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} | 2 | 2021-02-10T09:53:00Z | 2021-02-25T08:08:18Z | 2021-02-25T00:39:26Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146
This adds support for ignoring failures in `ArrayManager.reduce`.
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} | 17 | 2021-02-10T10:14:09Z | 2021-03-29T17:00:08Z | 2021-03-29T15:55:19Z | CONTRIBUTOR | null | - [x] closes #39593
- [x] closes #40645
- [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
This renames `Styling` with `Table Visualization` and aligns it more with `Chart Visualization`.
It re-organises some of the `Styler` user guide, removing outdated material, including new material with examples. | {
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} | 1 | 2021-02-10T10:22:20Z | 2021-02-10T13:54:05Z | 2021-02-10T13:51:47Z | MEMBER | null | xref #39146
Moved the common part (checking of dimension and indices) to the base class `equals` method so this can be shared, and the actual BlockManager/AraryManager then only need to check the column values. | {
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} | 2 | 2021-02-10T13:14:51Z | 2021-02-10T20:19:15Z | 2021-02-10T14:40:30Z | MEMBER | null | I think ideally the DataFrame does not need to be aware of how the underlying manager stores the data (as 2D, transposed or not), so moving the logic of ensuring 2D/transposed values from the DataFrame set_item-related method into `BlockManager.iset`.
This will help for the ArrayManager, so we don't have to re-reshape there. | {
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} | 1 | 2021-02-10T14:06:32Z | 2021-02-10T14:50:58Z | 2021-02-10T14:49:29Z | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
My question is quite simple, I have some code which builds a list of objects defined in a `Dataclass` and until today I was calling `asdict()` to convert them into dictionaries as I thought this was something I needed to do.
Today I removed this and the excel generation still works as expected but I would like to know if this now supported behaviour.
```python
# previously this was a list of dictionaries, and it is now a list of custom data classes
pd.DataFrame(renewals).reindex(columns=get_report_columns())
```
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https://api.github.com/repos/pandas-dev/pandas/issues/39724 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39724/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39724/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39724/events | https://github.com/pandas-dev/pandas/issues/39724 | 805,618,113 | MDU6SXNzdWU4MDU2MTgxMTM= | 39,724 | QST: Publish my personal modification of the official pandas cheat sheet | {
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] | closed | false | null | [] | null | 2 | 2021-02-10T15:29:29Z | 2021-03-29T15:24:00Z | 2021-03-29T15:24:00Z | CONTRIBUTOR | null | Hi all,
I found that the official cheat sheet could be optimized a bit.
I created my personalized version of the pandas cheat sheet and published it on my github [here](https://github.com/OliEfr/pandas_cheat_sheet).
Is this alright? I mentioned all sources and inspireations and chose the same license.
If you like the sheet, I can create a PR.
Let my know if I violate any license rights or if you feel uncomfortable with me publishing this. | {
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} | 0 | 2021-02-10T15:30:13Z | 2021-02-12T20:12:12Z | 2021-02-12T20:12:12Z | 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.
---
#### Code Sample
```python
import pandas as pd
pd.read_sas('dates_null.sas7bdat')
```
#### Problem description
When a column datetime contains null values it cannot be converted to a dataframe
### Traceback
<details>
```python
Traceback (most recent call last):
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sas7bdat.py", line 52, in _convert_datetimes
return pd.to_datetime(sas_datetimes, unit=unit, origin="1960-01-01")
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/core/tools/datetimes.py", line 805, in to_datetime
values = convert_listlike(arg._values, format)
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/core/tools/datetimes.py", line 345, in _convert_listlike_datetimes
result, tz_parsed = tslib.array_with_unit_to_datetime(
File "pandas/_libs/tslib.pyx", line 249, in pandas._libs.tslib.array_with_unit_to_datetime
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: cannot convert input with unit 'd'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sasreader.py", line 152, in read_sas
return reader.read()
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sas7bdat.py", line 723, in read
rslt = self._chunk_to_dataframe()
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sas7bdat.py", line 771, in _chunk_to_dataframe
rslt[name] = _convert_datetimes(rslt[name], "d")
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sas7bdat.py", line 59, in _convert_datetimes
return sas_datetimes.apply(
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/core/series.py", line 4135, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas/_libs/lib.pyx", line 2467, in pandas._libs.lib.map_infer
File "/home/wertha/source/pandas/pandas/tests/io/sas/data/.test/lib/python3.9/site-packages/pandas/io/sas/sas7bdat.py", line 60, in <lambda>
lambda sas_float: datetime(1960, 1, 1) + timedelta(days=sas_float)
ValueError: cannot convert float NaN to integer
```
</details>
#### Expected Output
A dataframe with NaT when nulls are found.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.14-arch1-1
Version : #1 SMP PREEMPT Sun, 07 Feb 2021 22:42:17 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | 2021-02-10T15:42:23Z | 2021-02-12T20:23:02Z | 2021-02-12T20:12:13Z | CONTRIBUTOR | null | - [x] closes #39725
- [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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] | open | false | null | [] | null | 4 | 2021-02-10T16:22:59Z | 2021-11-08T16:40:20Z | 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
```python
import datetime
import numpy as np
import pandas as pd
# This works fine:
pd.DataFrame([datetime.datetime(3015,1,1,0,0,0)])
# returned dtype is object, all good.
# But the same timestamp as a numpy object fails:
pd.DataFrame(np.array(['3015-01-01T00:00:00.000'], dtype='datetime64[ms]'))
# OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 3015-01-01 00:00:00
```
#### Problem description
pd.DataFrame tries to infer dtypes, and this works for datetime.datetime objects. If they are within the datetime64[ns] limits, they are converted, while if they are outside, the dtype is kept as `object`. For numpy datetime64 objects as input, the same flexibility is not in place, and pd.DataFrame() errors hard instead during auto-conversion.
#### Expected Output
dtype: object when pd.DataFrame is called on the numpy timestamps above
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-62-generic
Version : #70-Ubuntu SMP Tue Jan 12 12:45:47 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 21.0.1
setuptools : 44.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
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 : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2021-02-10T16:35:07Z | 2021-02-10T18:02:18Z | 2021-02-10T17:58:47Z | MEMBER | null | Precursor for https://github.com/pandas-dev/pandas/pull/39692 (just moving code into a separate helper function) | {
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} | 0 | 2021-02-10T17:22:48Z | 2021-02-10T18:51:15Z | 2021-02-10T18:43:28Z | 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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} | 0 | 2021-02-10T18:25:13Z | 2021-03-20T04:21:21Z | 2021-03-20T04:21:21Z | CONTRIBUTOR | null | I have enabled https://cloud.drone.io/ to connect to pandas. We need to configure it, ideally for arm64 builds (only).
cc @xhochy | {
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} | 8 | 2021-02-10T19:16:06Z | 2021-02-11T07:43:02Z | 2021-02-10T23:48:46Z | MEMBER | null | @jreback as discussed in https://github.com/pandas-dev/pandas/pull/39692
Since this is just an alias, this is certainly fine in theory. Although now doing it, I think there is still *some* value of the alias to signal that it is taking a 1D array which can provide useful context when *reading* code that uses this. | {
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} | 0 | 2021-02-10T19:31:43Z | 2021-02-25T06:05:05Z | 2021-02-25T06:05:05Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
This probably happens because we change the `mutated` attribues:
https://github.com/pandas-dev/pandas/blob/a2f42ac2f6a06975776597070075965d22bd2692/pandas/core/window/rolling.py#L532
```
In [1]: frame = DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
In [3]: g = frame.groupby("A")
In [4]: g.apply(lambda x: x.rolling(4).sum()).index
Out[4]:
Int64Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39],
dtype='int64')
In [5]: r = g.rolling(window=4)
In [6]: g.apply(lambda x: x.rolling(4).sum()).index
Out[6]:
MultiIndex([(1, 0),
(1, 1),
(1, 2),
(1, 3),
(1, 4),
(1, 5),
(1, 6),
(1, 7),
(1, 8),
(1, 9),
(1, 10),
(1, 11),
(1, 12),
(1, 13),
(1, 14),
(1, 15),
(1, 16),
(1, 17),
(1, 18),
(1, 19),
(2, 20),
(2, 21),
(2, 22),
(2, 23),
(2, 24),
(2, 25),
(2, 26),
(2, 27),
(2, 28),
(2, 29),
(2, 30),
(2, 31),
(3, 32),
(3, 33),
(3, 34),
(3, 35),
(3, 36),
(3, 37),
(3, 38),
(3, 39)],
names=['A', None])
``` | {
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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**:
#### Code Sample,
```python
import os
class Styler:
absolute_dir = os.pwd()
loader = jinja2.PackageLoader("pandas", absolute_dir +"/io/formats/templates")
env = jinja2.Environment(loader=loader, trim_blocks=True)
template = env.get_template("html.tpl")
```
#### Problem description
It allows including html.tpl as a local static file when styling of the dataframe is needed, and it works when is needed to transform python file into exe via pyinstaller.
