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https://api.github.com/repos/pandas-dev/pandas/issues/40919 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40919/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40919/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40919/events | https://github.com/pandas-dev/pandas/issues/40919 | 856,781,151 | MDU6SXNzdWU4NTY3ODExNTE= | 40,919 | QST: I have tried lots of different suggestions but still cannot import pandas / numpy into pycharm | {
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I have installed both using PIP as seen in photo
but always get this in pycharm
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```
params = {
'field1': 1,
'field2': 'XYZ',
'field3: 100
}
sql = """
select *
from table
where 1 = 1
and table.field1 = %(field1)s
and table.field2 = %(field2)s
and table.field3 = %(field3)s
"""
```
Everything works as it should if I am reading the sql like this:
`df = pd.read_sql(sql, params=params)`
But, if for example `params['field3'] is None` (instead of `100` or any other value) I would like to ignore condition `and table.field3 = %(field3)s`.
Can I use read_sql and have such behaviour with pandas read_sql or jinjasql is my only option? | {
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} | 6 | "2021-04-13T11:02:46Z" | "2021-04-28T15:18:04Z" | "2021-04-28T15:18:04Z" | NONE | null | Example:
```python
import pandas as pd
from scipy import sparse as sc
import numpy as np
np.random.seed(42)
a = np.random.randint(0, 10, size=(200, 5))
a_sp = sc.coo_matrix(a)
a_sp_pd = pd.DataFrame.sparse.from_spmatrix(a_sp)
a_sp_pd.max()
```
Expected output:
```
0 9
1 9
2 9
3 9
4 9
dtype: int64
```
Actual output:
```
Series([], dtype: float64)
```
System info:
```
- numpy [required: >=1.17, installed: 1.20.2]
- scipy [required: >=0.19.1, installed: 1.6.2]
- pandas [required: >=1.0.0,<=1.1.4, installed: 1.2.4]
``` | {
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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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- [x] tests added / passed
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} | 1 | "2021-04-13T15:34:33Z" | "2021-04-14T05:08:38Z" | "2021-04-13T23:19:12Z" | CONTRIBUTOR | null | The current structure is:
```
def highlight_between:
def f:
return x
return self.apply(f)
```
So that `Styler.highlight_quantile` can reuse code this PR refactors the above to:
```
def highlight_between:
return self.apply(_new_name)
def _new_name:
return x
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} | 1 | "2021-04-13T16:49:25Z" | "2021-04-13T20:55:35Z" | "2021-04-13T20:55:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/40932 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40932/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40932/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40932/events | https://github.com/pandas-dev/pandas/issues/40932 | 857,288,372 | MDU6SXNzdWU4NTcyODgzNzI= | 40,932 | Proposal: a new nullable Timestamp data type with support for non-ns resolutions | {
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] | open | false | null | [] | null | 24 | "2021-04-13T20:15:07Z" | "2021-10-17T13:43:55Z" | null | MEMBER | null | Motivation for this proposal: for full dtype support with nullable dtypes, we also need a nullable version of the datetime-like dtypes. For backwards compatibility, we need a new dtype (like we did for the other nullable dtypes), and that's what the proposal below is describing. And when creating a new dtype, I think it is the perfect opportunity to have a different default for the resolution (eg microsecond unit instead of nanosecond).
**Summary:** This proposal puts forward a new ``TimestampDtype``, a nullable extension dtype to hold timestamp data:
- A new timestamp data type that follows the pattern of the nullable dtypes (e.g. integer, boolean) with consistent missing value behaviour.
- A parameterized data type with support for multiple resolutions (from seconds through nanoseconds) and optionally time zones (unifying the tz-naive and tz-aware dtypes into a single ExtensionDtype).
- The new data type can have a better default resolution (e.g. microseconds instead of nanoseconds).
- I suggest using "timestamp" for the dtype name, because 1) we need a different name to differentiate from "datetime64" anyway and 2) this is then internally consistent with our Timestamp scalar. But an alternative could also be "Datetime64" (capitalized).
Full version at https://docs.google.com/document/d/1uCdxjlYAafdHD7f57kpkPsJV2Q9Oaxkg1a8V5steMBM/edit?usp=sharing
This would address #7307
Small illustrative code snippet:
```
>>> s
0 2020-01-01 00:00:00
1 <NA>
2 2020-01-01 02:00:00
dtype: timestamp[us]
>>> s_tz
0 2020-01-01 00:00:00+01:00
1 2020-01-01 01:00:00+01:00
2 2020-01-01 02:00:00+01:00
dtype: timestamp[ns, tz=Europe/Brussels]
```
Looking forward to your thoughts / comments.
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage | {
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} | 9 | "2021-04-13T21:44:48Z" | "2021-05-05T12:41:13Z" | "2021-05-05T12:41:13Z" | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
We got some inconsistencies with iloc between setting a numpy array and a pandas array
```python
df = pd.DataFrame(data={
'col1': [1, 2, 3, 4],
'col2': [3, 4, 5, 6],
'col3': [6, 7, 8, 9],
})
rhs = pd.array([1, 2, 3])
df.iloc[[1, 2, 3]] = rhs
```
#### Problem description
This returns
```
col1 col2 col3
0 1 3 6
1 1 1 1
2 2 2 2
3 3 3 3
```
When switching the rhs to a numpy array, this returns
```
rhs = np.array([1, 2, 3])
col1 col2 col3
0 1 3 6
1 1 2 3
2 1 2 3
3 1 2 3
```
Same happens when using ``[[1, 2, 3], :]`` as indexer
#### Expected Output
We should be consistent here, if we want to change iloc for the numpy case I think we need to deprecate?
Is this already known?
cc @jbrockmendel @jorisvandenbossche
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c8e077fa87a1e60bd6f8b7a9e5d718092bf70739
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-48-generic
Version : #54~20.04.1-Ubuntu SMP Sat Mar 20 13:40:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1320.gc8e077fa87.dirty
numpy : 1.20.0
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.2
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 1.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
None
</details>
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} | 1 | "2021-04-13T22:34:56Z" | "2021-04-20T23:05:19Z" | "2021-04-20T22:48:34Z" | MEMBER | null | Un-revert part of #40510 in the hopes of tracking down where the perf impact was. xref #40852 which un-reverted a different part.
Let's not merge for a few days to make sure that any surprising effects from #40852 show up in the asvs.
