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} | 8 | "2021-06-25T12:46:18Z" | "2021-11-17T02:02:18Z" | "2021-11-17T02:02:18Z" | CONTRIBUTOR | null | #### The problem
The following code works well, both in 1.4.0 and 1.2.5:
```python
df = pd.DataFrame(["=1+7"])
with pd.ExcelWriter("test.xlsx",engine="openpyxl") as writer:
df.to_excel(writer,sheet_name="Sheet2")
pd.DataFrame(["=1+1"]).to_excel(writer,sheet_name="Sheet2",header=None,index=False,startcol=4,startrow=3)
```
however, the following code raises an error in 1.4.0, but works in 1.2.5 (it creates a new sheet and writes both dataframes to it):
```python
pd.DataFrame(["=1+1"]).to_excel("test.xlsx")
df = pd.DataFrame(["=1+7"])
with pd.ExcelWriter("test.xlsx",engine="openpyxl",mode="a") as writer:
df.to_excel(writer,sheet_name="Sheet2")
pd.DataFrame(["=1+1"]).to_excel(writer,sheet_name="Sheet2",header=None,index=False,startcol=4,startrow=3)
```
Also other options cannot replicate the old behaviour.
#### Describe the solution you'd like
An added option to `if_sheet_exists="write_to"` that justs selects the sheet and writes into it, without first deleting its contents. This also allows to append a dataframe within a sheet.
**EDIT:** in #40230 this was also suggested behaviour, but it was not implemented:
> Originally I considered adding a flag like `overwrite=True` to `pd.ExcelWriter` but I don't think this would be sufficient to cover all the potential behaviours, so would suggest an enum along the lines of the `if_exists` option in `df.to_sql`, with the following options:
>
> * new_sheet: Create a new sheet with a different name.
>
> * overwrite_sheet: Delete the contents of the sheet, then write to it.
>
> * overwrite_cells: Write directly to the named sheet without deleting the previous contents.
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} | 17 | "2021-06-25T14:00:08Z" | "2021-11-17T02:02:23Z" | "2021-11-17T02:02:18Z" | CONTRIBUTOR | null | - [x] closes #42221, related to #40231
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry - not applicable
I mainly wanted this functionality myself, and am very open to input :) | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | "2021-06-25T16:59:35Z" | "2021-07-11T19:41:22Z" | "2021-07-11T19:41:22Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
print(pd.to_datetime(['2021-03-14 12:05:45 -0400', '2021-03-14 03:06:00 -0400']))
print(pd.to_datetime(['2021-03-14 01:06:30 -0500', '2021-03-13 23:23:40 -0500']))
print(pd.to_datetime(['2021-03-14 03:06:00 -0400', '2021-03-14 01:06:30 -0500'], errors='raise'))
print(pd.to_datetime(['2021-03-14 03:06:00 -0400', '2021-03-14 01:06:30 -0500'], errors='coerce'))
print(pd.to_datetime(['2021-03-14 03:06:00 -0400', '2021-03-14 01:06:30 -0500'], errors='ignore'))
```
The output is
```
DatetimeIndex(['2021-03-14 12:05:45-04:00', '2021-03-14 03:06:00-04:00'], dtype='datetime64[ns, pytz.FixedOffset(-240)]', freq=None)
DatetimeIndex(['2021-03-14 01:06:30-05:00', '2021-03-13 23:23:40-05:00'], dtype='datetime64[ns, pytz.FixedOffset(-300)]', freq=None)
Index([2021-03-14 03:06:00-04:00, 2021-03-14 01:06:30-05:00], dtype='object')
Index([2021-03-14 03:06:00-04:00, 2021-03-14 01:06:30-05:00], dtype='object')
Index([2021-03-14 03:06:00-04:00, 2021-03-14 01:06:30-05:00], dtype='object')
```
#### Problem description
The first two lines work as expected, but the following three lines do not return a `DatetimeIndex` but also don't raise any exceptions in the process. This behavior is undocumented, and quite hard to debug/track down, since the failure is completely silent. It seems that the reason is the input contains times from two different time zones.
#### Expected Output
Either a DatetimeIndex object or an exception to be raised.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.5
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.2.1
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.4.2
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-06-25T17:37:39Z" | "2021-06-25T22:15:25Z" | "2021-06-25T22:12:38Z" | MEMBER | null | This implements BlockManager._get_data_subset (which ArrayManager already has) to avoid making a copy in a `DataFrame.__getitem__` usage inside DataFrame._reduce.
This path is deprecated so this snippet will be removed in 2.0.
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- [x] tests added / passed
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- [x] whatsnew entry
Benchmarks:
```
before after ratio
[d61ace50] [7fb839ce]
<master~4> <gb_sample>
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- 808±20ms 75.9±1ms 0.09 groupby.Sample.time_sample_weights
```
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} | 2 | "2021-06-25T19:40:28Z" | "2021-06-29T12:26:37Z" | "2021-06-29T12:26:37Z" | MEMBER | null | Seeing lots of timeouts on `Windows py38_np18` builds, for example https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=62155&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a
The main tests showing up in the slowest durations are the groupby `numba` tests. Would it make sense to do something similar to #42024 in the groupby conftest to reduce the number of combinations run? Also in that run seeing warnings like
```
pandas/tests/window/test_numba.py: 14 warnings
D:\a\1\s\pandas\core\window\numba_.py:74: NumbaPerformanceWarning:
The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.
To find out why, try turning on parallel diagnostics, see https://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.
