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PERF: pd.Series.map too slow for huge dictionary
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| 2020-06-11T20:14:17Z
| 2020-09-13T20:28:44Z
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|
MEMBER
| null |
Just found out a performance issue with `pd.Series.map`, seems it is very slow when the input is a huge dictionary.
I noticed a similar issue reported before: #21278 and indeed for `Series` input, the first run might be slow and then for the later runs, they are very fast because hashable indexing is built. However, it doesn't seem to apply to `dict` input.
I slightly changed the example in #21278, and the runtime doesn't change if being run multiple times. And it is much faster using `apply` and `dict.get`.
So I am curious if this performance issue is being aware , and i would expect performance when a dict is assigned between `pd.Series.map` and `pd.Series.apply(lambda x: blabla)` is quite similar.
```python
n = 1000000
domain = np.arange(0, n)
ranges = domain+10
maptable = pd.Series(ranges, index=domain).sort_index().to_dict()
query_vals = pd.Series([1,2,3])
%timeit query_vals.map(maptable)
```
while much faster if doing below:
```python
query_vals.apply(lambda x: maptable.get(x))
```
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Removed __div__ impls from Cython
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MEMBER
| null |
Alternate to #34711 which may close #34704
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CI: Failing TestDtype.test_check_dtype on master
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CONTRIBUTOR
| null | ERROR: type should be string, got "https://travis-ci.org/github/pandas-dev/pandas/jobs/697381484#L3750\r\n\r\n```\r\n____________________ TestDtype.test_check_dtype[Int64Dtype] ____________________\r\n\r\n[gw1] linux -- Python 3.9.0 /home/travis/virtualenv/python3.9-dev/bin/python\r\n\r\nself = <pandas.tests.extension.test_integer.TestDtype object at 0x7f1440e52280>\r\n\r\ndata = <IntegerArray>\r\n\r\n[ 1, 2, 3, 4, 5, 6, 7, 8, <NA>, 10, 11, 12, 13,\r\n\r\n 14, 15, 16, 17,...5, 86, 87, 88, 89, 90, 91,\r\n\r\n 92, 93, 94, 95, 96, 97, <NA>, 99, 100]\r\n\r\nLength: 100, dtype: Int64\r\n\r\n def test_check_dtype(self, data):\r\n\r\n dtype = data.dtype\r\n\r\n \r\n\r\n # check equivalency for using .dtypes\r\n\r\n df = pd.DataFrame(\r\n\r\n {\"A\": pd.Series(data, dtype=dtype), \"B\": data, \"C\": \"foo\", \"D\": 1}\r\n\r\n )\r\n\r\n \r\n\r\n # np.dtype('int64') == 'Int64' == 'int64'\r\n\r\n # so can't distinguish\r\n\r\n if dtype.name == \"Int64\":\r\n\r\n expected = pd.Series([True, True, False, True], index=list(\"ABCD\"))\r\n\r\n else:\r\n\r\n expected = pd.Series([True, True, False, False], index=list(\"ABCD\"))\r\n\r\n \r\n\r\n # FIXME: This should probably be *fixed* not ignored.\r\n\r\n # See libops.scalar_compare\r\n\r\n with warnings.catch_warnings():\r\n\r\n warnings.simplefilter(\"ignore\", DeprecationWarning)\r\n\r\n result = df.dtypes == str(dtype)\r\n\r\n \r\n\r\n> self.assert_series_equal(result, expected)\r\n\r\npandas/tests/extension/base/dtype.py:84: \r\n\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\npandas/tests/extension/base/base.py:13: in assert_series_equal\r\n\r\n return tm.assert_series_equal(left, right, *args, **kwargs)\r\n\r\npandas/_libs/testing.pyx:68: in pandas._libs.testing.assert_almost_equal\r\n\r\n cpdef assert_almost_equal(a, b,\r\n\r\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \r\n\r\n> raise_assert_detail(obj, msg, lobj, robj, index_values=index_values)\r\n\r\nE AssertionError: Series are different\r\n\r\nE \r\n\r\nE Series values are different (25.0 %)\r\n\r\nE [index]: [A, B, C, D]\r\n\r\nE [left]: [True, True, False, False]\r\n\r\nE [right]: [True, True, False, True]\r\n\r\npandas/_libs/testing.pyx:183: AssertionError\r\n```\r\n\r\nThere's a note there about not ignoring the warning. I wonder if that's been changed."
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BUG #34621 added nanosecond support to class Period
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CONTRIBUTOR
| null |
Closes #34621
Closes #17053
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The class Period do not support nanosecond. I've made a quick and dirty code to support nanoseconds. I have struggled to find an alternative to dateutil.parser, but I didn't found an alternative.
The PR may be a performance issue as I've add a Timestamp constructor to the dateutil.parser. There is for sure a better solution. Please let me know !
|
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Debug CI Issue
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MDExOlB1bGxSZXF1ZXN0NDMzMzczOTEy
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CLN: remove verbose and private_key reference #34640
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NONE
| null |
- [ ] closes #34640
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MzczNzEzNTc=
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QST:Need to stop converting my regular values to date formats
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NONE
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
I have values in my tables as 5-7, 3-5, 5-6, these values are converting into date formats like 07-May, 05-Mar, 06-May. But those values are related to length, width and height, so i want to leave the values the way it is and to convert into date format only to date values. This is happening only when saving the data in 'to_csv' method and working properly in 'to_excel' method.
Also hyphen(—) values in csv are converted to — and working properly in excel.
Any help in any of the issues are much appreciated.
Thanks.
```
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CI: window 37_141 jobs needs to update numpy version
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| 2020-06-14T14:29:35Z
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CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/blob/master/ci/deps/azure-windows-37.yaml
we no longer support this, I guess it *still* works ok. But we should advance this job to be numpy 1.15* at least, i would say we should actually test numpy 1.18* here.
might also need to actually rename the job to reflect the update.
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| 2
| 2020-06-12T01:17:02Z
| 2020-10-10T22:32:29Z
| 2020-10-10T22:32:29Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import numpy as np
import pandas as pd
df = pd.DataFrame({'a':[1, 1, np.NaN, np.NaN], 'b':[10, np.NaN, 40, np.NaN]})
```
```
df
a b
0 1.0 10.0
1 1.0 NaN
2 NaN 40.0
3 NaN NaN
```
```
# Case 1 - Forward fill using fillna
df.groupby(["a"]).fillna(method="ffill")
df
b
0 10.0
1 10.0
2 NaN
3 NaN
```
```
# Case 2 - Forward fill using ffill
df.groupby(["a"]).ffill()
df
b
0 10.0
1 10.0
2 40.0
3 40.0
```
#### Problem description
`df.groupby(["a"]).ffill()` is not ignoring the NaN group while performing forward fill.
#### Expected Output
`df.groupby(["a"]).ffill()` should give the same output as `df.groupby(["a"]).fillna(method="ffill")` since NaN groups are ignored as per https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#na-values-in-groupby.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0.post20200106
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : 0.4.2
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDMzNTAyNTQ3
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DOC: changed 'providing' to 'that provides'
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CONTRIBUTOR
| null |
Changed this as it sounds more clear.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NDMzNTIxMjI1
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PERF: remove use of Python sets for interpolate
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| 2020-07-24T16:22:43Z
| 2020-07-24T16:22:42Z
|
MEMBER
| null |
have been investigating avoiding internals for interpolation. xref #34628
This PR address the issue of using Python sets, which is responsible for the bulk of the time in our current asv.
There are other improvements (will raise other PRs), but would need new benchmarks to show a benefit such as different index types and unsorted indexes. so this PR is targeting our current benchmark first.
prelim results (will post asv results if tests pass)
```
N = 10000
# this is the worst case, where every column has NaNs.
df = pd.DataFrame(np.random.randn(N, 100))
df.values[::2] = np.nan
df
%timeit df.interpolate()
# 189 ms ± 24.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
# 65.1 ms ± 635 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- PR
```
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PERF: interpolate_1d returns function to apply columnwise
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MEMBER
| null |
This doesn't provide a significant improvement for the existing asv due to the bulk of the time creating python sets which is in the function applied columnwise. see #34727
even without #34727 this could provide perf improvements for other index types ( and for unsorted indexes with creating a column-wise function for the numpy call.
will look at adding asvs for these cases.
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Changed the way a few sentences were written
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CONTRIBUTOR
| null |
Added comma before 'and'
- [ ] closes #xxxx
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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RLS: 1.1
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| 29
| 2020-06-12T12:25:07Z
| 2020-07-28T19:38:31Z
| 2020-07-28T18:13:32Z
|
CONTRIBUTOR
| null |
Tracking issue for the 1.1 release. I've set the target for July 15th. https://github.com/pandas-dev/pandas/milestone/68
Planning a release candidate for sometime in early July, 1-2 weeks before the final release.
