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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
inner = pd.DataFrame({"a": [1, np.nan]})
foo = pd.DataFrame({"df": [inner]})
bar = pd.DataFrame({"df": [inner]})
pd.testing.assert_frame_equal(foo, bar)
```
#### Problem description
Get `KeyError: 0`
The error goes away if `inner` does not contain missing values.
#### Expected Output
`None`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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https://pandas.pydata.org/docs/reference/api/pandas.core.window.rolling.Rolling.min.html
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
the return type of rolling methods (rolling.min / rolling.max) is mentioned to be same as the original object. But the default return types appears to be `float64`.
```python
>>> import pandas as pd
>>> s = pd.Series([4, 3, 5, 2, 6])
>>> s.dtypes
dtype('int64')
>>> s.rolling(3).min().dtypes
dtype('float64')
>>> s.rolling(3).max().dtypes
dtype('float64')
>>> s.rolling(3).std().dtypes
dtype('float64')
```
#### Suggested fix for documentation
Maybe the doc should mention the default return type
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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
Avoids expensive get_indexer calls.
```
from asv_bench.benchmarks.reshape import *
self = Unstack()
self.setup("category")
%timeit self.time_full_product("category")
58.8 ms ± 1.52 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
31.6 ms ± 1.98 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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} | 1 | "2021-08-13T20:29:34Z" | "2021-11-13T19:32:50Z" | "2021-08-19T02:01:55Z" | MEMBER | null | - [x] closes #43017
- [x] xref #43018 (if the reindex case is ok, then this is done too)
- [x] tests added / passed
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This ensures consistency with the DataFrame constructor | {
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} | 7 | "2021-08-13T23:20:50Z" | "2021-09-02T04:55:25Z" | "2021-09-01T23:01:48Z" | CONTRIBUTOR | null | * [x] xref #42717
* [x] xref #41153 & #42706
Deprecates Int64Index, UInt64Index & Float64Index.
I don't think it will be sensible to emit a deprecation warning for these because they have a lot of internal usage as is, so I've just added texts explaining that these are deprecated and will be removed in Pandas 2.0.
EDIT: it does now emit an deprecation warning when accessed from the main namespace. See changes to `pandas.__init__.py` for implementation.
```python
>>> pd.Int64Index
FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.NumericIndex with the appropriate dtype instead.
```
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} | 2 | "2021-08-14T00:08:31Z" | "2021-08-18T15:59:46Z" | "2021-08-18T15:58:27Z" | 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
Also adds a pre-commit check that errors in `doc/source/reference/general_utility_functions.rst` are synced with `pandas/errors/__init__.py` | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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] | open | false | null | [] | null | 3 | "2021-08-14T01:43:48Z" | "2021-11-03T14:25:16Z" | null | NONE | null | #### Is your feature request related to a problem?
I'm interested in adding additional options that would modify the behavior of merge, join, and concatenate operations when performed between IntervalArrays or IntervalIndexes.
There has been a fair amount of related discussion in a number of somewhat stale issues. I'd be happy to continue the discussion on one of those if it makes more sense.
* #19480
* #18975
* #22939
* #21998
#### Describe the solution you'd like
I'd like to be able to do merges on joins using Intervals where instead of finding exact matches the overlapping ranges are matched and split up as needed. Suppose we have the following two DataFrames.
``` python
idx_A = pd.IntervalIndex.from_tuples([(1, 4), (4, 6), (8, 9)])
df_A = pd.DataFrame(data={'A':['A1', 'A2', 'A3']}, index=idx_A)
idx_B = pd.IntervalIndex.from_tuples([(2, 3), (3, 5), (5, 10)])
df_B = pd.DataFrame(data={'B':['B1', 'B2', 'B3']}, index=idx_B)
```
<img width="247" alt="Screen Shot 2021-08-13 at 8 57 04 PM" src="https://user-images.githubusercontent.com/1461273/129429809-bfc16df4-8204-4706-9f06-5e9c90b89c90.png">
Currently, an outer join would not match any of the rows at all because it only joins Intervals that are equal.
`df_A.join(df_B, how='outer')`
<img width="255" alt="Screen Shot 2021-08-13 at 9 00 15 PM" src="https://user-images.githubusercontent.com/1461273/129429913-20a00f13-248c-40c6-b21d-d9e37d63a51b.png">
I'm interested in having an option that would change the behavior so that the overlapping portions of the intervals would be aligned. So the result would look like this.
``` python
df_a.join(df_B, how='overlapping') # not at all wedded to that parameter
idx_expected = pd.IntervalIndex.from_tuples([
(1,2), (2,3), (3,4), (4,5),
(5,6), (6,9),(9,10)])
df_expected = pd.DataFrame(
index=idx_expected, data={
'A': ['A1', 'A1', 'A1', 'A1', 'A2', 'A2', np.nan],
'B': [np.nan, 'B1', 'B2', 'B2', 'B3', 'B3', 'B3']
}
)
```
<img width="136" alt="Screen Shot 2021-08-13 at 9 06 22 PM" src="https://user-images.githubusercontent.com/1461273/129430015-10964b4e-90fe-45ab-8d56-22f15d77612e.png">
Ideally, the behavior would extend to multiple DataFrames or Series. So concat would work as well.
``` python
idx_C = pd.IntervalIndex.from_tuples([(3, 5), (8, 12), (14, 20)])
df_C = pd.DataFrame(data={'C':['C1', 'C2', 'C3']}, index=idx_C)
concat_df = pd.concat([df_A, df_B, df_C], axis=1)
```
<img width="101" alt="Screen Shot 2021-08-13 at 9 14 15 PM" src="https://user-images.githubusercontent.com/1461273/129430183-e1385398-c18f-4076-a64a-4b6d8feb911c.png">
<img width="195" alt="Screen Shot 2021-08-13 at 9 14 23 PM" src="https://user-images.githubusercontent.com/1461273/129430192-96d41067-dadb-41ce-931a-e870fa24c3d7.png">
#### API breaking implications
I imagine it would be best to avoid breaking the existing behavior of merge, join, concat, etc. on IntervalArrays. I could see either creating new functions (like `pd.merge_asof`) or adding either additional parameters to the various methods or additional options to existing parameters (such as `how`).
#### Describe alternatives you've considered
I've considered just writing my own functions on that use IntervalArrays to achieve the following and looked at a number of other libraries.
* [pyranges](https://pyranges.readthedocs.io/en/master/autoapi/pyranges/index.html)
* [intervaltree](https://pypi.org/project/intervaltree/)
* [typhon](https://www.radiativetransfer.org/misc/typhon/doc/_modules/typhon/trees.html)
#### Additional context
Some other related concepts or implementations:
* [Postgres Range Functions](https://www.postgresql.org/docs/13/functions-range.html)
* [kdb+ asof joins](https://code.kx.com/q/basics/joins/#as-of-joins)
* [Wikipedia: Interval Tree](https://en.wikipedia.org/wiki/Interval_tree)
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} | 3 | "2021-08-14T06:42:29Z" | "2021-08-17T05:20:53Z" | "2021-08-16T20:27:22Z" | CONTRIBUTOR | null | oversight in recent method addition for 1.4.0 / 1.3.2. backport.
no release note necessary. Can also be backported to 1.3.2.
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https://api.github.com/repos/pandas-dev/pandas/issues/43034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43034/events | https://github.com/pandas-dev/pandas/pull/43034 | 970,844,750 | MDExOlB1bGxSZXF1ZXN0NzEyNzMwODkx | 43,034 | BUG: don't manipulate `styler` object due to args in `to_html` | {
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} | 1 | "2021-08-14T07:05:08Z" | "2021-08-19T17:10:43Z" | "2021-08-19T16:13:56Z" | CONTRIBUTOR | null | This is the same as #42323 except for `to_html` instead of `to_latex`. | {
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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
nsmallest/nlargest are now consistent with x.sort_values(ascending=False).head(n)/x.sort_values(ascending=True).head(n)
Edit: I've changed test_nlargest_misc(self) in \tests\serie\methods\test_nlargest.py to reflect the fact that Nans are included | {
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- Added Plotting Functions
- Added a 3rd page consisting of various plots
- Adjusted the 2nd page and added a snippet to 'Drop a Column' | {
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} | 1 | "2021-08-14T14:51:27Z" | "2021-08-17T17:15:24Z" | "2021-08-17T16:03:19Z" | CONTRIBUTOR | null | addresses the issue whereby the set uuid at initialisation has a floating underscore:
```python
>>> styler = Styler(df, uuid="abc")
>>> print(styler.uuid)
abc_
```
Note: This is v minor but was discovered, and is useful to fix, due to an upcoming PR.
