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} | 3 | 2021-06-10T07:13:31Z | 2021-06-11T10:48:02Z | 2021-06-10T11:20:57Z | MEMBER | null | Address performance regression in https://github.com/pandas-dev/pandas/pull/41711/files#r647515492 (https://pandas.pydata.org/speed/pandas/#series_methods.All.time_all?python=3.8&Cython=0.29.21&p-N=1000000&p-case='fast'&p-dtype='bool'&commits=9231b495-44237a58)
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See #41891 for why this makes sense.
- [x] closes #41891
- [x] 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 | 2021-06-10T10:31:18Z | 2021-06-11T10:14:06Z | 2021-06-11T10:14:01Z | MEMBER | null | I think we should do this for the rc.
We will be publishing the docs at https://pandas.pydata.org/pandas-docs/version/1.3.0/ and linked from the rc announcement. (It's the same url for the actual release and the rc docs are replaced)
we haven't yet released 1.2.5 so the 1.2.5 tag does not yet exist. used 1.2.4 for now. | {
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- [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 datetime
times = pd.Series([pd.Timestamp(datetime.datetime.now(), tz='US/Central') for n in range(5)])
times.dtype # datetime64[ns, US/Central] == pd.DatetimeTZDtype(tz='US/Central')
mode_result = times.mode()
mode_result.dtype # dtype('<M8[ns]') == np.dtype('datetime64[ns]')
```
#### Problem description
The Timestamps are normalized to (I think) UTC time, and the original timezone and offset are lost. `Series.median()` does preserve timezones, so I assumed this is just an unhandled edge case. I'm working around this using `scipy.stats.mode`, which does preserve timezones.
#### Expected Output
```python
times.mode().dtype == pd.DatetimeTZDtype(tz='US/Central')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.7.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 : None
LOCALE : None.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.4
setuptools : 49.2.1
Cython : 0.29.14
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 : 2.11.3
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-06-10T18:06:00Z | 2021-07-01T21:49:35Z | null | 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.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
np.random.seed(1)
# Create one dataframe
dr = pd.date_range("2021-01-01", "2021-08-30")
ind = np.random.randint(0,5,len(dr))
something = np.random.random(len(ind))
df = pd.DataFrame({"date":dr, "ind":ind, "something":something})
# Define what to group on
GROUP_ON = ["ind", pd.Grouper(key="date", freq="2W")]
# Copy used if you uncomment lines below
GROUP_ON_COPY = ["ind", pd.Grouper(key="date", freq="2W")]
# Comment either of the two lines below, and it works
df.groupby(GROUP_ON).mean().reset_index().groupby(GROUP_ON).mean()
df.groupby(GROUP_ON).mean().reset_index()
# Use a different groupby in the second case, and it works
#df.groupby(GROUP_ON).mean().reset_index().groupby(GROUP_ON).sum()
#df.groupby(GROUP_ON_COPY).mean().reset_index()
# Bonus points: do only one groupby first, and it works
# df.groupby(GROUP_ON).mean().reset_index()
# df.groupby(GROUP_ON).mean().reset_index()
```
#### Problem description
In some cases it seems that pd.Grouper cannot be reused. I was not able to figure out exactly why, or if the output can sometimes corrupt. It leads to very painful debugging.
Tested with master branch, and with python 3.8 and 3.9
#### Expected Output
pd.Grouper should be able to be used any number of times, or should raise explicit error upon any reuse.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #488-Microsoft Mon Sep 01 13:43:00 PST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.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 : 57.0.0
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 : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
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 : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41931 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41931/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41931/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41931/events | https://github.com/pandas-dev/pandas/pull/41931 | 917,753,473 | MDExOlB1bGxSZXF1ZXN0NjY3MjUwNjE3 | 41,931 | BUG/REF: use sorted_rank_1d for rank_2d | {
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} | 3 | 2021-06-10T19:15:10Z | 2021-06-25T17:41:05Z | 2021-06-25T17:36:29Z | MEMBER | null | - [x] closes #19560 (All the inconsistencies are addressed, but could be left open if we want to remove object support)
- [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
Built on #41916
There is a slight slowdown in 3 benchmarks, due to use of `lexsort` instead of `argsort` to sort with both the data and mask so that `na_option` can be properly handled. I am not sure if this can be avoided (though as a plus there is also potential perf improvement for users running code in parallel since the ranking is in a `nogil` block now).
Benchmarks:
<details>
```
[499ef8c0] [d678bbf0]
<master> <ref/rank_2d_dedup>
7.14±0.1ms 7.04±0.4ms 0.99 categoricals.Rank.time_rank_int
7.85±0.3ms 7.34±0.2ms 0.93 categoricals.Rank.time_rank_int_cat
7.30±0.2ms 6.95±0.6ms 0.95 categoricals.Rank.time_rank_int_cat_ordered
121±3ms 122±10ms 1.01 categoricals.Rank.time_rank_string
8.66±1ms 8.13±1ms 0.94 categoricals.Rank.time_rank_string_cat
6.37±1ms 6.11±0.4ms 0.96 categoricals.Rank.time_rank_string_cat_ordered
9.34±2ms 10.1±1ms 1.08 frame_methods.Rank.time_rank('float')
3.81±1ms 4.16±0.4ms 1.09 frame_methods.Rank.time_rank('int')
59.0±20ms 48.0±0.9ms ~0.81 frame_methods.Rank.time_rank('object')
2.75±0.4ms 3.53±0.7ms ~1.28 frame_methods.Rank.time_rank('uint')
1.06±0.04ms 1.01±0.04ms 0.95 groupby.RankWithTies.time_rank_ties('datetime64', 'average')
1.13±0.06ms 1.02±0.04ms ~0.90 groupby.RankWithTies.time_rank_ties('datetime64', 'dense')
1.19±0.1ms 1.04±0.03ms ~0.87 groupby.RankWithTies.time_rank_ties('datetime64', 'first')
1.12±0.04ms 1.04±0.06ms 0.93 groupby.RankWithTies.time_rank_ties('datetime64', 'max')
1.03±0.05ms 1.03±0.04ms 1.00 groupby.RankWithTies.time_rank_ties('datetime64', 'min')
1.22±0.1ms 1.10±0.07ms ~0.90 groupby.RankWithTies.time_rank_ties('float32', 'average')
1.13±0.09ms 1.12±0.04ms 0.99 groupby.RankWithTies.time_rank_ties('float32', 'dense')
1.15±0.1ms 1.05±0.05ms 0.91 groupby.RankWithTies.time_rank_ties('float32', 'first')
- 1.19±0.05ms 1.00±0.03ms 0.84 groupby.RankWithTies.time_rank_ties('float32', 'max')
1.18±0.04ms 1.13±0.08ms 0.96 groupby.RankWithTies.time_rank_ties('float32', 'min')
1.25±0.1ms 1.17±0.03ms 0.94 groupby.RankWithTies.time_rank_ties('float64', 'average')
1.45±0.5ms 990±10μs ~0.68 groupby.RankWithTies.time_rank_ties('float64', 'dense')
1.21±0.2ms 1.11±0.08ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'first')
1.18±0.05ms 1.15±0.07ms 0.97 groupby.RankWithTies.time_rank_ties('float64', 'max')
1.32±0.3ms 963±40μs ~0.73 groupby.RankWithTies.time_rank_ties('float64', 'min')
1.03±0.1ms 971±80μs 0.94 groupby.RankWithTies.time_rank_ties('int64', 'average')
1.02±0.06ms 1.02±0.06ms 1.01 groupby.RankWithTies.time_rank_ties('int64', 'dense')
1.13±0.08ms 1.01±0.04ms ~0.89 groupby.RankWithTies.time_rank_ties('int64', 'first')
1.24±0.1ms 959±20μs ~0.77 groupby.RankWithTies.time_rank_ties('int64', 'max')
1.14±0.06ms 979±50μs ~0.86 groupby.RankWithTies.time_rank_ties('int64', 'min')
10.9±0.6ms 9.80±0.4ms ~0.90 series_methods.Rank.time_rank('float')
7.18±0.3ms 7.15±0.6ms 1.00 series_methods.Rank.time_rank('int')
53.7±1ms 49.6±4ms 0.92 series_methods.Rank.time_rank('object')
7.64±0.2ms 7.78±0.6ms 1.02 series_methods.Rank.time_rank('uint')
9.96±0.2ms 11.7±0.3ms ~1.17 stat_ops.Rank.time_average_old('DataFrame', False)
+ 9.89±0.2ms 11.9±0.6ms 1.20 stat_ops.Rank.time_average_old('DataFrame', True)
13.5±0.9ms 12.6±0.8ms 0.93 stat_ops.Rank.time_average_old('Series', False)
12.8±1ms 11.9±0.6ms 0.93 stat_ops.Rank.time_average_old('Series', True)
+ 9.84±0.3ms 11.7±0.4ms 1.19 stat_ops.Rank.time_rank('DataFrame', False)
+ 8.53±0.3ms 11.7±0.8ms 1.38 stat_ops.Rank.time_rank('DataFrame', True)
14.0±3ms 12.2±1ms ~0.87 stat_ops.Rank.time_rank('Series', False)
13.0±0.9ms 12.1±1ms 0.93 stat_ops.Rank.time_rank('Series', True)
```
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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
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} | 2 | 2021-06-10T21:13:41Z | 2021-06-16T00:22:58Z | 2021-06-16T00:11:35Z | 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
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} | 2 | 2021-06-10T21:58:36Z | 2021-06-17T18:50:02Z | 2021-06-17T17:52:17Z | 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
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Started with moving a fastpath from IntervalIndex to the base class. In the process found a bug in the IntervalIndex version, which is now fixed. | {
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} | 6 | 2021-06-10T23:17:19Z | 2021-11-01T23:46:25Z | null | MEMBER | null | @seberg this build is using numpy 1.22dev, looks like a bunch of the failures are raising in `np.iinfo(np.int64).max`
```
return np.iinfo(np.int64).max
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <[AttributeError("'iinfo' object has no attribute 'kind'") raised in repr()] iinfo object at 0x7f986b9aa830>
int_type = <class 'numpy.int64'>
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
> self.dtype = numeric.dtype(type(int_type))
E TypeError: 'numpy.dtype[bool_]' object is not callable
/opt/hostedtoolcache/Python/3.10.0-beta.2/x64/lib/python3.10/site-packages/numpy/core/getlimits.py:518: TypeError
``` | {
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} | 1 | 2021-06-10T23:24:06Z | 2021-06-16T15:32:45Z | 2021-06-15T23:50:03Z | 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
Not sure what the point of these were. | {
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} | 5 | 2021-06-11T01:02:31Z | 2021-06-11T23:56:04Z | 2021-06-11T23:56:04Z | MEMBER | null | See https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61339&view=logs&j=b516280a-8c75-541b-8ba0-ae13561718a7&t=a9217cb1-24f6-5625-717d-72085a70e1d3
and https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61324&view=logs&j=b516280a-8c75-541b-8ba0-ae13561718a7&t=a9217cb1-24f6-5625-717d-72085a70e1d3
for some examples on 2 different runs. I've only noticed it on this run, will look to try to see when it started (unless anyone knows :) | {
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} | 3 | 2021-06-11T01:04:35Z | 2021-06-11T02:33:17Z | 2021-06-11T02:33:10Z | CONTRIBUTOR | null | Fix bug introduced in https://github.com/pandas-dev/pandas/pull/41903
Mypy doesn't seem to like generic types with default values? | {
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- [ ] no tests added
- [ ] pre-commit hook, no python files added
- [ ] No docstrings added; docs not rebuilt. _**Where do pre-commit hooks get added to “What’s New"?**_
Implementation:
* Name set to always-use-future-annotations (rather than always-use-from-future-annotations)
* We filter with` types: [python]` [rather than](https://pre-commit.com/#filtering-files-with-types) `files: "\\.(py)$”`
* No minimum pre-commit version.
