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} | 0 | "2021-05-04T20:50:28Z" | "2021-05-05T14:52:58Z" | "2021-05-05T14:52:58Z" | MEMBER | null | Looks like it is discarding the axis=1
```
df = DataFrame({0: [1, 3, 5, 7], 1: [2, 4, 6, 8], 2: [1.5, 3.5, 5.5, 7.5]}, index=["a", "a", "b", "b"])
gb = df.groupby(level=0, axis=0)
res = gb.rank(axis=1)
# This should match what we get when "manually" operating group-by-group
expected = concat([df.loc["a"].rank(axis=1), df.loc["b"].rank(axis=1)], axis=0)
>>> res
0 1 2
a 1.0 1.0 1.0
a 2.0 2.0 2.0
b 1.0 1.0 1.0
b 2.0 2.0 2.0
>>> expected
0 1 2
a 1.0 3.0 2.0
a 1.0 3.0 2.0
b 1.0 3.0 2.0
b 1.0 3.0 2.0
``` | {
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} | 14 | "2021-05-04T21:04:13Z" | "2021-05-21T01:47:17Z" | "2021-05-21T01:44:13Z" | CONTRIBUTOR | null | - [x] closes #41225
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Added ability to check for multiple XLS signatures according to testing files available at [Spreadsheet Project](https://www.openoffice.org/sc/testdocs/). Also defer raising an error when the engine is specified but the file signature does not match one of the values in `SIGNATURES`: this allows a user to attempt to specify and use an engine even if the passed file doesn't match one of the provided values in `SIGNATURES`.
This is my first PR for this project, so please let me know if more is expected (writing tests, writing a whatsnew entry, etc). I have run Flake8 on the code and successfully opened BIFF2 through BIFF8 files with this method. Thanks!
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} | 3 | "2021-05-05T01:58:58Z" | "2021-05-05T21:08:13Z" | "2021-05-05T03:59:51Z" | NONE | null | - [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
I noticed that set comprehension of pandas index are being deprecated. These are extremely useful. I was wondering what the rationale for this change is amongst the developers?
This functionality is extremely useful:
```python
X_A = # Some dataframe (n,m)
X_B = # Some other dataframe (n,q)
X_C = X_A.loc[X_A.columns & X_B.columns]
features = X_A.columns | X_B.columns | X_C.columns
```
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} | 3 | "2021-05-05T03:46:20Z" | "2021-05-05T15:01:02Z" | "2021-05-05T14:52:58Z" | MEMBER | null | - [x] closes #41320
- [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 | "2021-05-05T09:00:03Z" | "2021-05-10T14:39:30Z" | "2021-05-10T14:39:30Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
With `pd.Series.value_counts()` it is possible to specify `dropna=False`, but that argument does not exist in `pd.DataFrame.value_counts()`. As a consequence, all rows that contain at least one `NA` element is dropped when using `df.value_counts()`.
#### Describe the solution you'd like
It should be possible to call `df.value_counts()` with `dropna=False` and get a count for each unique row, including rows that have `NA`s in them.
#### API breaking implications
Like with `pd.Series.value_counts()` the default should be `dropna=True`. This will keep consistency between the two implementations, and leave current behavior unchanged.
#### Describe alternatives you've considered
#### Additional context
```python
>>> import pandas as pd
>>> s1 = pd.Series([1, 2, 3, pd.NA, 3])
>>> s2 = pd.Series([pd.NA, 1, pd.NA, 4, 2])
>>> s1.value_counts(dropna=False)
3.0 2
NaN 1
1.0 1
2.0 1
dtype: int64
>>> df = pd.DataFrame(zip(s1, s2), columns=['s1', 's2'])
>>> df
s1 s2
0 1 <NA>
1 2 1
2 3 <NA>
3 <NA> 4
4 3 2
>>> df.value_counts()
s1 s2
2 1 1
3 2 1
dtype: int64
```
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```
[ 50.00%] ··· strings.Methods.time_match ok
[ 50.00%] ··· ============== ==========
dtype
-------------- ----------
str 28.3±0ms
string 22.5±0ms
arrow_string 2.46±0ms
============== ==========
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} | 1 | "2021-05-05T12:01:54Z" | "2021-05-05T12:58:45Z" | "2021-05-05T12:58:42Z" | MEMBER | null | Reverts pandas-dev/pandas#41315, closes #41313 | {
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not much burden on < 1, but let's keep the eco-system moving forward. | {
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} | 1 | "2021-05-05T16:12:13Z" | "2021-05-11T20:23:12Z" | "2021-05-11T20:23:12Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I'm trying to remove certain characters in a DataFrame, in this case, I am trying to remove the **ñ**.
#### Code Sample
```python
df = pd.DataFrame([['añoos','asññ','ÑolÑÑss'],['añoos','asññ','ÑolÑÑss']], dtype='string')
df.replace({'ñ':'n',
'Ñ':'N'},
regex = True, inplace = True)
```
The output is:
| | 0 | 1 | 2 |
|---:|:------|:-----|:--------|
| 0 | añoos | asññ | ÑolÑÑss |
| 1 | añoos | asññ | ÑolÑÑss |
#### Problem description
If I do not specify the dtype to ```string```, the output is the expected as follows:
#### Expected Output
```python
df = pd.DataFrame([['añoos','asññ','ÑolÑÑss'],['añoos','asññ','ÑolÑÑss']])
df.replace({'ñ':'n',
'Ñ':'N'},
regex = True, inplace = True)
print(df)
```
| | 0 | 1 | 2 |
|---:|:------|:-----|:--------|
| 0 | anoos | asnn | NolNNss |
| 1 | anoos | asnn | NolNNss |
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : es_419.UTF-8
LOCALE : Spanish_Colombia.1252
pandas : 1.2.4
numpy : 1.19.4
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.52.0
</details>
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} | 2 | "2021-05-05T16:18:56Z" | "2021-05-10T14:39:53Z" | "2021-05-10T14:39:30Z" | CONTRIBUTOR | null | - [x] closes #41325
- [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 | "2021-05-05T16:54:43Z" | "2021-07-01T14:17:34Z" | null | NONE | null | #### Is your feature request related to a problem?
I wish I could create temporary tables using the DataFrame.to_sql() method.
#### Describe the solution you'd like
Add a new boolean kwarg to the to_sql() method that if set to True, will cause the table to be created as a temporary table.
#### API breaking implications
One new argument, defaulting to False - shouldn't change any normalbehavior.
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} | 7 | "2021-05-05T17:21:32Z" | "2021-08-26T13:52:29Z" | "2021-05-24T06:02:23Z" | MEMBER | null | According to the change log of both "checkout" and "cache" this should increase performance.
---
Fell free to close this PR if this was already discussed, I haven't saw any discussion about this topic. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41337 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41337/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41337/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41337/events | https://github.com/pandas-dev/pandas/pull/41337 | 876,697,408 | MDExOlB1bGxSZXF1ZXN0NjMwNzg5OTcy | 41,337 | DEP: bump pyarrow to 1.0.0 | {
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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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} | 0 | "2021-05-05T17:56:45Z" | "2021-05-06T08:16:28Z" | "2021-05-06T01:50:39Z" | MEMBER | null | ```
before after ratio
[a43c42c3] [a5dd50ea]
<master> <small-perf>
- 26.8±0.05ms 26.1±0.1ms 0.97 strings.Methods.time_get('arrow_string')
- 21.2±0.1ms 20.3±0.1ms 0.96 strings.Methods.time_replace('arrow_string')
- 18.1±0.1ms 17.3±0.1ms 0.96 strings.Methods.time_lstrip('arrow_string')
- 18.5±0.1ms 17.7±0.2ms 0.95 strings.Methods.time_strip('arrow_string')
- 18.3±0.1ms 17.4±0.09ms 0.95 strings.Methods.time_rstrip('arrow_string')
- 28.0±0.3ms 26.6±0.07ms 0.95 strings.Methods.time_center('arrow_string')
- 21.2±0.2ms 20.1±0.1ms 0.95 strings.Methods.time_normalize('arrow_string')
- 28.1±0.2ms 26.5±0.2ms 0.94 strings.Methods.time_pad('arrow_string')
- 13.6±0.1ms 11.5±0.1ms 0.85 strings.Methods.time_isdigit('str')
- 13.6±0.2ms 11.3±0.04ms 0.83 strings.Methods.time_isnumeric('str')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41339 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41339/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41339/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41339/events | https://github.com/pandas-dev/pandas/issues/41339 | 876,829,008 | MDU6SXNzdWU4NzY4MjkwMDg= | 41,339 | BUG: parsing excel returns one row short | {
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} | 4 | "2021-05-05T20:58:18Z" | "2021-05-06T07:04:29Z" | "2021-05-06T07:04:15Z" | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [x ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
df = pd.read_excel("D:/Docs/Joop/Elektronica/uC/Raspberry Pi/Excel/PythonTest.xls")
# PythonTest.xls contains one column 4 rows on first sheet, rest empty
shape = df.shape
print(shape)
#### Problem description
Parsing excel returns one row short
#### Expected Output
(4, 1)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Dutch_Netherlands.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
[PythonTest.xls](https://github.com/pandas-dev/pandas/files/6430145/PythonTest.xls)
Python Debug PDB:
<details>
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\excel\_xlrd.py(112)get_sheet_data()
-> return data
(Pdb) data
[[1], [2], [3], [4]]
...