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} | 6 | "2021-08-14T15:50:55Z" | "2021-11-20T13:13:10Z" | 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
pd.api.types.pandas_dtype("int64") == "Int64"
```
#### Problem description
The code above fails (`TypeError: data type 'Int64' not understood`) while the following cases pass:
```Python
pd.api.types.pandas_dtype("int64") == "int64"
pd.api.types.pandas_dtype("Int64") == "Int64"
pd.api.types.pandas_dtype("Int64") == "int64"
```
#### Expected Output
`False`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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} | 1 | "2021-08-14T20:03:38Z" | "2021-09-14T15:30:07Z" | 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 pandas as pd
values = {'int64': pd.Series([0], dtype='>i8'),
'int32': pd.Series([1], dtype='>i4'),
'int64_2': pd.Series([2], dtype='>i8')}
df = pd.DataFrame(values)
print("series", [(v.dtype, v.dtype.byteorder) for k, v in values.items()])
print("df ", [(df[c].dtype, df[c].dtype.byteorder) for c in df.columns])
```
- The OS is Macos Catalina, 10.15.7.
- python_version(): 3.9.5
- pandas version: 1.3.1
#### Problem description
We specify the `byteorder`(endian) as `>` of `Series`, but when we construct `DataFrame` by `Series`, the `DataFrame`'s `byteorder` gets changed.
#### Expected Output
The expected output is, the byteorders of three columns are both '>'.
But the actual output is as follows:
```python
series [(dtype('>i8'), '>'), (dtype('>i4'), '>'), (dtype('>i8'), '>')]
df [(dtype('int64'), '='), (dtype('>i4'), '>'), (dtype('int64'), '=')]
```
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When a `styler` is copied this PR makes sure the set `uuid` is also copied. (also helps with an upcoming PR). | {
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} | 3 | "2021-08-15T09:33:55Z" | "2021-08-18T17:22:43Z" | "2021-08-18T17:22:25Z" | CONTRIBUTOR | null | - [X] closes #38017
- [ ] 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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} | 38 | "2021-08-15T10:23:54Z" | "2021-08-31T10:04:44Z" | "2021-08-31T10:04:18Z" | CONTRIBUTOR | null | This PR regards searching the **non-test codebase** (i.e. don't find code examples in pandas/tests) for examples like the following:
Try to find and replace logic like:
```python
if axis in [0, "index"]:
...
elif axis in [1, "columns"]:
...
else:
...
```
with the standard codebase method `get_axis_number(axis)`. This is a DataFrame method so often you need use local DataFrame objects to call this method.
It may also be necessary to change `axis` args in internal methods to consistently accept the same input format compatible with this method, i.e. axis as an integer (0 or 1).
This is an open issue and contributions are welcome to solve individual files in the code. As an example see #43078 or other merged contributions below.
- [x] `style.py` #43078
- [x] `test_label_or_level_utils.py` #43131
- [x] #43121 | {
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} | 7 | "2021-08-15T13:26:34Z" | "2021-09-12T20:30:04Z" | "2021-09-12T20:30:04Z" | MEMBER | null | Tracking issue for the 1.3.3 release. Currently scheduled for September 05, 2021.
https://github.com/pandas-dev/pandas/milestone/89
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression
performance regressions between 1.2.5 and 1.3.x https://simonjayhawkins.github.io/fantastic-dollop/#regressions?sort=3&dir=desc&branch=1.3.x
cc @pandas-dev/pandas-core @pandas-dev/pandas-triage | {
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} | 1 | "2021-08-15T13:39:41Z" | "2021-08-15T17:07:51Z" | "2021-08-15T17:07:44Z" | CONTRIBUTOR | null | Fixes typo in docs `Dataframe` -> `DataFrame`
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} | 4 | "2021-08-15T14:34:05Z" | "2021-08-16T21:01:55Z" | "2021-08-16T20:26:59Z" | MEMBER | null | - [x] closes #42333
- [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 (will move once 1.3.3 notes merged)
```
- 3.22±0.09ms 1.29±0.3ms 0.40 rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'mean')
- 2.92±0.1ms 1.08±0.1ms 0.37 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'int', 'mean')
- 2.68±0.1ms 959±100μs 0.36 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'mean')
- 3.00±0.2ms 1.02±0.03ms 0.34 rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'mean')
- 2.78±0.1ms 850±100μs 0.31 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'mean')
- 2.34±0.2ms 683±30μs 0.29 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'mean')
- 2.74±0.06ms 739±60μs 0.27 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'mean')
- 2.55±0.3ms 681±40μs 0.27 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'mean')
``` | {
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} | 3 | "2021-08-15T14:59:14Z" | "2021-08-16T22:51:54Z" | null | NONE | null | #### Is your feature request related to a problem?
In experimenting with openpyxl, I have found that I can load my files in faster when read_only=True, and I would like to pass in that argument to read_excel
#### Describe the solution you'd like
Similar to ExcelWriter, I would like to have engine_kwargs added to read_excel to pass in engine specific kwargs.
#### API breaking implications
This shouldn't break it.
#### Describe alternatives you've considered
Just use read_excel as is, works fine but I can load large excel files more quickly with using engine specific kwargs
#### Additional context
I imgine it would look like pd.read_excel('example.xlsx', engine='openpyxl', engine_kwargs={'read_only : True}) for am example | {
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} | 2 | "2021-08-15T15:41:09Z" | "2021-08-17T17:09:09Z" | "2021-08-17T16:04:17Z" | MEMBER | null | - [x] closes https://github.com/pandas-dev/pandas/issues/42390
- [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
Not sure what the defining characteristic of the failing cases is so there's a blank in the whatsnew note. | {
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Extremely naive implementation. Happy to take suggestions.
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} | 3 | "2021-08-15T18:55:51Z" | "2021-10-16T20:25:11Z" | "2021-10-16T20:25:10Z" | CONTRIBUTOR | null | - [ ] closes #42907
- [ ] tests added / passed
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} | 3 | "2021-08-15T19:42:59Z" | "2021-08-18T00:50:25Z" | "2021-08-18T00:07:52Z" | MEMBER | null | ```
from asv_bench.benchmarks.io.stata import *
self = StataMissing()
self.setup("tc")
%timeit self.time_read_stata("tc")
150 ms ± 3.3 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
114 ms ± 2.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
```
cc @bashtage | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43060 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43060/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43060/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43060/events | https://github.com/pandas-dev/pandas/pull/43060 | 971,249,081 | MDExOlB1bGxSZXF1ZXN0NzEzMDIwMDgw | 43,060 | BUG: nlargest/nsmallest can now consider nan values like sort_values(ascending=True).head(n) | {
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} | 5 | "2021-08-15T23:33:31Z" | "2021-09-07T23:17:44Z" | "2021-09-07T23:17:39Z" | CONTRIBUTOR | null | - [x] closes #28984
- [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
nsmallest/nlargest are now consistent with x.sort_values(ascending=False).head(n)/x.sort_values(ascending=True).head(n)
Edit: I've changed test_nlargest_misc(self) in \tests\serie\methods\test_nlargest.py to reflect the fact that Nans are included
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https://api.github.com/repos/pandas-dev/pandas/issues/43061 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43061/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43061/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43061/events | https://github.com/pandas-dev/pandas/pull/43061 | 971,250,126 | MDExOlB1bGxSZXF1ZXN0NzEzMDIwODMx | 43,061 | DOC: Updating grammar for clarity and consistency | {
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} | 1 | "2021-08-15T23:39:54Z" | "2021-08-17T18:47:25Z" | "2021-08-17T16:18:23Z" | CONTRIBUTOR | null | ### Location of the documentation
[10 minutes to pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html)
### Documentation problem
Throughout the page, there are code examples with an accompanying explanation of what is happening. The explanation will sometimes end in a colon, and other times end with a period. Here is a screenshot of the inconsistency:
### Suggested fix for documentation
I think that having a colon is the correct usage considering that the sentences are explaining the code block underneath it. I also think it would be better for the page to be consistent in punctuation. In my pull request, I've changed all the instances where I think this is applicable, as well as changing a couple of wordings to be easier to understand.
