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- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
### Link to question on StackOverflow
NA
### Question about pandas
linux env, how to build lib_lightgbm.so with "-fstack-protector-all" option? | {
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] | open | false | null | [] | null | 1 | "2021-08-20T10:34:00Z" | "2021-08-22T10:26:34Z" | null | CONTRIBUTOR | null | 5 Pandas docstrings discuss the `axis` argument with one use of `row/column-wise`, 2 docstrings use an opposite meaning (and possibly more examples exist, I picked these up using Google & DuckDuckGo).
I believe the first 5 usages are in agreement with the Numpy usage.
Examples of `axis: Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise` (i.e. 0 is row-wise):
* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.quantile.html
* https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.swaplevel.html
* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmin.html
* https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.idxmax.html
* https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html
Examples of `The axis to use. 0 or ‘index’ to compute column-wise, 1 or ‘columns’ for row-wise` (i.e. 0 is column-wise):
* https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corrwith.html
* https://pandas.pydata.org/pandas-docs/version/0.24.2/reference/api/pandas.Series.all.html
For `Series.all` the examples down the page note `Default behaviour (i.e. axis=0) checks if column-wise values all return True.` and `Specify axis='columns' (or axis=1) to check if row-wise values all return True.`.
On Numpy I can't find many examples, the few that I find are consistent with the first (5 case) usage:
* https://numpy.org/doc/stable/reference/generated/numpy.hsplit.html#numpy.hsplit `horizontally (column-wise)...equivalent to split with axis=1`
* https://numpy.org/doc/stable/reference/generated/numpy.vsplit.html#numpy.vsplit `vertically (row-wise)... equivalent to split with axis=0 (default)`
#### Suggested fix for documentation
I _think_ that the 2 opposing cases (`corrwith` and `Series.all`) don't need to have the inverted explanation so I _think_ that reversing them to fit the general case makes sense, but maybe I'm missing something?
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- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 8 | "2021-08-20T13:26:40Z" | "2021-09-18T02:31:52Z" | "2021-09-18T02:31:52Z" | NONE | null | #### Code Sample, a copy-pastable example
The problem is best shown in the following example:
```python
import pandas as pd
s = pd.Series(['1 day', '3 days', pd.NaT], dtype='timedelta64[ns]')
print(f'{s.mean()=}\n')
# as expected: result is '2 days'
print(f"{s.apply('mean')=}\n")
# as expected: result is '2 days'
print("transform('mean')")
print(s.groupby([1,1,1]).transform('mean'))
# whoops: (a series of 3x) -35583 days +08:04:14.381741568
# expectation was: (a series of 3x) '2 days'
# Fixing the problem
print("\ntransform(pd.Series.mean)")
print(s.groupby([1,1,1]).transform(pd.Series.mean))
# (a series of 3x) '2 days', sanity restored :-)
```
#### Problem description
When passing a function name by string to `transform`, pd.NaT is not handled correctly. This not only happens for 'mean' but also for other functions like 'sum'.
I don't know how transform is looking up the function if a string is passed (and it's not really documented), but it certainly doesn't select pd.Series.mean but some other non-NaT-aware mean function.
This is surprising since NaT is handled correctly when calling `apply('mean')`.
Tested with pd 1.3.2. | {
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} | 1 | "2021-08-20T14:41:11Z" | "2021-08-27T13:52:26Z" | "2021-08-27T13:52:26Z" | 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
df = pd.DataFrame({'group': ['A', 'A', 'B'], 'v': [4, 5, 6]}, index=[-1, -2, -3])
def f(values, index):
return np.mean(index)
df.groupby('group').aggregate(f, engine='numba')
# Result is:
# v
# group
# A 0.5
# B 2.0
```
#### Problem description
The behaviour when engine='numba' is inconsistent with engine='cython':
```python
def f_cython(x):
return np.mean(x.index)
df.groupby('group').aggregate(f_cython, engine='cython')
# Result is:
# v
# group
# A -1.5
# B -3.0
```
Furthermore engine='numba' would be more useful if it calls func with the DataFrame index.
#### Expected Output
```
v
group
A -1.5
B -3.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 19.2.3
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.1.1
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 2 | "2021-08-20T16:41:13Z" | "2021-08-20T17:38:00Z" | null | MEMBER | null | We should unify the process of creating the dataframe from index,names,data for the C and Python engines. This should be done after the pyarrow engine is merged, and will help get rid of some code nested within an if statement. Ideally, we make a method on ParserBase, and call that in the C and Python engine read code so that read always returns a DataFrame now. | {
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A unit test has been added to check the validity of pd.Categorical() . This PR is with respect to the GitHub issue no. 32414.
Thanks
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} | 0 | "2021-08-20T17:02:05Z" | "2021-08-21T02:09:22Z" | "2021-08-21T01:24:08Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43138 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43138/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43138/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43138/events | https://github.com/pandas-dev/pandas/pull/43138 | 975,778,421 | MDExOlB1bGxSZXF1ZXN0NzE2ODc5NjY3 | 43,138 | REF: remove create_block_manager_from_arrays | {
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} | 2 | "2021-08-20T17:03:49Z" | "2021-08-21T01:59:31Z" | "2021-08-21T01:52:18Z" | MEMBER | null | Looks like dask uses create_block_manager_from_blocks, but I haven't seen any evidence that any downstream package uses create_block_manager_from_arrays. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43139 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43139/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43139/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43139/events | https://github.com/pandas-dev/pandas/issues/43139 | 975,808,174 | MDU6SXNzdWU5NzU4MDgxNzQ= | 43,139 | BUG: Exactly same if check multiple times | {
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] | closed | false | null | [] | null | 2 | "2021-08-20T17:51:02Z" | "2021-08-20T18:16:29Z" | "2021-08-20T18:16:28Z" | CONTRIBUTOR | null | Hi, I came across the below while working on another bug.
https://github.com/pandas-dev/pandas/blob/e8f359348a601f303e19dc33fc66f0b602efce50/pandas/core/groupby/groupby.py#L2445-L2450 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43140 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43140/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43140/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43140/events | https://github.com/pandas-dev/pandas/issues/43140 | 975,887,765 | MDU6SXNzdWU5NzU4ODc3NjU= | 43,140 | pd.to_datetime returning string result (even with errors='raise') | {
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] | closed | false | null | [] | null | 13 | "2021-08-20T20:06:02Z" | "2021-08-24T08:53:20Z" | "2021-08-24T08:53:19Z" | NONE | null | Hello,
I've got a dataset and I can convert to naive time just fine, but I would like to include timezone information.
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : 5.4.3
hypothesis : 6.8.1
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.1.4
tables : None
tabulate : None
xarray : 0.18.2
xlrd : 2.0.1
xlwt : None
numba : 0.52.0
### Data
```
import pandas as pd
# https://nwis.waterdata.usgs.gov/usa/nwis/uv/?cb_00060=on&cb_00065=on&format=rdb&site_no=09333500&period=&begin_date=2000-01-01&end_date=2020-09-28
url = 'https://github.com/mattharrison/datasets/raw/master/data/dirtydevil.txt'
df = pd.read_csv(url, skiprows=lambda num: num <34 or num == 35, sep='\t')
```
The data has a *datetime* column and and a *tz_cd* column. I would like to combine them to get tz specific dates.
| | agency_cd | site_no | datetime | tz_cd | 144166_00060 | 144166_00060_cd | 144167_00065 | 144167_00065_cd |
|---:|:------------|----------:|:-----------------|:--------|---------------:|:------------------|---------------:|------------------:|
| 0 | USGS | 9333500 | 2001-05-07 01:00 | MDT | 71 | A:[91] | nan | nan |
| 1 | USGS | 9333500 | 2001-05-07 01:15 | MDT | 71 | A:[91] | nan | nan |
| 2 | USGS | 9333500 | 2001-05-07 01:30 | MDT | 71 | A:[91] | nan | nan |
| 3 | USGS | 9333500 | 2001-05-07 01:45 | MDT | 70 | A:[91] | nan | nan |
| 4 | USGS | 9333500 | 2001-05-07 02:00 | MDT | 70 | A:[91] | nan | nan |
### Attempt 1
```pd.to_datetime((df.datetime+df.tz_cd).iloc[:5000])```
Appears to work, but takes a LONG time (hence the ``.iloc`` and this issue).