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
List of open blockers: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3Ablocker+
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| 34,731
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BUG: DataFrame.melt gives unexpected result when column "value" already exists
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| 4
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| 2020-07-02T15:26:44Z
| 2020-07-02T15:26:44Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'col':list('ABC'),
'value':range(10,16,2)})
print(df, '\n')
dfm = df.melt(id_vars='value')
print(dfm)
col value
0 A 10
1 B 12
2 C 14
value variable value
0 A col A
1 B col B
2 C col C
```
#### Problem description
When the column `value` already exists, and we set it as index with `id_vars` it copies the `value` column of the melted dataframe and does not set the "old" `value` column of the original dataframe as `id_vars`
#### Expected Output
```python
# note change of column name 'val' instead of 'value'
df = pd.DataFrame({'col':list('ABC'),
'val':range(10,16,2)})
dfm = df.melt(id_vars='val')
print(dfm)
val variable value
0 10 col A
1 12 col B
2 14 col C
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: Skiprows with seperator within rows being skipped
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| 2
| 2020-06-12T15:46:48Z
| 2020-06-15T21:47:42Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
**This may be linked to issue #23067**
- [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
from io import StringIO
import pandas as pd
text='''# 1: 5
# 3: x
# F: 5.
# ID: 001
# No.: 2
# No.: 4
# Time: 20191216T122109
# Value: ";"
# Time: 4
# Time: ""
# Time ms: ""
# Date: ""
# Time separator: "T"
# J: 1000000
# Silent: false
# mode: true
Timestamp;T;ID;P
16T122109957;0;6;0006'''
pd.read_csv(StringIO(text),skiprows=11,sep=';')
pd.read_csv(StringIO(text),skiprows=16,sep=';')
```
#### Problem description
The seperator `sep` is causing issues when skiprows is requested to skip over rows that contain the seperator. Using 16 (the correct number of rows to skip) causes an `EmptyDataError`.
Instead the number of rows needs to be worked out by trial and error, in this case the skiprows value needs to be 11.
Using the call below without sep does not cause issues (in terms of skipping the correct number of rows):
`pd.read_csv(StringIO(text),skiprows=16)`
#### Expected Output
```
pd.read_csv('test.TXT',skiprows=16,sep=';')
Timestamp T ID P
0 16T122109957 0 6 6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.49.1
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BUG: Fixed Series.replace for EA with casting
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| 2020-06-12T16:30:49Z
| 2020-06-16T09:46:26Z
| 2020-06-15T19:21:21Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34530
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MDExOlB1bGxSZXF1ZXN0NDMzNzkxODg3
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PERF: Fixed perf regression in TimedeltaIndex.get_loc
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| 3
| 2020-06-12T16:59:19Z
| 2020-06-15T14:39:04Z
| 2020-06-15T14:38:54Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34510
1.0.3
```python
In [10]: index = pd.timedelta_range(start="1985", periods=1000, freq="D")
...: timedelta = index[500]
...:
...: %timeit index.get_loc(timedelta)
3.7 µs ± 275 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
This PR
```
3.25 µs ± 67.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
```
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MDU6SXNzdWU2Mzc5NTc4MzM=
| 34,735
|
API: Remove PeriodDtype.dtype_code from public API
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| 1
| 2020-06-12T19:22:15Z
| 2020-06-15T14:24:12Z
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|
CONTRIBUTOR
| null |
On master PeriodDtype has a `.dtype_code` attribute.
```python
In [5]: pd.PeriodDtype('D').dtype_code
Out[5]: 6000
```
That wasn't there in 1.0.x. I think we want it to be private.
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MDExOlB1bGxSZXF1ZXN0NDMzODYyNjk0
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BUG: Fixed regression in PeriodIndex loc
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| 1
| 2020-06-12T20:04:21Z
| 2020-06-14T16:40:03Z
| 2020-06-14T16:39:59Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/33964.
This is the smallest change I could get. I'm not sure what a more comprehensive fix would look like.
Regression only on master, so no need for a whatsnew.
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| 2020-06-14T14:34:41Z
| 2020-06-14T14:34:36Z
|
CONTRIBUTOR
| null |
Closes #34136.
Hopefully this preserves the right behavior. I could imagine breaking something if a caller was relying on `putmask(., inplace=False)` returning a copy.
```
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": 0, "B": 0}, index=range(4 * 10 ** 7))
# the 1 can be held in self._df.blocks[0], while the inf and -inf cant
%timeit df.replace([np.inf, -np.inf, 1], np.nan, inplace=False)
# 1.0.3
483 ms ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# master
900 ms ± 18.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# PR
490 ms ± 8.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
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MDExOlB1bGxSZXF1ZXN0NDMzODk3MDQ0
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|
BUG: pd.NA in format strings with formatting parameters
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|
CONTRIBUTOR
| null |
``pd.NA`` raises if passed to a format string and format parameters are supplied. This is different behaviour than ``np.nan`` and makes converting arrays containing ``pd.NA`` to strings very brittle and annoying.
Examples:
```python
>>> format(pd.NA)
'<NA>' # master and PR, ok
>>> format(pd.NA, ".1f")
TypeError # master
'<NA>' # this PR
>>> format(pd.NA, ">5")
TypeError # master
' <NA>' # this PR, tries to behave like a string, then falls back to '<NA>', like np.na
```
The new behaviour mirrors the behaviour of ``np.nan``.
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DOC: updated files for SS06 errors
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MDExOlB1bGxSZXF1ZXN0NDMzOTc3MTYy
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BUG: pd.NA.__format__ fails with format_specs
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|
CONTRIBUTOR
| null |
``pd.NA`` fails if passed to a format string and format parameters are supplied. This is different behaviour than ``np.nan`` and makes converting arrays containing ``pd.NA`` to strings very brittle and annoying.
Examples:
```python
>>> format(pd.NA)
'<NA>' # master and PR, ok
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TypeError # master
'<NA>' # this PR
>>> format(pd.NA, ">5")
TypeError # master
' <NA>' # this PR, tries to behave like a string, then falls back to '<NA>', like np.na
```
The new behaviour mirrors the behaviour of ``np.nan``.
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REGR: merge no longer accepts a list for suffixes keyword
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|
MEMBER
| null |
see https://github.com/pandas-dev/pandas/pull/34208#issuecomment-643598091
The linked PR changed the `merge` routines to now only accept tuples for the `suffixes` keyword, and no longer general sequence.
This is a breaking change (eg it breaks some GeoPandas functions), but actually also changing documented behaviour: the `merge` docstring indeed says "tuple", but eg `merge_asof` still says "tuple or list". And in our user guide, we were ourselves using a list in one example, and it explicitly says "tuple or list" (see https://pandas.pydata.org/pandas-docs/version/1.0/user_guide/merging.html#overlapping-value-columns)
In general, I am certainly fine with restricting the set of accepted types (eg to avoid the confusing issue with sets, the original report), but this is 1) breaking a documented behaviour and 2) a change that could also easily be done with a deprecation warning.
And given our versioning policy, if it is easy to do a change with a deprecation, then we should try to do that, IMO.
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MEMBER
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Noticed we still have the old theme customizations in the doc sources.
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MDExOlB1bGxSZXF1ZXN0NDMzOTg4MTc3
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CLN: make Info and DataFrameInfo subclasses
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MEMBER
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precursor to #31796
Makes Info class and DataFrameInfo subclass so there's no need for all the
```python
if isinstance(data, ABCDataFrame)
```
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CLN: clean and deduplicate in core.missing.interpolate_1d
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broken-off #34728
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MDExOlB1bGxSZXF1ZXN0NDM0MDAxOTQ1
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DOC: updated strings.py for SS06 errors
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CONTRIBUTOR
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API: validate `limit_direction` parameter of NDFrame.interpolate
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| 3
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broken off #31048
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MDU6SXNzdWU2MzgxNjUwMTI=
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BUG: read_excel issue - 1
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| 1
| 2020-06-13T13:47:02Z
| 2021-01-19T14:44:44Z
| 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.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
See Problem description.
