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} | 7 | 2021-03-09T12:35:04Z | 2021-03-11T13:02:50Z | 2021-03-11T10:45:01Z | CONTRIBUTOR | null | - [X] xref #38802
- [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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] | closed | false | null | [] | null | 2 | 2021-03-09T13:03:04Z | 2021-03-27T15:50:28Z | 2021-03-27T15:50:28Z | 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
from pathlib import Path
import pandas as pd
filename = 'file.xlsm'
template_df = pd.read_excel(filename, sheet_name="Muster", usecols="A:F", skiprows=1)
template_df.index = template_df["Beschreibung"]
path = 'file2.xlsx'
df = pd.read_excel(path, sheet_name="Januar", usecols="A:F", skiprows=1)
df.index = df["Beschreibung"]
template_df.loc['Fee EPEX ID Kundenhandel', "Short/Kauf/NEG"] = 0
template_df.update(df)
template_df.loc["Direktvermarktung Vertragspreis", "Long/Verkauf/POS"]
```
#### Problem description
First of all, I know that the example is not a proper minimal snippet. I also can not provide the excel files in question. However I have no idea how to debug this issue any further.
If I run the script above with python -X faulthandler I get:
```
Current thread 0x00007f76b30a3280 (most recent call first):
File ".../.venv/lib/python3.8/site-packages/pandas/core/frame.py", line 3133 in _get_value
File ".../.venv/lib/python3.8/site-packages/pandas/core/indexing.py", line 888 in __getitem__
File ".../debug.py", line 22 in <module>
fish: “python -X faulthandler .../de…” terminated by signal SIGSEGV (Address boundary error)
```
From what I can deduce there is something wrong with the template_df DataFrame. I was trying to generate an equivivalent copy of it without copying the memory so I ran it through
```
tempalte_df = pd.Dataframe.from_dict(template_df.to_dict()).
```
This does indeed seem to help. However by accident I discovered just doing
```
d = template_df.to_dict()
```
also fixes it. As if to_dict() would somehow alter the DataFrame?
So I might be completely on the wrong track. Any help on how to drill down on this would be appreciated.
#### Expected Output
No segfault :-)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
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 : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 28 | 2021-03-09T13:13:53Z | 2021-06-07T03:50:34Z | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
When using a "fluent"/"method chaining" style of programming with pandas, a common hiccup is the lack of a method for selecting columns from a `DataFrameGroupBy` object. For example, take this DataFrame
```python
import pandas as pd
df = pd.DataFrame(dict(
x=[1, 2, 3, 4, 5, 6],
y=[2, 4, 6, 1, 2, 3],
a=["a", "b", "c", "a", "b", "c"],
b=["x", "x", "y", "z", "z", "z"],
))
```
To group by one column and then return the mean of another column within each group, one has to use multiple syntactical constructs:
```python
(
df
.groupby("a")
["x"]
.mean()
)
```
There is also dot-attribute access columns:
```python
(
df
.groupby("a")
.x
.mean()
)
```
but that is not fully general: it won't work if your column name collides with an existing method, and it won't work if the column is defined by a variable.
Another option is to select the column first then groupby using a series, not a name:
```python
(
df["x"]
.groupby(df["a"])
.mean()
)
```
This is not so bad. Well, ideally each step in the pipeline would be on a new line, and it requires some duplicated typing. But the bigger issue is that it fails when you want to group by more than one column:
```python
(
df["x"]
.groupby(df[["a", "b"]])
.mean()
)
```
<details><summary>Traceback</summary>
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-edc38a1f02b1> in <module>
1 (
----> 2 df["x"]
3 .groupby(df[["a", "b"]])
4 .mean()
5 )
~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/series.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)
1689 axis = self._get_axis_number(axis)
1690
-> 1691 return SeriesGroupBy(
1692 obj=self,
1693 keys=by,
~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/groupby/groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)
558 from pandas.core.groupby.grouper import get_grouper
559
--> 560 grouper, exclusions, obj = get_grouper(
561 obj,
562 keys,
~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/groupby/grouper.py in get_grouper(obj, key, axis, level, sort, observed, mutated, validate, dropna)
826 # allow us to passing the actual Grouping as the gpr
827 ping = (
--> 828 Grouping(
829 group_axis,
830 gpr,
~/miniconda3/envs/py39/lib/python3.9/site-packages/pandas/core/groupby/grouper.py in __init__(self, index, grouper, obj, name, level, sort, observed, in_axis, dropna)
541 if getattr(self.grouper, "ndim", 1) != 1:
542 t = self.name or str(type(self.grouper))
--> 543 raise ValueError(f"Grouper for '{t}' not 1-dimensional")
544 self.grouper = self.index.map(self.grouper)
545 if not (
ValueError: Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional
```
</details>
(I actually feel like this really ought to work, independently of whether it's the best solution to this Feature Request, but let's leave it aside for now).
**So in summary:** There's no fully-general, non-awkward method for selecting a column from `DataFrameGroupBy`. While certainly not fatal, it does make fluent pandas harder to read[1] and write[2]. And this is not an esoteric application: groupby/select/apply must be one of the most common pandas workflows.
[1] The great advantage of this method chaining approach for understanding code is that you can read off the sequence of verbs that comprise the operation. Introducing the `[column]` syntax requires your brain to shift from comprehension mode to production mode — you need to generate a verb for what's happening — which is a costly operation that likely (on the margin) impairs comprehension.
[2] I really like fluent pandas but it currently can be a little bit annoying to fight with autoindent/autoformat tools when writing it. It likely would be easier to improve that tooling if one could assume each pipeline step begins with a dotted method access.
#### Describe the solution you'd like
The `DataFrameGroupBy` object could have a column selection method.
Possible names:
##### `DataFrameGroupBy.get`
This exists in `DataFrame` as a thin wrapper around `self[arg]`. Possibly adds confusion with `DataFrameGroupBy.get_group`
##### `DataFrameGroupBy.select`
This is the method I have reached for more than once. I'm broadly aware of the history of [`NDFrame.select`](https://github.com/pandas-dev/pandas/pull/17633); this name should now be available, though it could cause some confusion to reintroduce it with different semantics (but xref https://github.com/pandas-dev/pandas/issues/26642).
It probably wouldn't make sense to add `.select` *only* to `DataFrameGroupBy` and not to `DataFrame`/`NDFrame`. So this would be more work (but also more benefit?)
In code, that might look like
```python
(
df
.groupby("a")
.select("x")
.mean()
)
```
#### API breaking implications
No breakage, unless someone has very old code using `DataFrame.select` that completely missed the original deprecation cycle and gets resurrected now.
Adding new methods (especially new ways of indexing pandas objects) definitely has an API complexity cost. But I would argue that, by converging on "one way to do it" in method chains, it could provide a a net reduction in complexity from the user perspective.
#### Additional context
This originally got some discussion on [twitter](https://twitter.com/michaelwaskom/status/1369037397040726020), note especially @TomAugspurger's [response](https://twitter.com/TomAugspurger/status/1369073811035983879).
