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https://api.github.com/repos/pandas-dev/pandas/issues/30214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30214/events | https://github.com/pandas-dev/pandas/pull/30214 | 536,519,182 | MDExOlB1bGxSZXF1ZXN0MzUyMDM1Mzc1 | 30,214 | Parallelized Build / CI | {
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} | 9 | 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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Decided to create a PR for it. ;) | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
#29886 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30217 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30217/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30217/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30217/events | https://github.com/pandas-dev/pandas/issues/30217 | 536,605,392 | MDU6SXNzdWU1MzY2MDUzOTI= | 30,217 | CI Error AttributeError: 'ExcelFile' object has no attribute 'engine' | {
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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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... | open | false | null | [] | null | 3 | 2019-12-11T21:05:10Z | 2020-06-28T04:05:02Z | null | NONE | null | 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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} | 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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} | 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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} | 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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} | 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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} | 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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} | 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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} | 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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cc @jorisvandenbossche | {
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- [ ] 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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CLoses #30235 | {
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/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
```
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] | closed | false | null | [] | null | 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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} | 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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} | 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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} | 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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} | 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
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} | 1 | 2019-12-12T22:48:40Z | 2019-12-13T14:28:49Z | 2019-12-13T13:32:35Z | 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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} | 0 | 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
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} | 0 | 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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} | 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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] | closed | false | null | [] | null | 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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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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} | 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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} | 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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} | 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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https://api.github.com/repos/pandas-dev/pandas/issues/30261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30261/events | https://github.com/pandas-dev/pandas/issues/30261 | 537,690,201 | MDU6SXNzdWU1Mzc2OTAyMDE= | 30,261 | loop over dataframes to select rows didn't work | {
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```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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"... | closed | false | null | [] | null | 11 | 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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} | 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"<", 1)
671 val = val.replace(">", r">", 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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] | closed | false | null | [] | null | 2 | 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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} | [] | closed | false | null | [] | null | 4 | 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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} | 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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} | [] | closed | false | null | [] | null | 4 | 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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} | 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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} | 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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} | 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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"descript... | closed | false | null | [] | null | 1 | 2019-12-14T05:33:13Z | 2019-12-17T02:45:39Z | 2019-12-17T02:45:33Z | NONE | null | #### 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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} | 1 | 2019-12-14T07:43:41Z | 2019-12-15T12:39:43Z | 2019-12-15T12:35:23Z | CONTRIBUTOR | null | 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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"following_url": "https://api.github.com/users/shraddhazpy/following{/other_user}"... | null | 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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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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] | closed | false | null | [] | null | 1 | 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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} | 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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... | null | 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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- [ ] 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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} | 3 | 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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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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} | 10 | 2019-12-16T10:02:20Z | 2019-12-27T16:29:54Z | 2019-12-27T16:29:54Z | NONE | null | #### 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`.

#### Expected Output
Should look like this instead

#### 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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https://api.github.com/repos/pandas-dev/pandas/issues/30289 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30289/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30289/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30289/events | https://github.com/pandas-dev/pandas/issues/30289 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/30290 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30290/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30290/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30290/events | https://github.com/pandas-dev/pandas/issues/30290 | 538,620,566 | MDU6SXNzdWU1Mzg2MjA1NjY= | 30,290 | pandas/excel/tkinter | {
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} | [] | closed | false | null | [] | null | 4 | 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:

Here's the result:

Here's the values i want in right order:

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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https://api.github.com/repos/pandas-dev/pandas/issues/30291 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30291/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30291/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30291/events | https://github.com/pandas-dev/pandas/issues/30291 | 538,692,708 | MDU6SXNzdWU1Mzg2OTI3MDg= | 30,291 | Docs request: Specifying behavior of `dtype` parameter | {
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] | open | false | null | [] | null | 1 | 2019-12-16T22:04:19Z | 2020-02-25T18:55:06Z | null | NONE | 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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} | 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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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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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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} | 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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} | [] | closed | false | null | [] | null | 0 | 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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... | null | 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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https://api.github.com/repos/pandas-dev/pandas/issues/30299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30299/events | https://github.com/pandas-dev/pandas/pull/30299 | 538,863,729 | MDExOlB1bGxSZXF1ZXN0MzUzOTM3NzY3 | 30,299 | BUG: Make :func:'read_pickle' and :func:'to_pickle' accept FilePathOrBuffer | {
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} | [] | closed | false | null | [] | null | 0 | 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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] | closed | false | null | [] | null | 5 | 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()``
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} | 13 | 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`
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- [ ] tests added / passed
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"color": "fbc... | closed | false | null | [] | null | 15 | 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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- [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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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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The solutions I have found need external libraries.
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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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] | closed | false | null | [] | null | 0 | 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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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30311/events | https://github.com/pandas-dev/pandas/pull/30311 | 539,242,404 | MDExOlB1bGxSZXF1ZXN0MzU0MjUyODM4 | 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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https://api.github.com/repos/pandas-dev/pandas/issues/30312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30312/events | https://github.com/pandas-dev/pandas/pull/30312 | 539,268,007 | MDExOlB1bGxSZXF1ZXN0MzU0Mjc0MjQ4 | 30,312 | CI: Fixed travis-36-cov build | {
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Just debugging right now. Can't reproduce locally yet. | {
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