibs.dtypes import (
FreqGroup,
+ PeriodDtypeBase,
freq_to_period_freqstr,
)
from pandas._libs.tslibs.fields import isleapyear_arr
@@ -652,8 +653,10 @@ def to_timestamp(self, freq=None, how: str = "start") -> DatetimeArray:
return (self + self.freq).to_timestamp(how="start") - adjust
if freq is None:
- freq = self._dtype._get_to_timestamp_base()
- base = freq
+ freq_code = self._dtype._get_to_timestamp_base()
+ dtype = PeriodDtypeBase(freq_code, 1)
+ freq = dtype._freqstr
+ base = freq_code
else:
freq = Period._maybe_convert_freq(freq)
base = freq._period_dtype_code
diff --git a/pandas/tests/scalar/period/test_period.py b/pandas/tests/scalar/period/test_period.py
index 3e91264fdb3b1..06d82ccd72b8c 100644
--- a/pandas/tests/scalar/period/test_period.py
+++ b/pandas/tests/scalar/period/test_period.py
@@ -418,7 +418,7 @@ def test_parse_week_str_roundstrip(self):
def test_period_from_ordinal(self):
p = Period("2011-01", freq="M")
- res = Period._from_ordinal(p.ordinal, freq="M")
+ res = Period._from_ordinal(p.ordinal, freq=p.freq)
assert p == res
assert isinstance(res, Period)
| - [ ] 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).
- [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.
| https://api.github.com/repos/pandas-dev/pandas/pulls/56389 | 2023-12-07T23:01:40Z | 2023-12-08T00:15:01Z | 2023-12-08T00:15:01Z | 2023-12-08T00:18:40Z |
Backport PR #56160 on branch 2.1.x (BUG: reset_index not preserving object dtype for string option) | diff --git a/doc/source/whatsnew/v2.1.4.rst b/doc/source/whatsnew/v2.1.4.rst
index 519c3a4ab2d32..13df35a1477d0 100644
--- a/doc/source/whatsnew/v2.1.4.rst
+++ b/doc/source/whatsnew/v2.1.4.rst
@@ -32,6 +32,7 @@ Bug fixes
- Fixed bug in :meth:`Index.insert` casting object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
- Fixed bug in :meth:`Series.__ne__` resulting in False for comparison between ``NA`` and string value for ``dtype="string[pyarrow_numpy]"`` (:issue:`56122`)
- Fixed bug in :meth:`Series.mode` not keeping object dtype when ``infer_string`` is set (:issue:`56183`)
+- Fixed bug in :meth:`Series.reset_index` not preserving object dtype when ``infer_string`` is set (:issue:`56160`)
- Fixed bug in :meth:`Series.str.split` and :meth:`Series.str.rsplit` when ``pat=None`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56271`)
- Fixed bug in :meth:`Series.str.translate` losing object dtype when string option is set (:issue:`56152`)
-
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 956515aeaf289..623fb333e30c5 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -1652,7 +1652,7 @@ def reset_index(
return new_ser.__finalize__(self, method="reset_index")
else:
return self._constructor(
- self._values.copy(), index=new_index, copy=False
+ self._values.copy(), index=new_index, copy=False, dtype=self.dtype
).__finalize__(self, method="reset_index")
elif inplace:
raise TypeError(
diff --git a/pandas/tests/series/methods/test_reset_index.py b/pandas/tests/series/methods/test_reset_index.py
index db36221d8f510..272c6663a8dbd 100644
--- a/pandas/tests/series/methods/test_reset_index.py
+++ b/pandas/tests/series/methods/test_reset_index.py
@@ -11,6 +11,7 @@
RangeIndex,
Series,
date_range,
+ option_context,
)
import pandas._testing as tm
@@ -155,6 +156,14 @@ def test_reset_index_inplace_and_drop_ignore_name(self):
expected = Series(range(2), name="old")
tm.assert_series_equal(ser, expected)
+ def test_reset_index_drop_infer_string(self):
+ # GH#56160
+ pytest.importorskip("pyarrow")
+ ser = Series(["a", "b", "c"], dtype=object)
+ with option_context("future.infer_string", True):
+ result = ser.reset_index(drop=True)
+ tm.assert_series_equal(result, ser)
+
@pytest.mark.parametrize(
"array, dtype",
| #56160 | https://api.github.com/repos/pandas-dev/pandas/pulls/56387 | 2023-12-07T21:36:14Z | 2023-12-07T22:49:21Z | 2023-12-07T22:49:21Z | 2023-12-07T22:49:25Z |
DEPR: Series[categorical].replace special-casing | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index c878fd2664dc4..5dd04986dcd3e 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -473,6 +473,7 @@ Other Deprecations
- Deprecated the ``kind`` keyword in :meth:`Series.resample` and :meth:`DataFrame.resample`, explicitly cast the object's ``index`` instead (:issue:`55895`)
- Deprecated the ``ordinal`` keyword in :class:`PeriodIndex`, use :meth:`PeriodIndex.from_ordinals` instead (:issue:`55960`)
- Deprecated the ``unit`` keyword in :class:`TimedeltaIndex` construction, use :func:`to_timedelta` instead (:issue:`55499`)
+- Deprecated the behavior of :meth:`DataFrame.replace` and :meth:`Series.replace` with :class:`CategoricalDtype`; in a future version replace will change the values while preserving the categories. To change the categories, use ``ser.cat.rename_categories`` instead (:issue:`55147`)
- Deprecated the behavior of :meth:`Series.value_counts` and :meth:`Index.value_counts` with object dtype; in a future version these will not perform dtype inference on the resulting :class:`Index`, do ``result.index = result.index.infer_objects()`` to retain the old behavior (:issue:`56161`)
- Deprecated the default of ``observed=False`` in :meth:`DataFrame.pivot_table`; will be ``True`` in a future version (:issue:`56236`)
- Deprecated the extension test classes ``BaseNoReduceTests``, ``BaseBooleanReduceTests``, and ``BaseNumericReduceTests``, use ``BaseReduceTests`` instead (:issue:`54663`)
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index eec833c600177..f0aabbb863a79 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -2626,6 +2626,8 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
def _replace(self, *, to_replace, value, inplace: bool = False):
from pandas import Index
+ orig_dtype = self.dtype
+
inplace = validate_bool_kwarg(inplace, "inplace")
cat = self if inplace else self.copy()
@@ -2656,6 +2658,17 @@ def _replace(self, *, to_replace, value, inplace: bool = False):
new_dtype = CategoricalDtype(new_categories, ordered=self.dtype.ordered)
NDArrayBacked.__init__(cat, new_codes, new_dtype)
+ if new_dtype != orig_dtype:
+ warnings.warn(
+ # GH#55147
+ "The behavior of Series.replace (and DataFrame.replace) with "
+ "CategoricalDtype is deprecated. In a future version, replace "
+ "will only be used for cases that preserve the categories. "
+ "To change the categories, use ser.cat.rename_categories "
+ "instead.",
+ FutureWarning,
+ stacklevel=find_stack_level(),
+ )
if not inplace:
return cat
diff --git a/pandas/tests/arrays/categorical/test_replace.py b/pandas/tests/arrays/categorical/test_replace.py
index 0611d04d36d10..3c677142846d7 100644
--- a/pandas/tests/arrays/categorical/test_replace.py
+++ b/pandas/tests/arrays/categorical/test_replace.py
@@ -31,6 +31,9 @@
([1, 2, "3"], "5", ["5", "5", 3], True),
],
)
+@pytest.mark.filterwarnings(
+ "ignore:.*with CategoricalDtype is deprecated:FutureWarning"
+)
def test_replace_categorical_series(to_replace, value, expected, flip_categories):
# GH 31720
@@ -60,7 +63,13 @@ def test_replace_categorical(to_replace, value, result, expected_error_msg):
# GH#26988
cat = Categorical(["a", "b"])
expected = Categorical(result)
- result = pd.Series(cat, copy=False).replace(to_replace, value)._values
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ warn = FutureWarning if expected_error_msg is not None else None
+ with tm.assert_produces_warning(warn, match=msg):
+ result = pd.Series(cat, copy=False).replace(to_replace, value)._values
tm.assert_categorical_equal(result, expected)
if to_replace == "b": # the "c" test is supposed to be unchanged
@@ -69,14 +78,20 @@ def test_replace_categorical(to_replace, value, result, expected_error_msg):
tm.assert_categorical_equal(cat, expected)
ser = pd.Series(cat, copy=False)
- ser.replace(to_replace, value, inplace=True)
+ with tm.assert_produces_warning(warn, match=msg):
+ ser.replace(to_replace, value, inplace=True)
tm.assert_categorical_equal(cat, expected)
def test_replace_categorical_ea_dtype():
# GH49404
cat = Categorical(pd.array(["a", "b"], dtype="string"))
- result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values
expected = Categorical(pd.array(["c", pd.NA], dtype="string"))
tm.assert_categorical_equal(result, expected)
@@ -85,7 +100,12 @@ def test_replace_maintain_ordering():
# GH51016
dtype = pd.CategoricalDtype([0, 1, 2], ordered=True)
ser = pd.Series([0, 1, 2], dtype=dtype)
- result = ser.replace(0, 2)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = ser.replace(0, 2)
expected_dtype = pd.CategoricalDtype([1, 2], ordered=True)
expected = pd.Series([2, 1, 2], dtype=expected_dtype)
tm.assert_series_equal(expected, result, check_category_order=True)
diff --git a/pandas/tests/copy_view/test_replace.py b/pandas/tests/copy_view/test_replace.py
index eb3b1a5ef68e8..99178918ffdbf 100644
--- a/pandas/tests/copy_view/test_replace.py
+++ b/pandas/tests/copy_view/test_replace.py
@@ -161,13 +161,19 @@ def test_replace_to_replace_wrong_dtype(using_copy_on_write):
def test_replace_list_categorical(using_copy_on_write):
df = DataFrame({"a": ["a", "b", "c"]}, dtype="category")
arr = get_array(df, "a")
- df.replace(["c"], value="a", inplace=True)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ df.replace(["c"], value="a", inplace=True)
assert np.shares_memory(arr.codes, get_array(df, "a").codes)
if using_copy_on_write:
assert df._mgr._has_no_reference(0)
df_orig = df.copy()
- df2 = df.replace(["b"], value="a")
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ df2 = df.replace(["b"], value="a")
assert not np.shares_memory(arr.codes, get_array(df2, "a").codes)
tm.assert_frame_equal(df, df_orig)
@@ -177,7 +183,12 @@ def test_replace_list_inplace_refs_categorical(using_copy_on_write):
df = DataFrame({"a": ["a", "b", "c"]}, dtype="category")
view = df[:]
df_orig = df.copy()
- df.replace(["c"], value="a", inplace=True)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ df.replace(["c"], value="a", inplace=True)
if using_copy_on_write:
assert not np.shares_memory(
get_array(view, "a").codes, get_array(df, "a").codes
@@ -236,7 +247,13 @@ def test_replace_categorical_inplace_reference(using_copy_on_write, val, to_repl
df_orig = df.copy()
arr_a = get_array(df, "a")
view = df[:]
- df.replace(to_replace=to_replace, value=val, inplace=True)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ warn = FutureWarning if val == 1.5 else None
+ with tm.assert_produces_warning(warn, match=msg):
+ df.replace(to_replace=to_replace, value=val, inplace=True)
if using_copy_on_write:
assert not np.shares_memory(get_array(df, "a").codes, arr_a.codes)
@@ -251,7 +268,13 @@ def test_replace_categorical_inplace_reference(using_copy_on_write, val, to_repl
def test_replace_categorical_inplace(using_copy_on_write, val):
df = DataFrame({"a": Categorical([1, 2, 3])})
arr_a = get_array(df, "a")
- df.replace(to_replace=1, value=val, inplace=True)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ warn = FutureWarning if val == 1.5 else None
+ with tm.assert_produces_warning(warn, match=msg):
+ df.replace(to_replace=1, value=val, inplace=True)
assert np.shares_memory(get_array(df, "a").codes, arr_a.codes)
if using_copy_on_write:
@@ -265,7 +288,13 @@ def test_replace_categorical_inplace(using_copy_on_write, val):
def test_replace_categorical(using_copy_on_write, val):
df = DataFrame({"a": Categorical([1, 2, 3])})
df_orig = df.copy()
- df2 = df.replace(to_replace=1, value=val)
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ warn = FutureWarning if val == 1.5 else None
+ with tm.assert_produces_warning(warn, match=msg):
+ df2 = df.replace(to_replace=1, value=val)
if using_copy_on_write:
assert df._mgr._has_no_reference(0)