(Pdb) n
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\excel\_base.py(540)parse()
-> has_index_names = is_list_like(header) and len(header) > 1
(Pdb) data
[[1], [2], [3], [4]]
(Pdb) c
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\parsers.py(2231)TextParser()
-> kwds["engine"] = "python"
(Pdb) c
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\parsers.py(2468)read()
-> try:
(Pdb) r
--Return--
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\parsers.py(2509)read()->(None, [1], {1: array([2, 3, 4], dtype=int64)})
-> return index, columns, data
(Pdb) data
{1: array([2, 3, 4], dtype=int64)}
(Pdb) r
--Return--
> d:\docs\programs\python\python39\lib\site-packages\pandas\io\parsers.py(1077)read()-> 1
0 2
1 3
2 4
-> return df
(Pdb) df
1
0 2
1 3
2 4
(Pdb) c
(3, 1)
</details>
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} | 2 | "2021-05-05T21:17:40Z" | "2021-05-05T21:40:23Z" | "2021-05-05T21:40:14Z" | MEMBER | null | I might be doing something wrong here, but when executing
```python
import pandas as pd
```
I get an import error after pulling f40e58cc50abcd03efae372df0586467a957f695. The commit before is fine. Can someone reproduce?
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2021.1/scratches/scratch_4.py", line 566, in <module>
import pandas as pd
File "/home/developer/PycharmProjects/pandas/pandas/__init__.py", line 22, in <module>
from pandas.compat import (
File "/home/developer/PycharmProjects/pandas/pandas/compat/__init__.py", line 15, in <module>
from pandas.compat.numpy import (
File "/home/developer/PycharmProjects/pandas/pandas/compat/numpy/__init__.py", line 7, in <module>
from pandas.util.version import Version
File "/home/developer/PycharmProjects/pandas/pandas/util/__init__.py", line 1, in <module>
from pandas.util._decorators import ( # noqa
File "/home/developer/PycharmProjects/pandas/pandas/util/_decorators.py", line 14, in <module>
from pandas._libs.properties import cache_readonly # noqa
File "/home/developer/PycharmProjects/pandas/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 "pandas/_libs/hashtable.pyx", line 1, in init pandas._libs.hashtable
File "pandas/_libs/missing.pyx", line 30, in init pandas._libs.missing
ImportError: cannot import name IS64
Process finished with exit code 1
```
The show versions is from the last working commit
cc @fangchenli
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : a43c42c32d4f161679974e403b11784c824714bc
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 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+1544.ga43c42c32d
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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} | 0 | "2021-05-05T21:40:16Z" | "2021-05-06T15:08:04Z" | "2021-05-06T13:50:05Z" | MEMBER | null | In addressing #41291 i found that this test isn't constructing the DataFrame (i think) it intends to. When the construction is fixed, a bunch of the tests fail. This fixes the construction and xfails those tests. | {
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} | 5 | "2021-05-05T22:18:43Z" | "2021-05-26T17:15:55Z" | "2021-05-26T10:16:16Z" | 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
- [x] whatsnew entry
This addresses the SeriesGroupBy (easier) half of #41291:
1) When we have numeric_only=True and non-numeric data in a SeriesGroupBy reduction, raise NotImplementedError (matching Series behavior) instead of falling back to an often-wrong fallback (xref #41341)
2) When the user doesn't explicitly pass `numeric_only=True`, change the default to False for SeriesGroupBy, leaving DataFrameGroupBy unaffected. | {
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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
2 separate issues at play here - regex was ignored in #41333 (specific to the `replace_list` path) but then if `replace_regex` was hit with anything but an object type that would just raise.
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} | 0 | "2021-05-06T04:07:04Z" | "2021-05-06T23:23:13Z" | "2021-05-06T23:21:27Z" | MEMBER | null | Fix the need for verify_integrity=False | {
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} | 2 | "2021-05-06T09:55:00Z" | "2021-05-10T09:34:38Z" | "2021-05-10T09:34:29Z" | CONTRIBUTOR | null | - [x] closes #40769
- [x] tests added / passed
- [x] Ensure all linting tests pass, see here for how to run them
Color in boxplot.py should be a hex value. Letter based color will be at matplotlib level. Using same color as that of boxes for the caps as well. The color will be fetched based on the selected theme. We are over riding color with "k" , rather need to use the one set at matplotlib level.
Before Fix :
Caps for the box plot is not visible :
](https://user-images.githubusercontent.com/9417467/117279207-1a48f280-ae7f-11eb-9f0f-fe477d80dc77.png)
After Fix :
Caps for the box plow will be visible in dark background mode :

Whats New : Box Plot's Caps will have same color as boxes unless color is explicitly specified by user arguments. Hence theme changes will not adversly effect caps color. | {
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] | closed | false | null | [] | null | 2 | "2021-05-06T11:21:56Z" | "2021-08-20T02:54:23Z" | "2021-08-20T02:54:22Z" | NONE | null | We have many use-cases where we want to access the underlying column of a `DataFrame`, either as an Array or `numpy.ndarray`, and right now we have to go through a Series to do so:
```python
df["columnname"].array
df["columnname"].to_numpy()
```
Going through a Series has unwanted large overhead for us (ignoring the internal `_item_cache`), so it would be nice to be able to go directly to an `Array `or `numpy.ndarray`. If we want fast access we right now have to do:
```
df._mgr.iget_values(df.columns.get_loc("columnname"))
```
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] | null | 9 | "2021-05-06T13:34:31Z" | "2021-08-20T02:55:28Z" | 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
df = pd.DataFrame({'non_numeric': 10000*[pd.Timedelta(10)], 'numeric': 10000*[1]})
# Summing over axis 1 with numeric_only=True works as expected
assert_series_equal(df.sum(axis=1, numeric_only=True), df.numeric, check_names=False)
# Summing with the default numeric_only=None only works if df has <= 8192 = 2**13 rows
sample = df.sample(8192)
assert_series_equal(sample.sum(axis=1), sample.numeric, check_names=False)
# Summing over the whole dataframe we get a SystemError
df.sum(axis=1)
# The following also produces a SystemError
sample['numeric_2'] = 1
sample.sum(axis=1)
# The following does not produce a SystemError
sample.sample(1).sum(axis=1)
```
#### Problem description
In previous pandas versions the last line does not throw a TypeError. The default numeric_only=None will only sum over the numeric columns of the dataframe.
In pandas 1.2.4 this is the case if the length of the dataframe is <= 8192 = 2**13. As soon as the length exceeds 8192 we get a SystemErrorbecause it tries to sum a Timedelta column and an int column. We also get a SystemError when df has more than one numeric column. Then the sum only works if the length of the dataframe is 1
The behaviour of sum is the same for other aggregation methods like mean.
#### Expected Output
I expect df.sum(axis=1) to return a series whose entries are the sum of the numeric columns of the dataframe along the axis 1 as is the case for dataframes of length <= 8192.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.5.2.el7.x86_64
Version : #1 SMP Fri Oct 20 20:32:50 UTC 2017
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en.UTF-8
LOCALE : None.None
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
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 : 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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} | 1 | "2021-05-06T15:02:42Z" | "2021-05-12T01:52:52Z" | "2021-05-12T01:22:16Z" | MEMBER | null | Can move a bunch more, but the diff gets really garbled when moving more, so easier to do in chunks | {
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} | 2 | "2021-05-06T15:41:02Z" | "2021-05-06T23:38:44Z" | "2021-05-06T23:20:15Z" | CONTRIBUTOR | null | - [ ] xref #36893
- [ ] 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
Follow up to #40556, per this comment https://github.com/pandas-dev/pandas/pull/40556#issuecomment-831413175 | {
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- [x] tests added / passed (passed on azure checks)
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A proposed solution for issue #40820, to accept an optional argument ``initializer`` which allows for concatenating empty sequences and return the object passed to initializer -- which can be useful for cases where users are running pd.concat on sequence variables which might potentially be empty -- and would like the result of the concatenation to be initialized to an empty Series/DataFrame
The new argument is made optional, and the ValueError is still thrown for cases where the ``initializer`` is not specified -- this should maintain expected behaviour / backward compatibility
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} | 0 | "2021-05-06T18:36:50Z" | "2021-07-28T01:50:05Z" | "2021-07-28T01:50:05Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported (although it is somewhat related to #14869).