What else is needed here for a merge?
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"due_on": "2021-11-28T08:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/91",
"id": 7259169,
"labels_url": "https://api.github.com/repos/pandas-dev/pandas/milestones/91/labels",
"node_id": "MI_kwDOAA0YD84AbsQh",
"number": 91,
"open_issues": 23,
"state": "open",
"title": "1.3.5",
"updated_at": "2021-11-21T13:42:45Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/91"
} | 10 | 2021-06-11T02:41:54Z | 2021-10-16T18:47:09Z | null | MEMBER | null | A list of failures for pandas on Python 3.10.
Note: Some of these are flaky.
- [x] ``TestUnique.test_first_nan_kept``
<Details>
________________________ TestUnique.test_first_nan_kept ________________________
self = <pandas.tests.test_algos.TestUnique object at 0x7fe611cc19c0>
def test_first_nan_kept(self):
# GH 22295
# create different nans from bit-patterns:
bits_for_nan1 = 0xFFF8000000000001
bits_for_nan2 = 0x7FF8000000000001
NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0]
NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
assert NAN1 != NAN1
assert NAN2 != NAN2
for el_type in [np.float64, object]:
a = np.array([NAN1, NAN2], dtype=el_type)
result = pd.unique(a)
> assert result.size == 1
E assert 2 == 1
E + where 2 = array([nan, nan], dtype=object).size
pandas/tests/test_algos.py:798: AssertionError
</Details>
- [x] ``TestIsin.test_different_nans``
<Details>
_________________________ TestIsin.test_different_nans _________________________
self = <pandas.tests.test_algos.TestIsin object at 0x7fe611cc2b30>
def test_different_nans(self):
# GH 22160
# all nans are handled as equivalent
comps = [float("nan")]
values = [float("nan")]
assert comps[0] is not values[0] # different nan-objects
# as list of python-objects:
result = algos.isin(comps, values)
tm.assert_numpy_array_equal(np.array([True]), result)
# as object-array:
result = algos.isin(
np.asarray(comps, dtype=object), np.asarray(values, dtype=object)
)
> tm.assert_numpy_array_equal(np.array([True]), result)
pandas/tests/test_algos.py:1007:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([ True]), right = array([False]), err_msg = None
def _raise(left, right, err_msg):
if err_msg is None:
if left.shape != right.shape:
raise_assert_detail(
obj, f"{obj} shapes are different", left.shape, right.shape
)
diff = 0
for left_arr, right_arr in zip(left, right):
# count up differences
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
diff += 1
diff = diff * 100.0 / left.size
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
> raise_assert_detail(obj, msg, left, right, index_values=index_values)
E AssertionError: numpy array are different
E
E numpy array values are different (100.0 %)
E [left]: [True]
E [right]: [False]
pandas/_testing/asserters.py:726: AssertionError
</Details>
- [x] ``TestIsin.test_different_nan_objects``
<Details>
_____________________ TestIsin.test_different_nan_objects ______________________
self = <pandas.tests.test_algos.TestIsin object at 0x7fe61167ff40>
def test_different_nan_objects(self):
# GH 22119
comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
vals = np.array([float("nan")], dtype=object)
expected = np.array([False, False, True])
result = algos.isin(comps, vals)
> tm.assert_numpy_array_equal(expected, result)
pandas/tests/test_algos.py:1039:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([False, False, True]), right = array([False, False, False])
err_msg = None
def _raise(left, right, err_msg):
if err_msg is None:
if left.shape != right.shape:
raise_assert_detail(
obj, f"{obj} shapes are different", left.shape, right.shape
)
diff = 0
for left_arr, right_arr in zip(left, right):
# count up differences
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
diff += 1
diff = diff * 100.0 / left.size
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
> raise_assert_detail(obj, msg, left, right, index_values=index_values)
E AssertionError: numpy array are different
E
E numpy array values are different (33.33333 %)
E [left]: [False, False, True]
E [right]: [False, False, False]
pandas/_testing/asserters.py:726: AssertionError
</Details>
- [x] ``test_hash_equal``
<Details>
_________________________ test_hash_equal[a2-b2-True] __________________________
a = Sparse[float64, nan], b = Sparse[float64, nan], expected = True
@pytest.mark.parametrize(
"a, b, expected",
[
(SparseDtype(float, 0.0), SparseDtype(np.dtype("float"), 0.0), True),
(SparseDtype(int, 0), SparseDtype(int, 0), True),
(SparseDtype(float, float("nan")), SparseDtype(float, np.nan), True),
(SparseDtype(float, 0), SparseDtype(float, np.nan), False),
(SparseDtype(int, 0.0), SparseDtype(float, 0.0), False),
],
)
def test_hash_equal(a, b, expected):
result = a == b
assert result is expected
result = hash(a) == hash(b)
> assert result is expected
E assert False is True
pandas/tests/arrays/sparse/test_dtype.py:141: AssertionError
</Details>
- [x] ``TestReshaping.test_stack``
<Details>
____________________ TestReshaping.test_stack[nan-columns0] ____________________
self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fc4a6d10>
data = [0.6069529775354311, 0.6532416455715819, nan, 0.45409596635270144, 0.10948849972327801, nan, 0.08987488776518548, 0.19...66, 67, 69, 70, 72, 73, 75,
76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99],
dtype=int32)
columns = ['A', 'B']
@pytest.mark.parametrize(
"columns",
[
["A", "B"],
pd.MultiIndex.from_tuples(
[("A", "a"), ("A", "b")], names=["outer", "inner"]
),
],
)
def test_stack(self, data, columns):
df = pd.DataFrame({"A": data[:5], "B": data[:5]})
df.columns = columns
result = df.stack()
expected = df.astype(object).stack()
# we need a second astype(object), in case the constructor inferred
# object -> specialized, as is done for period.
expected = expected.astype(object)
if isinstance(expected, pd.Series):
assert result.dtype == df.iloc[:, 0].dtype
else:
assert all(result.dtypes == df.iloc[:, 0].dtype)
result = result.astype(object)
> self.assert_equal(result, expected)
pandas/tests/extension/base/reshaping.py:270:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'>
left = 0 A 0.606953
B 0.606953
1 A 0.653242
B 0.653242
3 A 0.454096
B 0.454096
4 A 0.109488
B 0.109488
dtype: Sparse[object, nan]
right = 0 A 0.606953
B 0.606953
1 A 0.653242
B 0.653242
3 A 0.454096
B 0.454096
4 A 0.109488
B 0.109488
dtype: object
kwargs = {}
@classmethod
def assert_equal(cls, left, right, **kwargs):
> return tm.assert_equal(left, right, **kwargs)
E AssertionError: Attributes of Series are different
E
E Attribute "dtype" are different
E [left]: Sparse[object, nan]
E [right]: object
pandas/tests/extension/base/base.py:9: AssertionError
____________________ TestReshaping.test_stack[nan-columns1] ____________________
self = <pandas.tests.extension.test_sparse.TestReshaping object at 0x7fe5fbea8a30>
data = [0.08642533151098042, 0.31948867044255314, nan, 0.7649119528014422, 0.8562776453950798, nan, 0.0026917407695283835, 0....66, 67, 69, 70, 72, 73, 75,
76, 78, 79, 81, 82, 84, 85, 87, 88, 90, 91, 93, 94, 96, 97, 99],
dtype=int32)
columns = MultiIndex([('A', 'a'),
('A', 'b')],
names=['outer', 'inner'])
@pytest.mark.parametrize(
"columns",
[
["A", "B"],
pd.MultiIndex.from_tuples(
[("A", "a"), ("A", "b")], names=["outer", "inner"]
),
],
)
def test_stack(self, data, columns):
df = pd.DataFrame({"A": data[:5], "B": data[:5]})
df.columns = columns
result = df.stack()
expected = df.astype(object).stack()