#### 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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} | 2 | 2021-02-10T20:58:42Z | 2021-02-15T19:06:55Z | 2021-02-15T19:06:52Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146/, follow-up on https://github.com/pandas-dev/pandas/pull/39722 | {
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} | 0 | 2021-02-10T21:12:33Z | 2021-03-10T14:50:17Z | 2021-03-10T14:50:07Z | 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.
---
Addressing #39396 and #39395 means that after `Styler._compute()` or `Styler.render()` the `_todo` list is cleared.
Since `Styler.export = lambda x: Styler._todo`
and `Styler.use = lambda x: Styler._todo.extend(x)`
This means that exporting `styles` cannot be achieved if a Styler has been rendered at least once.
Possible actions:
1) Update the docs to make users aware of the new behaviour and suffer any regression issues for `export` `use`.
2) Revert the original PR, suffering the duplicate CSS in HTML generated on each render.
3) Solve both issues by clearing the `.ctx` object before render and keeping the `_todo` list, suffering the need to recompute all applied styles on each render.
Perhaps 3 is best - If performance is an issue developers should know only to every render once?
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} | 0 | 2021-02-10T21:49:58Z | 2021-02-10T23:10:49Z | 2021-02-10T23:09:51Z | MEMBER | null | AFAICT this is a relic of using maybe_promote where we should use infer_dtype_from + find_common_type | {
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} | 1 | 2021-02-10T21:52:55Z | 2021-02-10T23:47:37Z | 2021-02-10T23:47:34Z | CONTRIBUTOR | null | - [x] closes #39710
- [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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} | 5 | 2021-02-10T23:30:46Z | 2021-03-05T01:09:48Z | 2021-03-05T01:09:48Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> df = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
>>> df
x y
0 1 4
1 2 5
2 3 6
>>> df.sum(min_count=10)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/generic.py", line 11067, in sum
self, axis, skipna, level, numeric_only, min_count, **kwargs
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/generic.py", line 10787, in sum
"sum", nanops.nansum, axis, skipna, level, numeric_only, min_count, **kwargs
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/generic.py", line 10774, in _min_count_stat_function
min_count=min_count,
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/frame.py", line 8847, in _reduce
res, indexer = df._mgr.reduce(blk_func, ignore_failures=ignore_failures)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 354, in reduce
nbs = blk.reduce(func, ignore_failures)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 400, in reduce
nb = self.make_block(res_values)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 286, in make_block
return make_block(values, placement=placement, ndim=self.ndim)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 2732, in make_block
return klass(values, ndim=ndim, placement=placement)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 143, in __init__
f"Wrong number of items passed {len(self.values)}, "
ValueError: Wrong number of items passed 1, placement implies 2
```
This actually works in `1.1.5`:
```python
>>> df
x y
0 1 4
1 2 5
2 3 6
>>> df.sum(min_count=10)
x NaN
y NaN
dtype: float64
```
#### Problem description
Reduction operations like `sum`, `prod`, etc.. are failing when `min_count` is larger in latest pandas, whereas this used to work in `1.1.5`.
#### Expected Output
```python
>>> df
x y
0 1 4
1 2 5
2 3 6
>>> df.sum(min_count=10)
x NaN
y NaN
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.2
hypothesis : 6.1.1
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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} | 10 | 2021-02-11T00:56:01Z | 2021-04-14T20:38:37Z | 2021-04-11T13:01:50Z | 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
In `1.2.1`:
```python
>>> import pandas as pd
>>> one = pd.Index([], dtype='object')
>>> two = pd.RangeIndex(start=0, stop=0, step=1)
>>> df_one = pd.DataFrame(index=one)
>>> df_two = pd.DataFrame(index=two)
>>> pd.testing.assert_frame_equal(df_one, df_two, check_like=True)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/_testing.py", line 1655, in assert_frame_equal
obj=f"{obj}.index",
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/_testing.py", line 773, in assert_index_equal
_check_types(left, right, obj=obj)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/_testing.py", line 740, in _check_types
assert_class_equal(left, right, exact=exact, obj=obj)
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/_testing.py", line 868, in assert_class_equal
raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
File "/nvme/0/pgali/envs/cudfdev/lib/python3.7/site-packages/pandas/_testing.py", line 1073, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.index are different
DataFrame.index classes are not equivalent
[left]: Index([], dtype='object')
[right]: RangeIndex(start=0, stop=0, step=1)
```
In `1.1.5`:
```python
>>> import pandas as pd
>>> one = pd.Index([], dtype='object')
>>> two = pd.RangeIndex(start=0, stop=0, step=1)
>>> df_one = pd.DataFrame(index=one)
>>> df_two = pd.DataFrame(index=two)
>>> pd.testing.assert_frame_equal(df_one, df_two, check_like=True)
```
#### Problem description
Upto `1.1.5`, both `df_one` and `df_two` were considered to be same dataframe when `check_like=True`, but in latest pandas this is broken.
#### Expected Output
```
>>> pd.testing.assert_frame_equal(df_one, df_two, check_like=True)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.1
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.2
hypothesis : 6.1.1
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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} | [] | closed | false | null | [] | null | 4 | 2021-02-11T01:51:50Z | 2021-03-06T20:41:03Z | 2021-03-06T20:41:03Z | NONE | null | …or axis=1.
- [ ] closes #39672
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- For axis = 1, periods = -periods.
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} | 2 | 2021-02-11T02:37:05Z | 2021-03-21T15:29:21Z | 2021-03-21T15:29:09Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
pd.set_option("display.max_rows", None)
df = pd.DataFrame(np.random.randint(0,100,size=(10000, 100)), columns=np.arange(0,100))
```
```python
print(df.columns)
dir(df)
```
#### Problem description
Changing the display option for max rows to `None` (unlimited) dramatically increases execution time of calls in the console that do not display rows. I observed similar results accessing attributes of non-pandas objects.
#### Expected Output
Execution time increase resulting from `display.max_rows` should only be incurred when rows are displayed.
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} | 11 | 2021-02-11T02:51:45Z | 2021-03-18T16:46:14Z | 2021-03-14T04:03:01Z | MEMBER | null | - [x] closes #39730
xref #36719
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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
- [ ] whatsnew entry
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} | 1 | 2021-02-11T04:12:38Z | 2021-02-11T17:27:05Z | 2021-02-11T07:43:30Z | MEMBER | null | Fix for issue noted by @lithomas1 in https://github.com/pandas-dev/pandas/pull/39731#issuecomment-777179836, which is causing the Benchmarks to fail.
- [ ] ~~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 | 2021-02-11T04:47:39Z | 2021-02-11T13:42:49Z | 2021-02-11T13:42:45Z | MEMBER | null | Split out from https://github.com/pandas-dev/pandas/pull/39392. Just a refactor; puts redundant setup steps in a shared place.
To be honest, I don't actually understand why different GitHub Actions set up pandas in different ways, so didn't change that here.
The `uses` calls can be moved into the composite actions once https://github.com/actions/runner/issues/862 is complete, DRYing it up even more.
- [ ] ~~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
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] | open | false | null | [] | null | 6 | 2021-02-11T04:47:57Z | 2021-06-06T17:38:49Z | null | NONE | null | #### Is your feature request related to a problem?