*Edit* My machine takes 2min 3s to convert all of the column (539,305 entries) to TZ aware. However, just converting to naive datetime takes 54ms! My attempts below are trying to lower that gap.
### Attempt 2 - Use ``format`` string
``pd.to_datetime((df.datetime+df.tz_cd).iloc[8730:8736],format='%Y-%m-%d %H:%M%Z', errors='raise')``
Fails with the exception:
```---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
508 try:
--> 509 values, tz = conversion.datetime_to_datetime64(arg)
510 dta = DatetimeArray(values, dtype=tz_to_dtype(tz))
~/envs/menv/lib/python3.8/site-packages/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-5-853da2332591> in <module>
1 # Can't use Z to speed up
----> 2 pd.to_datetime((df.datetime+df.tz_cd).iloc[8730:8736],format='%Y-%m-%d %H:%M%Z', errors='raise')
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
885 result = arg.map(cache_array)
886 else:
--> 887 values = convert_listlike(arg._values, format)
888 result = arg._constructor(values, index=arg.index, name=arg.name)
889 elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
391
392 if format is not None:
--> 393 res = _to_datetime_with_format(
394 arg, orig_arg, name, tz, format, exact, errors, infer_datetime_format
395 )
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
511 return DatetimeIndex._simple_new(dta, name=name)
512 except (ValueError, TypeError):
--> 513 raise err
514
515
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_format(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)
498
499 # fallback
--> 500 res = _array_strptime_with_fallback(
501 arg, name, tz, fmt, exact, errors, infer_datetime_format
502 )
~/envs/menv/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _array_strptime_with_fallback(arg, name, tz, fmt, exact, errors, infer_datetime_format)
434
435 try:
--> 436 result, timezones = array_strptime(arg, fmt, exact=exact, errors=errors)
437 if "%Z" in fmt or "%z" in fmt:
438 return _return_parsed_timezone_results(result, timezones, tz, name)
~/envs/menv/lib/python3.8/site-packages/pandas/_libs/tslibs/strptime.pyx in pandas._libs.tslibs.strptime.array_strptime()
ValueError: time data '2001-10-27 23:30MDT' does not match format '%Y-%m-%d %H:%M%Z' (match)
```
I've scoured the web and cannot find any examples of parsing dates using the the TZ names that work. Ideas?
*EDIT* - I'll anwser my own question here. For TZ names, you need to use what `pytz` supports. The following code get close, but fails because I have "ambiguous" dates and need to pass an `ambiguous` parameter at a lower level, but the parameter isn't exposed. See Attempt 4 for different option that exposes `ambiguous`
```
pd.to_datetime((df.datetime+df.tz_cd.str.replace('MDT', 'MST7MDT')),format='%Y-%m-%d %H:%M%Z', errors='raise')
```
### Attempt 3 - Convert TZ names to offsets
This appears to work
```
pd.to_datetime((df.datetime + df.tz_cd.str.replace('MST', '-07:00').str.replace('MDT', '-06:00')).iloc[8730:8736],
format='%Y-%m-%d %H:%M%z', errors='raise')
```
However, when I change the offsets, it fails (fails in that it returns strings and doesn't raise an error!):
```
# Fails with -07:00
pd.to_datetime((df.datetime + df.tz_cd.str.replace('MST', '-07:00').str.replace('MDT', '-06:00')).iloc[8730:8737],
format='%Y-%m-%d %H:%M%z', errors='raise')
```
Here is the output:
```
8730 2001-10-27 23:30:00-06:00
8731 2001-10-27 23:45:00-06:00
8732 2001-10-28 00:00:00-06:00
8733 2001-10-28 00:15:00-06:00
8734 2001-10-28 00:30:00-06:00
8735 2001-10-28 00:45:00-06:00
8736 2001-10-28 01:00:00-07:00
dtype: object
```
### (EDIT) Attempt 4 - @jreback's advice to use `.transform`
I was pointed to https://stackoverflow.com/questions/47822265/efficiently-convert-timezones-in-pandas-dataframe which suggested this code
```
(df
.assign(datetime=pd.to_datetime(df.datetime))
.iloc[8730:8737] # works with end slice 8736
.groupby('tz_cd')
.datetime
.transform(lambda g: g.dt.tz_localize(g.name.replace('MDT', 'MST7MDT')))#, ambiguous=False))
)
```
This runs much quicker (about 3-5 sec on the whole dataset), but also fails and gives the same result as attempt 3, a series with strings instead of objects.
### Summary
I would like to quickly and consistently parse tz information. Would appreciate any insight or advice. (Also, shouldn't ``errors='raise'`` cause an Exception in this case?)
Thanks much! 🙏
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} | 4 | "2021-08-20T21:08:02Z" | "2021-08-21T02:13:26Z" | "2021-08-21T00:11:31Z" | NONE | null | #### Is your feature request related to a problem?
Currently using Pandas on Python 3.10 RC 1 gets the wrong version of numpy (1.19) and builds the wheel manually, instead of getting the first wheel for NumPy + Python 3.10 (1.21).
#### Describe the solution you'd like
This problem has already been solved by https://github.com/scipy/oldest-supported-numpy/blob/master/setup.cfg - by using SciPy (the org, not the package)'s oldest-supported-numpy, you get a thorough and up-to-date listing of the oldest supported numpy's for compiling. You can further control it using normal requirements (such as setting a higher requirement for some version, etc). That's literally all the package does, and is maintained by NumPy maintainers.
#### API breaking implications
None, just a build improvement.
#### Describe alternatives you've considered
Otherwise, the contents of https://github.com/scipy/oldest-supported-numpy/blob/master/setup.cfg should be placed in pyproject.toml and maintained for each new Python release - 3.10 is currently missing.
#### Additional context
Before:
```python
# Numpy requirements for different OS/architectures
# Copied from https://github.com/scipy/scipy/blob/master/pyproject.toml (which is also licensed under BSD)
"numpy==1.17.3; python_version=='3.7' and (platform_machine!='arm64' or platform_system!='Darwin') and platform_machine!='aarch64'",
"numpy==1.18.3; python_version=='3.8' and (platform_machine!='arm64' or platform_system!='Darwin') and platform_machine!='aarch64'",
"numpy==1.19.3; python_version>='3.9' and (platform_machine!='arm64' or platform_system!='Darwin') and platform_machine!='aarch64'",
# Aarch64(Python 3.9 requirements are the same as AMD64)
"numpy==1.19.2; python_version=='3.7' and platform_machine=='aarch64'",
"numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64'",
# Darwin Arm64
"numpy>=1.20.0; python_version=='3.8' and platform_machine=='arm64' and platform_system=='Darwin'",
"numpy>=1.20.0; python_version=='3.9' and platform_machine=='arm64' and platform_system=='Darwin'"
```
After:
```python
"oldest-supported-numpy"
```
(Optionally pinned if you want) | {
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} | 0 | "2021-08-20T21:13:01Z" | "2021-08-21T01:12:43Z" | "2021-08-21T01:07:14Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_ctor import *
self = FromArrays()
self.setup()
%timeit self.time_frame_from_arrays_int()
2.77 ms ± 129 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.11 ms ± 57.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
``` | {
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] | open | false | null | [] | null | 7 | "2021-08-20T22:54:53Z" | "2021-08-28T12:22:12Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
It doesn't seem to be based on a search for read_excel and indexerror
- [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
Given an excel file with one tab that contains the text `header` in cell A1 and nothing else:
```python
In [1]: import pandas
In [2]: pandas.read_excel("~/temp/test_pandas/headers.xlsx", header=[0, 1])
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
*snip - see comment below for full traceback*
564 if index_col is not None:
IndexError: list index out of range
```
#### Problem description
The code throws a (presumably unintended) IndexError, which is difficult to respond to as the actual cause of the error is not specified.