# we need a second astype(object), in case the constructor inferred
# object -> specialized, as is done for period.
expected = expected.astype(object)
if isinstance(expected, pd.Series):
assert result.dtype == df.iloc[:, 0].dtype
else:
assert all(result.dtypes == df.iloc[:, 0].dtype)
result = result.astype(object)
> self.assert_equal(result, expected)
pandas/tests/extension/base/reshaping.py:270:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
cls = <class 'pandas.tests.extension.test_sparse.TestReshaping'>
left = outer A
inner
0 a 0.086425
b 0.086425
1 a 0.319489
b 0.319489
3 a 0.764912
b 0.764912
4 a 0.856278
b 0.856278
right = outer A
inner
0 a 0.086425
b 0.086425
1 a 0.319489
b 0.319489
3 a 0.764912
b 0.764912
4 a 0.856278
b 0.856278
kwargs = {}
@classmethod
def assert_equal(cls, left, right, **kwargs):
> return tm.assert_equal(left, right, **kwargs)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="A") are different
E
E Attribute "dtype" are different
E [left]: Sparse[object, nan]
E [right]: object
pandas/tests/extension/base/base.py:9: AssertionError
</Details>
- [x] ``TestDataFrameSelectReindex.test_reindex_nan``
<Details>
_________________ TestDataFrameSelectReindex.test_reindex_nan __________________
self = <pandas.tests.frame.methods.test_reindex.TestDataFrameSelectReindex object at 0x7fe5f9d03f40>
def test_reindex_nan(self):
df = DataFrame(
[[1, 2], [3, 5], [7, 11], [9, 23]],
index=[2, np.nan, 1, 5],
columns=["joe", "jim"],
)
i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1]
tm.assert_frame_equal(df.reindex(i), df.iloc[j])
df.index = df.index.astype("object")
> tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False)
E AssertionError: Attributes of DataFrame.iloc[:, 0] (column name="joe") are different
E
E Attribute "dtype" are different
E [left]: float64
E [right]: int64
pandas/tests/frame/methods/test_reindex.py:472: AssertionError
</Details>
- [x] ``TestIndex.test_isin_nan_common_object``
<Details>
_____________ TestIndex.test_isin_nan_common_object[float0-float1] _____________
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6a4d930>
nulls_fixture = nan, nulls_fixture2 = nan
def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
# Test cartesian product of null fixtures and ensure that we don't
# mangle the various types (save a corner case with PyPy)
# all nans are the same
if (
isinstance(nulls_fixture, float)
and isinstance(nulls_fixture2, float)
and math.isnan(nulls_fixture)
and math.isnan(nulls_fixture2)
):
> tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, True]),
)
pandas/tests/indexes/test_base.py:1006:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([False, False]), right = array([False, True]), err_msg = None
def _raise(left, right, err_msg):
if err_msg is None:
if left.shape != right.shape:
raise_assert_detail(
obj, f"{obj} shapes are different", left.shape, right.shape
)
diff = 0
for left_arr, right_arr in zip(left, right):
# count up differences
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
diff += 1
diff = diff * 100.0 / left.size
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
> raise_assert_detail(obj, msg, left, right, index_values=index_values)
E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]
pandas/_testing/asserters.py:726: AssertionError
_____________ TestIndex.test_isin_nan_common_object[float1-float0] _____________
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f5e186a0>
nulls_fixture = nan, nulls_fixture2 = nan
def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
# Test cartesian product of null fixtures and ensure that we don't
# mangle the various types (save a corner case with PyPy)
# all nans are the same
if (
isinstance(nulls_fixture, float)
and isinstance(nulls_fixture2, float)
and math.isnan(nulls_fixture)
and math.isnan(nulls_fixture2)
):
> tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, True]),
)
pandas/tests/indexes/test_base.py:1006:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([False, False]), right = array([False, True]), err_msg = None
def _raise(left, right, err_msg):
if err_msg is None:
if left.shape != right.shape:
raise_assert_detail(
obj, f"{obj} shapes are different", left.shape, right.shape
)
diff = 0
for left_arr, right_arr in zip(left, right):
# count up differences
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
diff += 1
diff = diff * 100.0 / left.size
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
> raise_assert_detail(obj, msg, left, right, index_values=index_values)
E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]
pandas/_testing/asserters.py:726: AssertionError
_____________ TestIndex.test_isin_nan_common_object[float1-float1] _____________
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7fe5f6ab3eb0>
nulls_fixture = nan, nulls_fixture2 = nan
def test_isin_nan_common_object(self, nulls_fixture, nulls_fixture2):
# Test cartesian product of null fixtures and ensure that we don't
# mangle the various types (save a corner case with PyPy)
# all nans are the same
if (
isinstance(nulls_fixture, float)
and isinstance(nulls_fixture2, float)
and math.isnan(nulls_fixture)
and math.isnan(nulls_fixture2)
):
> tm.assert_numpy_array_equal(
Index(["a", nulls_fixture]).isin([nulls_fixture2]),
np.array([False, True]),
)
pandas/tests/indexes/test_base.py:1006:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([False, False]), right = array([False, True]), err_msg = None
def _raise(left, right, err_msg):
if err_msg is None:
if left.shape != right.shape:
raise_assert_detail(
obj, f"{obj} shapes are different", left.shape, right.shape
)
diff = 0
for left_arr, right_arr in zip(left, right):
# count up differences
if not array_equivalent(left_arr, right_arr, strict_nan=strict_nan):
diff += 1
diff = diff * 100.0 / left.size
msg = f"{obj} values are different ({np.round(diff, 5)} %)"
> raise_assert_detail(obj, msg, left, right, index_values=index_values)
E AssertionError: numpy array are different
E
E numpy array values are different (50.0 %)
E [left]: [False, False]
E [right]: [False, True]
pandas/_testing/asserters.py:726: AssertionError
</Details>
- [ ] ``TestUltraJSONTests.test_invalid_double_precision``
<Details>
_____________ TestUltraJSONTests.test_invalid_double_precision[9] ______________
self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780>
invalid_val = '9'
@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
double_input = 30.12345678901234567890
expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
msg = (
r"Invalid value '.*' for option 'double_precision', max is '15'|"
r"an integer is required \(got type "
)
with pytest.raises(expected_exception, match=msg):
> ujson.encode(double_input, double_precision=invalid_val)
E TypeError: 'str' object cannot be interpreted as an integer
pandas/tests/io/json/test_ujson.py:260: TypeError
During handling of the above exception, another exception occurred:
self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d780>
invalid_val = '9'
@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
double_input = 30.12345678901234567890
expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
msg = (
r"Invalid value '.*' for option 'double_precision', max is '15'|"
r"an integer is required \(got type "
)
> with pytest.raises(expected_exception, match=msg):
E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \\(got type " does not match "'str' object cannot be interpreted as an integer".
pandas/tests/io/json/test_ujson.py:259: AssertionError
____________ TestUltraJSONTests.test_invalid_double_precision[None] ____________
self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990>
invalid_val = None
@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
double_input = 30.12345678901234567890
expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
msg = (
r"Invalid value '.*' for option 'double_precision', max is '15'|"
r"an integer is required \(got type "
)
with pytest.raises(expected_exception, match=msg):
> ujson.encode(double_input, double_precision=invalid_val)
E TypeError: 'NoneType' object cannot be interpreted as an integer
pandas/tests/io/json/test_ujson.py:260: TypeError
During handling of the above exception, another exception occurred:
self = <pandas.tests.io.json.test_ujson.TestUltraJSONTests object at 0x7fe5ee07d990>
invalid_val = None
@pytest.mark.parametrize("invalid_val", [20, -1, "9", None])
def test_invalid_double_precision(self, invalid_val):
double_input = 30.12345678901234567890
expected_exception = ValueError if isinstance(invalid_val, int) else TypeError
msg = (
r"Invalid value '.*' for option 'double_precision', max is '15'|"
r"an integer is required \(got type "
)
> with pytest.raises(expected_exception, match=msg):
E AssertionError: Regex pattern "Invalid value '.*' for option 'double_precision', max is '15'|an integer is required \\(got type " does not match "'NoneType' object cannot be interpreted as an integer".
pandas/tests/io/json/test_ujson.py:259: AssertionError
</Details>
- [x] ``TestGetDummies.test_get_dummies_basic_drop_first_NA``
<Details>
__________ TestGetDummies.test_get_dummies_basic_drop_first_NA[dense] __________
self = <pandas.tests.reshape.test_get_dummies.TestGetDummies object at 0x7fe5e7b1d7b0>
sparse = False
def test_get_dummies_basic_drop_first_NA(self, sparse):
# Test NA handling together with drop_first
s_NA = ["a", "b", np.nan]
res = get_dummies(s_NA, drop_first=True, sparse=sparse)
exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
if sparse:
exp = exp.apply(SparseArray, fill_value=0)
tm.assert_frame_equal(res, exp)
res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
["b", np.nan], axis=1
)
if sparse:
exp_na = exp_na.apply(SparseArray, fill_value=0)
> tm.assert_frame_equal(res_na, exp_na)
E AssertionError
pandas/tests/reshape/test_get_dummies.py:485: AssertionError
</Details>
- [x] ``test_reindex_nan``
<Details>
_______________________________ test_reindex_nan _______________________________
def test_reindex_nan():
ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8])
i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2]
tm.assert_series_equal(ts.reindex(i), ts.iloc[j])
ts.index = ts.index.astype("object")
# reindex coerces index.dtype to float, loc/iloc doesn't
> tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False)
E AssertionError: Attributes of Series are different
E
E Attribute "dtype" are different
E [left]: float64
E [right]: int64
</Details>
- [x] ``test_map_missing_mixed``
<Details>
_________________ test_map_missing_mixed[vals0-mapping0-exp0] __________________
[gw1] linux -- Python 3.10.0 /opt/hostedtoolcache/Python/3.10.0-beta.2/x64/bin/python
vals = ['a', 'b', 'c'], mapping = {nan: 'not NaN'}
exp = [nan, nan, nan, 'not NaN']
@pytest.mark.parametrize(
"vals,mapping,exp",
[
(list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]),
(list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3),
(list(range(3)), {0: 42}, [42] + [np.nan] * 3),
],
)
def test_map_missing_mixed(vals, mapping, exp):
# GH20495
s = Series(vals + [np.nan])
result = s.map(mapping)
> tm.assert_series_equal(result, Series(exp))
pandas/tests/apply/test_series_apply.py:821:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:53: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)
E AssertionError: Series are different
E
E Series values are different (25.0 %)
E [index]: [0, 1, 2, 3]
E [left]: [nan, nan, nan, nan]
E [right]: [nan, nan, nan, not NaN]
pandas/_libs/testing.pyx:168: AssertionError
</Details>
NOTE: This list is not complete since there are also some failing JSON tests that freeze pytest. Will look at those soon. | {
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] | closed | false | null | [] | null | 6 | 2021-06-11T02:53:40Z | 2021-08-17T02:29:26Z | 2021-08-17T02:29:26Z | NONE | null | - [X] closes #41901
- [ ] no tests added
- [ ] pre-commit hook, no python files added
- [ ] No docstrings added; docs not rebuilt. _**Where do pre-commit hooks get added to “What’s New"?**_
Implementation:
* Name set to always-use-future-annotations (rather than always-use-from-future-annotations)
* We filter with` types: [python]` [rather than](https://pre-commit.com/#filtering-files-with-types) `files: "\\.(py)$”`
* No minimum pre-commit version.