I wish to use pandas to support ppc64le cpu arch
#### Describe the solution you'd like
need tom modify this script ci/setup_env.sh to incorporate ppc64le in script and also the below works fine for conda installation in ppc64le
#### API breaking implications
NA
#### Describe alternatives you've considered
ie . CONDA_URL for ppc64le as below ,it works well in install conda and add package on using conda install
https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-ppc64le.sh
#### Additional context
N/A
```python
if [ "${TRAVIS_CPU_ARCH}" == "ppc64le" ]; then
CONDA_URL=" https://repo.anaconda.com/miniconda/Miniconda2-latest-Linux-ppc64le.sh "
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} | 1 | 2021-02-11T07:24:38Z | 2021-02-18T04:55:03Z | 2021-02-11T13:46:32Z | CONTRIBUTOR | null | - [x] closes #39686
Remove mention of MultiIndex from rolling docs. | {
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} | 4 | 2021-02-11T07:52:03Z | 2021-02-11T15:37:04Z | 2021-02-11T15:30:45Z | 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
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} | 3 | 2021-02-11T09:43:36Z | 2021-02-21T09:07:56Z | 2021-02-21T09:07:51Z | NONE | null | Hi ! I have some kind of hard time using panda to read a data base. My objective and my issue are simple, yet i'm unable to find a solution on other trending issues.
I'm trying to access the index of a column using it's name :
indicecol=df.columns.get_loc(nomF)
where nomF is the name of the column and is asked as an imput. I think the probleme is that my imput is "PK [m]" and the function doesn't like the brackets.
I have this error message :
File "pandas\_libs\hashtable_class_helper.pxi", line 1683, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'PK [m]'
Does anybody know how to get around this ?
Thx ! | {
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} | 2 | 2021-02-11T09:51:02Z | 2021-11-06T22:58:58Z | 2021-11-06T22:58:58Z | MEMBER | null |
```
In [27]: pd.array(["1 days", "2 days"], dtype="timedelta64[ns]") / np.array([2, 2])
Out[27]:
<TimedeltaArray>
['0 days 12:00:00', '1 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
In [28]: pd.array(["1 days", "2 days"], dtype="timedelta64[ns]") / np.array([2, 2], dtype=object)
Out[28]:
<TimedeltaArray>
['0 days 12:00:00', '1 days 00:00:00']
Length: 2, dtype: timedelta64[ns]
In [29]: pd.array(["NaT", "NaT"], dtype="timedelta64[ns]") / np.array([2, 2])
Out[29]:
<TimedeltaArray>
[NaT, NaT]
Length: 2, dtype: timedelta64[ns]
In [30]: pd.array(["NaT", "NaT"], dtype="timedelta64[ns]") / np.array([2, 2], dtype=object)
Out[30]: array([NaT, NaT], dtype=object)
```
The last case doesn't result in a TimedeltaArray as result.
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} | 8 | 2021-02-11T11:04:08Z | 2021-03-08T19:54:24Z | 2021-03-08T14:32:32Z | CONTRIBUTOR | null | Code proposed in the staled PR #38866
- [x] closes #38844
- [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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] | open | false | null | [] | null | 0 | 2021-02-11T11:13:09Z | 2021-08-15T17:42:31Z | 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.
---
**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
# build dataframe and write to hdf file
t = pd.DataFrame([i for i in range(100)],
columns=['A'],
index=pd.MultiIndex.from_arrays(
[[b for a in range(25) for b in ['a', 'b', 'c', 'd']],
range(100)],
names=('la','lb')
)).sort_index()
hdf_test = pd.HDFStore('test.hdf', mode='a', compression=9)
t.to_hdf(hdf_test,
'/test',
complevel=9,
format='table'
)
# build query strings
query1 = []
query2 = []
for idx in t.index:
query1.append(
f"(la == '{idx[0]}' & lb == '{idx[1]}')"
)
query2.append(
f"la == {idx[0]}"
)
query1 = ' | '.join(np.unique(query1))
query2 = ' | '.join(np.unique(query2))
# test queries
hdf_test.select('/test', where="(la == 'a' & lb == '0') | (la == 'a' & lb == '12')") # works
hdf_test.select('/test', where=query2) # works
hdf_test.select('/test', where=query1) # fails with ValueError: too many inputs
```
#### Problem description
The select query fails, I guess the reason is the long query string.
Note, in an earlier pandas version (1.2.1) this triggered a `MemoryError` instead of the `ValueError: too many inputs`.
#### Expected Output
dataframe according to query string. (In above case, the full dataframe `t`)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
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 : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-02-11T12:28:59Z | 2021-02-17T09:38:14Z | 2021-02-17T09:37:19Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146/ | {
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] | closed | false | null | [] | null | 1 | 2021-02-11T17:29:04Z | 2021-02-12T14:35:34Z | 2021-02-12T14:35:33Z | 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.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
This is about trying to reindex a MultiIndex level to add keys to avoid a `KeyError`.
This code could be more minimalist and more generic but the context in which the error arose might be helpful..
When using `groupby` unnecessary keys are eliminated..
```
idx = pd.IndexSlice
df = pd.DataFrame([
['A', 'cat', 10],
['A', 'cat', 20],
['B', 'dog', 30],
['B', 'cat', 40]
], columns = ['Label', 'Animal', 'Score'])
g = df.groupby(['Label', 'Animal']).agg('sum')
g.index
>>> MultiIndex([('A', 'cat'),
('B', 'cat'),
('B', 'dog')],
names=['Label', 'Animal'])
g.loc[idx[:, 'dog'], :] # <--- WORKS because 'dog' is an element of level 1.
```
But...
```
g = df[df['Label']=='A].groupby(['Label', 'Animal']).agg('sum')
g.index
>>> MultiIndex([('A', 'cat')],
names=['Label', 'Animal'])
g.loc[idx[:, 'dog'], :] # <--- KeyError because 'dog' is NOT an element of level 1.
```
I tried to solve this with
```
g2 = g.reindex(pd.Index(['cat', 'dog'], name='Other'), level=1)
g2.index
>>> MultiIndex([('A', 'cat')],
names=['Label', 'Animal']). # <-- Reindex did nothing.
```
The docs for `Index.reindex` don't mention the `level` argument or give any examples, the docs for `DataFrame.reindex` were better but still not actually consistent with the arguments and no multiindex examples. And I tried to find any relevant parts of the guide which deal with reindexing multi indexes but without success.
Now that Indexing with missing keys returns a `KeyError` is it more important to provide example for these issues? Or have I missed something very simple.
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} | 0 | 2021-02-11T18:55:24Z | 2021-02-12T01:21:36Z | 2021-02-12T01:21:36Z | MEMBER | null | ```
import numpy as np
import pandas as pd
mi = pd.MultiIndex.from_product([list("ABCDE"), range(2)])
dti = pd.date_range("2016-01-01", periods=10)
ser = pd.Series(dti, index=mi)
df = ser.unstack()
index = df.index.append(pd.Index([1]))
columns = df.columns.append(pd.Index(["foo"]))
axes = {"index": index, "columns": columns}
fv = np.timedelta64("NaT", "ns")
res = df.reindex(fill_value=fv, **axes)
>>> res
>>> res
0 1 foo
A 1451606400000000000 1451692800000000000 NaT
B 1451779200000000000 1451865600000000000 NaT
C 1451952000000000000 1452038400000000000 NaT
D 1452124800000000000 1452211200000000000 NaT
E 1452297600000000000 1452384000000000000 NaT
1 NaT NaT NaT
```
The underlying problem here is https://github.com/numpy/numpy/issues/12550 | {
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} | 6 | 2021-02-11T19:32:25Z | 2021-03-14T23:43:26Z | 2021-03-14T23:43:26Z | NONE | null | I'm getting this error when I try converting certain `Series` to datetime using `pd.to_datetime`, even though I pass `errors='ignore'`
```python
_ = pd.to_datetime(pd.Series([['a']]*50), errors='ignore') # works
_ = pd.to_datetime(pd.Series([['a']]*51), errors='ignore') # error
```
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Python38\lib\site-packages\pandas\core\tools\datetimes.py", line 801, in to_datetime
cache_array = _maybe_cache(arg, format, cache, convert_listlike)
File "C:\Python38\lib\site-packages\pandas\core\tools\datetimes.py", line 173, in _maybe_cache
if not should_cache(arg):
File "C:\Python38\lib\site-packages\pandas\core\tools\datetimes.py", line 137, in should_cache
unique_elements = set(islice(arg, check_count))
TypeError: unhashable type: 'list'
``` | {
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} | 5 | 2021-02-11T21:38:59Z | 2021-11-20T15:55:55Z | 2021-11-20T15:55:55Z | CONTRIBUTOR | null | The following raises a ValueError due to comparison of the two names in the function _maybe_match_name (core/ops/common.py).
