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2019-12-11T17:39:00Z
2019-12-19T20:06:48Z
2019-12-12T13:42:32Z
MEMBER
null
Modeled after https://github.com/cython/cython/pull/3187 which in 0.29.14 added parallel support A very slight boost to timing from a clean directory (bottleneck is in the Cythonize calls in both cases) ```sh # Master $ time python setup.py build_ext --inplace -j 4 : 62.93s user 3.18s system 97% cpu 1:07.77 total # this branch $ time python setup.py build_ext --inplace -j 4: 58.87s user 3.32s system 105% cpu 59.030 total ```
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536,553,266
MDExOlB1bGxSZXF1ZXN0MzUyMDYzODYw
30,215
[Doc Fix] Fix typo from Jupyter Notebook to Jupyter Lab in Ecosystem Doc.
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2019-12-11T18:53:37Z
2019-12-25T20:07:19Z
2019-12-25T20:07:19Z
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I saw this typo in the [doc](https://pandas.pydata.org/pandas-docs/stable/ecosystem.html#jupyter-notebook-jupyter-lab). Decided to create a PR for it. ;)
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STY: Replace .format() to fstrings and %r to repr()
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2019-12-11T19:33:17Z
2019-12-13T19:26:02Z
2019-12-13T19:14:32Z
CONTRIBUTOR
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- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry #29886
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CI Error AttributeError: 'ExcelFile' object has no attribute 'engine'
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2019-12-11T20:47:26Z
2019-12-13T17:31:05Z
2019-12-13T17:31:05Z
MEMBER
null
Showing up in https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=22986&view=logs ```python Traceback (most recent call last): File "D:\a\1\s\pandas\io\excel\_base.py", line 907, in __del__ self.close() File "D:\a\1\s\pandas\io\excel\_base.py", line 890, in close if self.engine == "openpyxl": AttributeError: 'ExcelFile' object has no attribute 'engine' ``` Looks to be caused by changes in #30096 - I think need to make it `self.reader.engine`. But definitely shows this code getting hit @jbrockmendel
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30,218
Feature request: Protocol for converting something to a pandas DataFrame
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2019-12-11T21:05:10Z
2020-06-28T04:05:02Z
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This is inspired by the discussion in https://github.com/scikit-learn/enhancement_proposals/pull/25. NumPy defines an ``__array__`` protocol that allows developers to implement classes that can be converted to an array by calling ``np.asarray()`` . That makes it easy to have a common interface between libraries and it's heavily used by pandas and sklearn. It would be great to have a similar protocol for converting something to a pandas ``DataFrame``. The goal would be to allow users to pass other data structures to libraries that expect a dataframe, say seaborn, as long as the data structures allow conversion to ``pd.DataFrame``. A workaround is for the developer of the new datastructure to provide an ``.asframe`` method, but that creates friction and requires the users to know what data type a particular library or function expects. If instead the developer of the datastructure can declare that conversion to a dataframe is possible, the library author (say seaborn) can request conversion to dataframe in a unified manner. The implementation of this is probably pretty simple as it requires "only" a special case in ``pd.DataFrame.__init__``. The main work is probably in adding it to developer documentation and publicizing it correctly. cc @jorisvandenbossche
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30,219
DEPR: deprecate label kwarg to lreshape, closes #29742
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2019-12-11T22:17:45Z
2019-12-12T15:31:49Z
2019-12-12T13:10:42Z
MEMBER
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- [x] closes #29742 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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30,220
CLN: no need for mixin, move non-index timedelta arithmetic test
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2019-12-11T23:29:13Z
2019-12-13T15:30:43Z
2019-12-13T14:09:10Z
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MDExOlB1bGxSZXF1ZXN0MzUyMTcyNzI3
30,221
CLN: remove keep_null_freq in ops
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2
2019-12-12T00:11:20Z
2019-12-12T23:36:59Z
2019-12-12T23:08:45Z
MEMBER
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This cleanup is viable now that integer-addition has been disallowed for dt64/td64.
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30,222
TST: tests for needs-test issues
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3
2019-12-12T01:06:29Z
2019-12-12T23:01:17Z
2019-12-12T21:05:10Z
MEMBER
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30,223
CLN: changed .format to f string in construction.py, dtypes/base.py, and dtypes/cast.py
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1
2019-12-12T08:10:25Z
2019-12-12T13:14:15Z
2019-12-12T13:14:12Z
CONTRIBUTOR
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Feature Request: Convenience method to rename a column
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2
2019-12-12T09:10:51Z
2020-01-01T16:02:32Z
2020-01-01T15:58:02Z
NONE
null
Usually when renaming a column you have to write: ``` df.rename(columns={"old": "new"}) ``` It would be nice to have a convenience method to rename one column like this: ``` df.renamecol("old", "new") ```
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https://api.github.com/repos/pandas-dev/pandas/issues/30225
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https://github.com/pandas-dev/pandas/pull/30225
536,879,704
MDExOlB1bGxSZXF1ZXN0MzUyMzMyNjQ2
30,225
BUG: loc-indexing with a slice on a CategoricalIndex
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1
2019-12-12T10:12:01Z
2019-12-12T15:04:22Z
2019-12-12T13:39:54Z
CONTRIBUTOR
null
- [x] followup to #29922 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry #29922 didn't handle loc-indexing using a slice on non-string CategoricalIndexes. This fixes that. E.g. we had: ```python >>> cat_idx = pd.CategoricalIndex([1,2,3], ordered=True) >>> df = pd.DataFrame({"A": ["foo", "bar", "baz"]}, index=cat_idx) >>> df.loc[1:2, "A"] TypeError: cannot do slice indexing on... ``` The above works now.
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536,920,968
MDExOlB1bGxSZXF1ZXN0MzUyMzY2NzQ4
30,226
DOC: Documentation for MultiIndex.get_locs() is missing in v0.25 docs
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1
2019-12-12T11:29:20Z
2019-12-12T15:57:30Z
2019-12-12T11:55:03Z
CONTRIBUTOR
null
Like a comment in #30056. I add "get_locs" to method - [ ] closes #30056 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,227
BUG: min/max on empty categorical fails
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1
2019-12-12T11:35:26Z
2019-12-18T07:15:23Z
2019-12-17T15:52:19Z
CONTRIBUTOR
null
- [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Currently on master, the following fails: ```python import pandas as pd # Empty Dataframe df = pd.DataFrame({ "cat": pd.Categorical([], categories=list("ABC"), ordered=True), "val": pd.Series([], dtype="int64") }) df["cat"].max() ``` `ValueError: zero-size array to reduction operation maximum which has no identity` Other dtypes return `NaN` when calling `min()` or `max()` on an empty Series. This PR changes the behavior of Categoricals to be the same. Note that current behavior causes a downstream bug in Dask here: https://github.com/dask/dask/issues/5645, so this PR could fix that bug as well.