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https://api.github.com/repos/pandas-dev/pandas/issues/43062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43062/events | https://github.com/pandas-dev/pandas/pull/43062 | 971,284,806 | MDExOlB1bGxSZXF1ZXN0NzEzMDUwMTI1 | 43,062 | Update frame.py | {
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- [x] 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
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43063 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43063/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43063/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43063/events | https://github.com/pandas-dev/pandas/pull/43063 | 971,293,883 | MDExOlB1bGxSZXF1ZXN0NzEzMDU3MzM0 | 43,063 | BENCH: fix misleading json asvs | {
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} | 0 | "2021-08-16T01:55:37Z" | "2021-08-17T14:22:08Z" | "2021-08-17T13:12:27Z" | MEMBER | null | a third of the measured time is in concat ATM, which isnt what we're trying to measure | {
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} | 2 | "2021-08-16T04:31:42Z" | "2021-08-17T16:29:23Z" | "2021-08-17T16:15:28Z" | CONTRIBUTOR | null | - [x] closes #32803
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-08-16T04:36:23Z" | "2021-08-19T00:02:49Z" | "2021-08-18T23:56:44Z" | 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
In preparation for unifying some `numba` code between groupby and rolling, moving `BaseIndexer`s to a new directory such that
`pandas/core/window.indexers.py` -> `pandas/core/indexers/objects.py` (`BaseIndexer`s and their subclasses)
`pandas/core/indexers.py` -> `pandas/core/indexers/utils.py` (misc indexer helper functions)
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} | 1 | "2021-08-16T07:00:36Z" | "2021-08-18T03:16:18Z" | "2021-08-17T13:13:50Z" | CONTRIBUTOR | null | - [x] closes #39881
- [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 (n/a - added test case only)
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} | 1 | "2021-08-16T15:04:17Z" | "2021-08-16T15:14:10Z" | "2021-08-16T15:13:50Z" | NONE | null | #### Problem description
When using `pd.concat` with the `keys` parameter, on a Series/Dataframe who's index contains duplicate entries, concat ignores these duplicate value and generates an **invalid MultiIndex**, with different "codes" for the same label.
further down the line, this breaks `df.groupby(level=[0,1]).sum()` because apparently groupby relies on the "codes" rather than the values (?), resulting in a groupby aggregation that produces duplicates.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.7.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.2.0
Cython : 0.29.24
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.22
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# Index with duplicate values
test = pd.Series(range(6), index=list('ABCDDE'))
# Prepend index level using concat
multi_idx_test = pd.concat([test, ], keys=['1',], sort=True)
# Other way to prepend an index level
multi_idx_test2 = test.reset_index().assign(index0='1').set_index(['index0', 'index']).rename_axis([None, None], axis=0).squeeze().rename(None)
assert multi_idx_test.equals(multi_idx_test2)
# Groupby uses index codes, not actual values, producing wrong results
bad_groupby = multi_idx_test.groupby(level=[0,1]).sum()
good_groupby = multi_idx_test.groupby([multi_idx_test.index.get_level_values(0),
multi_idx_test.index.get_level_values(1)]).sum()
good_groupby2 = multi_idx_test2.groupby(level=[0,1]).sum()
print(bad_groupby)
print(good_groupby)
print(good_groupby2)
# Bad groupby result, see D is repeated
1 A 0
B 1
C 2
D 3
D 4
E 5
dtype: int64
# Proper groupby result, the raw index values
1 A 0
B 1
C 2
D 7
E 5
dtype: int64
# Proper groupby result, when using a MultiIndex that was created without `pd.concat`
1 A 0
B 1
C 2
D 7
E 5
dtype: int64
```
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} | 6 | "2021-08-16T17:24:29Z" | "2021-08-17T20:46:48Z" | 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
# Your code here
writer = pd.ExcelWriter('existingFile.xlsx', mode='a')
df.to_excel(writer, sheet_name="existingSheet", startrow=writer.sheets["existingSheet"].max_row, index=False, header=False)
```
#### Problem description
It seems this function is broken in pandas 1.3.0, when trying to append to an existing sheet it will either raise an error, create a new one or replace it so it is impossible to append a data frame to an existing sheet. according to this [document](https://pandas.pydata.org/docs/reference/api/pandas.ExcelWriter.html)
> if_sheet_exists{‘error’, ‘new’, ‘replace’}, default ‘error’
> How to behave when trying to write to a sheet that already exists (append mode only).
>
> error: raise a ValueError.
>
> new: Create a new sheet, with a name determined by the engine.
>
> replace: Delete the contents of the sheet before writing to it.
>
> New in version 1.3.0
#### Expected Output
Able to append to an existing sheet without error
#### 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 | "2021-08-17T00:57:27Z" | "2021-08-17T20:25:31Z" | "2021-08-17T13:21:08Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This came up while I was benchmarking the pyarrow csv reader that I am planning to PR soon, which was erroring with an ImportError for pyarrow. Anyone know if this is the right patch? (It works correctly for me afterwards, however I am curious as to how ArrowStringArray was getting benchmarked)
pytest also should be removable I think. (there really shouldn't be any reason why we are benchmarking that)
cc @simonjayhawkins @TomAugspurger | {
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} | 16 | "2021-08-17T01:01:49Z" | "2021-09-01T01:47:06Z" | "2021-08-27T23:18:01Z" | MEMBER | null | - [ ] closes #23697
- [ ] 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
Picking up from #38370.
Perf is OK. (StringIO/Text buffer reading gets like 20% overhead b/c of BytesIOWrapper, we should probably look into more efficient ways of doing it/cythonize it. Bytes IO performance looks good though).
Benchmarks 100k rows, 5 col
```
[ 0.00%] ··· io.csv.ReadCSVEngine.time_read_bytescsv ok
[ 0.00%] ··· ========= ============
engine
--------- ------------
c 22.9±2ms
python 259±20ms
pyarrow 5.79±0.3ms
========= ============
[ 0.00%] ··· io.csv.ReadCSVEngine.time_read_stringcsv ok
[ 0.00%] ··· ========= ============
engine
--------- ------------
c 20.6±1ms
python 257±1ms
pyarrow 7.06±0.3ms
========= ============
```
Tests will still fail tho, working on that rn.
My machine is a 2019 MBP(Intel) with 6 cores. YMMV based on the number of CPUs you have. Surprised that it doesn't quite scale linearly, maybe the file is too small to feel the benefit of all 6 cores. | {
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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
x = []
with open(file=fn) as text:
for line in text.readlines():
if line.startswith('000651,') or line.startswith('651,'):
x.append(line)
dat = pd.read_csv(fn)
a = dat[dat['Id'] == '000651']
b = dat[dat['Id'] == 651]
```
#### Problem description
For very long csv file that I'm storing [here](https://mam-dev-khoa.s3.amazonaws.com/dummy.csv?AWSAccessKeyId=ASIA4TCV2CZ7FAE5OWBX&Signature=uX4%2FFIlESZ6v2%2BkoAU3Unz3iQ7s%3D&x-amz-security-token=FwoGZXIvYXdzEFcaDF5jyItKd0DwMh24wyKGAV454HK0YLObdSMHNseu4s96fpjBhy%2FRscSJFnFtCQtanim8mdia5vOKK4StoduCn1J%2FGoz%2F51zOVlUWWQ0e040v37nIkQDQzF7W2xEbqPNr0l4sLgzvdWUWX1R7hcGl8NA1SAvDhUiSEIBzNh6pgHk7%2FW4mXC1lCRlD2qk7boeAYNLi0PsoKO%2Ba7YgGMiijonJs5bU0s%2FhSCVsCThaPb%2BXtZD24DSMCiYaPCbpl1niY0ZKUUKt8&Expires=1629183449), the parser read some of the rows with `Id` as Integer then later some as strings. This leads to corrupted data when we later write the data in memory to SQL.