```python
>>> from pandas import Series
>>> import numpy as np
>>> series_a = Series([1,2,3], name=np.float64(1.0))
>>> series_b = Series([1,2,3], name=(np.float64(1.0), np.float64(2.0)))
>>> series_a + series_b
```
It seems to only occur when comparison a tuple of numpy dtypes to a single numpy dtype, but I didn't test every possible combination. It does not occur when using built-in floats, I imagine this is due to overloading of the == operator by the numpy dtypes when the other operand is a tuple.
There is a nearby TODO "what if they both have np.nan for their names?" I don't know how feasible it is to consider every case where equality fails, but it seems a try/except returning None would seem to resolve this issue, and be in line with the default return value if the names do not compare equal. I would be keen to implement this simple fix if it seems sensible.
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.0.3
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 47.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.8.1
bs4 : 4.9.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.51.2 | {
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} | 1 | 2021-02-11T21:43:11Z | 2021-03-19T14:22:49Z | 2021-03-19T14:22:49Z | MEMBER | null | Currently, we upload one coverage report from `actions-37-cov`. It takes the longest. But Codecov automatically merges multiple reports. See details https://docs.codecov.io/docs/merging-reports. So we could run coverage on multiple shorter builds.
We need to select a set of builds (create new ones if needed), add `-cov` to the name of their environment files (suggested by @jreback ), update their CI configs to upload reports to Codecov, and remove the `actions-37-cov` build if it's no longer required.
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} | 1 | 2021-02-11T23:06:21Z | 2021-02-12T01:39:54Z | 2021-02-12T01:22:32Z | MEMBER | null | - [ ] closes #xxxx
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} | 0 | 2021-02-11T23:09:03Z | 2021-02-12T15:24:22Z | 2021-02-12T01:27:36Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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started off just moving more tests to use SetitemCastingEquivalents, found this in TestSeriesNoneCoercion | {
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} | 1 | 2021-02-12T00:18:55Z | 2021-02-23T19:37:16Z | 2021-02-15T23:20:47Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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Ref: #12653. I added the message to apply, aggregate, transform, and groupby's filter. Not sure if this needs a whatsnew.
cc @jbrockmendel | {
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] | open | false | null | [] | null | 1 | 2021-02-12T00:23:07Z | 2021-08-15T18:49:25Z | null | MEMBER | null | cc @jorisvandenbossche this is coming up the quantile tests with --array-manager
```
index = pd.period_range("2016-01-01", periods=9, freq="D", name="A")
obj = pd.DataFrame(index).copy()
obj.iloc[:] = index._na_value
>>> obj.dtypes
A datetime64[ns]
dtype: object
```
We'd like to keep `period[D]` here. | {
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] | open | false | null | [] | null | 1 | 2021-02-12T00:48:51Z | 2021-08-15T17:45:25Z | 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
N/A
```
#### Problem description
I've been running into an issue lately where when is use `to_clipboard` the results paste into excel with a lot of garbled text. Any idea what can be going on here? I haven't found any minimal example to consistently reproduce, but it is happening a lot for me. `to_exce`l still works interestingly enough, but I do prefer the convenience of the clipboard.
#### Expected Output
Not this:
Here's another to verify that it is rather random what I get:
#### Output of ``pd.show_versions()``
<details>
Does work for me. I get the following error:
```
ImportError: Can't determine version for tables
```
I'm on pandas 1.2.2 though
</details>
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} | 4 | 2021-02-12T02:07:52Z | 2021-02-16T00:20:08Z | 2021-02-15T23:52:40Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Once enforced, maybe_promote behavior will match DatetimeArray._validate_setitem_value and infer_dtype_from_scalar
sits on top of #39743 | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 0 | 2021-02-12T06:12:32Z | 2021-02-12T12:04:30Z | 2021-02-12T12:04:21Z | CONTRIBUTOR | null | - [ ] closes #xxxx
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- [ ] whatsnew entry
The visualization doc had an example where a column in a DataFrame is repeatedly assigned.
```python
df["b"] = df["b"] = df["b"] + np.arange(1000)
```
The expression has been simplified.
---
Thanks for helping to maintain the library!
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} | 0 | 2021-02-12T07:17:24Z | 2021-05-31T17:41:02Z | 2021-05-31T17:41:02Z | MEMBER | null | This is a regression in master due to https://github.com/pandas-dev/pandas/pull/39606, so we should fix it before 1.3.
```
In [1]: pd.Series([1, 2, 3], dtype="Int64").quantile(0.75)
..
~/scipy/pandas/pandas/core/internals/blocks.py in quantile(self, qs, interpolation, axis)
1858 # shape[0] should be 1 as long as EAs are 1D
1859 assert result.shape == (1, len(qs)), result.shape
-> 1860 result = type(self.values)._from_factorized(result[0], self.values)
1861
1862 return make_block(result, placement=self.mgr_locs, ndim=2)
~/scipy/pandas/pandas/core/arrays/base.py in _from_factorized(cls, values, original)
255 ExtensionArray.factorize : Encode the extension array as an enumerated type.
256 """
--> 257 raise AbstractMethodError(cls)
258
259 # ------------------------------------------------------------------------
AbstractMethodError: This method must be defined in the concrete class type
```
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https://api.github.com/repos/pandas-dev/pandas/issues/39772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39772/events | https://github.com/pandas-dev/pandas/pull/39772 | 807,147,825 | MDExOlB1bGxSZXF1ZXN0NTcyNDQ0MjE1 | 39,772 | REF: move actual lookup and dispatch to array_op from frame into internals | {
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] | open | false | null | [] | null | 18 | 2021-02-12T11:10:27Z | 2021-05-26T00:08:18Z | null | MEMBER | null | Background: I am looking at ways to optimize ops for ArrayManager, and currently the manager hasn't much control (apart from the `operate_blockwise`): it is the DataFrame in `_dispatch_frame_op` that decides which `array_op` function to use and actually calls it on the columns or dispatches it to the manager (with apply or operate_blockwise). For ArrayManager, more control would be useful (one example: deciding whether to use numexpr or not now takes a lot of time in the benchmarks for ArrayManager, because it gets done column by column, while we could do it more efficiently inside the ArrayManager).
So the idea would then be:
- DataFrame is responsible for all preprocessing: checking the `right` argument, alignment, etc (which is already done right now in `DataFrame._arith_method` -> `ops.align_method_FRAME`). This happens on the DataFrame level because it involves frame-level logic (alignment) and can be shared for BlockManager/ArrayManager
- Manager is responsible for executing the actual operation on *clean* arguments: it gets either a scalar, array (with correct length to either match to axis 0 or 1) or another Manager (of the same size).
Note that making the Manager responsible to *call* the execution of the op, doesn't mean the actual code needs to live in the internals. It can still continue to use to array_ops code in /core/ops (and also the ArrayManager will continue to do that, but with some adaptations, eg with some other arguments like passing through a pre-determined `use_numexpr`).
So for the BlockManger, the diff is only small here (it's mainly moving the case of frame/series which works column by column into BlockManager). I think it's actually an OK change for BlockManager on itself as well, but so the main goal is to allow more customization for ArrayManager in follow-up PRs.
Thoughts on the general idea?
cc @jbrockmendel @jreback | {
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] | closed | false | null | [] | null | 2 | 2021-02-12T12:28:33Z | 2021-08-15T17:51:40Z | 2021-08-15T17:51:40Z | NONE | null | #### Is your feature request related to a problem?
I am able to filter columns of dataframes by using `df.filter(like="COLUMNNAME",axis=1)` and I would like to do the reverse, excluding these columns.
#### Describe the solution you'd like
I think a possible solution would be the implementation of a not_like argument. Alternatively, an operator similar to `~` in the `.loc[]` method.
#### API breaking implications
-
#### Describe alternatives you've considered
The alternative for achieving this goal is either using ´.filter()´ with an appropriate regex or using ´.loc[:,~df.columns.str.contains("COLUMNNAME")´. Both methods are feasible, but either more complicated or less readable than the proposed method.
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} | 4 | 2021-02-12T12:39:10Z | 2021-02-13T09:57:59Z | 2021-02-13T09:57:48Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
No longer limiting `abs-imports` to just pandas. This requires telling `isort` that `benchmarks` is a local folder, else it groups it with third-party imports | {
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] | open | false | null | [] | null | 0 | 2021-02-12T13:18:02Z | 2021-08-15T17:54:43Z | null | CONTRIBUTOR | null | Since the introduction of `KeyError` for missing keys in an `index` there have been quite a few use cases from different issues. I will try and link some of the issues if I see them.