diff --git a/pandas/tests/frame/methods/test_replace.py b/pandas/tests/frame/methods/test_replace.py
index 13e2c1a249ac2..53c45a5f4b5c6 100644
--- a/pandas/tests/frame/methods/test_replace.py
+++ b/pandas/tests/frame/methods/test_replace.py
@@ -1279,7 +1279,9 @@ def test_categorical_replace_with_dict(self, replace_dict, final_data):
b = pd.Categorical(final_data[:, 1], categories=ex_cat)
expected = DataFrame({"a": a, "b": b})
- result = df.replace(replace_dict, 3)
+ msg2 = "with CategoricalDtype is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg2):
+ result = df.replace(replace_dict, 3)
tm.assert_frame_equal(result, expected)
msg = (
r"Attributes of DataFrame.iloc\[:, 0\] \(column name=\"a\"\) are "
@@ -1288,7 +1290,8 @@ def test_categorical_replace_with_dict(self, replace_dict, final_data):
with pytest.raises(AssertionError, match=msg):
# ensure non-inplace call does not affect original
tm.assert_frame_equal(df, expected)
- return_value = df.replace(replace_dict, 3, inplace=True)
+ with tm.assert_produces_warning(FutureWarning, match=msg2):
+ return_value = df.replace(replace_dict, 3, inplace=True)
assert return_value is None
tm.assert_frame_equal(df, expected)
@@ -1438,9 +1441,14 @@ def test_replace_value_category_type(self):
)
# replace values in input dataframe
- input_df = input_df.replace("d", "z")
- input_df = input_df.replace("obj1", "obj9")
- result = input_df.replace("cat2", "catX")
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ input_df = input_df.replace("d", "z")
+ input_df = input_df.replace("obj1", "obj9")
+ result = input_df.replace("cat2", "catX")
tm.assert_frame_equal(result, expected)
@@ -1466,7 +1474,12 @@ def test_replace_dict_category_type(self):
)
# replace values in input dataframe using a dict
- result = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
+ msg = (
+ r"The behavior of Series\.replace \(and DataFrame.replace\) "
+ "with CategoricalDtype"
+ )
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"})
tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/groupby/test_groupby_dropna.py b/pandas/tests/groupby/test_groupby_dropna.py
index b40c8f45b6d19..4f54621b19b64 100644
--- a/pandas/tests/groupby/test_groupby_dropna.py
+++ b/pandas/tests/groupby/test_groupby_dropna.py
@@ -546,9 +546,9 @@ def test_categorical_reducers(reduction_func, observed, sort, as_index, index_ki
gb_filled = df_filled.groupby(keys, observed=observed, sort=sort, as_index=True)
expected = getattr(gb_filled, reduction_func)(*args_filled).reset_index()
- expected["x"] = expected["x"].replace(4, None)
+ expected["x"] = expected["x"].cat.remove_categories([4])
if index_kind == "multi":
- expected["x2"] = expected["x2"].replace(4, None)
+ expected["x2"] = expected["x2"].cat.remove_categories([4])
if as_index:
if index_kind == "multi":
expected = expected.set_index(["x", "x2"])
diff --git a/pandas/tests/io/pytables/test_file_handling.py b/pandas/tests/io/pytables/test_file_handling.py
index e292bc7fe251e..d93de16816725 100644
--- a/pandas/tests/io/pytables/test_file_handling.py
+++ b/pandas/tests/io/pytables/test_file_handling.py
@@ -341,7 +341,15 @@ def test_latin_encoding(tmp_path, setup_path, dtype, val):
ser.to_hdf(store, key=key, format="table", encoding=enc, nan_rep=nan_rep)
retr = read_hdf(store, key)
- s_nan = ser.replace(nan_rep, np.nan)
+ # TODO:(3.0): once Categorical replace deprecation is enforced,
+ # we may be able to re-simplify the construction of s_nan
+ if dtype == "category":
+ if nan_rep in ser.cat.categories:
+ s_nan = ser.cat.remove_categories([nan_rep])
+ else:
+ s_nan = ser
+ else:
+ s_nan = ser.replace(nan_rep, np.nan)
tm.assert_series_equal(s_nan, retr)
diff --git a/pandas/tests/series/methods/test_replace.py b/pandas/tests/series/methods/test_replace.py
index 477f36bdf4214..4330153c186ca 100644
--- a/pandas/tests/series/methods/test_replace.py
+++ b/pandas/tests/series/methods/test_replace.py
@@ -403,6 +403,7 @@ def test_replace_categorical(self, categorical, numeric):
# GH 24971, GH#23305
ser = pd.Series(categorical)
msg = "Downcasting behavior in `replace`"
+ msg = "with CategoricalDtype is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = ser.replace({"A": 1, "B": 2})
expected = pd.Series(numeric).astype("category")
@@ -418,7 +419,9 @@ def test_replace_categorical(self, categorical, numeric):
def test_replace_categorical_inplace(self, data, data_exp):
# GH 53358
result = pd.Series(data, dtype="category")
- result.replace(to_replace="a", value="b", inplace=True)
+ msg = "with CategoricalDtype is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result.replace(to_replace="a", value="b", inplace=True)
expected = pd.Series(data_exp, dtype="category")
tm.assert_series_equal(result, expected)
@@ -434,16 +437,22 @@ def test_replace_categorical_single(self):
expected = expected.cat.remove_unused_categories()
assert c[2] != "foo"
- result = c.replace(c[2], "foo")
+ msg = "with CategoricalDtype is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ result = c.replace(c[2], "foo")
tm.assert_series_equal(expected, result)
assert c[2] != "foo" # ensure non-inplace call does not alter original
- return_value = c.replace(c[2], "foo", inplace=True)
+ msg = "with CategoricalDtype is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ return_value = c.replace(c[2], "foo", inplace=True)
assert return_value is None
tm.assert_series_equal(expected, c)
first_value = c[0]
- return_value = c.replace(c[1], c[0], inplace=True)
+ msg = "with CategoricalDtype is deprecated"
+ with tm.assert_produces_warning(FutureWarning, match=msg):
+ return_value = c.replace(c[1], c[0], inplace=True)
assert return_value is None
assert c[0] == c[1] == first_value # test replacing with existing value
| - [x] closes #55147 (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/56385 | 2023-12-07T20:07:43Z | 2023-12-09T18:27:41Z | 2023-12-09T18:27:41Z | 2024-01-30T00:57:22Z |
TST: Added a test for groupby.prod | diff --git a/pandas/tests/groupby/test_reductions.py b/pandas/tests/groupby/test_reductions.py
index 3e78e728f5ea9..425079f943aba 100644
--- a/pandas/tests/groupby/test_reductions.py
+++ b/pandas/tests/groupby/test_reductions.py
@@ -1057,3 +1057,27 @@ def test_regression_allowlist_methods(op, axis, skipna, sort):
if sort:
expected = expected.sort_index(axis=axis)
tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_prod_with_int64_dtype():
+ # GH#46573
+ data = [
+ [1, 11],
+ [1, 41],
+ [1, 17],
+ [1, 37],
+ [1, 7],
+ [1, 29],
+ [1, 31],
+ [1, 2],
+ [1, 3],
+ [1, 43],
+ [1, 5],
+ [1, 47],
+ [1, 19],
+ [1, 88],
+ ]
+ df = DataFrame(data, columns=["A", "B"], dtype="int64")
+ result = df.groupby(["A"]).prod().reset_index()
+ expected = DataFrame({"A": [1], "B": [180970905912331920]}, dtype="int64")
+ tm.assert_frame_equal(result, expected)
| - [x] Closes #46573
- The bug related to issue #46573 has been fixed in the current version.
- Added a test related to this issue. The test is designed to check if `groupby.prod` returns the same results as `prod`.
| https://api.github.com/repos/pandas-dev/pandas/pulls/56384 | 2023-12-07T19:51:25Z | 2023-12-09T13:34:47Z | 2023-12-09T13:34:47Z | 2023-12-09T13:34:56Z |
Backport PR #56184 on branch 2.1.x (BUG: mode not preserving object dtype for string option) | diff --git a/doc/source/whatsnew/v2.1.4.rst b/doc/source/whatsnew/v2.1.4.rst
index 26fd23d80208c..519c3a4ab2d32 100644
--- a/doc/source/whatsnew/v2.1.4.rst
+++ b/doc/source/whatsnew/v2.1.4.rst
@@ -31,6 +31,7 @@ Bug fixes
- Fixed bug in :meth:`DataFrame.to_hdf` raising when columns have ``StringDtype`` (:issue:`55088`)
- Fixed bug in :meth:`Index.insert` casting object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
- Fixed bug in :meth:`Series.__ne__` resulting in False for comparison between ``NA`` and string value for ``dtype="string[pyarrow_numpy]"`` (:issue:`56122`)
+- Fixed bug in :meth:`Series.mode` not keeping object dtype when ``infer_string`` is set (:issue:`56183`)
- Fixed bug in :meth:`Series.str.split` and :meth:`Series.str.rsplit` when ``pat=None`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56271`)
- Fixed bug in :meth:`Series.str.translate` losing object dtype when string option is set (:issue:`56152`)
-
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 7b22d89bfe22d..956515aeaf289 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2218,7 +2218,11 @@ def mode(self, dropna: bool = True) -> Series:
# Ensure index is type stable (should always use int index)
return self._constructor(
- res_values, index=range(len(res_values)), name=self.name, copy=False
+ res_values,
+ index=range(len(res_values)),
+ name=self.name,
+ copy=False,
+ dtype=self.dtype,
).__finalize__(self, method="mode")
def unique(self) -> ArrayLike: # pylint: disable=useless-parent-delegation
diff --git a/pandas/tests/series/test_reductions.py b/pandas/tests/series/test_reductions.py
index 1e1ac100b21bf..75c7359fa81c0 100644
--- a/pandas/tests/series/test_reductions.py
+++ b/pandas/tests/series/test_reductions.py
@@ -29,6 +29,16 @@ def test_mode_extension_dtype(as_period):
tm.assert_series_equal(res, ser)
+def test_mode_infer_string():
+ # GH#56183
+ pytest.importorskip("pyarrow")
+ ser = Series(["a", "b"], dtype=object)
+ with pd.option_context("future.infer_string", True):
+ result = ser.mode()
+ expected = Series(["a", "b"], dtype=object)
+ tm.assert_series_equal(result, expected)
+
+
def test_reductions_td64_with_nat():
# GH#8617
ser = Series([0, pd.NaT], dtype="m8[ns]")
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index cb703d3439d44..c9aa410fed946 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -2102,7 +2102,7 @@ def test_timedelta_mode(self):
tm.assert_series_equal(ser.mode(), exp)
def test_mixed_dtype(self):
- exp = Series(["foo"])
+ exp = Series(["foo"], dtype=object)
ser = Series([1, "foo", "foo"])
tm.assert_numpy_array_equal(algos.mode(ser.values), exp.values)
tm.assert_series_equal(ser.mode(), exp)
| #56183/#56184 | https://api.github.com/repos/pandas-dev/pandas/pulls/56383 | 2023-12-07T19:41:52Z | 2023-12-07T22:48:40Z | 2023-12-07T22:48:40Z | 2023-12-07T22:49:33Z |
Backport PR #56123 on branch 2.1.x (BUG: ne comparison returns False for NA and other value) | diff --git a/doc/source/whatsnew/v2.1.4.rst b/doc/source/whatsnew/v2.1.4.rst
index 6cd44d954a708..26fd23d80208c 100644
--- a/doc/source/whatsnew/v2.1.4.rst
+++ b/doc/source/whatsnew/v2.1.4.rst
@@ -30,6 +30,7 @@ Bug fixes
- Fixed bug in :meth:`DataFrame.__setitem__` casting :class:`Index` with object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
- Fixed bug in :meth:`DataFrame.to_hdf` raising when columns have ``StringDtype`` (:issue:`55088`)
- Fixed bug in :meth:`Index.insert` casting object-dtype to PyArrow backed strings when ``infer_string`` option is set (:issue:`55638`)
+- Fixed bug in :meth:`Series.__ne__` resulting in False for comparison between ``NA`` and string value for ``dtype="string[pyarrow_numpy]"`` (:issue:`56122`)
- Fixed bug in :meth:`Series.str.split` and :meth:`Series.str.rsplit` when ``pat=None`` for :class:`ArrowDtype` with ``pyarrow.string`` (:issue:`56271`)
- Fixed bug in :meth:`Series.str.translate` losing object dtype when string option is set (:issue:`56152`)
-
diff --git a/pandas/core/arrays/string_arrow.py b/pandas/core/arrays/string_arrow.py
index 3b474774b7873..803e032b452cf 100644
--- a/pandas/core/arrays/string_arrow.py
+++ b/pandas/core/arrays/string_arrow.py
@@ -1,6 +1,7 @@
from __future__ import annotations
from functools import partial
+import operator
import re
from typing import (
TYPE_CHECKING,
@@ -600,7 +601,10 @@ def _str_find(self, sub: str, start: int = 0, end: int | None = None):