What else is needed here for a merge?
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] | closed | false | null | [] | null | 6 | 2021-06-11T04:50:13Z | 2021-06-14T22:48:42Z | 2021-06-14T22:48:42Z | 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
pd.read_excel("foo.xlsx")
```
Example (empty) excel file: https://github.com/pandas-dev/pandas/files/6635871/foo.xlsx
#### Problem description
Running the code example emits an error message that could be very helpful:
```
ImportError: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.
```
However, as of version 2, the xlrd library only supports xls files, not xlsx (https://github.com/pandas-dev/pandas/issues/38410). This instruction is thus misleading: running `pip install xlrd` and then rerunning the code still fails:
```
ValueError: Your version of xlrd is 2.0.1. In xlrd >= 2.0, only the xls format is supported. Install openpyxl instead.
```
The correct/final resolution is thus to run `pip install openpyxl`. It's a bit of papercut to go though several steps here, especially as I suspect xlsx files are more common than xls these days (at least, it's been ~15-20 years since .xlsx was released according to https://en.wikipedia.org/wiki/Office_Open_XML).
#### Expected Output
It would be nice if the original error message made it clearer what was going on. For instance, options might be:
1. detect the file type/extension and reference the appropriate library (i.e. for .xlsx `ImportError: Missing optional dependency 'openpyxl'. Install openpyxl for .xlsx Excel support. Use pip or conda to install openpyxl.`)
2. mention both libraries so the human can make the decision `ImportError: Missing optional dependency 'xlrd' (for xls) 'openpyxl' (for xlsx). Install the appropriate one of these for Excel support. Use pip or conda to install xlrd or openpyxl.`)
2. suggest installing `xlrd>=1.0.0,<2.0.0`
(I personally feel that 1 would be the nicest.)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:40:21 PST 2020; root:xnu-7195.60.75~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.4
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
After `pip install xlrd`, it reports: xlrd: 2.0.1.
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] | open | false | null | [] | null | 0 | 2021-06-11T05:43:20Z | 2021-06-11T05:43:20Z | null | MEMBER | null | From 2021-06-09 dev call, new discussion of https://github.com/pandas-dev/pandas/issues/22864
**Problem**: Frequency string `'D'` and `pd.offsets.Day` can mean 24 hours or calendar day (23/24/25H) depending on the operation
**Original Settled Solution**: Deprecate all behavior where Frequency string `'D'` and `pd.offsets.Day` is a fixed 24 hours in favor of `"24H"`. A private `_Day` offset would be used where appropriate internally and swapped out once the deprecation is enforced.
(Note: I lost steam last time catching all the warnings issued in the testing suite given the above solution touches datetimes, timedeltas, offsets, methods, etc)
**Other Solutions**
1. Implement a new offset, e.g. `"'DayDST'"/pd.offsets.CalendarDay"`, that users can migrate to.
2. Deprecate `Tick`s (`Day` is a subclass) all together since they are redundant with `Timedelta`s
cc @pandas-dev/pandas-core
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} | 3 | 2021-06-11T09:03:05Z | 2021-06-11T11:52:12Z | 2021-06-11T11:20:52Z | MEMBER | null | This doesn't "fix" anything, but by falling back for now to the BlockManager-specific implementation of `to_hdf` writer, this avoids failures when using ArrayManager, which should make it easier to test it. | {
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} | 3 | 2021-06-11T09:51:13Z | 2021-07-02T19:12:48Z | 2021-06-25T22:16:43Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/41807#issuecomment-859372500
this does not update the `__array__` to `to_numpy` methods of EA, see https://github.com/pandas-dev/pandas/pull/41185#issuecomment-852469918 but does need a couple of ignores till that is done.
(we are removing 3 anyway, so a net reduction in ignores :smile:, and reducing false positives for users)
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} | 11 | 2021-06-11T09:53:35Z | 2021-07-29T14:59:15Z | 2021-07-28T23:01:05Z | CONTRIBUTOR | null | Checks an item off the list in #41693
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When resetting the index of a `DataFrame`, and when needing to rename the "index" column to something more useful, it would be nice to do it directly with the call to`reset_index` (and not afterwards with `df.rename`).
#### Describe the solution you'd like
`DataFrame.reset_index` should get a new parameter `name` that renames the "index" column after resetting.
#### API breaking implications
I don't see any obvious implications. | {
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} | 6 | 2021-06-11T11:30:36Z | 2021-06-12T08:31:36Z | 2021-06-11T23:56:04Z | MEMBER | null | Closes #41937 | {
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} | 1 | 2021-06-11T11:48:36Z | 2021-06-24T13:07:20Z | 2021-06-21T13:18:36Z | MEMBER | null | Currently we give a deprecation warning when unpickling:
```
In [10]: idx = pd.date_range("2012", periods=3, freq="D")
In [11]: res = pickle.dumps(idx[0])
In [12]: pickle.loads(res)
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: The 'freq' argument in Timestamp is deprecated and will be removed in a future version.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[12]: Timestamp('2012-01-01 00:00:00', freq='D')
```
We should ensure that unpickling a Timestamp does not generate a warning, only if the user afterwards accesses the freq as normal it should warn. | {
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} | 2 | 2021-06-11T12:22:09Z | 2021-06-11T14:37:41Z | 2021-06-11T14:33:27Z | MEMBER | null | That failed the doc build. In addition also removed the msgpack row in the table at the top of the page, since those functions don't exist anymore. | {
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} | 4 | 2021-06-11T12:33:10Z | 2021-06-15T23:34:24Z | 2021-06-15T23:34:24Z | NONE | null | DataFrame.columns.get_indexer fails if there are duplicate column names.
https://github.com/pandas-dev/pandas/blob/05552d354a430f17a45b5fda8a1fd417f4c60c2e/pandas/core/reshape/melt.py#L109
This is really unfortunate -- melting should handle such cases
id value value
id1 2 3
should go to
id1 2
id1 3
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} | 14 | 2021-06-11T15:06:58Z | 2021-06-16T22:15:53Z | 2021-06-16T22:15:37Z | CONTRIBUTOR | null | - [x] closes #41836
- [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
NaNs as Float are already handled correctly (all nans are the same equavalency class), this PR adds similar functionality for complex and tuple.
It doesn't handle `frozenset` (see also discussion in #41836) because it would require to duplicate a lot of code/internal details from setobject. | {
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} | 0 | 2021-06-11T16:24:03Z | 2021-06-17T19:05:36Z | 2021-06-17T19:05:36Z | 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.
---
Due to this change in CPython: https://github.com/python/cpython/commit/a07da09ad5bd7d234ccd084a3a0933c290d1b592
the following assumption is no longer true:
https://github.com/pandas-dev/pandas/blob/059c8bac51e47d6eaaa3e36d6a293a22312925e6/pandas/_libs/src/klib/khash_python.h#L183-L187
Here is a failing example:
```python
_____________________ TestIsin.test_different_nan_objects ______________________
self = <pandas.tests.test_algos.TestIsin object at 0x7f9ba1fe7250>
def test_different_nan_objects(self):
# GH 22119
comps = np.array(["nan", np.nan * 1j, float("nan")], dtype=object)
vals = np.array([float("nan")], dtype=object)
expected = np.array([False, False, True])
result = algos.isin(comps, vals)
> tm.assert_numpy_array_equal(expected, result)
pandas/tests/test_algos.py:1039:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
left = array([False, False, True]), right = array([False, False, False])
```
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} | 15 | 2021-06-11T18:16:38Z | 2021-07-04T17:47:26Z | 2021-07-04T10:06:54Z | CONTRIBUTOR | null | - [x] closes #40876
- [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://github.com/pandas-dev/pandas/milestone/86
Currently scheduled for December 31, 2021 (with a release candidate around 2 weeks before)
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression | {
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This surfaces another bug in `StringArray.__getitem__` that I haven't looked into yet. | {
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orthogonal but related to #41956 | {
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} | 1 | 2021-06-12T07:29:50Z | 2021-06-12T13:20:56Z | 2021-06-12T13:20:48Z | MEMBER | null | Backport PR #41924 | {
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} | 5 | 2021-06-12T10:49:11Z | 2021-06-18T10:43:11Z | 2021-06-15T23:34:25Z | MEMBER | null | - [x] closes #41951
- [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
@simonjayhawkins I think this changed in May last year, so probably not woth backporting? | {
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} | 1 | 2021-06-12T12:22:58Z | 2021-07-01T20:33:27Z | null | NONE | null |
after dropna('rows'), the dataframe index changes from RangeIndex to Int64Index even when nothing changed in the dataframe.
reset_index after dropna will fix it, but it should be avoided in the first place.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame ({'a':range(1000)})
df.info()
df = df.dropna ('rows')
print ('\nafter dropna\n')
df.info()
```
output:
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 a 1000 non-null int64
dtypes: int64(1)
memory usage: 7.9 KB
after dropna
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000 entries, 0 to 999
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 a 1000 non-null int64
dtypes: int64(1)
memory usage: 15.6 KB
```
#### Problem description
changing from RangeIndex to Int64Index increases the dataframe size, for no good reason.
#### Output of ``pd.show_versions()``
<details>
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Israel.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.10.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 5 | 2021-06-12T14:20:59Z | 2021-10-04T01:37:37Z | 2021-10-04T01:37:37Z | NONE | null | #### Is your feature request related to a problem?
For sparce data it is particuarly helpful to preserve null values when doing reduction operations. I end up using this (see below) wrapper function to achieve the intended result.