File "pandas\core\util\numba_.py", line 109:
def impl(data, *_args):
^
result[i] = numba_func(window, *args)
```
so the `parallel=True` case might not be testing anything. cc @mroeschke | {
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} | 4 | "2021-06-25T19:51:31Z" | "2021-06-25T22:55:37Z" | "2021-06-25T21:59:05Z" | MEMBER | null | - [x] closes #42236
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Limiting the numba `engine_kwargs` parameterization for groupby tests similar to #42024
cc @mzeitlin11 | {
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} | 0 | "2021-06-25T20:34:45Z" | "2021-06-25T22:12:25Z" | "2021-06-25T22:07:28Z" | MEMBER | null | - [ ] closes #xxxx
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- [ ] whatsnew entry
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} | 0 | "2021-06-25T20:40:01Z" | "2021-06-30T09:01:10Z" | "2021-06-30T09:01:10Z" | MEMBER | null | This seems to have caused several performance regressions, see https://pandas.pydata.org/speed/pandas/#regressions?sort=1&dir=desc (the ones for commit "bf72b70b")
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/41833#issuecomment-858560156_ | {
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} | 17 | "2021-06-25T20:46:34Z" | "2021-07-01T12:01:19Z" | "2021-06-30T06:47:31Z" | MEMBER | null | There were several regressions related to the set op methods of IntervalIndex. For example https://github.com/pandas-dev/pandas/pull/41929#issuecomment-860514675 which was fixed (and backported) in https://github.com/pandas-dev/pandas/pull/42197.
But as far as I can see, that didn't fix the regression in https://pandas.pydata.org/speed/pandas/#index_object.IntervalIndexMethod.time_intersection?python=3.8&Cython=0.29.21&p-param1=100000&commits=de07087a-6f953a8c (50x slow down of `index_object.IntervalIndexMethod.time_intersection(100000)`)
This one might be caused by one of the other interval index related commits in the range (#41832, #41824, #41883)
cc @jbrockmendel
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} | 1 | "2021-06-25T22:59:26Z" | "2021-07-11T20:14:10Z" | "2021-07-11T19:47:14Z" | CONTRIBUTOR | null | - [X] closes #42009
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them | {
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} | 1 | "2021-06-25T23:01:18Z" | "2021-07-04T22:00:35Z" | "2021-07-04T21:37:34Z" | MEMBER | null | - [x] closes #38100
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
The perf gap is pretty crazy:
```
In [10]: rng = np.random.default_rng()
In [11]: ser = pd.Series(np.zeros(10 ** 6))
In [12]: %timeit ser.sample(n=10 ** 4)
13.8 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [13]: %timeit ser.sample(n=10 ** 4, random_state=rng)
569 µs ± 8.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Leaving #28440 open because I think the end goal should still be to make using these the default. But assuming that change isn't too easy since will be a breaking change? | {
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} | 4 | "2021-06-26T00:02:49Z" | "2021-07-12T08:06:37Z" | "2021-07-11T19:41:22Z" | CONTRIBUTOR | null | Updated the return type section of to_datetime to include list-like mixed timezone inputs
- [x] closes #42229
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry - Updated the return type section of to_datetime to include list-like mixed timezone inputs
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} | 2 | "2021-06-26T02:59:19Z" | "2021-07-04T21:45:20Z" | "2021-07-04T21:37:03Z" | MEMBER | null | I think this can be simplified further, but need to fix a newly-surfaced get_indexer_non_unique bug first | {
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} | 4 | "2021-06-26T07:42:54Z" | "2021-06-30T07:36:36Z" | null | MEMBER | null | Performance regression in `shift` caused by #40536
I don't know if it's a particularly useful benchmark, but there is a big slowdown potentially related to this PR: https://pandas.pydata.org/speed/pandas/#frame_methods.Shift.time_shift?python=3.8&Cython=0.29.21&p-axis=1&commits=05a0e98a-bc62e768
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/40536#issuecomment-808265872_ | {
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} | 4 | "2021-06-26T07:46:09Z" | "2021-06-29T14:24:40Z" | "2021-06-29T14:24:40Z" | MEMBER | null | #41670 seems to have caused slowdowns in several of the Period indexing benchmarks: see https://pandas.pydata.org/speed/pandas/#regressions?sort=1&dir=desc, scrolldown to "2021-05-28 17:58"
One example: https://pandas.pydata.org/speed/pandas/#period.Indexing.time_get_loc?python=3.8&Cython=0.29.21&commits=ddc28a42
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/41670#issuecomment-856835712_ | {
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} | 1 | "2021-06-26T07:50:08Z" | "2021-07-08T12:42:31Z" | "2021-07-08T12:42:31Z" | MEMBER | null | #41785 removed some cython code to handle this case (because it could also be handled by existing python code, AFAIU), but this de-duplication caused a big slowdown in one of the benchmarks: https://pandas.pydata.org/speed/pandas/#frame_ctor.FromDicts.time_nested_dict_int64?python=3.8&Cython=0.29.21
It's of course a trade-off between the maintenance cost of having the cython version versus the performance benefit. But if the benchmark is representative, a 5-6x slowdown seems quite a lot for getting rid of a relatively small piece of cython code.
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/41785#issuecomment-856827815_ | {
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} | 1 | "2021-06-26T08:46:40Z" | "2021-06-28T14:58:01Z" | "2021-06-28T14:58:01Z" | MEMBER | null | The fix in #42089 (or caused by the PR that this one was fixing) seems to have caused a large slowdown on the `get_indexer` benchmarks: https://pandas.pydata.org/speed/pandas/#indexing.CategoricalIndexIndexing.time_get_indexer_list?python=3.8&Cython=0.29.21&p-index='monotonic_incr'&commits=cf5852bf-fce7f9eb
The regression overview (https://pandas.pydata.org/speed/pandas/#regressions?sort=1&dir=desc) lists it as a 1000x slowdown, but that's only because https://github.com/pandas-dev/pandas/pull/42042 first improved the performance a lot (which might be a bit suspicious?). Compared to the timing before that, it's only 4-5x slowdown. With the below code, I see locally a ~9x slowdown on master compared to 1.2.5.