def _cmp_method(self, other, op):
result = super()._cmp_method(other, op)
- return result.to_numpy(np.bool_, na_value=False)
+ if op == operator.ne:
+ return result.to_numpy(np.bool_, na_value=True)
+ else:
+ return result.to_numpy(np.bool_, na_value=False)
def value_counts(self, dropna: bool = True):
from pandas import Series
diff --git a/pandas/tests/arithmetic/test_object.py b/pandas/tests/arithmetic/test_object.py
index 5ffbf1a38e845..76e38500df5a2 100644
--- a/pandas/tests/arithmetic/test_object.py
+++ b/pandas/tests/arithmetic/test_object.py
@@ -8,10 +8,13 @@
import numpy as np
import pytest
+import pandas.util._test_decorators as td
+
import pandas as pd
from pandas import (
Series,
Timestamp,
+ option_context,
)
import pandas._testing as tm
from pandas.core import ops
@@ -31,20 +34,24 @@ def test_comparison_object_numeric_nas(self, comparison_op):
expected = func(ser.astype(float), shifted.astype(float))
tm.assert_series_equal(result, expected)
- def test_object_comparisons(self):
- ser = Series(["a", "b", np.nan, "c", "a"])
+ @pytest.mark.parametrize(
+ "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))]
+ )
+ def test_object_comparisons(self, infer_string):
+ with option_context("future.infer_string", infer_string):
+ ser = Series(["a", "b", np.nan, "c", "a"])
- result = ser == "a"
- expected = Series([True, False, False, False, True])
- tm.assert_series_equal(result, expected)
+ result = ser == "a"
+ expected = Series([True, False, False, False, True])
+ tm.assert_series_equal(result, expected)
- result = ser < "a"
- expected = Series([False, False, False, False, False])
- tm.assert_series_equal(result, expected)
+ result = ser < "a"
+ expected = Series([False, False, False, False, False])
+ tm.assert_series_equal(result, expected)
- result = ser != "a"
- expected = -(ser == "a")
- tm.assert_series_equal(result, expected)
+ result = ser != "a"
+ expected = -(ser == "a")
+ tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("dtype", [None, object])
def test_more_na_comparisons(self, dtype):
diff --git a/pandas/tests/arrays/string_/test_string.py b/pandas/tests/arrays/string_/test_string.py
index 68b2b01ddcaf3..b9c59018fa797 100644
--- a/pandas/tests/arrays/string_/test_string.py
+++ b/pandas/tests/arrays/string_/test_string.py
@@ -2,6 +2,8 @@
This module tests the functionality of StringArray and ArrowStringArray.
Tests for the str accessors are in pandas/tests/strings/test_string_array.py
"""
+import operator
+
import numpy as np
import pytest
@@ -221,7 +223,10 @@ def test_comparison_methods_scalar(comparison_op, dtype):
result = getattr(a, op_name)(other)
if dtype.storage == "pyarrow_numpy":
expected = np.array([getattr(item, op_name)(other) for item in a])
- expected[1] = False
+ if comparison_op == operator.ne:
+ expected[1] = True
+ else:
+ expected[1] = False
tm.assert_numpy_array_equal(result, expected.astype(np.bool_))
else:
expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
@@ -236,7 +241,10 @@ def test_comparison_methods_scalar_pd_na(comparison_op, dtype):
result = getattr(a, op_name)(pd.NA)
if dtype.storage == "pyarrow_numpy":
- expected = np.array([False, False, False])
+ if operator.ne == comparison_op:
+ expected = np.array([True, True, True])
+ else:
+ expected = np.array([False, False, False])
tm.assert_numpy_array_equal(result, expected)
else:
expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean"
@@ -262,7 +270,7 @@ def test_comparison_methods_scalar_not_string(comparison_op, dtype):
if dtype.storage == "pyarrow_numpy":
expected_data = {
"__eq__": [False, False, False],
- "__ne__": [True, False, True],
+ "__ne__": [True, True, True],
}[op_name]
expected = np.array(expected_data)
tm.assert_numpy_array_equal(result, expected)
@@ -282,12 +290,18 @@ def test_comparison_methods_array(comparison_op, dtype):
other = [None, None, "c"]
result = getattr(a, op_name)(other)
if dtype.storage == "pyarrow_numpy":
- expected = np.array([False, False, False])
- expected[-1] = getattr(other[-1], op_name)(a[-1])
+ if operator.ne == comparison_op:
+ expected = np.array([True, True, False])
+ else:
+ expected = np.array([False, False, False])
+ expected[-1] = getattr(other[-1], op_name)(a[-1])
tm.assert_numpy_array_equal(result, expected)
result = getattr(a, op_name)(pd.NA)
- expected = np.array([False, False, False])
+ if operator.ne == comparison_op:
+ expected = np.array([True, True, True])
+ else:
+ expected = np.array([False, False, False])
tm.assert_numpy_array_equal(result, expected)
else:
| #56123 | https://api.github.com/repos/pandas-dev/pandas/pulls/56382 | 2023-12-07T19:40:45Z | 2023-12-07T21:19:16Z | 2023-12-07T21:19:16Z | 2023-12-08T22:37:11Z |
CoW: Warn for transform inplace modification | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 1630c2c31920d..179279cc08bab 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -4895,7 +4895,6 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
inplace = validate_bool_kwarg(inplace, "inplace")
kwargs["level"] = kwargs.pop("level", 0) + 1
- # TODO(CoW) those index/column resolvers create unnecessary refs to `self`
index_resolvers = self._get_index_resolvers()
column_resolvers = self._get_cleaned_column_resolvers()
resolvers = column_resolvers, index_resolvers
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 464e066b4e86a..de4b3e750a7e8 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -4740,7 +4740,11 @@ def transform(
) -> DataFrame | Series:
# Validate axis argument
self._get_axis_number(axis)
- ser = self.copy(deep=False) if using_copy_on_write() else self
+ ser = (
+ self.copy(deep=False)
+ if using_copy_on_write() or warn_copy_on_write()
+ else self
+ )
result = SeriesApply(ser, func=func, args=args, kwargs=kwargs).transform()
return result
diff --git a/pandas/tests/copy_view/test_methods.py b/pandas/tests/copy_view/test_methods.py
index 1e3c95dbc1ca3..558b483933f25 100644
--- a/pandas/tests/copy_view/test_methods.py
+++ b/pandas/tests/copy_view/test_methods.py
@@ -1938,8 +1938,8 @@ def func(ser):
ser.iloc[0] = 100
return ser
- # TODO(CoW-warn) should warn?
- ser.transform(func)
+ with tm.assert_cow_warning(warn_copy_on_write):
+ ser.transform(func)
if using_copy_on_write:
tm.assert_series_equal(ser, ser_orig)
| xref https://github.com/pandas-dev/pandas/issues/56019 | https://api.github.com/repos/pandas-dev/pandas/pulls/56381 | 2023-12-07T19:20:30Z | 2023-12-07T21:15:39Z | 2023-12-07T21:15:39Z | 2023-12-07T21:18:14Z |
Backport PR #55703 on branch 2.1.x (Use DeprecationWarning instead of FutureWarning for is_.._dtype deprecations) | diff --git a/pandas/core/dtypes/common.py b/pandas/core/dtypes/common.py
index 3db36fc50e343..143dc46359af5 100644
--- a/pandas/core/dtypes/common.py
+++ b/pandas/core/dtypes/common.py
@@ -211,8 +211,8 @@ def is_sparse(arr) -> bool:
warnings.warn(
"is_sparse is deprecated and will be removed in a future "
"version. Check `isinstance(dtype, pd.SparseDtype)` instead.",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
dtype = getattr(arr, "dtype", arr)
@@ -329,8 +329,8 @@ def is_datetime64tz_dtype(arr_or_dtype) -> bool:
warnings.warn(
"is_datetime64tz_dtype is deprecated and will be removed in a future "
"version. Check `isinstance(dtype, pd.DatetimeTZDtype)` instead.",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
if isinstance(arr_or_dtype, DatetimeTZDtype):
# GH#33400 fastpath for dtype object
@@ -408,8 +408,8 @@ def is_period_dtype(arr_or_dtype) -> bool:
warnings.warn(
"is_period_dtype is deprecated and will be removed in a future version. "
"Use `isinstance(dtype, pd.PeriodDtype)` instead",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
if isinstance(arr_or_dtype, ExtensionDtype):
# GH#33400 fastpath for dtype object
@@ -454,8 +454,8 @@ def is_interval_dtype(arr_or_dtype) -> bool:
warnings.warn(
"is_interval_dtype is deprecated and will be removed in a future version. "
"Use `isinstance(dtype, pd.IntervalDtype)` instead",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
if isinstance(arr_or_dtype, ExtensionDtype):
# GH#33400 fastpath for dtype object
@@ -498,9 +498,9 @@ def is_categorical_dtype(arr_or_dtype) -> bool:
# GH#52527
warnings.warn(
"is_categorical_dtype is deprecated and will be removed in a future "
- "version. Use isinstance(dtype, CategoricalDtype) instead",
- FutureWarning,
- stacklevel=find_stack_level(),
+ "version. Use isinstance(dtype, pd.CategoricalDtype) instead",
+ DeprecationWarning,
+ stacklevel=2,
)
if isinstance(arr_or_dtype, ExtensionDtype):
# GH#33400 fastpath for dtype object
@@ -838,8 +838,8 @@ def is_int64_dtype(arr_or_dtype) -> bool:
warnings.warn(
"is_int64_dtype is deprecated and will be removed in a future "
"version. Use dtype == np.int64 instead.",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
return _is_dtype_type(arr_or_dtype, classes(np.int64))
@@ -1241,8 +1241,8 @@ def is_bool_dtype(arr_or_dtype) -> bool:
"The behavior of is_bool_dtype with an object-dtype Index "
"of bool objects is deprecated. In a future version, "
"this will return False. Cast the Index to a bool dtype instead.",
- FutureWarning,
- stacklevel=find_stack_level(),
+ DeprecationWarning,
+ stacklevel=2,
)
return True
return False
diff --git a/pandas/tests/dtypes/test_common.py b/pandas/tests/dtypes/test_common.py
index 165bf61302145..9419cb693dd70 100644
--- a/pandas/tests/dtypes/test_common.py
+++ b/pandas/tests/dtypes/test_common.py
@@ -164,7 +164,9 @@ def get_is_dtype_funcs():
return [getattr(com, fname) for fname in fnames]
-@pytest.mark.filterwarnings("ignore:is_categorical_dtype is deprecated:FutureWarning")
+@pytest.mark.filterwarnings(
+ "ignore:is_categorical_dtype is deprecated:DeprecationWarning"
+)
@pytest.mark.parametrize("func", get_is_dtype_funcs(), ids=lambda x: x.__name__)
def test_get_dtype_error_catch(func):
# see gh-15941
@@ -180,7 +182,7 @@ def test_get_dtype_error_catch(func):
or func is com.is_categorical_dtype
or func is com.is_period_dtype
):
- warn = FutureWarning
+ warn = DeprecationWarning
with tm.assert_produces_warning(warn, match=msg):
assert not func(None)
@@ -200,7 +202,7 @@ def test_is_object():
)
def test_is_sparse(check_scipy):
msg = "is_sparse is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert com.is_sparse(SparseArray([1, 2, 3]))
assert not com.is_sparse(np.array([1, 2, 3]))
@@ -230,7 +232,7 @@ def test_is_datetime64_dtype():
def test_is_datetime64tz_dtype():
msg = "is_datetime64tz_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_datetime64tz_dtype(object)
assert not com.is_datetime64tz_dtype([1, 2, 3])
assert not com.is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3]))
@@ -246,7 +248,7 @@ def kind(self) -> str:
not_tz_dtype = NotTZDtype()
msg = "is_datetime64tz_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_datetime64tz_dtype(not_tz_dtype)
assert not com.needs_i8_conversion(not_tz_dtype)
@@ -268,7 +270,7 @@ def test_is_timedelta64_dtype():
def test_is_period_dtype():
msg = "is_period_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_period_dtype(object)
assert not com.is_period_dtype([1, 2, 3])
assert not com.is_period_dtype(pd.Period("2017-01-01"))
@@ -279,7 +281,7 @@ def test_is_period_dtype():
def test_is_interval_dtype():
msg = "is_interval_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_interval_dtype(object)
assert not com.is_interval_dtype([1, 2, 3])
@@ -292,7 +294,7 @@ def test_is_interval_dtype():
def test_is_categorical_dtype():
msg = "is_categorical_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_categorical_dtype(object)
assert not com.is_categorical_dtype([1, 2, 3])
@@ -433,7 +435,7 @@ def test_is_not_unsigned_integer_dtype(dtype):