Other relevant context would be the [select verb](https://dplyr.tidyverse.org/reference/select.html) in `dplyr`. | {
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} | 3 | 2021-03-09T13:31:19Z | 2021-03-14T09:31:23Z | 2021-03-12T21:48:08Z | MEMBER | null | xref #39146
Extracting part of the test changes of https://github.com/pandas-dev/pandas/pull/39578. This PR is doing `pandas/tests/frame/indexing`. The `pandas/tests/indexing` tests are handled in https://github.com/pandas-dev/pandas/pull/40323.
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} | 1 | 2021-03-09T14:32:35Z | 2021-03-10T00:17:16Z | 2021-03-09T22:06:15Z | MEMBER | null | - [x] closes https://github.com/pandas-dev/pandas/issues/40258
- [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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Extracting part of the test changes of https://github.com/pandas-dev/pandas/pull/39578. To keep the size a bit more limited, I am splitting the PRs, so this PR is doing `pandas/tests/indexing`. The `pandas/tests/frame/indexing` are handled in https://github.com/pandas-dev/pandas/pull/40323
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} | 2 | 2021-03-09T15:22:41Z | 2021-03-15T07:29:53Z | 2021-03-12T22:05:45Z | MEMBER | null | xref #39146
Getting the tests passing for the indexing tests, her for `pandas/tests/series/indexing` (`pandas/tests/indexing` is done in https://github.com/pandas-dev/pandas/pull/40325 and `pandas/tests/frame/indexing` is done in https://github.com/pandas-dev/pandas/pull/40323) | {
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https://pandas.pydata.org/docs/user_guide/window.html#exponentially-weighted-window
#### Documentation problem
Currently, it reads
However, I don't understand why the denominator doesn't also sum over time, as is shown in the general formula at the beginning of the section:
#### Suggested fix for documentation
----
I haven't done much windowing at all, so this may very well be me misunderstanding something
cc @mroeschke in #40099 , then if the timedelta is zero, then I think the current formula would give an expanding sum rather than an expanding mean | {
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} | 8 | 2021-03-09T15:44:09Z | 2021-10-16T15:35:07Z | 2021-10-16T15:35:07Z | CONTRIBUTOR | null | Just noticed that this is poorly documented.
It has signature `(target, method=None, level=None, limit=None, tolerance=None)` and only documents the behaviour of `target`
Is a good candidate to revisit.
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} | 10 | 2021-03-09T16:21:29Z | 2021-04-12T12:38:16Z | 2021-04-12T12:38:06Z | MEMBER | null | The property cache was using the attribute `_cache` which occasionally is used by other classes as a class attribute. Most prominently this is true for all subclasses of `PandasExtensionDtype`, see https://github.com/pandas-dev/pandas/blob/ce082fb04a0cf597f673d35bc47afd6c2e3c3168/pandas/core/dtypes/dtypes.py#L89
This would cause the same cash to be used for the property caching as for the extension dtype. The dtype cache is a class attribute whereas the property cache is supposed to be instance only. I fixed this by using a longer name for the property caching attribute, hoping this would lead to less collisions.
Introducing a new attribute leads to failures for Cython extension types which previously were handled with an `AttributeError`. Firstly, I believe these cases should not silently pass and secondly the previous behaviour was wrong by not executing the function w/out cache but it returned the instance of the `CachedProperty` class instead. I addressed this but enabling the caching for extension classes requires defining the attribute explicitly. I went ahead and created a mixin class for this but I'm open to other suggestions as well.
- [x] closes N/A
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 4 | 2021-03-09T18:07:53Z | 2021-03-09T21:15:26Z | 2021-03-09T20:17:10Z | MEMBER | null | The only non-trivial change here is changing `_id` from `_id = _Identity(object)` to `_id = object()`, which I found in profiling (https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422) makes a non-trivial difference as the non-cython performance inches closer to the cython performance.
Share DTI/TDI/PI's _simple_new with Categorical.
Simplify DTI/TDI `__contains__`
Annotations.
Docstring follow-up to #40241 | {
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} | 1 | 2021-03-09T18:14:29Z | 2021-03-09T22:28:11Z | 2021-03-09T22:14:27Z | MEMBER | null | Broken off from #40149 | {
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} | 4 | 2021-03-09T19:16:16Z | 2021-03-10T10:18:20Z | 2021-03-10T08:58:31Z | CONTRIBUTOR | null | - [X] xref #38802
- [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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} | 8 | 2021-03-09T19:33:48Z | 2021-04-19T16:04:59Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
(this was also posted to Stack Overflow, where I was told this seems like a bug https://stackoverflow.com/questions/66541816/pandas-read-csv-not-erroring-on-a-bad-line-with-extra-columns)
#### Code Sample, a copy-pastable example
I have the below simple csv
```
ownername,streetno,streetname
me,320,main st,just,absolute,garbage
you,40,mint ave
```
The command I'm using to read the file is
```python
import csv
import pandas
file = 'path_to_csv'
data = pandas.read_csv(file,',',header=0, quotechar=None, quoting = csv.QUOTE_NONE, index_col=False)
```
As long as the extra values (just,absolute,garbage) occur on the first row of data, it will parse the file without errors, giving me the below DataFrame
```
ownername streetno streetname
0 me 320 main st
1 you 40 mint ave
```
#### Problem description
This is not erroring on the bad line with extra columns as long as the bad line is the first line of data. Explicitly setting `error_bad_lines=True` has no effect.
#### Expected Output
`Error tokenizing data. C error: Expected 3 fields in line 2, saw 6`, e.g. the same as what happens when the extra columns are only on the 2nd line of data.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.2.final.0
python-bits : 32
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 : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 3 | 2021-03-09T19:37:11Z | 2021-03-30T05:04:15Z | 2021-03-10T14:50:07Z | CONTRIBUTOR | null | - [x] closes #39735
#39396 solved a bug that CSS was duplicated everytime a Styler rendered. The solution maintained the computed `ctx` object and cleared the `_todo` list of methods so it wasn't recalculated. This broke the `export` and `use` methods since they rely on passing methods from the `_todo` list.
This PR solves the above duplication problem by clearing the `ctx` object before each `render`, maintaining the `_todo` from one render to another, and therefore fixes the broken methods.
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] | closed | false | null | [] | null | 1 | 2021-03-09T19:58:42Z | 2021-03-09T20:50:44Z | 2021-03-09T20:46:31Z | 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.
---
**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
On macOS Big Sur 11.2.3 on MacBook Pro M1, with Python 3.9.2, pandas _master_ or _v1.2.3_ branch:
```bash
% python3.9 -c "import panda3d.core"
Traceback (most recent call last):
File "<string>", line 1, in <module>
ImportError: dlopen(/opt/homebrew/lib/python3.9/site-packages/panda3d/core.cpython-39-darwin.so, 2): no suitable image found. Did find:
/opt/homebrew/lib/python3.9/site-packages/panda3d/core.cpython-39-darwin.so: code signature in (/opt/homebrew/lib/python3.9/site-packages/panda3d/core.cpython-39-darwin.so) not valid for use in process using Library Validation: Trying to load an unsigned library
```
Note below that pandas itself was compiled with a signature.