```python
import string, itertools
data_unique = pd.CategoricalIndex(
["".join(perm) for perm in itertools.permutations(string.printable, 3)]
)
cat_list = ["a", "c"]
%timeit data_unique.get_indexer(cat_list)
52.8 ms ± 5.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- pandas 1.2.5
417 ms ± 22.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
```
I _think_ it has to do with the fact that before we called the Engine.get_indexer on the codes, while now in the base class version we do that with the `.categories`, which means in this case that both `self` and `target` are cast to object dtype and thus use the Engine.get_indexer for object dtype.
_Originally posted by @jorisvandenbossche in https://github.com/pandas-dev/pandas/issues/42089#issuecomment-868970458_ | {
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} | 9 | "2021-06-26T10:16:43Z" | "2021-09-21T19:15:48Z" | "2021-09-21T19:15:44Z" | CONTRIBUTOR | null | - [x] closes #24306
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-06-26T12:14:55Z" | "2021-07-11T19:35:44Z" | "2021-07-11T19:35:36Z" | CONTRIBUTOR | null | dateutil.relativedelta can be constructed either from two date/datetime objects passed as positional arguments, or explicitly constructed in terms of year, month, day etc passed as keyword arguments. DateOffset only supports the keyword argument form of relativedelta.
- [x] related to #41847
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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@TomAugspurger @lithomas1 I have made a fix to this issue and I wonder if do you mind have a review, please? I will add the whatsnew when this PR is about to be merged. | {
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} | 4 | "2021-06-26T16:17:55Z" | "2021-10-19T03:46:02Z" | "2021-06-28T14:27:50Z" | MEMBER | null | - [x] closes #41998
- [x] tests added / passed
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No whatsnew because this was introduced in 1.3
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- [x] closes #24306
- Can use the existing #42250 to extend documentation for each of the `to_*` methods (@Joeperdefloep)
- [x] tests added / passed
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Seeing if this helps for #42236
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-06-26T19:16:21Z" | "2021-07-22T13:40:26Z" | "2021-07-04T21:32:21Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
from pandas import NaT, to_datetime, Series, Timestamp
from pandas._testing import assert_series_equal
input_ser = Series([None] + [NaT] * 50 + ["2012 July 26", Timestamp("2012-07-26")], dtype="object")
expected_ser = Series([NaT] * 51 + [Timestamp("2012-07-26"), Timestamp("2012-07-26")], dtype="datetime64[ns]")
result = to_datetime(input_ser)
assert_series_equal(result, expected_ser)
# AssertionError: numpy array are different
# actual result is [NaT] * 51 + [Timestamp("2012-07-26"), NaT]
# ie. below passes
# assert_series_equal(result, Series([NaT] * 51 + [Timestamp("2012-07-26"), NaT], dtype="datetime64[ns]"))
```
#### Problem description
The current to_datetime will incorrectly parse and omit data in certain situations due to a slightly erroneous deduplication to fix
GH#39882 and GH#35888.
#### Expected Output
to parse the datetime correctly.
Eg. return Series([NaT] * 51 + [Timestamp("2012-07-26"), Timestamp("2012-07-26")], dtype="datetime64[ns]") in the example above.
#### Output of ``pd.show_versions()``
<details>
pandas : 1.4.0.dev0+108.gfa6b96e128
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.0
sphinx : 4.0.2
blosc : 1.10.4
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : 2021.05.0
scipy : 1.7.0
sqlalchemy : 1.4.19
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 2 | "2021-06-26T19:24:10Z" | "2021-07-01T23:45:18Z" | "2021-07-01T23:42:10Z" | MEMBER | null | Saves some unnecessary memory allocations, plus moving towards making `rank_sorted_1d` easier to call. Also have done something similar for allowing `mask` to be None, but split that off this pr to keep it simpler.
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https://api.github.com/repos/pandas-dev/pandas/issues/42261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42261/events | https://github.com/pandas-dev/pandas/pull/42261 | 930,782,409 | MDExOlB1bGxSZXF1ZXN0Njc4NDA1MDg0 | 42,261 | BUG: Fix to_datetime() cache behaviour to not omit duplicated output values | {
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} | 3 | "2021-06-26T19:45:12Z" | "2021-07-22T12:25:42Z" | "2021-07-04T21:32:21Z" | CONTRIBUTOR | null | - [x] closes #42259
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42262/events | https://github.com/pandas-dev/pandas/pull/42262 | 930,783,489 | MDExOlB1bGxSZXF1ZXN0Njc4NDA1OTIx | 42,262 | Set threadsafe behavior for numba parallelism | {
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} | 4 | "2021-06-26T19:52:40Z" | "2021-06-28T08:41:58Z" | "2021-06-28T02:10:40Z" | MEMBER | null | - xref #https://github.com/numba/numba/issues/6839#issuecomment-867977583
- [x] tests added / passed
Applicable for 1.3.0 since this behavior can appear for the new `method="table"` feature for rolling/expanding
cc @jbrockmendel
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https://api.github.com/repos/pandas-dev/pandas/issues/42263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42263/events | https://github.com/pandas-dev/pandas/pull/42263 | 930,786,668 | MDExOlB1bGxSZXF1ZXN0Njc4NDA4Mjg2 | 42,263 | CLN: simplify take2d_axis0 generated code | {
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} | 1 | "2021-06-26T20:14:00Z" | "2021-07-01T23:45:06Z" | "2021-07-01T23:40:14Z" | MEMBER | null | For example, on master we generate:
```
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_int32(const int8_t[:, :] values,
ndarray[intp_t] indexer,
int32_t[:, :] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, j, k, n, idx
int32_t fv
n = len(indexer)
k = values.shape[1]
fv = fill_value
IF False:
cdef:
const int32_t *v
int32_t *o
# GH#3130
if (values.strides[1] == out.strides[1] and
values.strides[1] == sizeof(int32_t) and
sizeof(int32_t) * n >= 256):
for i in range(n):
idx = indexer[i]
if idx == -1:
for j in range(k):
out[i, j] = fv
else:
v = &values[idx, 0]
o = &out[i, 0]
memmove(o, v, <size_t>(sizeof(int32_t) * k))
return
for i in range(n):
idx = indexer[i]
if idx == -1:
for j in range(k):
out[i, j] = fv
else:
for j in range(k):
out[i, j] = values[idx, j]
```
On this pr:
```
@cython.wraparound(False)
@cython.boundscheck(False)
def take_2d_axis0_int8_int32(const int8_t[:, :] values,
ndarray[intp_t] indexer,
int32_t[:, :] out,
fill_value=np.nan):
cdef:
Py_ssize_t i, j, k, n, idx
int32_t fv
n = len(indexer)
k = values.shape[1]
fv = fill_value
for i in range(n):
idx = indexer[i]
if idx == -1:
for j in range(k):
out[i, j] = fv
else:
for j in range(k):
out[i, j] = values[idx, j]
```
Not too impactful since just generated code, but makes looking at all the take helpers clearer (and the file shorter).