)
def test_is_int64_dtype(dtype):
msg = "is_int64_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert com.is_int64_dtype(dtype)
@@ -471,7 +473,7 @@ def test_type_comparison_with_signed_int_ea_dtype_and_signed_int_numpy_dtype(
)
def test_is_not_int64_dtype(dtype):
msg = "is_int64_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not com.is_int64_dtype(dtype)
diff --git a/pandas/tests/dtypes/test_dtypes.py b/pandas/tests/dtypes/test_dtypes.py
index f57093c29b733..d5286d3fadcd0 100644
--- a/pandas/tests/dtypes/test_dtypes.py
+++ b/pandas/tests/dtypes/test_dtypes.py
@@ -166,7 +166,7 @@ def test_is_dtype(self, dtype):
def test_basic(self, dtype):
msg = "is_categorical_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_categorical_dtype(dtype)
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
@@ -291,7 +291,7 @@ def test_subclass(self):
def test_compat(self, dtype):
msg = "is_datetime64tz_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_datetime64tz_dtype(dtype)
assert is_datetime64tz_dtype("datetime64[ns, US/Eastern]")
assert is_datetime64_any_dtype(dtype)
@@ -352,14 +352,14 @@ def test_equality(self, dtype):
def test_basic(self, dtype):
msg = "is_datetime64tz_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_datetime64tz_dtype(dtype)
dr = date_range("20130101", periods=3, tz="US/Eastern")
s = Series(dr, name="A")
# dtypes
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_datetime64tz_dtype(s.dtype)
assert is_datetime64tz_dtype(s)
assert not is_datetime64tz_dtype(np.dtype("float64"))
@@ -530,7 +530,7 @@ def test_equality(self, dtype):
def test_basic(self, dtype):
msg = "is_period_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_period_dtype(dtype)
pidx = pd.period_range("2013-01-01 09:00", periods=5, freq="H")
@@ -618,7 +618,7 @@ def test_construction(self, subtype):
i = IntervalDtype(subtype, closed="right")
assert i.subtype == np.dtype("int64")
msg = "is_interval_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_interval_dtype(i)
@pytest.mark.parametrize(
@@ -641,7 +641,7 @@ def test_construction_generic(self, subtype):
i = IntervalDtype(subtype)
assert i.subtype is None
msg = "is_interval_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_interval_dtype(i)
@pytest.mark.parametrize(
@@ -814,7 +814,7 @@ def test_name_repr_generic(self, subtype):
def test_basic(self, dtype):
msg = "is_interval_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_interval_dtype(dtype)
ii = IntervalIndex.from_breaks(range(3))
@@ -829,7 +829,7 @@ def test_basic(self, dtype):
def test_basic_dtype(self):
msg = "is_interval_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_interval_dtype("interval[int64, both]")
assert is_interval_dtype(IntervalIndex.from_tuples([(0, 1)]))
assert is_interval_dtype(IntervalIndex.from_breaks(np.arange(4)))
@@ -1158,7 +1158,7 @@ def test_is_dtype_no_warning(check):
or check is is_datetime64tz_dtype
or check is is_period_dtype
):
- warn = FutureWarning
+ warn = DeprecationWarning
with tm.assert_produces_warning(warn, match=msg):
check(data)
diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
index df7c787d2b9bf..2198ed0c6af78 100644
--- a/pandas/tests/dtypes/test_inference.py
+++ b/pandas/tests/dtypes/test_inference.py
@@ -1821,7 +1821,7 @@ def test_is_datetime_dtypes(self):
assert is_datetime64_any_dtype(ts)
assert is_datetime64_any_dtype(tsa)
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert not is_datetime64tz_dtype("datetime64")
assert not is_datetime64tz_dtype("datetime64[ns]")
assert not is_datetime64tz_dtype(ts)
@@ -1833,7 +1833,7 @@ def test_is_datetime_dtypes_with_tz(self, tz):
assert not is_datetime64_dtype(dtype)
msg = "is_datetime64tz_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
+ with tm.assert_produces_warning(DeprecationWarning, match=msg):
assert is_datetime64tz_dtype(dtype)
assert is_datetime64_ns_dtype(dtype)
assert is_datetime64_any_dtype(dtype)
diff --git a/pandas/tests/series/test_constructors.py b/pandas/tests/series/test_constructors.py
index d585a56665901..fb9e465238d62 100644
--- a/pandas/tests/series/test_constructors.py
+++ b/pandas/tests/series/test_constructors.py
@@ -17,7 +17,6 @@
from pandas.errors import IntCastingNaNError
import pandas.util._test_decorators as td
-from pandas.core.dtypes.common import is_categorical_dtype
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
@@ -393,18 +392,12 @@ def test_constructor_categorical(self):
def test_construct_from_categorical_with_dtype(self):
# GH12574
- cat = Series(Categorical([1, 2, 3]), dtype="category")
- msg = "is_categorical_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- assert is_categorical_dtype(cat)
- assert is_categorical_dtype(cat.dtype)
+ ser = Series(Categorical([1, 2, 3]), dtype="category")
+ assert isinstance(ser.dtype, CategoricalDtype)
def test_construct_intlist_values_category_dtype(self):
ser = Series([1, 2, 3], dtype="category")
- msg = "is_categorical_dtype is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- assert is_categorical_dtype(ser)
- assert is_categorical_dtype(ser.dtype)
+ assert isinstance(ser.dtype, CategoricalDtype)
def test_constructor_categorical_with_coercion(self):
factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
| Backport of https://github.com/pandas-dev/pandas/pull/55703 | https://api.github.com/repos/pandas-dev/pandas/pulls/56377 | 2023-12-07T13:15:49Z | 2023-12-07T16:52:03Z | 2023-12-07T16:52:03Z | 2023-12-14T20:36:01Z |
TYP: require_matching_freq | diff --git a/pandas/_libs/tslibs/period.pyi b/pandas/_libs/tslibs/period.pyi
index 846d238beadbd..df6ce675b07fc 100644
--- a/pandas/_libs/tslibs/period.pyi
+++ b/pandas/_libs/tslibs/period.pyi
@@ -63,7 +63,7 @@ class PeriodMixin:
def end_time(self) -> Timestamp: ...
@property
def start_time(self) -> Timestamp: ...
- def _require_matching_freq(self, other, base: bool = ...) -> None: ...
+ def _require_matching_freq(self, other: BaseOffset, base: bool = ...) -> None: ...
class Period(PeriodMixin):
ordinal: int # int64_t
diff --git a/pandas/_libs/tslibs/period.pyx b/pandas/_libs/tslibs/period.pyx
index 22f96f8f6c3fa..6b105f5974f9b 100644
--- a/pandas/_libs/tslibs/period.pyx
+++ b/pandas/_libs/tslibs/period.pyx
@@ -1714,21 +1714,16 @@ cdef class PeriodMixin:
"""
return self.to_timestamp(how="end")
- def _require_matching_freq(self, other, base=False):
+ def _require_matching_freq(self, other: BaseOffset, bint base=False):
# See also arrays.period.raise_on_incompatible
- if is_offset_object(other):
- other_freq = other
- else:
- other_freq = other.freq
-
if base:
- condition = self.freq.base != other_freq.base
+ condition = self.freq.base != other.base
else:
- condition = self.freq != other_freq
+ condition = self.freq != other
if condition:
freqstr = freq_to_period_freqstr(self.freq.n, self.freq.name)
- other_freqstr = freq_to_period_freqstr(other_freq.n, other_freq.name)
+ other_freqstr = freq_to_period_freqstr(other.n, other.name)
msg = DIFFERENT_FREQ.format(
cls=type(self).__name__,
own_freq=freqstr,
@@ -1803,7 +1798,7 @@ cdef class _Period(PeriodMixin):
return False
elif op == Py_NE:
return True
- self._require_matching_freq(other)
+ self._require_matching_freq(other.freq)
return PyObject_RichCompareBool(self.ordinal, other.ordinal, op)
elif other is NaT:
return op == Py_NE
@@ -1893,7 +1888,7 @@ cdef class _Period(PeriodMixin):
):
return self + (-other)
elif is_period_object(other):
- self._require_matching_freq(other)
+ self._require_matching_freq(other.freq)
# GH 23915 - mul by base freq since __add__ is agnostic of n
return (self.ordinal - other.ordinal) * self.freq.base
elif other is NaT:
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index a8c21cfbb6e2f..5f267a9f816e7 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -370,10 +370,15 @@ def _unbox_scalar( # type: ignore[override]
def _scalar_from_string(self, value: str) -> Period:
return Period(value, freq=self.freq)
- def _check_compatible_with(self, other) -> None:
+ # error: Argument 1 of "_check_compatible_with" is incompatible with
+ # supertype "DatetimeLikeArrayMixin"; supertype defines the argument type
+ # as "Period | Timestamp | Timedelta | NaTType"
+ def _check_compatible_with(self, other: Period | NaTType | PeriodArray) -> None: # type: ignore[override]
if other is NaT:
return
- self._require_matching_freq(other)
+ # error: Item "NaTType" of "Period | NaTType | PeriodArray" has no
+ # attribute "freq"
+ self._require_matching_freq(other.freq) # type: ignore[union-attr]
# --------------------------------------------------------------------
# Data / Attributes
| - [ ] 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/56374 | 2023-12-07T03:03:35Z | 2023-12-07T19:15:44Z | 2023-12-07T19:15:44Z | 2023-12-07T19:39:21Z |
PERF: datetimelike addition | diff --git a/asv_bench/benchmarks/arithmetic.py b/asv_bench/benchmarks/arithmetic.py
index 5e23cba2e1074..6b1f75187f887 100644
--- a/asv_bench/benchmarks/arithmetic.py
+++ b/asv_bench/benchmarks/arithmetic.py
@@ -12,7 +12,6 @@
date_range,
to_timedelta,
)
-from pandas.core.algorithms import checked_add_with_arr
from .pandas_vb_common import numeric_dtypes
@@ -389,42 +388,6 @@ def time_add_timedeltas(self, df):
df["timedelta"] + df["timedelta"]
-class AddOverflowScalar:
- params = [1, -1, 0]
- param_names = ["scalar"]
-
- def setup(self, scalar):
- N = 10**6
- self.arr = np.arange(N)
-
- def time_add_overflow_scalar(self, scalar):
- checked_add_with_arr(self.arr, scalar)
-
-
-class AddOverflowArray:
- def setup(self):
- N = 10**6
- self.arr = np.arange(N)
- self.arr_rev = np.arange(-N, 0)
- self.arr_mixed = np.array([1, -1]).repeat(N / 2)
- self.arr_nan_1 = np.random.choice([True, False], size=N)
- self.arr_nan_2 = np.random.choice([True, False], size=N)
-
- def time_add_overflow_arr_rev(self):
- checked_add_with_arr(self.arr, self.arr_rev)
-
- def time_add_overflow_arr_mask_nan(self):
- checked_add_with_arr(self.arr, self.arr_mixed, arr_mask=self.arr_nan_1)
-
- def time_add_overflow_b_mask_nan(self):
- checked_add_with_arr(self.arr, self.arr_mixed, b_mask=self.arr_nan_1)
-
- def time_add_overflow_both_arg_nan(self):
- checked_add_with_arr(
- self.arr, self.arr_mixed, arr_mask=self.arr_nan_1, b_mask=self.arr_nan_2
- )
-
-
hcal = pd.tseries.holiday.USFederalHolidayCalendar()
# These offsets currently raise a NotImplementedError with .apply_index()
non_apply = [
diff --git a/pandas/_libs/tslibs/__init__.py b/pandas/_libs/tslibs/__init__.py
index c622121578dcb..b626959203295 100644
--- a/pandas/_libs/tslibs/__init__.py
+++ b/pandas/_libs/tslibs/__init__.py
@@ -34,6 +34,7 @@
"npy_unit_to_abbrev",
"get_supported_reso",
"guess_datetime_format",
+ "add_overflowsafe",
]
from pandas._libs.tslibs import dtypes # pylint: disable=import-self
@@ -55,6 +56,7 @@
from pandas._libs.tslibs.np_datetime import (
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
+ add_overflowsafe,
astype_overflowsafe,
is_unitless,
py_get_unit_from_dtype as get_unit_from_dtype,
diff --git a/pandas/_libs/tslibs/np_datetime.pxd b/pandas/_libs/tslibs/np_datetime.pxd
index a87c3d3f0955d..cb2658d343772 100644
--- a/pandas/_libs/tslibs/np_datetime.pxd
+++ b/pandas/_libs/tslibs/np_datetime.pxd
@@ -118,3 +118,5 @@ cdef int64_t convert_reso(
NPY_DATETIMEUNIT to_reso,
bint round_ok,
) except? -1
+
+cpdef cnp.ndarray add_overflowsafe(cnp.ndarray left, cnp.ndarray right)
diff --git a/pandas/_libs/tslibs/np_datetime.pyi b/pandas/_libs/tslibs/np_datetime.pyi
index c42bc43ac9d89..5a4ba673dbeff 100644
--- a/pandas/_libs/tslibs/np_datetime.pyi
+++ b/pandas/_libs/tslibs/np_datetime.pyi
@@ -19,3 +19,7 @@ def is_unitless(dtype: np.dtype) -> bool: ...