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30,228
DEPR: log of deprecations in 1.x (to be removed in 2.0)
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2
2019-12-12T12:25:57Z
2021-11-20T21:16:41Z
null
MEMBER
null
Similar issue as https://github.com/pandas-dev/pandas/issues/6581, but starting a fresh one for keeping a log of the deprecations we do in the 1.x releases. ## 1.0.0 - [ ] #27929 numeric_only to Categorical.min/max - [ ] #29457 is_extension_type - [ ] #29731 remove `keep_tz` kwarg from `DatetimeIndex.to_series` - [ ] #28621 Index.set_value (presumably also IndexEngine.set_value) - [ ] #29812 Remove truediv kwarg from pd.eval - [ ] #27745 Categorical.take_nd - [ ] #29405 Use object dtype for empty Series - [ ] #29909 ``str.__iter__`` - [ ] #30338 "fname" argument in `to_parquet`, `to_stata`, `to_feather` changed to "path" - [ ] #30482 `RangeIndex._start, _stop, _step` - [ ] #30340 `onOffset`, `isAnchored`, `get_offset` - [ ] #30386 ``pd.np`` - [ ] #30489 ``pd.datetime`` - [ ] #30546 groupby single-list indexing - [ ] #30615 ``is_copy`` arg in `.take` - [ ] #30636 ``numpy`` arg in ``read_json`` - [ ] #30656 deprecated ``pd.SparseArray`` (leave in designated pd.array.SparseArray) - [ ] #30702 `CategoricalIndex.take_nd` - [ ] #30745 `pandas.util.testing` module. ## 1.0.1 - [ ] #31569 accepting a negative int in `display.max_colwidth` ## 1.1.0 - [ ] #31333 allowing single-item lists containing slice in `Series.__getitem__` - [ ] #34798 `DataFrame/Series.describe` treats datelike as numeric. - [ ] #29941 `DataFrame.mean` and `DataFrame.median` with ``numeric_only=None`` will include datetime64 and datetime64tz columns in a future version - [ ] #31840 `loc.__setitem__` incorrectly accepting positional slices - [ ] #32516 .to_dict(orient=), for abbreviations - [ ] #32639 `Categorical.to_dense` - [ ] #32516 ``.to_dict()`` aliases - [ ] #33291 `Index.is_mixed` - [ ] #27573 `read_html` and `read_json` non-keyword args - [ ] #34418 `read_excel` non-keyword args - [ ] #33385 `is_categorical` - [ ] #33595 week, weekofyear in Series.dt,DatetimeIndex - [ ] #33637 ``_AXIS_NUMBERS`` and ``_AXIS_NAMES`` properties on NDFrame and child classes - [ ] #33907 `Index.get_value` - [ ] #31809 `.resample(base=, loffset=) - [ ] #34171 `DateOffset.__call__` - [ ] #33218 `groupby(squeeze=)` - [ ] #34193 `Index.__getitem__` with float key - [ ] #34522 `Period.to_timestamp` "tz" kwarg - [ ] #34545 `DataFrame/Series.tshift` - [ ] #34810 `pd.merge` handling `suffixes` as a set. - [ ] #34853 `DatetimeIndex.to_perioddelta` - [ ] #34887 `center` keyword in `.expanding()` - [ ] #30562 `check_less_precise` in `assert_frame_equal/assert_series_equal` - [ ] #35003 deprecate a same column as the `value` parameter in `DataFrame.melt` - [ ] #35141 multi-dimensional key to `Series.__getitem__` - [ ] #34998 `group_keys` ignored for transform-like apply. - [ ] #35165 `Offset.apply_index` ## 1.2.0 - [ ] #35626 ``inplace`` parameter in ``MultiIndex.set_codes`` and ``MultiIndex.set_levels`` - [ ] #35853 `dtype` parameters in `Index.copy` - [ ] #35741 `pandas/io/date_converters.py` - [ ] #36418 `Index.to_native_type` - [ ] #36179 string indexing along index for datetimes - [ ] #36685 `MultiIndex.copy(level, codes)` - [ ] #36649 `.rolling(min_periods=None ...).count()` - [ ] #36795 automatic alignment on DataFrame cmp Series - [ ] #36148 Indexing tz-aware with tz-naive - [ ] #36695 regex=True in str.replace() - [ ] #36900 Index.ravel - [ ] #36695 default value of regex for Series.str.replace - [ ] #36838 'Y', 'M', 'y' units in to_timedelta - [ ] #37374 `Index.__and__`, `__or__`, `__xor__` behaving as setops - [ ] #37545 `Categorical.is_dtype_equal` and `CategoricalIndex.is_dtype_equal` - [ ] #37601 `DataFrame.slice_shift` - [ ] #37819 partial indexing of a non-monotonic DTI - [ ] #37877 Index.asi8 - [ ] #37918 `Categorical.remove_unused_categories` - [ ] #37982 `how` keyword in `Period.astype` - [ ] #37999 `null_counts` in `DataFrame.info` - [ ] #35029 default for `read_excel` with `engine=None` changing - [ ] #38134 `ndim` arg in `Block.__init__` - [ ] #35967 `observed=False` keyword in groupby (this is pending) - [ ] #38317 `xlwt` in excel writing ## 1.2.5 - [ ] #41789 lists in `.xs` ## 1.3.0 - [ ] #38544 `.astype(int)` on datetimelikes - [ ] #38597 `Index` constuction sub-class keywords, [additional tests needed](https://github.com/pandas-dev/pandas/pull/38597#issue-543156552) - [ ] #38472 passing a scalar to `Categorical` constructor - [ ] #36697 `Index.is_all_dates` (tentative as reverted) - [ ] #36131 `Timestamp` comparsion to `datetime.date` - [ ] #38836 `try_cast` in `.where/.mask` - [ ] #38701 `MultiIndex.is_lexsorted` - [ ] #38963 `Rolling.win_type` returning `"freq"` & `Rolling.is_datetimelike` - [ ] #38963 `Rolling.is_datetimelike` - [ ] #39022 `DataFrame.iloc[DataFrame.indexer] = ....` - [ ] #39220 `EWM.vol` - [ ] #39258 `.astype()` with a tz-naive timestamp/index. - [ ] #39022 `DataFrame.iloc` alignment with boolean DataFrame input - [ ] #39767 .reindex with an incorrect fill value - [ ] #40134 `Styler.set_precision` and `Styler.set_na_rep` - [ ] #40238 partial failure in `Series.transform` or `DataFrame.transform` - [ ] #40430 kwargs in `read_excel` - [ ] #40363 MaskedRecords - [ ] #40527 `CategoricalBlock` - [ ] #40571 Block.is_categorical - [ ] #40869 `level=` kwarg in `DataFrame` reduction methods - [ ] #40991 merging with duplicated suffixes - [ ] #40993 merging with non-equal number of levels - [ ] #37981 `Categorial.remove_categories` - [ ] #41118 `Categorical.add_categories` - [ ] #41133 `Categorical.reorder_categories` - [ ] #40821 `Styler.where` - [ ] #41176 `ExcelFile` are `convert_float` - [ ] #41378 `Index._maybe_slice_bound` loc keyword - [ ] #41429 setting `Categorical._codes` - [ ] #41130 `usecols` in `read_csv` that are out of bounds - [ ] #41458 `DatatimeIndex.union` with mixed-timezones - [ ] #41480 DataFrame reduction with nuiscance columns - [ ] #41475 DataFrame groupby reductions with nuiscance columns - [ ] #41510 Positional args in .interpolate - [ ] #41559 Positional args in .fillna - [ ] #41551 Positional args in .set_names - [ ] #41523 Positional args in .where - [ ] #41580 Positional args in .mask - [ ] #41506 Positional args in .sort_index - [ ] #41486 Positional args in .drop - [ ] #41504 Positional args in .dropna - [ ] #41505 Positional args in .sort_values - [ ] #41508 Positional args in ffill/bfill - [ ] #41511 Positional args in .clip - [ ] #41491 Postiional args in .set_axis - [ ] #41495 Positional args in .set_index - [ ] #41496 Positional args in .reset_index - [ ] #41589 Positional args in .set_levels - [ ] #41650 Positional args in set_codes - [ ] #41557 Dataframe construction with categorical in a list of arrays - [ ] #41555 Series/DataFrame with tzaware data and tznaive dtype - [ ] #40413 `read_csv`, `error_bad_lines`, `warn_bad_lines` - [ ] #41657 Positional args in `read_csv` - [ ] #41717 Positional args in `read_table` - [ ] #41662 Series construction with i8 and DatetimeTZDtype as UTC - [ ] #41734 Series construction with smaller dtypes than possible - [ ] #41733 Dataframe construction ignoring invalid dtypes - [ ] #41731 Series inference of M8, m8 w/o dtype - [ ] #41770 Dataframe construction with int dtypes and floats. - [ ] #40628 `Series.between`, inclusive options ## 1.4.0 - [ ] #42113 `Index.is_type_compatible` - [ ] #42215 Setitem with integer and `Float64Index` - [ ] #42269 `Index.get_loc` with `method=` - [ ] #42351 Indexing with a MultiIndex and missing keys - [ ] #43028 `Int64Index`, `Float64Index`, `UInt64Index` - [ ] #43265 .ewm(times=) as a string - [ ] #43427 squeeze arg in `pd.read_csv`, `pd.read_excel` - [ ] #43523 `index` arg to `SparseArray` - [ ] #43504 `closed` arg in `pd.date_range` - [ ] #43248 `includes_*` args in `.between_time` - [ ] #43665 Rolling/Expanding.validate - [ ] #43743,#43741 silent dropping of nuscance columns in .transform - [ ] #43771 .hide_index / .hide_columns in Styler - [ ] #44193 DeprecationWarning on fastparquet checks - [ ] #44243 comparision of tzs - [ ] #44294 'errors' kw in `.mask` and `.where` - [ ] #44398 `PeriodArray.astype` to dti - [ ] #44522 `DateOffset.apply` - [ ] #44539 `Series.append` & `DataFrame.append` - [ ] #44411 `DataFrame.to_latex` to use `Styler.to_latex`
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537,009,630
MDExOlB1bGxSZXF1ZXN0MzUyNDQwNjg0
30,229
Groupby crash on a single level
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4
2019-12-12T14:24:54Z
2019-12-13T17:06:45Z
2019-12-13T17:06:38Z
CONTRIBUTOR
null
- [/] closes #30208 - [/] tests added / passed - [/] passes `black pandas` - [/] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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30,230
CLN: replace str.format() in pandas/io/formats
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1
2019-12-12T14:51:02Z
2019-12-13T00:37:40Z
2019-12-13T00:37:39Z
MEMBER
null
- [x] ref #29547 and ref #30020 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,231
StringArray comparisions return BooleanArray
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2019-12-12T15:14:37Z
2019-12-18T08:30:33Z
2019-12-18T08:30:27Z
CONTRIBUTOR
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xref https://github.com/pandas-dev/pandas/issues/29556 cc @jorisvandenbossche
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30,232
Move 'For Developers' content from wiki to contributing docs.