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} | 1 | "2021-06-26T20:57:47Z" | "2021-07-09T13:54:31Z" | "2021-07-09T13:15:46Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Reorg of apply tests, grouping all those that take a string argument together. Plan to next create `test_list` and `test_dict`, then all other test files in apply will have (at least partially) UDFs, which make up a majority of the tests.
Had to rename a few tests because of collisions, but otherwise purely a move. | {
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} | 0 | "2021-06-26T22:52:34Z" | "2021-07-01T23:39:02Z" | "2021-07-01T23:18:58Z" | MEMBER | null | - [x] closes #24892
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
xref #42258 which makes the same change for PeriodIndex._can_partial_date_slice but its behavior-neutral there.
cc @mroeschke | {
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] | closed | false | null | [] | null | 2 | "2021-06-26T23:14:35Z" | "2021-06-27T02:03:42Z" | "2021-06-27T02:03:42Z" | NONE | null | #### Is your feature request related to a problem?
I would like to apply `map` to some columns and not others.
Then, I could apply dataframe methods to the resulting dataframe.
For example,
```
# Apply map to column "cat" and compute correlations on the resulting dataframe
df.map({'cat': {'n': 0, 'y': 1}}).corr()
```
#### Describe the solution you'd like
Add a `map` method that is an extension of the `Series.map` method, except that, here, a nested dictionary is specified -- the key(s) of the first level of the dictionary specify the column names of the dataframe.
Example dataframe:
``` py
df = pd.DataFrame({
'num': [10, 10, 20, 20],
'cat': list('ynyn'),
'res': [42.3, 88.4, 64.8, 124.9],
})
```
Would like to do this:
``` py
df.map({'cat': {'n': 0, 'y': 1}})
```
This would return:
``` py
num cat res
0 10 1 42.3
1 10 0 88.4
2 20 1 64.8
3 20 0 124.9
```
So that I can do
``` py
df.map({'cat': {'n': 0, 'y': 1}}).corr()
```
Which would result in:
``` py
num cat res
num 1.00000 0.00000 0.48245
cat 0.00000 1.00000 -0.86841
res 0.48245 -0.86841 1.00000
```
The method could instead be named `colmap` to avoid confusion with `applymap`.
#### API breaking implications
Since this is a new method for DataFrame, I don't think this should break API.
One other point of consideration: the `applymap` method which maps every element may be confusing. At a future date, `applymap` could be renamed to `mapelem` or something and/or the method requested in this ticket could be named `colmap` or something.
#### Describe alternatives you've considered
Current workaround:
Right now, I can create a new dataframe and take three steps to do what I would want to do in one:
``` py
df1 = df.copy()
df1['cat'] = df1['cat'].map({'n': 0, 'y': 1})
df1.corr()
```
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} | 3 | "2021-06-27T16:35:16Z" | "2021-07-01T23:55:16Z" | "2021-07-01T23:55:16Z" | CONTRIBUTOR | null | - [x] closes #21796
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 4 | "2021-06-27T16:42:14Z" | "2021-06-28T16:16:17Z" | "2021-06-28T14:25:14Z" | MEMBER | null | - [x] closes #42240
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
```
import pandas as pd
import numpy as np
N = 10 ** 5
left = pd.IntervalIndex.from_breaks(np.arange(N))
right = pd.IntervalIndex.from_breaks(np.arange(N - 3, 2 * N - 3))
%timeit left.intersection(right)
146 ms ± 1.82 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
5.6 ms ± 277 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- PR
```
We're also going to get a nice bump in join_monotonic/_inner_indexer, but not big enough to warrant not-special-casing IntervalIndex's intersection. | {
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} | 1 | "2021-06-27T18:21:14Z" | "2021-08-20T22:39:29Z" | "2021-07-01T23:15:30Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
We only use it one place internally; this will allow us to simplify get_loc, which will marginally improve perf. | {
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} | 4 | "2021-06-27T19:57:46Z" | "2021-06-28T16:14:21Z" | "2021-06-28T14:58:01Z" | MEMBER | null | - [x] closes #42249
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
```
import pandas as pd
import string, itertools
data_unique = pd.CategoricalIndex(
["".join(perm) for perm in itertools.permutations(string.printable, 3)]
)
cat_list = ["a", "c"]
%timeit data_unique.get_indexer(cat_list)
106 µs ± 6.14 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
39 ms ± 616 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- 1.2.4
290 ms ± 3.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
``` | {
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} | 1 | "2021-06-27T21:43:56Z" | "2021-07-03T07:25:23Z" | "2021-07-01T23:56:26Z" | CONTRIBUTOR | null | - [x] follows #42187
- [x] checks an item off #40675
The current mechanics are unstructured and the tests is not future proof (if new attributes are added tests will not pick these up and fail). This PR generalises the construction and future proofs the tests. No user facing change.