def compare_mismatched_resolutions(
left: np.ndarray, right: np.ndarray, op
) -> npt.NDArray[np.bool_]: ...
+def add_overflowsafe(
+ left: npt.NDArray[np.int64],
+ right: npt.NDArray[np.int64],
+) -> npt.NDArray[np.int64]: ...
diff --git a/pandas/_libs/tslibs/np_datetime.pyx b/pandas/_libs/tslibs/np_datetime.pyx
index 9958206c51b7a..5f5e75b1e64d0 100644
--- a/pandas/_libs/tslibs/np_datetime.pyx
+++ b/pandas/_libs/tslibs/np_datetime.pyx
@@ -1,3 +1,4 @@
+cimport cython
from cpython.datetime cimport (
PyDateTime_CheckExact,
PyDateTime_DATE_GET_HOUR,
@@ -678,3 +679,43 @@ cdef int64_t _convert_reso_with_dtstruct(
raise OutOfBoundsDatetime from err
return result
+
+
+@cython.overflowcheck(True)
+cpdef cnp.ndarray add_overflowsafe(cnp.ndarray left, cnp.ndarray right):
+ """
+ Overflow-safe addition for datetime64/timedelta64 dtypes.
+
+ `right` may either be zero-dim or of the same shape as `left`.
+ """
+ cdef:
+ Py_ssize_t N = left.size
+ int64_t lval, rval, res_value
+ ndarray iresult = cnp.PyArray_EMPTY(
+ left.ndim, left.shape, cnp.NPY_INT64, 0
+ )
+ cnp.broadcast mi = cnp.PyArray_MultiIterNew3(iresult, left, right)
+
+ # Note: doing this try/except outside the loop improves performance over
+ # doing it inside the loop.
+ try:
+ for i in range(N):
+ # Analogous to: lval = lvalues[i]
+ lval = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 1))[0]
+
+ # Analogous to: rval = rvalues[i]
+ rval = (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 2))[0]
+
+ if lval == NPY_DATETIME_NAT or rval == NPY_DATETIME_NAT:
+ res_value = NPY_DATETIME_NAT
+ else:
+ res_value = lval + rval
+
+ # Analogous to: result[i] = res_value
+ (<int64_t*>cnp.PyArray_MultiIter_DATA(mi, 0))[0] = res_value
+
+ cnp.PyArray_MultiIter_NEXT(mi)
+ except OverflowError as err:
+ raise OverflowError("Overflow in int64 addition") from err
+
+ return iresult
diff --git a/pandas/core/algorithms.py b/pandas/core/algorithms.py
index 82de8ae96160f..03f06da5f84e1 100644
--- a/pandas/core/algorithms.py
+++ b/pandas/core/algorithms.py
@@ -1119,98 +1119,6 @@ def rank(
return ranks
-def checked_add_with_arr(
- arr: npt.NDArray[np.int64],
- b: int | npt.NDArray[np.int64],
- arr_mask: npt.NDArray[np.bool_] | None = None,
- b_mask: npt.NDArray[np.bool_] | None = None,
-) -> npt.NDArray[np.int64]:
- """
- Perform array addition that checks for underflow and overflow.
-
- Performs the addition of an int64 array and an int64 integer (or array)
- but checks that they do not result in overflow first. For elements that
- are indicated to be NaN, whether or not there is overflow for that element
- is automatically ignored.
-
- Parameters
- ----------
- arr : np.ndarray[int64] addend.
- b : array or scalar addend.
- arr_mask : np.ndarray[bool] or None, default None
- array indicating which elements to exclude from checking
- b_mask : np.ndarray[bool] or None, default None
- array or scalar indicating which element(s) to exclude from checking
-
- Returns
- -------
- sum : An array for elements x + b for each element x in arr if b is
- a scalar or an array for elements x + y for each element pair
- (x, y) in (arr, b).
-
- Raises
- ------
- OverflowError if any x + y exceeds the maximum or minimum int64 value.
- """
- # For performance reasons, we broadcast 'b' to the new array 'b2'
- # so that it has the same size as 'arr'.
- b2 = np.broadcast_to(b, arr.shape)
- if b_mask is not None:
- # We do the same broadcasting for b_mask as well.
- b2_mask = np.broadcast_to(b_mask, arr.shape)
- else:
- b2_mask = None
-
- # For elements that are NaN, regardless of their value, we should
- # ignore whether they overflow or not when doing the checked add.
- if arr_mask is not None and b2_mask is not None:
- not_nan = np.logical_not(arr_mask | b2_mask)
- elif arr_mask is not None:
- not_nan = np.logical_not(arr_mask)
- elif b_mask is not None:
- # error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has
- # incompatible type "Optional[ndarray[Any, dtype[bool_]]]";
- # expected "Union[_SupportsArray[dtype[Any]], _NestedSequence
- # [_SupportsArray[dtype[Any]]], bool, int, float, complex, str
- # , bytes, _NestedSequence[Union[bool, int, float, complex, str
- # , bytes]]]"
- not_nan = np.logical_not(b2_mask) # type: ignore[arg-type]
- else:
- not_nan = np.empty(arr.shape, dtype=bool)
- not_nan.fill(True)
-
- # gh-14324: For each element in 'arr' and its corresponding element
- # in 'b2', we check the sign of the element in 'b2'. If it is positive,
- # we then check whether its sum with the element in 'arr' exceeds
- # np.iinfo(np.int64).max. If so, we have an overflow error. If it
- # it is negative, we then check whether its sum with the element in
- # 'arr' exceeds np.iinfo(np.int64).min. If so, we have an overflow
- # error as well.
- i8max = lib.i8max
- i8min = iNaT
-
- mask1 = b2 > 0
- mask2 = b2 < 0
-
- if not mask1.any():
- to_raise = ((i8min - b2 > arr) & not_nan).any()
- elif not mask2.any():
- to_raise = ((i8max - b2 < arr) & not_nan).any()
- else:
- to_raise = ((i8max - b2[mask1] < arr[mask1]) & not_nan[mask1]).any() or (
- (i8min - b2[mask2] > arr[mask2]) & not_nan[mask2]
- ).any()
-
- if to_raise:
- raise OverflowError("Overflow in int64 addition")
-
- result = arr + b
- if arr_mask is not None or b2_mask is not None:
- np.putmask(result, ~not_nan, iNaT)
-
- return result
-
-
# ---- #
# take #
# ---- #
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index a88f40013b3f6..b4d9964c10ebd 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -35,6 +35,7 @@
Tick,
Timedelta,
Timestamp,
+ add_overflowsafe,
astype_overflowsafe,
get_unit_from_dtype,
iNaT,
@@ -112,7 +113,6 @@
ops,
)
from pandas.core.algorithms import (
- checked_add_with_arr,
isin,
map_array,
unique1d,
@@ -1013,7 +1013,7 @@ def _get_i8_values_and_mask(
self, other
) -> tuple[int | npt.NDArray[np.int64], None | npt.NDArray[np.bool_]]:
"""
- Get the int64 values and b_mask to pass to checked_add_with_arr.
+ Get the int64 values and b_mask to pass to add_overflowsafe.
"""
if isinstance(other, Period):
i8values = other.ordinal
@@ -1069,9 +1069,7 @@ def _add_datetimelike_scalar(self, other) -> DatetimeArray:
self = cast("TimedeltaArray", self)
other_i8, o_mask = self._get_i8_values_and_mask(other)
- result = checked_add_with_arr(
- self.asi8, other_i8, arr_mask=self._isnan, b_mask=o_mask
- )
+ result = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
res_values = result.view(f"M8[{self.unit}]")
dtype = tz_to_dtype(tz=other.tz, unit=self.unit)
@@ -1134,9 +1132,7 @@ def _sub_datetimelike(self, other: Timestamp | DatetimeArray) -> TimedeltaArray:
raise type(err)(new_message) from err
other_i8, o_mask = self._get_i8_values_and_mask(other)
- res_values = checked_add_with_arr(
- self.asi8, -other_i8, arr_mask=self._isnan, b_mask=o_mask
- )
+ res_values = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
res_m8 = res_values.view(f"timedelta64[{self.unit}]")
new_freq = self._get_arithmetic_result_freq(other)
@@ -1202,9 +1198,7 @@ def _add_timedeltalike(self, other: Timedelta | TimedeltaArray):
self = cast("DatetimeArray | TimedeltaArray", self)
other_i8, o_mask = self._get_i8_values_and_mask(other)
- new_values = checked_add_with_arr(
- self.asi8, other_i8, arr_mask=self._isnan, b_mask=o_mask
- )
+ new_values = add_overflowsafe(self.asi8, np.asarray(other_i8, dtype="i8"))
res_values = new_values.view(self._ndarray.dtype)
new_freq = self._get_arithmetic_result_freq(other)
@@ -1272,9 +1266,7 @@ def _sub_periodlike(self, other: Period | PeriodArray) -> npt.NDArray[np.object_
self._check_compatible_with(other)
other_i8, o_mask = self._get_i8_values_and_mask(other)
- new_i8_data = checked_add_with_arr(
- self.asi8, -other_i8, arr_mask=self._isnan, b_mask=o_mask
- )
+ new_i8_data = add_overflowsafe(self.asi8, np.asarray(-other_i8, dtype="i8"))
new_data = np.array([self.freq.base * x for x in new_i8_data])
if o_mask is None:
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index a8c21cfbb6e2f..26eef379909c3 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -25,6 +25,7 @@
NaT,
NaTType,
Timedelta,
+ add_overflowsafe,
astype_overflowsafe,
dt64arr_to_periodarr as c_dt64arr_to_periodarr,
get_unit_from_dtype,
@@ -71,7 +72,6 @@
)
from pandas.core.dtypes.missing import isna
-import pandas.core.algorithms as algos
from pandas.core.arrays import datetimelike as dtl
import pandas.core.common as com
@@ -847,7 +847,7 @@ def _addsub_int_array_or_scalar(
assert op in [operator.add, operator.sub]
if op is operator.sub:
other = -other
- res_values = algos.checked_add_with_arr(self.asi8, other, arr_mask=self._isnan)
+ res_values = add_overflowsafe(self.asi8, np.asarray(other, dtype="i8"))
return type(self)(res_values, dtype=self.dtype)
def _add_offset(self, other: BaseOffset):
@@ -912,12 +912,7 @@ def _add_timedelta_arraylike(
"not an integer multiple of the PeriodArray's freq."