- [x] I have confirmed this bug exists on the latest version of pandas. [1.2.4]
- [x] (optional) I have confirmed this bug exists on the master branch of pandas. [1.3.0.dev0+1544.ga43c42c32d.dirty]
---
**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
from datetime import timedelta
print(pd.__version__) # 1.3.0.dev0+1544.ga43c42c32d.dirty
offset_1 = pd.offsets.CustomBusinessMonthBegin(n=1) # 1 month BMS
offset_1_plus_5d = pd.offsets.CustomBusinessMonthBegin(n=1, offset=timedelta(days=5)) # 1 month BMS + 5 days
offset_2_plus_5d = pd.offsets.CustomBusinessMonthBegin(n=2, offset=timedelta(days=5)) # 2 month BMS + 5 days
test_timestamp = pd.Timestamp('2021-03-01')
print(test_timestamp + offset_1) # Expected: 2021-04-01, Actual: 2021-04-01 [Correct]
print(test_timestamp + offset_1_plus_5d) # Expected: 2021-04-06, Actual: 2021-04-01 [Incorrect]
print(test_timestamp + offset_2_plus_5d) # Expected: 2021-05-08, Actual: 2021-05-08 [Correct]
```
#### Expected Output
I am using 2021-03-01 as the example in the code snippet but I am listing below the expected vs. actual outputs with 2021-04-01 as well.
| Date | offset | Expected | **Actual** | Correct |
| --- | --- | --- | --- | --- |
| 2021-03-01 | `offset_1` | 2021-04-01 | 2021-04-01 | :heavy_check_mark: |
| 2021-03-01 | `offset_1_plus_5d` | 2021-04-06 | 2021-04-01 | :x: |
| 2021-03-01 | `offset_2_plus_5d` | 2021-05-08 | 2021-05-08 | :heavy_check_mark: |
| 2021-04-01 | `offset_1` | 2021-05-03 | 2021-05-03 | :heavy_check_mark: |
| 2021-04-01 | `offset_1_plus_5d` | 2021-05-08 | 2021-05-08 | :heavy_check_mark: |
| 2021-04-01 | `offset_2_plus_5d` | 2021-06-06 | 2021-06-01 | :x: |
#### Problem description
From reading the [implementations](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/tslibs/offsets.pyx), I think the expected behavior of `test_timestamp + offset_1_plus_5d` for example should be essentially `test_timestamp + offset_1 + timedelta(days=5)`, but we are seeing some inconsistent behaviors.
The issue lies with the `.apply` logic in `_CustomBusinessMonth` [[link here](https://github.com/pandas-dev/pandas/blob/3cce96f515917170ea9bce731ffcc913750464b8/pandas/_libs/tslibs/offsets.pyx#L3386-L3397)] , which first rolls with a vanilla `MonthBegin` or `MonthEnd` (to variable `new`) and then uses `CustomBusinessDay` to roll `new` to the corresponding business day + extra offset (variable `result`). However, if `new` is already on offset, then no further rolling will be applied and no extra offset (e.g. 5 days) will be added either, leading to the inconsistencies we are seeing here. See implementation for [rollforward](https://github.com/pandas-dev/pandas/blob/3cce96f515917170ea9bce731ffcc913750464b8/pandas/_libs/tslibs/offsets.pyx#L604-L616) and [rollback](https://github.com/pandas-dev/pandas/blob/3cce96f515917170ea9bce731ffcc913750464b8/pandas/_libs/tslibs/offsets.pyx#L590-L602).
As an example, with `CustomBusinessMonthBegin(n=1, offset=timedelta(days=5))`, 2021-03-01 first gets rolled to 2021-04-01. Since 2021-04-01 is already a business day, it is not rolled to the next business day and the extra 5-day offset is not applied either.
Since `_CustomBusinesMonth` is the underlying implementation for both `CustomBusinessMonthBegin` and `CustomBusinessMonthEnd`, this issue applies to both classes (although I am only showing `CustomBusinessMonthBegin` ).
Fix: #41488
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : a43c42c32d4f161679974e403b11784c824714bc
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-50-generic
Version : #56~20.04.1-Ubuntu SMP Mon Apr 12 21:46:35 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+1544.ga43c42c32d.dirty
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.8.1
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.1
sqlalchemy : 1.4.2
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import gc
import pandas as pd
@pd.api.extensions.register_dataframe_accessor("test")
class TestAccessor:
def __init__(self, pandas_obj):
self._obj = pandas_obj
def dump_garbage():
gc.collect()
print(f"\nUNCOLLECTABLE OBJECTS: {len(gc.garbage)}")
for x in gc.garbage:
s = str(x)
if len(s) > 80:
s = s[:77]+'...'
print(type(x), "\n ", s)
gc.enable()
gc.set_debug(gc.DEBUG_SAVEALL)
df = pd.DataFrame({"a": [1, 2, 3]})
del df
dump_garbage() # <- No uncollectable objects found
df = pd.DataFrame({"a": [1, 2, 3]})
df.test # <- Initialize custom accessor
del df
dump_garbage() # <- Multiple uncollectable objects found
```
#### Problem description
When using a custom accessor, memory is not being freed as expected. This appears to be a result of the accessor storing a reference to the dataframe, as demonstrated in the example above.
The output of the example code is included below showing that after deletion of the dataframe without the accessor initialized, there are 0 uncollectible objects, but after deleting the dataframe with the accessor initialized 17 uncollectible objects remain.
For additional context refer to [this issue](https://github.com/alteryx/woodwork/issues/880) and this [pull request](https://github.com/alteryx/woodwork/pull/894).
Note, this problem appears to be solvable by storing a weak reference to the dataframe in the accessor instead, but I am concerned that using that approach could create other issues.
<details>
```
UNCOLLECTABLE OBJECTS: 0
UNCOLLECTABLE OBJECTS: 17
<class 'pandas.core.frame.DataFrame'>
a
0 1
1 2
2 3
<class 'pandas.core.indexes.base.Index'>
Index(['a'], dtype='object')
<class 'dict'>
{'_data': array(['a'], dtype=object), '_index_data': array(['a'], dtype=objec...
<class 'pandas.core.indexes.range.RangeIndex'>
RangeIndex(start=0, stop=3, step=1)
<class 'dict'>
{'_range': range(0, 3), '_name': None, '_cache': {}, '_id': <object object at...
<class 'pandas.core.internals.blocks.IntBlock'>
IntBlock: slice(0, 1, 1), 1 x 3, dtype: int64
<class 'pandas._libs.internals.BlockPlacement'>
BlockPlacement(slice(0, 1, 1))
<class 'slice'>
slice(0, 1, 1)
<class 'pandas.core.internals.managers.BlockManager'>
BlockManager
Items: Index(['a'], dtype='object')
Axis 1: RangeIndex(start=0, ...
<class 'list'>
[Index(['a'], dtype='object'), RangeIndex(start=0, stop=3, step=1)]
<class 'tuple'>
(IntBlock: slice(0, 1, 1), 1 x 3, dtype: int64,)
<class 'dict'>
{'_is_copy': None, '_mgr': BlockManager
Items: Index(['a'], dtype='object')
A...
<class 'pandas.core.flags.Flags'>
<Flags(allows_duplicate_labels=True)>
<class 'weakref'>
<weakref at 0x150273900; dead>
<class 'dict'>
{'_allows_duplicate_labels': True, '_obj': <weakref at 0x150273900; dead>}
<class '__main__.TestAccessor'>
<__main__.TestAccessor object at 0x10af93520>
<class 'dict'>
{'_obj': a
0 1
1 2
2 3}
<class 'pandas.core.series.Series'>
0 a
1 0 1
2 1 2
3 2 3
dtype: object
<class 'pandas.core.internals.blocks.ObjectBlock'>
ObjectBlock: 4 dtype: object
<class 'pandas._libs.internals.BlockPlacement'>
BlockPlacement(slice(0, 4, 1))
<class 'slice'>
slice(0, 4, 1)
<class 'pandas.core.internals.managers.SingleBlockManager'>
SingleBlockManager
Items: RangeIndex(start=0, stop=4, step=1)
ObjectBlock: 4 ...
<class 'list'>
[RangeIndex(start=0, stop=4, step=1)]
<class 'tuple'>
(ObjectBlock: 4 dtype: object,)
<class 'dict'>
{'_is_copy': None, '_mgr': SingleBlockManager
Items: RangeIndex(start=0, stop...
<class 'pandas.core.flags.Flags'>
<Flags(allows_duplicate_labels=True)>
<class 'weakref'>
<weakref at 0x1502ba810; dead>
<class 'dict'>
{'_allows_duplicate_labels': True, '_obj': <weakref at 0x1502ba810; dead>}
<class 'pandas.core.strings.accessor.StringMethods'>
<pandas.core.strings.accessor.StringMethods object at 0x1502aa0d0>
<class 'dict'>
{'_inferred_dtype': 'string', '_is_categorical': False, '_is_string': False, ...