#### Problem description
`panda3d/core.cpython-39-darwin.so` is not code signed on macOS Big Sur
```bash
% codesign -v -v /opt/homebrew/lib/python3.9/site-packages/panda3d/core.cpython-39-darwin.so
/opt/homebrew/lib/python3.9/site-packages/panda3d/core.cpython-39-darwin.so: code object is not signed at all
In architecture: arm64
```
#### Expected Output
`import panda3d.core` should load as well.
#### Output of ``pd.show_versions()``
```bash
% python3.9 -c "import pandas; pandas.show_versions()"
```
<details>
INSTALLED VERSIONS
------------------
commit : 921477ed71b93b9f0162fd018431f1076d48b37f
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:06:51 PST 2021; root:xnu-7195.81.3~1/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : nl_NL.UTF-8
LOCALE : nl_NL.UTF-8
pandas : 1.3.0.dev0+999.g921477ed71
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : 0.29.22
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 : None
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 : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
pd.date_range('2020-03-09 00:00:00-07:00', '2021-03-09 00:00:00-08:00', tz='America/Los_Angeles')
...
TypeError: Start and end cannot both be tz-aware with different timezones
```
#### Problem description
The start and end timestamps in the above line of code are both valid timestamps in the `America/Los_Angeles` timezone, but they fail when passed directly
#### Expected Output
I would expect this to match the output from the following:
```
>>> pd.date_range('2020-03-09', '2021-03-09', freq='H', tz='America/Los_Angeles')
DatetimeIndex(['2020-03-09 00:00:00-07:00', '2020-03-09 01:00:00-07:00',
'2020-03-09 02:00:00-07:00', '2020-03-09 03:00:00-07:00',
'2020-03-09 04:00:00-07:00', '2020-03-09 05:00:00-07:00',
'2020-03-09 06:00:00-07:00', '2020-03-09 07:00:00-07:00',
'2020-03-09 08:00:00-07:00', '2020-03-09 09:00:00-07:00',
...
'2021-03-08 15:00:00-08:00', '2021-03-08 16:00:00-08:00',
'2021-03-08 17:00:00-08:00', '2021-03-08 18:00:00-08:00',
'2021-03-08 19:00:00-08:00', '2021-03-08 20:00:00-08:00',
'2021-03-08 21:00:00-08:00', '2021-03-08 22:00:00-08:00',
'2021-03-08 23:00:00-08:00', '2021-03-09 00:00:00-08:00'],
dtype='datetime64[ns, America/Los_Angeles]', length=8762, freq='H')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.16.6
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 47.1.1
Cython : 0.29.21
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : 0.6.2
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
</details>
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} | 2 | 2021-03-10T04:45:06Z | 2021-03-10T15:39:34Z | 2021-03-10T13:54:40Z | CONTRIBUTOR | null | `c_encoding` has always been `NULL` since #36997. This allows removing/simplifying a lot of code in pandas/_libs/parsers.pyx. | {
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} | 3 | 2021-03-10T05:03:54Z | 2021-05-03T01:11:47Z | 2021-03-10T08:50:54Z | CONTRIBUTOR | null | …of times? #40327
- [x] closes #40327
- [ ] 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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} | 1 | 2021-03-10T05:18:11Z | 2021-03-10T23:04:04Z | 2021-03-10T05:25:54Z | NONE | null | - [ ] 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.
---
**Pandas Version:**
`pandas==1.1.0`
**Data file:** [penguins1@csv](https://observablehq.com/@visnup/hello-danfo-js)
`df.head(10).describe()`
gives me:
`std` value for `bill_length_mm` is around `2`
`df.head(10)['bill_length_mm'].values
`
result in:
`array([39.1, 39.5, 40.3, nan, 36.7, 39.3, 38.9, 39.2, 34.1, 42. ])
```
import numpy as np
x = [39.1, 39.5, 40.3, 0, 36.7, 39.3, 38.9, 39.2, 34.1, 42. ]
np.std(x, ddof=1)
```
gives:
`12.445923027240687`
`pd.Series(x).std()` gives `12.445923027240687` (same)
but why describe is giving me `2`??
`
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} | 11 | 2021-03-10T08:27:07Z | 2021-04-14T12:55:40Z | 2021-04-14T12:55:40Z | 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
df.groupby('teamId').idxmax()
tmp.groupby('teamId').idxmax()
```
#### Problem description
DataFrame df is consist of **teamId(int64)** and **neutralMinionsKilled(int64)** and 'tmp' is consist of **teamId(Int64)** and **neutralMinionsKilled(Int64)**
`df.groupby('teamId').idxmax()` is working
But `tmp.groupby('teamId').idxmax()` is not working and showing 'TypeError: argmax() takes 1 positional argument but 2 were given'
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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Also, if https://github.com/python/mypy/issues/10194 was recognised as a bug and resolved, then the extra overload for the `bool` case in #40200 (and any others of that kind) could be removed | {
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} | 2 | 2021-03-10T13:50:38Z | 2021-04-02T19:09:57Z | 2021-04-02T19:09:57Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import pandas.testing as pdt
y = pd.Series(["A"], dtype="object").astype("category")
x = pd.Series(["A"], dtype="string").astype("category")
pdt.assert_series_equal(x,y)
```
`pandas==1.2.3`: runs through, doesn't raise
`pandas==1.3.0.dev0+1005.g93c52e4e1d` (from the nightly wheels):
```python-traceback
Traceback (most recent call last):
File "/home/simon/Documents/QuantCo/quantcore.thek/test.py", line 5, in <module>
x = pd.Series(["A"], dtype="string").astype("category")
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/generic.py", line 5944, in astype
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 659, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/internals/managers.py", line 524, in apply
applied = getattr(b, f)(**kwargs)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/internals/blocks.py", line 658, in astype
new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/dtypes/cast.py", line 1352, in astype_array_safe
new_values = astype_array(values, dtype, copy=copy)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/dtypes/cast.py", line 1297, in astype_array
values = values.astype(dtype, copy=copy)
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/pandas/core/arrays/string_.py", line 328, in astype
elif np.issubdtype(dtype, np.floating):
File "/home/simon/miniconda3/envs/quantcore.thek/lib/python3.9/site-packages/numpy/core/numerictypes.py", line 419, in issubdtype
arg1 = dtype(arg1).type
TypeError: Cannot interpret 'CategoricalDtype(categories=None, ordered=None)' as a data type
```
#### Problem description
Seems like a regression. Converting the `string` series to `object` first works without raising:
```python
x = pd.Series(["A"], dtype="string").astype("object").astype("category")
```
#### Expected Output
`nightly` should behave like `pandas=1.2.3`, running through and producing two equal series.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 93c52e4e1d172512b32e0e76e9b09c0cf76daf21
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-44-generic
Version : #50-Ubuntu SMP Tue Feb 9 06:29:41 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1005.g93c52e4e1d
numpy : 1.21.0.dev0+934.g6222e283f
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.2
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 : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.1.0.dev317
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 1 | 2021-03-10T14:41:25Z | 2021-03-10T14:53:05Z | 2021-03-10T14:53:05Z | MEMBER | null | Currently, it seems that the only job which uploads coverage reports to codecov is https://github.com/pandas-dev/pandas/blob/master/.github/workflows/database.yml
Ideally, the jobs in https://github.com/pandas-dev/pandas/blob/master/ci/azure/posix.yml would also upload coverage reports to codecov
Marking as "good first issue" as it doesn't require knowledge of the pandas codebase, though probably requires some experience/knowledge of CI systems | {
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} | 6 | 2021-03-10T16:45:51Z | 2021-03-16T17:05:03Z | 2021-03-16T16:55:42Z | MEMBER | null | This cuts another 25% off of the benchmark discussed https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422, getting us to within 3x of the cython-path performance. | {
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} | 2 | 2021-03-10T17:21:53Z | 2021-03-11T16:14:39Z | 2021-03-11T02:18:24Z | CONTRIBUTOR | null | I noticed that these doctests lost some formatting in a refactor. It's a little surprising the tests were still passing, presumably doctests is configured to ignore leading whitespace? | {
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} | 1 | 2021-03-10T17:34:29Z | 2021-03-29T03:46:49Z | 2021-03-29T03:46:49Z | NONE | null | https://github.com/pandas-dev/pandas/blob/f2c8480af2f25efdbd803218b9d87980f416563e/pandas/core/strings/accessor.py#L1906
Please shift over example column names to properly align with dataframe (currently aligned starting with index). Example is confusing.