```
def any_nan(x):
if pd.isnull(x).all():
return pd.NA
else:
return x.any(skipna=True)
output = df.column_to_operate_on.apply(any_nan)
```
#### Describe the solution you'd like
The ask is to add a configuration parameter to. `.any()` and `.all()` that optionally preserves null values when returning the array. Skipna doesn't seem to have to correct functionality as it will return false, if all the values of the array are null. Ideally, with the toggleable parameter, `.any()` would return `np.nan/pd.NA` if all values in the array are null.
#### API breaking implications
This shouldn't change the api. It will just add an optional parameter
#### Describe alternatives you've considered
`pd.NA`
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} | 0 | 2021-06-12T17:59:57Z | 2021-06-16T00:30:28Z | 2021-06-15T23:48:02Z | MEMBER | null | - [x] closes #31479
- [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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} | 4 | 2021-06-12T19:19:37Z | 2021-09-21T20:59:42Z | 2021-09-21T14:09:19Z | MEMBER | null | - [x] xref #41967
- [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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This gives consistency for nullable dtype treatment across `.any/.all` and the groupby versions which already used Kleene logic. Diff looks big, but this is just moving `BooleanArray.any` and `BooleanArray.all` to `BaseMaskedArray` | {
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} | 7 | 2021-06-12T19:43:59Z | 2021-06-15T04:52:36Z | 2021-06-15T01:12:49Z | MEMBER | null | Seems like the new online tests added increase test runtime a lot, making failures like https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61437&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a and
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=61437&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a
more common. Slowest durations showing this are below, have marked these as slow as a potential solution
```
============================ slowest 30 durations =============================
99.91s call pandas/tests/resample/test_base.py::test_resample_interpolate[period_range-pi-_index_start1-_index_end1]
47.56s call pandas/tests/resample/test_period_index.py::TestPeriodIndex::test_weekly_upsample[end-D-FRI]
47.36s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-True-obj1]
45.55s teardown pandas/tests/window/test_rolling.py::test_rolling_zero_window
30.25s call pandas/tests/tslibs/test_timezones.py::test_cache_keys_are_distinct_for_pytz_vs_dateutil[America/Argentina/San_Luis]
28.37s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-True-obj0]
23.03s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-True-obj1]
23.00s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-True-obj1]
20.95s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-True-obj0]
20.30s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-True-obj0]
19.94s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-False-obj1]
19.81s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-False-obj0]
19.37s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-False-obj1]
19.25s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-False-False-obj0]
18.13s call pandas/tests/test_sorting.py::TestSorting::test_int64_overflow_moar
18.10s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-True-True-False-obj0]
18.07s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-True-False-obj1]
17.17s call pandas/tests/window/test_numba.py::TestEngine::test_numba_vs_cython_apply[True-False-False-False-True]
17.16s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-True-obj1]
16.84s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-False-True-obj1]
16.75s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-True-obj0]
16.48s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-True-True-obj1]
16.47s setup pandas/tests/io/test_fsspec.py::test_from_s3_csv
16.36s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-True-True-obj1]
15.93s call pandas/tests/io/test_compression.py::test_with_missing_lzma
15.83s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-False-True-obj1]
15.13s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-False-obj1]
14.46s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-True-True-True-obj0]
14.14s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[True-True-False-False-False-obj0]
14.00s call pandas/tests/window/test_online.py::TestEWM::test_online_vs_non_online_mean[False-True-False-True-False-obj0]
```
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} | 5 | 2021-06-12T20:08:10Z | 2021-06-15T21:44:19Z | 2021-06-15T21:05:57Z | MEMBER | null | - [x] closes #41873
- [ ] 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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] | open | false | null | [] | null | 0 | 2021-06-12T20:54:05Z | 2021-07-01T20:39:14Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported - **_briefly_**
- [ ] 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.
---
```python
s = Series(["a", "b"])
np.char.find(s, "b")
# or np.capitalize(s)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-a924fee0248d> in <module>
----> 1 np.char.capitalize(s)
<__array_function__ internals> in capitalize(*args, **kwargs)
~/envPandas38/lib/python3.8/site-packages/numpy/core/defchararray.py in capitalize(a)
414 """
415 a_arr = numpy.asarray(a)
--> 416 return _vec_string(a_arr, a_arr.dtype, 'capitalize')
417
418
TypeError: string operation on non-string array
```
#### Problem description
This fails even though `s` is composed of strings and even things like `s.str` `s.astype(str)`, `dtype=str` make no difference.
#### Expected Output
numpy string functions should work
<details>
pandas master as of 12 jun 21.
numpy 1.20.3
</details>
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} | 4 | 2021-06-12T21:11:03Z | 2021-06-16T00:26:00Z | 2021-06-15T23:49:03Z | 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
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] | closed | false | null | [] | null | 3 | 2021-06-12T21:35:55Z | 2021-06-13T22:37:22Z | 2021-06-13T22:37:22Z | MEMBER | null | I think the current behavior is left over from the long long ago.
```
mi = pd.MultiIndex.from_product([range(3), ["A", "B"]])
ser = pd.Series(len(mi), index=mi)
>>> ser.loc[1, ["A", "B", "not a key"]]
1 A 6
B 6
dtype: int64
>>> ser.loc[[99999], ["A", "B"]]
Series([], dtype: int64)
>>> ser.loc[[99999]]
KeyError: '[99999] not in index'
```
I think we should deprecate this and eventually raise like we would with any other missing label.
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} | 0 | 2021-06-12T23:08:01Z | 2021-06-16T00:21:47Z | 2021-06-15T23:48:23Z | 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
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] | open | false | null | [] | null | 3 | 2021-06-13T03:52:39Z | 2021-08-21T02:28:48Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
There was a issue 5 years ago mentioned that `.to_hdf()` acts inconsistently across Python2 & 3 on `PeriodIndex` for fixed format
https://github.com/pandas-dev/pandas/issues/16781
- [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.
The bug exists, but behavior is different - see next comment
---
I noticed that the deserialization of a pandas Series/DataFrame with `PeriodIndex` from HDF5 file is inconsistent when using PyTables format: The retrieved series/df index will be converted to `Int64Index` instead of `PeriodIndex`: See code below for example
```python
import pandas as pd
store = pd.HDFStore('test.h5')
series = pd.Series(index=pd.date_range(start='2015-01', end='2016-01', freq='M'), data=0).to_period('M')
df = pd.DataFrame(index=pd.date_range(start='2015-01', end='2016-01', freq='M'), data=0, columns=['a']).to_period('M')
store.put('/a/a', series, format='table')
store.put('/a/b', df, format='table')
```
```python
store.select('/a/a')
```
Output:
```
540 0
541 0
542 0
543 0
544 0
545 0
546 0
547 0
548 0
549 0
550 0
551 0
dtype: int64
```
```python
store.select('/a/b').index
```
Output:
```
Int64Index([540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551], dtype='int64')
```
#### Problem description
Inconsistent output with HDF5 file & PyTables format
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 25 Model 33 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.1
setuptools : 51.0.0.post20201207
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 3 | 2021-06-13T09:44:51Z | 2021-06-18T05:22:27Z | 2021-06-18T01:46:49Z | CONTRIBUTOR | null | There are currently some issues in `_check_dtypes` in `assert_index_equal` related to #41153:
* `assert_attr_equal("dtype", left, right, obj=obj)` currently isn't run if `check_categorical` is False (even if the indexes are not CategoricalIndexes)
* `assert_class_equal` with `exact='equiv'` should not be restricted to accept equivalence of `Int64Index` and `RangeIndex`, but should accept equivalence of all subclasses of `NumericIndex` (in preparation for #41153).
So, comparing numeric indexes with `assert_index_equal(idx1, idx2, exact="equiv")` we should for otherwise equal content have:
* RangeIndex and Int64Index should pass, because they're both numeric indexes and have the same dtype
* Int64Index and Float64Index should fail, because even though they're both numeric indexes, they have different dtypes
* Int64Index and NumericIndex[int64] should pass, because they're both numeric indexes and have the same dtype
* RangeIndex and NumericIndex[int64] should pass, because they're both numeric indexes and have the same dtype
* Float64Index and NumericIndex[int64] should fail, because even though they're both numeric indexes, they have different dtypes
Conversely, with `exact=True` class comparison should be strict, so:
* RangeIndex and Int64Index should fail, because they're different classes, even though they have the same dtype
* Int64Index and NumericIndex[int64] should fail, because they're different classes, even though they have the same dtype
* etc.
This refactoring does not change behaviour in master, but will make #41153 easier. | {
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xref https://github.com/numpy/numpy/issues/19146 | {
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} | 1 | 2021-06-13T17:53:39Z | 2021-07-19T12:00:54Z | 2021-07-14T23:47:06Z | CONTRIBUTOR | null | - [x] based on discussion in #41807
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- N/A since only doc changes
- [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
- N/A since only doc changes
- [x] whatsnew entry
- Not needed
Based on discussion, I went through all docstrings that had the regex `":.*array-like"` and added a scalar as an accepted argument where appropriate.
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```yaml
- id: rst
name: rst
entry: rst-lint --encoding utf-8
files: ^(RELEASING.rst|README.rst|TIDELIFT.rst)$
language: python
additional_dependencies: [pygments, restructuredtext_lint]
```
Would this be worth including in pandas? Investigations would be welcome | {
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} | 6 | 2021-06-13T19:05:06Z | 2021-06-17T11:36:01Z | 2021-06-14T16:31:45Z | 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
Makes 3.8 npdev green. | {
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} | 4 | 2021-06-13T20:13:41Z | 2021-06-17T19:06:14Z | 2021-06-17T19:05:36Z | CONTRIBUTOR | null | - [x] closes #41953
- [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
This builds upon #41952, which should be integrated first.
We no longer can assume that the CPython's hash function for elements with NaN (float, complex, tuple) will do the right thing (see https://github.com/python/cpython/commit/a07da09ad5bd7d234ccd084a3a0933c290d1b592) and thus need to replace the default CPython's hash-function with our own, similar to the way we handle equality-operator.
We need to use old CPython hash functions for `float` and `complex`, because we would like to keep the equivalency `int(k)==float(k)==complex(k, 0.0)`.