#### Problem description
```python
import pandas as pd
pd.read_excel('test.xlsx', header=[0, 1], index_col=0, engine='openpyxl')
```
produces
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
pd.read_excel('test.xlsx', header=[0, 1], index_col=0, engine='openpyxl')
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 521, in parse
].columns.set_names(header_names)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1325, in set_names
idx._set_names(names, level=level)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1239, in _set_names
raise ValueError(f"Length of new names must be 1, got {len(values)}")
ValueError: Length of new names must be 1, got 2
```
```python
import pandas as pd
pd.read_excel('test.xlsx', header=[0, 1], index_col=0, engine='xlrd')
```
produces
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
pd.read_excel('test.xlsx', header=[0, 1], index_col=0, engine='xlrd')
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 512, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2201, in TextParser
return TextFileReader(*args, **kwds)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 880, in __init__
self._make_engine(self.engine)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1126, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2298, in __init__
self.columns, self.index_names, self.col_names
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1495, in _extract_multi_indexer_columns
for n in range(len(columns[0])):
IndexError: list index out of range
```
[test.xlsx](https://github.com/pandas-dev/pandas/files/4774703/test.xlsx)
#### Expected Output
No exceptions.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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MDU6SXNzdWU2MzgxNjY4Njg=
| 34,748
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BUG: read_excel issue - 2 (when "column name" is a date/time)
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| 1
| 2020-06-13T13:59:23Z
| 2020-07-08T22:03:48Z
| 2020-07-08T22:03:48Z
|
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('test.xlsx', header=[0, 1], index_col=0, engine='openpyxl')
```
(`engine` could be `xlrd` or `openpyxl`, doesn't matter.)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4774723/test.xlsx)
#### Problem description
Exception:
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
pd.read_excel('test.xlsx', header=[0, 1], index_col=0, engine='xlrd')
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 512, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2201, in TextParser
return TextFileReader(*args, **kwds)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 880, in __init__
self._make_engine(self.engine)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1126, in _make_engine
self._engine = klass(self.f, **self.options)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 2298, in __init__
self.columns, self.index_names, self.col_names
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1508, in _extract_multi_indexer_columns
for r in header
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/parsers.py", line 1508, in <listcomp>
for r in header
TypeError: object of type 'datetime.datetime' has no len()
```
#### Expected Output
No exceptions.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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[WIP] fork of #31048 for CI testing DO NOT MERGE
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fork of #31048 for CI testing
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MDExOlB1bGxSZXF1ZXN0NDM0MDMyMDc1
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Bump up minimum numpy version in windows37 job
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CONTRIBUTOR
| null |
- [x] closes #34724
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Hypothesis test failure - test_on_offset_implementations
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| 1
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| 2020-10-24T02:55:20Z
| 2020-10-24T02:55:20Z
|
MEMBER
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=37199&view=logs&j=76104ccd-8dcc-5006-a17c-28bcdd709542&t=cd420693-444f-5c55-14e0-1be238e189a1
```
2020-06-13T14:27:46.0206662Z ================================== FAILURES ===================================
2020-06-13T14:27:46.0207049Z _______________________ test_on_offset_implementations ________________________
2020-06-13T14:27:46.0207455Z [gw2] win32 -- Python 3.7.7 C:\Miniconda\envs\pandas-dev\python.exe
2020-06-13T14:27:46.0207694Z
2020-06-13T14:27:46.0207905Z @given(gen_random_datetime, gen_yqm_offset)
2020-06-13T14:27:46.0208239Z > def test_on_offset_implementations(dt, offset):
2020-06-13T14:27:46.0208612Z
2020-06-13T14:27:46.0208902Z pandas\tests\tseries\offsets\test_offsets_properties.py:89:
2020-06-13T14:27:46.0209414Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-06-13T14:27:46.0209969Z pandas\tests\tseries\offsets\test_offsets_properties.py:94: in test_on_offset_implementations
2020-06-13T14:27:46.0210468Z compare = (dt + offset) - offset
2020-06-13T14:27:46.0210950Z pandas\_libs\tslibs\offsets.pyx:438: in pandas._libs.tslibs.offsets.BaseOffset.__add__
2020-06-13T14:27:46.0211305Z return other.__add__(self)
2020-06-13T14:27:46.0211774Z pandas\_libs\tslibs\offsets.pyx:440: in pandas._libs.tslibs.offsets.BaseOffset.__add__
2020-06-13T14:27:46.0212253Z return self.apply(other)
2020-06-13T14:27:46.0212687Z pandas\_libs\tslibs\offsets.pyx:135: in pandas._libs.tslibs.offsets.apply_wraps.wrapper
2020-06-13T14:27:46.0213172Z result = result.tz_localize(tz)
2020-06-13T14:27:46.0213673Z pandas\_libs\tslibs\timestamps.pyx:1258: in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
2020-06-13T14:27:46.0215173Z value = tz_localize_to_utc(np.array([self.value], dtype='i8'), tz,
2020-06-13T14:27:46.0215769Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-06-13T14:27:46.0216156Z
2020-06-13T14:27:46.0216385Z > raise pytz.NonExistentTimeError(stamp)
2020-06-13T14:27:46.0216849Z E pytz.exceptions.NonExistentTimeError: 1906-01-01 00:00:00
2020-06-13T14:27:46.0217233Z
2020-06-13T14:27:46.0217489Z pandas\_libs\tslibs\tzconversion.pyx:276: NonExistentTimeError
2020-06-13T14:27:46.0217864Z --------------------------------- Hypothesis ----------------------------------
2020-06-13T14:27:46.0218223Z Falsifying example: test_on_offset_implementations(
2020-06-13T14:27:46.0218570Z dt=datetime.datetime(1900, 1, 1, 0, 0, tzinfo=tzfile('Asia/Calcutta')),
2020-06-13T14:27:46.0219018Z offset=<72 * MonthBegins>,
2020-06-13T14:27:46.0219213Z )
```
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REF: refactor NDFrame.interpolate to avoid dispatching to fillna
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| 1
| 2020-06-13T18:41:21Z
| 2020-06-14T15:25:57Z
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/31048#issuecomment-643658604, #33959
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BUG: build from source failed on Ubuntu 20.04
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MEMBER
| 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.
---
#### Output of the build script
```bash
(pandas-dev) xxx@xxx-home-office:~/Workspace/pandas-VirosaLi$ python setup.py build_ext --inplace -j 4
running build_ext
building 'pandas._libs.interval' extension
gcc -pthread -B /home/xxx/miniconda3/envs/pandas-dev/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs/tslibs -Ipandas/_libs/src/klib -I/home/xxx/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/include -I/home/xxx/miniconda3/envs/pandas-dev/include/python3.8 -c pandas/_libs/interval.c -o build/temp.linux-x86_64-3.8/pandas/_libs/interval.o -Werror
building 'pandas._libs.tslibs.nattype' extension
gcc -pthread -B /home/xxx/miniconda3/envs/pandas-dev/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs/tslibs -I/home/xxx/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/numpy/core/include -I/home/xxx/miniconda3/envs/pandas-dev/include/python3.8 -c pandas/_libs/tslibs/nattype.c -o build/temp.linux-x86_64-3.8/pandas/_libs/tslibs/nattype.o -Werror
pandas/_libs/tslibs/nattype.c:4998:18: error: ‘__pyx_pw_6pandas_5_libs_6tslibs_7nattype_4_NaT_11__div__’ defined but not used [-Werror=unused-function]
4998 | static PyObject *__pyx_pw_6pandas_5_libs_6tslibs_7nattype_4_NaT_11__div__(PyObject *__pyx_v_self, PyObject *__pyx_v_other) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/interval.c:8301:18: error: ‘__pyx_pw_6pandas_5_libs_8interval_8Interval_25__div__’ defined but not used [-Werror=unused-function]
8301 | static PyObject *__pyx_pw_6pandas_5_libs_8interval_8Interval_25__div__(PyObject *__pyx_v_self, PyObject *__pyx_v_y) {
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cc1: all warnings being treated as errors
cc1: all warnings being treated as errors
error: command 'gcc' failed with exit status 1
```
#### Problem description
Recently I decide to try out Ubuntu 20.04. The build process failed. See the output in the section above. I replaced my user name with xxx.
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MDU6SXNzdWU2MzgyMTI4MzE=
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ENH: add masked algorithm for mean()
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| 6
| 2020-06-13T18:55:35Z
| 2021-01-01T21:46:48Z
| 2021-01-01T21:46:48Z
|
MEMBER
| null |
Similarly as we now have masked implementations for sum, prod, min and max for the nullable integer array (first PR https://github.com/pandas-dev/pandas/pull/30982, now lives at https://github.com/pandas-dev/pandas/blob/master/pandas/core/array_algos/masked_reductions.py), we can add one for the `mean` reduction as well.