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} | 4 | "2021-04-13T23:34:30Z" | "2021-04-20T12:52:12Z" | "2021-04-20T12:52:12Z" | MEMBER | null | This came up in #40651, seems worth resolving. Right now `BaseMaskedArray` just directly returns `self._mask` in the implementation of `isna`. This could lead to some unintuitive user-facing behavior like:
```
>>> ser = pd.Series([1, 1, 1], dtype="Int64")
>>> mask = ser.isna()
>>> mask[:] = True
>>> ser
0 <NA>
1 <NA>
2 <NA>
dtype: Int64
```
Seems like `isna` should return a copy instead to avoid this. One potential downside is losing an API-supported way to obtain 0-cost access to the mask (xref #34873). | {
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] | null | 4 | "2021-04-14T08:08:50Z" | "2021-04-20T00:33:32Z" | "2021-04-20T00:33:32Z" | NONE | null | #### Question about pandas
pandas series append will lost original name if new series name is different, probably can improve by adding an arg like retain_name=True?
```python
# Your code here, if applicable
s1 = pd.Series([1,2,3], name='A')
s2 = pd.Series([4,5,6], name='B')
s3 = pd.Series([7,8,9])
s4 = pd.Series([10,11,12], name='A')
print(s1.append(s2).name)
print(s1.append(s3).name)
print(s1.append(s4).name)
```
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} | 3 | "2021-04-14T08:17:33Z" | "2021-04-15T11:41:31Z" | "2021-04-15T08:56:14Z" | CONTRIBUTOR | null | some of the builds have failed recently due to this ipython directive, so I added the fix as advised in the error warning. | {
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- Is there a reason for the magic number? Why not remove it?
- Alternatively, make it optional with something like pd.set_option('display.max_dir_items', None)?
Which is better?
https://github.com/pandas-dev/pandas/blob/88df92f221a6f7634a825125a54937f1ad01b919/pandas/core/indexes/base.py#L760 | {
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```sh
python3 -m pytest # ...
```
This allows running the tests in a venv. (Otherwise, if pytest is not installed in the venv, it falls back to the system pytest, leading to some surprises down the road.) | {
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} | 2 | "2021-04-14T10:54:48Z" | "2021-04-16T16:24:09Z" | "2021-04-16T16:24:04Z" | CONTRIBUTOR | null | A simple hopefully no-brainer documentation patch; came across this while reading https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.tz_localize.html#pandas-datetimeindex-tz-localize.
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} | 1 | "2021-04-14T14:04:23Z" | "2021-04-16T16:16:25Z" | "2021-04-16T16:14:42Z" | MEMBER | null | - [x] closes #40824
- [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 | "2021-04-14T14:21:19Z" | "2021-04-20T19:59:52Z" | "2021-04-20T19:59:47Z" | NONE | null | If I create a view on a subset of a dataframe with a Multiindex using `.loc`, if I print the index of the subset, it get the expected subset of the Multiindex. However `index.levels` and `index.levshape ` return the values for the original dataframe, not the subset.
```python
import pandas as pd
import numpy as np
index = [[1,1,1,1,2,2,2,2,3,3,3,3],
[1,2,3,4,1,2,3,4,1,2,3,4]]
data = pd.DataFrame(np.random.randn(12,2),index=index)
smalldata = data.loc[(slice(None),slice(1,2)),:]
print(data.index)
print(smalldata.index)
print(smalldata.index.levels)
print(smalldata.index.levshape)
```
#### Problem description
If the index of the created view is only the subset of values which appear in the view, the index methods `levels` and `levshape` should reflect this subset of values, not the whole index of the original dataframe
#### Expected Output
```python
>>> print(smalldata.index.levels)
[[1, 2, 3], [1, 2]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.1.3.el7.x86_64
Version : #1 SMP Thu Nov 29 14:49:43 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2
setuptools : 49.2.0.post20200712
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] tests added / passed
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- [ ] whatsnew entry
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- [ ] tests added / passed
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] | open | false | null | [] | null | 1 | "2021-04-14T15:20:33Z" | "2021-06-07T17:38:18Z" | null | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# create 1us Timedelta - it can be successfully converted to float
a = pd.Timedelta("1us")
print(a)
print(a.total_seconds())
# create 500ns Timedelta - it is not converted successfully to float - result is zero
b = pd.Timedelta("500ns")
print(b)
print(b.total_seconds())
```
#### Problem description
Starting with version 1.1.0 Timedelta objects that represent time shorter than 1 microsecond cannot be successfully converted to float -> the result is always zero. This is also present on the current version (1.2.4). when trying same code on pandas 1.0.5, situation is the opposite: Printed output for timedeltas smaller then 1us is bad; it shows all zeros, but converted float value is valid.
Output in pandas 1.0.5:
```
0 days 00:00:00.000001
1e-06
0 days 00:00:00.000000
5e-07
```
third line has unexpected value, but it doesn't affect float conversion, as can be seen in the 4th line.
Output in pandas 1.2.4:
```
0 days 00:00:00.000001
1e-06
0 days 00:00:00.000000500
0.0
```
now the timedelta representation is fixed, but its float value is 0.0 insteaad of 5e-7
#### Expected Output
```
0 days 00:00:00.000001
1e-06
0 days 00:00:00.000000500
5e-7
```
I am also interested in contributing and helping to fix this issue, if that is needed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Serbian (Latin)_Serbia.1250
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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> At runtime it is a plain dict | {
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] | open | false | null | [] | null | 0 | "2021-04-14T17:51:14Z" | "2021-08-19T04:55:35Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print("Timedelta")
print(pd.Timestamp("2020-01-01 18:00:00.000000001") + pd.Timedelta(days=1))
print("Dateoffset")
print(pd.Timestamp("2020-01-01 18:00:00.000000001") + pd.DateOffset(days=1))
```
Output:
```
$ python bug.py
Timedelta
2020-01-02 18:00:00.000000001
Dateoffset
sys:1: UserWarning: Discarding nonzero nanoseconds in conversion
2020-01-02 18:00:00.000000001
```
#### Problem description
This program produces correct answers in both cases, but gives a warning about discarding nanoseconds when using DateOffset. I think this is because internally it is converted to a pydatetime somewhere. The warning does not occur with Timedelta, presumably because it's just adding the raw values.
#### Expected Output
I expect not to get a warning
```
Timedelta
2020-01-02 18:00:00.000000001
Dateoffset
2020-01-02 18:00:00.000000001
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-33-generic
Version : #36-Ubuntu SMP Wed Aug 15 16:00:05 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
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 : 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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} | 13 | "2021-04-14T19:10:43Z" | "2021-10-18T15:16:36Z" | "2021-10-18T15:16:36Z" | CONTRIBUTOR | null | Need to fix the procedures so that the PDFs (English, Japanese) for the cheat sheet here: https://github.com/pandas-dev/pandas/tree/master/doc/cheatsheet
get uploaded to here:
https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
(and when the Japanese one is there, can update the docs to link to it as well)
Note: If we decide to move the location on the web site, then we should do a redirect, because there are links out there to the cheat sheet in the current location.