We have more freedom with tuple's hash-function, but still use CPython's implementation, so we get old hash-values.
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Most of the new minimum support versions were released between Oct 14, 2019, Python 3.8's initial release date, and June/July 2020. | {
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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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- [ ] No tests added
- [X] All linting passed (used `isort` to add imports)
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Built a [script](https://gist.github.com/aacosta13/eb1cb4b22ece354c60f0e7aab90137b0) using `isort` to add `from __future__ import annotations` to necessary files as referenced by issue #41901. | {
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<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
</details>
<hr>
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} | 0 | 2021-06-14T07:30:25Z | 2021-08-12T17:02:45Z | 2021-08-12T15:52:05Z | CONTRIBUTOR | null | This is a follow-on to #41893.
It makes the new `Styler.apply_index` and `Styler.applymap_index` methods compatible with `Styler.to_latex`.

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```
df = pd.DataFrame({"A": [0, 1, 2], "B": [-0.61, -1.22, -2.22], "C": ["ab", "cd", "de"]})
cidx = pd.MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")])
ridx = pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")])
df.index, df.columns = ridx, cidx
styler = df.style
func = lambda v: 'cellcolor:{red}; color:{white}; bfseries:--rwrap;' if "A" in v or "Z" in v or "c" in v else None
print(styler.applymap_header(func, axis="columns").applymap_header(func).to_latex(hrules=True))
```
```
\begin{tabular}{llrrl}
\toprule
{} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\
{} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\
\midrule
\multirow[c]{2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & a & 0 & -0.610000 & ab \\
& b & 1 & -1.220000 & cd \\
B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\
\bottomrule
\end{tabular}
```

Even if the code is amended to print the styles in problem cells, albeit with a blank display value the problem is not solved because the text is overwritten:
```
\begin{tabular}{llrrl}
\toprule
{} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\
{} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\
\midrule
\multirow[c]{2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & a & 0 & -0.610000 & ab \\
\cellcolor{red} \color{white} \bfseries{} & b & 1 & -1.220000 & cd \\
B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\
\bottomrule
\end{tabular}
```

One solution is to apply the multirow last so that it overwrites previous rendered cells, using a negative rowspan.
```
\begin{tabular}{llrrl}
\toprule
{} & {} & \multicolumn{2}{r}{\cellcolor{red} \color{white} \bfseries{Z}} & {Y} \\
{} & {} & {a} & {b} & {\cellcolor{red} \color{white} \bfseries{c}} \\
\midrule
\cellcolor{red} \color{white} \bfseries{} & a & 0 & -0.610000 & ab \\
\multirow[c]{-2}{*}{\cellcolor{red} \color{white} \bfseries{A}} & b & 1 & -1.220000 & cd \\
B & \cellcolor{red} \color{white} \bfseries{c} & 2 & -2.220000 & de \\
\bottomrule
\end{tabular}
```

Albeit this might be quite difficult to code and have other repercussions.
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} | 3 | 2021-06-14T09:01:41Z | 2021-10-19T05:31:41Z | 2021-10-19T00:44:24Z | CONTRIBUTOR | null | This is a follow-on to #41893.
It makes the new `Styler.apply_index` and `Styler.applymap_index` methods compatible with `Styler.to_excel`. If the methods are not used the output reverts to the old format.

**MultiIndex Case**
```python
midx = pd.MultiIndex.from_product([['ix', 'jy'], [0, 1], ['x3', 'z4']])
df = pd.DataFrame([np.arange(8)], columns=midx)
def highlight_x(s):
return ["background-color: yellow;" if 'x' in v else "" for v in s]
df.style.apply_index(highlight_x, axis="columns", level=[0, 2]).to_excel("mi_test.xlsx")
```

# NOTE on TESTS
The tests for styler_to_excel were broken and were xfailed out of production in 2019. Since they dont work I have replaced them with some other, cleaner, tests and add tests relevant to this PR also.
Closes #25351
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} | 1 | 2021-06-14T09:22:04Z | 2021-06-16T16:58:02Z | null | NONE | null | #### Is your feature request related to a problem?
[this should provide a description of what the problem is, e.g. "I wish I could use pandas to do [...]"]
There are many places where `dtype` and `converters` can be modified
#### Describe the solution you'd like
[this should provide a description of the feature request, e.g. "`DataFrame.foo` should get a new parameter `bar` that [...]", try to write a docstring for the desired feature]
This should gather all the `dtype` and `convertors` processing in one place.
#### API breaking implications
[this should provide a description of how this feature will affect the API]
The changes are absolutely internal.
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
It is maintainers' idea.
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
See discussion in #41664 | {
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} | 15 | 2021-06-14T10:26:05Z | 2021-11-19T22:42:12Z | null | CONTRIBUTOR | null | #### Problem description
The following (toy) snippet worked with 1.2:
```python
df = pd.DataFrame(columns=["a", "b"])
df["a"] = df.apply(lambda x: x["a"], axis=1)
```
With 1.3 it fails with `ValueError: Columns must be same length as key`
Technically this is correct - the apply on an empty frame returns the empty frame so things do not really match.
#### Expected Output
It still works? Just reporting it here if this is an unintended change. Maybe I missed it, but I did not see this mentioned in the changelog.
The fix is to only call apply when the frame is not empty I guess? I stumbled upon this one when running our test suite.
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} | 7 | 2021-06-14T10:39:23Z | 2021-06-28T14:27:50Z | 2021-06-28T14:27:50Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example
```python
pd.DataFrame(columns=["date", "crew_member_id"])
.groupby(["date", "crew_member_id"], as_index=False)
.sum()
```
#### Problem description
With 1.3rc1 this fails with `ValueError: Length of values (0) does not match length of index (1)` while 1.2 returns and empty frame.
#### Expected Output
If this is the expected behavior fine. Just wanted to ensure that it is not an accidental regression.
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} | 10 | 2021-06-14T11:21:04Z | 2021-11-19T22:54:06Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example
```python
def apply_func(df):
del df["MEMBER_ID"]
return df.head(1)
df = pd.DataFrame.from_dict(
{
"MONTH": {0: "April", 1: "April", 2: "April"},
"MEMBER_ID": {0: "Member A", 1: "Member A", 2: "Member B"},
"ACTIVITY_CATEGORY": {
0: "Activity 1",
1: "Days off at homebase",
2: "Activity 1",
},
"FTE": {0: 1.0, 1: 1.0, 2: 0.75},
"FTE_OFF_DAYS": {0: 5, 1: 5, 2: 10},
}
)
df.groupby(["MONTH", "FTE"], observed=True)[
["MEMBER_ID", "FTE_OFF_DAYS", "ACTIVITY_CATEGORY"]
].apply(apply_func)
```
#### Problem description
Deleting the row passed to `apply_func` in combination with the call to `head` causes an exception in 1.3rc1.
When `apply_func` is called the second time the `MEMBER_ID` column does not exist anymore.
Doing a `df = df.copy()` in `apply_func` fixes the problem.
#### Expected Output
While one could argue that the code is a bit bogus (and should be possibly rewritten), I wonder if this change happened on purpose. | {
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} | 2 | 2021-06-14T12:56:50Z | 2021-06-16T09:34:33Z | 2021-06-16T09:34:33Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Let's consider the following snippet:
```python
pd.DataFrame(index=[pd.to_datetime("2019"), pd.to_datetime("2019"), pd.to_datetime("2020")]).asfreq("M")
```
This will raise an exception as the to be resampled dataframe has an axis with duplicates. The problem: I stumpbled at least twice on this problem and never understood what the error message wanted to tell me on first sight.
When seeing just the message (wihtout looking at the backtrace) I always thought this is about providing duplicate axis names or something like this.
```python
ValueError: cannot reindex from a duplicate axis
```
#### Describe the solution you'd like
Maybe the exception message could be clarified. The comment two lines above the exception always saves me:
```python
# trying to reindex on an axis with duplicates
```
So maybe something like this could help?
```python
raise ValueError("Cannot reindex on an axis with duplicate values")
```
#### API breaking implications
None I guess.
The message would additionally need to be changed in some tests and in the docs.
#### Additional context
If there is interest I could draft a PR. | {
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] | open | false | null | [] | null | 0 | 2021-06-14T13:58:52Z | 2021-08-21T02:30:36Z | null | CONTRIBUTOR | 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
The documentation on `result_type` for `DataFrame.apply` states
>‘broadcast’ : results will be broadcast to the original shape of the DataFrame, the original index and columns will be retained.
It doesn't mention enforcing dtypes, yet:
```
>>> df = pd.DataFrame([[1, 2], [3, 4]], index=["A", "B"], columns=["X", "Y"])
>>> df.apply(lambda s: [1, 2], axis=1, result_type="broadcast")
X Y
A 1 2
B 1 2
>>> df.apply(lambda s: ["s", "t"], axis=1, result_type="broadcast")
# ValueError: invalid literal for int() with base 10: 's'
>>> df.apply(lambda s: [3.999, 2], axis=1, result_type="broadcast")
X Y
A 3 2
B 3 2
>>> df.apply(lambda s: [3+1j, 2+2j], axis=1, result_type="broadcast")
# TypeError: can't convert complex to int
```
This seems like a bug to me. Can anyone confirm? | {
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} | 2 | 2021-06-14T15:23:48Z | 2021-06-15T15:49:02Z | 2021-06-15T15:49:02Z | 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.
---
Probably an intended change, but I didn't find anything related to it in the whatsnew.