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19
2019-12-12T15:30:16Z
2020-12-30T18:23:04Z
null
MEMBER
null
Content from the 'For Developers' content section of the wiki should be included in the pandas documentation where relevant. - [ ] Code Style and Conventions - [ ] Testing #20501 - [ ] Documenting new features and bug fixes - [ ] Using Git - [ ] Choosing Exceptions to Raise - [ ] Tips & Tricks for pandas dev - [ ] A quick overview of pandas.util - [ ] Git Workflows - [ ] Pandas Development FAQ - [ ] Building On Windows PRs welcome for any of the above sections.
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537,058,395
MDExOlB1bGxSZXF1ZXN0MzUyNDgxMzg4
30,233
CI: restore npdev build
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3
2019-12-12T15:45:25Z
2019-12-12T21:13:13Z
2019-12-12T21:10:53Z
MEMBER
null
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537,065,853
MDExOlB1bGxSZXF1ZXN0MzUyNDg3NzAy
30,234
DOC: Add MultiIndex.get_locs to generated reference
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4
2019-12-12T15:57:44Z
2019-12-12T19:26:35Z
2019-12-12T17:39:53Z
MEMBER
null
This should fix the failing CI, see https://github.com/pandas-dev/pandas/pull/30226 CLoses #30235
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30,235
CI: Doc build failing
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1
2019-12-12T16:13:03Z
2019-12-12T17:39:53Z
2019-12-12T17:39:53Z
CONTRIBUTOR
null
``` /home/vsts/work/1/s/pandas/core/indexes/multi.py:docstring of pandas.MultiIndex.rst:91: WARNING: autosummary: stub file not found 'pandas.MultiIndex.get_locs'. Check your autosummary_generate setting. looking for now-outdated files... none found ``` https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=23019
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537,080,830
MDU6SXNzdWU1MzcwODA4MzA=
30,236
CI: 32-bit build failing.
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6
2019-12-12T16:23:36Z
2019-12-18T19:24:35Z
2019-12-18T19:24:35Z
CONTRIBUTOR
null
We're failing to compile the C extension on linux 32. See e.g. https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=23011
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537,081,420
MDExOlB1bGxSZXF1ZXN0MzUyNTAwNjU0
30,237
Removed parallel CI
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1
2019-12-12T16:24:41Z
2019-12-12T17:07:51Z
2019-12-12T17:07:48Z
MEMBER
null
I think this was responsible for some of the Py36 failures showing up in CI this morning, so reverting this piece for now (can still locally use parallel build_ext) until investigated further
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30,238
Appending to DataFrame removes the timezone-awareness
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2
2019-12-12T16:30:04Z
2019-12-24T14:58:53Z
2019-12-24T14:58:53Z
MEMBER
null
#### Code Sample, a copy-pastable example if possible ```python import pytz import pandas as pd df = pd.DataFrame( {"a": [pd.Timestamp("2019-07-19 07:04:57+0100", tz=pytz.FixedOffset(60))]} ) df.append(df.iloc[0]) ``` #### Problem description This returns: ``` | | a | |---:|:--------------------------| | 0 | 2019-07-19 07:04:57+01:00 | | 0 | 2019-07-19 06:04:57 | ``` However, I was expecting ``` | | a | |---:|:--------------------------| | 0 | 2019-07-19 07:04:57+01:00 | | 0 | 2019-07-19 07:04:57+01:00 | ``` which indeed is what happens if I instead write ``` df.append(df) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.5.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-72-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.6.0.post20191030 Cython : 0.29.14 pytest : 5.3.1 hypothesis : None sphinx : 2.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.9.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.1.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.15.1 pytables : None s3fs : 0.4.0 scipy : 1.3.3 sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None </details>
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537,104,256
MDExOlB1bGxSZXF1ZXN0MzUyNTE5NjUx
30,239
API: Return BoolArray for string ops when backed by StringArray
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4
2019-12-12T17:04:50Z
2019-12-19T17:28:38Z
2019-12-19T17:09:21Z
CONTRIBUTOR
null
ref https://github.com/pandas-dev/pandas/issues/29556
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537,167,742
MDExOlB1bGxSZXF1ZXN0MzUyNTcyNDY5
30,240
REF: dont set self.data in pytables take_data
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2019-12-12T19:24:41Z
2019-12-12T20:56:36Z
2019-12-12T20:42:33Z
MEMBER
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537,184,670
MDExOlB1bGxSZXF1ZXN0MzUyNTg2NzUw
30,241
BUG: to_datetime with unit with Int64
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12
2019-12-12T20:01:05Z
2020-01-03T06:14:23Z
2020-01-02T20:38:41Z
MEMBER
null
- [X] closes #30050 - [X] tests added 1 / passed 1 - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [X] whatsnew entry Test output: ``` $ py.test pandas/tests/tools/test_datetime.py ============================= test session starts ============================= platform win32 -- Python 3.7.3, pytest-5.3.1, py-1.8.0, pluggy-0.13.0 rootdir: C:\git_contrib\pandas\pandas, inifile: setup.cfg plugins: hypothesis-4.50.6, cov-2.8.1, forked-1.1.2, xdist-1.30.0 collected 1 item pandas\tests\tools\test_datetime.py . [100%] ============================== 1 passed in 0.03s ============================== ``` Notes: Hello. The fix introduces one additional type check if the Series passed to `to_datetime` stores anything except and IntegerArray. It does nothing else in this case. If we try to pass an IntegerArray, it pulls all the values that aren't NaN into an ndarray, converts that into datetime and adds NaT in the places where NaNs were. No precision is lost.
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30,242
TST: Add message checks to tests/arrays/categorical/
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1
2019-12-12T21:00:04Z
2019-12-16T00:35:03Z
2019-12-15T21:35:54Z
MEMBER
null
- [ ] xref #23922 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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537,219,175
MDExOlB1bGxSZXF1ZXN0MzUyNjE1NjAx
30,243
Remove yapf references
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3
2019-12-12T21:19:44Z
2020-01-16T00:33:18Z
2019-12-13T13:29:30Z
MEMBER
null
I'm not aware that we actually use this
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537,230,022
MDExOlB1bGxSZXF1ZXN0MzUyNjI0NzMy
30,244
TST: Add message checks to tests/arrays/sparse/
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2
2019-12-12T21:44:36Z
2019-12-12T23:16:21Z
2019-12-12T23:09:16Z
MEMBER
null
- [ ] xref #23922 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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537,231,558
MDExOlB1bGxSZXF1ZXN0MzUyNjI2MDE0
30,245
Implement NA.__array_ufunc__
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19
2019-12-12T21:48:00Z
2020-01-05T20:43:25Z
2020-01-05T20:43:08Z
CONTRIBUTOR
null
This gives us consistent comparisons with NumPy scalars. I have a few implementation questions: 1. I wanted to reuse `maybe_dispatch_ufunc_to_dunder_op`. That was in `core/ops/dispatch.py`, but NA is defend in `_libs/missing.pyx`. For now I just moved it to `_libs/missing.pyx`, is there a better home for it? I don't think `_libs/ops.pyx` is an option, as it imports missing. 2. How should we handle comparisons with ndarrays? I see a few options: a. Return an `ndarray[object]`, where each element is NA b. Really take over and return our extension arrays. So `np.array([1, 2]) == pd.NA` would return an `IntegerArray([NA, NA])`. Boolean results would return a BooleanArray. And float results would return a ... PandasArray? (so maybe this isn't a good idea. But worth considering) 3. What scalars should we handle (in `__array_ufunc__` and in the regular methods)? Any of date time, Timestamp, time delta, Period, Interval? One item 2, I seem to have messed something up, as we return just a scalar `NA` right now. Will want to sort that out.