Followup to discussion here: https://github.com/pandas-dev/pandas/issues/40168#issuecomment-819671740 | {
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} | 0 | "2021-04-14T19:15:25Z" | "2021-04-16T01:15:17Z" | "2021-04-16T01:07:49Z" | 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 | "2021-04-14T19:45:33Z" | "2021-04-16T18:42:55Z" | "2021-04-16T18:42:55Z" | 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, a copy-pastable example
This refers to the code that is currently on **master** https://github.com/pandas-dev/pandas/commit/84d9c5ed7965b3cc667b5a3a61d5911b1748af49 (2021-04-14). The issues also exist on the latest version of pandas but are different.
```python
import pandas as pd
halflife = "23 days"
baseline_df = pd.DataFrame(
{
"A": ["a", "b", "a", "b", "a", "b"],
"B": [0, 0, 1, 1, 2, 2],
"C": pd.to_datetime(
[
"2020-01-01",
"2020-01-01",
"2020-01-10",
"2020-01-02",
"2020-01-23",
"2020-01-03",
]
)
}
)
cython_result = baseline_df.groupby("A").ewm(halflife=halflife, times="C").mean()
print("cython")
print(cython_result)
print("numba")
numba_result = baseline_df.groupby("A").ewm(halflife=halflife, times="C").mean(engine="numba")
print(numba_result)
expected_result_a = pd.DataFrame([0, 1, 2]).ewm(
halflife=halflife, times=pd.to_datetime(["2020-01-01", "2020-01-10", "2020-01-23"])
).mean()
expected_result_b = pd.DataFrame([0, 1, 2]).ewm(
halflife=halflife, times=pd.to_datetime(["2020-01-01", "2020-01-02", "2020-01-03"])
).mean()
print("expected")
print(" group a")
print(expected_result_a)
print(" group b")
print(expected_result_b)
```
Output:
<details>
```python
cython
B
A
a 0 0.000000
2 0.500000
4 1.094088
b 1 0.000000
3 0.500000
5 1.094088
numba
B
A
a 0 0.000000
2 0.666667
4 1.428571
b 1 0.000000
3 0.666667
5 1.428571
expected
group a
0
0 0.000000
1 0.567395
2 1.221209
group b
0
0 0.000000
1 0.507534
2 1.020088
```
</details>
#### Problem description
There are three problems with the current groupby ewm implementation in the case of non-None times.
1. numba implementation: ignores the times
2. cython implementation: does not use the correct times/deltas in aggregations.pyx in case of multiple groups
3. if the groups are non-trivial the time vector and values become out of sync
I have a branch that fixes these issues, will link to it in a bit.
#### Expected Output
<details>
```python
cython
B
A
a 0 0.000000
2 0.567395
4 1.221209
b 1 0.000000
3 0.507534
5 1.020088
numba
B
A
a 0 0.000000
2 0.567395
4 1.221209
b 1 0.000000
3 0.507534
5 1.020088
expected
group a
0
0 0.000000
1 0.567395
2 1.221209
group b
0
0 0.000000
1 0.507534
2 1.020088
```
</details>
#### Output of ``pd.show_versions()``
<details>
</details>
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} | 5 | "2021-04-14T19:55:40Z" | "2021-04-16T18:43:53Z" | "2021-04-16T18:42:55Z" | CONTRIBUTOR | null | - [x] closes #40951
- [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 | "2021-04-14T20:29:23Z" | "2021-04-21T20:40:19Z" | "2021-04-20T22:50:55Z" | MEMBER | null | - [x] closes #36227
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
I did something like this in the past but this got lost back then between prs.
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} | 3 | "2021-04-14T21:14:15Z" | "2021-04-21T20:40:35Z" | "2021-04-20T23:23:08Z" | MEMBER | null | - [x] closes #39143
- [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 adds support for sort=False to pivot_table if we want to do this | {
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} | 3 | "2021-04-14T22:47:18Z" | "2021-07-09T06:51:17Z" | null | NONE | null | #### Is your feature request related to a problem?
DataFrame.corr(method="spearman") is extremely slow.
method="pearson" is quite slow too.
I can see from my machine resource monitor that the implementation is single threaded. Is it a design choice? If so, there should be at least an optional argument to parallelize it (at C++ level, of course).
I did not check the actual code implementing this method.
#### Describe the solution you'd like
scipy.stats.spearmanr implements this computation on a numpy array in 1/20 of the time in my 6-core machine.
#### API breaking implications
None.
#### Describe alternatives you've considered
Add an optional argument (ex. "parallelize"=[True, False]) so that you give the user this option.
Then, the method should either be reimplemented from scratch at C++ level or we must use the existing scipy.stats function
on the DataFrame.values, wrapping the returned array in a new DataFrame.
#### Additional context
IMPORTANT: DataFrame.corr and spearmanr gives slightly different results (some kind of small rounding error of about 10e-15)
```python
import numpy as np
from scipy.stats import spearmanr
import pandas as pd
df = pd.DataFrame(np.random.rand(1000, 2000))
pd_corr = df.corr(method='spearman') # a few seconds
scipy_corr, p_value = spearmanr(df.values) # <1 sec
np.equal(pd_corr.values, scipy_corr) # False
np.sum(np.abs(corr_m.values - corr_m_sci) > 1e-15) # 0
```
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https://api.github.com/repos/pandas-dev/pandas/issues/40958 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40958/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40958/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40958/events | https://github.com/pandas-dev/pandas/pull/40958 | 858,580,694 | MDExOlB1bGxSZXF1ZXN0NjE1Nzk2MTA4 | 40,958 | CLN: refactor tests outside of class setup in `style/test_matplotlib.py` | {
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} | 1 | "2021-04-15T07:26:55Z" | "2021-04-15T21:55:44Z" | "2021-04-15T17:49:00Z" | CONTRIBUTOR | null | This is a refactor to remove the pytest class setup structure, reverting to class-less fixtures.
It also parametrises some of the tests.
No test is altered or removed.