```py
import pandas as pd
df = pd.DataFrame({"a": [1]})
reveal_type(df.values)
reveal_type(df.index.values)
```
pyright with 1.2.4
```sh
info: Type of "df.values" is "ndarray"
info: Type of "df.index.values" is "ndarray"
```
pyright with 1.3.0.rc1
```sh
info: Type of "df.values" is "ndarray"
info: Type of "df.index.values" is "ExtensionArray | ndarray"
``` | {
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] | open | false | null | [] | null | 8 | 2021-06-14T15:39:49Z | 2021-06-15T18:00:01Z | null | CONTRIBUTOR | 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. (does not exist in 1.2.4, only seen in 1.3.0rc1 and master)
- [X] (optional) I have confirmed this bug exists on the master branch of pandas. (confirmed on 0b68d87a443)
---
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
In [2]: pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin'))
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-389898c5af02> in <module>
----> 1 pd.util.hash_array(pd.DatetimeIndex(['2018-10-28 01:20:00'], tz='Europe/Berlin'))
~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
287 elif not isinstance(vals, np.ndarray):
288 # i.e. ExtensionArray
--> 289 vals, _ = vals._values_for_factorize()
290
291 return _hash_ndarray(vals, encoding, hash_key, categorize)
AttributeError: 'DatetimeIndex' object has no attribute '_values_for_factorize'
In [3]: pd.util.hash_array(pd.Index([1,2,3]))
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-e66efa244441> in <module>
----> 1 pd.util.hash_array(pd.Index([1,2,3]))
~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
287 elif not isinstance(vals, np.ndarray):
288 # i.e. ExtensionArray
--> 289 vals, _ = vals._values_for_factorize()
290
291 return _hash_ndarray(vals, encoding, hash_key, categorize)
AttributeError: 'Int64Index' object has no attribute '_values_for_factorize'
In [4]: pd.util.hash_array(pd.RangeIndex(1,3))
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-af0900aae979> in <module>
----> 1 pd.util.hash_array(pd.RangeIndex(1,3))
~/working_dir/pandas/pandas/core/util/hashing.py in hash_array(vals, encoding, hash_key, categorize)
287 elif not isinstance(vals, np.ndarray):
288 # i.e. ExtensionArray
--> 289 vals, _ = vals._values_for_factorize()
290
291 return _hash_ndarray(vals, encoding, hash_key, categorize)
AttributeError: 'RangeIndex' object has no attribute '_values_for_factorize'
```
#### Problem description
This issue looks similar to https://github.com/pandas-dev/pandas/issues/41817, but that is specifically for DateTimeIndex with tz defined, while this seems to happen for any Index instance.
#### Expected Output
A hash of the input index, as in pandas < 1.3.0.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2dd9e9ba9009a40191c0c0b96262fa3939d609f0
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.28-1rodete1-amd64
Version : #1 SMP Debian 5.10.28-1rodete1 (2021-04-30)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0rc1
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.4.0
sphinx : 3.5.1
blosc : 1.10.2
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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] | open | false | null | [] | null | 0 | 2021-06-14T17:13:30Z | 2021-08-21T02:31:13Z | null | CONTRIBUTOR | null | - [ ] I have checked that this issue has not already been reported.
- [ ] 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.
---
Currently `DataFrame.apply` works asymmetrically.
```python
>>> df = pd.DataFrame([[1,2], [3,4]], index=["a", "b"], columns=["x", "y"])
>>> df.apply(lambda s: ["one", "two"], axis=0)
x y
a one one
b two two
>>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=0)
x y
c one one
d two two
>>> df.apply(lambda s: ["one", "two", "three"], axis=0)
x y
0 one one
1 two two
2 three three
```
Along axis=0, the native index is maintained, if the shapes match and it is not overwritten by the function result object. When shapes don't match the created index is the default RangeIndex.
```python
>>> df.apply(lambda s: ["one", "two"], axis=1, result_type="expand")
0 1
a one two
b one two
>>> df.apply(lambda s: pd.Series(["one", "two"], index=["c", "d"]), axis=1, result_type="expand")
c d
a one two
b one two
>>> df.apply(lambda s: ["one", "two", "three"], axis=1, result_type="expand")
0 1 2
a one two three
b one two three
```
Along axis=1 (with result_type=expand), the native columns are overwritten by a default RangeIndex (even if it has same shape) unless overwritten by the function result object index.
#### Expected Output
Would anyone object to a PR that looks to unify the two cases and apply the columns index where the shapes were the same?
Currently I use the workaround:
```
df.apply(func, axis=1, result_type="expand") -> df.T.apply(func, axis=0).T
```
### Bug 1
Documentation states that for `result_type` arguments - `These only act when axis=1 (columns):`.
This is not true since:
```python
>>> df = pd.DataFrame([[1, 2], [3, 4]], index=["a", "b"], columns=["a", "b"])
>>> df.apply(lambda s: [1,1,1], axis=0, result_type=None) # or "expand"
a b
0 1 1
1 1 1
2 1 1
>>> df.apply(lambda s: [1,1,1], axis=0, result_type="broadcast")
ValueError: cannot broadcast result
>>> df.apply(lambda s: [1,1,1], axis=0, result_type="reduce")
a [1, 1, 1]
b [1, 1, 1]
dtype: object
```
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} | 4 | 2021-06-14T18:41:30Z | 2021-06-16T09:34:44Z | 2021-06-16T09:34:33Z | CONTRIBUTOR | null | - [x] closes #42000
- [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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} | 0 | 2021-06-15T01:07:38Z | 2021-07-11T19:47:14Z | 2021-07-11T19:47:14Z | CONTRIBUTOR | null | #### Location of the documentation
I'm proposing the addition of another community tutorial to the [Community tutorials](https://pandas.pydata.org/docs/getting_started/tutorials.html) page.
#### Documentation problem
Not a problem, but an opportunity for improvement.
#### Suggested fix for documentation
I recently built a [pandas workshop](https://github.com/stefmolin/pandas-workshop) for the Open Data Science Conference (ODSC) Europe event aimed at new users of pandas. It focuses on real-world datasets (making it easier for beginners to understand), includes detailed environment setup instructions (and binder), and provides notebooks and exercises to practice the concepts. I think it would make a great addition to the [Community tutorials](https://pandas.pydata.org/docs/getting_started/tutorials.html) page -- the more learning resources the better 😃
I'm happy to make a PR.
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https://api.github.com/repos/pandas-dev/pandas/issues/42011 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42011/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42011/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42011/events | https://github.com/pandas-dev/pandas/pull/42011 | 921,000,172 | MDExOlB1bGxSZXF1ZXN0NjcwMDY1OTA2 | 42,011 | Update frame.py | {
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import pandas as pd
x = pd.Series(range(3), index=pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2, 3]], names=['letters', 'numbers']))
print(x.reset_index('numbers', drop=True))
- [ ] 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
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https://api.github.com/repos/pandas-dev/pandas/issues/42012 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42012/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42012/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42012/events | https://github.com/pandas-dev/pandas/pull/42012 | 921,010,934 | MDExOlB1bGxSZXF1ZXN0NjcwMDc1MDg3 | 42,012 | ENH/PERF: optional mask in nullable dtypes | {
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] | closed | false | null | [] | null | 4 | 2021-06-15T05:45:44Z | 2021-06-20T01:41:59Z | 2021-06-20T01:41:59Z | MEMBER | null | - [x] closes #30435
- [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
Benchmarks:
<details>
```
before after ratio
[e3d57c98] [ea9f1a89]
<master> <perf/optional_mask~1>
+ 985±70ns 2.42±0.04μs 2.45 array.IntegerArray.time_constructor
+ 1.11±0.08μs 2.66±0.1μs 2.38 array.BooleanArray.time_constructor
+ 11.5±0.06μs 15.3±0.3μs 1.34 series_methods.NanOps.time_func('argmax', 1000, 'boolean')
+ 12.6±0.4μs 16.3±0.6μs 1.29 boolean.TimeLogicalOps.time_and_array
+ 25.9±1μs 32.4±3μs 1.25 boolean.TimeLogicalOps.time_or_scalar
+ 13.3±0.5μs 16.2±0.6μs 1.22 boolean.TimeLogicalOps.time_or_array
+ 27.5±1μs 33.3±2μs 1.21 boolean.TimeLogicalOps.time_and_scalar
+ 14.5±0.4μs 17.4±0.4μs 1.20 boolean.TimeLogicalOps.time_xor_array
+ 34.8±0.6μs 41.8±2μs 1.20 series_methods.Isna.time_isna_no_nans('Int64')
+ 29.2±0.4μs 34.7±1μs 1.19 boolean.TimeLogicalOps.time_xor_scalar
+ 27.2±0.3μs 31.0±3μs 1.14 array.IntegerArray.time_from_integer_array
- 66.9±0.6μs 58.6±0.7μs 0.88 series_methods.NanOps.time_func('skew', 1000, 'Int64')
- 78.0±2μs 66.9±1μs 0.86 series_methods.NanOps.time_func('std', 1000, 'boolean')
- 67.4±0.9μs 57.6±0.8μs 0.85 series_methods.NanOps.time_func('kurt', 1000, 'boolean')
- 66.9±0.8μs 56.9±0.7μs 0.85 series_methods.NanOps.time_func('kurt', 1000, 'Int64')
- 67.6±2μs 57.4±0.6μs 0.85 series_methods.NanOps.time_func('var', 1000, 'Int64')
- 10.8±0.2μs 9.12±0.5μs 0.85 series_methods.NanOps.time_func('sum', 1000, 'boolean')
- 75.4±5μs 63.5±1μs 0.84 series_methods.NanOps.time_func('std', 1000, 'int32')
- 5000 4016 0.80 array.NullableArrayMemory.track_array_size('Float32')
- 150±20μs 119±0.6μs 0.80 series_methods.NanOps.time_func('sem', 1000, 'int8')
- 8.81±0.2μs 6.92±0.2μs 0.79 series_methods.NanOps.time_func('max', 1000, 'boolean')
- 6.25±0.06μs 4.83±0.1μs 0.77 series_methods.All.time_all(1000, 'slow', 'boolean')
- 9.78±0.08μs 7.44±0.2μs 0.76 series_methods.NanOps.time_func('prod', 1000, 'Int64')
- 1.40±0.02ms 1.03±0.08ms 0.73 series_methods.NanOps.time_func('prod', 1000000, 'boolean')
- 9.44±0.1μs 6.83±0.1μs 0.72 series_methods.NanOps.time_func('min', 1000, 'Int64')
- 12.9±0.2μs 9.12±2μs 0.70 series_methods.NanOps.time_func('mean', 1000, 'Int64')
- 4.26±0.06ms 2.97±0.4ms 0.70 series_methods.NanOps.time_func('std', 1000000, 'boolean')
- 170±10μs 108±8μs 0.64 algos.isin.IsIn.time_isin('boolean')
- 10.2±0.3μs 6.50±0.1μs 0.63 series_methods.NanOps.time_func('max', 1000, 'Int64')
- 4.14±0.2ms 2.60±0.2ms 0.63 series_methods.NanOps.time_func('var', 1000000, 'boolean')
- 14.4±0.2μs 8.83±0.1μs 0.61 series_methods.NanOps.time_func('mean', 1000, 'boolean')
- 6.27±1ms 3.70±0.1ms 0.59 series_methods.NanOps.time_func('std', 1000000, 'Int64')
- 1.01±0.01ms 557±10μs 0.55 series_methods.NanOps.time_func('sum', 1000000, 'boolean')
- 1.27±0.04ms 696±10μs 0.55 series_methods.NanOps.time_func('prod', 1000000, 'Int64')
- 2000 1016 0.51 array.NullableArrayMemory.track_array_size('Int8')
- 2000 1016 0.51 array.NullableArrayMemory.track_array_size('boolean')
- 116±5μs 58.2±2μs 0.50 algos.isin.IsIn.time_isin_mismatched_dtype('boolean')
- 925±400μs 338±6μs 0.37 series_methods.NanOps.time_func('sum', 1000000, 'Int64')
- 1.03±0.06ms 371±30μs 0.36 series_methods.NanOps.time_func('mean', 1000000, 'Int64')
- 1.95±0.2ms 329±5μs 0.17 series_methods.NanOps.time_func('max', 1000000, 'Int64')
- 1.98±0.2ms 324±3μs 0.16 series_methods.NanOps.time_func('min', 1000000, 'Int64')
- 454±7μs 69.6±1μs 0.15 series_methods.All.time_all(1000000, 'slow', 'boolean')
- 454±10μs 64.4±2μs 0.14 series_methods.Any.time_any(1000000, 'slow', 'boolean')
- 430±6μs 43.7±2μs 0.10 series_methods.Any.time_any(1000000, 'fast', 'boolean')
- 431±7μs 42.2±1μs 0.10 series_methods.All.time_all(1000000, 'fast', 'boolean')
- 505±4μs 42.4±3μs 0.08 series_methods.NanOps.time_func('min', 1000000, 'boolean')
- 496±10μs 13.6±0.6μs 0.03 series_methods.NanOps.time_func('max', 1000000, 'boolean')
```
</details>
- The decrease in the `isna` benchmark was somewhat surprising, but happens because of the following:
```
In [12]: mask = np.zeros(100000, dtype=np.bool_)
In [13]: %timeit mask.copy() # what happens in master
2 µs ± 19.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [14]: %timeit np.zeros(100000, dtype=np.bool_) # essentially what happens in this pr
2.7 µs ± 182 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
- The `argmax` performance decrease is caused by the `isna` slowdown because there are a bunch of `isna.any()` calls. I think this decrease could be prevented (and performance increased overall) by adding `argmax` to our masked reductions (or avoiding the repeated `isna.any()` some other way), but that seems better left for a different pr.