```
</details>
#### Expected Output
Both example cases above should release the memory used by the dataframe once it is deleted.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Aug 31 20:53:32 PDT 2020; root:xnu-4903.278.44~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 47.1.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 6 | "2021-05-06T20:44:43Z" | "2021-05-12T09:19:14Z" | "2021-05-12T01:20:35Z" | MEMBER | null | - [x] closes #41170
- [x] closes #40235
- [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
stumbled across a bug in MultiIndex.reindex, which caused the ValueError from the op | {
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} | 1 | "2021-05-06T20:55:17Z" | "2021-05-07T13:09:43Z" | "2021-05-07T12:43:27Z" | CONTRIBUTOR | null | The invalid dtype tests for all the numeric index types are currently done in `tests.numeric.test_numeric.py::TestFloat64Index::test_invalid_dtype`. This PR moves them to the appropriate test class (`TestInt64Index` etc.). | {
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] | open | false | null | [] | null | 2 | "2021-05-06T22:12:32Z" | "2021-10-19T00:27:53Z" | 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
from datetime import datetime
now = datetime.now()
df = pd.DataFrame({'ts_pd': [now.timestamp()], 'ts_py': [now.timestamp()]})
print(f"It is now: {now.timestamp()} seconds from Unix Epoch (00:00:00 UTC on 1 January 1970)")
df['ts_pd'] = pd.to_datetime(df['ts_pd'], unit='s')
df['ts_py'] = df['ts_py'].apply(datetime.fromtimestamp)
print(df)
# It is now: 1620338266.89658 seconds from Unix Epoch (00:00:00 UTC on 1 January 1970)
# ts_pd ts_py
# 2021-05-06 21:57:46 2021-05-06 17:57:46
```
#### Problem description
I would expect `to_datetime` to behave the same as `fromtimestamp`. That is, if returning a TZ-naive time, it should probably be a localtime. If parsing a unix epoch timestamp, we know the value is in UTC, so leaving it TZ-unaware is a footgun as I think a naive user would expect either (1) localtime or (2) to have UTC tzinfo attached to prevent mingling with true localtimes.
I encountered this problem because I was parsing timestamps from a couple of files. In one file were text-form timestamps, which were parsed as local-time. In the other were epoch-form timestamps, which were parsed as TZ-unaware UTC. If I unconditionally pass utc=True, now my text-form timestamps are mistakenly given UTC tzinfo. In the end I wrote a helper which only passes utc=True if the column is numerical.
This table shows the outputs and workaround:
| | str | ts | comment |
|---:|:----------------------------------------------------|:------------------------------------|--|
| 0 | pd.to_datetime(now.isoformat()) | 2021-05-06 18:03:31.766199 ||
| 1 | datetime.fromtimestamp(now.timestamp()) | 2021-05-06 18:03:31.766199 ||
| 2 | datetime.utcfromtimestamp(now.timestamp()) | 2021-05-06 22:03:31.766199 |This is OK, I specifically asked for UTC not localtime.|
| 3 | pd.to_datetime(now.timestamp(), unit="s") | 2021-05-06 22:03:31.766199112 |Not OK. I've now parsed a different time than if I had parsed isoformat() of the same time.|
| 4 | pd.to_datetime(now.timestamp(), unit="s", utc=True) | 2021-05-06 22:03:31.766199112+00:00 |OK, but should be default.|
#### Expected Output
| ts_pd | ts_py |
|--------|--------|
| 2021-05-06 21:53:42+00:00 | 2021-05-06 17:53:42|
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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} | 0 | "2021-05-06T23:10:06Z" | "2021-05-07T15:59:19Z" | "2021-05-07T15:53:05Z" | 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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} | 2 | "2021-05-07T01:02:32Z" | "2021-05-09T19:44:25Z" | "2021-05-07T09:17:22Z" | 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
import numpy as np
mi = pd.MultiIndex.from_product([[1,2,3], ["a", "b"]])
ser = pd.Series([1,np.nan]*3, index = mi)
# b in index, as expected
print("b" in ser.index.levels[1])
# b still in index even though all cells with (anything, b) have been dropped
ser0 = ser.dropna()
print("b" in ser0.index.levels[1])
# resetting and setting index produces expected behavior after dropna
ser1 = ser.dropna().reset_index().set_index(["level_0", "level_1"])
print("b" in ser1.index.levels[1])
```
#### Problem description
I'm not sure if this is a bug or if there is simply a function that does what I expect that I can't find.
When dropping NaN's from a series/dataframe with a multiindex (or just slicing it arbitrarily for that matter), removing all rows that correspond to one value in the index doesn't actually remove it from the index. In that case, tests like `value in series.index.levels[x]` will return true even though there are no rows that have `value` in level `x`.
Furthermore, this is inconsistent in comparison to series/dataframes with a non-multiindex; in these cases the index values are updated as the rows change.
#### Expected Output
It is expected that the values in each level of the multiindex reflect what is currently in the dataframe/series. The workaround (the last two lines in the code above) produce the desired behavior.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.33-1-lts-native
Version : #1 SMP PREEMPT Sun, 02 May 2021 04:17:56 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
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 : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.6
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 9 | "2021-05-07T10:47:22Z" | "2021-05-14T17:13:47Z" | "2021-05-14T17:13:47Z" | MEMBER | null | It looks like the fastparquet release broke our ci
Successful build:
https://github.com/pandas-dev/pandas/runs/2522988276
Erroneous build:
https://github.com/pandas-dev/pandas/runs/2524444950
This is the full version diff:
```
packages version_error_build version_successful_build
38 boto3 1.17.68 1.17.67
59 cramjam 2.3.0 NaN
79 fastparquet 0.6.0 0.5.0
228 pandas 1.3.0.dev0+1562.g6ea277f241 1.3.0.dev0+1561.gebf3b98596
316 sqlalchemy 1.4.14 1.4.13
```
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________________________ TestUnique.test_first_nan_kept ________________________
self = <pandas.tests.test_algos.TestUnique object at 0x7efe1274c760>
def test_first_nan_kept(self):
# GH 22295
# create different nans from bit-patterns:
bits_for_nan1 = 0xFFF8000000000001
bits_for_nan2 = 0x7FF8000000000001
NAN1 = struct.unpack("d", struct.pack("=Q", bits_for_nan1))[0]
NAN2 = struct.unpack("d", struct.pack("=Q", bits_for_nan2))[0]
assert NAN1 != NAN1
assert NAN2 != NAN2
for el_type in [np.float64, object]:
a = np.array([NAN1, NAN2], dtype=el_type)
result = pd.unique(a)
> assert result.size == 1
E assert 2 == 1
E + where 2 = array([nan, nan], dtype=object).size
pandas/tests/test_algos.py:798: AssertionError
```
Note: I cannot reproduce it on M1 mac. | {
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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
This popped up randomly for some of my dataframes, but only a few.
EDIT: Thanks to @mzeitlin11 below for a properly minimal example.
```python
import pandas as pd
df = pd.DataFrame([1, 1, 2, 2])
df.groupby([0, 0, 0, 0]).agg([pd.Series.mode])
```
</details>
This results in a `ValueError: no results`:
<details>
```python
ValueError Traceback (most recent call last)
c:\tmp\zonal-mode\debug.py in
----> 7 selection.groupby("id").agg([pd.Series.mode])
C:\Miniconda3\envs\main\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
943 func = maybe_mangle_lambdas(func)
944
--> 945 result, how = aggregate(self, func, *args, **kwargs)
946 if how is None:
947 return result
C:\Miniconda3\envs\main\lib\site-packages\pandas\core\aggregation.py in aggregate(obj, arg, *args, **kwargs)
584 # we require a list, but not an 'str'
585 arg = cast(List[AggFuncTypeBase], arg)
--> 586 return agg_list_like(obj, arg, _axis=_axis), None
587 else:
588 result = None
C:\Miniconda3\envs\main\lib\site-packages\pandas\core\aggregation.py in agg_list_like(obj, arg, _axis)
672 raise ValueError("no results")
673
--> 674 try:
675 return concat(results, keys=keys, axis=1, sort=False)
676 except TypeError as err:
ValueError: no results
```
</details>
other methods seem to work okay:
```python
selection.groupby("id").agg([pd.Series.sum])
selection.groupby("id").agg([pd.Series.mean])
selection.groupby("id").agg([pd.Series.std])
selection.groupby("id").agg(["count"])
```
Oddly enough, slightly changing the selection makes it run:
```python
selection.iloc[100:].groupby("id").agg([pd.Series.mode]) # works
selection.iloc[:900].groupby("id").agg([pd.Series.mode]) # works
selection.iloc[:917].groupby("id").agg([pd.Series.mode]) # works
selection.iloc[:918].groupby("id").agg([pd.Series.mode]) # errors
selection.iloc[18:918].groupby("id").agg([pd.Series.mode]) # works
selection.iloc[17:918].groupby("id").agg([pd.Series.mode]) # errors
```
`scipy.stats.mode` also works fine:
```python
import scipy.stats
selection.groupby("id").agg([scipy.stats.mode])
```
#### Problem description
I don't understand why it seems to randomly fail, there are no oddities with the data as far as I can tell.
Given the difference the selections make, I guess it's related with the specific number of occurences, which would suggest something internally in implementation of pd.Series.mode.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Dutch_Netherlands.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.0
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/41369 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41369/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41369/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41369/events | https://github.com/pandas-dev/pandas/issues/41369 | 879,161,119 | MDU6SXNzdWU4NzkxNjExMTk= | 41,369 | BUG: Accessing a cell in a pandas DataFrame with MultiIndex loses type information | {
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} | 2 | "2021-05-07T14:58:06Z" | "2021-05-25T12:28:54Z" | "2021-05-25T12:28:54Z" | 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
df2 = pd.DataFrame([{"a":1.0,"b":2,"c":3,"d":4}]).set_index(["c","d"])
df2.loc[(3,4),"b"]
```
#### Problem description
the above returns `2.0` of type `numpy.float64` instead of `2` of type `numpy.int64` as it should since `df2.dtypes["b"]`
is `dtype('int64')`.