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MDExOlB1bGxSZXF1ZXN0MzUyNjI3MjY5
30,246
ERR: stringify error message from parsers.pyx, closes #29233
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1
2019-12-12T21:51:36Z
2019-12-13T15:29:08Z
2019-12-13T14:21:39Z
MEMBER
null
- [x] closes #29233 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,247
CLN: more consistent error message for ExtensionDtype.construct_from_string
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2019-12-12T22:48:40Z
2019-12-13T14:28:49Z
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MEMBER
null
message check added to `test_construct_from_string` in `pandas/tests/extension/base/dtype.py` and error messages changed to be consistent with error message on class method in `pandas/core/dtypes/base.py` xref Provide ExtensionDtype.construct_from_string by default #26562
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537,262,885
MDExOlB1bGxSZXF1ZXN0MzUyNjUyMDA1
30,248
CLN changed .format to f-string for test_converter.py, test_datetimel…
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2019-12-12T23:11:53Z
2019-12-27T23:45:17Z
2019-12-13T13:33:34Z
CONTRIBUTOR
null
…ike.py, test_series.py #29547 https://github.com/pandas-dev/pandas/issues/29547 - [x] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,249
TST: Fix apparent typo skip_if_no lxml->bs4
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2019-12-13T01:03:46Z
2019-12-13T15:28:11Z
2019-12-13T13:31:34Z
MEMBER
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30,250
CLN : f-string instead of str.format() in pandas/utils and pandas/io/formats
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2019-12-13T01:05:53Z
2019-12-14T16:02:35Z
2019-12-13T13:17:24Z
MEMBER
null
- [x] ref #29547 and ref #30020 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry I accidentally deleted the branch for #30230 which closed it, but I applied all the requested changes in this PR.
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DEPR: change DataFrame.append default sort kwarg
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3
2019-12-13T01:15:27Z
2019-12-16T01:06:10Z
2019-12-15T21:46:58Z
MEMBER
null
xref #20613 AFAICT this doesn't break any tests, was this supposed to have been changed along with the default in `concat` #29786 ?
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30,252
Make resample documentation better
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4
2019-12-13T04:51:57Z
2021-04-20T17:08:06Z
2020-01-03T13:07:54Z
NONE
null
I removed the checkboxes because they were not relevant. This is a docstring update. I found the answer in https://stackoverflow.com/questions/17001389/pandas-resample-documentation but it should have been present in the docs
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30,253
Groupby agg works with pd.Series.nunique, but groupby nunique fails with axis=1
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4
2019-12-13T09:43:47Z
2019-12-18T18:17:49Z
2019-12-18T18:17:49Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python df = pd.DataFrame({ ('A', 'B'): [1, 2], ('A', 'C'): [1, 3], ('D', 'B'): [0, 0] }, index=pd.bdate_range('20191212', '20191213')) # DOESNT CRASH df.groupby(axis=1, level=0).agg(pd.Series.nunique) # CRASH df.groupby(axis=1, level=0).nunique() ``` #### Problem description The groupby nunique crashes with error "ValueError: all keys need to be the same shape", whereas the groupby agg with pd.Series.nunique does not crash #### Expected Output pd.DataFrame({ 'A': [1, 2], 'D': [1, 1] }, index=pd.bdate_range('20191212', '20191213')) #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 3.6.8.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-642.6.2.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8 pandas: 0.21.1 pytest: 4.4.1 pip: 18.1 setuptools: 40.8.0.post20190628 Cython: 0.27.3 numpy: 1.13.3 scipy: 0.19.1 pyarrow: None xarray: 0.8.2 IPython: 5.1.0 sphinx: 1.8.5 patsy: 0.4.1 dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: None tables: 3.4.4 numexpr: 2.6.5 feather: None matplotlib: 2.1.2 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: 0.9999999 sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.8 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None </details>
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30,254
How to import data from web with no tables with python
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2019-12-13T10:07:56Z
2019-12-13T10:23:36Z
2019-12-13T10:23:36Z
NONE
null
Hi I want to import and save data from the following page into a list variable in python. "http://www.fipiran.com/Symbol/HistoryPrice?symbolpara=%D8%A8%D8%AA%D8%B1%D8%A7%D9%86%D8%B3" Unfortunately, data did not store into a table in this page. Please note that table data in middle of page is required. Can any one help me to save this data? Regards
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30,255
CI: wrap ExcelFile.__del__ in try/except
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3
2019-12-13T10:08:40Z
2019-12-14T07:45:52Z
2019-12-13T17:31:05Z
MEMBER
null
Closes https://github.com/pandas-dev/pandas/issues/30217
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30,256
Copying values by columns from Series to DataFrame results in NaNs
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1
2019-12-13T11:04:00Z
2019-12-13T11:08:02Z
2019-12-13T11:07:40Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd # version 0.22.0 columns = ['a', 'b', 'c'] df1 = pd.DataFrame([[0, 0, 0]], columns=columns) df2 = pd.DataFrame( [[1, 1, 1, 1, 1], [2, 2, 2, 2, 2]], index=['ones', 'twos'], columns=['d', 'a', 'b', 'c', 'e'] ) df1[columns] = df2.loc['twos', columns] # results in NaNs in df1 df1[columns] = list(df2.loc['twos', columns]) # results in NaNs in df1 ``` #### Problem description The result of the second-last line in the code above (`df1` filled with `NaN`s) is surprising. #### Expected Output The last line works as I would have expected, i.e. it copies `[2.0, 2.0, 2.0]` into `df1`. If the first line is somehow under-specified perhaps an exception is preferable to `NaN` values in `df1`? #### Output of ``pd.show_versions()`` ``` INSTALLED VERSIONS ------------------ commit: None python: 2.7.15.final.0 python-bits: 64 OS: Linux OS-release: 5.0.0-37-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_ZA.UTF-8 LOCALE: en_ZA.UTF-8 pandas: 0.22.0 pytest: 3.3.2 pip: 9.0.1 setuptools: 39.0.1 Cython: None numpy: 1.13.3 scipy: 0.19.1 pyarrow: None xarray: None IPython: 5.5.0 sphinx: None patsy: 0.4.1+dev dateutil: 2.6.1 pytz: 2018.3 blosc: None bottleneck: None tables: 3.4.2 numexpr: 2.6.4 feather: None matplotlib: 2.1.1 openpyxl: 2.4.9 xlrd: 1.1.0 xlwt: 0.7.5 xlsxwriter: None lxml: 4.2.1 bs4: 4.6.0 html5lib: 0.999999999 sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None ```
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30,257
DOC: .get_slice_bound in MultiIndex needs documentation.
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2
2019-12-13T13:56:19Z
2020-06-17T11:31:50Z
2020-01-01T17:27:21Z
CONTRIBUTOR
null
- [x] closes #29967 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry I'm not good at English. So if there are wrong sentences or awkward sentences, please let me know.
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CLN: various lgtm.com cleanups
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2019-12-13T16:11:36Z
2019-12-16T00:33:55Z
2019-12-15T21:46:17Z
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30,259
CLN: more lgtm.com cleanups
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2019-12-13T17:03:49Z
2019-12-16T00:34:04Z
2019-12-15T21:39:26Z
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CLN: Assorted cleanups
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0
2019-12-13T17:45:36Z
2019-12-16T01:04:30Z
2019-12-15T21:43:42Z
MEMBER
null
Many of these are broken off from #30035.
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537,690,201
MDU6SXNzdWU1Mzc2OTAyMDE=
30,261
loop over dataframes to select rows didn't work
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3
2019-12-13T18:03:40Z
2019-12-13T19:43:05Z
2019-12-13T19:29:21Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import seaborn as sns import pandas as pd df = sns.load_dataset('tips') df_1 = df[['tip', 'time', 'day']].copy() df_2 = df[['total_bill', 'time', 'day']].copy() for each_df in [df_1, df_2]: each_df = each_df[each_df['day'] != 'Sun'] ``` #### Problem description I tried to loop over two dataframes but the selecting operation didn't work and I checked `df_1` and `df_2`, and rows with 'Sun' are still in the dataframe. I also check the iteration variable `each_df`, the rows with `Sun` were indeed dropped. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 0.25.3 numpy : 1.16.2 pytz : 2019.3 dateutil : 2.8.0 pip : 19.1 setuptools : 41.4.0 Cython : 0.29.7 pytest : 4.5.0 hypothesis : None sphinx : 2.0.1 blosc : None feather : None xlsxwriter : 1.1.7 lxml.etree : 4.3.0 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.8.0 pandas_datareader: None bs4 : 4.7.1 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.3.0 matplotlib : 3.1.1 numexpr : 2.7.0 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.2.1 sqlalchemy : None tables : None xarray : 0.12.1 xlrd : 1.2.0 xlwt : None xlsxwriter : 1.1.7 </details>
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537,761,094
MDU6SXNzdWU1Mzc3NjEwOTQ=
30,262
Builtin loading of files from multi-file zip archives
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2019-12-13T20:20:39Z
2020-05-02T10:49:38Z
2020-05-02T04:37:15Z
CONTRIBUTOR
null
Could a solution be implemented to simplify loading a dataset from a multi-file zip archive? I know it is a bit insignificant compared to most other issues (especially since there are workarounds), but I'd be happy to implement it myself if I'm given the go-ahead so as to not waste your time. Thanks for this amazing library!