Same issue applies to L1912. | {
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} | 3 | 2021-03-10T19:05:37Z | 2021-03-21T22:50:06Z | 2021-03-21T21:08:13Z | CONTRIBUTOR | null | - [x] closes #12147
when copying a table from Jupyter to Excel the column header row is translated to the left because there is no value to copy.
this PR puts a non-breaking space in the empty cell so that structure is preserved.
my excel above is quite old, but this effect is worse on more recent excels since I believe they merge cells which have a rowspan (in the multiindex column headers) or a colspan (in a multiindex index labels). With columns merging in wrong place it is a bit of a nuisance. | {
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] | open | false | null | [] | null | 2 | 2021-03-10T19:49:16Z | 2021-08-19T01:56:24Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': [0, 1]}, index=[pd.Timestamp('2002-12-30T20'), pd.Timestamp('2003-01-03T20')])
df['T 1.75 1/3'] = [1, 2]
```
#### Problem description
When using `__setitem__` or `__getitem__` with a str key on a pandas DataFrame where the index has ._supports_partial_string_indexing = True, pandas first tries to convert the str key to a slice using index._get_string_slice(key).
It seems to me that we have too loose of an interpretation of what one of these partial string slices looks like (or perhaps when / how one might use them).
In my use case I have some timeseries data for some US treasury bills, where the names are something like f"T {coupon} {date}", e.g. "T 1.75 1/3" and I want to assign a new column to a DataFrame with some data on this instrument.
However `df['T 1.75 1/3'] = value` - as in the example above - raises "ValueError: cannot set using a slice indexer with a different length than the value"
#### Expected Output
A new column with label "T 1.75 1/3" and values [1, 2] is assigned to the pandas DataFrame `df`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.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 :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.21.0
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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} | 2 | 2021-03-10T20:23:23Z | 2021-05-03T01:12:28Z | 2021-03-29T03:46:50Z | CONTRIBUTOR | null | - [x] closes #40355
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-03-10T21:40:39Z | 2021-03-15T00:57:46Z | 2021-03-15T00:52:27Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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We fixed this for DTI/TDI/PI in #39068. Also did CategoricalIndex recently, not sure where off the top. | {
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] | closed | false | null | [] | null | 2 | 2021-03-10T22:49:42Z | 2021-03-11T00:44:05Z | 2021-03-11T00:44:05Z | NONE | null | #### Code Sample, a copy-pastable example
```python
# Your code here
import hvplot.pandas
```
#### Problem description
When I try the import, I get the following when running from a Jupyter Notebook on Chrome or Edge:
```Javascript error adding output!
Error: Cannot find module '@bokehjs/styles/buttons'
See your browser Javascript console for more details.
```
With Internet Explorer, I get the following (both messages):
```
Javascript error adding output!
SyntaxError: Expected ';'
See your browser Javascript console for more details.
Javascript error adding output!
SyntaxError: Syntax error
See your browser Javascript console for more details.
```
After that, any calls to hvplot produce no output. I have checked the Javascript console, but it did not illuminate the problem for me.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
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 : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
Thank you! If this is a bokeh issue rather than a pandas issue, please advise.
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} | 4 | 2021-03-11T02:01:37Z | 2021-11-14T02:13:44Z | 2021-11-14T02:13:44Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.all.html
#### Documentation problem
This documentation is about 'pandas.Index.all' but it has examples of 'pandas.Index.any'.
The examples of 'pandas.Index.any' are also included in [its own page](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.any.html).
#### Suggested fix for documentation
Delete 'any()' examples on the documentation of 'all()', I think this is wrong place.
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} | 0 | 2021-03-11T03:33:28Z | 2021-03-15T00:55:16Z | 2021-03-15T00:09:58Z | MEMBER | null | - [x] closes #38399
- [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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} | 1 | 2021-03-11T04:04:55Z | 2021-03-20T02:01:27Z | 2021-03-20T01:25:12Z | MEMBER | null | After fixing the CategoricalIndex behavior (first commit), the "kind" keyword is no longer needed in _maybe_cast_slice_bound, so removed that.
After this the keyword is no longer needed in get_slice_bound, slice_locs, slice_indexer, but those are technically public so didnt rip them out. | {
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} | 2 | 2021-03-11T04:14:35Z | 2021-03-11T15:08:16Z | 2021-03-11T09:38:49Z | MEMBER | null | Looks like a couple of individually-correct things got merged that together are causing failures on master. This should fix it. | {
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] | open | false | null | [] | null | 0 | 2021-03-11T06:04:43Z | 2021-08-19T01:57:16Z | 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
@jreback
This is an issue I came across while working on https://github.com/pandas-dev/pandas/pull/40127.
Most Index objects can be constructed with None values:
```
pd.Index(['a', 'b', None], dtype='category')
pd.Index([1.0, 2.0, None], dtype='float64')
pd.Index(['2000-01', '2000-02', None], dtype='datetime64[ns]')
```
But (U)Int64Index and cannot accept None values:
```
>>> pd.Index([1, 2, None], dtype='int64')
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
```
This seems pretty reasonable. However, inside of a MultiIndex, None values and the int dtype can actually coexist:
```
>>> pd.MultiIndex.from_arrays([[1, 2, None]]).levels[0]
Int64Index([1, 2], dtype='int64')
```
So if we construct a DataFrame like so:
```
>>> df = pd.DataFrame([10, 20, 30], index=pd.MultiIndex.from_arrays([[1, 2, None]]))
0
1 10
2 20
NaN 30
```
Indeed, the dtype of the first and only level is correct:
```
>>> df.index.levels[0]
Int64Index([1, 2], dtype='int64')
```
The reason this works is because of `codes`, which can encode None values as `-1`.