My view is that `KeyErrors` for `Index` is fine, but `MultiIndexes` should be treated differently: you cannot always raise a `KeyError` for a single keys in a `MultiIndex` slice since a `MultiIndex` **cannot** always be reindexed.
## Index
```
indexes = [
pd.Index(['a','b','c','e','d'], name='Unique Non-Monotonic'),
pd.Index(['a','b','c','e','d'], name='Unique Monotonic').sort_values(),
pd.Index(['a','b','b','e','d'], name='Non-Unique Non-Monotonic'),
pd.Index(['a','b','b','e','d'], name='Non-Unique Monotonic').sort_values(),
]
```
<details>
<summary>Code generator</summary>
```
ret = None
def do(command):
try:
exec(f'global ret; ret={command}', globals())
except KeyError:
return 'KeyError'
else:
if isinstance(ret, (np.int64)):
return 'int64'
elif isinstance(ret, (pd.Series)):
return 'Series'
elif isinstance(ret, (pd.DataFrame)):
return 'DataFrame'
return 'OtherType'
cases = [
"'a'", # single valid key
"'!'", # single invalid key
"['a']", # single valid key as pseudo multiple valid keys
"['!']", # single invalid key as pseudo multip valid keys
"['a','e']", # multiple valid keys
"['a','!']", # at least one invalid keys
"'a':'e'", # valid key slice
"'a':'!'", # at least one invalid slice key
"'!':", # at least one invalid slice key
"'b'", # single valid non-unique key
"['b']", # single valid non-unique key as pseudo multiple keys
"'b':'d'", # slice with non-unique key
]
base = [
[f's.loc[{case}]', # use regular s.loc[]
f's.loc[ix[{case}]]'] # and with index slice as comparison s.loc[ix[{}]]
for case in cases
]
commands = [
command for sublist in base for command in sublist
]
indexes = [
pd.Index(['a','b','c','e','d'], name='Unique Non-Monotonic'),
pd.Index(['a','b','c','e','d'], name='Unique Monotonic').sort_values(),
pd.Index(['a','b','b','e','d'], name='Non-Unique Non-Monotonic'),
pd.Index(['a','b','b','e','d'], name='Non-Unique Monotonic').sort_values(),
]
results = pd.DataFrame('', index=commands, columns=['Uq Non-Mono', 'Uq Mono', 'Non-Uq Non-Mono', 'Non-Uq Mono'])
for j, index in enumerate(indexes):
s = pd.Series([1,2,3,4,5], index=index)
for i, command in enumerate(commands):
results.iloc[i, j] = do(command)
```
</details>
This seems to be pretty consistent. The only inconsistency is perhaps highlighted in red, and a minor niggle for dynamic coding might be the different return types in the case of non-unique indexes.
Obviously the solution to dealing with any case where you need to index by pre-defined levels that may have been filtered is to `reindex` with your pre-defined keys. Any this is quite easy to do in RAM.
## MultiIndex
```
indexes = [
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'z'), ('a','x'), ('a', 'z')]),
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'z'), ('a','x'), ('a', 'z')]).sortlevel()[0],
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'x'), ('a','x'), ('a', 'z')]),
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'x'), ('a','x'), ('a', 'z')]).sortlevel()[0],
]
```
MultiIndexing is different. You **cannot** always reindex for one of two reasons:
- The number of possible combinations of the index level values exceeds ram and is computationally slow.
- If you are to add in a value or set of values to a MultiIndex level the process is ambiguous and expanding all combinations will lead to above problems.
For example, consider the MultiIndex levels: (a,b), (x,y,z). There are a maximum of 6 index tuples but practically one will work with indexes of much less than the maximum combinations *(Since the combinations scale exponentially with the number of levels)*. Your MultiIndex is thus [(a,x), (a,z), (b,x), (b,y)].
I think **you need** to be able to index MultiIndexes with keys that are missing. As a rule I would suggest that slices which are an iterable do not yield KeyErrors. Here is a summary of some of the observances below for current behaviour:
```
[a, y] : KeyError
[a, [y]] : KeyError but should return empty (a in level0)
[[a], y] : KeyError but should return empty (y in level1)
[[a], [y]] : KeyError but should return empty
[a, !] : KeyError
[a, [!]] : returns empty
[[a], !] : KeyError (maybe OK since ! not in level1)
[[!], x] : returns empty (x in level1)
[[!], [!]] : returns empty
[!, !] : KeyError
```
<details>
<summary>Code generator</summary>
```
cases_level0 = [
"'a'", # single valid key on level0
"'!'", # single invalid key on level0
"['a']", # single valid key on level0 as pseudo multiple valid keys
"['!']", # single invalid key on level0 as pseudo multiple valid keys
"['a', 'b']", # multiple valid key on level0
"['a', '!']", # at least one invalid key on level0
"'a':'b'", # valid level0 index slice
"'a':'!'", # invalid level0 index slice
"'!':", # fully invalid level0 index slice
]
comments_level0 = [
'0: valid single, ',
'0: invalid single, ',
'0: valid single as multiple, ',
'0: invalid single as multiple, ',
'0: multiple valid, ',
'0: one invalid in multiple, ',
'0: valid slice, ',
'0: semi-invalid slice, ',
'0: invalid slice, ',
]
base = [
[f's.loc[{case}]', f's.loc[ix[{case}, :]]'] for case in cases_level0
]
commands = [
command for sublist in base for command in sublist
]
indexes = [
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'z'), ('a','x'), ('a', 'z')]),
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'z'), ('a','x'), ('a', 'z')]).sortlevel()[0],
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'x'), ('a','x'), ('a', 'z')]),
pd.MultiIndex.from_tuples([('b','x'), ('b', 'y'), ('b', 'x'), ('a','x'), ('a', 'z')]).sortlevel()[0],
]
results = pd.DataFrame('', index=commands, columns=['Uq Non-Mono', 'Uq Mono', 'Non-Uq Non-Mono', 'Non-Uq Mono'])
for j, index in enumerate(indexes):
s = pd.Series([1,2,3,4,5], index=index)
for i, command in enumerate(commands):
results.iloc[i,j] = do(command)
base = [
[com, com] for com in comments_level0
]
comments = [
com for sublist in base for com in sublist
]
results['Comment'] = comments
results.style
cases_level1 = [
"'x'", # single valid key on level1
"'y'", # single sometimes-valid key on level1
"'!'", # single invalid key on level1
"['x']", # single valid key on level1 as pseudo multiple valid keys
"['y']", # single sometimes-valid key on level1 as pseudo multiple valid keys
"['!']", # single invalid key on level1 as pseudo multiple valid keys
"['x', 'y']", # multiple sometimes-valid key on level1
"['x', '!']", # at least one invalid key on level1
"'x':'y'", # sometimes-valid level0 index slice
"'x':'!'", # invalid level1 index slice
"'!':", # fully invalid level1 index slice
]
comments_level1 = [
'1: valid single',
'1: semi-valid single',
'1: invalid single',
'1: valid single as multiple',
'1: semi-valid single as multiple',
'1: invalid single as multiple',
'1: multiple semi-valid',
'1: one invalid in multiple',
'1: semi-valid slice',
'1: semi-invalid slice',
'1: invalid slice',
]
from itertools import product
multi_cases = list(product(cases_level0, cases_level1))
multi_comments = list(product(comments_level0, comments_level1))
base = [
[f's.loc[{case[0]}, {case[1]}]', f's.loc[ix[{case[0]}, {case[1]}]]'] for case in multi_cases
]
commands = [
command for sublist in base for command in sublist
]
results = pd.DataFrame('', index=commands, columns=['Uq Non-Mono', 'Uq Mono', 'Non-Uq Non-Mono', 'Non-Uq Mono'])
for j, index in enumerate(indexes):
s = pd.Series([1,2,3,4,5], index=index)
for i, command in enumerate(commands):
results.iloc[i,j] = do(command)
base = [
[com, com] for com in multi_comments
]
comments = [
com for sublist in base for com in sublist
]
results['comment'] = comments
results.style\
.applymap(lambda v: 'background-color:red;', subset=ix[["s.loc['a', ['!']]", "s.loc['a', ['y']]", "s.loc[['!'], ['!']]", "s.loc[['a'], ['y']]"], :])\
.applymap(lambda v: 'background-color:LemonChiffon;', subset=ix[["s.loc['a', 'x':'y']"], :])
```
</details>
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] | open | false | null | [] | null | 0 | 2021-02-12T13:20:57Z | 2021-08-15T17:57:51Z | null | MEMBER | null | Specific use case comes from https://github.com/pandas-dev/pandas/pull/39612#discussion_r570932180, but in general it can sometimes be useful to be able to create an empty or all-NA array in an efficient way.