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30,263
iloc erroneously truncates underlying data for timestamps with timezone
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4
2019-12-13T20:22:05Z
2021-05-31T14:27:04Z
2021-05-31T14:27:04Z
CONTRIBUTOR
null
This is a strange one... Here's a short repro of the error, which I can reproduce in pandas 0.21-0.25 and numpy 0.15-0.17 (I didn't try further back than that): ```python-traceback >>> import pandas as pd >>> df = pd.DataFrame({'x': pd.date_range('2019', periods=10, tz='UTC')}) >>> df = df.iloc[:, :5] >>> df._repr_html_() --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-3-f6e53eba0a8f> in <module>() 2 df = pd.DataFrame({'x': pd.date_range('2019', periods=10, tz='UTC')}) 3 df = df.iloc[:, :5] ----> 4 df._repr_html_() /usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in _repr_html_(self) 667 buf = StringIO("") 668 self.info(buf=buf) --> 669 # need to escape the <class>, should be the first line. 670 val = buf.getvalue().replace("<", r"&lt;", 1) 671 val = val.replace(">", r"&gt;", 1) /usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook, decimal, border) 1732 1733 >>> df.to_records(column_dtypes={"A": "int32"}) -> 1734 rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)], 1735 dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')]) 1736 /usr/local/lib/python3.6/dist-packages/pandas/io/formats/format.py in to_html(self, classes, notebook, border) 732 ) 733 ) --> 734 735 def _join_multiline(self, *strcols): 736 lwidth = self.line_width /usr/local/lib/python3.6/dist-packages/pandas/io/formats/format.py in write_result(self, buf) 1206 else: 1207 threshold = None -> 1208 1209 # if we have a fixed_width, we'll need to try different float_format 1210 def format_values_with(float_format): /usr/local/lib/python3.6/dist-packages/pandas/io/formats/format.py in _write_body(self, indent) 1369 1370 Examples -> 1371 -------- 1372 Keeps all entries different after rounding: 1373 /usr/local/lib/python3.6/dist-packages/pandas/io/formats/format.py in _write_regular_rows(self, fmt_values, indent) 1403 to_begin = unique_pcts[0] if unique_pcts[0] > 0 else None 1404 to_end = 100 - unique_pcts[-1] if unique_pcts[-1] < 100 else None -> 1405 1406 # Least precision that keeps percentiles unique after rounding 1407 prec = -np.floor( /usr/local/lib/python3.6/dist-packages/pandas/io/formats/format.py in <genexpr>(.0) 1403 to_begin = unique_pcts[0] if unique_pcts[0] > 0 else None 1404 to_end = 100 - unique_pcts[-1] if unique_pcts[-1] < 100 else None -> 1405 1406 # Least precision that keeps percentiles unique after rounding 1407 prec = -np.floor( IndexError: list index out of range ``` It seems that somehow, the second argument of iloc is truncating the underlying data along the wrong axis: ```python-traceback >>> df.shape (10, 1) >>> df.values.shape (5, 1) >>> print(df) x 0 2019-01-01 00:00:00+00:00 1 2019-01-02 00:00:00+00:00 2 2019-01-03 00:00:00+00:00 3 2019-01-04 00:00:00+00:00 4 2019-01-05 00:00:00+00:00 5 6 7 8 9 ``` This only appears to happen if the data contains timestamps with a timezone specified. If I remove ``tz='UTC'`` above, everything works properly.
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WIP/ERR: raise consistent errors for non-existent files
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2019-12-13T20:42:47Z
2021-11-20T23:21:55Z
2019-12-21T19:18:11Z
MEMBER
null
- [x] closes #29125 - [x] closes #13872 - [ ] closes #17918 <-- needs tests and double-checking - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry xref #29102 the json case is xfailed here.
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[WIP]: Indexing with BooleanArray propagates NA
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2019-12-13T20:46:44Z
2019-12-18T14:26:54Z
2019-12-16T21:40:46Z
CONTRIBUTOR
null
This implements indexing a Series with a BooleanArray for some dtypes. NA values in the mask propagate. It's not clear that we want to propagate NAs. This required updating each EA's `__getitem__` to correctly propagate NA. Doing the same for ndarray-backed Series is possible, but will require some additional hacking and we first need to determine the result dtype. No need to review in detail. The primary motivation for pushing this up is to better understand what indexing with NA values will look like in practice. xref https://github.com/pandas-dev/pandas/issues/28778, and https://github.com/pandas-dev/pandas/issues/28778#issuecomment-565538145 in particular. --- (copied from https://github.com/pandas-dev/pandas/issues/28778#issuecomment-565538145) On indexing, propagating NAs presents some challenging dtype casting issues. For example, what is the dtype on the output of ```python In [3]: s = pd.Series([1, 2, 3]) In [4]: mask = pd.array([True, False, None]) In [5]: s[mask] ``` Would that be an Int64 dtype, with the last value being NA? Would we cast to float64 and use `np.nan`? object-dtype? And what if the original series was float dtype? A float-dtype with NaN is about the only option we have right now, since we lack a float64 array that can hold NA. I don't think that an indexing operation that introduces missing values should change the dtype of the array. I don't think anyone would realistically want that. So... do we just raise for now? What about cases when we are able to index without changing the dtype? Those would be 1. Indexing with a BooleanArray with no missing values. 2. Indexing a dtype that supports missing values (datetime, timedelta, string, object, Int64, boolean...). Basically anything but NumPy's bool, int float. IMO, which shouldn't have value-dependent behavior, so if ```python >>> pd.Series([1, 2])[pd.array([True, None]) ``` raises, then so should `pd.Series([1, 2])[pd.array([True, False])` (no missing values). I think supporting 2 is fine, since it just depends on the dtypes. ```python >>> pd.Series([1, 2], dtype="Int64")[pd.array([True, None])] Series([1, NA], dtype="Int64") >>> pd.Series([1, 2], dtype="Int64")[pd.array([True, False])] Series([1], dtype="Int64") ```
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Made date time sources usage more explicit in setup.py
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7
2019-12-13T22:08:24Z
2019-12-18T18:34:06Z
2019-12-18T18:34:02Z
MEMBER
null
Not sure this fully solves #30236 but doesn't hurt and makes things more explicit
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PERF: slow multiplication of DataFrame and a scalar
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2019-12-13T22:22:11Z
2019-12-14T02:44:31Z
2019-12-14T02:44:30Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python df = pd.DataFrame(np.random.randn(500, 1000)) %timeit x1 = df * 50 # 258 ms ± 14.6 ms per loop # For comparison: %timeit x2 = df * df # 1.57 ms ± 9.16 µs per loop %timeit x3 = np.multiply(df, 50) # 878 µs ± 71.7 µs per loop ``` #### Problem description Multiplication by a scalar takes enormously large time (e.g. in comparison with an element-wise multiplication of two dataframes or `numpy.multiply`). Same problem holds for division. Pandas version: 0.25.3 #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.1.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.25.3 numpy : 1.17.2 pytz : 2019.3 dateutil : 2.8.1 pip : 19.3.1 setuptools : 42.0.1.post20191125 Cython : None pytest : 5.3.0 hypothesis : None sphinx : 2.2.0 blosc : None feather : None xlsxwriter : None lxml.etree : 4.4.1 html5lib : None pymysql : 0.9.3 psycopg2 : None jinja2 : 2.10.1 IPython : 7.8.0 pandas_datareader: None bs4 : 4.6.3 bottleneck : None fastparquet : 0.3.2 gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.0.1 numexpr : 2.7.0 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.12.1 pytables : None s3fs : 0.4.0 scipy : 1.3.1 sqlalchemy : 1.3.8 tables : 3.4.4 xarray : None xlrd : 1.1.0 xlwt : None xlsxwriter : None </details>
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Int64 with null value mangles large-ish integers
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5
2019-12-13T22:40:04Z
2021-05-11T03:12:49Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np x = 9999999999999999 y = 123123123123123123 z = 10000000000000543 s = pd.Series([1, 2, 3, x, y, z], dtype="Int64") s[3] == x # True s s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64") s2[3] == x # False s2 np.iinfo(np.int64).max ``` With interpreter output: ```python >>> import pandas as pd >>> import numpy as np >>> >>> x = 9999999999999999 >>> y = 123123123123123123 >>> z = 10000000000000543 >>> s = pd.Series([1, 2, 3, x, y, z], dtype="Int64") >>> s[3] == x # True True >>> s 0 1 1 2 2 3 3 9999999999999999 4 123123123123123123 5 10000000000000543 dtype: Int64 >>> s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64") >>> s2[3] == x # False False >>> s2 0 1 1 2 2 3 3 10000000000000000 4 123123123123123120 5 10000000000000544 6 NaN dtype: Int64 >>> np.iinfo(np.int64).max 9223372036854775807 ``` #### Problem description It seams that the presence of `np.nan` values in a column being typed as `Int64` causes some non-null values to be mangled. This seems to happen with large-ish values (but still below the max int limit). Given that Int64 is the "Nullable integer" data type, null values should be allowed, and should certainly not silently change the values of other elements in the data frame. #### Expected Output ```python >>> import pandas as pd >>> import numpy as np >>> >>> x = 9999999999999999 >>> y = 123123123123123123 >>> z = 10000000000000543 >>> s = pd.Series([1, 2, 3, x, y, z], dtype="Int64") >>> s[3] == x # True True >>> s 0 1 1 2 2 3 3 9999999999999999 4 123123123123123123 5 10000000000000543 dtype: Int64 >>> s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64") >>> s2[3] == x # True (was False above) True >>> s2 0 1 1 2 2 3 3 9999999999999999 4 123123123123123123 5 10000000000000543 6 NaN dtype: Int64 >>> np.iinfo(np.int64).max 9223372036854775807 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.1.final.0 python-bits : 64 OS : Darwin OS-release : 19.0.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_CA.UTF-8 LOCALE : en_CA.UTF-8 pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.1 pip : 10.0.1 setuptools : 39.0.1 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None </details>