#### Expected Output
Consistent data type for each column or throw error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.0
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
</details>
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} | 4 | "2021-08-17T06:11:52Z" | "2021-09-01T19:36:43Z" | "2021-09-01T19:36:43Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
I've searched listing of open issues for "parquet" and found no matching entry describing this problem
- [x] I have confirmed this bug exists on the latest version of pandas.
Exists in 1.3.2
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
Exists also in the master branch.
---
**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_parquet("df.parquet")
```
This fails with the following stack trace:
```
In [8]: df = pd.read_parquet("df.parquet")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-3c208bf199a1> in <module>
----> 1 df = pd.read_parquet("df.parquet")
~/.virtual_envs/kapi2/lib/python3.9/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, **kwargs)
493 impl = get_engine(engine)
494
--> 495 return impl.read(
496 path,
497 columns=columns,
~/.virtual_envs/kapi2/lib/python3.9/site-packages/pandas/io/parquet.py in read(self, path, columns, storage_options, **kwargs)
342 path = handles.handle
343
--> 344 parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
345
346 result = parquet_file.to_pandas(columns=columns, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'pandas_nulls'
```
#### Problem description
AFAICT, the root cause of the problem is the "api" version test in [io/parquet.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/parquet.py#L314) It needs to test `self.api.__version__ > Version("0.7.0")`. `ParquetFile` constructor in `fastparquet` 0.7.0 does not support `pandas_nulls` argument. This argument is supported from version 0.7.1.
`fastparquet` 0.7.1 unfortunately does not build/install on darwin/arm64 (well, at least for me) :-/.
#### Expected Output
Obviously, the expected behavior is that the `DataFrame` is read from the file.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:31 PDT 2021; root:xnu-7195.121.3~9/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 56.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : 0.7.0
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
df = pandas.DataFrame(
{
"a": [1, 1, 2, 3],
"b": [3, 4, 5, 6],
"c": [7, 8, 9, 10],
"d": [11, 12, 13, 14],
}, index= ["1", "2", "3", "4"]
)
a = df["a"] = pandas.Series([5, 5, 5, 5])
a
0 5
1 5
2 5
3 5
dtype: int64
df
a b c d
1 NaN 3 7 11
2 NaN 4 8 12
3 NaN 5 9 13
4 NaN 6 10 14
```
#### Problem description & Expected Output
Shouldn't the column "a" contain `Series([5, 5, 5, 5])`?
#### Output of ``pd.show_versions()``
<details>
pandas : 1.3.2
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : 0.15.0
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : 0.19.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
### Code Sample
```python
import itertools
import pandas as pd
ts_list = pd.date_range(start='2021-01-01T02:00:00', periods=3, freq='1D').to_list()
df = pd.DataFrame(data=list(itertools.islice(itertools.cycle(ts_list), 9)),
index=[2, 4, 5, 6, 7, 8, 11, 14, 15],
columns=['pure_datetime'])
df['categorical_datetime'] = df['pure_datetime'].astype(pd.CategoricalDtype())
"""
>>> df
pure_datetime categorical_datetime
2 2021-01-01 02:00:00 2021-01-01 02:00:00
4 2021-01-02 02:00:00 2021-01-02 02:00:00
5 2021-01-03 02:00:00 2021-01-03 02:00:00
6 2021-01-01 02:00:00 2021-01-01 02:00:00
7 2021-01-02 02:00:00 2021-01-02 02:00:00
8 2021-01-03 02:00:00 2021-01-03 02:00:00
11 2021-01-01 02:00:00 2021-01-01 02:00:00
14 2021-01-02 02:00:00 2021-01-02 02:00:00
15 2021-01-03 02:00:00 2021-01-03 02:00:00
>>> df.dtypes
pure_datetime datetime64[ns]
categorical_datetime category
dtype: object
"""
# Now apply some function from .dt namespace.
"""
>>> df['pure_datetime'].dt.tz_localize('Europe/Berlin')
2 2021-01-01 02:00:00+01:00
4 2021-01-02 02:00:00+01:00
5 2021-01-03 02:00:00+01:00
6 2021-01-01 02:00:00+01:00
7 2021-01-02 02:00:00+01:00
8 2021-01-03 02:00:00+01:00
11 2021-01-01 02:00:00+01:00
14 2021-01-02 02:00:00+01:00
15 2021-01-03 02:00:00+01:00
Name: pure_datetime, dtype: datetime64[ns, Europe/Berlin]
"""
# All is fine with pure datetime column
# BUT, when applied to categorical column, the index is reset:
"""
>>> df['categorical_datetime'].dt.tz_localize('Europe/Berlin')
0 2021-01-01 02:00:00+01:00
1 2021-01-02 02:00:00+01:00
2 2021-01-03 02:00:00+01:00
3 2021-01-01 02:00:00+01:00
4 2021-01-02 02:00:00+01:00
5 2021-01-03 02:00:00+01:00
6 2021-01-01 02:00:00+01:00
7 2021-01-02 02:00:00+01:00
8 2021-01-03 02:00:00+01:00
Name: categorical_datetime, dtype: datetime64[ns, Europe/Berlin]
"""
# Naive construction of new columns yields convoluted results
df['localized_pure'] = df['pure_datetime'].dt.tz_localize('Europe/Berlin')
df['localized_categorical'] = df['categorical_datetime'].dt.tz_localize('Europe/Berlin')
"""
>>> df
pure_datetime categorical_datetime localized_pure localized_categorical
2 2021-01-01 02:00:00 2021-01-01 02:00:00 2021-01-01 02:00:00+01:00 2021-01-03 02:00:00+01:00
4 2021-01-02 02:00:00 2021-01-02 02:00:00 2021-01-02 02:00:00+01:00 2021-01-02 02:00:00+01:00
5 2021-01-03 02:00:00 2021-01-03 02:00:00 2021-01-03 02:00:00+01:00 2021-01-03 02:00:00+01:00
6 2021-01-01 02:00:00 2021-01-01 02:00:00 2021-01-01 02:00:00+01:00 2021-01-01 02:00:00+01:00
7 2021-01-02 02:00:00 2021-01-02 02:00:00 2021-01-02 02:00:00+01:00 2021-01-02 02:00:00+01:00
8 2021-01-03 02:00:00 2021-01-03 02:00:00 2021-01-03 02:00:00+01:00 2021-01-03 02:00:00+01:00
11 2021-01-01 02:00:00 2021-01-01 02:00:00 2021-01-01 02:00:00+01:00 NaT
14 2021-01-02 02:00:00 2021-01-02 02:00:00 2021-01-02 02:00:00+01:00 NaT
15 2021-01-03 02:00:00 2021-01-03 02:00:00 2021-01-03 02:00:00+01:00 NaT
"""
```
#### Problem description
When working on categorical columns, I would expect all methods, that can be called without error or warning, to yield the same results as when applied to the pure (category-expanded) columns. Some methods from the `.dt`-namespace (at least `tz_localize()` and `tz_convert()`) seem to return non-consistent results.
In particular, they reset the index of the input column (a Series in fact).
#### Expected Output
I expect the method `.dt.tz_localize()` to not reset the index when applied to a Series of datetime categoricals.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [X] 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
import numpy as np
columns = pd.MultiIndex.from_arrays([[1, 1, 2], ['a', 'b', 'b']])
index = range(1000)
df = pd.DataFrame(
np.random.normal(size=(len(index), len(columns))), index=index,
columns=columns)
df.ewm(alpha=0.05).cov()
```
#### Problem description
`cov` works fine when the `MultiIndex` is created with `from_product`, but fails here since the `MultiIndex` here cannot be created in this way. I would expect the `.cov` to work on any MultiIndex since the form of the column names should not impact covariance computations. It works for `pd.DataFrame(df.values).ewm(alpha=0.1).cov()`. This issue is closely related to [this older bug](https://github.com/pandas-dev/pandas/issues/34440).