```
def test_background_gradient(styler):
def test_background_gradient_color(styler):
def test_background_gradient_axis(styler, axis, expected):
def test_text_color_threshold(cmap, expected):
def test_background_gradient_vmin_vmax():
def test_background_gradient_int64():
def test_background_gradient_gmap_array(styler_blank, axis, gmap, expected):
def test_background_gradient_gmap_array_raises(gmap, axis):
def test_background_gradient_gmap_dataframe_align(styler_blank, gmap, subset, exp_gmap):
def test_background_gradient_gmap_series_align(styler_blank, gmap, axis, exp_gmap):
def test_background_gradient_gmap_wrong_dataframe(styler_blank, gmap, axis):
def test_background_gradient_gmap_wrong_series(styler_blank):
``` | {
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} | 0 | "2021-04-15T09:11:13Z" | "2021-04-16T07:11:28Z" | "2021-04-15T17:49:44Z" | MEMBER | null | follow-up to #40708
in #40708 the str accessor was enabled and tests changed in `pandas/tests/arrays/string_/test_string.py` and `pandas/tests/strings/test_string_array.py`
This PR colocates the str accessor test in `pandas/tests/arrays/string_/test_string.py` with those in `pandas/tests/strings/test_string_array.py` and renames it `test_string_methods` -> `test_capitalize` | {
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] | open | false | null | [] | null | 12 | "2021-04-15T12:34:43Z" | "2021-07-19T18:21:37Z" | null | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/35169#issuecomment-712372349
this PR is draft as this change has not yet been discussed. (maybe could keep the StringArray the same and create a StringArrayBase instead)
also probably want to do this after #39908 (which is not yet ready) and then move the common base class into a separate module along with the dtype. (this could be string_base.py or create subdir with maybe base.py, string_arrow.py and string_python.py)
we should leave moving things around for a follow-up to keep the diff simpler to review.
This PR could be a precusor to a follow-up to #40708 to remove duplication https://github.com/pandas-dev/pandas/pull/40708#discussion_r605106321 (or could be done with common helper function or mixin https://github.com/pandas-dev/pandas/pull/40708#discussion_r605674743)
No deduplication here. just creating base class and changes to make the tests pass
This is API breaking, but for experimental API, so we would probably need some documentation or add to #40747 if we do this after #39908 | {
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} | 0 | "2021-04-15T16:56:11Z" | "2021-04-16T07:03:33Z" | "2021-04-16T01:03:57Z" | MEMBER | null | still outstanding:
`test_astype_string` in `pandas/tests/extension/base/casting.py` requires separate PR with fix
`test_convert_dtypes` in `pandas/tests/frame/methods/test_convert_dtypes.py` requires discussion https://github.com/pandas-dev/pandas/pull/40747#issuecomment-812508672... probably need to include in (or follow on to #39908) to at least use the global default
`test_to_html_formatters` in `pandas/tests/io/formats/test_to_html.py`
paramaterisation where pd.StringArray is used directly... could potentially combine with #40962 and expose ArrowStringArray publicly
the changes here to `test_repeat_with_null` need a separate PR to fix so could be excluded here. | {
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} | 3 | "2021-04-15T17:04:55Z" | "2021-04-28T15:54:28Z" | "2021-04-28T14:53:58Z" | MEMBER | null | There's no real perf bump to calling union_categoricals inside cython, better to do it in the python code where we can e.g. get the benefit of mypy. Plus gets us closer to dependency structure goals.
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} | 3 | "2021-04-15T17:45:48Z" | "2021-10-14T15:54:31Z" | null | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html
#### Documentation problem
System:
```
Windows 10 10.0.18363 Build 18363
Firefox 78.9esr (64-bit)
Chrome 89.0.4389.128 (Official Build) (64-bit)
MS Edge 89.0.774.75 (Official build) (64-bit)
Resolution: 1920x1080
```
The `main` element on the page referenced renders too narrowly. This truncates longer entries.
#### Suggested fix for documentation
The definition for the `main` element is as follows:
```
<main class="col-12 col-md-9 col-xl-7 py-md-5 pl-md-5 pr-md-4 bd-content" role="main">
```
Adjusting the flex breakpoint `col-xl` to use eight columns provides enough space for the content.
```
<main class="col-12 col-md-9 col-xl-8 py-md-5 pl-md-5 pr-md-4 bd-content" role="main">
```
It might be that an `overflow` declaration would work, but this seems straightforward and there seems to be room on the right of the page. Of course I don't know how this change would cascade. | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import numpy as np
import pandas as pd
print('Example 1')
df1 = pd.DataFrame(dict(x=[1, 2],
t=[np.datetime64(1, 's'), np.datetime64(3, 's')]))
ewm1 = df1.ewm(halflife='1s', times='t').mean()['x']
ewm2 = df1.ewm(halflife='1s', times='t')['x'].mean()
print(df1, ewm1, ewm2, sep=2 * '\n')
print('\n\nExample 2')
df2 = pd.concat([df1, df1], keys=['a', 'b'], names=['group', None])
ewm3 = (df2
.groupby('group', group_keys=False)
.ewm(halflife='1s', times='t')
.mean())
print(df2, ewm3, sep=2 * '\n')
```
#### Problem description
This code computes a rolling EWM of a two element time series `[1, 2]` with a one second half life, assuming two seconds between the data points. You get different answers depending on how you do the computation. The output I get is the following.
```
Example 1
x t
0 1 1970-01-01 00:00:01
1 2 1970-01-01 00:00:03
0 1.0
1 1.8
Name: x, dtype: float64
0 1.0
1 2.0
Name: x, dtype: float64
Example 2
x t
group
a 0 1 1970-01-01 00:00:01
1 2 1970-01-01 00:00:03
b 0 1 1970-01-01 00:00:01
1 2 1970-01-01 00:00:03
x
group
a 0 1.0
1 2.0
b 0 1.0
1 2.0
```
I would have thought that `ewm1` and `ewm2` should the same, but to me it looks like `ewm2` is incorrect. Also when you combine `ewm()` with `groupby()`, it looks like you get the same wrong answer in `ewm3` as in `ewm2`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.0-15-amd64
Version : #1 SMP Debian 4.9.258-1 (2021-03-08)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
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} | 2 | "2021-04-16T06:25:47Z" | "2021-04-25T02:41:50Z" | "2021-04-21T12:41:03Z" | CONTRIBUTOR | null | - [x] closes #40918
- [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
---
I am not sure if this is a reasonable solution. Cause If users install PyArrow from PyPI on MacOS or Win10(works fine on Linux), it will always raise an `AttributeError` exception.
Should we add some operating system requirments to `pd.read_orc`? ([Related to PyArrow's JIRA #7811](https://issues.apache.org/jira/browse/ARROW-7811)).
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`Series.quantile` does not have the `numeric_only` keyword argument, and obviously doesn't work with the above dtypes.
The work around is to convert the Series to DataFrame and extract the leading column of results.
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} | 2 | "2021-04-16T08:18:19Z" | "2021-04-20T04:26:46Z" | "2021-04-20T04:26:26Z" | NONE | null | In the "getting started tutorial" here: https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html
The following sentence is incorrect
Many pandas operations return a DataFrame or a Series. The describe() method is an example of a pandas operation returning a pandas **Series**.