) from err
- b_mask = np.isnat(delta)
-
- res_values = algos.checked_add_with_arr(
- self.asi8, delta.view("i8"), arr_mask=self._isnan, b_mask=b_mask
- )
- np.putmask(res_values, self._isnan | b_mask, iNaT)
+ res_values = add_overflowsafe(self.asi8, np.asarray(delta.view("i8")))
return type(self)(res_values, dtype=self.dtype)
def _check_timedeltalike_freq_compat(self, other):
diff --git a/pandas/tests/test_algos.py b/pandas/tests/test_algos.py
index 5356704cc64a2..50a935d96cdab 100644
--- a/pandas/tests/test_algos.py
+++ b/pandas/tests/test_algos.py
@@ -1798,57 +1798,6 @@ def test_pct_max_many_rows(self):
assert result == 1
-def test_int64_add_overflow():
- # see gh-14068
- msg = "Overflow in int64 addition"
- m = np.iinfo(np.int64).max
- n = np.iinfo(np.int64).min
-
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([m, m]), m)
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([m, m]), np.array([m, m]))
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([n, n]), n)
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([n, n]), np.array([n, n]))
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([m, n]), np.array([n, n]))
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(
- np.array([m, m]), np.array([m, m]), arr_mask=np.array([False, True])
- )
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(
- np.array([m, m]), np.array([m, m]), b_mask=np.array([False, True])
- )
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(
- np.array([m, m]),
- np.array([m, m]),
- arr_mask=np.array([False, True]),
- b_mask=np.array([False, True]),
- )
- with pytest.raises(OverflowError, match=msg):
- algos.checked_add_with_arr(np.array([m, m]), np.array([np.nan, m]))
-
- # Check that the nan boolean arrays override whether or not
- # the addition overflows. We don't check the result but just
- # the fact that an OverflowError is not raised.
- algos.checked_add_with_arr(
- np.array([m, m]), np.array([m, m]), arr_mask=np.array([True, True])
- )
- algos.checked_add_with_arr(
- np.array([m, m]), np.array([m, m]), b_mask=np.array([True, True])
- )
- algos.checked_add_with_arr(
- np.array([m, m]),
- np.array([m, m]),
- arr_mask=np.array([True, False]),
- b_mask=np.array([False, True]),
- )
-
-
class TestMode:
def test_no_mode(self):
exp = Series([], dtype=np.float64, index=Index([], dtype=int))
diff --git a/pandas/tests/tslibs/test_api.py b/pandas/tests/tslibs/test_api.py
index b52bc78d58296..e02cea2fef426 100644
--- a/pandas/tests/tslibs/test_api.py
+++ b/pandas/tests/tslibs/test_api.py
@@ -58,6 +58,7 @@ def test_namespace():
"get_supported_reso",
"npy_unit_to_abbrev",
"guess_datetime_format",
+ "add_overflowsafe",
]
expected = set(submodules + api)
| ```
In [1]: import pandas as pd
In [2]: dti = pd.date_range("2016-01-01", periods=10_000)
In [3]: td = pd.Timedelta(days=1)
In [4]: %timeit dti + td
108 µs ± 1.85 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each) # <- main
94.3 µs ± 3.27 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each) # <- PR
``` | https://api.github.com/repos/pandas-dev/pandas/pulls/56373 | 2023-12-07T02:55:40Z | 2023-12-09T18:36:28Z | 2023-12-09T18:36:28Z | 2023-12-09T18:37:46Z |
CLN/TYP: "how" parameter in merge ops | diff --git a/pandas/core/reshape/merge.py b/pandas/core/reshape/merge.py
index f8575b1b53908..0756b25adedcd 100644
--- a/pandas/core/reshape/merge.py
+++ b/pandas/core/reshape/merge.py
@@ -718,7 +718,7 @@ class _MergeOperation:
"""
_merge_type = "merge"
- how: MergeHow | Literal["asof"]
+ how: JoinHow | Literal["asof"]
on: IndexLabel | None
# left_on/right_on may be None when passed, but in validate_specification
# get replaced with non-None.
@@ -739,7 +739,7 @@ def __init__(
self,
left: DataFrame | Series,
right: DataFrame | Series,
- how: MergeHow | Literal["asof"] = "inner",
+ how: JoinHow | Literal["asof"] = "inner",
on: IndexLabel | AnyArrayLike | None = None,
left_on: IndexLabel | AnyArrayLike | None = None,
right_on: IndexLabel | AnyArrayLike | None = None,
@@ -1106,6 +1106,8 @@ def _maybe_add_join_keys(
def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""return the join indexers"""
+ # make mypy happy
+ assert self.how != "asof"
return get_join_indexers(
self.left_join_keys, self.right_join_keys, sort=self.sort, how=self.how
)
@@ -1114,8 +1116,6 @@ def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]
def _get_join_info(
self,
) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
- # make mypy happy
- assert self.how != "cross"
left_ax = self.left.index
right_ax = self.right.index
@@ -1658,7 +1658,7 @@ def get_join_indexers(
left_keys: list[ArrayLike],
right_keys: list[ArrayLike],
sort: bool = False,
- how: MergeHow | Literal["asof"] = "inner",
+ how: JoinHow = "inner",
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""
@@ -1684,12 +1684,12 @@ def get_join_indexers(
left_n = len(left_keys[0])
right_n = len(right_keys[0])
if left_n == 0:
- if how in ["left", "inner", "cross"]:
+ if how in ["left", "inner"]:
return _get_empty_indexer()
elif not sort and how in ["right", "outer"]:
return _get_no_sort_one_missing_indexer(right_n, True)
elif right_n == 0:
- if how in ["right", "inner", "cross"]:
+ if how in ["right", "inner"]:
return _get_empty_indexer()
elif not sort and how in ["left", "outer"]:
return _get_no_sort_one_missing_indexer(left_n, False)
@@ -1699,7 +1699,7 @@ def get_join_indexers(
# get left & right join labels and num. of levels at each location
mapped = (
- _factorize_keys(left_keys[n], right_keys[n], sort=sort, how=how)
+ _factorize_keys(left_keys[n], right_keys[n], sort=sort)
for n in range(len(left_keys))
)
zipped = zip(*mapped)
@@ -1712,7 +1712,7 @@ def get_join_indexers(
# `count` is the num. of unique keys
# set(lkey) | set(rkey) == range(count)
- lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort, how=how)
+ lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)
# preserve left frame order if how == 'left' and sort == False
kwargs = {}
if how in ("inner", "left", "right"):
@@ -2166,7 +2166,6 @@ def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]
left_join_keys[n],
right_join_keys[n],
sort=False,
- how="left",
)
for n in range(len(left_join_keys))
]
@@ -2310,10 +2309,7 @@ def _left_join_on_index(
def _factorize_keys(
- lk: ArrayLike,
- rk: ArrayLike,
- sort: bool = True,
- how: MergeHow | Literal["asof"] = "inner",
+ lk: ArrayLike, rk: ArrayLike, sort: bool = True
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
"""
Encode left and right keys as enumerated types.
@@ -2329,8 +2325,6 @@ def _factorize_keys(
sort : bool, defaults to True
If True, the encoding is done such that the unique elements in the
keys are sorted.
- how : {'left', 'right', 'outer', 'inner'}, default 'inner'
- Type of merge.