Very rough check gives a nice speed-up:
```
In [27]: arr = pd.array(np.random.randint(0, 1000, 1_000_000), dtype="Int64")
In [28]: arr[np.random.randint(0, 1_000_000, 1000)] = pd.NA
In [30]: arr._reduce("mean")
Out[30]: 499.27095868772903
In [31]: %timeit arr._reduce("mean")
7.26 ms ± 335 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [32]: arr._data.sum(where=~arr._mask, dtype="float64") / (~arr._mask).sum()
Out[32]: 499.27095868772903
In [33]: %timeit arr._data.sum(where=~arr._mask, dtype="float64") / (~arr._mask).sum()
2.08 ms ± 6.89 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
The `nanmean` version lives here: https://github.com/pandas-dev/pandas/blob/master/pandas/core/nanops.py#L517
And as reference, numpy is also adding a version that accepts a mask: https://github.com/numpy/numpy/pull/15852 (which could be used in the future, and as inspiration for the implementation now).
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TST: Period with Timestamp overflow
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| 8
| 2020-06-13T19:04:44Z
| 2021-01-02T08:31:59Z
| 2020-10-05T20:52:52Z
|
CONTRIBUTOR
| null |
- [x] closes #13346
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The first part of the test checks that the period inside boundaries does not raise exception when calling `start_time` and `end_time` methods. The second part checks that methods raise exceptions when outside of boundaries.
The parameters are `Timestamp.min` and `Timestamp.max`, so the tests will follow any change of the boundaries.
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BUG: DataFrameGroupBy.quantile raises for non-numeric dtypes rather than dropping columns
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| 6
| 2020-06-13T20:17:17Z
| 2020-10-11T13:22:14Z
| 2020-07-16T22:48:30Z
|
MEMBER
| null |
- [x] closes #27892
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Unlike what is mentioned in #27892, this will raise if there are no columns to aggregate. Both mean and median raise with "No numeric types to aggregate" in such a case, so I was thinking perhaps we should be consistent with them. Any thoughts @WillAyd and @TomAugspurger?
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typo: rows -> columns
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| 1
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| 2020-06-14T13:56:04Z
| 2020-06-14T01:30:27Z
|
CONTRIBUTOR
| null |
I'm new to pandas and reading the docs for the first time. By my reading of the first part of this paragraph, there's a dictionary of lists and the lists are `["Braund...", "Allen...", ...]`, `[22, 35, 58]`, and `["male", "male", "female"]`.
The original sentence says: "... and the values in each list as rows of the DataFrame", but the values in the first list (`["Baund...]`) are the values in the first **column** of the illustration. Likewise the list `[22,...]` is the second column and `["male",...]` is the third.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
typo: pivot_table -> pivot
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| 1
| 2020-06-13T23:28:48Z
| 2020-06-14T01:29:40Z
| 2020-06-14T01:29:28Z
|
CONTRIBUTOR
| null |
I'm new to pandas and reading the docs for the first time.
I believe that the reference in the *pivot* section should be to `pivot`, not `pivot_table`, to match the code in the example.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 34,759
|
BUG: problem converting between new dtypes ('string' and 'Int8' / 'Int64')
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| 2020-06-14T00:48:14Z
| 2020-08-21T17:05:15Z
| 2020-06-14T01:35:23Z
|
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.
---
Attempts to convert between the new nullable integer and string dtypes result in an error. This might be a different manifestation of the same underlying problem as in: https://github.com/pandas-dev/pandas/issues/32073 and https://github.com/pandas-dev/pandas/issues/32234
```python
import pandas as pd
import numpy as np
test = {
'a': [0, 1, np.nan],
'b': ['0', '1', np.nan]
}
df = pd.DataFrame(test).astype({'a': 'Int8', 'b': 'string'})
df.b.astype('Int8')
# TypeError: data type not understood
```
Here's the traceback:
<details>
---------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-13-fcd2b8190e95> in <module>
----> 1 df.b.astype('Int8')
~/myenv/lib/python3.7/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors)
5696 else:
5697 # else, only a single dtype is given
-> 5698 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors)
5699 return self._constructor(new_data).__finalize__(self)
5700
~/myenv/lib/python3.7/site-packages/pandas/core/internals/managers.py in astype(self, dtype, copy, errors)
580
581 def astype(self, dtype, copy: bool = False, errors: str = "raise"):
--> 582 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
583
584 def convert(self, **kwargs):
~/myenv/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~/myenv/lib/python3.7/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors)
605 if self.is_extension:
606 # TODO: Should we try/except this astype?
--> 607 values = self.values.astype(dtype)
608 else:
609 if issubclass(dtype.type, str):
~/myenv/lib/python3.7/site-packages/pandas/core/arrays/string_.py in astype(self, dtype, copy)
258 return self.copy()
259 return self
--> 260 return super().astype(dtype, copy)
261
262 def _reduce(self, name, skipna=True, **kwargs):
~/myenv/lib/python3.7/site-packages/pandas/core/arrays/base.py in astype(self, dtype, copy)
448 NumPy ndarray with 'dtype' for its dtype.
449 """
--> 450 return np.array(self, dtype=dtype, copy=copy)
451
452 def isna(self) -> ArrayLike:
TypeError: data type not understood
</details>
#### Expected Output
It should be possible to convert between different dtypes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.12.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1.post20200529
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fastparquet : 0.4.0
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.15.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDM0MDc4NTAw
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REF: De-duplicate roll_yearday/roll_qtrday
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MDExOlB1bGxSZXF1ZXN0NDM0MDgxMjU5
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REF: implement shift_bday
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| 2020-06-14T01:33:30Z
| 2020-06-14T23:06:50Z
| 2020-06-14T22:55:41Z
|
MEMBER
| null |
Avoids depending on DatetimeArray/PeriodArray methods, also avoids a couple of array allocations
```
In [2]: dti = pd.date_range("2016-01-01", periods=10**5, freq="S")
In [3]: off = pd.offsets.BDay()
In [4]: %timeit dti + off
24.1 ms ± 202 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
20.1 ms ± 664 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- PR
```
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CLN: day->day_opt, remove unused case in liboffsets.get_day_of_month
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| 1
| 2020-06-14T01:46:21Z
| 2020-06-14T15:20:08Z
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MEMBER
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Removing this unused case turns out to be a blocker to making get_day_of_month nogil, which in turn will allow a bunch of de-duplication in this file.
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REF: remove roll_check, use roll_convention
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| 2020-06-14T01:49:07Z
| 2020-06-14T17:01:22Z
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|
MEMBER
| null |
Avoid bespoke logic for these two cases. This will make it feasible to collapse shift_months down to a single case (following #34762 and the one after that that makes get_day_of_month nogil)
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REF: make get_day_of_month nogil
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BUG: read_excel issue - 3 (header is a list, index_col is not range(N))
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| 2020-06-14T10:54:51Z
| 2020-09-07T03:41:09Z
| 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.
- [ ] (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('test.xlsx', header=[0, 1], index_col=1, engine='openpyxl')
# or
pd.read_excel('test.xlsx', header=[0, 1], index_col=[0, 2], engine='openpyxl')
# or
pd.read_excel('test.xlsx', header=[0, 1], index_col=[1, 2], engine='openpyxl')
```
(`engine` could be `xlrd` or `openpyxl`, doesn't matter.)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4776274/test.xlsx) (but could be different)
#### Problem description
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
pd.read_excel('test.xlsx', header=[0, 1], index_col=[0, 2], engine='openpyxl')
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 334, in read_excel
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 888, in parse
**kwds,
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 521, in parse
].columns.set_names(header_names)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1325, in set_names
idx._set_names(names, level=level)
File "/home/sasha/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1239, in _set_names
raise ValueError(f"Length of new names must be 1, got {len(values)}")
ValueError: Length of new names must be 1, got 2
```
#### Expected Output
No exceptions.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.42-calculate
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : ru_RU.utf8
LOCALE : ru_RU.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.8
numba : None
</details>
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| 34,766
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API: read_excel's header and index_col handling rules
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| 2020-06-14T11:05:44Z
| 2021-08-08T00:43:36Z
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NONE
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Good day!
`read_excel`'s `header` and `index_col` handling rules I see (a little chaotic [demo_suite.zip](https://github.com/pandas-dev/pandas/files/4776287/demo_suite.zip)):
- in all cases of `header`:
- cells `(header, outside of index_col)` are used as `df.columns`.
- if `header` is a integer/one-item list:
- `df.columns.name` is untouched,
- cells `(greater than max(header), index_col)` are used for `df.index`,
- cells `(header, index_col)` are used for `df.index.name`.