#### Expected Output
A `ParserError` (or other appropriate error type) with an error message that makes the cause of the problem clear.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-154-generic
Version : #161-Ubuntu SMP Fri Jul 30 13:04:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.3
setuptools : 51.3.3
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 : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 5 | "2021-08-21T00:17:09Z" | "2021-08-23T01:47:48Z" | "2021-08-23T00:49:41Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_ctor import *
self = FromArrays()
self.setup()
%timeit self.time_frame_from_arrays_int()
2.28 ms ± 67 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
1.82 ms ± 5.34 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
```
Orthogonal to #43142, one more optimization after these two are in. | {
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} | 8 | "2021-08-21T02:19:26Z" | "2021-11-11T01:38:11Z" | "2021-09-01T19:36:43Z" | MEMBER | null | - [x] closes #43075
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
My bad on this one, pinning one of the CI to ensure that this works. | {
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- [x] closes #40307
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Two things to consider: first, for Python 3.7, this sets an older _build time_ requirement for NumPy than is there currently. According to the NumPy devs, this is fine, older build time requirements than the run requirement is okay. But we could use selectors and manually set NumPy for this one case, it's not something that changes.
Second, we could pin here, but given that this just pins old versions, I don't think it needs pinning. We could set a minimum, though, >=0.10 for example. | {
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} | 0 | "2021-08-21T04:45:25Z" | "2021-08-21T15:24:51Z" | "2021-08-21T14:11:37Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_ctor import *
self = FromDicts()
self.setup()
%timeit self.time_dict_of_categoricals()
# 530 µs ± 21.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
# 471 µs ± 2.85 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
``` | {
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} | 1 | "2021-08-21T11:57:46Z" | "2021-09-01T05:45:49Z" | "2021-08-31T23:58:07Z" | CONTRIBUTOR | null | - [x] closes #33090
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 46 | "2021-08-21T14:20:06Z" | "2021-09-10T17:32:15Z" | "2021-09-09T17:59:38Z" | CONTRIBUTOR | null | - [x] closes #43108 and #42395
It sets the numeric_only value based on the columns on which aggregate functions are applied
- [x] tests added / passed
New Test is added to `test_groupby_aggregation_mixed_dtype` function
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Checking the column DataTypes in `_resolve_numeric_only` function
I am absolutely new to contributing on Open Source . So please guide me through it .
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] | open | false | null | [] | null | 17 | "2021-08-21T15:09:58Z" | "2021-10-18T03:30:40Z" | null | CONTRIBUTOR | null | Hi Folks,
I'm experimenting with a new shuffle algorithm for Dask dataframe. This is what backs distributed versions of join, set_index, groupby-apply, or anything that requires the large movement of rows around a distributed dataframe.
Things are coming along well, but I'm running into a performance challenge with pandas and I would like to solicit feedback here before diving more deeply. If this isn't the correct venue please let me know and I'll shift this elsewhere.
We've constructed a script (thanks @gjoseph92 for starting this) that creates a random dataframe and a column on which to split, rearrange, serialize/deserialize, and concat a couple of times. This is representative of the operations that we're trying to do, except that in between a couple of steps there the shards/groups end up coming from different machines, rather than being the same shards
```python
import time
import random
import pickle
import numpy as np
import pandas as pd
# Parameters
n_groups = 10_000
n_cols = 1000
n_rows = 30_000
# Make input data
df = pd.DataFrame(np.random.random((n_rows, n_cols)))
df["partitions"] = (df[0] * n_groups).astype(int) # random values 0..10000
start = time.time()
_, groups = zip(*df.groupby("partitions")) # split into many small shards
groups = list(groups)
random.shuffle(groups) # rearrange those shards
groups = [pickle.dumps(group) for group in groups] # Simulate sending across the network
groups = [pickle.loads(group) for group in groups]
df = pd.concat(groups) # reassemble shards
_, groups = zip(*df.groupby("partitions")) # and resplit
stop = time.time()
import dask
print(dask.utils.format_bytes(df.memory_usage().sum() / (stop - start)), "/s")
```
With 10,000 groups I get around 40 MB/s bandwidth.
With 1,000 groups I get around 230 MB/s bandwidth
With 200 or fewer groups I get around 500 MB/s bandwidth
Obviously, one answer here is "use fewer groups, Pandas isn't designed to operate efficiently with only a few rows". That's fair and we're trying to design for that, but there is always pressure to shrink things down so I would like to explore and see if there is anything we can do on the pandas side to add tolerance here. | {
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Related to #43155 I'm running into memory issues when pickling many small pandas dataframes. The following script creates a pandas dataframe, splits it up, and then pickles each little split and brings them back again. It then deletes all objects from memory but something is still sticking around. Here is the script, followed by the outputs of using `memory_profiler`
```python
import numpy as np
import pandas as pd
import pickle
@profile
def test():
df = pd.DataFrame(np.random.random((20000, 1000)))
df["partitions"] = (df[0] * 10000).astype(int)
_, groups = zip(*df.groupby("partitions"))
del df
groups = [pickle.dumps(group) for group in groups]
groups = [pickle.loads(group) for group in groups]
del groups
if __name__ == "__main__":
test()
```
```
python -m memory_profiler memory_issue.py
```
```
Filename: memory_issue.py
Line # Mem usage Increment Occurences Line Contents
============================================================
7 76.574 MiB 76.574 MiB 1 @profile
8 def test():
9 229.445 MiB 152.871 MiB 1 df = pd.DataFrame(np.random.random((20000, 1000)))
10 230.738 MiB 1.293 MiB 1 df["partitions"] = (df[0] * 10000).astype(int)
11 398.453 MiB 167.715 MiB 1 _, groups = zip(*df.groupby("partitions"))
12 245.633 MiB -152.820 MiB 1 del df
13
14 445.688 MiB 47.273 MiB 8631 groups = [pickle.dumps(group) for group in groups]
15 712.285 MiB 266.598 MiB 8631 groups = [pickle.loads(group) for group in groups]
16
17 557.488 MiB -154.797 MiB 1 del groups
```
As you can see, we start with 70 MiB in memory and end with 550 MiB, despite all relevant objects being released. This leak increases with the number of groups (scale the 10000 number to move the leak up or down). Any help or pointers on how to track this down would be welcome.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This cleans up the SDist build slightly. First, it uses PyPA's "build" instead of directly calling `setup.py`, which will respect PEP 518 requirements and avoids the workaround of installing NumPy to be able to run setup.py. It also uses pipx to avoid needless setup; PyPA's pipx is provides as a first-class package manager on GitHub Actions (and Azure, they share images).
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} | 1 | "2021-08-21T16:19:31Z" | "2021-08-27T01:47:16Z" | "2021-08-26T23:11:36Z" | MEMBER | null | xref #42951, #43009
Screenshot of current version:
<img width="829" alt="Screen Shot 2021-08-21 at 12 12 44 PM" src="https://user-images.githubusercontent.com/37011898/130328264-29ea829e-b664-4a68-bb7d-8a3b21c5bfee.png">
https://github.com/mzeitlin11/issue-forms/issues if anyone wants to play around with opening issues with the form. | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Whats new::
Add a test to `pandas/test/dtypes/test_dtypes.py`
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} | 1 | "2021-08-22T04:02:07Z" | "2021-08-30T15:15:12Z" | "2021-08-30T15:13:02Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_methods import *
self = ToNumpy()
self.setup()
%timeit self.time_to_numpy_mixed_tall()
24.2 ms ± 324 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
11.8 ms ± 609 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
``` | {
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} | 15 | "2021-08-22T12:01:44Z" | "2021-11-16T17:33:53Z" | "2021-11-16T17:33:52Z" | CONTRIBUTOR | null | This is being worked towards in #41693. Please look at that issue and comment if there are specific features we need to keep/discuss. This is a medium term project I've been working on but would be great if someone else in team can be sounding board.