It should say "The describe() method is an example of a pandas operation returning a pandas **DataFrame**"
Steps to reproduce
>>> import pandas as pd
>>> df = pd.DataFrame({
... "Name" : ["Ally", "Bob", "Charly"],
... "Age" : [15,25,36],
... "Cash": [1000,5000, 20000]});
>>> d = df.describe()
>>> type(d)
<class 'pandas.core.frame.DataFrame'>
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- turning off the black formatter for the overloads, keeping them in `.py` files (what I've done here)
- moving overloads to `.pyi` files, writing a script to keep them in sync (I tried that this, and it seems that `mypy` behaves a bit differently with a `.pyi` file than it does with a `.py` file - will look into this more closely, but it'll take more time)
- keeping overloads out of `.py` files and "injecting" them before running `mypy` via a script - IMO this might be slightly disruptive to the usual workflow of just running `mypy pandas`, but other than that seems like a potentially good idea
- doing away with overloads, and just using `cast` and `assert` - TBH this strikes me as more error-prone, and if pandas will expose types to users, then I think it'd be better to have the correct type revealed rather than having them do `cast` / `assert`
Even if `inplace` is removed in version 2, there are other places in the codebase where there's a parameter which changes the return type, so I do think finding a solution here is worthwhile.
For now, I've gone with the first solution, though I will give some more thought to the second one | {
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] | closed | false | null | [] | null | 2 | "2021-04-16T10:01:41Z" | "2021-04-19T17:22:28Z" | "2021-04-19T17:22:28Z" | MEMBER | null | This method is declared as `final`:
https://github.com/pandas-dev/pandas/blob/526d52f91ee06ba9134762373ab3a0bb38395d8a/pandas/core/generic.py#L3629-L3630
It's then overridden in `series`:
https://github.com/pandas-dev/pandas/blob/526d52f91ee06ba9134762373ab3a0bb38395d8a/pandas/core/series.py#L866
The reason `mypy` doesn't detect this failure is that the override in `series` in untyped, and so `mypy` doesn't check it
---
@jbrockmendel do you know which one is correct? Remove `final` or remove the override? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40975 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40975/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40975/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40975/events | https://github.com/pandas-dev/pandas/pull/40975 | 859,681,865 | MDExOlB1bGxSZXF1ZXN0NjE2NzE5ODc1 | 40,975 | TYP add some missing types to series | {
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] | closed | false | null | [] | null | 1 | "2021-04-16T10:21:50Z" | "2021-04-16T17:31:23Z" | "2021-04-16T16:54:14Z" | MEMBER | null | Noticed this while working on #40973
Those without any types won't be checked by `mypy`
e.g.:
```console
$ cat t.py
from typing import final
class Foo:
@final
def foo(x) -> None:
pass
class Bar(Foo):
def foo(x):
pass
$ mypy t.py
Success: no issues found in 1 source file
```
Adding in `-> None`:
```console
$ cat t.py
from typing import final
class Foo:
@final
def foo(x) -> None:
pass
class Bar(Foo):
def foo(x) -> None:
pass
$ mypy t.py
t.py:9: error: Cannot override final attribute "foo" (previously declared in base class "Foo") [misc]
Found 1 error in 1 file (checked 1 source file)
``` | {
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} | 6 | "2021-04-16T12:10:23Z" | "2021-05-26T02:04:15Z" | "2021-05-26T02:04:15Z" | 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.
---
Between 1.1.5 and 1.2.0rc2 the behavior of slicing MultiIndexes has changed. Up to 1.1.5 slicing a MultiIndex like `df.loc[(slice(None), somel_items), :]` preserved the sequence of the sliced `DataFrame`. From 1.2.0rc0 the sequence is changed.
#### Code Sample
```python
import pandas as pd
print("pandas version %s" % pd.__version__)
index = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (1, 7), (1, 6),
(2, 2), (2, 3), (2, 8), (2, 7)])
all_items = index.get_level_values(1) # all items from level 1
df = pd.DataFrame({'x': range(8)}, index=index)
df_sliced = df.loc[(slice(None), all_items), :]
# df_sliced, should be identical with df, as all_items contains all items from level 1
print(df_sliced)
pd.testing.assert_frame_equal(df, df_sliced)
# works if and only if pd.__version__ < 1.2.0rc0
print("Success")
```
#### Problem description
Running the sample code in 1.1.5 gives the following output:
```
pandas version 1.1.5
x
1 1 0
2 1
7 2
6 3
2 2 4
3 5
8 6
7 7
Success
```
whereas in 1.2.4 it gives
```
pandas version 1.2.4
x
1 1 0
6 3
2 1
2 2 4
3 5
8 6
1 7 2
2 7 7
Traceback (most recent call last):
File "tmp/pandas_demo.py", line 19, in <module>
pd.testing.assert_frame_equal(df, df_sliced)
File "/home/jmu3si/Devel/pylife/.venv/lib/python3.8/site-packages/pandas/_testing.py", line 1657, in assert_frame_equal
assert_index_equal(
File "/home/jmu3si/Devel/pylife/.venv/lib/python3.8/site-packages/pandas/_testing.py", line 805, in assert_index_equal
assert_index_equal(
File "/home/jmu3si/Devel/pylife/.venv/lib/python3.8/site-packages/pandas/_testing.py", line 825, in assert_index_equal
_testing.assert_almost_equal(
File "pandas/_libs/testing.pyx", line 46, in pandas._libs.testing.assert_almost_equal
File "pandas/_libs/testing.pyx", line 161, in pandas._libs.testing.assert_almost_equal
File "/home/jmu3si/Devel/pylife/.venv/lib/python3.8/site-packages/pandas/_testing.py", line 1073, in raise_assert_detail
raise AssertionError(msg)
AssertionError: MultiIndex level [0] are different
MultiIndex level [0] values are different (25.0 %)
[left]: Int64Index([1, 1, 1, 1, 2, 2, 2, 2], dtype='int64')
[right]: Int64Index([1, 1, 1, 2, 2, 2, 1, 2], dtype='int64')
```
I am not sure if this is necessarily a problem, I stumbled across it because a test suite that used `pd.testing.assert_frame_equal()` failed due to this. So either the actual sequence should be preserved when slicing a `DataFrame` or `pd.testing.assert_frame_equal()` should not fail if the sequence is shuffled (but the index is correct).