- The `TimeLogicalOps` benchmark slowdown is occurring because of the `mask.any()` check added in the `BooleanArray` constructor - this slowdown could be avoided by more aggressively figuring out when `mask` can (or won't) be None, but that would complicate the logic somewhat, so would plan to leave as a potential followup.
- Finally, the slowdown in `IntegerArray` and `BooleanArray` is also due to this added `mask.any()` check. This could be mitigated by determining earlier when missing values are not possible in the `coerce_to_array` functions, but that adds reasonable complexity to already complex functions. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42013 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42013/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42013/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42013/events | https://github.com/pandas-dev/pandas/pull/42013 | 921,068,190 | MDExOlB1bGxSZXF1ZXN0NjcwMTI0OTEz | 42,013 | Updated the docstring for reset_index in frame.py | {
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import pandas as pd
x = pd.Series(range(3), index=pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2, 3]], names=['letters', 'numbers']))
print(x.reset_index('numbers', drop=True))
- [ ] 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
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} | 2 | 2021-06-15T07:22:49Z | 2021-08-20T05:31:08Z | 2021-08-19T21:41:49Z | CONTRIBUTOR | null | - [x] closes #13657
This issue is due to the asymmetric application of `DataFrame.apply` (#42005).
**The bug** is that when using `Styler.apply` the treatment of Series return objects is applied for `axis=0` and overwritten for `axis=1`.
```python
df = pd.DataFrame([[1,2],[3,4]], index=["X", "Y"], columns=["X", "Y"])
s = df.style
def series_ret(s):
return pd.Series(["color:red;", "color:blue;"], index=["Y", "X"]) # note X-Y order
```
| Command | Current Behaviour | New Behaviour |
| ------------ | ------------------------- | ---------------------- |
| `s.apply(series_ret, axis=0)` | ![][image5] | ![][image5] |
| `s.apply(series_ret, axis=1)` | ![][image6] | ![][image7] |
Also the application has been expanded to **allow** (for axis=0 | 1) Series return objects that are **not** the same size, or (for axis=None) DataFrame return objects that are **not** the same size, if they contain only relevant index labels. This allows for more flexible user defined functions. Potentially it also increases performance by not having to loop over labels that no longer exist.
```python
def series_ret(s):
return pd.Series(["color:red;"], index=["Y"]) # note only returning 1 element
def array_ret(s):
return ["color:red;"]
def ser_no_idx_ret(s):
return pd.Series(["color:red;", "color:blue;"]) # note returning a Series of right shape with no index
```
| Command | Current Behaviour | New Behaviour |
| ------------ | ------------------------- | ---------------------- |
| `s.apply(series_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | ![][image1] |
| `s.apply(series_ret, axis=1)` | `ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` | ![][image2] |
| `s.apply(array_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | `ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` |
| `s.apply(array_ret, axis=1)` |`ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` |`ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` |
| `s.apply(ser_no_idx_ret, axis=0)` | `KeyError: 0` | `ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` |
| `s.apply(ser_no_idx_ret, axis=1)` | ![][image6] |`ValueError: Function created invalid index labels. Usually, this is the result of the function returning a Series which contains invalid labels, or returning an incorrectly shaped, list-like object which cannot be mapped to labels, possibly due to applying the function along the wrong axis.` |
Note all errors do report the shapes, I just didn't include in the table for brevity.
### Edge Cases
The edge case here is that if a user has a DataFrame with a default range index (or a DataFrame with some indexes the same as those in default, e.g. 0 or 1) then even returning arrays of the wrong size may work, since `DataFrame.apply` assigns them to a RangeIndex.
To be consistent with the above this should raise, but it is impossible to hook into the return value of the user-defined function and determine if it is a Series (where non-matching shapes are OK) or list-like (where matching shapes should be enforced)
```python
df = pd.DataFrame([[1,2],[3,4]])
s = df.style
```
| Command | Current Behaviour | New Behaviour |
| ------------ | ------------------------- | ---------------------- |
| `s.apply(array_ret, axis=0)` | `ValueError: Function returned the wrong shape.` | ![][image3] |
| `s.apply(array_ret, axis=1)` | `ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements` | ![][image4] |
### ASV
Benchmarks unchanged.
[image1]: https://user-images.githubusercontent.com/24256554/122002332-e59b5580-cdb1-11eb-85c2-993c7e20bdfc.png
[image2]: https://user-images.githubusercontent.com/24256554/122002344-e9c77300-cdb1-11eb-8a01-f50ec660b300.png
[image3]: https://user-images.githubusercontent.com/24256554/122004954-57c16980-cdb5-11eb-8ba4-ec14b1a6b06f.png
[image4]: https://user-images.githubusercontent.com/24256554/122005013-69a30c80-cdb5-11eb-900c-0d4be813cb58.png
[image5]: https://user-images.githubusercontent.com/24256554/122006522-3497b980-cdb7-11eb-892e-f34c80e3c7ff.png
[image6]: https://user-images.githubusercontent.com/24256554/122006532-36fa1380-cdb7-11eb-8b08-f89d6991536c.png
[image7]: https://user-images.githubusercontent.com/24256554/122006816-93f5c980-cdb7-11eb-8aff-09b658c594d9.png
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} | 1 | 2021-06-15T07:24:21Z | 2021-06-15T22:40:31Z | 2021-06-15T22:40:31Z | CONTRIBUTOR | null | #### Location of the documentation
[pandas.Series.str.isspace](https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.str.isspace.html)
#### Documentation problem
> Check whether all characters in each string are alphanumeric.
>
> This is equivalent to running the Python string method str.isalnum() for each element of the Series/Index. If a string has zero characters, False is returned for that check. Seems like it was somehow copied from `pandas.Series.str.isalnum`.
That does not seem to match the function.
#### Suggested fix for documentation
Not copy the doc of `pandas.Series.str.isalnum` but rather provide a specific one.
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- [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
df_list = [[1, 1, 1, 'A'], [1, None, 1, 'A'], [1, None, 2, 'A'], [1, None, 3, 'A']]
df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c", 'partner'])
# with 'unique', groupby result contains duplicates
partners =\
df_dropna.groupby(['a', 'b', 'c'], dropna=False)\
.agg({'partner': ['nunique', 'unique']})
partners.index.is_unique
# when using another aggregate function than 'unique', it is working as expected
partners =\
df_dropna.groupby(['a', 'b', 'c'], dropna=False)\
.agg({'partner': ['nunique', 'sum']})
partners.index.is_unique
```
#### Problem description
When using 'unique' and another aggregate function in case of a groupby on key containing NA values, the result index contains duplicates but it shouldn't:
```
partner
nunique unique
a b c
1 1.0 1 1 [A]
NaN 1 1 [A]
2 1 [A]
3 1 [A]
1 1 [A]
2 1 [A]
3 1 [A]
```
#### Expected Output
Groupby result index shouldn't contain duplicates as below:
```
partner
nunique unique
a b c
1 1.0 1 1 [A]
NaN 1 1 [A]
2 1 [A]
3 1 [A]
```
#### 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 : AMD64 Family 23 Model 96 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United Kingdom.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 : None
pytest : 6.2.2
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-06-15T11:11:25Z | 2021-06-16T11:09:48Z | 2021-06-15T22:40:31Z | CONTRIBUTOR | null | cc @simonjayhawkins
- [ ] closes #42015
- [ ] 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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