#### Expected Output
A standard `ewm.cov` output.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
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 : 2.8.6 (dt dec pq3 ext lo64)
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
```
</details>
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} | 1 | "2021-08-17T17:41:57Z" | "2021-08-18T23:46:19Z" | null | NONE | null | #### Is your feature request related to a problem?
While using the `scipy-notebook` image from Jupyter, which installs `pandas` and `xlrd` but not `openpyxl` (full dependency list [here](https://github.com/jupyter/docker-stacks/blob/master/scipy-notebook/Dockerfile#L19)), I attempted to open a `.xlsx` file using `pd.read_excel`. Code example:
```python
import pandas as pd
df = pd.read_excel('cities.xlsx')
```
The error message I got was:
```
ImportError: Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl.
```
This was confusing since previously all I needed was `xlrd` for this code to work.
When I look in the [docs for `pd.read_excel`](https://pandas.pydata.org/docs/reference/api/pandas.read_excel.html) and scroll down through the params to `engine` (which I had left as the default in my code), I see that this is because my `xlrd` version has been upgraded to greater than 2.0, which means that it only supports `.xls` files now, not `.xlsx`.
I would like the error message to reflect this more clearly.
#### Describe the solution you'd like
There is already a `ValueError` in the code that would be much clearer:
```python
# Pass through if ext is None, otherwise check if ext valid for xlrd
if ext and ext != "xls" and xlrd_version >= Version("2"):
raise ValueError(
f"Your version of xlrd is {xlrd_version}. In xlrd >= 2.0, "
f"only the xls format is supported. Install openpyxl instead."
)
```
(Link to source [here](https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_base.py#L1215))
However this is only triggered if the user explicitly specifies `engine="xlrd"`, e.g.
```python
import pandas as pd
df = pd.read_excel('cities.xlsx', engine='xlrd')
```
I propose that the logic be rearranged so that if:
1. The user passes in a file with a `.xlsx` extension to `pd.read_excel`, and
2. No `engine` param is specified, and
3. `xlrd` with version > 2.0 is installed, and
4. `openpyxl` is not installed
then the user will see the value error message explaining that `xlrd` > 2.0 only supports `.xls`, and that `openpyxl` is required for `.xlsx`.
In other words, this code:
```python
import pandas as pd
df = pd.read_excel('cities.xlsx')
```
should produce an error message like this:
```
Your version of xlrd is {xlrd_version}. In xlrd >= 2.0,
only the xls format is supported. Install openpyxl instead.
```
rather than an error message like this:
```
ImportError: Missing optional dependency 'openpyxl'. Use pip or conda to install openpyxl.
```
#### API breaking implications
I'm not sure if it technically is part of the "API", but this changes what type of error is thrown from an `ImportError` to a `ValueError` in a specific circumstance.
#### Describe alternatives you've considered
Another alternative might be to move the documentation about `xlrd` and `openpxl` out from under the `engine` param. If that were moved to the top or bottom of the `pd.read_excel` page (rather than in the middle like it is now), that would make it easier to understand what's happening with an error like this.
Specifically this section of the documentation could be moved:
```
.. versionchanged:: 1.2.0
The engine `xlrd <https://xlrd.readthedocs.io/en/latest/>`_
now only supports old-style ``.xls`` files.
When ``engine=None``, the following logic will be
used to determine the engine:
- If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
then `odf <https://pypi.org/project/odfpy/>`_ will be used.
- Otherwise if ``path_or_buffer`` is an xls format,
``xlrd`` will be used.
- Otherwise if ``path_or_buffer`` is in xlsb format,
``pyxlsb`` will be used.
.. versionadded:: 1.3.0
- Otherwise ``openpyxl`` will be used.
.. versionchanged:: 1.3.0
```
(Link to source [here](https://github.com/pandas-dev/pandas/blob/v1.3.2/pandas/io/excel/_base.py#L128))
I still think changing the error message would be better and clearer.
#### Additional context
**The current error message seems to assume that the user has the ability to install new dependencies on the system, and that the user does not have the ability to convert the file type they are working with. I think that the opposite is often true.** In my specific situation, I am not actually using any of the functionality that is specific to a `.xlsx` file, so the easiest workaround is to convert the file to `.xls`. But the `ImportError` implies that the best approach is to install additional dependencies, which would require waiting for updates to be made on the Jupyter project.
If there is support for this feature request I'm happy to put together a PR!
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} | 4 | "2021-08-17T17:55:46Z" | "2021-08-18T18:17:03Z" | "2021-08-18T18:17:02Z" | 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
date_index = pd.date_range('1/1/2010', periods=6, freq='D')
df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
index=date_index)
df2
date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
df2.reindex(date_index2)
df2
```python
date_index = pd.date_range('1/1/2010', periods=6, freq='D')
df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
index=date_index)
df2
date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
df2.reindex(date_index2)
df2
```
#### Problem description
Unable to expand a dataframe using reindex, if the index is datetime. It works for other data types. The code is copied from the [API reference](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex.html) of Pandas
#### Expected Output
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19043
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_India.1252
pandas : 1.3.2
numpy : 1.21.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43086 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43086/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43086/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43086/events | https://github.com/pandas-dev/pandas/pull/43086 | 972,960,656 | MDExOlB1bGxSZXF1ZXN0NzE0NDc4MDEy | 43,086 | #fixed syntax and grammatical errors, Added quick notes for simple go - through/ follow-ups | {
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#fixed syntax and grammatical errors, Added quick notes for simple go through. (Open to further updates)
- [x] 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
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43087 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43087/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43087/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43087/events | https://github.com/pandas-dev/pandas/issues/43087 | 973,036,906 | MDU6SXNzdWU5NzMwMzY5MDY= | 43,087 | BUG: Inconsistent column order result using Left Merge depending if left dataframe is empty or not | {
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] | open | false | null | [] | null | 1 | "2021-08-17T20:23:33Z" | "2021-08-19T13:32:44Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
There is a similar report but using "outer join" instead of "left join": https://github.com/pandas-dev/pandas/issues/9937
- [X] I have confirmed this bug exists on the latest version of pandas.
Is present at least in the last version I can install with pip3 "1.1.5".
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
Partially, the relevant underlying code that I identified in master looks identical to my version, although I have not upgrade it
---
#### Code Sample, a copy-pastable example
First the regular and expected behavior
```python
left_df = pandas.DataFrame({
"id": [1, 2],
"full_name": ["Long Name", "Long Name 2"],
"short_name": ["Short", "Short 2"],
"counts": [1000, 500],
"percent": [20, 10],
"constant": [5, 1],
})
right_df = pandas.DataFrame({
"full_name": ["Long Name"],
"copies": [20000],
"norm_copies": [10000],
"unit": ["abcd"],
"calculated_value": [2000],
})
merged_df = pandas.merge(
left_df,
right_df,
how="left",
on="full_name",
)
print(merged_df)
id full_name short_name counts percent constant copies norm_copies unit calculated_value
0 1 Long Name Short 1000 20 5 20000.0 10000.0 abcd 2000.0
1 2 Long Name 2 Short 2 500 10 1 NaN NaN NaN NaN
```
Note that it preserves the left_df order and adds the new columns at the end
Then when the left_df is empty
```python
left_df = pandas.DataFrame({
"id": [],
"full_name": [],
"short_name": [],
"counts": [],
"percent": [],
"constant": [],
})
right_df = pandas.DataFrame({
"full_name": [],
"copies": [],
"norm_copies": [],
"unit": [],
"calculated_value": [],
})
merged_df = pandas.merge(
left_df,
right_df,
how="left",
on="full_name",
)
print(merged_df)
Empty DataFrame
Columns: [id, short_name, counts, percent, constant, full_name, copies, norm_copies, unit, calculated_value]
Index: []
```
#### Problem description
There is an inconsistency in the column order depending only if the `left_df` is empty or not. When its not empty the merged dataframe have the expected column ordering, keeping the ON `full_name` column in its original place, while when its empty the right_df overrides this and imposes its own column ordering at the end of the merged dataframe, moving the ON `full_name` column to a different place.