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] | closed | false | null | [] | null | 4 | "2021-08-22T12:14:11Z" | "2021-08-23T03:42:18Z" | "2021-08-23T03:42:18Z" | NONE | null | Hello,
i tried using [pandas.Series.searchsorted](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.searchsorted.html) method in series.
**Example-1**
```
input:
import pandas as pd
ser_order = pd.Series([1, 1, 2,3,4])
ser_order.searchsorted([2,3])
output:
array([2,3]) #correct result.
```
**Example-2**
```
input:
import pandas as pd
ser_order = pd.Series([2, 2,3,5,6,3,1])
ser_order.searchsorted([2,4])
output:
array([0,3]) #wrong result.
```
So above two example clearly shows that [pandas.Series.searchsorted](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.searchsorted.html) will work only for ordered array.if we could add parameter **ascending**,then we can resolve this issue.
Thanks | {
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} | 8 | "2021-08-22T12:22:52Z" | "2021-08-25T17:55:56Z" | "2021-08-25T17:55:55Z" | CONTRIBUTOR | null | I have fixed all the warning.warn messages in the entire code base for "Minor typo on warnings #39158" reported issue.
closes #39158
Summary of the changes .
[Fix-Tracking-Sheet (2).xlsx](https://github.com/pandas-dev/pandas/files/7030053/Fix-Tracking-Sheet.2.xlsx)
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] | open | false | null | [] | null | 5 | "2021-08-22T16:54:07Z" | "2021-09-30T17:58:52Z" | null | CONTRIBUTOR | null | Can someone confirm that this is a bug:
```
pd.options.display.chop_threshold = 0.5
pd.options.display.date_dayfirst = True
df = pd.DataFrame([[1.00, 0.1, 0.01, 0.001], [2.22, 2.22, 2.22, 2.22]],
index=pd.date_range(start="01-01-2000", periods=2))
print(df)
0 1 2 3
2000-01-01 1.00 0.00 0.00 0.00
2000-01-02 2.22 2.22 2.22 2.22
```
Also it seems weird to have the options `display.date_dayfirst` and `display.date_yearfirst` with both set to `False` by default, and yet print year first..
Docs say: `When True, prints and parses dates with the day first, eg 20/01/2005`. Maybe this is just for parsing? | {
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] | open | false | null | [] | null | 4 | "2021-08-22T17:16:05Z" | "2021-09-30T18:00:36Z" | null | NONE | null | I found the following behavior a bit unexpected from the name of the argument.
> The default (`copy=False`) may return a copy instead of a view. `copy=True` always returns a copy, but `copy=False` does not always return a view.
I think changing the argument name to something like `always_copy` would be more clear about what it is doing. | {
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} | 3 | "2021-08-22T18:20:34Z" | "2021-08-22T21:32:27Z" | "2021-08-22T21:30:23Z" | MEMBER | null | - [x] xref #43168
Aiobotocore got an upgrade from 1.3.3 to 1.4.0, looks like this broke our ci
This comes with s3fs. The new release handles the dependency, but is not on conda-forge yet | {
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Failure:
https://github.com/pandas-dev/pandas/runs/3394467341
Working:
https://github.com/pandas-dev/pandas/runs/3389494290
This comes as a dependency of s3fs, which changed the requirements already, but the 2021.08.0 release is not on conda-forge yet. We can remove the pin after it is available
https://github.com/dask/s3fs/blob/main/requirements.txt | {
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} | 1 | "2021-08-22T22:59:49Z" | "2021-08-23T23:12:14Z" | "2021-08-23T23:06:31Z" | MEMBER | null | - [x] closes #33036
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```
from asv_bench.benchmarks.rolling import *
self = Engine()
self.setup("DataFrame", "float", sum, "cython", "sum")
self.time_rolling_methods("DataFrame", "float", sum, "cython", "sum")
%timeit self.time_rolling_methods("DataFrame", "float", sum, "cython", "sum")
150 µs ± 889 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
224 µs ± 3.18 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
```
cc @mroeschke | {
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} | 2 | "2021-08-23T02:00:52Z" | "2021-08-26T21:16:33Z" | "2021-08-26T21:16:10Z" | CONTRIBUTOR | null | - [x] closes #42295
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
This makes sure that df.where does not cast floats to integers. | {
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} | 4 | "2021-08-23T03:32:35Z" | "2021-09-06T10:54:12Z" | "2021-08-31T19:54:23Z" | MEMBER | null | - [x] closes #43016
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43175 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43175/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43175/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43175/events | https://github.com/pandas-dev/pandas/issues/43175 | 976,706,185 | MDU6SXNzdWU5NzY3MDYxODU= | 43,175 | BUG: column with type float32 is skipped by rolling | {
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] | closed | false | null | [] | null | 1 | "2021-08-23T07:09:25Z" | "2021-08-23T07:21:07Z" | "2021-08-23T07:20:50Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
...: import numpy as np
...: arr = np.zeros((5,5))
...: df = pd.DataFrame(arr)
...: df[3] = 1
...: df[3] = df[3].astype(np.float32)
...: df.rolling(2, axis=1).max()
Out[1]:
0 1 2 4
0 NaN 0.0 0.0 0.0
1 NaN 0.0 0.0 0.0
2 NaN 0.0 0.0 0.0
3 NaN 0.0 0.0 0.0
4 NaN 0.0 0.0 0.0
In [2]: df[3] = df[3].astype(np.float64)
...: df.rolling(2, axis=1).max()
Out[2]:
0 1 2 3 4
0 NaN 0.0 0.0 1.0 1.0
1 NaN 0.0 0.0 1.0 1.0
2 NaN 0.0 0.0 1.0 1.0
3 NaN 0.0 0.0 1.0 1.0
4 NaN 0.0 0.0 1.0 1.0
```
#### Problem description
It seems that the column with type float32 is skipped by the operation rolling.
#### Expected Output
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
<details>
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-123-generic
Version : #126-Ubuntu SMP Wed Oct 21 09:40:11 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | [] | closed | false | null | [] | null | 0 | "2021-08-23T07:11:45Z" | "2021-08-23T08:02:43Z" | "2021-08-23T08:02:43Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.14.0-py2.py3-none-any.whl (191 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.13-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.7.2-py2.py3-none-any.whl (5.3 MB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting backports.entry-points-selectable>=1.0.4
Downloading backports.entry_points_selectable-1.1.0-py2.py3-none-any.whl (6.2 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting platformdirs<3,>=2
Downloading platformdirs-2.2.0-py3-none-any.whl (13 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.13 nodeenv-1.6.0 platformdirs-2.2.0 pre-commit-2.14.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.7.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.23.3 -> v2.24.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for local:pyyaml,toml.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
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[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
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[INFO] Installing environment for https://github.com/cpplint/cpplint.
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[INFO] Installing environment for https://github.com/PyCQA/isort.
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[INFO] Installing environment for https://github.com/asottile/pyupgrade.
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[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
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[INFO] Installing environment for local.