Since the ExtensionDtype/Array knows best how to do this efficiently, we can add a method to the extension array interface for this.
One question, though: I mentioned above "empty or all-NA", but this is not the same of course. An actually uninitialized array (`np.empty(n, dtype)`) can still be faster to create as an all-NA array (`np.full(n, NA, dtype)`, or variant with the appropriate NA-sentinel).
But maybe we don't care much about this distinction, and just a version to create all-NA is fine? (or another constant fill_value)
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} | 24 | 2021-02-12T14:06:32Z | 2021-03-09T22:55:44Z | 2021-03-09T22:03:54Z | CONTRIBUTOR | null | - [x] closes #39450
- [x] closes #39017
- [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
Should `encoding_errors` be added to `to_csv` and should `errors` in `to_csv` show a `DeprecationWarning` (for consistent naming)? | {
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} | 1 | 2021-02-12T17:48:32Z | 2021-02-16T09:55:16Z | 2021-02-15T22:31:47Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
xref #39624 in which it took ~15 minutes for @rhshadrach to be told
```
Heading capitalization formatted incorrectly. Please correctly capitalize "pytest.xfail" to "Pytest.xfail"
```
, like this such feedback will be provided much faster | {
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} | 2 | 2021-02-12T18:42:19Z | 2021-02-16T15:43:26Z | 2021-02-16T15:41:51Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
As discussed in the monthly dev meeting - motivation here is to reduce the number of merge conflicts, which single-line imports seem to be a major source of.
some examples of such conflicts (which `force_grid_wrap` would avoid):
#37467 :
```python
<<<<<<< GH37278
from typing import TYPE_CHECKING, Optional
=======
from __future__ import annotations
from typing import TYPE_CHECKING
>>>>>>> master
```
#34426 :
```python
<<<<<<< 8745-from_dummies
from typing import TYPE_CHECKING, Any, Dict, Hashable, List, Optional, Type, Union, cast
=======
from typing import (
TYPE_CHECKING,
Dict,
Hashable,
List,
Optional,
Sequence,
Type,
TypeVar,
Union,
cast,
)
>>>>>>> master
```
#29944 :
```python
<<<<<<< subplot_groups
from collections import Counter, Iterable
from typing import TYPE_CHECKING, Hashable, List, Optional, Sequence, Tuple, Union
=======
from __future__ import annotations
from typing import TYPE_CHECKING, Hashable, List, Optional, Tuple
>>>>>>> master
```
----
This changes a huge number of files in one go, but all I've done is
```
git reset --hard upstream/master
# add `force_grid_wrap` option to setup.cfg
pre-commit run isort -a
git commit -m 'force grid-wrap' -a
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} | 1 | 2021-02-12T21:34:44Z | 2021-04-23T18:51:49Z | 2021-02-15T23:32:25Z | CONTRIBUTOR | null | openpyxl already converts cell values to Python types, preferring
integers where possible. For backwards-compatibility, we have to convert
integers back to float if not `convert_float`
- [ ] closes #xxxx
- [x] tests 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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} | 2 | 2021-02-13T00:07:59Z | 2021-02-15T22:43:49Z | 2021-02-15T22:43:18Z | CONTRIBUTOR | null | This PR adds a reference to our Text Extensions for Pandas library, which adds extension types for representing natural language processing data in Pandas DataFrames, to the "Pandas ecosystem" section of the documentation.
Main web page for our project: https://ibm.biz/text-extensions-for-pandas
Source code: https://github.com/CODAIT/text-extensions-for-pandas
API docs: https://text-extensions-for-pandas.readthedocs.io/
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} | 3 | 2021-02-13T01:14:05Z | 2021-02-26T14:46:40Z | 2021-02-26T14:46:40Z | NONE | null | - [yes ] I have checked that this issue has not already been reported.
- [yes ] 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
idx=pd.date_range('20000101','20201231',periods=50000)
df=pd.DataFrame(data=range(50000),index=idx)
%%time
df.ewm(halflife=pd.Timedelta('100d'),times=df.index).mean()
CPU times: user 1min 13s, sys: 0 ns, total: 1min 13s
Wall time: 1min 13s
%%time
df.ewm(halflife=100).mean()
CPU times: user 1.72 ms, sys: 26 µs, total: 1.75 ms
Wall time: 1.16 ms
```
#### Problem description
ewm passed with times is so slow compared to ewm pased with int
#### Expected Output
ewm passed with time should be similar speed as ewm passed with int
<details>
version 1.2.2
</details>
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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
- [x] A colleague of mine complained about the quality of the `cut` description. I took a chance to update the description of `cut` and `qcut`. If there are some suggestions to improve the description - please let me know.
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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
This makes it feasible to share some of the SetitemCastingEquivalents tests. | {
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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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] | open | false | null | [] | null | 7 | 2021-02-13T10:44:09Z | 2021-08-15T17:59:24Z | null | NONE | null | I need to add a timedelta to a datetime object.
Using the following code it produces unexpected results:
```pyhton
import pandas
samplingRate = 200000000
n = pandas.to_datetime('now')
for index in range (0,3):
sampleTime = float((1 / samplingRate) * index)
td = pandas.Timedelta(sampleTime, unit='s')
print(f'index: {index} -> nanoseconds: {td.nanoseconds:>2} final timestamp: {n + td}')
```
Results are:
```
index: 0 -> nanoseconds: 0 -> final timestamp: 2021-02-13 10:28:32.985419
index: 1 -> nanoseconds: 5 -> final timestamp: 2021-02-13 10:28:32.985419005
index: 2 -> nanoseconds: 10 -> final timestamp: 2021-02-13 10:28:32.985419010
```
as can be seen nanoseconds in the line with index 0 are missing.
Expected: 2021-02-13 10:28:32.985419**000**
Unexpected: 2021-02-13 10:28:32.985419 (nanoseconds value, all 0, is missing)
The data is passed to an influxdb using telegraf which claims that it can't parse the line with index 0 because of the missing **000**
I would assume that this as a bug.
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This will be blocking using numpy 1.20 on Github actions Checks, xref https://github.com/pandas-dev/pandas/pull/36092#issuecomment-771112784 | {
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} | 5 | 2021-02-13T16:41:32Z | 2021-02-16T23:21:23Z | 2021-02-16T23:08:53Z | MEMBER | null | - [x] closes #38884
- [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
One thing I noticed while working on this is that xlrd is in requirements-dev but not environment.yml; not sure if it should be removed from requirements-dev.
cc @jreback @jorisvandenbossche | {
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} | 1 | 2021-02-13T18:49:31Z | 2021-02-16T20:30:47Z | 2021-02-16T17:34:01Z | CONTRIBUTOR | null | Now that `Styler.apply` can accept `ndarray` in all cases we can reorganise the module and wrap all the UI facing functions into one:
- highlight null
- highlight max
- highlight min | {
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} | 0 | 2021-02-13T19:25:47Z | 2021-02-22T15:34:09Z | 2021-02-21T17:18:15Z | MEMBER | null | Also avoid passing unnecesary arg to libreduction, and use a lower-level sanitize function in sanitize_index | {
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] | open | false | null | [] | null | 2 | 2021-02-13T20:53:26Z | 2021-08-15T18:00:19Z | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Currently, in `isin`, `unique` and `duplicated` temporary hash-maps are created, albeit sets would be enough (for `isin` and for some inputs to `unique` and `duplicated`). Thus has at least two drawbacks:
1) Quite a memory overhead: For every key there are additional 8 bytes overhead for values. This ratio will become even worse for 32bit/16bit keys, once they are be used.
2) For big datasets, the running time of the above algorithms is dominated by cache-misses. Thus having twice as many cache-misses, because also values are touched could mean a factor 2 slowdown.