My own problem was that some tests I made for my code expects consistency on the merged dataframe structure (which is later transformed in exported tables), but now I see that the ordering changes on different scenarios. Of course I can try to compensate this problem by always reordering columns after the fact, or some other adjustments to ensure a consistent output, but the main point is that merged column order should follow a consistent behavior (either the original order which I prefer, or the new order imposed by the right dataframe) regardless of the left dataframe being empty or not.
I'm not sure about what is the "right" behavior in the outer join case I referred above, but at least in a left join the column order of the left dataframe should mandate in all cases.
#### Expected Output
Preserve original column order
```
Empty DataFrame
Columns: [id, full_name, short_name, counts, percent, constant, copies, norm_copies, unit, calculated_value]
Index: []
```
I looked deeper with my debugger and traced to the relevant merge code, https://github.com/pandas-dev/pandas/blob/master/pandas/core/reshape/merge.py#L1097-L1102:
```python
if lk is not None and lk == rk:
# avoid key upcast in corner case (length-0)
if len(left) > 0:
right_drop.append(rk)
else:
left_drop.append(lk)
```
In this place the code decides which redundant column (the ON column) to remove, If the left dataframe is empty, it adds it to a drop list that is executed later, removing the column from there, while if not empty it removes from the right dataframe, which is exactly what I'm observing.
Doing some archaelogy, I went back until I found the commit where this was first implemented 9 years ago, https://github.com/pandas-dev/pandas/commit/0da0066c2687898656bbf509ffd42f14b1c0a727:
> - Fix IndexError in left merges on a DataFrame with 0-length (#1628)
Apparently, it always dropped from the right dataframe before but changed because of an error with the left dataframe being empty. So the relevant question is if that would still be a problem today, since in my example the right dataframe was also empty but I saw no error with that... could it be that the fix is not needed anymore because the old error is resolved in other ways and go back to a consistent behavior??
I made a temporal local edit of my files, removing the IF to always drop from the dataframe to test this case, and it worked without errors!:
```python
if lk is not None and lk == rk:
right_drop.append(rk)
print(merged_df)
Empty DataFrame
Columns: [id, full_name, short_name, counts, percent, constant, copies, norm_copies, unit, calculated_value]
Index: []
```
The columns have the correct order.
Since the removal of the IF was successful, I decided to also test the code in the old issue report with the error associated to this IF mentioned above:
```python
>>> from pandas import *
>>> a = DataFrame(columns=['column1'], data=[1])
>>> b = DataFrame(columns=['column1'])
>>> a.merge(b, on=['column1'], how='left')
column1
0 1
```
And no errors!! So it means that fixing the column order by removing that IF will not cause a regression on that other report.
Please consider implementing this if it does not cause any test regression, it would be an easy fix.
Best regards!
#### Output of ``pd.show_versions()``
<details>
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90~18.04.1-Ubuntu SMP Tue Jul 13 19:40:02 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.4
setuptools : 39.0.1
Cython : 0.29.20
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43088 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43088/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43088/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43088/events | https://github.com/pandas-dev/pandas/issues/43088 | 973,048,411 | MDU6SXNzdWU5NzMwNDg0MTE= | 43,088 | ENH: Rename ExcelWriter to make clear attributes are not public | {
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} | 15 | "2021-08-17T20:40:30Z" | "2021-09-10T22:48:05Z" | null | MEMBER | null | Ref: https://github.com/pandas-dev/pandas/issues/43068#issuecomment-900504112
Copying from the comment above:
> The [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.ExcelWriter.html) of `ExcelWriter` says: "None of the methods and properties are considered public.".
It seems this line the documentation would be easy to miss, and it'd be better to make this more clear via the standard naming convention of a single leading underscore. | {
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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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https://api.github.com/repos/pandas-dev/pandas/issues/43090 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43090/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43090/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43090/events | https://github.com/pandas-dev/pandas/pull/43090 | 973,097,589 | MDExOlB1bGxSZXF1ZXN0NzE0NTkzNjYx | 43,090 | Here is the solution to issue #43085 Basically, you have to make sure that the df2 after reindexing is stored again in df2 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43091 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43091/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43091/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43091/events | https://github.com/pandas-dev/pandas/issues/43091 | 973,211,770 | MDU6SXNzdWU5NzMyMTE3NzA= | 43,091 | BUG: Change of behavior in casting of datetime-like types in MultiIndex | {
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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
[Edited to inform a much simpler example.]
```python
import datetime
import pandas as pd
print(f"Pandas version:\t{pd.__version__}\n")
df = pd.DataFrame({'date': [datetime.date(2021, 8, 1),
datetime.date(2021, 8, 2),
datetime.date(2021, 8, 3)],
'ticker': ['aapl', 'goog', 'yhoo'],
'value': [5.63269, 4.45609, 2.74843]})
df.set_index(['date', 'ticker'], inplace=True)
print(df.index.get_level_values(0))
```
#### Output
The output below has been generated with pandas 1.3.0 or higher.
```
Pandas version: 1.3.0
Index([2021-08-01, 2021-08-02, 2021-08-03], dtype='object', name='date')
```
#### Expected Output
The output below has been generated with pandas 1.2.5.
```
Pandas version: 1.2.5
DatetimeIndex(['2021-08-01', '2021-08-02', '2021-08-03'], dtype='datetime64[ns]', name='date', freq=None)
```
#### Problem description
Starting from pandas 1.3.0, the observed behavior changed: in a `MultiIndex` creation, `datetime.date` objects are not cast to `datetime64` anymore. I fail to find in the [What’s new](https://pandas.pydata.org/docs/whatsnew/v1.3.0.html) page the reason for that change of behavior. Is it by design or a bug?
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/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 : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : None
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.22
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43092 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43092/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43092/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43092/events | https://github.com/pandas-dev/pandas/pull/43092 | 973,217,020 | MDExOlB1bGxSZXF1ZXN0NzE0Njk1NTQ1 | 43,092 | rom | {
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-
[answer.pdf](https://github.com/pandas-dev/pandas/files/7004115/answer.pdf)
[ ] 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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] | closed | false | null | [] | null | 2 | "2021-08-18T04:40:35Z" | "2021-08-19T16:09:43Z" | "2021-08-19T00:51:24Z" | NONE | null | In computer science, we always try to design algorithms and procedures in a logically consistent manner so that other people can follow easily without making wrong assumptions. For example, when we group dates into intervals, for every group we can choose to use either the left or the right boundary to label that group. So if you decide to choose the left timestamp to label every interval, then you should do it for all types of intervals.
However, this is not so in Pandas.
```
>>> stmp=pd.to_datetime('2021-01-01 09:31:04.34')
>>> [print(sc, list(pd.DataFrame({'a':0}, index=[stmp]).groupby(pd.Grouper(freq=sc)))[0][0]) for sc in ['s', 'min', 'H', 'D', '7D', 'W', 'M', 'Y']]
s 2021-01-01 09:31:04
min 2021-01-01 09:31:00
H 2021-01-01 09:00:00
D 2021-01-01 00:00:00
7D 2021-01-01 00:00:00
W 2021-01-03 00:00:00
M 2021-01-31 00:00:00
Y 2021-12-31 00:00:00
```
As you can see above, when grouping into seconds, minutes, hours, days, weeks and months respectively:
- for seconds, minutes, hours and days, the group time label lies to the left of the sample
- but for weeks, months and years, the group time label lies to the right the sample, what is even worse is that the boundary does not align to the beginning of the next interval, rather, it aligns to the beginning of the last day in the interval, which is even more weird (does it mean the last day of a month/year belongs to the next month/year??? absolutely non-sense)
In general, we can group dates by arbitrary interval, e.g., 10.5 days or 1.5 weeks (some astronomical events monitoring can leads to such use case). This inconsistency is going to cause errors in applications based on human assumption because who the hell will know that for weeks, months, and years, the library decide to do it differently from the rest?? | {
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] | closed | false | null | [] | null | 1 | "2021-08-18T07:03:22Z" | "2021-08-18T08:28:26Z" | "2021-08-18T08:28:26Z" | NONE | null | ### The Problem
Currently, we can select the `axis` by typing `0` `1` `index`.
But why we can't select the axis also by typing `column`.