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absolufy-imports................................................................................................Passed
black...........................................................................................................Passed
codespell.......................................................................................................Passed
Debug Statements (Python).......................................................................................Passed
Fix End of Files................................................................................................Passed
Trim Trailing Whitespace........................................................................................Passed
cpplint.........................................................................................................Passed
flake8..........................................................................................................Passed
flake8 (cython).................................................................................................Passed
flake8 (cython template)........................................................................................Passed
isort...........................................................................................................Passed
pyupgrade.......................................................................................................Passed
rst ``code`` is two backticks...................................................................................Passed
rst directives end with two colons..............................................................................Passed
rst ``inline code`` next to normal text.........................................................................Passed
Strip unnecessary `# noqa`s.....................................................................................Passed
flake8-rst......................................................................................................Passed
Unwanted patterns...............................................................................................Passed
Check Cython casting is `<type>obj`, not `<type> obj`...........................................................Passed
Check for backticks incorrectly rendering because of missing spaces.............................................Passed
Check for unnecessary random seeds in asv benchmarks............................................................Passed
Check for invalid EA testing....................................................................................Passed
Generate pip dependency from conda..............................................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml........................................Passed
Validate correct capitalization among titles in documentation...................................................Passed
Import pandas.array as pd_array in core.........................................................................Passed
Use bool_t instead of bool in pandas/core/generic.py............................................................Passed
Ensure pandas errors are documented in doc/source/reference/general_utility_functions.rst.......................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
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} | 0 | "2021-08-23T13:23:45Z" | "2021-09-01T16:22:47Z" | "2021-09-01T15:53:48Z" | CONTRIBUTOR | null | This pandas option is added to allow the `Styler` options to be independent from the others. See #41395
- [x] add option
- [x] add tests
- [x] whats new
- [x] update function docstrings `to_latex` and `to_html`
will update styler user guide (do as a follow on).
encoding also applies to saving to files where appropriate, as well as HTML metatag
```python
>>> df = pd.DataFrame([[1,2],[3,4]])
>>> print(df.style.to_html(doctype_html=True)[:76])
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<style type="text/css">
>>> pd.options.styler.render.encoding = "ANSI"
>>> print(df.style.to_html(doctype_html=True)[:75])
<!DOCTYPE html>
<html>
<head>
<meta charset="ANSI">
<style type="text/css">
```
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} | 4 | "2021-08-23T13:26:13Z" | "2021-08-26T11:10:54Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
>>> x = pd.Series([pd.Timedelta(100000, "days"), pd.Timedelta(100000, "days")])
# Results in ValueError
>>> x.sum()
# Results in OverflowError
>>> pd.Timedelta(100000, "days")*2
# Results in OverflowError
>>> x + x
# Permits overflow
>>> x*2
0 -13504 days +00:25:26.290448384
1 -13504 days +00:25:26.290448384
dtype: timedelta64[ns]
```
#### Problem description
It would seem that pandas.Series calcs with Timedeltas are inconsistent in raising overflow errors, or not.
The exact error is also inconsistent (OverflowError vs ValueError).
#### Expected Output
Ideally `x*2` in above example raises an error, and all overflow related errors are of type OverflowError.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
The major issue is that the parser is reading 1 as a boolean expression and not as an integer.
So, making 2 different tests based on the data type of the input or return of a function can help split the parsing as two seperate entities.
Reference: https://github.com/pandas-dev/pandas/pull/42944 | {
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} | 2 | "2021-08-23T13:35:35Z" | "2021-09-02T05:13:11Z" | "2021-09-01T23:03:25Z" | CONTRIBUTOR | null | offers option switching between "html" and "latex" output.
adds items to #41395.
also xref #39911 (technically after DataFrame.to_html is deprecated)
- [x] adds option
- [x] adds tests
- [x] updates options.rst docs
- [x] no public method docstrings
- [x] whatsnew
update to styler user guide as follow-on after all options are merged.
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} | 5 | "2021-08-23T14:28:37Z" | "2021-09-03T11:32:42Z" | "2021-08-24T12:55:15Z" | MEMBER | null | - [ ] closes #42946
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
IMPORTANT: This depends on MacPython/pandas-wheels#152, so please merge that first.
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} | 2 | "2021-08-23T15:10:35Z" | "2021-09-14T18:29:26Z" | "2021-09-14T18:17:49Z" | MEMBER | null | Follow-up cleanup to get_indexer work | {
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} | 10 | "2021-08-23T15:30:56Z" | "2021-10-17T11:04:06Z" | "2021-10-17T11:04:06Z" | NONE | null | #### Code Sample, a copy-pastable example
This test will pass on pandas 1.2.5 but fail on 1.3 onwards (checked 1.3, 1.3.2).
```python
def test_convert_dtypes_on_byte_string():
byte_str = b'binary-string'
dataframe = pd.DataFrame(
data={
"data": byte_str,
},
index=[0]
)
converted_dtypes = dataframe.convert_dtypes()
assert converted_dtypes['data'][0].decode('ascii') == "binary-string"
```
#### Problem description
Error on 1.3 onwards:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 10, in test_convert_dtypes_on_byte_string
AttributeError: 'str' object has no attribute 'decode'
```
The issue is that converted_dtypes() will convert the `data` column values to the string `b'binary-data'`, almost like it has had `str(val)` called on it. On 1.2.5 it is left correctly as a byte array.
#### Expected Output
Test should pass. Byte strings should not be converted to string.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.11.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : 0.7.0
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 2021.07.0
scipy : None
sqlalchemy : 1.4.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-08-23T16:52:36Z" | "2021-08-26T17:19:27Z" | "2021-08-26T17:04:55Z" | CONTRIBUTOR | null | - [x] closes #42145
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
This has been resolved in the master. I think adding a test will close it. | {
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} | 1 | "2021-08-23T17:03:28Z" | "2021-08-25T19:12:57Z" | "2021-08-25T19:08:27Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
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} | 6 | "2021-08-23T21:10:15Z" | "2021-09-08T01:54:45Z" | "2021-09-08T01:54:01Z" | CONTRIBUTOR | null | - [x] closes #38015
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This is my first real contribution, I appreciate the feedback! | {
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} | 5 | "2021-08-23T23:03:30Z" | "2021-08-26T15:33:19Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas (1.3.1)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# These 2-adic invocations don't fail and return a Series/DataFrame/ndarray respectively:
np.fmin(pd.Series(np.array([1, 2])), pd.Series(np.array([3, 4])))
np.fmin(pd.DataFrame(np.array([[1, 2]])), pd.DataFrame(np.array([[3, 4]])))
np.fmin(np.array([1, 2]), np.array([3, 4]), np.array([5, 6]))
# These 3-adic invocations fail (RecursionError: maximum recursion depth exceeded)
np.fmin(pd.Series(np.array([1, 2])), pd.Series(np.array([3, 4])), pd.Series(np.array([5, 6])))
np.minimum(pd.Series(np.array([1, 2])), pd.Series(np.array([3, 4])), pd.Series(np.array([5, 6])))
np.fmin(pd.DataFrame(np.array([[1, 2]])), pd.DataFrame(np.array([[3, 4]])), pd.DataFrame(np.array([[5, 6]])))
```
#### Problem description
I'm not sure if this is strictly a Pandas bug since it involves a Numpy function, but the infinite recursion goes through Pandas functions only so it seems like the only place it could be solved is within Pandas itself.
The end of the stack trace looks like this:
```
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\generic.py", line 1936, in __array_ufunc__
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\arraylike.py", line 358, in array_ufunc
result = getattr(ufunc, method)(*inputs, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\generic.py", line 1936, in __array_ufunc__
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\arraylike.py", line 358, in array_ufunc
result = getattr(ufunc, method)(*inputs, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\generic.py", line 1936, in __array_ufunc__
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\arraylike.py", line 357, in array_ufunc
inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\arraylike.py", line 357, in <genexpr>
inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
File "C:\Users\mboling\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\construction.py", line 396, in extract_array
if isinstance(obj, (ABCIndexClass, ABCSeries)):
```
(This RecursionError actually manifested as a segfault in my application so was a bit of a pain to track down.)