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537,813,901
MDExOlB1bGxSZXF1ZXN0MzUzMTAzNTkw
30,269
Remove skiplist.pxd
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2
2019-12-13T22:55:08Z
2019-12-14T03:58:00Z
2019-12-14T03:57:40Z
MEMBER
null
Just placed header definitions where they are actually used follow up to #27465
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30,270
ENH: pass through schema keyword in to_parquet for pyarrow
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5
2019-12-13T23:47:53Z
2019-12-18T10:39:46Z
2019-12-18T10:39:32Z
CONTRIBUTOR
null
- [X] closes #30189 - [x] tests added / passed - [X] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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537,859,643
MDExOlB1bGxSZXF1ZXN0MzUzMTM3OTcw
30,271
Simplified objToJSON signatures
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2019-12-14T03:33:15Z
2020-01-16T00:33:21Z
2019-12-15T17:40:01Z
MEMBER
null
Pre-cursor to a change that splits datetime functions that return `char *` off from datetime functions that return int. Right now these are intermixed in functions that return void pointers, which makes type checking and grokking more difficult than they need to be
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MDU6SXNzdWU1Mzc4NzA1ODg=
30,272
Unable to read number_format with pandas / openpyxl (engine)
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2019-12-14T05:33:13Z
2019-12-17T02:45:39Z
2019-12-17T02:45:33Z
NONE
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#### Code Sample ```python # Your code here import pandas as pd data = pd.read_excel('temp.xlsx', engine="openpyxl") print(data.iloc[0][4].number_format) ######Via openpyxl from openpyxl import load_workbook wb = load_workbook('temp.xlsx') ws = wb.worksheets[0] col_d = ws.cell(row=2, column=5) print(col_d.value) col_nf = col_d.number_format print(col_nf) ``` #### Problem description Unable to access number_format in pandas dataframe using engine openpyxl. But i am able to acccess the number_format in plain openoyxl via above code. #### Expected Output "$"#,##0 #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] INSTALLED VERSIONS ------------------ commit : None python : 3.6.8.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 0.25.2 numpy : 1.17.3 pytz : 2019.2 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.6.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : 0.9.3 psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : 3.0.2 pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : 1.3.10 tables : None xarray : None xlrd : 1.2.0 xlwt : None xlsxwriter : None None </details>
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30,273
CLN: changed .format to f-string in pandas/core/indexes
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2019-12-14T07:43:41Z
2019-12-15T12:39:43Z
2019-12-15T12:35:23Z
CONTRIBUTOR
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towards #29547 - [ ] closes - [x] tests passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,274
DOC: Make pyplot import explicit in the 10 minutes to pandas page
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2019-12-14T08:25:07Z
2019-12-26T12:44:29Z
2019-12-26T12:44:29Z
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,275
Very frustrating not to be able to apply per-column date formats exporting to Excel
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9
2019-12-14T10:25:53Z
2021-06-11T13:49:40Z
null
NONE
null
When exporting to Excel, it is possible to add multiple number formats and then use `set_column` to apply any desired numeric format to any column. Attempting to do the same for date or date-time columns has no effect however. It is possible to set a format for all date columns or all date-time columns using the `date_format` and `datetime_format` arguments to `ExcelWriter()`, but that applies a single format to all dates and a single format to all date-time values. In my particular case, I have a column that is intended to represent a whole month, and I want to format it as such in the exported data, and I have other date columns that represent specific dates. There is apparently no way to accomplish that. Why? Why not simply allow applying date formats to columns just as one can apply numeric formats to columns?
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537,949,007
MDU6SXNzdWU1Mzc5NDkwMDc=
30,276
Only reading first sheet without warning
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2019-12-14T18:37:05Z
2020-01-20T01:20:57Z
2019-12-17T02:42:46Z
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#29491 ## Problem description When there are multiple sheets in an excel file, read_excel only returns the first. This is fine, but I think it should also warn that there are multiple sheets
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30,277
[bug] don't remove timezone-awareness when using the method from Dat…
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2019-12-14T18:38:20Z
2019-12-24T16:08:38Z
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MEMBER
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…aFrame - [x] closes #30238 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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STY: Replace .format() to fstrings
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2019-12-18T09:57:32Z
2019-12-18T09:57:19Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry #29547
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30,279
Unhide plt, beginners don't know how to import it.
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2019-12-15T01:53:52Z
2019-12-16T04:03:26Z
2019-12-16T04:03:26Z
CONTRIBUTOR
null
Alter https://github.com/pandas-dev/pandas/pull/30274. - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,280
Can't initialise DataFrame with ExtensionDtype, but can do so for Series
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0
2019-12-15T08:39:54Z
2019-12-17T13:25:57Z
2019-12-17T13:25:57Z
MEMBER
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import pytz temp = pd.Series([pd.Timestamp("2019-07-19 07:04:57+0100", tz=pytz.FixedOffset(60))]) pd.Series(["2019-06-19 07:04:57"], dtype=temp.dtype) # works pd.DataFrame(["2019-06-19 07:04:57"]).astype(temp.dtype) # works pd.DataFrame(["2019-06-19 07:04:57"], dtype=temp.dtype) # fails ``` #### Problem description I get the following error: ``` ValueError: failed to cast to 'datetime64[ns, pytz.FixedOffset(60)]' (Exception was: data type not understood) ``` #### Expected Output I would have expected the same output as I got in the first two examples above labelled `#works`. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-70-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.4.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 7.9.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None </details>
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30,281
read_html docs inconsistent with behaviour if lxml not installed
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1
2019-12-15T12:55:59Z
2021-08-01T18:28:32Z
null
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python >>> dfs = pd.read_html('<table><tr><td>1</td></tr></table>') ImportError: lxml not found, please install it >>> dfs = pd.read_html('<table><tr><td>1</td></tr></table>', flavor='bs4') [ 0 0 1] ``` #### Problem description The documentation explictly states, in the HTML-parsing-gotchas page and the argument docstring that the fall back is to 'bs4+html5lib' if 'lxml' fails. For me there is no fallback, just an ImportError since I do not have lxml installed. #### Expected Output Docs should be changed to require explicit `flavor` input, or the ImportError is caught. #### Output of ``pd.show_versions()`` <details> pandas == 0.25.3 </details>
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30,282
solve "Int64 with null value mangles large-ish integers" problem
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2019-12-15T14:29:59Z
2020-11-26T19:04:42Z
2020-11-26T19:04:41Z
CONTRIBUTOR
null
- [x] closes #30268 - [ ] tests added/passed - [x] passes black pandas - [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff - [x] whatsnew entry The `pd.Series` was giving less precise output when inputting a list containing large integers and np.nan values and passing `dtype="Int64"`. Which was due to converting it to `np.array`. This PR aims to solve that bug.