Personally, I prefer to use `index` as an axis as this makes my code clean.
#### The solution
Check for the key world `column` as an axis if so this means it is the same as `1`
After doing this it will be like this
```python
0 == index
1 == column
```
#### API breaking implications
This code here throw an error.
```python
frame = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])
f = lambda x: x.max() - x.min()frame.apply()
frame.apply(f, axis="column")
```
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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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} | 1 | "2021-08-18T13:09:59Z" | "2021-08-18T17:05:13Z" | "2021-08-18T17:04:58Z" | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
I was reviewing the recent PR for anti-joins and was attempting to come up with a standard test structure, and came across this:
```python
left = pd.DataFrame([[1,1],[2,2],[3,3]], index=[1,2,3], columns=["A", "C"])
A C
1 1 1
2 2 2
3 3 3
right = pd.DataFrame([[1,1],[2,2],[4,4]], index=[1,2,4], columns=["B", "C"])
B C
1 1 1
2 2 2
4 4 4
pd.merge(left, right, how="left", left_index=True, right_on="B")
A C_x B C_y
1.0 1 1 1 1.0
2.0 2 2 2 2.0
NaN 3 3 3 NaN
```
### Expected
```python
pd.merge(left, right, how="left", left_index=True, right_on="B")
A C_x B C_y
1 1 1 1 1
2 2 2 2 2
3 3 3 NaN NaN
```
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} | 2 | "2021-08-18T14:57:04Z" | "2021-08-19T05:23:02Z" | "2021-08-18T20:18:14Z" | CONTRIBUTOR | null | This was actually never true from the start #40134. It might have been true at one point during the PR and then was overwritten without docs being corrected before final merge.
I.e. these docs have always been wrong since introduction.
Should be back portable.
```python
df = DataFrame([[1.0, 2.0]], columns=["A", "B"])
df.style.format({"A": "{:.1f}", "B": "{:.2f}"}, subset = ["A"])
# does not raise a KeyError for "B", just ignores.
``` | {
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} | 1 | "2021-08-18T16:11:24Z" | "2021-08-19T01:57:14Z" | "2021-08-19T01:57:06Z" | MEMBER | null | xref #40686
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} | 0 | "2021-08-18T18:20:34Z" | "2021-08-19T01:50:24Z" | "2021-08-19T01:04:45Z" | MEMBER | null | ATM we pass both 'columns' and `arr_names` to arrays_to_mgr and create_block_manager_from_arrays. This is part of en effort to align these before calling arrays_to_mgr, which will let us improve the performance of _form_blocks. | {
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} | 9 | "2021-08-18T20:57:36Z" | "2021-09-20T22:41:11Z" | "2021-09-16T22:16:23Z" | CONTRIBUTOR | null | - [x] adds features and method with docstrings
- [x] update user guide
- [x] whats new
- [x] add tests
This PR adds the method `format_index` so that the typical `format` routines can also be applied to display the index labels and columns headers of a Styler.
With all the recent functionality here is a complete example:
**Notes**
Also had to move `_refactor_levels` function from `style.py` to `style_render.py` for use here.
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I couldn't find it with a search on `indexerror read_csv` anyway
- [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 [1]: import pandas
In [2]: from io import StringIO
In [3]: data = """
...: head1, head2, head3
...: sub1, sub2
...: """[1:]
In [4]: data
Out[4]: 'head1, head2, head3\nsub1, sub2\n'
In [5]: pandas.read_csv(StringIO(data), sep=",", header=[0, 1], on_bad_lines="sk
...: ip")
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-5-1bf8982a1449> in <module>
----> 1 pandas.read_csv(StringIO(data), sep=",", header=[0, 1], on_bad_lines="skip")
*snip - I can provide the entire trace on request, but leave it out here for brevity*
~/.local/share/virtualenvs/py3.9playground--W4ZB8cD/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py in <genexpr>(.0)
343
344 def extract(r):
--> 345 return tuple(r[i] for i in range(field_count) if i not in sic)
346
347 columns = list(zip(*(extract(r) for r in header)))
IndexError: list index out of range
```
#### Problem description
A generic index error is thrown rather than a pandas specific error, or skipping the row as per `on_bad_lines`.
#### Expected Output
I would expect one of two things:
1. a `pandas.errors.ParseError` describing the problem
2. skipping the header. This seems a little odd to me so maybe 1 is the better option.
Maybe there are other reasonable responses as well.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-154-generic
Version : #161-Ubuntu SMP Fri Jul 30 13:04:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.3
setuptools : 51.3.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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The parameters `order` and `frozen` is set equal to True to make sure that that behaviour of OutputKey does not change.
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} | 8 | "2021-08-19T15:16:30Z" | "2021-09-10T15:47:23Z" | "2021-08-24T12:27:24Z" | 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
Version 1.3.2:
```python
>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
Empty DataFrame
Columns: []
Index: [M, W]
>>> df.groupby("MW").v.sum()
MW
M [1, 2]
W [10, 20]
Name: v, dtype: object
```
Version 1.2.5:
```python
>>> df=pd.DataFrame([["M", [1]], ["M", [2]], ["W", [10]], ["W", [20]]], columns=["MW", "v"])
>>> df.groupby("MW").sum()
v
MW
M [1, 2]
W [10, 20]
>>> df.groupby("MW").v.sum()
MW
M [1, 2]
W [10, 20]
Name: v, dtype: object
```
#### Problem description
With 1.2.5 and earlier, if the elements of one of the columns are objects that are lists, then the `groupby` operation with `sum` creates the aggregation of the lists. With 1.3.2, it doesn't.
#### Expected Output
Same as 1.2.5
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3af1a4fa27a7d48d8e6aea66bf2da6fdf2e1550e
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 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.4.0.dev0+475.g3af1a4fa27
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : 6.14.1
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.6.3
gcsfs : 2021.05.0
matplotlib : 3.3.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.0
sqlalchemy : 1.4.20
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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- [x] I have confirmed this bug exists on the latest version of pandas. (1.3.1)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
This succeeds:
```python
pd.DataFrame(columns=['A', 'A'], index=['T', 'U']).stack(dropna=False)
```
But this fails ("cannot reindex from a duplicate axis"):
```python
pd.DataFrame(columns=pd.MultiIndex.from_tuples([
('A', 'B'), ('A', 'B')
]), index=['T', 'U']).stack(level=[0, 1], dropna=False)
```
If you remove the duplicate entry then it does of course work:
```python
pd.DataFrame(columns=pd.MultiIndex.from_tuples([
('A', 'B'), ('A', 'C')
]), index=['T', 'U']).stack(level=[0, 1], dropna=False)
```
#### Problem description
I think you should be able to stack `MultiIndex` axes that contain duplicates, since you can stack `Index` axes that contain duplicates. Conceptually a `MultiIndex` is very similar to a `Index` of tuples so it's surprising that they differ in this respect.