- else (i.e., `header` is a (2-up-items) list):
- according to #34765, `index_col` should be `None` or `0` or `range(0, max_index_col+1, 1)`.
- if row `max(header)+1` is empty except columns `index_col`:
- cells `(greater than max(header)+1, index_col)` are used for `df.index`,
- cells `(max(header)+1, index_col)` are used for `df.index.names`,
- else:
- cells `(greater than max(header), index_col)` are used for `df.index`,
- `df.index.names` is untouched.
- cells `(header, max(index_col))` are used for `df.columns.names`.
- non-`max` columns of `index_col` aren't used for `df.index`/`df.columns`.
Other observations:
- rows above `min(header)` are omitted.
- `header` is 0-based or `skiprows-1`-based?
So,
1. Isn't it too complicated, is it?
2. Is each this rule is documented?
3. Is each this rule is tested?
Any thoughts? Thanks!
/cc @TomAugspurger @jreback
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MEMBER
| null |
- [x] closes #32599
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This regression was a symptom of `DataFrameGroupBy.size` ignoring `as_index=False`. Fixing this now makes the result either a frame or series, so where the internals use `size()` they now use `grouper.size()` which always returns a series.
I also implemented @dsaxton's suggestion to make the column named "size" when `as_index=False` (https://github.com/pandas-dev/pandas/issues/5755#issuecomment-522321259).
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| 2
| 2020-06-14T14:02:33Z
| 2020-06-16T19:34:12Z
| 2020-06-16T19:29:38Z
|
MEMBER
| null |
Here's something I noticed while working on #34334 : `self._check_ticks_props(ax, ylabelsize=0)` always passes!
This is because of
```python
if ylabelsize
```
instead of
```python
if ylabelsize is not None
```
being used.
The test I've added fails on master:
```python-traceback
(pandas-dev) marco@marco-Predator-PH315-52:~/pandas-dev$ pytest pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_plot_with_rot
============================================================== test session starts ==============================================================
platform linux -- Python 3.8.2, pytest-5.4.1, py-1.8.1, pluggy-0.13.1
rootdir: /home/marco/pandas-dev, inifile: setup.cfg
plugins: cov-2.8.1, xdist-1.31.0, asyncio-0.10.0, hypothesis-5.8.0, forked-1.1.2
collected 1 item
pandas/tests/plotting/test_frame.py . [100%]
=============================================================== 1 passed in 0.33s ===============================================================
(pandas-dev) marco@marco-Predator-PH315-52:~/pandas-dev$ git checkout upstream/master -- pandas/tests/plotting/common.py
(pandas-dev) marco@marco-Predator-PH315-52:~/pandas-dev$ pytest pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_plot_with_rot
============================================================== test session starts ==============================================================
platform linux -- Python 3.8.2, pytest-5.4.1, py-1.8.1, pluggy-0.13.1
rootdir: /home/marco/pandas-dev, inifile: setup.cfg
plugins: cov-2.8.1, xdist-1.31.0, asyncio-0.10.0, hypothesis-5.8.0, forked-1.1.2
collected 1 item
pandas/tests/plotting/test_frame.py F [100%]
=================================================================== FAILURES ====================================================================
_____________________________________________________ TestDataFramePlots.test_plot_with_rot _____________________________________________________
self = <pandas.tests.plotting.test_frame.TestDataFramePlots object at 0x7f8c7578f3a0>
def test_plot_with_rot(self):
# GH 34768
df = pd.DataFrame({"b": [0, 1, 0], "a": [1, 2, 3]})
ax = _check_plot_works(df.plot, rot=30)
ax.yaxis.set_tick_params(rotation=30)
msg = "expected 0.00000 but got "
with pytest.raises(AssertionError, match=msg):
> self._check_ticks_props(ax, xrot=0)
E Failed: DID NOT RAISE <class 'AssertionError'>
pandas/tests/plotting/test_frame.py:3360: Failed
============================================================ short test summary info ============================================================
FAILED pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_plot_with_rot - Failed: DID NOT RAISE <class 'AssertionError'>
=============================================================== 1 failed in 0.46s ===============================================================
```
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| 638,367,645
|
MDU6SXNzdWU2MzgzNjc2NDU=
| 34,769
|
TST: split up tests/plotting/test_frame.py into subdir & modules
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| 6
| 2020-06-14T14:54:47Z
| 2020-11-13T02:51:08Z
| 2020-11-13T02:51:08Z
|
CONTRIBUTOR
| null |
we should split up this test file into a separate subdir and multiple test modules for better groking. first PR should be a straight move, then can split (or all in one is ok)
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| 0
| 2020-06-14T16:53:17Z
| 2020-06-14T18:36:26Z
| 2020-06-14T18:36:23Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
1. Move `min_periods` validation to constructor
2. Stronger cython type defintions
3. Typing EWM methods
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| 34,771
|
PERF: DataFrameGroupBy var and std
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| 2020-06-14T17:18:24Z
| 2020-07-07T13:39:22Z
| 2020-07-07T13:39:22Z
|
MEMBER
| null |
Performance on wide and short frames decreased due to #34372, timings are in the OP there. std is computed by calling var.
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MDExOlB1bGxSZXF1ZXN0NDM0MTc4MDkz
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REF: inline get_day_of_month
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| 2020-06-14T17:45:56Z
| 2020-06-14T19:52:45Z
| 2020-06-14T19:45:25Z
|
MEMBER
| null |
Makes the logic in all cases of shift_months identical, same for shift_quarters. Next pass does de-duplication.
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MDU6SXNzdWU2MzgzOTgzMzA=
| 34,773
|
PERF: Significant speed difference between `arr.mean()` and `arr.values.mean()` for common `dtype` columns
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| 2020-06-14T17:49:29Z
| 2021-08-08T00:46:24Z
| 2021-08-08T00:46:23Z
|
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.
---
I'm seeing a significant variance in timings for common math operations (e.g. `mean`, `std`, `max`) on a large Pandas `Series` vs the underlying NumPy `array`. An code example is shown below with 1 million elements and a 10x speed difference. The screenshot below uses 10 million elements.
I've generated a testing module (https://github.com/ianozsvald/dtype_pandas_numpy_speed_test) which several people have tried on Intel & AMD hardware: https://github.com/ianozsvald/dtype_pandas_numpy_speed_test/issues/1
This module confirms the general trend that all of these operations are faster on the underlying NumPy array (not unsurprising as it avoids the despatch machinery) _but_ for float operations the speed hit using Pandas seems to be extreme:
#### Code Sample, a copy-pastable example
A Python module exists in this repo along with reports from several other users with screenshots of their graphs, the same general behaviour is seen across different machines: https://github.com/ianozsvald/dtype_pandas_numpy_speed_test
```python
# note this is copied from my README linked above.
# paste into IPython or a Notebook
import pandas as pd
import numpy as np
arr = pd.Series(np.ones(shape=1_000_000))
arr.values.dtype
Out[]: dtype('float64')
arr.values.mean() == arr.mean()
Out[]: True
# call arr.mean() vs arr.values.mean(), note circa 10* speed difference
# with 4ms vs 0.4ms
%timeit arr.mean()
4.59 ms ± 44.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit arr.values.mean()
485 µs ± 5.73 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
# note that arr.values dereference is very cheap (nano seconds)
%timeit arr.values
456 ns ± 0.828 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
#### Problem description
Is this slow-down expected? The slowdown feels extreme but perhaps my testing methodology is flawed? I expect the float & integer math to operate at approximately the same speed but instead we see a significant slow-down for Pandas float operations vs their NumPy counterparts.
I've added some extra graphs:
* https://github.com/ianozsvald/dtype_pandas_numpy_speed_test/blob/master/timings_1e7_std.png (10M elements with `std`)
* https://github.com/ianozsvald/dtype_pandas_numpy_speed_test/blob/master/timings_1e8_mean.png (100M elements with `mean` to contrast against the picture shown above in this report)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.7-050607-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1.post20200529
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDM0MTc4NTkw
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CI: move arm to non-failure builds
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| 0
| 2020-06-14T17:50:10Z
| 2020-06-15T00:59:29Z
| 2020-06-15T00:59:29Z
|
CONTRIBUTOR
| null |
make pre-commit language generic
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MDExOlB1bGxSZXF1ZXN0NDM0MTkwNzEx
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DOC: updated multi.py docstring for SS06 errors
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CI: move 3.6,slow or 3.6 locale builds from travis to azure
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| 2
| 2020-06-14T20:52:16Z
| 2020-07-07T13:10:07Z
| 2020-07-07T13:10:07Z
|
CONTRIBUTOR
| null |
we have capacity on azure (we are using 8/10) and slightly overloaded on travis.