Returns
-------
@@ -2419,8 +2413,6 @@ def _factorize_keys(
)
if dc.null_count > 0:
count += 1
- if how == "right":
- return rlab, llab, count
return llab, rlab, count
if not isinstance(lk, BaseMaskedArray) and not (
@@ -2491,8 +2483,6 @@ def _factorize_keys(
np.putmask(rlab, rmask, count)
count += 1
- if how == "right":
- return rlab, llab, count
return llab, rlab, count
| cleanup and tighten typing of `how` parameter in merge ops. | https://api.github.com/repos/pandas-dev/pandas/pulls/56372 | 2023-12-07T02:10:50Z | 2023-12-07T17:15:34Z | 2023-12-07T17:15:34Z | 2023-12-07T17:15:41Z |
BUG: resample with ArrowDtype | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index c878fd2664dc4..aa5de99b69f57 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -644,6 +644,7 @@ Groupby/resample/rolling
- Bug in :meth:`DataFrame.asfreq` and :meth:`Series.asfreq` with a :class:`DatetimeIndex` with non-nanosecond resolution incorrectly converting to nanosecond resolution (:issue:`55958`)
- Bug in :meth:`DataFrame.ewm` when passed ``times`` with non-nanosecond ``datetime64`` or :class:`DatetimeTZDtype` dtype (:issue:`56262`)
- Bug in :meth:`DataFrame.resample` not respecting ``closed`` and ``label`` arguments for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55282`)
+- Bug in :meth:`DataFrame.resample` when resampling on a :class:`ArrowDtype` of ``pyarrow.timestamp`` or ``pyarrow.duration`` type (:issue:`55989`)
- Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
- Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
-
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index fc914831b7a72..4703c12db602d 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -330,7 +330,6 @@ def _get_grouper(
return grouper, obj
- @final
def _set_grouper(
self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 8af81cd43d62e..3831af23dc16e 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -38,6 +38,7 @@
rewrite_warning,
)
+from pandas.core.dtypes.dtypes import ArrowDtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
@@ -48,6 +49,7 @@
ResamplerWindowApply,
warn_alias_replacement,
)
+from pandas.core.arrays import ArrowExtensionArray
from pandas.core.base import (
PandasObject,
SelectionMixin,
@@ -68,6 +70,7 @@
from pandas.core.groupby.grouper import Grouper
from pandas.core.groupby.ops import BinGrouper
from pandas.core.indexes.api import MultiIndex
+from pandas.core.indexes.base import Index
from pandas.core.indexes.datetimes import (
DatetimeIndex,
date_range,
@@ -109,7 +112,6 @@
from pandas import (
DataFrame,
- Index,
Series,
)
@@ -511,6 +513,9 @@ def _wrap_result(self, result):
result.index = _asfreq_compat(obj.index[:0], freq=self.freq)
result.name = getattr(obj, "name", None)
+ if self._timegrouper._arrow_dtype is not None:
+ result.index = result.index.astype(self._timegrouper._arrow_dtype)
+
return result
@final
@@ -2163,6 +2168,7 @@ def __init__(
self.fill_method = fill_method
self.limit = limit
self.group_keys = group_keys
+ self._arrow_dtype: ArrowDtype | None = None
if origin in ("epoch", "start", "start_day", "end", "end_day"):
# error: Incompatible types in assignment (expression has type "Union[Union[
@@ -2213,7 +2219,7 @@ def _get_resampler(self, obj: NDFrame, kind=None) -> Resampler:
TypeError if incompatible axis
"""
- _, ax, indexer = self._set_grouper(obj, gpr_index=None)
+ _, ax, _ = self._set_grouper(obj, gpr_index=None)
if isinstance(ax, DatetimeIndex):
return DatetimeIndexResampler(
obj,
@@ -2495,6 +2501,17 @@ def _get_period_bins(self, ax: PeriodIndex):
return binner, bins, labels
+ def _set_grouper(
+ self, obj: NDFrameT, sort: bool = False, *, gpr_index: Index | None = None
+ ) -> tuple[NDFrameT, Index, npt.NDArray[np.intp] | None]:
+ obj, ax, indexer = super()._set_grouper(obj, sort, gpr_index=gpr_index)
+ if isinstance(ax.dtype, ArrowDtype) and ax.dtype.kind in "Mm":
+ self._arrow_dtype = ax.dtype
+ ax = Index(
+ cast(ArrowExtensionArray, ax.array)._maybe_convert_datelike_array()
+ )
+ return obj, ax, indexer
+
def _take_new_index(
obj: NDFrameT, indexer: npt.NDArray[np.intp], new_index: Index, axis: AxisInt = 0
diff --git a/pandas/tests/resample/test_datetime_index.py b/pandas/tests/resample/test_datetime_index.py
index 8a725c6e51e3f..760ed35bab678 100644
--- a/pandas/tests/resample/test_datetime_index.py
+++ b/pandas/tests/resample/test_datetime_index.py
@@ -7,6 +7,8 @@
from pandas._libs import lib
from pandas._typing import DatetimeNaTType
+from pandas.compat import is_platform_windows
+import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
@@ -2195,3 +2197,27 @@ def test_resample_b_55282(unit):
index=exp_dti,
)
tm.assert_series_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+@pytest.mark.parametrize(
+ "tz",
+ [
+ None,
+ pytest.param(
+ "UTC",
+ marks=pytest.mark.xfail(
+ condition=is_platform_windows(),
+ reason="TODO: Set ARROW_TIMEZONE_DATABASE env var in CI",
+ ),
+ ),
+ ],
+)
+def test_arrow_timestamp_resample(tz):
+ # GH 56371
+ idx = Series(date_range("2020-01-01", periods=5), dtype="timestamp[ns][pyarrow]")
+ if tz is not None:
+ idx = idx.dt.tz_localize(tz)
+ expected = Series(np.arange(5, dtype=np.float64), index=idx)
+ result = expected.resample("1D").mean()
+ tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/resample/test_timedelta.py b/pandas/tests/resample/test_timedelta.py
index 5d6876343a0c9..7c70670d42908 100644
--- a/pandas/tests/resample/test_timedelta.py
+++ b/pandas/tests/resample/test_timedelta.py
@@ -3,6 +3,8 @@
import numpy as np
import pytest
+import pandas.util._test_decorators as td
+
import pandas as pd
from pandas import (
DataFrame,
@@ -207,3 +209,12 @@ def test_resample_closed_right():
),
)
tm.assert_series_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_arrow_duration_resample():
+ # GH 56371
+ idx = pd.Index(timedelta_range("1 day", periods=5), dtype="duration[ns][pyarrow]")
+ expected = Series(np.arange(5, dtype=np.float64), index=idx)
+ result = expected.resample("1D").mean()
+ tm.assert_series_equal(result, expected)
| - [x] closes #55989 (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.
The current resample path heavily uses `DatetimeIndex`/`TimedeltaIndex` APIs, so it's kinda onerous to try to allow `Index` API methods. This essentially wraps the Arrow types into one of the two classes above and saves an `_arrow_dtype` for the output | https://api.github.com/repos/pandas-dev/pandas/pulls/56371 | 2023-12-06T23:18:34Z | 2023-12-09T01:06:34Z | 2023-12-09T01:06:34Z | 2023-12-09T01:06:37Z |
BUG: rolling with datetime ArrowDtype | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index d1481639ca5a0..58a70a2b66a84 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -831,6 +831,7 @@ Groupby/resample/rolling
- Bug in :meth:`DataFrame.resample` when resampling on a :class:`ArrowDtype` of ``pyarrow.timestamp`` or ``pyarrow.duration`` type (:issue:`55989`)
- Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.BusinessDay` (:issue:`55281`)
- Bug in :meth:`DataFrame.resample` where bin edges were not correct for :class:`~pandas.tseries.offsets.MonthBegin` (:issue:`55271`)
+- Bug in :meth:`DataFrame.rolling` and :meth:`Series.rolling` where either the ``index`` or ``on`` column was :class:`ArrowDtype` with ``pyarrow.timestamp`` type (:issue:`55849`)
Reshaping
^^^^^^^^^
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 11a0c7bf18fcb..a0e0a1434e871 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -92,6 +92,7 @@
pandas_dtype,
)
from pandas.core.dtypes.dtypes import (
+ ArrowDtype,
CategoricalDtype,
DatetimeTZDtype,
ExtensionDtype,
@@ -2531,7 +2532,7 @@ def _validate_inferred_freq(
return freq
-def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype) -> str:
+def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype | ArrowDtype) -> str:
"""
Return the unit str corresponding to the dtype's resolution.
@@ -2546,4 +2547,8 @@ def dtype_to_unit(dtype: DatetimeTZDtype | np.dtype) -> str:
"""
if isinstance(dtype, DatetimeTZDtype):
return dtype.unit
+ elif isinstance(dtype, ArrowDtype):
+ if dtype.kind not in "mM":
+ raise ValueError(f"{dtype=} does not have a resolution.")
+ return dtype.pyarrow_dtype.unit
return np.datetime_data(dtype)[0]
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index e78bd258c11ff..68cec16ec9eca 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -14,7 +14,6 @@
Any,
Callable,
Literal,
- cast,
)
import numpy as np
@@ -39,6 +38,7 @@
is_numeric_dtype,
needs_i8_conversion,
)
+from pandas.core.dtypes.dtypes import ArrowDtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
@@ -104,6 +104,7 @@
NDFrameT,
QuantileInterpolation,
WindowingRankType,
+ npt,
)
from pandas import (
@@ -404,11 +405,12 @@ def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
result[name] = extra_col
@property
- def _index_array(self):
+ def _index_array(self) -> npt.NDArray[np.int64] | None:
# TODO: why do we get here with e.g. MultiIndex?
- if needs_i8_conversion(self._on.dtype):
- idx = cast("PeriodIndex | DatetimeIndex | TimedeltaIndex", self._on)
- return idx.asi8
+ if isinstance(self._on, (PeriodIndex, DatetimeIndex, TimedeltaIndex)):
+ return self._on.asi8
+ elif isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM":
+ return self._on.to_numpy(dtype=np.int64)
return None
def _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame:
@@ -439,7 +441,7 @@ def _apply_series(
self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None
) -> Series:
"""
- Series version of _apply_blockwise
+ Series version of _apply_columnwise
"""
obj = self._create_data(self._selected_obj)
@@ -455,7 +457,7 @@ def _apply_series(
index = self._slice_axis_for_step(obj.index, result)
return obj._constructor(result, index=index, name=obj.name)
- def _apply_blockwise(
+ def _apply_columnwise(
self,
homogeneous_func: Callable[..., ArrayLike],
name: str,
@@ -614,7 +616,7 @@ def calc(x):
return result
if self.method == "single":
- return self._apply_blockwise(homogeneous_func, name, numeric_only)
+ return self._apply_columnwise(homogeneous_func, name, numeric_only)
else:
return self._apply_tablewise(homogeneous_func, name, numeric_only)
@@ -1232,7 +1234,9 @@ def calc(x):
return result
- return self._apply_blockwise(homogeneous_func, name, numeric_only)[:: self.step]
+ return self._apply_columnwise(homogeneous_func, name, numeric_only)[
+ :: self.step
+ ]
@doc(
_shared_docs["aggregate"],
@@ -1868,6 +1872,7 @@ def _validate(self):
if (
self.obj.empty
or isinstance(self._on, (DatetimeIndex, TimedeltaIndex, PeriodIndex))
+ or (isinstance(self._on.dtype, ArrowDtype) and self._on.dtype.kind in "mM")
) and isinstance(self.window, (str, BaseOffset, timedelta)):
self._validate_datetimelike_monotonic()
diff --git a/pandas/tests/window/test_timeseries_window.py b/pandas/tests/window/test_timeseries_window.py
index c99fc8a8eb60f..bd0fadeb3e475 100644
--- a/pandas/tests/window/test_timeseries_window.py
+++ b/pandas/tests/window/test_timeseries_window.py
@@ -1,9 +1,12 @@
import numpy as np
import pytest
+import pandas.util._test_decorators as td
+
from pandas import (
DataFrame,
DatetimeIndex,
+ Index,
MultiIndex,
NaT,
Series,
@@ -697,3 +700,16 @@ def test_nat_axis_error(msg, axis):
with pytest.raises(ValueError, match=f"{msg} values must not have NaT"):
with tm.assert_produces_warning(FutureWarning, match=warn_msg):
df.rolling("D", axis=axis).mean()
+
+
+@td.skip_if_no("pyarrow")
+def test_arrow_datetime_axis():
+ # GH 55849
+ expected = Series(
+ np.arange(5, dtype=np.float64),
+ index=Index(
+ date_range("2020-01-01", periods=5), dtype="timestamp[ns][pyarrow]"
+ ),
+ )
+ result = expected.rolling("1D").sum()
+ tm.assert_series_equal(result, expected)
| - [x] closes #55849 (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.
Additionally renamed _apply_blockwise to _apply_columnwise to fit the actual methodology | https://api.github.com/repos/pandas-dev/pandas/pulls/56370 | 2023-12-06T21:55:41Z | 2023-12-28T19:32:01Z | 2023-12-28T19:32:01Z | 2023-12-28T19:32:03Z |
Update nlargest nsmallest doc | diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index e741fa7b37f33..8ba9926c054ba 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -7505,8 +7505,8 @@ def nlargest(
- ``first`` : prioritize the first occurrence(s)
- ``last`` : prioritize the last occurrence(s)
- - ``all`` : do not drop any duplicates, even it means
- selecting more than `n` items.
+ - ``all`` : keep all the ties of the smallest item even if it means
+ selecting more than ``n`` items.
Returns
-------
@@ -7568,7 +7568,9 @@ def nlargest(
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
- When using ``keep='all'``, all duplicate items are maintained:
+ When using ``keep='all'``, the number of element kept can go beyond ``n``
+ if there are duplicate values for the smallest element, all the
+ ties are kept:
>>> df.nlargest(3, 'population', keep='all')
population GDP alpha-2
@@ -7578,6 +7580,16 @@ def nlargest(
Maldives 434000 4520 MV
Brunei 434000 12128 BN
+ However, ``nlargest`` does not keep ``n`` distinct largest elements:
+
+ >>> df.nlargest(5, 'population', keep='all')
+ population GDP alpha-2
+ France 65000000 2583560 FR
+ Italy 59000000 1937894 IT
+ Malta 434000 12011 MT
+ Maldives 434000 4520 MV
+ Brunei 434000 12128 BN
+
To order by the largest values in column "population" and then "GDP",
we can specify multiple columns like in the next example.
@@ -7614,8 +7626,8 @@ def nsmallest(
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
- - ``all`` : do not drop any duplicates, even it means
- selecting more than `n` items.
+ - ``all`` : keep all the ties of the largest item even if it means
+ selecting more than ``n`` items.
Returns
-------
@@ -7669,7 +7681,9 @@ def nsmallest(
Tuvalu 11300 38 TV
Nauru 337000 182 NR
- When using ``keep='all'``, all duplicate items are maintained:
+ When using ``keep='all'``, the number of element kept can go beyond ``n``
+ if there are duplicate values for the largest element, all the
+ ties are kept.