#### Expected Output
The failing example should stack as follows:
```python
T A B NaN
B NaN
U A B NaN
B NaN
dtype: object
```
#### Full backtrace
<details>
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\frame.py in stack(self, level, dropna)
7206
7207 if isinstance(level, (tuple, list)):
-> 7208 result = stack_multiple(self, level, dropna=dropna)
7209 else:
7210 result = stack(self, level, dropna=dropna)
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\reshape\reshape.py in stack_multiple(frame, level, dropna)
571 for index in range(len(level)):
572 lev = level[index]
--> 573 result = stack(result, lev, dropna=dropna)
574 # Decrement all level numbers greater than current, as these
575 # have now shifted down by one
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\reshape\reshape.py in stack(frame, level, dropna)
498
499 if isinstance(frame.columns, MultiIndex):
--> 500 return _stack_multi_columns(frame, level_num=level_num, dropna=dropna)
501 elif isinstance(frame.index, MultiIndex):
502 new_levels = list(frame.index.levels)
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\reshape\reshape.py in _stack_multi_columns(frame, level_num, dropna)
670 chunk = this.loc[:, this.columns[loc]]
671 chunk.columns = level_vals.take(chunk.columns.codes[-1])
--> 672 value_slice = chunk.reindex(columns=level_vals_used).values
673 else:
674 if frame._is_homogeneous_type and is_extension_array_dtype(
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
310 @wraps(func)
311 def wrapper(*args, **kwargs) -> Callable[..., Any]:
--> 312 return func(*args, **kwargs)
313
314 kind = inspect.Parameter.POSITIONAL_OR_KEYWORD
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\frame.py in reindex(self, *args, **kwargs)
4174 kwargs.pop("axis", None)
4175 kwargs.pop("labels", None)
-> 4176 return super().reindex(**kwargs)
4177
4178 def drop(
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
4809
4810 # perform the reindex on the axes
-> 4811 return self._reindex_axes(
4812 axes, level, limit, tolerance, method, fill_value, copy
4813 ).__finalize__(self, method="reindex")
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\frame.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4014 columns = axes["columns"]
4015 if columns is not None:
-> 4016 frame = frame._reindex_columns(
4017 columns, method, copy, level, fill_value, limit, tolerance
4018 )
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\frame.py in _reindex_columns(self, new_columns, method, copy, level, fill_value, limit, tolerance)
4059 new_columns, method=method, level=level, limit=limit, tolerance=tolerance
4060 )
-> 4061 return self._reindex_with_indexers(
4062 {1: [new_columns, indexer]},
4063 copy=copy,
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
4875
4876 # TODO: speed up on homogeneous DataFrame objects
-> 4877 new_data = new_data.reindex_indexer(
4878 index,
4879 indexer,
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\internals\managers.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate, only_slice)
1299 # some axes don't allow reindexing with dups
1300 if not allow_dups:
-> 1301 self.axes[axis]._can_reindex(indexer)
1302
1303 if axis >= self.ndim:
~\Anaconda3\envs\py3_2\lib\site-packages\pandas\core\indexes\base.py in _can_reindex(self, indexer)
3474 # trying to reindex on an axis with duplicates
3475 if not self._index_as_unique and len(indexer):
-> 3476 raise ValueError("cannot reindex from a duplicate axis")
3477
3478 def reindex(self, target, method=None, level=None, limit=None, tolerance=None):
ValueError: cannot reindex from a duplicate axis
</details>
#### Output of ``pd.show_versions()``
(This from an earlier environment because my Pandas 1.3.0 environment doesn't have a working `show_versions`)
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18363
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.0
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.0
xlrd : 2.0.1
xlwt : None
numba : 0.53.0
</details>
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} | 0 | "2021-08-19T16:35:09Z" | "2021-08-19T19:21:43Z" | "2021-08-19T19:02:59Z" | 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
In turn this means that create_block_manager_from_arrays doesnt need `columns`, but bc that is in the pseudo-public API the existing version will need to be moved to internals.api before being simplified. Saving that for a separate step. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43112/events | https://github.com/pandas-dev/pandas/pull/43112 | 974,894,317 | MDExOlB1bGxSZXF1ZXN0NzE2MTMwOTcz | 43,112 | REF: use _get_axis_number instead of if axis in [...]: #43048 | {
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} | 1 | "2021-08-19T18:24:35Z" | "2021-08-24T16:26:48Z" | "2021-08-24T16:26:48Z" | MEMBER | null | e.g. in `Index._get_fill_indexer` we can work out that `target.dtype` should match `self.dtype`. It would be nice if we could get that into the annotation. cc @simonjayhawkins
```
# current
def _get_fill_indexer(self, target: Index, ...):
# desired?
def _get_fill_indexer(self: Index[MyDtype], target: Index[MyDtype], ...):
``` | {
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} | 9 | "2021-08-19T21:22:58Z" | "2021-08-20T18:11:31Z" | "2021-08-20T00:57:56Z" | MEMBER | null | 1) copy/paste create_block_manager_from_arrays and _form_blocks to the pseudo-public internals.api
2) rename create_block_manager_from_arrays -> create_block_manager_from_column_arrays
3) prune no-longer-needed args from create_block_manager_from_arrays, _form_blocks
Two follow-ups after this:
4) remove no-longer-needed checks in no-longer-pseudo-public create_block_manager_from_column_arrays
5) deprecate pseudo-public create_block_manager_from_arrays | {
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} | 0 | "2021-08-19T22:58:09Z" | "2021-08-20T01:53:24Z" | "2021-08-20T00:56:55Z" | MEMBER | null | The relevant cython code already supports 2D, so this is a super-simple fix. | {
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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] Fixed typo in pandas/io/parser/c_parser_wrapper.py
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} | 11 | "2021-08-20T05:28:07Z" | "2021-09-05T17:39:58Z" | "2021-09-05T15:59:21Z" | CONTRIBUTOR | null | (GH43102)
- closes #43102
- Fixed uncaught IndexError that is raised when the iterator `i` in `extract(r)` inside base_parser.py exceeds the length of a header row (when `field_count > len(r)`).
- A Parser error is raised when the header row had unequal columns. Outputs the first row where a mismatch is found.
- Added a test to match the error being raised. | {
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} | 3 | "2021-08-20T06:10:16Z" | "2021-08-28T17:54:16Z" | 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 pandas as pd
pd.show_versions()
broken = pd.DataFrame([], columns=["a"], dtype="string")
broken.assign(b=[1]) # ValueError: StringArray requires a sequence of strings or pandas.NA
working1 = pd.DataFrame([pd.NA], columns=["a"], dtype="string")
working1.assign(b=[1]) # 0 <NA> 1
working2 = pd.DataFrame([], columns=["a"], dtype="object")
working2.assign(b=[1]) # 0 NaN 1
```
Full traceback:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../site-packages/pandas/core/frame.py", line 4481, in assign
data[k] = com.apply_if_callable(v, data)
File ".../site-packages/pandas/core/frame.py", line 3607, in __setitem__
self._set_item(key, value)
File ".../site-packages/pandas/core/frame.py", line 3779, in _set_item
value = self._sanitize_column(value)
File ".../site-packages/pandas/core/frame.py", line 4497, in _sanitize_column
self._ensure_valid_index(value)
File ".../site-packages/pandas/core/frame.py", line 3853, in _ensure_valid_index
self._mgr = self._mgr.reindex_axis(index_copy, axis=1, fill_value=np.nan)
File ".../site-packages/pandas/core/internals/base.py", line 89, in reindex_axis
return self.reindex_indexer(
File ".../site-packages/pandas/core/internals/managers.py", line 680, in reindex_indexer
new_blocks = [
File ".../site-packages/pandas/core/internals/managers.py", line 681, in <listcomp>
blk.take_nd(
File ".../site-packages/pandas/core/internals/blocks.py", line 1512, in take_nd
new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
File ".../site-packages/pandas/core/arrays/_mixins.py", line 108, in take
return self._from_backing_data(new_data)
File ".../site-packages/pandas/core/arrays/numpy_.py", line 118, in _from_backing_data
return type(self)(arr)
File ".../site-packages/pandas/core/arrays/string_.py", line 318, in __init__
self._validate()
File ".../site-packages/pandas/core/arrays/string_.py", line 323, in _validate
raise ValueError("StringArray requires a sequence of strings or pandas.NA")
ValueError: StringArray requires a sequence of strings or pandas.NA
```
#### Problem description
The `broken` code doesn't seem meaningfully different to the working versions, but throws an exception; from a very quick look (and as hinted by the `working2` output), it looks like the `StringArray` is being constructed with an array containing NaN/`float('nan')`, and that value is considered/treated different to `pd.NA`.
There's a few other issues mentioning this error, but it wasn't obvious to me if they're the same:
- #40800
- #40823
- #40824
#### Expected Output
The `broken` code should behave similarly to the very similar `working1` and `working2`. The output should be the same as the `working1`:
```
a b
0 <NA> 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:31 PDT 2021; root:xnu-7195.121.3~9/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.3.2
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.4
setuptools : 49.2.1
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43120 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43120/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43120/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43120/events | https://github.com/pandas-dev/pandas/pull/43120 | 975,327,490 | MDExOlB1bGxSZXF1ZXN0NzE2NDk0Mzkw | 43,120 | REF: used _get_axis_number instead of if axis in [...]: #43048 | {
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