```
>>> df.index.codes
FrozenList([[0, 1, -1]])
```
More examples of None behavior in various scenarios:
```
>>> df = pd.DataFrame(np.zeros((3, 3)), columns=pd.MultiIndex.from_arrays([['a', 'b', 'c'], [1, 2, None]]))
a b c
1 2 NaN
0 0.0 0.0 0.0
1 0.0 0.0 0.0
2 0.0 0.0 0.0
>>> df.stack(0).columns
Int64Index([1, 2], dtype='int64')
>>> df.columns.droplevel(0)
Float64Index([1.0, 2.0, nan], dtype='float64')
>>> pd.Index([1, 2, None])
Index([1, 2, None], dtype='object')
```
I'm not sure what the best solution is to normalize the treatment of Nones inside indices. So I thought I would raise this issue and see what others think about this. | {
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] | open | false | null | [] | null | 0 | 2021-03-11T07:24:10Z | 2021-08-19T01:57:44Z | null | NONE | null | i noticed that the 'loffset' arguments in Grouper and resample is deprecated since v1.1.0 #31809.The new arguments 'origin' and 'offset' are use to specify the start of bin.The 'loffset' is use for ajusting the result labels, which is a useful Feature.without the 'loffset',we need to add the offset to the index after group/resample,it's a little boring。
```python
from pandas.tseries.frequencies import to_offset
loffset = '19min'
ts_out = ts.resample('17min').sum()
ts_out.index = ts_out.index + to_offset(loffset)
```
To avoid the confusion,you can change 'loffset' to 'label_offset' .
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} | 1 | 2021-03-11T08:36:27Z | 2021-03-15T14:30:16Z | 2021-03-15T00:04:19Z | CONTRIBUTOR | null | couple of comments in issues recently about complexity of `_translate` and its readability.
small commit here to group setup variables by type and function, and add some comments.
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} | 5 | 2021-03-11T12:43:42Z | 2021-06-01T12:36:36Z | 2021-06-01T12:36:36Z | 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.
- [ ] I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
pd.Series([1]).replace(np.array([1.]), [0])
```
#### Problem description
As of version 1.1.5 (and earlier), the code snippet above yielded `pd.Series([0])` (i.e. the replacement took place). As of version 1.2.0 (and later), the code snippet above yields `pd.Series([1])` (i.e. the replacement does not take place). In both versions, the replacement takes place if we pass either an integer array (`np.array([1])`) or a list (`[1.]`) to `replace` instead of a float array (`np.array([1.])`). Is this change in behavior intentional?
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1038-aws
Version : #40~18.04.1-Ubuntu SMP Sat Feb 6 01:56:56 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : None
numba : 0.52.0
```
</details>
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} | 2 | 2021-03-11T13:11:55Z | 2021-03-11T20:20:21Z | 2021-03-11T19:03:30Z | CONTRIBUTOR | null | - [x] closes #38802
- [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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} | 7 | 2021-03-11T14:26:48Z | 2021-05-26T03:27:26Z | 2021-05-26T03:27:26Z | 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.
---
**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
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
for group in df.rolling(5, on='index'):
print(len(group))
```
Prints:
```
1
2
3
4
5
5
5
5
5
5
```
#### Problem description
With a rolling window all groups should be always the same size (5 in the above example) and so the first 4 groups should be skipped. The first groups are an expanding window which then turn into a rolling window.
Additionally, the API is a bit unexpected when you compare to a `for idx, group in df.groupby('col')` operation. With a `groupby` each element also provides the index, while `rolling` it just provides the grouped data and removes the grouping index column (i.e. the column `index` does not exist in `group`).
#### Expected Output
```
5
5
5
5
5
5
```
Even better it would also include the index so that:
```
for idx, group in df.rolling(5, on='index'):
print(len(group))
```
Would work
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1100-azure
Version : #111~16.04.1-Ubuntu SMP Thu Nov 19 06:49:21 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.17
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 4 | 2021-03-11T14:38:03Z | 2021-03-19T17:53:57Z | null | NONE | null | #### Is your feature request related to a problem?
With a `group.apply` the applied function acts on all columns available to provide a result. With `rolling.apply` it applies the function to each column individually - it would be great if you could apply it to all the columns as well.
e.g. the following is possible with `groupby.apply`
```
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df['index'] = df['index'] % 3
df.groupby('index').apply(lambda df: df['A'] + df['B'])
```
However, you can't do the same equivalent with `rolling.apply`:
```
df = pd.DataFrame({'A': range(10), 'B': range(10,0,-1)}).reset_index()
df.rolling(3, on='index').apply(lambda df: (df['A'] * df['B']).sum())
```
Gives the error: `KeyError: 'A'`
This is because the `apply` function is applied to each column separately. The series is passed to the function not the dataframe. This feels inconsistent and annoying if you want to apply a function to the grouped rolling data. It would be great if there were a way to do it 😃
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} | 9 | 2021-03-11T14:48:54Z | 2021-03-13T16:03:48Z | 2021-03-12T22:06:45Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.query.html
#### Documentation problem
#### Suggested fix for documentation
Backticks should be shown for the second `Area (cm^2)`. | {
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] | closed | false | null | [] | null | 4 | 2021-03-11T14:51:42Z | 2021-03-13T01:05:11Z | 2021-03-13T01:04:48Z | NONE | null | #### Is your feature request related to a problem?
`pd.pivot_table` reshapes a table and aggregates values using a `aggfunc` (default `mean`) when rows has the same `index`+`columns` values. However, it is also common to use this function to purely reshape the table without aggregation - based on the assumption that no duplicates exist in the input table. However, if the upstream data does contain duplicates the output of `pd.pivot_table` "silently" changes to an aggregation instead of a pure reshape.
This issue is a parallel issue with `pd.merge` and `1:m` "silently" converting to a `m:m` join when input tables contain duplicates.
#### Describe the solution you'd like
`pd.merge` introduced a `validate` switch which checks the inputs do not have duplicates (depending on which setting is used). I propose that `pd.pivot_table` also introduce a `validate` switch to check that the input table doesn't have duplicate rows with the same `index`+`columns` values.
#### API breaking implications
None
#### Describe alternatives you've considered
`assert df.duplicated(keys).sum() == 0`
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} | 3 | 2021-03-11T15:06:03Z | 2021-03-11T21:03:51Z | 2021-03-11T20:34:28Z | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.rolling.Rolling.apply.html
#### Documentation problem
For `engine` we have `None : Defaults to 'cython' or globally setting compute.use_numba`
#### Suggested fix for documentation
I'm very confident that this should read `None : Defaults to 'cython' or the global setting in compute.use_numba`.