See [here](https://stackoverflow.com/q/67436529/850781) for more information.
#### Expected Output
`2`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Apr 12 20:57:45 PDT 2021; root:xnu-6153.141.28.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : C
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 54.2.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.23.1
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 : 1.6.3
sqlalchemy : 1.4.14
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-05-07T14:59:58Z" | "2021-06-01T14:58:14Z" | "2021-05-07T16:41:07Z" | MEMBER | null | - [x] xref #41366
Pinning for now to get ci passing
Labeled the issue with 1.3 for now so that we don't forget it | {
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} | 6 | "2021-05-07T16:32:36Z" | "2021-05-12T01:57:02Z" | "2021-05-07T22:07:35Z" | MEMBER | null | - [x] xref #41170
The removed tests don't make sense anymore, since all is printed now but without a fixed order | {
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before after ratio
[170b4391] [62cc9c38]
<master> <_str_extract>
- 138±0.4ms 117±2ms 0.85 strings.Methods.time_extract('string')
- 14.4±0.6ms 11.9±0.06ms 0.82 strings.Methods.time_isdigit('str')
- 109±0.4ms 81.1±0.9ms 0.74 strings.Split.time_split(True)
- 82.3±0.3ms 46.3±0.2ms 0.56 strings.Split.time_rsplit(True)
- 50.0±0.4ms 27.3±0.4ms 0.55 strings.Methods.time_partition('string')
- 49.0±0.2ms 26.4±0.4ms 0.54 strings.Methods.time_rpartition('string')
- 55.3±0.5ms 28.8±0.3ms 0.52 strings.Methods.time_partition('str')
- 54.8±0.4ms 27.8±0.2ms 0.51 strings.Methods.time_rpartition('str')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
marked as draft since it may be better to split the perf improvements from the dispatch to array refactor and also may be better to finish #41085 first.
also need to complete the parameterisation of the extract tests.
also maybe able to reduce the _wrap_result changes by passing a 2d array without expand kwarg so that expand is inferred and the code removed here will be bypassed. so will look into that before marking as ready for review.
if I can reduce the diff here (even if only by doing the test parameterisation as a precursor), then may be worth adding the pyarrow native implementation here also.
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} | 8 | "2021-05-07T20:27:00Z" | "2021-07-25T14:42:04Z" | "2021-07-25T14:23:16Z" | MEMBER | null | - [ ] closes #xxxx
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} | 7 | "2021-05-07T21:53:12Z" | "2021-06-03T22:08:41Z" | "2021-05-25T12:28:55Z" | MEMBER | null | - [x] closes #41369
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In theory this would solve the issue, but I am not sure if this is desirable. We cast the row to a series, which forces the dtype conversion. If we loop in reverse we retrieve a column as a series which would avoid the conversion. Would you mind having a look @jbrockmendel ? | {
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} | 4 | "2021-05-07T21:53:45Z" | "2021-05-17T14:41:07Z" | "2021-05-17T14:25:50Z" | MEMBER | null | making it incrementally easier to reason about what `self.grouper` is | {
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} | 2 | "2021-05-08T01:02:29Z" | "2021-05-17T19:25:12Z" | "2021-05-17T19:18:11Z" | 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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] | open | false | null | [] | null | 0 | "2021-05-08T01:02:36Z" | "2021-08-20T02:58:06Z" | 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.
---
#### Problem description
I am implementing arrow-backed extension arrays with non-primitive data types. I got it to work well: almost all relevant unit tests from `pandas.tests.extension.base` are passing. One of the remaining issues is instantiation of Series objects with missing data, which results in infinite recursion with dict-like arrow data types. Consider this example of an ExtensionArray (I am not implementing all abstract methods: only showing what is necessary for demonstrating the issue)
```python
import numpy as np
import pyarrow as pa
from pandas.api.extensions import ExtensionDtype, ExtensionArray
class MyDtype(ExtensionDtype):
type = pa.StructType
name = 'arrow_struct_dtype'
arrow_type = pa.struct([('a', pa.int32())])
na_value = pa.scalar(None, arrow_type)
@classmethod
def construct_array_type(cls):
return MyArray
class MyArray(ExtensionArray):
dtype = MyDtype()
def __init__(self, array):
self.data = array
def __array__(self, *args, **kwargs):
"""Convert to numpy array"""
# can't use np.asarray here because it produces a 2-dimensional
# array when scalars are iterable and have the same same length
result = np.empty(shape=(len(self),), dtype='object')
for i, scalar in enumerate(self):
result[i] = scalar
return result
def __len__(self):
return len(self.data)
def __getitem__(self, item):
# assuming item is an integer
return self.data.__getitem__(item)
@classmethod
def _from_sequence(cls, scalars, *, dtype=None, copy=False):
# assuming scalars is Iterable[pyarrow.Scalar]
return cls(pa.array([s.as_py() for s in scalars],
type=cls.dtype.arrow_type))
dtype = MyDtype()
valid_value = pa.scalar({'a': 5}, MyDtype.arrow_type)
na_value = MyDtype.na_value
```
Now if we try to create a Series object, all 3 cases below result in infinite recursion:
```python
pd.Series(data=na_value, index=[1,2], dtype=dtype)
pd.Series(data={}, index=[1,2], dtype=dtype)
pd.Series(data=None, index=[1,2], dtype=dtype)
```
Being more explicit (to avoid broadcasting) works fine
```python
>>> pd.Series(data=[na_value] * 2, index=[1,2], dtype=dtype)
1 None
2 None
dtype: arrow_struct_dtype
>>> pd.Series(data=[na_value, valid_value], index=[1,2], dtype=dtype)
1 None
2 {'a': 5}
dtype: arrow_struct_dtype
>>> pd.Series(data=[valid_value] * 2, index=[1,2], dtype=dtype)
1 {'a': 5}
2 {'a': 5}
dtype: arrow_struct_dtype
```
The root case seems to be that pandas attempts to analyze internal structure of `pyarrow.Scalar` instances in Series constructor. Specifically:
```python
>>> from pandas.core.dtypes.common import is_dict_like
>>> is_dict_like(MyDtype.na_value)
True
```
#### Expected Output
I believe some special treatment is needed for all instances of `pyarrow.Scalar` and its subclasses because the type itself communicates that the object represents a "primitive-like" data container, and pandas should not use its internal structure to decide how to treat it. For example, this seems reasonable:
* `is_scalar` should return True
* `is_dict_like`, `is_list_like` and `is_array_like` should all return False
Although pyarrow itself has some inconsistencies in how it treats scalar objects (see [ARROW-12695](https://issues.apache.org/jira/projects/ARROW/issues/ARROW-12695)), I think in this case pyarrow is not causing this issue.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.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 : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.4.0
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-05-08T04:29:48Z" | "2021-07-22T21:59:56Z" | "2021-05-10T23:33:09Z" | 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
When I tried just removing it that broke some dask tests. | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [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.
---
```python
#------ pandas 1.2.* ------
import pandas as pd
df = pd.DataFrame(index = pd.DatetimeIndex([]))
print(df.T.sum().index) # output: Index([], dtype='object')
print(df.sum(axis = 1).index) # output: DatetimeIndex([], dtype='datetime64[ns]', freq=None)
```
#### Problem description
In pandas versions >= 1.2, if we take the sum of the transpose of an empty DataFrame with DatetimeIndex, it results in an output with normal Index instead of DatetimeIndex.
But if we sum the DataFrame with axis=1, which is an equivalent operation, the DatetimeIndex is preserved.
#### Expected Output
In pandas versions <= 1.1, the output correctly has a DatetimeIndex in both cases.