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] | open | false | null | [] | null | 2 | "2021-06-28T01:52:01Z" | "2021-08-21T02:48:10Z" | null | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
a = pd.DataFrame(range(10),
index=pd.MultiIndex.from_arrays(
[
pd.Index([1]*10, name="shared_and_not_unique"),
pd.Index(range(10), name="unique_and_not_in_b")]))
b = pd.DataFrame(range(2),
index=pd.Index([1, 2], name="shared_and_not_unique"))
c = pd.merge(a, b, left_index=True, right_index=True, validate="1:1")
d = pd.merge(a.droplevel("unique_and_not_in_b"), b, left_index=True, right_index=True, validate="1:1")
```
#### Problem description
pd.merge checks whether the index for the right and left are unique. It does not check whether the levels used for the join are unique. In the example above, a has a unique index, but the subset of a's index levels that overlap with b's index is not unique. As a result, a many to one merge is performed and 1:1 validation check is passed.
#### Expected Output
MergeError for c.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 4 | "2021-06-28T05:49:30Z" | "2021-07-02T08:21:58Z" | "2021-06-29T12:24:59Z" | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 4 | "2021-06-28T07:10:51Z" | "2021-06-30T07:47:28Z" | "2021-06-30T07:47:25Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.7-py2.py3-none-any.whl (7.2 MB)
Collecting identify>=1.0.0
Downloading identify-2.2.10-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.10 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.7
```
### stderr:
```Shell
WARNING: You are using pip version 21.1.2; however, version 21.1.3 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.5/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.9.0 -> 5.9.1.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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} | 8 | "2021-06-28T07:17:59Z" | "2021-07-16T06:56:23Z" | null | NONE | null | #### Is your feature request related to a problem?
In pandas version 1.2.5., using groupby.max() on a large matrix of int8 datatype 0/1 values, pandas casts the dataframe to int64, resulting in
```MemoryError: Unable to allocate 76.4 GiB for an array with shape (1915674, 5356) and data type int64```
Traceback:
```
/python3.9/site-packages/pandas/core/dtypes/common.py in ensure_int_or_float(arr, copy)
143 try:
144 # error: Unexpected keyword argument "casting" for "astype"
--> 145 return arr.astype("int64", copy=copy, casting="safe") # type: ignore[call-arg]
146 except TypeError:
147 pass
```
#### Describe the solution you'd like
Keep the original datatype, in this case int8. | {
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} | 1 | "2021-06-28T07:46:52Z" | "2021-10-20T15:45:18Z" | null | CONTRIBUTOR | null | This is a tracker / explainer for the various issues:
- (#21221)
- (#25185)
- (#30008)
- (#34438)
Essentially these issues record 3 things:
### Set_table_styles
`Styler.set_table_styles` is **not** exported to excel. This will not be changed (at least by me). To write xlsx, excel styling needs to be attached on a per-cell basis, whereas in HTML indirect references can be created in the CSS language that the browser will parse, for example `thead th` will apply to all header cells in the header section, and `tbody th:nth-child(3n+0)` will apply to every third header cell in the body section starting with first. Without writing our own HTML to cell translator for the CSS language it is therefore impossible to map and account for all the complex CSS rules that can be used within `set_table_styles`. **This is well documented**.
Todo:
- [x] We will instead propose changes that allow styling to header cells, to complement that already in place for body cells, (#41995)
### Exporting formatting
The number of possible formatting constructs allowed by Python is greater than what excel offers. Excel also has specified structures that differ from Pythons structures. It is impossible to dynamically code these relationships in some ambiguous cases. Therefore this will not be implemented.
Todo:
- [ ] There is currently a pseudo CSS attribute: `number-format` which can be used to apply specific Excel based formatting. This should be much better documented with examples.
### Border styles bug
Borders in CSS can be specified in many different ways, and the parsing code to translate this into excel's border structure is broken
Todo
- [ ] Review the border CSS translation code and propose a solution, if only to document a single way of getting this to work.
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} | 3 | "2021-06-28T08:30:47Z" | "2021-06-28T16:01:22Z" | "2021-06-28T15:42:38Z" | CONTRIBUTOR | null | - [x] closes #29075
- [ ] whatsnew entry
I've expanded on the use of a context manager to handle the closing of the database connection after the connection has been opened. I've also included a warning at the end of the section highlighting problems with leaving the database connection open. I think these additions tackle the docs problem associated with this issue.
Whilst psycopg2 is supported through sqlalchemy it appears to not be explicitly supported in its own right. I linked to the section in the sqlalchemy docs which discusses what happens when you use a Python context manager to handle connecting to a database. Hopefully users will now have a better idea on how or why database connections are being made in the recommended manner and can access the relevant documentation quickly. | {
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} | 2 | "2021-06-28T13:07:55Z" | "2021-07-01T23:46:37Z" | "2021-07-01T23:46:31Z" | CONTRIBUTOR | null | This PR simple adds a test for upcasting to object when concatinating on categorical indexes with non-identical categories.
- [x] closes #17629
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-06-28T16:13:46Z" | "2021-06-28T17:52:27Z" | "2021-06-28T17:18:45Z" | MEMBER | null | Backport PR #42268 | {
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} | 5 | "2021-06-28T16:29:36Z" | "2021-07-23T18:19:48Z" | "2021-07-23T00:01:02Z" | CONTRIBUTOR | null | - [ ] xref #27085
- [x] tests added / passed (no new tests added; all tests pass)
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Move custom month business cases from the `test_month.py` file into their own `test_custom_business_month.py` file per the current pattern in the `tseries.offsets` tests that split offset business and/or custom subclasses into their own files with ample cases (see `test_custom_business_hour.py`)
There is much refactoring work here to do that could be linked to this issue (#27085) going forward, but the simplest pieces that probably belong under this issue are simple organization of existing tests into smaller, logical files that make it easier for contributors to find a tests' home.