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ENH: Support Multi-Index for columns in parquet format
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| 4
| 2020-06-14T21:19:18Z
| 2020-11-19T02:09:10Z
| 2020-11-19T02:09:10Z
|
NONE
| null |
#### Is your feature request related to a problem?
I would like to save DataFrame with Multi-Index used for columns into parquet format.
This is currently not possible.
```python
import pandas as pd
df = pd.DataFrame([[1,2,3],[4,5,6],[7,8,9]], columns= pd.MultiIndex.from_product([['1'],['a', 'b', 'c']]))
df.to_parquet('test.parquet')
""" doesn't work, whatever the engine """
ValueError: parquet must have string column names
```
#### Describe alternatives you've considered
To do so with actual state of library, a [piece of code has been shared on SO](https://stackoverflow.com/questions/54861430/how-do-i-save-multi-indexed-pandas-dataframes-to-parquet).
I will use it for now.
```python
import pyarrow as pa
import pyarrow.parquet as pq
table = pa.Table.from_pandas(df)
pq.write_table(table, 'test.parquet')
df_test_read = pd.read_parquet('test.parquet')
```
Thanks for your help!
Bests,
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MDExOlB1bGxSZXF1ZXN0NDM0MjA4MTAw
| 34,778
|
REF: de-duplicate code in liboffsets
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| 2
| 2020-06-14T21:58:07Z
| 2020-06-15T00:59:10Z
| 2020-06-15T00:17:51Z
|
MEMBER
| null |
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, freq="S")
In [3]: off = pd.offsets.BMonthEnd(2)
In [4]: %timeit dti + off
3.36 ms ± 14 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- master
998 µs ± 12.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
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MDU6SXNzdWU2Mzg0NTg1ODY=
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|
CI: gbq tests failing
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| 9
| 2020-06-15T00:02:51Z
| 2020-07-09T23:41:00Z
| 2020-07-09T23:41:00Z
|
CONTRIBUTOR
| null |
https://travis-ci.org/github/pandas-dev/pandas/builds/698328374
and specificallly these 2 builds: https://travis-ci.org/github/pandas-dev/pandas/jobs/698328378
cc @alimcmaster1 @tswast
if you have any insights
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|
TST: remove super slow cases on upsample_nearest_limit
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| 2020-06-15T02:19:40Z
| 2020-06-15T02:19:39Z
|
CONTRIBUTOR
| null |
make multi_thread csv parsing test slow
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REF: reuse roll_qtrday in liboffsets
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| 1
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| 2020-06-15T03:13:37Z
| 2020-06-15T02:19:54Z
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MEMBER
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Last one in this sequence.
|
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REF: avoid using DTA/PA methods in Week.apply_index
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| 1
| 2020-06-15T01:35:35Z
| 2020-06-15T03:14:31Z
| 2020-06-15T02:20:08Z
|
MEMBER
| null |
We avoid a couple of array allocations in the process.
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, freq="S")
In [3]: off = pd.offsets.Week(1, False, 3)
In [4]: %timeit dti + off
3.22 ms ± 30.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- master
344 µs ± 4.97 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
|
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| 34,783
|
REF: avoid DTA/PA methods in SemiMonthOffset.apply_index
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| 0
| 2020-06-15T03:31:47Z
| 2020-06-15T15:00:32Z
| 2020-06-15T12:23:06Z
|
MEMBER
| null |
I think this gets the last implicit-external-dependencies in tslibs.
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, freq="S")
In [3]: off = pd.offsets.SemiMonthEnd(-2)
In [4]: %timeit dti + off
3.08 ms ± 108 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <-- master
486 µs ± 7.16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
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MDU6SXNzdWU2Mzg2Mjc1Mjg=
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|
BUG: pd.Series.rolling - center=True doesnt work when using numba engine
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| 2
| 2020-06-15T07:53:59Z
| 2020-06-16T12:47:57Z
| 2020-06-16T12:47:57Z
|
NONE
| null |
**Rolling over a Series with center=True gives different results depending on the engine used:**
```python
import pandas as pd
test_ser = pd.Series(np.arange(0,10))
rolled_without_numba =test_ser.rolling(3, center=True).apply(lambda x:x[1],raw=True)
rolled_with_numba = test_ser.rolling(3, center=True).apply(lambda x:x[1],raw=True, engine='numba', engine_kwargs={'no_python':True})
```
#### Problem description
wheras the result for rolling without the engine is as expected:
```python
rolled_without_numba.head()
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
```
rolling with the numba engine gives:
```python
rolled_with_numba.head()
0 NaN
1 NaN
2 1.0
3 2.0
4 3.0
```
The behavior is problematic at least because its not documented and causes frustrating bug tracing sessions. That the use of numba engine may change the result numerically, is not the first guess.
.
Since rolling with offset is not possible when using numba engine anyway, finding the center labels after rolling is not too big a problem - so maybe instead of a fix, a documentation or a warning would be sufficient.
#### Expected Output
```python
rolled_with_numba.equals(rolled_without_numba)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-53-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 46.1.3
Cython : 0.29.17
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDM0MzYzMzE3
| 34,785
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DOC: add release note about revert for 1.0.5
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| 3
| 2020-06-15T08:08:22Z
| 2020-06-15T13:38:02Z
| 2020-06-15T13:36:13Z
|
MEMBER
| null |
Whatsnew for https://github.com/pandas-dev/pandas/pull/34632
cc @simonjayhawkins @alimcmaster1
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Backport PR #34667 on branch 1.0.x (BLD: pyproject.toml for Py38)
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| 2020-06-15T11:23:53Z
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|
MEMBER
| null |
xref #34667
@jorisvandenbossche another set of eyes needed as this was not a clean cherry-pick
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Backport Test Only from PR #34500 on branch 1.0.x (REG: Fix read_parquet from file-like objects)
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| 2
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MEMBER
| null |
xref #34500
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Backport PR #34721 on branch 1.0.x (Debug CI Issue)
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| 0
| 2020-06-15T09:24:33Z
| 2020-06-15T09:58:01Z
| 2020-06-15T09:57:55Z
|
MEMBER
| null |
xref #34721
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MDU6SXNzdWU2Mzg3MDY0OTc=
| 34,789
|
BUG: Replace raises TypeError if to_replace is Dict with numeric DataFrame and key of Dict is String
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| 2020-06-15T09:47:46Z
| 2020-09-05T03:21:50Z
| 2020-09-05T03:21:50Z
|
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"Y0": [1, 2], "Y1": [3, 4]})
df = df.replace({"replace_string": "test"})
```
#### Problem description
This raises a TypeError with the following Traceback:
```python
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharmCE2020.1/scratches/scratch_1.py", line 7, in <module>
df = df.replace({"replace_string": "test"})
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6598, in replace
return self.replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/frame.py", line 4277, in replace
return super().replace(
File "/home/developer/PycharmProjects/pandas/pandas/core/generic.py", line 6641, in replace
new_data = self._mgr.replace_list(
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in replace_list
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 616, in <listcomp>
masks = [comp(s, regex) for s in src_list]
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 614, in comp
return _compare_or_regex_search(values, s, regex)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1946, in _compare_or_regex_search
_check_comparison_types(False, a, b)
File "/home/developer/PycharmProjects/pandas/pandas/core/internals/managers.py", line 1925, in _check_comparison_types
raise TypeError(
TypeError: Cannot compare types 'ndarray(dtype=int64)' and 'str'
Process finished with exit code 1
```
#### Expected Output
The original DataFrame.