```python
#------ pandas 1.1.* ------
import pandas as pd
df = pd.DataFrame(index = pd.DatetimeIndex([]))
print(df.T.sum().index) # output: DatetimeIndex([], dtype='datetime64[ns]', freq=None)
print(df.sum(axis = 1).index) # output: DatetimeIndex([], dtype='datetime64[ns]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.6.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 : None.None
pandas : 1.2.4
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.1.0
Cython : 0.29.16
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 1 | "2021-05-08T16:15:30Z" | "2021-05-11T18:12:41Z" | "2021-05-11T18:12:41Z" | NONE | null | - [x ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ n/a] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
[40/40] Cythonizing pandas/io/sas/sas.pyx
Traceback (most recent call last):
File "/usr/lib/python3.7/urllib/request.py", line 1317, in do_open
encode_chunked=req.has_header('Transfer-encoding'))
File "/usr/lib/python3.7/http/client.py", line 1229, in request
self._send_request(method, url, body, headers, encode_chunked)
File "/usr/lib/python3.7/http/client.py", line 1275, in _send_request
self.endheaders(body, encode_chunked=encode_chunked)
File "/usr/lib/python3.7/http/client.py", line 1224, in endheaders
self._send_output(message_body, encode_chunked=encode_chunked)
File "/usr/lib/python3.7/http/client.py", line 1016, in _send_output
self.send(msg)
File "/usr/lib/python3.7/http/client.py", line 956, in send
self.connect()
File "/usr/lib/python3.7/site-packages/setuptools/ssl_support.py", line 174, in connect
(self.host, self.port), getattr(self, 'source_address', None)
File "/usr/lib/python3.7/socket.py", line 707, in create_connection
for res in getaddrinfo(host, port, 0, SOCK_STREAM):
File "/usr/lib/python3.7/socket.py", line 748, in getaddrinfo
for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
socket.gaierror: [Errno -2] Name or service not known
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 764, in open_url
return open_with_auth(url, self.opener)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 951, in _socket_timeout
return func(*args, **kwargs)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 1070, in open_with_auth
fp = opener(request)
File "/usr/lib/python3.7/urllib/request.py", line 525, in open
response = self._open(req, data)
File "/usr/lib/python3.7/urllib/request.py", line 543, in _open
'_open', req)
File "/usr/lib/python3.7/urllib/request.py", line 503, in _call_chain
result = func(*args)
File "/usr/lib/python3.7/site-packages/setuptools/ssl_support.py", line 161, in https_open
lambda host, **kw: VerifyingHTTPSConn(host, self.ca_bundle, **kw), req
File "/usr/lib/python3.7/urllib/request.py", line 1319, in do_open
raise URLError(err)
urllib.error.URLError: <urlopen error [Errno -2] Name or service not known>
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "setup.py", line 761, in <module>
setup_package()
File "setup.py", line 754, in setup_package
**setuptools_kwargs,
File "/usr/lib/python3.7/site-packages/setuptools/__init__.py", line 128, in setup
_install_setup_requires(attrs)
File "/usr/lib/python3.7/site-packages/setuptools/__init__.py", line 123, in _install_setup_requires
dist.fetch_build_eggs(dist.setup_requires)
File "/usr/lib/python3.7/site-packages/setuptools/dist.py", line 513, in fetch_build_eggs
replace_conflicting=True,
File "/usr/lib/python3.7/site-packages/pkg_resources/__init__.py", line 774, in resolve
replace_conflicting=replace_conflicting
File "/usr/lib/python3.7/site-packages/pkg_resources/__init__.py", line 1057, in best_match
return self.obtain(req, installer)
File "/usr/lib/python3.7/site-packages/pkg_resources/__init__.py", line 1069, in obtain
return installer(requirement)
File "/usr/lib/python3.7/site-packages/setuptools/dist.py", line 580, in fetch_build_egg
return cmd.easy_install(req)
File "/usr/lib/python3.7/site-packages/setuptools/command/easy_install.py", line 661, in easy_install
not self.always_copy, self.local_index
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 653, in fetch_distribution
dist = find(requirement)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 633, in find
loc = self.download(dist.location, tmpdir)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 577, in download
found = self._download_url(scheme.group(1), spec, tmpdir)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 822, in _download_url
return self._attempt_download(url, filename)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 828, in _attempt_download
headers = self._download_to(url, filename)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 727, in _download_to
fp = self.open_url(url)
File "/usr/lib/python3.7/site-packages/setuptools/package_index.py", line 778, in open_url
% (url, v.reason))
distutils.errors.DistutilsError: Download error for https://files.pythonhosted.org/packages/a9/03/5b216f6a55ffc6bdac3ad3a896c58d4f17d99e18ff82fc68363df7d6a7b3/numpy-1.20.2-cp37-cp37m-manylinux1_x86_64.whl#sha256=9cab23439eb1ebfed1aaec9cd42b7dc50fc96d5cd3147da348d9161f0501ada5: [Errno -2] Name or service not known
```
I am building on a machine that CANNOT access the internet. I already have numpy etc., yet setup.py will not
allow me to build pandas.
Q: how to build offline?
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} | 3 | "2021-05-08T18:37:08Z" | "2021-05-11T00:13:48Z" | "2021-05-10T23:28:48Z" | 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
@simonjayhawkins thoughts on the FrameOrSeries vs FrameOrSeriesUnion that ive ignored here? | {
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} | 2 | "2021-05-08T18:44:34Z" | "2021-08-20T02:58:28Z" | null | NONE | null | #### Is your feature request related to a problem?
No
#### Describe the solution you'd like
I would like a built-in shift function that shifts each row by a different period, given by a Series.
#### API breaking implications
It would improve the shift possibilities.
#### Describe alternatives you've considered
Using shift with apply and lambda.
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
def shift_df_by_series(self, df=pd.DataFrame, s=pd.Series) -> pd.DataFrame:
return df.apply(lambda shifted_df: shifted_df.shift(periods=s[shifted_df.name], fill_value=0), axis='columns')
```
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] | closed | false | null | [] | null | 6 | "2021-05-08T18:46:09Z" | "2021-08-18T00:23:57Z" | "2021-08-18T00:23:37Z" | CONTRIBUTOR | null | - [X] follows up on #40884
- [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
- [X] user_guide/io entry
### Proposed RData I/O module interfaces to the C library: [librdata](https://github.com/WizardMac/librdata).
Overall, this PR includes following changes:
- **setup.py**: pandas/setup.py (new rdata section at the bottom)
- **librdata**: pandas/_libs/src/librdata (C and header files and iconv scripts)
- **rdata IO**: pandas/io/rdata (Cython and Python scripts)
- **frame.py**: pandas/core/frame.py (for `DataFrame.to_rdata`)
- **tests**: pandas/tests/io/test_rdata.py
- **tests data**: pandas/tests/io/data/rdata (R data files in gzip compression)
- **docs**: librdata license, user_guide/io.rst, whatsnew/v1.3.0.rst
Note: special handling of `iconv`, a system resource built-in to Unix machines, is required:
- For Linux, to centralize from different locations, the `iconv.h` header of the GNU C library is included.
- For Mac, setup.py points to system folders, `/usr/include` and `/usr/lib`, which may differ from users installs.
- For Windows , since `iconv` is not built-in, two counterpart files (.h and .c script) were added from this repo, [win-iconv](https://github.com/win-iconv/win-iconv), where its readme indicates code is placed in the public domain. | {
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} | 2 | "2021-05-09T00:41:45Z" | "2021-05-24T13:44:06Z" | "2021-05-24T13:44:06Z" | 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.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
df = pd.DataFrame(
{
"Node": ["Atmosphere"],
"Description": [
"The Planetary Atmospheres Node (ATM) is responsible for the acquisition, preservation, and distribution of all non-imaging atmospheric data from NASA's planetary missions, including higher-order products derived from those data. In addition to providing user consultation, the node makes available a number of tools and services supporting researchers."
],
}
)
df.to_latex('table.tex')
```
output created:
```bash
$ cat table.tex (py39)
\begin{tabular}{lll}
\toprule
{} & Node & Description \\
\midrule
0 & Atmosphere & The Planetary Atmospheres Node (ATM) is responsible for the acquisition, preservation, and distr... \\
\bottomrule
\end{tabular}
```
#### Problem description
According to what I have read in previous issues about `to_latex()`, it should not truncate by default for the file output (and it would be a bad API if it would).
#### Expected Output
A complete table in the output tex file.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.4.2
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 4.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.18.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</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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} | 2 | "2021-05-09T07:51:33Z" | "2021-05-12T16:09:17Z" | "2021-05-12T15:03:20Z" | CONTRIBUTOR | null | This is not a user facing change.
It adds only the necessary code to permit separate control of index/column MultIndex sparsification. Essentially it removes the `get_option("display.multi_sparse")` from a low level method to a higher level method where it can be overwritten by user-args later.
partly addresses #41142 | {
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} | 2 | "2021-05-09T10:13:04Z" | "2021-05-10T12:14:31Z" | "2021-05-10T11:58:53Z" | MEMBER | null | the test (and benchmark) changes broken off from #41085 as a precursor to #41085 and #41372 (which currently makes changes to the str.split path, although I may break that PR up also) | {
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} | 4 | "2021-05-09T14:13:46Z" | "2021-05-11T02:00:34Z" | "2021-05-10T23:21:43Z" | 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
ref #40869, cc @phofl | {
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] | closed | false | null | [] | null | 0 | "2021-05-09T14:44:25Z" | "2021-10-26T18:36:26Z" | "2021-10-26T18:36:11Z" | CONTRIBUTOR | null | The [pandas options](https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html#available-options) seem to be little haphazard in the way they have been accumulated and implemented in pandas.