I'm a new contributor to the codebase so place let me know if I misunderstood the scope or what you all were thinking with this issue. Happy to adjust. | {
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} | 3 | "2021-06-28T18:18:36Z" | "2021-07-11T20:15:39Z" | "2021-07-11T20:15:33Z" | CONTRIBUTOR | null | - [x] closes #34633
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Have added clarification to the user guide on the default behaviour of `to_timedelta` when a string is passed. | {
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} | 0 | "2021-06-28T20:08:36Z" | "2021-06-29T12:29:22Z" | "2021-06-29T12:29:22Z" | MEMBER | null | ```
with pd.ExcelWriter('asdf.xlsx', engine='xlsxwriter', options={'nan_inf_to_errors': True}) as writer:
pass
```
gives
```
scratch.py:38: FutureWarning: Use of **kwargs is deprecated, use engine_kwargs instead.
with pd.ExcelWriter('asdf.xlsx', engine='xlsxwriter', options={'nan_inf_to_errors': True}) as writer:
Traceback (most recent call last):
File "scratch.py", line 38, in <module>
with pd.ExcelWriter('asdf.xlsx', engine='xlsxwriter', options={'nan_inf_to_errors': True}) as writer:
TypeError: __init__() got an unexpected keyword argument 'options'
```
#40430 deprecated the use of kwargs and is the most likely culprit, but need to confirm. | {
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} | 2 | "2021-06-28T20:14:43Z" | "2021-07-04T18:13:18Z" | "2021-07-04T18:13:18Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
def expanding_multiply(values, x):
arr = values[-1]
arr[1] = values[:,0].sum() * x
return arr
df = pd.DataFrame({'id': [1, 2, 3], 'value': [0, 0, 0]})
def call_expanding(x):
expanded = df.expanding(method="table").apply(expanding_multiply, raw=True, engine="numba", args=x)
return expanded
call_expanding(tuple([1]))
# 1.0 1.0
# 2.0 3.0
# 3.0 6.0
```
```python
call_expanding(tuple([2]))
# 1.0 1.0
# 2.0 3.0
# 3.0 6.0
```
#### Problem description
When using expanding.apply with engine='numba' calling the same function with different arguments gives the same results as the first time it was called. I believe this is because the function is being cached with its arguments.
https://github.com/pandas-dev/pandas/blob/e8dbdb07782437ad592736f6d212d0af5e7324be/pandas/core/window/rolling.py#L1172-L1175
#### Expected Output
```python
call_expanding(tuple([2]))
# 1.0 2.0
# 2.0 6.0
# 3.0 12.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Thu Apr 30 18:25:59 PDT 2020; root:xnu-6153.121.1~7/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.19.1
pytz : 2019.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-06-28T20:56:12Z" | "2021-07-08T15:19:09Z" | "2021-07-08T12:42:13Z" | MEMBER | null | - [ ] xref #24559
- [x] tests added / passed
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Once enforced this will match the DatetimeIndex/DatetimeArray/Series behavior | {
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One of the tests is copied from #35498 | {
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} | 3 | "2021-06-28T21:33:37Z" | "2021-07-13T16:52:39Z" | "2021-07-13T16:52:33Z" | CONTRIBUTOR | null | I noticed that this note was mistakenly added to all methods that use `num_ddof_doc`, e.g. including [`sem`](https://pandas.pydata.org/docs/reference/api/pandas.Series.sem.html), which is misleading - this statement is only true for `std`. This PR makes sure we only add the note in that case.
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https://api.github.com/repos/pandas-dev/pandas/issues/42291 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42291/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42291/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42291/events | https://github.com/pandas-dev/pandas/issues/42291 | 932,150,687 | MDU6SXNzdWU5MzIxNTA2ODc= | 42,291 | ENH: DataFrame.interpolate limit to support all-or-none filling | {
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] | open | false | null | [] | null | 2 | "2021-06-29T02:40:56Z" | "2021-08-21T03:44:20Z" | null | NONE | null | Currently with `df.interpolate(limit, limit_direction)` , I must choose 1 or both sides to fill when I am limiting the interpolation. What I find more useful is a all-or-none strategy rather than only fill up to the limit count, so I can fill up some short-term missing data and keep long-term missing data to be filtered after. Demonstrated as here:
```python
>>> df = pd.DataFrame([[0,1,2,3],[1,np.nan,np.nan,np.nan],[np.nan,np.nan,np.nan,5],[3,4,5,6]],columns=list('abcd'))
>>> df
a b c d
0 0.0 1.0 2.0 3.0
1 1.0 NaN NaN NaN
2 NaN NaN NaN 5.0
3 3.0 4.0 5.0 6.0
>>> # Current options
>>> df.interpolate(axis=0,limit=1,limit_direction='forward')
a b c d
0 0.0 1.0 2.0 3.0
1 1.0 2.0 3.0 4.0
2 2.0 NaN NaN 5.0
3 3.0 4.0 5.0 6.0
>>> df.interpolate(axis=0,limit=1,limit_direction='backward')
a b c d
0 0.0 1.0 2.0 3.0
1 1.0 NaN NaN 4.0
2 2.0 3.0 4.0 5.0
3 3.0 4.0 5.0 6.0
>>> df.interpolate(axis=0,limit=1,limit_direction='both')
a b c d
0 0.0 1.0 2.0 3.0
1 1.0 2.0 3.0 4.0
2 2.0 3.0 4.0 5.0
3 3.0 4.0 5.0 6.0
>>> # What is desired
>>> interpolated_df = pd.DataFrame([[0,1,2,3],[1,np.nan,np.nan,4],[2,np.nan,np.nan,5],[3,4,5,6]],columns=list('abcd'))
>>> interpolated_df # NaNs at column b and c not filtered for exceeding limit 1
a b c d
0 0 1.0 2.0 3
1 1 NaN NaN 4
2 2 NaN NaN 5
3 3 4.0 5.0 6
```
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Seeing some open resource test failure in excel writer, but not sure why. Checking if they occur on CI too.