>>> df.nsmallest(3, 'population', keep='all')
population GDP alpha-2
@@ -7678,6 +7692,16 @@ def nsmallest(
Iceland 337000 17036 IS
Nauru 337000 182 NR
+ However, ``nsmallest`` does not keep ``n`` distinct
+ smallest elements:
+
+ >>> df.nsmallest(4, 'population', keep='all')
+ population GDP alpha-2
+ Tuvalu 11300 38 TV
+ Anguilla 11300 311 AI
+ Iceland 337000 17036 IS
+ Nauru 337000 182 NR
+
To order by the smallest values in column "population" and then "GDP", we can
specify multiple columns like in the next example.
| - [x] closes #45041 (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/56369 | 2023-12-06T20:26:06Z | 2023-12-12T00:39:24Z | 2023-12-12T00:39:24Z | 2023-12-12T00:39:32Z |
BUG: Series.__mul__ for pyarrow strings | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index 67b4052b386c0..c878fd2664dc4 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -576,6 +576,7 @@ Strings
^^^^^^^
- Bug in :func:`pandas.api.types.is_string_dtype` while checking object array with no elements is of the string dtype (:issue:`54661`)
- Bug in :meth:`DataFrame.apply` failing when ``engine="numba"`` and columns or index have ``StringDtype`` (:issue:`56189`)
+- Bug in :meth:`Series.__mul__` for :class:`ArrowDtype` with ``pyarrow.string`` dtype and ``string[pyarrow]`` for the pyarrow backend (:issue:`51970`)
- Bug in :meth:`Series.str.startswith` and :meth:`Series.str.endswith` with arguments of type ``tuple[str, ...]`` for ``string[pyarrow]`` (:issue:`54942`)
Interval
diff --git a/pandas/core/arrays/arrow/array.py b/pandas/core/arrays/arrow/array.py
index 1609bf50a834a..e7a50dbba9935 100644
--- a/pandas/core/arrays/arrow/array.py
+++ b/pandas/core/arrays/arrow/array.py
@@ -668,16 +668,22 @@ def _evaluate_op_method(self, other, op, arrow_funcs):
pa_type = self._pa_array.type
other = self._box_pa(other)
- if (pa.types.is_string(pa_type) or pa.types.is_binary(pa_type)) and op in [
- operator.add,
- roperator.radd,
- ]:
- sep = pa.scalar("", type=pa_type)
- if op is operator.add:
- result = pc.binary_join_element_wise(self._pa_array, other, sep)
- else:
- result = pc.binary_join_element_wise(other, self._pa_array, sep)
- return type(self)(result)
+ if pa.types.is_string(pa_type) or pa.types.is_binary(pa_type):
+ if op in [operator.add, roperator.radd, operator.mul, roperator.rmul]:
+ sep = pa.scalar("", type=pa_type)
+ if op is operator.add:
+ result = pc.binary_join_element_wise(self._pa_array, other, sep)
+ elif op is roperator.radd:
+ result = pc.binary_join_element_wise(other, self._pa_array, sep)
+ else:
+ if not (
+ isinstance(other, pa.Scalar) and pa.types.is_integer(other.type)
+ ):
+ raise TypeError("Can only string multiply by an integer.")
+ result = pc.binary_join_element_wise(
+ *([self._pa_array] * other.as_py()), sep
+ )
+ return type(self)(result)
if (
isinstance(other, pa.Scalar)
diff --git a/pandas/tests/arrays/string_/test_string.py b/pandas/tests/arrays/string_/test_string.py
index 524a6632e5544..3e11062b8384e 100644
--- a/pandas/tests/arrays/string_/test_string.py
+++ b/pandas/tests/arrays/string_/test_string.py
@@ -176,12 +176,7 @@ def test_add_sequence(dtype):
tm.assert_extension_array_equal(result, expected)
-def test_mul(dtype, request, arrow_string_storage):
- if dtype.storage in arrow_string_storage:
- reason = "unsupported operand type(s) for *: 'ArrowStringArray' and 'int'"
- mark = pytest.mark.xfail(raises=NotImplementedError, reason=reason)
- request.applymarker(mark)
-
+def test_mul(dtype):
a = pd.array(["a", "b", None], dtype=dtype)
result = a * 2
expected = pd.array(["aa", "bb", None], dtype=dtype)
diff --git a/pandas/tests/extension/test_arrow.py b/pandas/tests/extension/test_arrow.py
index 9e70a59932701..3ce3cee9714e4 100644
--- a/pandas/tests/extension/test_arrow.py
+++ b/pandas/tests/extension/test_arrow.py
@@ -965,8 +965,16 @@ def _get_arith_xfail_marker(self, opname, pa_dtype):
def test_arith_series_with_scalar(self, data, all_arithmetic_operators, request):
pa_dtype = data.dtype.pyarrow_dtype
- if all_arithmetic_operators == "__rmod__" and (pa.types.is_binary(pa_dtype)):
+ if all_arithmetic_operators == "__rmod__" and pa.types.is_binary(pa_dtype):
pytest.skip("Skip testing Python string formatting")
+ elif all_arithmetic_operators in ("__rmul__", "__mul__") and (
+ pa.types.is_binary(pa_dtype) or pa.types.is_string(pa_dtype)
+ ):
+ request.applymarker(
+ pytest.mark.xfail(
+ raises=TypeError, reason="Can only string multiply by an integer."
+ )
+ )
mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
if mark is not None:
@@ -981,6 +989,14 @@ def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
pa.types.is_string(pa_dtype) or pa.types.is_binary(pa_dtype)
):
pytest.skip("Skip testing Python string formatting")
+ elif all_arithmetic_operators in ("__rmul__", "__mul__") and (
+ pa.types.is_binary(pa_dtype) or pa.types.is_string(pa_dtype)
+ ):
+ request.applymarker(
+ pytest.mark.xfail(
+ raises=TypeError, reason="Can only string multiply by an integer."
+ )
+ )
mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
if mark is not None:
@@ -1004,6 +1020,14 @@ def test_arith_series_with_array(self, data, all_arithmetic_operators, request):
),
)
)
+ elif all_arithmetic_operators in ("__rmul__", "__mul__") and (
+ pa.types.is_binary(pa_dtype) or pa.types.is_string(pa_dtype)
+ ):
+ request.applymarker(
+ pytest.mark.xfail(
+ raises=TypeError, reason="Can only string multiply by an integer."
+ )
+ )
mark = self._get_arith_xfail_marker(all_arithmetic_operators, pa_dtype)
if mark is not None:
| - [x] closes #51970 (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/56368 | 2023-12-06T19:50:49Z | 2023-12-07T05:57:39Z | 2023-12-07T05:57:39Z | 2023-12-07T05:57:43Z |
BUG: `concat` should keep series names unless `ignore_index=True` | diff --git a/doc/source/whatsnew/v2.2.0.rst b/doc/source/whatsnew/v2.2.0.rst
index dea986c401b60..52a354b8e54bd 100644
--- a/doc/source/whatsnew/v2.2.0.rst
+++ b/doc/source/whatsnew/v2.2.0.rst
@@ -636,6 +636,7 @@ Groupby/resample/rolling
Reshaping
^^^^^^^^^
- Bug in :func:`concat` ignoring ``sort`` parameter when passed :class:`DatetimeIndex` indexes (:issue:`54769`)
+- Bug in :func:`concat` renaming :class:`Series` when ``ignore_index=False`` (:issue:`15047`)
- Bug in :func:`merge_asof` raising ``TypeError`` when ``by`` dtype is not ``object``, ``int64``, or ``uint64`` (:issue:`22794`)
- Bug in :func:`merge` returning columns in incorrect order when left and/or right is empty (:issue:`51929`)
- Bug in :meth:`DataFrame.melt` where an exception was raised if ``var_name`` was not a string (:issue:`55948`)
diff --git a/pandas/core/reshape/concat.py b/pandas/core/reshape/concat.py
index 1bc548de91f01..d46348fff7a02 100644
--- a/pandas/core/reshape/concat.py
+++ b/pandas/core/reshape/concat.py
@@ -464,7 +464,7 @@ def __init__(
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
if len(ndims) > 1:
- objs, sample = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
+ objs = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
self.objs = objs
@@ -580,7 +580,7 @@ def _sanitize_mixed_ndim(
sample: Series | DataFrame,
ignore_index: bool,
axis: AxisInt,
- ) -> tuple[list[Series | DataFrame], Series | DataFrame]:
+ ) -> list[Series | DataFrame]:
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
@@ -601,19 +601,21 @@ def _sanitize_mixed_ndim(
else:
name = getattr(obj, "name", None)
if ignore_index or name is None:
- name = current_column
- current_column += 1
-
- # doing a row-wise concatenation so need everything
- # to line up
- if self._is_frame and axis == 1:
- name = 0
+ if axis == 1:
+ # doing a row-wise concatenation so need everything
+ # to line up
+ name = 0
+ else:
+ # doing a column-wise concatenation so need series
+ # to have unique names
+ name = current_column
+ current_column += 1
obj = sample._constructor({name: obj}, copy=False)
new_objs.append(obj)
- return new_objs, sample
+ return new_objs
def get_result(self):
cons: Callable[..., DataFrame | Series]
diff --git a/pandas/tests/reshape/concat/test_concat.py b/pandas/tests/reshape/concat/test_concat.py
index ea0d510d2b8f8..9e34d02091e69 100644
--- a/pandas/tests/reshape/concat/test_concat.py
+++ b/pandas/tests/reshape/concat/test_concat.py
@@ -267,11 +267,10 @@ def test_with_mixed_tuples(self, sort):
# it works
concat([df1, df2], sort=sort)
- def test_concat_mixed_objs(self):
- # concat mixed series/frames
+ def test_concat_mixed_objs_columns(self):
+ # Test column-wise concat for mixed series/frames (axis=1)
# G2385
- # axis 1
index = date_range("01-Jan-2013", periods=10, freq="h")
arr = np.arange(10, dtype="int64")
s1 = Series(arr, index=index)
@@ -324,13 +323,41 @@ def test_concat_mixed_objs(self):
result = concat([s1, df, s2], axis=1, ignore_index=True)
tm.assert_frame_equal(result, expected)
- # axis 0
+ def test_concat_mixed_objs_index(self):
+ # Test row-wise concat for mixed series/frames with a common name
+ # GH2385, GH15047
+
+ index = date_range("01-Jan-2013", periods=10, freq="h")
+ arr = np.arange(10, dtype="int64")
+ s1 = Series(arr, index=index)
+ s2 = Series(arr, index=index)
+ df = DataFrame(arr.reshape(-1, 1), index=index)
+
expected = DataFrame(
np.tile(arr, 3).reshape(-1, 1), index=index.tolist() * 3, columns=[0]
)
result = concat([s1, df, s2])
tm.assert_frame_equal(result, expected)
+ def test_concat_mixed_objs_index_names(self):
+ # Test row-wise concat for mixed series/frames with distinct names
+ # GH2385, GH15047
+
+ index = date_range("01-Jan-2013", periods=10, freq="h")
+ arr = np.arange(10, dtype="int64")
+ s1 = Series(arr, index=index, name="foo")
+ s2 = Series(arr, index=index, name="bar")
+ df = DataFrame(arr.reshape(-1, 1), index=index)
+
+ expected = DataFrame(
+ np.kron(np.where(np.identity(3) == 1, 1, np.nan), arr).T,
+ index=index.tolist() * 3,
+ columns=["foo", 0, "bar"],
+ )
+ result = concat([s1, df, s2])
+ tm.assert_frame_equal(result, expected)
+
+ # Rename all series to 0 when ignore_index=True
expected = DataFrame(np.tile(arr, 3).reshape(-1, 1), columns=[0])
result = concat([s1, df, s2], ignore_index=True)
tm.assert_frame_equal(result, expected)
| - [x] closes #15047
- [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/v2.2.0.rst` file if fixing a bug or adding a new feature.
| https://api.github.com/repos/pandas-dev/pandas/pulls/56365 | 2023-12-06T18:15:11Z | 2023-12-09T19:07:44Z | 2023-12-09T19:07:44Z | 2023-12-09T21:02:17Z |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.