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30,283
Replaced void pointers with Types in JSON Datetime Conversions
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2019-12-15T23:45:07Z
2019-12-24T17:28:50Z
2019-12-24T17:27:47Z
MEMBER
null
This also incorporates the clean up from #30271 Benchmarks show the following benefit, though most likely just noise: ```sh . before after ratio [04fce81f] [f8ed75d0] <stash~6^2> <json-dates> - 336±9ms 302±4ms 0.90 io.json.ToJSON.time_to_json('columns', 'df_int_floats') SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. PERFORMANCE INCREASED. ``` One follow up to this can drastically speed up datetime index handling - right now these are done in the Python space but can reuse these same functions
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BUG: DataFrame constructor raised ValueError with list-like data and …
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2
2019-12-15T23:47:35Z
2019-12-17T14:01:33Z
2019-12-17T13:25:58Z
MEMBER
null
…dtype specified - [ ] closes #30280 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,285
ENH: Add StataWriter 118 for unicode support
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10
2019-12-16T00:46:04Z
2020-07-28T14:41:35Z
2019-12-31T21:05:42Z
CONTRIBUTOR
null
Add StataWriter with unicode support - [X] closes #23573 - [X] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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30,286
AttributeError: module 'pandas' has no attribute 'ewm'
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1
2019-12-16T06:38:46Z
2019-12-16T11:10:25Z
2019-12-16T11:10:25Z
NONE
null
#### Code Sample, a copy-pastable example if possible def get_technical_indicators(dataset): dataset['26ema'] = pd.ewm(dataset['Close'], span=26) dataset['12ema'] = pd.ewm(dataset['Close'], span=12) dataset['MACD'] = (dataset['12ema']-dataset['26ema']) def get_technical_indicators(dataset): ```python # Your code here when running those command is shows the error pandas has no attribute ewma ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution.] **Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates! **Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary. For documentation-related issues, you can check the latest versions of the docs on `master` here: https://pandas-docs.github.io/pandas-docs-travis/ If the issue has not been resolved there, go ahead and file it in the issue tracker. #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] </details>
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30,287
CLN: changed .format to f-string in pandas/core/dtypes
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3
2019-12-16T09:02:01Z
2019-12-18T23:09:26Z
2019-12-18T23:08:54Z
CONTRIBUTOR
null
- [ ] ref #29547 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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The setting xrot=0 in DataFrame.hist() doesn't work with by and subplots
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2019-12-16T10:02:20Z
2019-12-27T16:29:54Z
2019-12-27T16:29:54Z
NONE
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#### Code Sample, a copy-pastable example if possible ```python df = pd.DataFrame({ 'length': [1.5, 0.5, 1.2, 0.9, 3], 'animal': ['pig', 'rabbit', 'pig', 'pig', 'rabbit'] }) df.hist(column='length', by='animal', bins=5, xrot=0); ``` #### Problem description The setting `xrot=0` doesn't work when I use the option `by` in DataFrame.hist() (see the screenshot below). The temporary workaround is to set it to a very small value like `xrot=0.001`. ![image](https://user-images.githubusercontent.com/16855796/70897591-38e0ec00-1ff3-11ea-9f35-c924727fad39.png) #### Expected Output Should look like this instead ![image](https://user-images.githubusercontent.com/16855796/70897647-5615ba80-1ff3-11ea-925d-f67377d5001e.png) #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.6.9.final.0 python-bits : 64 OS : Linux OS-release : 3.10.0-1062.4.1.el7.x86_64 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.25.2 numpy : 1.17.2 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 41.4.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.8.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.1.1 numexpr : None odfpy : None openpyxl : 3.0.2 pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.3.1 sqlalchemy : None tables : None xarray : None xlrd : 1.2.0 xlwt : None xlsxwriter : None </details>
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538,506,717
MDU6SXNzdWU1Mzg1MDY3MTc=
30,289
groupby.quantile(<arraylike>) fails with AssertionError
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4
2019-12-16T16:08:32Z
2019-12-27T16:16:49Z
2019-12-27T16:16:49Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python # Your code here df = pd.DataFrame(np.array([10*[_%4] for _ in range(100)])) df.groupby(0).quantile(0.5) # Out[19]: # 1 2 3 4 5 6 7 8 9 # 0 # 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 # 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 # 2 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 # 3 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 df.groupby(0).quantile([0.5,0.99]) --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-20-21c92d2481c9> in <module> ----> 1 df.groupby(0).quantile([0.5,0.99]) ~/PycharmProjects/netsim_stats/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation) 1950 1951 indices = np.concatenate(arrays) -> 1952 assert len(indices) == len(result) 1953 return result.take(indices) 1954 AssertionError: df.quantile([0.5,0.99]) # 0 1 2 3 4 5 6 7 8 9 # 0.50 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 # 0.99 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 df.groupby(0)[1].quantile(0.5) # 0 # 0 0.0 # 1 1.0 # 2 2.0 # 3 3.0 # Name: 1, dtype: float64 df.groupby(0)[1].quantile([0.5,0.99]) --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-24-ebf6ade716ff> in <module> ----> 1 df.groupby(0)[1].quantile([0.5,0.99]) ~/PycharmProjects/netsim_stats/venv/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation) 1950 1951 indices = np.concatenate(arrays) -> 1952 assert len(indices) == len(result) 1953 return result.take(indices) 1954 AssertionError: ``` #### Problem description The above is a constructed minimal example. I am not sure how much I should elaborate on the "why this is a problem". groupby.quantile() fails with an assertion error for "larger" dataframes, smaller dataframes seem to work fine. #### Expected Output #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.5.final.0 python-bits : 64 OS : Linux OS-release : 5.3.15-200.fc30.x86_64 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : de_DE.UTF-8 LOCALE : de_DE.UTF-8 pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.1 pip : 19.1.1 setuptools : 40.8.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.2.5 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.1 IPython : 7.2.0 pandas_datareader: None bs4 : 4.6.3 bottleneck : None fastparquet : None gcsfs : None lxml.etree : 4.2.5 matplotlib : 3.1.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.1.0 sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None </details>
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pandas/excel/tkinter
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2019-12-16T19:57:08Z
2019-12-17T02:40:53Z
2019-12-17T02:40:53Z
NONE
null
Hi, there! How are? I'm trying to import the values from excel with pandas to a tkinter treeview, but i'm having some issues. Here's the code: ![image](https://user-images.githubusercontent.com/49736581/70938361-c7219600-2024-11ea-81b2-f317424acf0f.png) Here's the result: ![image](https://user-images.githubusercontent.com/49736581/70938333-b83ae380-2024-11ea-816f-c87caf0ac413.png) Here's the values i want in right order: ![image](https://user-images.githubusercontent.com/49736581/70938431-ec160900-2024-11ea-9161-99fd06b5ae30.png) If someone could help me, i wish I could see the informations of Excel on treeview in the same way i see on excel
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30,291
Docs request: Specifying behavior of `dtype` parameter
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2019-12-16T22:04:19Z
2020-02-25T18:55:06Z
null
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null
#### Code Sample, a copy-pastable example if possible ```python # My data comes in with some empty IDs, but these rows are still usable. data_with_nan = {'ID':['1000','1001','1002', np.nan], 'Price':['900','100','150', '600']} # I set dtype to str. df_with_nan = pd.DataFrame(data_with_nan,dtype=str) # That nan cell is still arriving as a float, type(df_with_nan.iloc[3,0]) # type(df_with_nan.iloc[3,0]) # <class 'float'> # and perceived as a nan in pandas. df_with_nan.notnull() # ID Price # 0 True True # 1 True True # 2 True True # 3 False True ``` #### Problem description `dtype` does not explicitly say that it ignores NaN in the documentation of - `pd.DataFrame` - `read_excel` - `read_csv` For instance, in `read_csv` docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html), this is the description of the `dtype` param: > dtype : Type name or dict of column -> type, optional > > Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’} Use str or object together with suitable na_values settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. For the longest time, I kept reading in my dataframes as `str` in the belief that it would convert `nan` to "NaN". This, obviously, drained performance. The docs don't explicitly say that specifying a dtype ignores the null values. I don't know if I'm just oblivious, but that would've helped me several months ago. #### Expected Output #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.1 pip : 19.0.3 setuptools : 40.8.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : None numexpr : None odfpy : None openpyxl : 3.0.0 pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : 1.2.0 xlwt : None xlsxwriter : None </details>
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30,292
[Feature Request] Plot T/F mask directly as ones and zeros
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1
2019-12-16T22:12:55Z
2019-12-17T00:20:40Z
2019-12-17T00:15:41Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python x = pd.Series([False,True,True,False]) x.plot() # Error thrown! # Running version 0.24.2 ``` #### Problem description It would be nice to be able to directly use .plot() on true / false masks. Matplotlib currently supports plotting true / false masks with the below code (using the same x variable as above): plt.plot(x.index, x.values) #### Expected Output Would be great if the code in the "code sample" section outputted the same as the matplotlib equivalent of ( plt.plot(x.index, x.values) ), where True translates to 1, and False to zero, and plots against the index values of the series. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit: None python: 2.7.13.final.0 python-bits: 64 OS: Windows OS-release: 10 pandas: 0.24.2 numpy: 1.16.4 scipy: 1.2.2 pandas_gbq: None pandas_datareader: None </details>
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30,293
test CI
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2
2019-12-16T22:33:15Z
2019-12-17T15:01:09Z
2019-12-17T15:01:05Z
CONTRIBUTOR
null
Debugging an issue noted in https://github.com/pandas-dev/pandas/pull/30139 and maybe in https://github.com/pandas-dev/pandas/pull/30245
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30,294
CLN: .format to f-string formatting in various files
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3
2019-12-16T23:08:48Z
2019-12-17T16:49:46Z
2019-12-17T16:49:40Z
CONTRIBUTOR
null
- [ ] ref https://github.com/pandas-dev/pandas/issues/29547 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,295
JSON Support for parsing NaN, Infinity and -Infinity
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2
2019-12-17T04:50:17Z
2020-01-02T00:40:50Z
2020-01-02T00:40:41Z
MEMBER
null
- [X] closes #12213 - [X] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [X] whatsnew entry Not sure if we totally want this but figured I'd give it a shot any way. `simplejson` and the stdlib by comparison would decode these values
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30,296
DEPR: Remove pandas.np
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20
2019-12-17T05:02:13Z
2020-04-02T15:23:30Z
2019-12-27T16:52:54Z
MEMBER
null
Not sure if it was added intentionally, but it's possible to call numpy with the `np` attribute of the pandas module: ```python import pandas x = pandas.np.array([1, 2, 3]) ``` While this is not documented, I've seen couple of places suggesting this as a "trick" to avoid importing numpy directly. I personally find this hacky, and I think should be removed.