Following the source code for `apply` to https://github.com/pandas-dev/pandas/blob/v1.2.3/pandas/core/window/rolling.py#L1307 and https://github.com/pandas-dev/pandas/blob/v1.2.3/pandas/core/util/numba_.py#L17 neither place sets the global variable, they just read from it. | {
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} | 1 | 2021-03-11T16:08:31Z | 2021-03-12T03:14:24Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
df = pandas.DataFrame({"col1": ["a", "b", "c"]})
df.groupby("col1", level=0) # Fine
df.index = pandas.MultiIndex.from_arrays([["a", "a", "b"], [1, 2, 1]], names=["x", "y"])
df.groupby(["col1", "x"]) # Fine
df.groupby("col1", level=0) # TypeError
```
<details><summary>Traceback</summary>
```
Traceback (most recent call last):
File "rofl.py", line 7, in <module>
res = df.groupby("col1", level=0) # TypeError
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/frame.py", line 6727, in groupby
dropna=dropna,
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 568, in __init__
dropna=self.dropna,
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/grouper.py", line 839, in get_grouper
if not isinstance(gpr, Grouping)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/grouper.py", line 485, in __init__
) = index._get_grouper_for_level(self.grouper, level)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 1487, in _get_grouper_for_level
grouper = level_values.map(mapper)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 5098, in map
new_values = super()._map_values(mapper, na_action=na_action)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/base.py", line 937, in _map_values
new_values = map_f(values, mapper)
File "pandas/_libs/lib.pyx", line 2467, in pandas._libs.lib.map_infer
TypeError: 'numpy.ndarray' object is not callable
```
</details>
#### Problem description
Getting a TypeError when specifying `by` and `level` params in `.groupby()` at the same time. It seems that grouping on column name and index level is supported since there is no error when level name is located in `by` parameter, so specifying it with `level` should also be possible.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-65-generic
Version : #73-Ubuntu SMP Mon Jan 18 17:25:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : 0.13.2
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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} | 2 | 2021-03-11T17:05:05Z | 2021-03-12T11:17:01Z | 2021-03-12T02:33:29Z | MEMBER | null | follow-on from #36092
The first commit is the change in the alias.
Although this does get rid of many of the false positives, as well as removing ignores this also results is some changes in the mypy error codes and some new errors where we might need a typevar (or more likely an overload)
The subsequent commits were an attempt to ensure that this PR only removed ignores.
The PR is starting to get a bit difficult to review imo. so I have stopped for now.
I could break off the first commit and then submit the subsequent commits as separate PRs if it makes review easier. | {
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Sits on top of #39510 | {
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DOC: Slightly broken grammar for `groupby.apply` with fix [#40377](https://github.com/pandas-dev/pandas/issues/40377)
Made the grammar change as raised in the issue. | {
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] | closed | false | null | [] | null | 4 | 2021-03-11T20:48:01Z | 2021-09-22T19:15:01Z | 2021-07-10T18:21:23Z | NONE | null | Hello,
please accept my apologies for not adhering to the bug report template. I felt this issue doesn't quite fit it.
There is a huge increase in installation times on aarch64 Linux machines (e.g. AWS Graviton 2 based EC2 instances) running CPython 3.8 when comparing the current and previous releases of pandas:
- [``pip install pandas==1.2.2``](https://pypi.org/project/pandas/1.2.2/#files) -> completes fast, leverages the [manylinux2014 wheel](https://files.pythonhosted.org/packages/86/c2/dbb6f8b0e42da7c556fc07668b97d1614d9bb39a9dcab69eb91a78f04ba9/pandas-1.2.2-cp38-cp38-manylinux2014_aarch64.whl)
- [``pip install pandas==1.2.3``](https://pypi.org/project/pandas/1.2.3/#files) -> takes several times longer, performs source build
The cause appears to be **manylinux2014 wheels for pandas 1.2.3 missing from PyPi**. Could these please be added? Are there plans to include these for future releases?
My workaround is simply to pin the 1.2.2 release on aarch64 for the time being.
Thanks for all the effort! | {
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} | 1 | 2021-03-11T23:05:10Z | 2021-03-15T00:43:16Z | 2021-03-15T00:03:14Z | MEMBER | null | - [ ] closes #xxxx
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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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} | 4 | 2021-03-11T23:38:48Z | 2021-03-23T23:52:13Z | 2021-03-23T17:01:39Z | CONTRIBUTOR | null | - [x] closes #39805
- [x] tests added / passed
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- [x] whatsnew entry
This prevents `frame_or_series.asfreq()` from dropping rows/items if the index is not sorted.
This seemed like a relatively easy fix, and it should probably be done sooner than later. However, there wasn't much discussion in the issue so I still have some questions:
1. How should we respond to unsorted indices? For now, I went ahead with option a.
a. Silently "sort" the index before reindexing. Or we could
b. Raise a warning before "sorting." Or
c. Raise an error.
2. If it should be in the whatsnew, which version would it be added to?
3. I was thinking of converting the `asfreq` tests from class-based to functions. Can I do that in this PR or should I do that in another one? | {
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} | 0 | 2021-03-12T00:08:55Z | 2021-04-05T16:19:43Z | 2021-04-05T16:19:43Z | MEMBER | null | it calls np.delete, which casts to ndarray, which for e.g. PeriodDtype will cast to object
```
dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
ser = pd.Series(dti)
del ser[1]
>>> ser
0 2016-01-01 00:00:00-08:00
2 2016-01-03 00:00:00-08:00
dtype: object
``` | {
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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] dataframe.to_csv with zip compression now works like gzip/xz, when use 'infer' and destination path 'xxxx.csv.zip' which endwith '.zip', to_csv will create a zip file with archive_name=xxxx.csv
close old PR,do some work after read pre commit guidelines | {
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} | 4 | 2021-03-12T01:10:31Z | 2021-03-23T17:03:14Z | 2021-03-23T17:03:14Z | CONTRIBUTOR | null | `Styler.format` could allow a parameter `raw_html` (bool, False for current behaviour), enabling richer output
For example, `df.style.format(lambda s: f"<a href='https://en.wikipedia.org/wiki/{html.escape(s)}'>{html.escape(s)}</a>", raw_html=True)` could be used to turn a column of values into a column of links to English Wikipedia articles, when exported with `to_html`.
To achieve this, we would need to always pass raw HTML to the jina templates, changing how formatting is currently done in `Styler.to_html`.