If at least one column is from dtype object, the replace works, for example the following works:
```python
import pandas as pd
df = pd.DataFrame({"Y0": [1, "2"], "Y1": [3, 4]})
df = df.replace({"replace_string": "test"})
```
Unexpectedly the following Code works:
```python
import pandas as pd
df = pd.DataFrame({"Y0": [1, 2], "Y1": [3, 4]})
df = df.replace(to_replace="replace_string", value="test")
```
This is probably related to #16784
Tested on master.
|
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MDU6SXNzdWU2Mzg3NDE4NzE=
| 34,790
|
BUG: `DataFrame.to_parquet` does not correctly write index information if `partition_cols` are provided
|
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[] | null | 6
| 2020-06-15T10:35:53Z
| 2020-09-04T14:02:05Z
| 2020-09-04T14:02:05Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'Data': [1, 2], 'partition': [1, 2]}, index=['2000-01-01', '2010-01-02'])
data_path_with_partitions = 'with_partitions.parquet'
df.to_parquet(data_path_with_partitions, partition_cols=['partition'])
df_read_with_partitions = pd.read_parquet(data_path_with_partitions)
pd.testing.assert_frame_equal(df, df_read_with_partitions) # <-- this fails because the index has been turned into an extra column __index_level_0
```
results in an `AssertionError`:
```
> pd.testing.assert_frame_equal(df, df_read_with_partitions) # <-- this fails because the index has been turned into an extra column __index_level_0
E AssertionError: DataFrame are different
E
E DataFrame shape mismatch
E [left]: (2, 2)
E [right]: (2, 3)
```
Because of an extra column `__index_level_0__` containing the index values
```
Data __index_level_0__ partition
0 1 2000-01-01 1
1 2 2010-01-02 2
```
On the other hand, without partitions works as expected:
```python
data_path_without_partitions = 'without_partitions.parquet'
df.to_parquet(data_path_without_partitions)
df_read_without_partitions = pd.read_parquet(data_path_without_partitions)
pd.testing.assert_frame_equal(df, df_read_without_partitions) # <-- this passes
```
#### Problem description
If serializing data with pyarrow parquet using the `partition_cols` parameter, deserializing does not correctly reset the index, even though it has been serialized.
In the above example, `df_read_with_partitions` contains an extra column `__index_level_0__`.
Debugging into this, I believe this is a problem with the pandas integration inside pyarrow. In `pyarrow/parquet.py:1723` the sub-tables for each of the partitions get generated, but the `b'pandas'` metadata incorrectly overwrites the index column information of `subschema`.
#### Expected Output
I expect `read_parquet` to yield the same output whether or not it has been written with `partition_cols`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.4
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 5.2.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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|
ENH: Inconsistency when string refers both to index level and column label
|
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[] | null | 2
| 2020-06-15T10:39:59Z
| 2020-09-04T14:04:01Z
| 2020-09-04T14:04:01Z
|
NONE
| null |
While [groupby](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups) throws a ValueError when the string parameter refers both to an index level and column name, [query](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html?highlight=query%20method#the-query-method) gives the column precedence in such a case:
```python
>>> df = pd.DataFrame({'a':[1,2,3,4,5], 'b':[3,3,3,3,3]})
>>> df.index.name = 'a'
>>> df.query('a < b')
a b
a
0 1 3
1 2 3
>>> df.groupby('a')
Traceback (most recent call last):
ValueError: 'a' is both an index level and a column label, which is ambiguous.
```
I am sure there are many other places where a string can refer to both an index level and column name. #27652 and #8162 are somehow linked.
Even though those issues might *not* be solved, it would be desirable to handle the above ambiguity consistently in places where both index levels and column labels can be referenced by a string argument.
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CI: test failure, raises unexpected warning
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|
MEMBER
| null |
I merged master into #33247 . This results in a failing test:
`pandas/tests/plotting/test_converter.py::TestRegistration::test_pandas_plots_register`
in Windows py37_np_18
```
AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning("Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working")
```
Was this already detected?
[test results](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=37342&view=logs&j=ea1e4a48-f1c1-5933-c4f7-7e56de389fce&t=967459d8-2b8b-5603-ef1c-c585363eb056&l=92)
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CI testing for s3 reads from public buckets on 1.0.x - DO NOT MERGE
|
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| 2020-06-17T07:29:33Z
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|
MEMBER
| null |
xref #34626
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Backport PR #34711 on branch 1.0.x (BLD: Pin cython for 37-locale build)
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|
MEMBER
| null |
xref #34711
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CI: Update pipelines config to trigger on PRs on 1.0.x
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| 2020-06-15T12:01:56Z
| 2020-06-15T17:52:30Z
| 2020-06-15T13:55:59Z
|
MEMBER
| null |
xref #32706
I think maybe the additions should also go in master. let's see what happens here first.
|
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API: Removed PeriodDtype.dtype_code from public API
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| 0
| 2020-06-15T12:41:52Z
| 2020-06-15T14:24:13Z
| 2020-06-15T14:24:12Z
|
CONTRIBUTOR
| null |
Closes #34735
|
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Backport PR #34785 on branch 1.0.x (DOC: add release note about revert for 1.0.5)
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| 2020-06-15T14:20:08Z
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NONE
| null |
Backport PR #34785: DOC: add release note about revert for 1.0.5
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API: Make describe changes backwards compatible
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| 22
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|
CONTRIBUTOR
| null |
Adds the new behavior as a feature flag / deprecation.
Closes https://github.com/pandas-dev/pandas/issues/33903
(Do we have a list of issues for deprecations introduced in 1.x?)
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ENH: At top-level dataframe function for single-dispatched construction of a DataFrame
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| 2020-10-06T01:49:01Z
| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
This might help with two things
1. A coordination point for 3rd-party libraries creating objects they'd like to turn into DataFrames, and users of those libraries
2. Possibly, simplification of `DataFrame.__init__`
#### Describe the solution you'd like
A new top-level `pd.dataframe` function.
```python
def dataframe(data: Any, index: Index, columns: Index, copy: bool = False):
"""
Create a pandas DataFrame from data.
"""
@singledispatch.register(np.ndarray)
def dataframe(...):
pass
```
#### API breaking implications
None
#### Describe alternatives you've considered
xref https://github.com/pandas-dev/pandas/pull/32844. Which attempted this for `DataFrame.__init__`. That was a non-starter since it exposed our internal BlockManager too publicly. https://github.com/pandas-dev/pandas/pull/32844#issuecomment-601494850. So we'd need to do this on a top-level function instead.
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Check API changes in whatsnew
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| 11
| 2020-06-15T14:39:15Z
| 2020-07-17T16:30:02Z
| 2020-07-17T16:30:02Z
|
CONTRIBUTOR
| null |
Our whatsnew has several sections detailing API changes:
- https://pandas.pydata.org/pandas-docs/dev/whatsnew/v1.1.0.html#other-api-changes
- https://pandas.pydata.org/pandas-docs/dev/whatsnew/v1.1.0.html#backwards-incompatible-api-changes. According to our version policy we shouldn't have any API-breaking changes until 2.0
Many of these, like adding `DataFrame.value_counts` are fine and should just be moved to a different section.
Others like https://github.com/pandas-dev/pandas/pull/31905 will need to be looked at closely, and we'll need to determine if they're API breaking or bug fixes.
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MDExOlB1bGxSZXF1ZXN0NDM0NTg5NTQ4
| 34,802
|
Backport PR #34718 on branch 1.0.x (Removed __div__ impls)
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| 2
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|
MEMBER
| null |
https://github.com/pandas-dev/pandas/pull/34711#issuecomment-644148383
@WillAyd on 1.0.x. we have cython>=0.29.13 and on master cython>=0.29.16. is this an issue?
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| 638,940,025
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MDU6SXNzdWU2Mzg5NDAwMjU=
| 34,803
|
BUG: `read_parquet` gives "Passed non-file path" in v1.0.4 but works in v1.0.3
|
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[] | null | 1
| 2020-06-15T15:23:13Z
| 2020-06-15T15:32:22Z
| 2020-06-15T15:32:15Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
pd.read_parquet("https://www.tangel.se/wind/wind_frames/wind-2018.parquet")
```
#### Problem description
There is some regression in the interaction of Pandas v1.0.4 and Pyarrow.
The above code downloads a Parquet file when Pandas is v1.0.3.
However, in v1.0.4 it generates the error:
```
File "<python-env>/lib/python3.8/site-packages/pandas/io/parquet.py", line 315, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "<python-env>/lib/python3.8/site-packages/pandas/io/parquet.py", line 130, in read
parquet_ds = self.api.parquet.ParquetDataset(
File "<python-env>/lib/python3.8/site-packages/pyarrow/parquet.py", line 1171, in __init__
self.metadata_path) = _make_manifest(
File "<python-env>/lib/python3.8/site-packages/pyarrow/parquet.py", line 1367, in _make_manifest
raise OSError('Passed non-file path: {}'
OSError: Passed non-file path: https://www.tangel.se/wind/wind_frames/wind-2018.parquet
```
I can confirm that the bug appears with Pyarrow of versions 0.15.1 and 0.17.1 on Python 3.7 and 3.8.
#### Expected Output
```
x y u v ... altitude height date time
0 361912.978162 6.139641e+06 5.338025 5.958648 ... 5 10 2018-01-01 06:00
1 361912.978162 6.139641e+06 10.008798 11.172465 ... 5 10 2018-01-01 09:00
2 361912.978162 6.139641e+06 14.252365 4.676546 ... 5 10 2018-01-01 12:00
3 361912.978162 6.139641e+06 14.107182 7.549001 ... 5 10 2018-01-01 15:00
4 361912.978162 6.139641e+06 8.650998 6.794132 ... 5 10 2018-01-01 18:00
... ... ... ... ... ... ... ... ... ...