#### Expected Output
As discussed above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-71-lowlatency
Version : #79-Ubuntu SMP PREEMPT Wed Mar 24 12:38:51 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.2.4 (resp. 1.1.5)
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/40979 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40979/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40979/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40979/events | https://github.com/pandas-dev/pandas/pull/40979 | 859,858,001 | MDExOlB1bGxSZXF1ZXN0NjE2ODY4OTIx | 40,979 | TYP: Signature of "rename" incompatible with supertype "NDFrame" | {
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] | open | false | null | [] | null | 6 | "2021-04-16T14:17:39Z" | "2021-11-16T17:31:13Z" | null | MEMBER | null | - [x] closes #40977
- [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
---
@WillAyd this should still be backwards compatible, it just makes the signature compatible with its parent's | {
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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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- [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
```
#### Problem description
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.9/site-packages/pandas/__init__.py", line 29, in <module>
from pandas._libs import hashtable as _hashtable, lib as _lib, tslib as _tslib
File "/usr/lib/python3.9/site-packages/pandas/_libs/__init__.py", line 13, in <module>
from pandas._libs.interval import Interval
File "pandas/_libs/interval.pyx", line 1, in init pandas._libs.interval
File "/usr/lib/python3.9/site-packages/pandas/_libs/tslibs/__init__.py", line 30, in <module>
from .conversion import OutOfBoundsTimedelta, localize_pydatetime
File "pandas/_libs/tslibs/conversion.pyx", line 58, in init pandas._libs.tslibs.conversion
File "pandas/_libs/tslibs/parsing.pyx", line 1, in init pandas._libs.tslibs.parsing
File "pandas/_libs/tslibs/offsets.pyx", line 1, in init pandas._libs.tslibs.offsets
File "pandas/_libs/tslibs/timestamps.pyx", line 47, in init pandas._libs.tslibs.timestamps
File "pandas/_libs/tslibs/fields.pyx", line 40, in init pandas._libs.tslibs.fields
File "pandas/_libs/tslibs/strptime.pyx", line 627, in init pandas._libs.tslibs.strptime
File "pandas/_libs/tslibs/strptime.pyx", line 532, in pandas._libs.tslibs.strptime.TimeRE.__init__
File "pandas/_libs/tslibs/strptime.pyx", line 408, in pandas._libs.tslibs.strptime.LocaleTime.__init__
File "pandas/_libs/tslibs/strptime.pyx", line 428, in pandas._libs.tslibs.strptime.LocaleTime.__calc_weekday
AttributeError: module 'calendar' has no attribute 'day_abbr'
```
Also I have built the latest 1.2.4 and the bug persists.
#### Expected Output
No output, successful import
<details>
$ pacman -Qi python-pandas [17:56:31]
Name : python-pandas
Version : 1.2.3-1
Description : High-performance, easy-to-use data structures and data analysis tools for Python
Architecture : x86_64
URL : https://pandas.pydata.org/
Licenses : BSD
Groups : None
Provides : None
Depends On : python-numpy python-dateutil python-pytz python-setuptools
Optional Deps : python-pandas-datareader: pandas.io.data replacement (recommended) [installed]
python-numexpr: needed for accelerating certain numerical operations (recommended)
python-bottleneck: needed for accelerating certain types of nan evaluations (recommended)
python-beautifulsoup4: needed for read_html function [installed]
python-jinja: needed for conditional HTML formatting [installed]
python-pyqt5: needed for read_clipboard function (only one needed) [installed]
python-pytables: needed for HDF5-based storage
python-sqlalchemy: needed for SQL database support [installed]
python-scipy: needed for miscellaneous statistical functions [installed]
python-xlsxwriter: alternative Excel XLSX output [installed]
python-blosc: for msgpack compression using blosc
python-html5lib: needed for read_hmlt function (and/or python-lxml) [installed]
python-lxml: needed for read_html function (and/or python-html5lib) [installed]
python-matplotlib: needed for plotting [installed]
python-openpyxl: needed for Excel XLSX input/output [installed]
python-psycopg2: needed for PostgreSQL engine for sqlalchemy [installed]
python-pymysql: needed for MySQL engine for sqlalchemy
python-qtpy: needed for read_clipboard function (only one needed)
python-tabulate: needed for printing in Markdown-friendly format [installed]
python-fsspec: needed for handling files aside from local and HTTP
xclip: needed for read_clipboard function (only one needed) [installed]
python-xlrd: needed for Excel XLS input [installed]
python-xlwt: needed for Excel XLS output [installed]
xsel: needed for read_clipboard function (only one needed)
zlib: needed for compression for msgpack [installed]
Required By : python-arch python-pandas-datareader python-pyportfolioopt-git python-statsmodels
Optional For : python-cclib python-openpyxl python-tabulate
Conflicts With : None
Replaces : None
Installed Size : 55.24 MiB
Packager : Morten Linderud <[email protected]>
Build Date : Thu 11 Mar 2021 01:24:21
Install Date : Fri 16 Apr 2021 17:56:31
Install Reason : Explicitly installed
Install Script : No
Validated By : Signature
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [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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} | 9 | "2021-04-16T17:37:44Z" | "2021-05-24T12:20:54Z" | "2021-04-26T12:20:56Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'name': ['Raphael', 'Donatello', 'Miguel Angel', 'Leonardo'],
'mask': ['red', 'purple', 'orange', 'blue'],
'weapon': ['sai', 'bo staff', 'nunchunk', 'katana']})
df.to_csv("tmnt.csv", index=False, encoding="utf-16")
pd.read_csv(filepath_or_buffer="tmnt.csv", encoding="utf-16", sep=",", header=0, decimal=".", memory_map=True)
```
#### Problem description
This works perfectly with version 1.1.1, but now it doesn't and since this is a nice feature because it removes I/O overhead, I think is good look into this and also because it could break many things
#### Expected Output
```
name mask weapon
0 Raphael red sai
1 Donatello purple bo staff
2 Miguel Angel orange nunchunk
3 Leonardo blue katana
```
#### Traceback
<details>
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "./lib/python3.8/site-packages/pandas/io/parsers.py", line 610, in read_csv
return _read(filepath_or_buffer, kwds)
File "./lib/python3.8/site-packages/pandas/io/parsers.py", line 462, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "./lib/python3.8/site-packages/pandas/io/parsers.py", line 819, in __init__
self._engine = self._make_engine(self.engine)
File "./lib/python3.8/site-packages/pandas/io/parsers.py", line 1050, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "./lib/python3.8/site-packages/pandas/io/parsers.py", line 1898, in __init__
self._reader = parsers.TextReader(self.handles.handle, **kwds)
File "pandas/_libs/parsers.pyx", line 518, in pandas._libs.parsers.TextReader.__cinit__
File "pandas/_libs/parsers.pyx", line 649, in pandas._libs.parsers.TextReader._get_header
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
```
</details>
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.1
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
```
</details>
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} | 3 | "2021-04-16T20:26:55Z" | "2021-05-27T10:26:39Z" | "2021-05-26T02:04:16Z" | MEMBER | null | - [x] closes #40978
- [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 fixes the ordering, but I am not sure if an indexer with duplicates in this case should return the same as an indexer which is unique with the same elements. | {
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] | open | false | null | [] | null | 1 | "2021-04-16T20:30:23Z" | "2021-10-26T20:43:52Z" | null | MEMBER | null | This came up recently in #40861
Depending on the way forward this would allow us to get downcast out from Block or be stricter about it
Thougts?