This issue is a master tracker for considering `pandas.options` relevant to `styler` and also in relation to deprecation of `DataFrame.to_html` and `DataFrame.to_latex` (#41649 #41693)
The complete list of existing `pandas.options` is below with their description and view on what to port to `styler`. The summary is below:
#### Pending Options:
- [x] `styler.sparse.index`: #41512
- [x] `styler.sparse.columns`: #41512
- [x] `styler.render.max_elements`: dynamic render trimming for v large dataframes #41635
- [x] `styler.render.max_rows`: #42972
- [x] `styler.render.max_columns`: #42972
- [x] `styler.render.encoding`: doctype format ("utf-8") #43177
- [x] `styler.render.repr`: notebook default ("html") #43180
- [x] `styler.format.precision` #43256
- [x] `styler.format.thousands` #43256
- [x] `styler.format.escape` #43256
- [x] `styler.format.formatter` #43256
- [x] `styler.format.na_rep` #43256
- [x] `styler.format.decimal` #43256
- [x] `styler.html.mathjax` #43283 (if not legacy would have not included)
- [x] `styler.latex.multirow_align` #43284 (if not legacy would have not included)
- [x] `styler.latex.multicolumn_align` #43284 (if not legacy would have not included)
- [x] `styler.latex.longtable` #43284
- [x] `styler.latex.hrules` #43670
- [ ] <s>`styler.latex.siunitx`</s> too esoteric, not worthy of global namespace.
- [ ] <s>`styler.latex.css_convert`</s> too esoteric, not worthy of global namespace.
Status
- **Exclude**: the option is not relevant for styler and will not be added.
- **Exclude Pending**: the option is currently considered not relevant for styler pending a discussion or future development.
- **Pending**: the option is scheduled for development.
- **Implemented**: the option already exists in `styler` options.
Option | Default | Function | Decision | Discussion
-- | -- | -- | -- | --
display.chop_threshold | None | If set to a float value, all float values smaller then the given threshold will be displayed as exactly 0 by repr and friends. | Exclude | Rarely used, doesn't warrant `styler` option addition. Can, alternatively, be specifically coded by a UDF `formatter` option.
display.colheader_justify | right | Controls the justification of column headers. used by DataFrameFormatter. | Exclude Pending | Should be coded with CSS, very specific option to add in?
display.column_space | 12 | No description available. | Exclude | not relevant, console use only.
display.date_dayfirst | False | When True, prints and parses dates with the day first, eg 20/01/2005 | Exclude Pending | May have possible use. Future dev?
display.date_yearfirst | False | When True, prints and parses dates with the year first, eg 2005/01/20 | Exclude Pending | ..
display.encoding | UTF-8 | Defaults to the detected encoding of the console. Specifies the encoding to be used for strings returned by to_string, these are generally strings meant to be displayed on the console. | Pending | Styler should have its own option
display.expand_frame_repr | True | Whether to print out the full DataFrame repr for wide DataFrames across multiple lines, max_columns is still respected, but the output will wrap-around across multiple “pages” if its width exceeds display.width. | Exclude | Relevant for console printing not HTML / LaTeX.
display.float_format | None | The callable should accept a floating point number and return a string with the desired format of the number. This is used in some places like SeriesFormatter. See core.format.EngFormatter for an example. | Pending | Should be wrapped up into `styler.format` options.
display.large_repr | truncate | For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can show a truncated table (the default), or switch to the view from df.info() (the behaviour in earlier versions of pandas). allowable settings, [‘truncate’, ‘info’] | Exclude Pending | Can decide if this is completely necessary for HTML.
display.latex.repr | False | Whether to produce a latex DataFrame representation for Jupyter frontends that support it. | Pending | Should be implemented into `styler` options.
display.latex.escape | True | Escapes special characters in DataFrames, when using the to_latex method. | Pending | included in `styler.format` options.
display.latex.longtable | False | Specifies if the to_latex method of a DataFrame uses the longtable format. | Pending | included in `styler.latex` options
display.latex.multicolumn | True | Combines columns when using a MultiIndex | Pending | included in `styler.latex` options
display.latex.multicolumn_format | ‘l’ | Alignment of multicolumn labels | Pending | included in `styler.latex` options
display.latex.multirow | False | Combines rows when using a MultiIndex. Centered instead of top-aligned, separated by clines. | Pending | included in `styler.latex` options.
display.max_columns | 0 or 20 | max_rows and max_columns are used in __repr__() methods to decide if to_string() or info() is used to render an object to a string. In case Python/IPython is running in a terminal this is set to 0 by default and pandas will correctly auto-detect the width of the terminal and switch to a smaller format in case all columns would not fit vertically. The IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to do correct auto-detection, in which case the default is set to 20. ‘None’ value means unlimited. | Pending | styler has own maxs
display.max_colwidth | 50 | The maximum width in characters of a column in the repr of a pandas data structure. When the column overflows, a “…” placeholder is embedded in the output. ‘None’ value means unlimited. | Excluded Pending | styler has no cell overflow currently
display.max_rows | 60 | This sets the maximum number of rows pandas should output when printing out various output. For example, this value determines whether the repr() for a dataframe prints out fully or just a truncated or summary repr. ‘None’ value means unlimited. | Pending | styler render options
display.min_rows | 10 | The numbers of rows to show in a truncated repr (when max_rows is exceeded). Ignored when max_rows is set to None or 0. When set to None, follows the value of max_rows. | Excluded | no need for min rows with HTML LaTex
display.multi_sparse | True | “Sparsify” MultiIndex display (don’t display repeated elements in outer levels within groups)
display.notebook_repr_html | True | When True, IPython notebook will use html representation for pandas objects (if it is available). | Pending | styler options
display.precision | 6 | Floating point output precision in terms of number of places after the decimal, for regular formatting as well as scientific notation. Similar to numpy’s precision print option | Pending | styler.format options
display.show_dimensions | truncate | Whether to print out dimensions at the end of DataFrame repr. If ‘truncate’ is specified, only print out the dimensions if the frame is truncated (e.g. not display all rows and/or columns) | Exclude Pending | maybe necessary for backwards compatability with display trimming.
display.width | 80 | Width of the display in characters. In case Python/IPython is running in a terminal this can be set to None and pandas will correctly auto-detect the width. Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a terminal and hence it is not possible to correctly detect the width. | Excluded | by note not relevant for HTML / LaTeX
display.html.table_schema | False | Whether to publish a Table Schema representation for frontends that support it. | Excluded | not relevant for HTML/LaTeX
display.html.border | 1 | A `border=value` attribute is inserted in the table tag for the DataFrame HTML repr. | Excluded | this is deprecated HTML.
display.html.use_mathjax | True | When True, Jupyter notebook will process table contents using MathJax, rendering mathematical expressions enclosed by the dollar symbol. | Pending | styler render option. | {
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} | 5 | "2021-05-09T15:00:55Z" | "2021-05-10T22:12:56Z" | "2021-05-10T21:57:20Z" | 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
ref #36695
When passing a callable repl, str.replace will raise if regex is False. So it seems to me that in the future, when the default is changed, we should be ignoring the regex argument completely (and update the documentation to that effect). This is slightly magical, but I think better than having a default that raises.
If this is the correct way forward, then we don't need to warn when repl is callable.
cc @dsaxton @jorisvandenbossche
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ref #37374 cc @jbrockmendel
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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
New duplicate of #15772 since I had lost the previous branch. Work in progress.
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} | 4 | "2021-05-09T16:20:45Z" | "2021-05-10T20:05:35Z" | "2021-05-10T14:37:40Z" | 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
Related to #26788
This PR gets rid of all warnings of the form "UserWarning: Code input with no code at..." when building the docs. When there are multiple blank lines, e.g.
```
foo = 5
def bar():
pass
```
one of the blank lines is reported as having no code. That said, I'm not sure if this is the right approach, as the double blank likes are PEP8 compliant. Perhaps this should be reported upstream instead and the multiple blank lines left in the docs.
cc @jorisvandenbossche @TomAugspurger
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https://api.github.com/repos/pandas-dev/pandas/issues/41401 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41401/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41401/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41401/events | https://github.com/pandas-dev/pandas/pull/41401 | 882,518,785 | MDExOlB1bGxSZXF1ZXN0NjM2MDQ5NTU4 | 41,401 | REF: groupby remove _selection_name | {
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} | 1 | "2021-05-09T16:33:47Z" | "2021-05-12T01:37:26Z" | "2021-05-12T01:11:45Z" | MEMBER | null | We use self._selected_obj and self._obj_with_exclusions in different places with no clear distinction as to when to use which. I _think_ in pretty much all cases we really want obj_with_exclusions, am moving towards pinning that down.
The only non-trivial thing here is getting rid of pinning _selection in [here](https://github.com/pandas-dev/pandas/compare/master...jbrockmendel:cln-selection_name?expand=1#diff-f1ec980d06b0b54c8263f663f767636c3f5921f04f8a7ee91dfc71a2100b05cdL326). We have no tests that reach that. I can cook up a case that gets there, but haven't found any cases where removing it actually changes anything. | {
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} | 3 | "2021-05-09T16:57:45Z" | "2021-05-12T14:07:35Z" | "2021-05-12T14:07:30Z" | CONTRIBUTOR | 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
This PR adds a link and a description about Cylon to the ecosystem page.