#### Expected Output
The failing examples involving `Series` should evaluate to `pd.Series([1, 2])`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43192/events | https://github.com/pandas-dev/pandas/pull/43192 | 977,590,336 | MDExOlB1bGxSZXF1ZXN0NzE4MzE0NzI3 | 43,192 | DOC: OSX -> macOS | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43194 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43194/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43194/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43194/events | https://github.com/pandas-dev/pandas/issues/43194 | 977,730,537 | MDU6SXNzdWU5Nzc3MzA1Mzc= | 43,194 | Ambiguous behaviour when index is bool type? | {
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] | open | false | null | [] | null | 6 | "2021-08-24T05:51:51Z" | "2021-09-30T23:00:23Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
pandas.Series([True, True, False]).value_counts()[[False]]
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
```python
pandas/core/indexers.py in check_array_indexer(array, indexer)
468 # GH26658
469 if len(indexer) != len(array):
--> 470 raise IndexError(
471 f"Boolean index has wrong length: "
472 f"{len(indexer)} instead of {len(array)}"
IndexError: Boolean index has wrong length: 1 instead of 2
```
It seems that `Series.__getitem__` and `check_bool_indexer` do not work well for bool-typed index
#### Expected Output
```python
<pandas.Series>
False 1
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Wed Jun 23 00:26:31 PDT 2021; root:xnu-7195.141.2~5/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43195/events | https://github.com/pandas-dev/pandas/issues/43195 | 977,848,938 | MDU6SXNzdWU5Nzc4NDg5Mzg= | 43,195 | BUG: Implementation of is_list_like causes TypeError when working with Extension Arrays of ambiguous type | {
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- [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
# Your code here
import pint
import pandas as pd
import pint_pandas
import numpy as np
import datetime
u = pint.UnitRegistry()
Q = pint.Quantity(np.random.rand(20), u.hertz)
df = pd.DataFrame(Q , dtype='pint[hertz]')
limit1 = -0.4*u.hertz
limit2 = 4*u.hertz
df.clip(limit1, limit2)
```
#### Problem description
This makes it difficult for downstream libraries to correctly advertise their type when they must be able to accommodate array-like data or scalar date. In addition, having dtype information (important for pint in particular because it handles physical units) makes this type of class evaluate true for `is_array_like` even when it is a scalar, due to `is_array_like` using `is_list_like`.
Since Quantity does not know what type of data it will receive, it has an `iter` function. Calling this function - and thus allowing it to advertise whether it has scalar or array-like data - instead of checking for the presence of an iter function would make is_list_like more robust.
<details> <summary>Traceback</summary>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-13539a13f5ad> in <module>
----> 1 df.diff().clip(limit1, limit2)
~/opt/anaconda3/envs/aug20/lib/python3.9/site-packages/pandas/core/generic.py in clip(self, lower, upper, axis, inplace, *args, **kwargs)
7713 result = self
7714 if lower is not None:
-> 7715 result = result._clip_with_one_bound(
7716 lower, method=self.ge, axis=axis, inplace=inplace
7717 )
~/opt/anaconda3/envs/aug20/lib/python3.9/site-packages/pandas/core/generic.py in _clip_with_one_bound(self, threshold, method, axis, inplace)
7586 return self._clip_with_scalar(threshold, None, inplace=inplace)
7587
-> 7588 subset = method(threshold, axis=axis) | isna(self)
7589
7590 # GH #15390
~/opt/anaconda3/envs/aug20/lib/python3.9/site-packages/pandas/core/ops/__init__.py in f(self, other, axis, level)
447 axis = self._get_axis_number(axis) if axis is not None else 1
448
--> 449 self, other = align_method_FRAME(self, other, axis, flex=True, level=level)
450
451 new_data = self._dispatch_frame_op(other, op, axis=axis)
~/opt/anaconda3/envs/aug20/lib/python3.9/site-packages/pandas/core/ops/__init__.py in align_method_FRAME(left, right, axis, flex, level)
255 elif is_list_like(right) and not isinstance(right, (ABCSeries, ABCDataFrame)):
256 # GH 36702. Raise when attempting arithmetic with list of array-like.
--> 257 if any(is_array_like(el) for el in right):
258 raise ValueError(
259 f"Unable to coerce list of {type(right[0])} to Series/DataFrame"
~/opt/anaconda3/envs/aug20/lib/python3.9/site-packages/pint/quantity.py in __iter__(self)
262 # as one calls iter(self) without waiting for the first element to be drawn from
263 # the iterator
--> 264 it_magnitude = iter(self.magnitude)
265
266 def it_outer():
TypeError: 'float' object is not iterable
```
</details>
#### Expected Output
Expected output is that the values of `df` should be constrained between `limit1` and `limit2`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2. \#Also tested on 1.4.0.dev0+496.g8814de9fbc with same result.
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : 6.13.14
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 1 | "2021-08-24T09:06:11Z" | "2021-09-30T23:01:42Z" | null | NONE | null | #### Is your feature request related to a problem?
Yes, this would improve predictability of behavior with pandas extension arrays.
#### Describe the solution you'd like
`pandas.api.types.is_list_like` can check for iterability in a more pythonic way according to https://docs.python.org/3/glossary.html#term-iterable. This would result in the appropriate call being `iter(obj)` rather than checking for `__iter__` attribute.
#### API breaking implications
This will not affect the API.
#### Describe alternatives you've considered
Alternative solutions include communicating to third parties that they must change their extension arrays to include `hasattr(q, "__iter__") == False` to make sure that classes with ambiguous data iterability (may contain either scalar or array-like data) are perceived as utterable when they contain scalar data.
#### Additional context
Opened a bug report for this issue. I think an enhancement request is more appropriate, but I am linking the previous (closed) bug report as it includes useful information.
#43195
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my name is Zbigniew Królikowski. I am a software developer at VirtusLab.
Together with my colleagues we started working on our own [pandas type stubs library](https://github.com/VirtusLab/pandas-stubs), and at some point decided to make it open-source.
We wanted to ask you:
1. Is the separation between our library and pandas itself clear? We wanted to be clear on the authorship so we added a [disclaimer](https://github.com/VirtusLab/pandas-stubs).
2. Do you see any issues with how we promote our library, especially in terms of our graphics?
We'd be very grateful to hear your thoughts on this.
Best regards,
Zbigniew Królikowski | {
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] | open | false | null | [] | null | 0 | "2021-08-24T12:30:26Z" | "2021-09-30T23:05:02Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas. (1.3.1)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# ValueError: Values falls before first bin
pd.Series(index=pd.DatetimeIndex(['2013-04-01 22:00:00'])).resample('2D', closed='right').max()
# Works OK
pd.Series(index=pd.DatetimeIndex(['2013-04-01 22:00:00'])).resample('1D', closed='right').max()
pd.Series(index=pd.DatetimeIndex(['2013-04-01 22:00:00'])).resample('2D').max()
```
(Any number of days > 1 shows the same bug)
Stack trace from failure:
<details>
```
ValueError Traceback (most recent call last)
<ipython-input-171-dd7aa6747f09> in <module>
----> 1 itensors.unwrap_1d(pd.Series(np.arange(len(de.times)), index=de.times).resample('14D', closed='right', label='right').max())
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in f(self, _method, min_count, *args, **kwargs)
955 def f(self, _method=method, min_count=0, *args, **kwargs):
956 nv.validate_resampler_func(_method, args, kwargs)
--> 957 return self._downsample(_method, min_count=min_count)
958
959 f.__doc__ = getattr(GroupBy, method).__doc__
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in _downsample(self, how, **kwargs)
1051 **kwargs : kw args passed to how function
1052 """
-> 1053 self._set_binner()
1054 how = self._get_cython_func(how) or how
1055 ax = self.ax
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in _set_binner(self)
193 """
194 if self.binner is None:
--> 195 self.binner, self.grouper = self._get_binner()
196
197 def _get_binner(self):
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in _get_binner(self)
200 has already been called.