#### Describe the solution you'd like
Pandas uses khash, which also provides set-implementations, which would solve the above two problems.
Given the current infrastructure it would be no big deal to add sets as well.
Another advantage would be: prior investigations have shown, that the usages of sets (as in `isin`) and of maps lead to different optimal strategies for hash-functions and collision-resolution-strategies (see e.g. https://github.com/pandas-dev/pandas/pull/36729#issuecomment-716843946)
#### Describe alternatives you've considered
Given that the most OSes nowdays don't really reserve memory for a proccess if it is not used/written to, one could claim, that the above two problems aren't problems at all (at least for bigger datasets) - because in the above algorithms no values are written to `values`.
The above claim is true, as long as there is no rehashing involved. Once a map is rehashed, the garbage values are copied to the new location, which leads OS to reserve this memory to the process. Thus, preallocating big enough maps/hashes would not trigger the usage of additional memory. However, right now it doesn't not happend for different reasons, one of them being the limitation of the size of the preallocated table to 2²⁰ elements (which is a sane approach imo).
However, using sets and not maps would be a more robust and explicit way to avoid unnecessary memory usage. | {
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} | 3 | 2021-02-13T21:07:24Z | 2021-02-18T19:46:12Z | 2021-02-15T22:42:17Z | MEMBER | null | - [x] closes #39695
- [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 | 2021-02-13T21:24:56Z | 2021-02-15T22:37:49Z | 2021-02-15T22:37:46Z | MEMBER | null | As noted in https://github.com/pandas-dev/pandas/pull/39364#issuecomment-778678278. A little unclear to me why it's not failing in other pull requests, as I can reproduce the issue locally.
- [ ] ~~closes #xxxx~~
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] ~~whatsnew entry~~
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] | open | false | null | [] | null | 18 | 2021-02-13T21:49:29Z | 2021-08-15T18:10:19Z | null | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
```
#### Problem description
Pandas import is failing.
#### Expected Output
Pandas to work.
#### Output of ``pd.show_versions()``
<details>
```
Traceback (most recent call last):
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/__init__.py", line 29, in <module>
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/__init__.py", line 13, in <module>
ImportError: /me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/interval.cpython-37m-x86_64-linux-gnu.so: invalid ELF header
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/__init__.py", line 37, in <module>
ImportError: C extension: /me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/interval.cpython-37m-x86_64-linux-gnu.so: invalid ELF header not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --force' to build the C extensions first.Traceback (most recent call last):
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/__init__.py", line 29, in <module>
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/__init__.py", line 13, in <module>
ImportError: /me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/interval.cpython-37m-x86_64-linux-gnu.so: invalid ELF header
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/__init__.py", line 37, in <module>
ImportError: C extension: /me/anaconda3/envs/py37/lib/python3.7/site-packages/pandas/_libs/interval.cpython-37m-x86_64-linux-gnu.so: invalid ELF header not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --force' to build the C extensions first.
```
</details>
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} | 0 | 2021-02-14T02:43:01Z | 2021-02-15T22:38:21Z | 2021-02-15T22:37:28Z | 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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} | 3 | 2021-02-14T09:15:59Z | 2021-08-15T18:10:59Z | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
To prevent casting str to float. `read_json`, `read_csv`, `read_table` have all supported `dtype` param already , `read_html` should not be left behind.
Maybe related: #21379 #30589
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} | 3 | 2021-02-14T10:27:28Z | 2021-03-23T17:01:39Z | 2021-03-23T17:01:39Z | NONE | null | `pd.DataFrame.asfreq` seems to silently drop rows when the data is not sorted.
```python
d=pd.DataFrame(range(3), index=pd.to_datetime(["2021-01-01", "2021-03-01", "2021-02-01"])).asfreq("MS")
d
# output:
# 2021-01-01 0
# 2021-02-01 2
```
Silent data loss is very dangerous in data analysis as it's hard to detect.
Expected behavior:
* an exception, because dropping rows is not useful
* or automatic sorting (with possibly a parameter in asfreq to be explicit)
<details>
```
INSTALLED VERSIONS
------------------
commit : 9d598a5e1eee26df95b3910e3f2934890d062caa
python : 3.8.5.final.0
pandas : 1.2.1
numpy : 1.19.2
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/39806 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39806/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39806/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39806/events | https://github.com/pandas-dev/pandas/pull/39806 | 807,946,230 | MDExOlB1bGxSZXF1ZXN0NTczMDgwMjM3 | 39,806 | DOC: update cheatsheet | {
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- [x] tests added / passed (not required)
- [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 (not required)
- [x] whatsnew entry (not required)
#### Major changes
1) **Replaced Heading "Data Wrangling ..." with section "General"**
- I found it useful to have the hyperlinks to pandas api reference and user guide included
- Also, all the time time when you work with pandas you need other packages (matplotlib etc.). That's why added hyperlinks for them as well
- There is some place left for other useful links!
2) **Replaces explanatory section "Tidy data" with section "Display & Visualize data"**
- Cheat sheets are for users who already have basic knowledge. So this explanation of Tidy Data is of no use here
- Displaying and visualizing data is one of the main tasks. So this section should be included in the cheat sheet
- On the previous cheat sheet there also was a section "Plotting". But it was very small at the end of the second page.
3) **Added section "Apply Funtions"** (bottom of second page)
- There are many ways to apply functions and I always find it confusing to remember them. So I added them here with a short description
- However, if you have a better suggestion for this section, it can be interchanged
4) **Refractor section "Subset Observation"**
- This section was pretty confusing in styling and layout before. E.g. there where multiple places where you can find the df.loc[] and df.iloc [] functions.
- I reordered this section
- I added df.iloc[row_ind, column_ind] and df.loc[row_ind, column_ind]
Let me know what you think about it and make suggestions.
Oli | {
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} | 3 | 2021-02-14T13:09:25Z | 2021-02-17T07:04:44Z | 2021-02-17T00:16:18Z | CONTRIBUTOR | null | - [x] closes #39716
- [x] tests passed
- [x] linting and typing checks | {
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} | 23 | 2021-02-14T14:38:16Z | 2021-06-09T03:57:22Z | 2021-06-09T03:57:22Z | NONE | null | #### Is your feature request related to a problem?
Reading Stack Overflow, there has been several posts trying to skip blank lines when reading ([post 1](https://stackoverflow.com/questions/39334543/python-pandas-read-excel-doesnt-recognize-null-cell), [post 2](https://stackoverflow.com/questions/54890013/dont-skip-blank-lines-in-pandas-read-excel), [post 3](https://stackoverflow.com/questions/59279177/read-excel-file-pd-read-excel-by-skipping-blank-rows-only), ...). The proposed solution works for pandas v<0.24 but there is no mention about this feature and trying to use it I get:
In pandas 1.1.4 and 1.2: `TypeError: read_excel() got an unexpected keyword argument 'skip_blank_lines'` and no mention in the documentation posted above.
#### Describe the solution you'd like
Pandas `read_excel` and/or `ExcelFile` should get back this argument and functionality
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```
df = DataFrame(columns=["A", "B", "C"])
print(df.groupby(["A"]).sum())
print(df.groupby(["A", "B"]).sum())
```
gives:
```
Empty DataFrame
Columns: [B, C]
Index: []
Empty DataFrame
Columns: []
Index: []
```
This is due to taking two different paths through agg - when `nkeys > 1`, using `_python_agg_general` and otherwise using `GroupByApply.agg`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39812/events | https://github.com/pandas-dev/pandas/pull/39812 | 808,056,543 | MDExOlB1bGxSZXF1ZXN0NTczMTYxNzgw | 39,812 | TYP: typing refactor mypy fix | {
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} | 2 | 2021-02-14T21:57:39Z | 2021-02-16T18:24:35Z | 2021-02-15T17:17:44Z | CONTRIBUTOR | null | `pandas/io/formats/style.py:168: error: Incompatible types in assignment (expression has type "Union[DataFrame, Series]", variable has type "DataFrame") [assignment]`
refactor to remove the `assert` statement otherwise needed to solve the above `mypy` error. | {
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] | open | false | null | [] | null | 3 | 2021-02-14T23:00:29Z | 2021-03-09T14:52:51Z | null | CONTRIBUTOR | null | This describes a procedure for using the command line tool `pyright` (https://github.com/microsoft/pyright/blob/master/docs/command-line.md) to identify places in the pandas code that are missing type declarations. xref #28142
1. Install pyright: See https://github.com/microsoft/pyright#command-line
2. In your pandas development folder, create an empty file `py.typed` in the same folder as `pandas\__init__.py`
3. To get the complete analysis as a text file, in your shell, `cd` to the folder containing `README.md` from pandas, and type `pyright --verifytypes pandas! > pyright.out`
4. To determine the modules that need the most work, use the script shown below named `verifytypes.py` which can be run from the command line as `python verifytypes.py` and will print the top 20 modules that need fixing.