Before this PR, I didn't notice that only xlsxwriter actually utilizes the kwargs/engine_kwargs. This makes the non-xlsxwriter tests slightly awkward - we pass the kwargs and just make sure the call to ExcelWriter doesn't fail because I don't see anything else to check. | {
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} | 6 | "2021-06-29T04:38:51Z" | "2021-06-29T18:49:04Z" | "2021-06-29T12:31:40Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Should fix #42240, #42247, not checking the box because I jumped the gun last time. | {
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} | 6 | "2021-06-29T08:58:21Z" | "2021-08-26T21:16:09Z" | "2021-08-26T21:16:09Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
# Float column casted to int
d1 = pd.DataFrame({"a": [1.0, 2.0], 'b': [3, np.nan]})
print('\nd1:')
print(d1)
print('\nd1.dtypes:')
print(d1.dtypes)
print('\nd1.where(pd.notnull(d1), None):')
print(d1.where(pd.notnull(d1), None))
print()
# Float column not casted to int
d2 = pd.DataFrame({"a": [1.0, 2.0], 'b': [3, 4]})
print('\nd2:')
print(d2)
print('\nd2.dtypes:')
print(d2.dtypes)
print('\nd2.where(pd.notnull(d2), None):')
print(d2.where(pd.notnull(d2), None))
```
#### Problem description
In both previous examples, the column `a` is identical (a float column containing 1.0 and 2.0), but the outcome of `df.where(pd.notnull(df), None)` differs.
#### Expected Output
For a given column, I would expect the outcome of `df.where(pd.notnull(df), None)` to be consistent, regardless of the other columns of the dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 49.6.0
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.4.17
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 2 | "2021-06-29T13:14:18Z" | "2021-07-09T18:02:13Z" | "2021-07-09T13:15:05Z" | CONTRIBUTOR | null | This refactors `Styler.bar` to allow more flexible arguments to align bars, and an additional keyword `props` akin to other builtin methods that allow some customisation, demonstrated in the new user guide (image below)
- [x] closes #26070
- [x] tests added / passed
- [x] whats new
**All previous tests pass with minor formatting alterations.**
As a follow-on I should improve the original testing for this function - it is sporadic and unstructured.
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] | open | false | null | [] | null | 1 | "2021-06-29T14:17:16Z" | "2021-08-21T04:03:52Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
IIUC for pandas extension arrays, for each location where `_mask` is `True`, the value of `_data` should be irrelevant. However this is not the case for `__array_ufunc__`:
```python
In [1]: import pandas as pd
...: import numpy as np
...:
...: arr = pd.array([True, False, pd.NA], dtype="boolean")
...: arr
Out[1]:
<BooleanArray>
[True, False, <NA>]
Length: 3, dtype: boolean
In [2]: y = np.ones(3, bool)
...: out = np.ones(3, bool)
...:
...: np.logical_and(arr, y, out=out)
...: out
Out[2]: array([ True, False, False])
In [3]: arr._data[2] = True # This does not change arr since arr._mask[2] = True
...:
...: np.logical_and(arr, y, out=out)
...: out
Out[3]: array([ True, False, True])
```
Relevant code:
https://github.com/pandas-dev/pandas/blob/17ecb56c219d8d01399faffcffa8f059ecf6fd45/pandas/core/arrays/boolean.py#L366-L390
Here we are calling
```python
result = np.logical_and(arr._data, y, out=out)
return BooleanArray(result, arr._mask)
```
which adjusts `out` in-place, ignoring the mask and depending on `arr._data[2]`.
This issue shows how this might be an issue:
https://github.com/numpy/numpy/issues/19374
#### Output of ``pd.show_versions()``
<details>
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-74-generic
Version : #83-Ubuntu SMP Sat May 8 02:35:39 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-06-29T14:58:59Z" | "2021-08-21T04:04:52Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
This seems to be similar to much older bugs 14621 and 7825, but I can't see an open one,
- [x] I have confirmed this bug exists on the latest version of pandas.
Occurs with 1.2.4 which seems to be the current one I can get.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
Not done.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python -i
>>> import pandas as pd
>>> import datetime
>>> datetime.datetime.replace(pd.Timestamp('2021-01-01'), tzinfo=None)
Segmentation fault
```
#### Problem description
Unexpected segmentation fault, which shouldn't occur with basic function usage.
#### Expected Output
It's not entirely clear whether this should work, but it shouldn't crash.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
Also occurs on python 3.8.8. | {
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} | 6 | "2021-06-29T15:21:17Z" | "2021-10-31T00:40:25Z" | null | MEMBER | null | Frequently one of the posix github action builds will fail because conda won't download. Hitting the re-run button causes _all_ of them to re-run instead of just that one. Can we find a way to only re-run the affected build? | {
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} | 0 | "2021-06-29T15:38:33Z" | "2021-07-01T23:25:57Z" | "2021-07-01T23:06:07Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Code is still pretty messy at this point, opening mainly for CI purposes. Also, this is my first time messing around with BlockManager so would appreciate a little extra scrutiny on that part.