201 """
--> 202 binner, bins, binlabels = self._get_binner_for_time()
203 assert len(bins) == len(binlabels)
204 bin_grouper = BinGrouper(bins, binlabels, indexer=self.groupby.indexer)
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in _get_binner_for_time(self)
1040 if self.kind == "period":
1041 return self.groupby._get_time_period_bins(self.ax)
-> 1042 return self.groupby._get_time_bins(self.ax)
1043
1044 def _downsample(self, how, **kwargs):
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\resample.py in _get_time_bins(self, ax)
1526
1527 # general version, knowing nothing about relative frequencies
-> 1528 bins = lib.generate_bins_dt64(
1529 ax_values, bin_edges, self.closed, hasnans=ax.hasnans
1530 )
pandas\_libs\lib.pyx in pandas._libs.lib.generate_bins_dt64()
ValueError: Values falls before first bin
```
</details>
#### Problem description
If nothing falls exactly on the bin edge, `closed=right` should give exactly the same results as `closed=left`. However, for this example the `closed=right` case just fails even though `closed=left` succeeds.
#### Expected Output
```
2013-04-01 NaN
Freq: D, dtype: float64
```
#### Output of ``pd.show_versions()``
(`pd.show_versions` doesn't work on my 1.3.1 install so this is from a different environment.)
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.0
</details>
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} | 45 | "2021-08-24T13:42:46Z" | "2021-10-17T16:56:34Z" | "2021-10-17T11:04:06Z" | CONTRIBUTOR | null | - [x] closes #43183
- [x] tests added / passed in the test_convert_dtypes.py file
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Added extra try except block to identify the correct type to solve the problem. | {
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} | 1 | "2021-08-24T16:27:50Z" | "2021-08-25T19:11:46Z" | "2021-08-25T19:04:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 4 | "2021-08-24T16:59:23Z" | "2021-10-06T01:08:43Z" | "2021-10-06T01:08:43Z" | NONE | null | - [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version of pandas.
- [ x ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import functools
class SubclassedSeries(pd.Series):
@property
def _constructor(self):
return SubclassedSeries
@property
def _constructor_expanddim(self):
return SubclassedDataFrame
class SubclassedDataFrame(pd.DataFrame):
_metadata = ['my_extra_data']
def __init__(self, my_extra_data, *args, **kwargs):
self.my_extra_data = my_extra_data
super().__init__(*args, **kwargs)
@property
def _constructor(self):
return functools.partial(self.__class__, self.my_extra_data)
@property
def _constructor_sliced(self):
return SubclassedSeries
x = SubclassedDataFrame("some_data", {"A":[1,2,3], "B":[4,5,6]})
x*2
```
#### Problem description
This code raises an exception, ultimately because function ```_dispatch_frame_op``` in \pandas\core\frame.py returns ```type(self)(bm)```. Shouldn't instead return ```self._constructor(bm)```?
#### Expected Output
```python
A B
0 2 8
1 4 10
2 6 12
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-81-generic
Version : #91-Ubuntu SMP Thu Jul 15 19:09:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.2
numpy : 1.20.1
pytz : 2019.3
dateutil : 2.7.3
pip : 21.0.1
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-08-24T21:01:08Z" | "2021-08-24T21:15:06Z" | "2021-08-24T21:08:51Z" | 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
import pandas as pd
df = pd.DataFrame(
{
"A": pd.Series(1, index=list(range(2)))
}
)
# 0) Start with an empty test_dataset. test_table does not exist at this point.
# 1) This successfully creates a table named test_table in dataset test_dataset in project willyn_data_project
df.to_gbq('willyn_data_project.test_dataset.test_table', project_id='willyn_job_project')
# 2) This fails with pandas_gbq.gbq.NotFoundException: Table does not exist
df.to_gbq('willyn_data_project.test_dataset.test_table', project_id='willyn_job_project', if_exists='replace')
```
#### Problem description
I am working on a project where researchers can write to a dataset in one GCP project, but must run query jobs in another. In the above code example, `willyn_data_project` is the name of the project that contains the dataset, and `willyn_job_project` is the name of the project where query jobs are executed.
When attempting to use `if_exists='replace'`, we get the error `pandas_gbq.gbq.NotFoundException: Table does not exist`. I believe what is happening is we are attempting to delete a table from `willyn_job_project`, when we should be deleting a table in `willyn_data_project`.
#### Expected Output
No error.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.40-1rodete2-amd64
Version : #1 SMP Debian 5.10.40-1rodete2 (2021-06-22)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.4
setuptools : 52.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.15.0
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-08-25T02:47:24Z" | "2021-08-30T17:37:20Z" | "2021-08-30T15:56:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ O ] 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.
---
```python
import pandas as pd
series_index = ["a", "b", "c"]
series_data = [1.0, 0, "X"]
series = pd.Series(index=series_index, data=series_data)
df_from_series = series.to_frame().T
print(df_from_series["a"].dtype)
# >> dtype('O')
df_from_constructor = pd.DataFrame(columns=series.index, data=[series.values])
print(df_from_constructor["a"].dtype)
# >> dtype('float64')
```
#### Problem description
When converting a series to a single row dataframe, dtypes are not maintained. This cause a problem when a subsequent groupby with sum is called, which dropped the now non-numeric column.
Above behaviour is inconsistent to the resulting dataframe initialized from the DataFrame constructor with the series index and values attribute passed.
Above behaviour has not been observed in previous versions and I can see some transpose() related changes in the release-notes.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
Can neither log into github from my corporate machine nor send the version information out without getting compliance issues, but pandas is 1.3.2
</details>
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} | 1 | "2021-08-25T07:18:02Z" | "2021-09-06T15:19:37Z" | "2021-09-06T15:19:37Z" | NONE | null | (Using pandas 1.3.1 and numpy 1.20.3)
This is a repetition of the SO question found [here](https://stackoverflow.com/questions/68918019/pandas-groupby-with-dropna-set-to-true/68918101?noredirect=1#comment121798992_68918101). (I did not have this issue previously when working with version 1.1.3).
In the following snippet:
```
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6, 7, 8, 9],
"b": [1, np.nan, 1, np.nan, 2, 1, 2, np.nan, 1]
}
)
df_again = df.groupby("b", dropna=False).apply(lambda x: x)
```
I was expecting df and df_again to be identical. But they are not:
```
df
a b
0 1 1.0
1 2 NaN
2 3 1.0
3 4 NaN
4 5 2.0
5 6 1.0
6 7 2.0
7 8 NaN
8 9 1.0
df_again
a b
0 1 1.0
2 3 1.0
4 5 2.0
5 6 1.0
6 7 2.0
8 9 1.0
```
Now, if I tweak slightly the lambda expression slightly to "see" what is going on by `df.groupby("b", dropna=False).apply(lambda x: print(x))` I can actually visualize that also the portion of the `df` where `b` was `NaN` was processed. | {
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} | 14 | "2021-08-25T10:16:56Z" | "2021-10-16T19:29:54Z" | null | NONE | null | I see strange `groupby.apply` behaviour: resulting index depends on the existence of the `Timestamp` dtype column. MWE:
```python
import pandas as pd
df = pd.DataFrame({
'date_val': {
0: pd.Timestamp('2017-01-01 00:00:00'),
1: pd.Timestamp('2017-01-01 00:00:00'),
2: pd.Timestamp('2017-02-01 00:00:00'),
3: pd.Timestamp('2017-02-01 00:00:00')
},
'uid': {
0: '1',
1: '2',
2: '1',
3: '2'
},
'str_val': {
0: str('2017-01-01 00:00:00'),
1: str('2017-01-01 00:00:00'),
2: str('2017-02-01 00:00:00'),
3: str('2017-02-01 00:00:00')
}
})
df1 = df.groupby('uid', as_index=False)[['uid', 'str_val', 'date_val']].apply(lambda x: x.sort_values(by='str_val',ascending=True))
df2 = df.groupby('uid', as_index=False)[['uid', 'str_val']].apply(lambda x: x.sort_values(by='str_val',ascending=True))
df3 = df.groupby('uid', as_index=False)[['uid', 'date_val']].apply(lambda x: x.sort_values(by='date_val',ascending=True))
print(df1)
print(df2)
print(df3)
```
Output:
```
uid str_val date_val
0 1 2017-01-01 00:00:00 2017-01-01
1 2 2017-01-01 00:00:00 2017-01-01
2 1 2017-02-01 00:00:00 2017-02-01
3 2 2017-02-01 00:00:00 2017-02-01
uid str_val
0 0 1 2017-01-01 00:00:00
2 1 2017-02-01 00:00:00
1 1 2 2017-01-01 00:00:00
3 2 2017-02-01 00:00:00
uid date_val
0 1 2017-01-01
1 2 2017-01-01
2 1 2017-02-01
3 2 2017-02-01
```
I expect the index to be like in the second output (`df2`). Indexes in `df1` and `df3` seem strange.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Apr 27 20:09:39 PDT 2020; root:xnu-4903.278.35~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 8 | "2021-08-25T10:33:03Z" | "2021-09-09T06:30:25Z" | "2021-09-09T00:19:44Z" | CONTRIBUTOR | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Added information about pandas-stubs to both the .rst and .md ecosystem doc files.