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BUG: :func:'read_pickle' and :func:'to_pickle' now accept URL and buf…
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2019-12-17T05:33:41Z
2019-12-17T06:09:25Z
2019-12-17T06:09:25Z
CONTRIBUTOR
null
…fer as well as file path GH30163 - [ ] closes #30163 - [ ] tests passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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CI: Checks job aborted if a step fails
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4
2019-12-17T06:13:15Z
2019-12-30T20:53:06Z
2019-12-30T20:53:06Z
MEMBER
null
In our job that checks that the code is ok (flake8, mypy...) [1], we want to run all the steps whether any of them fail or not. This is implemented by using `if: true` in each step. While this should work, it doesn't. A better way to implement this that probably fixes the issue is to use `if: always()` instead. What we should do is: - Replace all instances of `if: true` by `if: always` in the yaml file. - Introduce couple of temporary errors. Something PEP-8 invalid (so flake8 reports the error), and change the order of the imports of a file (so isort reports the error). - Open a pull request - See if the job behaves as expected, and wait for a code review - Once the pull request is approved, revert the errors introduced, so the changes to the yaml file can be merged 1. https://github.com/pandas-dev/pandas/blob/master/.github/workflows/ci.yml
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30,299
BUG: Make :func:'read_pickle' and :func:'to_pickle' accept FilePathOrBuffer
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2019-12-17T06:23:45Z
2019-12-17T06:55:18Z
2019-12-17T06:40:21Z
CONTRIBUTOR
null
- [ ] closes #30297 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,300
Ability to Unnest Series of Dicts
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2019-12-17T06:31:45Z
2019-12-18T19:16:17Z
2019-12-18T19:16:16Z
CONTRIBUTOR
null
#### Code Sample, a copy-pastable example if possible ```python s = pd.Series([{'a': {'b': 1, 'c': 2}}, {'a': {'b': 3, 'c': 4}}], index=[1, 3]) s 1 {'a': {'b': 1, 'c': 2}} 3 {'a': {'b': 3, 'c': 4}} dtype: object json_normalize(s) a.b a.c 0 1 2 1 3 4 ``` #### Problem description Maybe I'm missing some nuance of nesting or something that would potentially result in a different number of rows...? But it seems like if the rows of the output correspond to those of the input then the index should match as well. #### Output of ``pd.show_versions()`` `pandas==0.25.3`
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30,301
ENH: meth 'DataFrame.to_pickle' and func 'read_pickle' to accept URL GH#30163
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2019-12-17T06:56:11Z
2020-01-10T04:10:38Z
2020-01-09T13:16:26Z
CONTRIBUTOR
null
- [ ] closes #30163 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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BUG: func 'to_pickle' and 'read_pickle' where not accepting URL GH30299
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2019-12-17T07:51:43Z
2019-12-17T07:54:33Z
2019-12-17T07:52:57Z
CONTRIBUTOR
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- [ ] closes #30298 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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CI: Checks job aborted if a step fails GH30298
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2019-12-17T08:30:56Z
2019-12-30T20:53:06Z
2019-12-30T20:53:06Z
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null
- [ ] closes #30298 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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BUG: fix DataFrame.apply returning wrong result when dealing with dtype (#28773)
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2019-12-17T12:35:06Z
2020-05-22T11:22:41Z
2020-05-22T11:22:41Z
CONTRIBUTOR
null
- [x] closes #28773 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry The DataFrame.apply was sometimes returning wrong result when we passed function, that was dealing with dtypes. It was caused by retrieving the DataFrame.values of whole DataFrame, and applying the function to it: values are represented by NumPy array, which has one type for all data inside. It sometimes caused treating objects in DataFrame as if they had one common type. What's worth mentioning, the problem only existed, when we were applying function on columns. The implemented solution "cuts" the DataFrame by columns and applies function to each part, as it was whole DataFrame. After that, all results are concatenated into final result on whole DataFrame. The "cuts" are done in following way: the first column is taken, and then we iterate through next columns and take them into first cut while their dtype is identical as in the first column. The process is then repeated for the rest of DataFrame.
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30,305
ENH: Add support for DataFrame(ExtensionArray)
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1
2019-12-17T14:18:48Z
2019-12-19T07:34:31Z
2019-12-18T22:20:12Z
CONTRIBUTOR
null
- [x] closes #11363 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Problem: In core.frame ``` elif isinstance(data, abc.Iterable) and not isinstance(data, (str, bytes)): if not isinstance(data, (abc.Sequence)): data = list(data) ``` Categorical Object cast as list. So In core.construction it is not identified as Categorical.
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30,306
CLN: changed .format to f string in pandas/core/arrays/base.py
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5
2019-12-17T17:13:22Z
2019-12-17T19:31:46Z
2019-12-17T18:24:12Z
CONTRIBUTOR
null
- [ ] ref #29547 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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539,211,796
MDU6SXNzdWU1MzkyMTE3OTY=
30,307
Loading practice datasets without internet connection
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2019-12-17T17:34:15Z
2019-12-17T17:45:37Z
2019-12-17T17:45:37Z
NONE
null
Should Pandas has a feature that allows to load practice datasets (i.e. Iris) without internet connection such as R feature? The solutions I have found need external libraries.
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MDExOlB1bGxSZXF1ZXN0MzU0MjM3MTc5
30,308
DOC/TST: Indexing with NA raises
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27
2019-12-17T18:00:10Z
2020-10-07T15:52:55Z
2020-01-03T02:21:27Z
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/issues/29556, https://github.com/pandas-dev/pandas/issues/28778 We're already doing the right thing on master. This just documents that behavior, and adds a handful of tests. I'm not sure if there are existing tests I should be parameterizing. I only found a couple in `tests/indexing/`, which I parametrized over bool and boolean dtype. Will add more if I've missed any. In the meantime, I've written new tests.
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30,309
CI: travis-36-cov failing
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2019-12-17T18:18:24Z
2019-12-18T12:32:51Z
2019-12-18T12:32:51Z
CONTRIBUTOR
null
I'm seeing ``` from ._openmp_helpers import _openmp_parallelism_enabled ImportError: dlopen: cannot load any more object with static TLS ``` in e.g. https://travis-ci.org/pandas-dev/pandas/jobs/626304911#L648 Still debugging. scikit-learn 0.22 was just released to defaults. Some of our deps look fishy there (mixing defaults and conda-forge a bunch).
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Fixings fstrings in pandas/io/stata.py & pandas/io/sql.py
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2019-12-31T00:12:32Z
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- [x] ref #29547 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,311
Groupby agg works with pd.Series.nunique, but groupby nunique fails with axis=1
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4
2019-12-17T18:39:21Z
2019-12-19T01:13:40Z
2019-12-18T18:17:49Z
CONTRIBUTOR
null
- [x] closes #30253 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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539,268,007
MDExOlB1bGxSZXF1ZXN0MzU0Mjc0MjQ4
30,312
CI: Fixed travis-36-cov build
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10
2019-12-17T19:33:54Z
2019-12-18T12:32:54Z
2019-12-18T12:32:51Z
CONTRIBUTOR
null
Closes #30309 Just debugging right now. Can't reproduce locally yet.
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539,301,624
MDExOlB1bGxSZXF1ZXN0MzU0MzAyNDU4
30,313
xfail test_scikit_learn
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1
2019-12-17T20:47:35Z
2019-12-17T21:33:16Z
2019-12-17T21:33:12Z
CONTRIBUTOR
null
xref https://github.com/pandas-dev/pandas/issues/30309, this is a bit tricky. Will do a proper fix in https://github.com/pandas-dev/pandas/pull/30312, but xfailing for now.
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