Github: [https://github.com/cylondata/cylon](https://github.com/cylondata/cylon)
Web: [https://cylondata.org/](https://cylondata.org/)
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} | 13 | "2021-05-09T21:59:12Z" | "2021-06-18T15:43:42Z" | "2021-06-18T15:43:42Z" | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
hash_pandas_object(test[ columns_names[i] ], index=True, encoding='utf8', hash_key='012' , categorize=True)
0 3713087409444908179
1 7478705303072568462
2 12024724921319894105
3 12785939622558835299
4 9788992550609991128
5 1239052552041868816
6 9610202078597672705
7 12287384021013641209
8 10264240190786022141
9 10535148974563425818
10 10238940258630658604
11 15446383648481672096
12 14265484681526586699
13 8862960024351814462
dtype: uint64
hash_pandas_object(test[ columns_names[i] ], index=True, encoding='utf8', hash_key='01298768755' , categorize=True)
0 3713087409444908179
1 7478705303072568462
2 12024724921319894105
3 12785939622558835299
4 9788992550609991128
5 1239052552041868816
6 9610202078597672705
7 12287384021013641209
8 10264240190786022141
9 10535148974563425818
10 10238940258630658604
11 15446383648481672096
12 14265484681526586699
13 8862960024351814462
dtype: uint64
hash_pandas_object(test[ [ columns_names[i] , columns_names[j] ] ], index=True, encoding='utf8', hash_key='01' , categorize=True)
0 11107058607426530111
1 15666232225746534312
2 1136675766145783381
3 14892489092684772659
4 8519430825150424018
5 550646855301521146
6 3846031041217881485
7 2936614219041217571
8 16182698869780262111
9 2895548739675332954
10 677258434224654732
11 6105852029672525672
12 15095703462911844621
13 6081994522921680694
dtype: uint64
hash_pandas_object(test[ [ columns_names[i] , columns_names[j] ] ], index=True, encoding='utf8', hash_key='0198076674534' , categorize=True)
0 11107058607426530111
1 15666232225746534312
2 1136675766145783381
3 14892489092684772659
4 8519430825150424018
5 550646855301521146
6 3846031041217881485
7 2936614219041217571
8 16182698869780262111
9 2895548739675332954
10 677258434224654732
11 6105852029672525672
12 15095703462911844621
13 6081994522921680694
dtype: uint64
test[ [ columns_names[i] , columns_names[j] ] ]
A B
0 0 -1
1 0 -1
2 0 0
3 0 0
4 1 0
5 1 0
6 2 0
7 2 2
8 2 2
9 2 2
10 2 2
11 2 2
12 -1 2
13 -1 2
hashing's are the same for different key values
```
#### Problem description
it should be different hashing values for different keys
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
print("sklearn.__version__ = ", sklearn.__version__)
sklearn.__version__ = 0.24.2
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.7.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 : English_United States.1252
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
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 : 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 : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 8 | "2021-05-10T03:49:50Z" | "2021-05-26T03:27:29Z" | "2021-05-26T03:27:26Z" | MEMBER | null | - [x] closes #40373
- [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 | "2021-05-10T04:52:06Z" | "2021-05-10T14:29:11Z" | "2021-05-10T14:29:06Z" | CONTRIBUTOR | null | … DatetimeIndex (#37366)
- [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/41407 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41407/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41407/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41407/events | https://github.com/pandas-dev/pandas/issues/41407 | 883,573,819 | MDU6SXNzdWU4ODM1NzM4MTk= | 41,407 | DOC: Unclear first example in pandas.DataFrame.append | {
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} | 2 | "2021-05-10T07:16:11Z" | "2021-05-17T13:18:28Z" | "2021-05-17T13:18:28Z" | CONTRIBUTOR | null | #### Location of the documentation
pandas.DataFrame.append
#### Documentation problem
I think the example chosen to show the difference between ignore_index=False and ignore_index = True (the very first on the page) could make this difference more clear.
#### Suggested fix for documentation
Choosing a custom index for the first df (the one whose append function is called) could help to especially point out the fact that ignore_index=True actually overwrites the whole index and does not keep the index of the first dataframe, as it could be interpreted from this example (given somebody dared to not read the doc above 😉 )
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https://api.github.com/repos/pandas-dev/pandas/issues/41408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41408/events | https://github.com/pandas-dev/pandas/issues/41408 | 883,616,747 | MDU6SXNzdWU4ODM2MTY3NDc= | 41,408 | BUG: read_json changes dtype (object => int) | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
dct1 = {'text': "I ran to the store.",
'e1_span': '17',
'e2_span': '18_19',
'value1': 0.0,
'value2': 0.2}
dct2 = {'text': "I ran to the store.",
'e1_span': '11',
'e2_span': '12_13_14',
'value1': 0.4,
'value2': 0.67}
df = pd.DataFrame([dct1, dct2])
print(f"initial value: {df['e2_span'].values[0]}")
print(f"value type: {type(df['e2_span'].values[0])}")
json_string = df.to_json(orient="records", lines=True)
df2 = pd.read_json(json_string, orient="records", lines=True)
print(f"final value: {df2['e2_span'].values[0]}")
print(f"value type: {type(df2['e2_span'].values[0])}")
```
#### Problem description
When the `json_string` is read by `pd.read_json` function, it converts the `str` type of `e2_span` to `int64`.
The current output is:
```python
initial value: 18_19
value type: <class 'str'>
final value: 1819
value type: <class 'numpy.int64'>
```
#### Expected Output
```python
initial value: 18_19
value type: <class 'str'>
final value: 18_19
value type: <class 'str'>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-120-generic
Version : #122~16.04.1-Ubuntu SMP Wed Sep 30 15:09:00 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.1
setuptools : 56.0.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.23.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.04.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 7 | "2021-05-10T11:05:45Z" | "2021-06-15T21:04:40Z" | "2021-06-15T21:04:40Z" | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/38562#issuecomment-836438843 cc @jbrockmendel
old behaviour
```
>>> pd.__version__
'1.2.4'
>>>
>>> tdi = pd.timedelta_range("1 Day", periods=3)
>>> ser = pd.Series(tdi)
>>> ser
0 1 days
1 2 days
2 3 days
dtype: timedelta64[ns]
>>> ser.astype("string")
0 1 days
1 2 days
2 3 days
dtype: string
>>>
>>> dti = pd.date_range("2021", periods=3)
>>> ser = pd.Series(dti)
>>> ser
0 2021-01-01
1 2021-01-02
2 2021-01-03
dtype: datetime64[ns]
>>> ser.astype("string")
0 2021-01-01
1 2021-01-02
2 2021-01-03
dtype: string
```
new behaviour
```
>>> pd.__version__
'1.3.0.dev0+1567.g67c9385787'
>>>
>>> tdi = pd.timedelta_range("1 Day", periods=3)
>>> ser = pd.Series(tdi)
>>> ser
0 1 days
1 2 days
2 3 days
dtype: timedelta64[ns]
>>> ser.astype("string")
0 86400000000000 nanoseconds
1 172800000000000 nanoseconds
2 259200000000000 nanoseconds
dtype: string
>>>
>>> dti = pd.date_range("2021", periods=3)
>>> ser = pd.Series(dti)
>>> ser
0 2021-01-01
1 2021-01-02
2 2021-01-03
dtype: datetime64[ns]
>>> ser.astype("string")
0 2021-01-01T00:00:00.000000000
1 2021-01-02T00:00:00.000000000
2 2021-01-03T00:00:00.000000000
dtype: string
>>>
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Looking at OP #30980 this was done to ensure that a double pass is not made over data, but I don't think I'm doing that here.
This is a precursor for #40687
Marking as needs discussion since it might be controversial which nan-likes to allow in the constructor. | {
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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Is it correct behavior when indexing over empty DataFrame?
```python
import pandas
df = pandas.DataFrame()
df
Empty DataFrame
Columns: []
Index: []
df.iloc[:, 3:9] # this doesn't throw an exception
Empty DataFrame
Columns: []
Index: []
df.iloc[:, 3] # this throws the exception
IndexError: single positional indexer is out-of-bounds
df[3:9] # this doesn't throw an exception
Empty DataFrame
Columns: []
Index: []
df[3] # this throws the exception
KeyError: 3
```
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] | closed | false | null | [] | null | 3 | "2021-05-11T09:38:19Z" | "2021-08-20T03:02:50Z" | "2021-08-20T03:02:50Z" | NONE | null | I am developing some code that derives various pandas classes, and I have stumbled what seems to me like a bug. I do not have too much experience with pandas, so pardon me if this is obviously wrong.
In function `maybe_droplevels` from the `multiindex` file, lines [3878](https://github.com/pandas-dev/pandas/blob/15dff11b799f665b50e9078ae52dd820ac1bc678/pandas/core/indexes/multi.py#L3878) and [3884](https://github.com/pandas-dev/pandas/blob/15dff11b799f665b50e9078ae52dd820ac1bc678/pandas/core/indexes/multi.py#L3884) disregard the content of `key` and directly drop the top-most level. It has been like that for at least some years. I am not sure if this was by design in the beginning, to avoid issues, but after a brief look I cannot find the reason why it is would not possible to drop only the level where `key` is.
My code derives `MultiIndex`, and when I return a slice that shrinks the DataFrame size, line [3463](https://github.com/pandas-dev/pandas/blob/15dff11b799f665b50e9078ae52dd820ac1bc678/pandas/core/frame.py#L3463) wants to shrink the size of the index too. | {
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