closes https://github.com/pandas-dev/pandas/issues/43197 | {
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I am using pandas to read both csv files and lists of lists.
I have a dictionary mapping some colum headers to a conversion function:
`dict_converters = {"column_a": func_a, "column_b": func_b, "column_e": func_a} `
I find the converters argument in pandas.read_csv to be very useful to pass a dictionary mapping columns to functions so as to apply a bunch of transformations in a single pass:
`df = pd.read_csv("data.csv", converters=dict_converters)`
Doing the same for a dataframe constructed from a list of lists implies to iterate over each transformation:
```
df = pd.DataFrame(data[1:], columns=data[0])
for k, v in dict_converters.items():
df[k] = df[k].apply(v)
```
#### The solution I would like
It would be nice to be able to use the same syntax as in the read_csv function:
`df = pd.DataFrame(data[1:], columns=data[0], converters=dict_converters)`
#### API breaking implications
Not sure, but as an added feature this should maintain backward compatibility.
#### Example code
[data.csv](https://github.com/pandas-dev/pandas/files/7046279/data.csv)
[converters.py.md](https://github.com/pandas-dev/pandas/files/7046291/converters.py.md)
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} | 2 | "2021-08-25T10:58:27Z" | "2021-09-29T22:47:17Z" | "2021-09-29T22:47:17Z" | 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
import pandas as pd
df = pd.DataFrame([[1, 2, 3, 4, 5, 6]]*3)
df.columns = pd.MultiIndex.from_product([['a', 'b'], ['i', 'j', 'k']])
print('all normal int:')
print(df.groupby(level=1, axis=1).sum())
for col in df.columns:
if col[-1] == 'j':
df[col] = df[col].astype('Int64')
print('mixed int:')
print(df.groupby(level=1, axis=1).sum())
for col in df.columns:
df[col] = df[col].astype('Int64')
print('all nullable int:')
print(df.groupby(level=1, axis=1).sum())
```
#### Problem description
If columns have mixed datatypes, in the example partially normal ints and partially nullable ints, several aggregation methods in the example .sum(), of the DataFrameGroupBy objects unexpectedly return empty DataFrames. This behavior was not yet present in version 1.2.5.
#### Expected Output
Output should be the same for mixed and non mixed
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.19.1.el7.x86_64
Version : #1 SMP Tue Aug 11 19:12:04 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C
LOCALE : None.None
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.1.2
blosc : None
feather : 0.4.1
xlsxwriter : 1.4.5
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.2.0
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 8 | "2021-08-25T12:29:47Z" | "2021-08-30T18:23:18Z" | "2021-08-30T16:50:38Z" | CONTRIBUTOR | null | - [x] closes #42424
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
The bug is specifically in the Interval index. The error arises in the example code from the issue while reindexing the categorical index with another categorical index(with new categories).
The test (test_cut_with_nonexact_categorical_indices) from PR [42425](https://github.com/pandas-dev/pandas/pull/42425) implicitly works in a similar way.
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changed file generic.py and style.py
replaced if axis in [...] with get_axis_number(axis)
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} | 1 | "2021-08-25T17:44:51Z" | "2021-08-30T15:12:24Z" | "2021-08-30T15:12:24Z" | MEMBER | null | ```
left = pd.MultiIndex.from_tuples([(np.nan, 6), (np.nan, 6), ("a", 4)])
right = pd.MultiIndex.from_tuples([("a", 1), ("a", 1), ("d", 1)])
mask = np.array([True, True, False])
result = left.putmask(mask, right)
>>> result
[...]
IndexError: list index out of range
```
The result here somehow ended up with `len(result.levels) == 0` | {
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} | 20 | "2021-08-25T18:49:07Z" | "2021-10-19T22:02:01Z" | "2021-09-29T22:47:18Z" | CONTRIBUTOR | null | - [x] closes #43209
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43214 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43214/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43214/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43214/events | https://github.com/pandas-dev/pandas/issues/43214 | 979,568,429 | MDU6SXNzdWU5Nzk1Njg0Mjk= | 43,214 | CLN: avoid use of service account keys in `to_gbq` and `read_gbq` tests | {
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] | null | 2 | "2021-08-25T19:51:41Z" | "2021-08-26T18:26:35Z" | "2021-08-26T18:26:35Z" | CONTRIBUTOR | null | The GCP project we (are? were?) using to test the `to_gbq` and `read_gbq` is scheduled to get a policy applied to it that prevents the use of service account key files. https://cloud.google.com/resource-manager/docs/organization-policy/restricting-service-accounts#disable_service_account_key_creation
In anticipation of this change:
1. Are the `to_gbq` and `read_gbq` tests still running in the CI for this project? It's been a while since I've last looked and I've forgotten where they would be running.
2. If they are running somewhere and we want to continue running them, we need to do something about this upcoming restriction.
a. Ideally we'd migrate these tests to a service that doesn't require a GCP service account key file to connect to BigQuery. [Cloud Build](https://cloud.google.com/build) could be a good candidate, as it has a service account associated with it that doesn't require a key file. I could help migrate these tests if that's what we choose to do.
b. We continue to use a key file, but it'd have to be a project not associated with Google. This may be more difficult to get billing figured out (not that the tests actually cost that much to run, but there's overhead in getting the billing accounts setup plus whomever sets them up is a bit at risk if someone abuses the credentials)
I'd appreciate your thoughts so that we can continue to ensure these methods work well. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43215 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43215/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43215/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43215/events | https://github.com/pandas-dev/pandas/issues/43215 | 979,579,754 | MDU6SXNzdWU5Nzk1Nzk3NTQ= | 43,215 | TYP: Type hints in merge incorrect for ``on`` | {
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```
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
```
imply that we pass keys here.
We have tests, which pass arrays as on argument, for example
``test_handle_join_key_pass_array``
Not sure if this is tested well...
related: #42844 | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-08-25T22:29:44Z" | "2021-08-28T05:42:58Z" | "2021-08-27T23:56:07Z" | CONTRIBUTOR | null | - [ ] xref #27085 (chips away at, yes)
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* https://github.com/pandas-dev/pandas/pull/42284
* https://github.com/pandas-dev/pandas/pull/42896
* https://github.com/pandas-dev/pandas/pull/42909
* https://github.com/pandas-dev/pandas/pull/42930
* https://github.com/pandas-dev/pandas/pull/42932
* https://github.com/pandas-dev/pandas/pull/42952
* https://github.com/pandas-dev/pandas/pull/42989
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- [x] tests added / passed
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} | 2 | "2021-08-26T04:18:49Z" | "2021-09-01T15:55:44Z" | "2021-09-01T15:50:51Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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