Formats performed with `raw_html=True` would have no effect on `Styler.to_excel`. | {
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} | 0 | 2021-03-12T03:30:43Z | 2021-03-12T22:42:08Z | 2021-03-12T22:33:24Z | MEMBER | null | i get a bunch of noise in my local mypy output, so will have to see what the CI says | {
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} | 1 | 2021-03-12T04:42:40Z | 2021-03-12T20:35:49Z | 2021-03-12T19:41:24Z | 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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} | 1 | 2021-03-12T05:53:55Z | 2021-03-12T18:41:13Z | 2021-03-12T09:03:41Z | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
I believe https://github.com/pandas-dev/pandas/pull/40293 may have caused the current typing failures in the CI | {
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} | 5 | 2021-03-12T08:27:07Z | 2021-03-15T14:00:57Z | 2021-03-15T14:00:57Z | 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
# Your code here
import pandas as pd
pd.DataFrame().convert_dtypes()
```
#### Problem description
convert_dtypes throws a "ValueError: No objects to concatenate" exception when called on an empty DataFrame. I find that a problem because I have use cases of chaining up DataFrame transform calls fluently that may result in an empty DataFrame before the convert_dtypes call. I don't want want to break up the transform calls to check for an empty DataFrame or handle the ValueError.
#### Expected Output
I expect calling convert_dtypes on an empty DataFrame should return an empty DataFrame without error.
```python
import pandas as pd
df = pd.DataFrame().convert_dtypes()
assert df.empty
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20200917
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.2
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.2.2
scipy : 1.5.3
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 2.0.1
xlwt : None
numba : 0.51.2
</details>
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} | 7 | 2021-03-12T10:17:53Z | 2021-03-17T21:09:44Z | 2021-03-17T21:08:52Z | MEMBER | null | - [x] closes #39758
Moving the posix builds to GitHubActions + uploading coverage on every build, as they're merged by default by Codecov
----
Trying to get this in so that the codecov check in #40078 will pass | {
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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
import numpy as np
import pandas as pd
s = pd.Series(np.arange(100), dtype=pd.Int64Dtype())
s.quantile(0.75)
```
produces
```
AbstractMethodError: This method must be defined in the concrete class type
```
#### Problem description
A regression in the extension dtype not raises `AbstractMethodError`. This is only true in 1.3.0dev. Found in statsmodels prerelease testing.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 65abf6b2d8a1c109fca070ed95f9bc2106cfb818
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1026.g65abf6b2d8
numpy : 1.21.0.dev0+934.g6222e283f
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.1
Cython : 0.29.22
pytest : 6.2.2
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 : 1.7.0.dev0+5e9eb01
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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} | 4 | 2021-03-12T10:40:11Z | 2021-03-18T14:34:37Z | 2021-03-17T21:10:35Z | MEMBER | null | Currently, we suppress numpy warnings with `np.errstate(all="ignore")` inside the `evaluate` function in `core.computation.expressions`, which is is only used in `core.ops.array_ops._na_arithmetic_op`, which in itself is only used in `core.ops.array_ops` `arithmetic_op()` and `comparison_op()` (where we actually called `np.errstate` again, duplicatively).
So, in summary, we suppress the warnings at the level of the "array op". For the ArrayManager, we call this array op many times for each column, and repeatedly calling `np.errstate(all="ignore")` gives a big overhead. Luckily, it is easy to suppress the warnings once at a higher level, at the DataFrame/Series level, where those array ops are called.
That's what this PR is doing: removing `np.errstate(all="ignore")` in the actual array ops, and adding it in all places where we currently call the array ops.
With the benchmark case of an arithmetic op with two dataframes, this gives a considerable improvement:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000, 1000))
df_am = df._as_manager("array")
```
```
In [2]: %timeit df_am + df_am
18.1 ms ± 1.04 ms per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
8.57 ms ± 167 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
For BM it doesn't matter for this case, since that's a single block and `np.errstate` would be called only once anyway. But for certain cases of `df+s` ops where we potentially also work column-wise for BM, it should benefit there as well. | {
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---
This makes it easier for users and other libraries to inspect pandas' behavior without importing private modules (xref https://github.com/dask/dask/pull/7375).
I'm not sure what public namespace is best. | {
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} | 0 | 2021-03-12T14:05:39Z | 2021-03-17T07:22:13Z | 2021-03-17T07:22:07Z | MEMBER | null | The `elif isinstance(obj, np.ndarray) and obj.dtype.kind == "m":` part is ever reached, because just before calling `_maybe_upcast_for_op`, we already did a `ensure_wrapped_if_datetimelike` which basically does the same (codedov also confirms it's not covered). So therefore removing that check.
In addition, moving the Datetime/TimedeltaArray inline within the `if/elif` checks, this gives a little bit less overhead for the non-datetimelike cases. | {
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} | 2 | 2021-03-12T14:49:42Z | 2021-03-15T05:22:40Z | 2021-03-12T22:06:45Z | CONTRIBUTOR | null | - [ ] closes #40375
- [ ] 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
I also noticed that in the same sentence double quotes were missing, so I added those as well. | {
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- [ ] tests added / passed
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} | 4 | 2021-03-12T16:29:49Z | 2021-03-15T14:35:58Z | 2021-03-15T14:00:57Z | CONTRIBUTOR | null | Added a conditional to check a DataFrame had at least one column and if not return the original (empty) DataFrame if so.
- [x] closes #40393
- [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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} | 0 | 2021-03-12T17:37:45Z | 2021-03-16T14:54:22Z | 2021-03-16T13:51:26Z | 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
We do expose create_block_manager_from_blocks in core.internals, so could put the old version in internals.api and continue exposing that. Not sure if any downstream libraries are actually using it. | {
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Python 3.7.4 (default, Aug 9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
import pandas as pd
pd.version
'1.1.5'
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
df
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py", line 751, in repr
show_dimensions=show_dimensions,
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py", line 883, in to_string
return formatter.to_string(buf=buf, encoding=encoding)
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\formats\format.py", line 921, in to_string
return self.get_result(buf=buf, encoding=encoding)
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\formats\format.py", line 520, in get_result
self.write_result(buf=f)
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\formats\format.py", line 844, in write_result
max_len = Series(lines).str.len().max()
File "C:\Users\wellington.souza\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\strings\accessor.py", line 2579, in len
result = self._data.array._str_len()
AttributeError: 'PandasArray' object has no attribute '_str_len' | {
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} | 2 | 2021-03-12T19:05:06Z | 2021-03-13T09:46:14Z | 2021-03-12T22:07:21Z | MEMBER | null | it looks like some code is unreachable following #37140 (assuming 1d or 2d only) cc @jbrockmendel | {
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- [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
xref: https://github.com/pandas-dev/pandas/pull/40392#discussion_r593013628 | {
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} | 1 | 2021-03-13T00:16:12Z | 2021-03-15T03:18:11Z | 2021-03-14T23:45:27Z | MEMBER | null | - [x] closes #21311
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
We can go a step further and re-use sanitize_array for the `dtype is None` case, but that ever-so-slightly changes our datetime-inference behavior, so we'd need to either deprecate or call it a bugfix. | {
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} | 2 | 2021-03-13T04:16:37Z | 2021-03-13T15:05:20Z | 2021-03-13T13:34:11Z | MEMBER | null | cc @simonjayhawkins
Locally when I run mypy on master I get 427 errors mostly "unused 'type: ignore' comment". Same result when i switch from py39 to py38, or from OSX to Ubuntu. mypy==0.812 in all cases. Any guesses what config the CI has different? | {
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} | 5 | 2021-03-13T04:56:48Z | 2021-05-28T22:31:39Z | 2021-05-28T16:58:22Z | MEMBER | null | - [x] closes #15122
- [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
Summary of contents
- Adds new on_bad_lines parameter (I found on_bad_lines a more explicit name than bad_lines)
- This defaults to None at first in order to preserve compatibility, however it should be changed to error in 2.0 after
error_bad_lines and warn_bad_lines are removed.