cc @jbrockmendel @jreback | {
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} | 1 | "2021-04-16T20:44:57Z" | "2021-08-20T13:50:30Z" | "2021-08-20T13:50:30Z" | 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
In [6]: df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...: [3, 4, np.nan, 1],
...: [np.nan, np.nan, np.nan, 5],
...: [np.nan, 3, np.nan, 4]],
...: columns=list('ABCD'))
In [7]: df
Out[7]:
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4
In [8]: df.fillna(axis='columns', value=100, limit=2)
Out[8]:
A B C D
0 100.0 2.0 100.0 0
1 3.0 4.0 100.0 1
2 100.0 100.0 NaN 5
3 NaN 3.0 NaN 4
In [9]: df.fillna(axis='columns', value=100, limit=1)
Out[9]:
A B C D
0 100.0 2.0 100.0 0
1 3.0 4.0 NaN 1
2 NaN 100.0 NaN 5
3 NaN 3.0 NaN 4
```
#### Problem description
Based on the [description](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) of `limit`:
> If method is not specified, this is the maximum number of entries *along the entire axis where NaNs will be filled.* Must be greater than 0 if not None.
I would expect it to apply along the columns axis in this case, but it seems to be applied along the index. It's possible this is intentional, but if that's the case I'd like to update the documentation
#### Expected Output
```python
In [8]: df.fillna(axis='columns', value=100, limit=2)
Out[8]:
A B C D
0 100.0 2.0 100.0 0
1 3.0 4.0 100.0 1
2 100.0 100.0 NaN 5
3 100.0 3.0 100.0 4
In [9]: df.fillna(axis='columns', value=100, limit=1)
Out[9]:
A B C D
0 100.0 2.0 NaN 0
1 3.0 4.0 100.0 1
2 100.0 NaN NaN 5
3 100.0 3.0 NaN 4
```
#### Output of ``pd.show_versions()``
<details>
pandas : 1.2.4
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.13
pytest : 4.6.11
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-04-16T21:30:41Z" | "2021-05-05T14:47:17Z" | "2021-05-05T12:52:29Z" | 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 | "2021-04-16T23:50:24Z" | "2021-04-21T20:39:54Z" | "2021-04-20T22:47:30Z" | MEMBER | null | - [x] closes #22818
- [x] tests added / passed
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I think we should not allow these column collisions at all. Hence deprecating and raising in 2.0
Alternative here would be to add an ``errors`` keyword to the merge and join functions as @simonjayhawkins mentioned in the op. | {
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] | closed | false | null | [] | null | 6 | "2021-04-17T00:14:18Z" | "2021-04-20T01:23:52Z" | "2021-04-19T10:45:53Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.read_json('file.jsonl', orient='records', lines=True)
df['uuid']
```
#### Problem description
I have a `jsonlines` file (`file.jsonl`) that looks like this
```
{"id": 1, "uuid": "1344800117571260417"}
{"id": 2, "uuid": "1344900117571260918"}
```
After reading that file using the above code, this is what I get:
```
id uuid
0 1 1344800117571260416
1 2 1344900117571260928
```
So the `uuid`s have changed
#### Expected Output
My output should look like
```
id uuid
0 1 1344800117571260417
1 2 1344900117571260918
```
On Stackoverflow, someone answered [my post](https://stackoverflow.com/questions/67133093/pandas-changing-values-when-inferring-dtypes?noredirect=1#comment118664602_67133093), with the following explanation:
> int(float("1344900117571260918")) is 1344900117571260928 . I assume pandas first uses floats and afterwards converts it to int - so precision is lost.
So the problem is how `pandas` is inferring the types.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-49-generic
Version : #55~20.04.1-Ubuntu SMP Fri Mar 26 01:01:07 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.18.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.4.3
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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} | 2 | "2021-04-17T00:22:07Z" | "2021-04-21T20:40:07Z" | "2021-04-20T22:47:54Z" | MEMBER | null | - [x] closes #34862
- [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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I think deprecating this is a good idea, I stumbled across this accidentally this week and found this really odd | {
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} | 4 | "2021-04-17T02:09:57Z" | "2021-06-05T20:50:11Z" | "2021-04-26T12:20:57Z" | CONTRIBUTOR | null | - [x] closes #40986
- [x] tests added / passed
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My best guess is that `memory_map=True` always assumed UTF-8 with the python engine. Now that the c and python engine use the same IO code, the c engine assumed UTF-8 as well.
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dti = pd.date_range("2016-01-01", periods=3)
ci = pd.Categorical(dti._values)
>>> pd.Categorical(["foo"], dtype=ci.dtype)
[NaT]
Categories (3, datetime64[ns]): [2016-01-01, 2016-01-02, 2016-01-03]
```
Casting "foo" to pd.NaT here makes very little sense to me, and does not match the behavior of any of our other constructors. I'm pretty sure this is why we have to do so much special-casing for Categorical in dtypes.cast and groupby ops.
Suggestion: we change/deprecate the behavior to raise instead of cast. | {
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I have followed the guidance of **README.md -> Installation from sources** , download the source code of master and excute the install commands. However, I got some errors when running this command
``` bash
python setup.py install
```
It looks like that the installation script called gcc and got some problems, here's the error report:
``` bash
gcc: error: unrecognized command line option '-ffile-prefix-map=/builder/shared-workdir/build/sdk/build_dir/target-aarch64_cortex-a72_musl/Python-3.9.2=Python-3.9.2'
gcc: error: unrecognized command line option '-ffile-prefix-map=/builder/shared-workdir/build/sdk/build_dir/target-aarch64_cortex-a72_musl/Python-3.9.2=Python-3.9.2'
gcc: error: unrecognized command line option '-ffile-prefix-map=/builder/shared-workdir/build/sdk/build_dir/target-aarch64_cortex-a72_musl/Python-3.9.2=Python-3.9.2'
error: command '/usr/bin/gcc' failed with exit code 1
```
How can I solve this problem? Should I change the different gcc version or else?
## Device Information
Platform: Raspberry Pi 4B
CPU: BCM2711 ARM Cortex-A71 64bit
OS: OpenWrt( a linux distribution )
gcc: gcc (OpenWrt GCC 7.4.0) 7.4.0
g++: g++ (OpenWrt GCC 7.4.0) 7.4.0
Python: 3.9.2
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} | 1 | "2021-04-17T11:41:39Z" | "2021-04-30T17:57:25Z" | "2021-04-30T17:24:25Z" | MEMBER | null | this also fixes Interval, but that test doesn't fail in #39908 for a single StringDtype parameterized by storage.