1449984 804603.972910 7.610557e+06 -1.789940 0.190043 ... 330 10 2018-12-31 19:00
1449985 804603.972910 7.610557e+06 -1.646950 -0.947394 ... 330 10 2018-12-31 20:00
1449986 804603.972910 7.610557e+06 -1.078928 -0.725200 ... 330 10 2018-12-31 21:00
1449987 804603.972910 7.610557e+06 0.302022 -1.057725 ... 330 10 2018-12-31 22:00
1449988 804603.972910 7.610557e+06 0.920168 -0.391524 ... 330 10 2018-12-31 23:00
[1449989 rows x 9 columns]
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.1
```
</details>
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MDExOlB1bGxSZXF1ZXN0NDM0NjEzMDU1
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|
TST: ensure read_parquet filter argument is correctly passed though (pyarrow engine)
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| 3
| 2020-06-15T15:25:52Z
| 2020-06-16T09:24:46Z
| 2020-06-15T16:25:10Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/26551#issuecomment-643882543
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|
BUG: Inconsistent behavior for isin() over columns with different datatypes
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| 2020-06-15T16:32:20Z
| 2020-08-21T17:05:16Z
| 2020-06-17T18:13:20Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
s_obj = pd.Series(['1', '2', '3'])
s_int = pd.Series([1, 2, 3])
s_obj.isin(s_int).sum() # 0
s_int.isin(s_obj).sum() # 3
s_obj.astype(np.int).isin(s_int).sum() # 3
```
#### Problem description
It is not uncommon for numeric fields to be read into a dataframe column as text. The behavior of the `isin` function is inconsistent in these cases, working as expected in the `s_int.isin(s_obj)` case, but failing in the `s_obj.isin(s_int)` case unless the text field is coerced to numeric first by the user.
#### Expected Output
**Behavior should be consistent in both directions.** Either `isin()` should always fail when there is a type mismatch, or it should perform necessary coersions automatically. It would be helpful to throw a warning in either case to inform the user either that they should standardize their schemas or that a potentially costly coersion is going to happen automatically. At the very least, we could keep the current behavior and show the user a warning explaining that an empty result set could be due to a type mismatch.
Middle ground solution: add something like a `coerce_if_necessary` option to `isin()`. Defaulting to False would return the current behavior (which preserves backwards compatibility and ensures we don't surprise users with expensive coersions). We could augment this behavior with a new warning, alerting users to the type mismatch and recommending solutions. Users who aren't comfortable with datatypes (or are just feeling lazy) could then set `coerce=True` to have necessary coersions attempted for them behind the scenes.
Interested to hear other's thoughts regarding what the preferred behavior should be.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
Regression in to_timedelta with errors="coerce" and unit
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| 1
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| 2020-06-15T20:16:28Z
| 2020-06-15T17:27:29Z
|
CONTRIBUTOR
| null |
Introduced in https://github.com/pandas-dev/pandas/commit/d3f686bb50c14594087171aa0493cb07eb5a874c
In pandas 1.0.3
```python
In [2]: pd.to_timedelta([1, 2, 'error'], errors="coerce", unit="ns")
Out[2]: TimedeltaIndex(['00:00:00.000000', '00:00:00.000000', NaT], dtype='timedelta64[ns]', freq=None)
```
In master, we raise.
```pytb
In [2]: pd.to_timedelta([1, 2, 'error'], errors="coerce", unit="ns")
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-a3691c044041> in <module>
----> 1 pd.to_timedelta([1, 2, 'error'], errors="coerce", unit="ns")
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/tools/timedeltas.py in to_timedelta(arg, unit, errors)
101 arg = arg.item()
102 elif is_list_like(arg) and getattr(arg, "ndim", 1) == 1:
--> 103 return _convert_listlike(arg, unit=unit, errors=errors)
104 elif getattr(arg, "ndim", 1) > 1:
105 raise TypeError(
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/tools/timedeltas.py in _convert_listlike(arg, unit, errors, name)
140
141 try:
--> 142 value = sequence_to_td64ns(arg, unit=unit, errors=errors, copy=False)[0]
143 except ValueError:
144 if errors == "ignore":
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py in sequence_to_td64ns(data, copy, unit, errors)
927 if is_object_dtype(data.dtype) or is_string_dtype(data.dtype):
928 # no need to make a copy, need to convert if string-dtyped
--> 929 data = objects_to_td64ns(data, unit=unit, errors=errors)
930 copy = False
931
~/Envs/dask-dev/lib/python3.7/site-packages/pandas/core/arrays/timedeltas.py in objects_to_td64ns(data, unit, errors)
1037 values = np.array(data, dtype=np.object_, copy=False)
1038
-> 1039 result = array_to_timedelta64(values, unit=unit, errors=errors)
1040 return result.view("timedelta64[ns]")
1041
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.array_to_timedelta64()
ValueError: unit must not be specified if the input contains a str
```
This restores the 1.0.3 behavior, and adds an additional test for `errors="ignore"`, and cleans up the `to_timedelta` docstring.
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Backport PR #34804 on branch 1.0.x (TST: ensure read_parquet filter argument is correctly passed though (pyarrow engine))
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MEMBER
| null |
xref #34804
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Inconsistent output in GroupBy.apply returning a DataFrame
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| 2021-08-08T00:52:03Z
| null |
CONTRIBUTOR
| null |
This is a continuation of https://github.com/pandas-dev/pandas/issues/13056, https://github.com/pandas-dev/pandas/issues/14927, and https://github.com/pandas-dev/pandas/issues/13056, which were closed by https://github.com/pandas-dev/pandas/pull/31613. I think that PR ensured that we consistently take one of two code paths. This issue is to verify that we actually want the behavior on master.
Focusing on a specific pair of examples that differ only in whether the returned index is the same or not:
```python
# master
In [10]: def f(x):
...: return x.copy() # same index
In [11]: def g(x):
...: return x.copy().rename(lambda x: x + 1) # different index
In [12]: df = pd.DataFrame({"A": ['a', 'b'], "B": [1, 2]})
In [13]: df.groupby("A").apply(f)
Out[13]:
A B
0 a 1
1 b 2
In [14]: df.groupby("A").apply(g)
Out[14]:
A B
A
a 1 a 1
b 2 b 2
```
```python
# 1.0.4
In [8]: df.groupby("A").apply(f)
Out[8]:
A B
A
a 0 a 1
b 1 b 2
In [9]: df.groupby("A").apply(g)
Out[9]:
A B
A
a 1 a 1
b 2 b 2
```
So the 1.0.4 behavior is to always prepend the group keys to the result as an index level.
In pandas 1.1.0, whether the group keys are prepended depends on whether the udf returns a dataframe with an identical index. Do we want that kind of value-dependent behavior?
@jorisvandenbossche's [notebook](http://nbviewer.jupyter.org/gist/jorisvandenbossche/16f511fe111a8b9fa0eac74e920c5251) from https://github.com/pandas-dev/pandas/issues/13056#issuecomment-403300216 might be helpful, though it might be out of date.
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| 1
| 2020-06-15T21:03:27Z
| 2020-07-06T13:14:06Z
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CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/34741, while
retaining the spirit of the spirit of
https://github.com/pandas-dev/pandas/pull/34208.
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| null |
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew note entry
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| 2
| 2020-06-16T02:04:17Z
| 2020-06-16T15:51:10Z
| 2020-06-16T12:51:02Z
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MEMBER
| null |
This is pretty much a clean move, in preparation for making FreqGroup an enum and trying to de-duplicate our 3+ enum-like classes
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ENH: add masked algorithm for mean() function
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| 29
| 2020-06-16T02:57:34Z
| 2021-01-01T21:46:52Z
| 2021-01-01T21:46:48Z
|
CONTRIBUTOR
| null |
- [x] closes #34754
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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CLN: liboffsets annotations
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| 0
| 2020-06-16T03:35:11Z
| 2020-06-16T15:34:51Z
| 2020-06-16T12:49:30Z
|
MEMBER
| null |
It looks like rollforward/rollback we have some tests that pass `date` instead of `datetime`. That and a couple other outliers means there are some things that are not yet annotated.
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| 34,816
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BUG: Respect center=True in rolling.apply when numba engine is used
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| 1
| 2020-06-16T04:07:34Z
| 2020-06-16T15:40:30Z
| 2020-06-16T12:47:57Z
|
MEMBER
| null |
- [x] closes #34784
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