- Cleanup of some C/Python Parser code ( add enum instead of using 2 variables for C and only use on_bad_lines in Python) | {
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} | 2 | 2021-03-13T07:34:37Z | 2021-03-16T01:06:01Z | 2021-03-14T23:43:26Z | CONTRIBUTOR | null | - [x] closes #39756
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | closed | false | null | [] | null | 1 | 2021-03-13T12:24:30Z | 2021-08-19T02:02:33Z | 2021-08-19T02:02:33Z | 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
```bash
pydoc -p 8000
```
#### Problem description
I tried to use pydoc to view documentation of pandas offline and it works fine at most time. When I want to view docs of DataFrame, I click the link and it brings me to `http://localhost:8000/pandas.core.frame.html`. However, it just shows an error like this
```
pandas.core.frame.html
--
pydoc.ErrorDuringImport: problem in pandas.core.frame - ImportError: cannot import name 'concat_compat' from partially initialized module 'pandas.core.dtypes.concat' (most likely due to a circular import) (/home/user/.local/lib/python3.9/site-packages/pandas/core/dtypes/concat.py)
```
I have tested on different platforms, from Windows 7 to Arch Linux, from Python 3.6 with Anaconda to Python 3.9. And the problem remains the same. In addition, some other pydoc page in `core` hava similar problems.
Just now I found that `pydoc -w pandas.core.frame` create correct html file for the documentation! That is interesting :smile:
#### Expected Output
It should show the documentation page.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.19-1-MANJARO
Version : #1 SMP PREEMPT Fri Feb 26 11:38:34 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0
setuptools : 53.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
None
</details>
I have searched for this problem and it seems that there have been no discussion about it, so I submit this issue to report this weird behavior to you although it may not be your responsibility. But I cannot find place to make issue regarding to `pydoc` :cry:
I found this error out put in the [`CPython`](https://github.com/python/cpython/blob/master/Python/ceval.c) code below (at line 6135), hoping this will help you. And that is why I submit this issue as BUG instead of DOC, since it says `due to a circular import`.
```C
static PyObject *
import_from(PyThreadState *tstate, PyObject *v, PyObject *name)
{
PyObject *x;
PyObject *fullmodname, *pkgname, *pkgpath, *pkgname_or_unknown, *errmsg;
if (_PyObject_LookupAttr(v, name, &x) != 0) {
return x;
}
/* Issue #17636: in case this failed because of a circular relative
import, try to fallback on reading the module directly from
sys.modules. */
pkgname = _PyObject_GetAttrId(v, &PyId___name__);
if (pkgname == NULL) {
goto error;
}
if (!PyUnicode_Check(pkgname)) {
Py_CLEAR(pkgname);
goto error;
}
fullmodname = PyUnicode_FromFormat("%U.%U", pkgname, name);
if (fullmodname == NULL) {
Py_DECREF(pkgname);
return NULL;
}
x = PyImport_GetModule(fullmodname);
Py_DECREF(fullmodname);
if (x == NULL && !_PyErr_Occurred(tstate)) {
goto error;
}
Py_DECREF(pkgname);
return x;
error:
pkgpath = PyModule_GetFilenameObject(v);
if (pkgname == NULL) {
pkgname_or_unknown = PyUnicode_FromString("<unknown module name>");
if (pkgname_or_unknown == NULL) {
Py_XDECREF(pkgpath);
return NULL;
}
} else {
pkgname_or_unknown = pkgname;
}
if (pkgpath == NULL || !PyUnicode_Check(pkgpath)) {
_PyErr_Clear(tstate);
errmsg = PyUnicode_FromFormat(
"cannot import name %R from %R (unknown location)",
name, pkgname_or_unknown
);
/* NULL checks for errmsg and pkgname done by PyErr_SetImportError. */
PyErr_SetImportError(errmsg, pkgname, NULL);
}
else {
_Py_IDENTIFIER(__spec__);
PyObject *spec = _PyObject_GetAttrId(v, &PyId___spec__);
const char *fmt =
_PyModuleSpec_IsInitializing(spec) ?
"cannot import name %R from partially initialized module %R "
"(most likely due to a circular import) (%S)" :
"cannot import name %R from %R (%S)";
Py_XDECREF(spec);
errmsg = PyUnicode_FromFormat(fmt, name, pkgname_or_unknown, pkgpath);
/* NULL checks for errmsg and pkgname done by PyErr_SetImportError. */
PyErr_SetImportError(errmsg, pkgname, pkgpath);
}
Py_XDECREF(errmsg);
Py_XDECREF(pkgname_or_unknown);
Py_XDECREF(pkgpath);
return NULL;
}
``` | {
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] | closed | false | null | [] | null | 1 | 2021-03-13T14:33:57Z | 2021-03-13T14:51:33Z | 2021-03-13T14:51:23Z | NONE | null | - [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).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
```
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} | 0 | 2021-03-13T15:57:12Z | 2021-03-15T00:00:07Z | 2021-03-14T23:33:31Z | MEMBER | null | We catch the flaky byte in one place, this catches it in one more | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40418 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40418/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40418/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40418/events | https://github.com/pandas-dev/pandas/issues/40418 | 830,932,487 | MDU6SXNzdWU4MzA5MzI0ODc= | 40,418 | BUG: Series/DataFrame.rank returns empty object on failure | {
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] | open | false | null | [] | null | 4 | 2021-03-13T16:17:35Z | 2021-08-19T02:03:02Z | null | MEMBER | null | This was noticed when working on #40288
Example:
```
df = DataFrame({"A": 3 * [object]})
print(df.rank())
print(df.A.rank())
```
The last two lines are an empty DataFrame and empty Series respectively.
The docstring for numeric_only says:
> For DataFrame objects, rank only numeric columns if set to True.
The current behavior with `numeric_only=None` (the default value) is:
Try with all columns. If a TypeError is raised, try with numeric_only=True.
When numeric_only is True and the Series/DataFrame contain no numeric columns, rank then operates on an empty object returning an empty result.
This is causing issues when rank is used in transform lists and dictionaries. Namely, we'd like to have partial-failure for TypeErrors, but rank is returning an empty result instead of raising a TypeError. This could be special-cased, but it seems to me that returning an empty object (at least when numeric_only is not True) is undesirable itself.
Some options I see are:
- Remove None as being an option and replace the default numeric_only=None with numeric_only=False.
- If numeric_only is None and the fallback of selecting only numeric columns results in an empty object, raise a TypeError.
- If selecting only numeric columns results in an empty object (even when numeric_only=True), raise a TypeError.
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