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} | 1 | "2021-04-17T14:43:17Z" | "2021-04-19T23:20:21Z" | "2021-04-19T14:02:53Z" | MEMBER | null | - [x] closes #40885
- [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
Regression on master so no whatsnew
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- [ ] closes #40682
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} | 2 | "2021-04-17T21:34:34Z" | "2021-04-21T20:40:13Z" | "2021-04-20T22:49:56Z" | MEMBER | null | - [x] closes #39882
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None and NaT are different for unique while convert_listlike casts None to NaT, hence causing dups
Not sure if we could do something better. | {
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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
I throw a value error when a dataframe column is assigned to an object with `ndim == 2` with more than one column.
I have added a test to test dataframe column assignment to an array with more than one 'column' and a test to show that assignment still works with an array with only one 'column'.
The tests I added pass but I am unsure why ~600 existing tests fail when I run the entire suite (whether I have my change or not). I doubt the tests are failing because of something on my machine but I want to see if that's the case anyway.
~500 linting tests fail as well when I run `./ci/code_checks.sh`
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} | 1 | "2021-04-18T00:27:11Z" | "2021-04-18T00:31:02Z" | "2021-04-18T00:30:38Z" | NONE | null | Just imagine that I create a dataset with multicolumns like that:
`import pandas as pd`
`arrays = [["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],]`
`tuples = list(zip(*arrays))`
`index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])`
`df = pd.DataFrame(np.random.randn(3, 8), index=["A", "B", "C"], columns=index)`
`df`
OK, I created a df dataset, and I need to keep the name for the dataset using the **columns.name** attribute:
`df.columns.name = 'BOO'`
`df.columns.name`
gives: 'BOO'
Then I made a little alteration in my dataset (e.g. deleting, or adding a column) like this:
`df = df.drop([('bar', 'one')], axis=1)`
`df.columns.name`
gives: None
On single-column datasets, the column.name persist. But on multicolumns, column.name is lost every time I make a even small change on my columns!
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : pt_BR.cp1252
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.4
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### 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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} | 1 | "2021-04-18T03:23:27Z" | "2021-04-20T22:59:10Z" | "2021-04-20T22:56:41Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
In master:
```
df = pd.DataFrame({"key": ["a"] * 4, "val": [1] * 4})
df["val"] = df["val"].astype("Int64")
>>> result = df.groupby("key").rank()
TypeError: cannot safely cast non-equivalent float64 to int64
```
Moves in the direction of sharing/generalizing _ea_wrap_cython_operation.
If I did this right, should avoid overlap with #40651. | {
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} | 0 | "2021-04-18T04:01:23Z" | "2021-04-19T14:43:15Z" | "2021-04-19T13:51:57Z" | MEMBER | null | - [x] closes #27506
- [ ] 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
No need for a try/except; we can reason out which branch we go down in each of the places this is used. | {
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} | 7 | "2021-04-18T05:52:26Z" | "2021-04-20T04:26:36Z" | "2021-04-20T04:26:26Z" | CONTRIBUTOR | null | - [x] closes #40972
- [ ] 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
It feels more verbose, but necessary? | {
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} | 6 | "2021-04-18T06:02:13Z" | "2021-04-22T10:34:56Z" | "2021-04-21T18:31:39Z" | CONTRIBUTOR | null | I added small changes to the Styler User Guide to exemplify the new builtin highlighting functions.
Also removed some of the longer detail which has been moved to individual documentation pages (for example for `background_gradient`) | {
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Dataframe RTT columns: ['TIMESTAMP', 'ID', 'RTT']
Dataframe RSRP columns: ['TIMESTAMP', 'ID', 'RSRP']
Result dataframe columns: ['TIMESTAMP', 'ID', 'RTT', 'RSRP']
Below are my codes:
```python
def map_RSRP(timestamp, id):
rsrp = np.nan
time_diff = np.nan
past_5mins = timestamp - timedelta(minutes = 5)
future_5mins = timestamp + timedelta(minutes = 5)
#Get the RSRP data for same ID with timestamp within 5 minutes difference from RTT timestamp
df_rsrp = df_RSRP.loc[(df_RSRP['ID'] == id) &
(df_RSRP['TIMESTAMP'] >= past_5mins) &
(df_RSRP['TIMESTAMP'] <= future_5mins)]
df_rsrp['Time Difference (minutes)'] = ((df_rsrp['TIMESTAMP'] - timestamp).dt.total_seconds()/60).astype(int)
if len(df_rsrp) > 0:
time_nearest = abs(df_rsrp['Time Difference (minutes)']).min()
df_rsrp = df_rsrp[abs(df_rsrp['Time Difference (minutes)']) == time_nearest]
rsrp = df_rsrp['RSRP'].mean()
return rsrp
#Result dataframe columns: ['TIMESTAMP', 'ID', 'RTT', 'RSRP']
df_rtt['RSRP'] = df_rtt.apply(lambda x : pd.Series(map_RSRP(x['TIMESTAMP'],x['ID'])), axis = 1)
```
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} | 5 | "2021-04-18T11:13:42Z" | "2021-04-18T16:03:01Z" | "2021-04-18T15:55:39Z" | MEMBER | null | - [x] closes #40874
new numpy wheel is out, bug was fixed during the week
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} | 3 | "2021-04-18T11:56:17Z" | "2021-04-20T10:54:59Z" | "2021-04-20T10:54:59Z" | MEMBER | null | #### Is your feature request related to a problem?
xref #33634
```python
ser1 = pd.Series(["1","3"], index=pd.Index(["a", "c"], dtype="object"), dtype="object", name="test1")
ser2 = pd.Series([3, 4, 5], index=pd.Index(["a", "b", "c"], dtype="object"), dtype="float64", name="test2")
ser3 = pd.Series([6,7,8], index=pd.Index(["a", "b", "c"], dtype="object"), dtype="float64", name="test3")
pd.concat([ser1, ser2, ser3], axis=1, sort=False, keys=["test1", "test2", "test3"])
test1 test2 test3
a 1 3.0 6.0
c 3 5.0 8.0
b NaN 4.0 7.0
```
When concatenating, `concat` uses the index from the first series to determine the order of the non-concatenated axis.
It would be neat to have a way specify this order with either a function or a list. Currently, `sort=True` can be used, but it only allows for sorting.
#### Describe the solution you'd like
The solution could be either to:
- add a new argument
- expand the sort argument to take functions or lists
- use the `key` argument for this.
#### API breaking implications
I think this change can be backward compatible
#### Describe alternatives you've considered
As an alternative, reindexing could be used. But this requires the extraction of the largest index first.
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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
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