Benchmarks
<Details>
```
before after ratio
[9f6a91ae] [77c05634]
<master> <perf-fillna>
- 1.16±0.3ms 772±70μs 0.67 replace.FillNa.time_fillna(True)
- 6.78±0.7ms 4.48±0.3ms 0.66 replace.FillNa.time_fillna_df(False)
- 8.86±0.8ms 2.96±0.2ms 0.33 replace.FillNa.time_fillna_limit(False)
- 7.92±0.6ms 2.46±0.6ms 0.31 replace.FillNa.time_fillna_df(True)
- 6.15±0.5ms 810±60μs 0.13 replace.FillNa.time_fillna_limit(True)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
before after ratio
[9f6a91ae] [77c05634]
<master> <perf-fillna>
+ 644±10μs 1.15±0.04ms 1.79 replace.FillNa.time_replace(True)
+ 4.14±0.08ms 5.43±0.3ms 1.31 replace.FillNa.time_replace(False)
- 4.10±0.2ms 2.30±0.2ms 0.56 replace.FillNa.time_fillna(False)
- 1.24±0.09ms 682±20μs 0.55 replace.FillNa.time_fillna(True)
- 6.98±0.4ms 2.61±0.4ms 0.37 replace.FillNa.time_fillna_limit(False)
- 6.61±0.3ms 1.57±0.1ms 0.24 replace.FillNa.time_fillna_df(True)
- 4.99±0.1ms 622±10μs 0.12 replace.FillNa.time_fillna_limit(True)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
before after ratio
[9f6a91ae] [77c05634]
<master> <perf-fillna>
- 1.14±0.3ms 612±40μs 0.54 replace.FillNa.time_replace(True)
- 7.22±2ms 1.61±0.1ms 0.22 replace.FillNa.time_fillna_df(True)
- 8.97±2ms 2.00±0.2ms 0.22 replace.FillNa.time_fillna_limit(False)
- 5.47±0.2ms 639±30μs 0.12 replace.FillNa.time_fillna_limit(True)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
</Details> | {
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} | 0 | "2021-06-29T17:12:13Z" | "2021-07-01T23:16:11Z" | "2021-07-01T23:04:34Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
At first I was thinking of deprecating, but decided that silently ignoring should be considered a bug, better to loudly raise. | {
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] | open | false | null | [] | null | 0 | "2021-06-29T18:19:31Z" | "2021-08-21T04:05:44Z" | null | NONE | null | #### Problem
Right now `json_normalize` will leave lists encountered within dictionaries intact:
```
import pandas as pd
df = pd.json_normalize([{"a": [1, 1]}, {"a": [1, 2]}])
print(df)
```
output:
```
a
0 [1, 1]
1 [1, 2]
```
Each entry is a `list` object in this case. I am not really sure how this is of any use really. If I for example like to do anything with the first element of each row I would have to convert this first into yet another `DataFrame` with something like:
```
df2 = pd.DataFrame({f"a.{k}": [i[k] for i in df['a']] for k in range(len(df['a'][0]))})
print(df2)
```
output
```
a.0 a.1
0 1 1
1 1 2
```
#### Solution
It would be really useful I think, if there is a flag or something that would enable to directly flatten lists as well. Something like `json_normalize(data, flatten_list=True)`. The list index is then used as a string in the record name, e.g. `"a.0.b", "a.1.b"` etc.
#### API breaking implications
Don't think this would break any API.
#### Alternatives
There are a few packages that already have some of this ability, but require additional dependencies and intermediate products, so are slowing down conversion:
* https://github.com/amirziai/flatten
* https://github.com/metinsenturk/flat_table
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} | 0 | "2021-06-29T19:22:51Z" | "2021-07-09T13:16:22Z" | "2021-07-09T13:16:22Z" | MEMBER | null | We've got two versions of our logo, one for light backgrounds, and another one for dark backgrounds: https://pandas.pydata.org/about/citing.html#brand-and-logo.
Modern browsers usually support a dark mode, and when users have it activated, our favicon (our logo for light backgrounds) doesn't look really nice:
Looks like there are ways to change the favicon colors (or the favicon itself I guess) based on the light theme of the browser user. For example, see this article: https://catalin.red/svg-favicon-light-dark-theme/
It'd be nice to show the dark background logo as a favicon to web visitors using a dark theme for their browser. | {
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} | 2 | "2021-06-29T20:59:49Z" | "2021-07-01T23:31:07Z" | "2021-07-01T23:05:29Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/42316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42316/events | https://github.com/pandas-dev/pandas/issues/42316 | 933,245,943 | MDU6SXNzdWU5MzMyNDU5NDM= | 42,316 | DOC: timedelta return type | {
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] | open | false | null | [] | null | 0 | "2021-06-30T02:22:51Z" | "2021-08-21T04:06:07Z" | null | CONTRIBUTOR | null | #### Location of the documentation
https://github.com/pandas-dev/pandas/blob/master/pandas/core/tools/timedeltas.py#L70
#### Documentation problem
Is `numpy.array of timedelta64` the correct return time for to_timedelta? I see it here as `TimedeltaIndex`: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/indexes/timedeltas/test_timedelta.py#L27
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https://api.github.com/repos/pandas-dev/pandas/issues/42317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42317/events | https://github.com/pandas-dev/pandas/pull/42317 | 933,271,493 | MDExOlB1bGxSZXF1ZXN0NjgwNTAzMzk2 | 42,317 | Revert "REF: move shift logic from BlockManager to DataFrame" | {
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} | 2 | "2021-06-30T03:30:42Z" | "2021-06-30T07:26:26Z" | "2021-06-30T07:25:47Z" | MEMBER | null | Reverts pandas-dev/pandas#40536
Discussed in #42246, we can revert for 1.3 without breaking anything, then un-revert in 1.4 and have some time to sort out the perf trouble. | {
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} | 5 | "2021-06-30T04:29:57Z" | "2021-06-30T14:25:17Z" | "2021-06-30T09:01:11Z" | MEMBER | null | - [ ] closes #42239
- [ ] tests added / passed
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aimed at #42239 | {
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} | 2 | "2021-06-30T05:11:39Z" | "2021-07-04T21:44:44Z" | "2021-07-04T21:35:37Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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There are a bunch of issues about inconsistent dropping of MultiIndex levels: #12827, #18631, #10552, #42102, #41416, none of which are closed by this. | {
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"merged_at": "2021-07-04T21:35:37Z",
"patch_url": "https://github.com/pandas-dev/pandas/pull/42319.patch",
"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/42319"
} |
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