Open issues for adding types:
1. We will need to systematically bring over the typing work done by Microsoft in https://github.com/microsoft/python-type-stubs/tree/main/pandas to help enhance our type declarations.
2. Using `pyright` to determine where thing are missing will not determine if we are missing appropriate overloads. See example below.
3. Most likely, the best way to test if we have all the overloads correct is by fully typing our `tests` code, and adding `# ignore` comments when we are specifically testing for incorrect types.
<details>
<summary>verifytypes.py utility</summary>
```python
import subprocess
import json
import pandas as pd
def getpyrightout() -> bytes:
try:
pyrightout = subprocess.run(
["pyright", "--outputjson", "--verifytypes", "pandas!"],
capture_output=True,
shell=True,
)
except Exception as e:
raise e
return pyrightout.stdout
def processjson(jsonstr: bytes):
d = json.loads(jsonstr)
msgsSeries = pd.Series([k["message"] for k in d["diagnostics"]])
msgsdf = msgsSeries.str.split('"', n=2, expand=True)
msgsdf.columns = ["primary", "element", "extra"]
typemsgs = msgsdf[msgsdf.primary.str.startswith("Type")].copy()
typemsgs["module"] = typemsgs["element"].str.replace(r"\.[A-Z][a-z_A-Z\.]*$", "")
notest = typemsgs[~typemsgs.module.str.startswith("pandas.tests")]
print(
notest.groupby(["module", "primary"])
.size()
.sort_values(ascending=False)
.head(20)
)
if __name__ == "__main__":
processjson(getpyrightout())
```
</details>
<details>
<summary>Example using DataFrame.rename() where overloads are needed</summary>
This is taken from https://github.com/microsoft/python-type-stubs/blob/main/pandas/core/frame.pyi
```python
@overload
def fillna(
self,
value: Optional[Union[Scalar, Dict, Series, DataFrame]] = ...,
method: Optional[Literal["backfill", "bfill", "ffill", "pad"]] = ...,
axis: Optional[AxisType] = ...,
limit: int = ...,
downcast: Optional[Dict] = ...,
*,
inplace: Literal[True]
) -> None: ...
@overload
def fillna(
self,
value: Optional[Union[Scalar, Dict, Series, DataFrame]] = ...,
method: Optional[Literal["backfill", "bfill", "ffill", "pad"]] = ...,
axis: Optional[AxisType] = ...,
limit: int = ...,
downcast: Optional[Dict] = ...,
*,
inplace: Literal[False] = ...
) -> DataFrame: ...
@overload
def fillna(
self,
value: Optional[Union[Scalar, Dict, Series, DataFrame]] = ...,
method: Optional[Union[_str, Literal["backfill", "bfill", "ffill", "pad"]]] = ...,
axis: Optional[AxisType] = ...,
*,
limit: int = ...,
downcast: Optional[Dict] = ...,
) -> Union[None, DataFrame]: ...
@overload
def fillna(
self,
value: Optional[Union[Scalar, Dict, Series, DataFrame]] = ...,
method: Optional[Union[_str, Literal["backfill", "bfill", "ffill", "pad"]]] = ...,
axis: Optional[AxisType] = ...,
inplace: Optional[_bool] = ...,
limit: int = ...,
downcast: Optional[Dict] = ...,
) -> Union[None, DataFrame]: ...
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39814/events | https://github.com/pandas-dev/pandas/pull/39814 | 808,074,373 | MDExOlB1bGxSZXF1ZXN0NTczMTc1MjI1 | 39,814 | REF: implement HybridBlock | {
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} | 2 | 2021-02-14T23:39:53Z | 2021-02-16T17:45:41Z | 2021-02-16T16:59:12Z | MEMBER | null | Methods that would work with any NDArrayBackedExtensionArray, not just DTA/TDA. Written to facilitate experimenting with Blocks backed by 2D EAs. | {
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} | 0 | 2021-02-15T02:48:00Z | 2021-02-15T22:50:11Z | 2021-02-15T22:46:23Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | 2021-02-15T07:24:15Z | 2021-02-15T09:34:35Z | 2021-02-15T09:34:22Z | 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-2.10.1-py2.py3-none-any.whl (185 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.2-py2.py3-none-any.whl (7.2 MB)
Collecting identify>=1.0.0
Downloading identify-1.5.13-py2.py3-none-any.whl (97 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.5.13 nodeenv-1.5.0 pre-commit-2.10.1 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/python/black ... 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.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
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/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
already up to date.
Updating https://github.com/MarcoGorelli/abs-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/abs-imports.
updating v0.1.2 -> v0.2.1.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/abs-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................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
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for use of pytest.xfail..........................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
no-string-hints........................................................................................Passed
abs-imports............................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/39817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/39817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/39817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/39817/events | https://github.com/pandas-dev/pandas/issues/39817 | 808,410,263 | MDU6SXNzdWU4MDg0MTAyNjM= | 39,817 | API: common dtype for bool + numeric: upcast to object or coerce to numeric? | {
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] | open | false | null | [] | null | 7 | 2021-02-15T10:33:11Z | 2021-02-16T20:18:54Z | null | MEMBER | null | We currently have an inconsistency in how we determine the common dtype for bool + numeric.
Numpy coerces booleans to numeric values when combining with numeric dtype:
```python
>> np.concatenate([np.array([True]), np.array([1])])
array([1, 1])
```
In pandas, Series does the same:
```python
>>> pd.concat([pd.Series([True], dtype=bool), pd.Series([1.0], dtype=float)])
0 1.0
0 1.0
dtype: float64
```
except if they are empty, then we ensure the result is object dtype:
```python
>>> pd.concat([pd.Series([], dtype=bool), pd.Series([], dtype=float)])
Series([], dtype: object)
```
And for DataFrame we return object dtype in all cases:
```python
>>> pd.concat([pd.DataFrame({'a': np.array([], dtype=bool)}), pd.DataFrame({'a': np.array([], dtype=float)})]).dtypes
a object
dtype: object
>>> pd.concat([pd.DataFrame({'a': np.array([True], dtype=bool)}), pd.DataFrame({'a': np.array([1.0], dtype=float)})]).dtypes
a object
dtype: object
```
For the nullable dtypes, we also have implemented this a bit inconsistently up to now:
```python
>>> pd.concat([pd.Series([True], dtype="boolean"), pd.Series([1], dtype="Int64")])
0 1
0 1
dtype: Int64
>>> pd.concat([pd.Series([True], dtype="boolean"), pd.Series([1], dtype="Float64")])
0 True
0 1.0
dtype: object
```
So here we preserve numeric dtype for Integer, but convert to object for Float. Now, the reason for this is because `IntegerDtype._get_common_dtype` handles the case of boolean dtype and then uses the numpy rules to determine the result dtype, while the FloatingDtype doesn't yet handle non-float dtypes and thus results in object dtype for anything else (also for float numpy dtype, which is obviously a bug / missing feature)
---
Basically we need to decide what the desired behaviour is for the bool + numeric dtype combination: coerce to numeric or upcast to object? (and then fix the inconsistencies according to the decided rule)
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} | 2 | 2021-02-15T11:31:09Z | 2021-02-21T09:09:14Z | 2021-02-21T09:09:07Z | NONE | null | I have a dataset consisting of 10,00,000 rows and one column has a data type of value eg. 'FY2020-Q1' and I am trying to convert this to a String Data type because I would like to apply a filter of == but it does not work when using with **astype.**
Basically, the conversion does not work on a huge dataset but on a small dataset, it works.
`df['fyq'] = df['fyq'].astype(str) and also`
`df['fyq'] = df['fyq'].astype('string')`
I would like to filter like: `df[df.fyq== 'FY2020-Q4']`
Pandas version in use is **1.2.2**
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