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https://api.github.com/repos/pandas-dev/pandas/issues/38519 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38519/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38519/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38519/events | https://github.com/pandas-dev/pandas/issues/38519 | 768,669,929 | MDU6SXNzdWU3Njg2Njk5Mjk= | 38,519 | BUG: Augmented arithmetic assignments will modify original series starting version 1.1.4 | {
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"id": 7171206... | open | false | null | [] | null | 8 | 2020-12-16T10:09:53Z | 2021-02-14T11:21:16Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series([1, 2, 3])
s1 = s.iloc[1:]
s1 -= 4
s
# output
# 0 1
# 1 -2
# 2 -1
# dtype: int64
s = pd.Series([1, 2, 3])
s1 = s.iloc[1:]
s1 = s1 - 4
s
# output
# 0 1
# 1 2
# 2 3
# dtype: int64
```
#### Problem description
Starting with version `1.1.4` when applying either of `+=`, `-=`, `*=` or `/=` operators on a series `s1` that was sliced from original series `s`, the change propagates to original series `s`. When using normal assignment operators `s1 = s1 - 4` the problem is not present, which leads to inconsistent behavior.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.4
numpy : 1.17.2
pytz : 2020.4
dateutil : 2.8.0
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : 0.28.5
pytest : 3.8.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/38520 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38520/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38520/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38520/events | https://github.com/pandas-dev/pandas/issues/38520 | 768,751,011 | MDU6SXNzdWU3Njg3NTEwMTE= | 38,520 | BUG: method fillna not working when supplied value is a dict | {
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{
"color": "d7e102",
"default": false,
"description": "np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate",
"id": 2822342,
"name": "Missing-data",
"node_id": "MDU6TGFiZWwyODIyMzQy",
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"color": "0052cc"... | closed | false | null | [] | null | 2 | 2020-12-16T11:28:29Z | 2020-12-16T12:24:44Z | 2020-12-16T12:24:44Z | NONE | null | #### Reproducible example
```python
import pandas as pd
s = pd.Series([{"a": 1}, None])
s.fillna({"a": 0})
```
returns:
```python
0 {'a': 1}
1 NaN
dtype: object
```
The error is the same when using `np.nan` instead of None.
#### Problem description
The fillna method does not work when the supplied value is a dict.
#### Expected Output
```python
0 {'a': 1}
1 {'a': 0}
dtype: object
```
#### Problem identification
I check the internals of `NDFrame.fillna` the problem seems to be the following lines :
https://github.com/pandas-dev/pandas/blob/122d50246bcffcf8c3f252146340ac02676a5bf6/pandas/core/generic.py#L6379-L6382
Indeed these lines build the value as :
```python
value = create_series_with_explicit_dtype(dict(a=0), dtype_if_empty=object)
```
which returns
```
a 0
dtype: int64
```
This index has no overlap with `s.index` and reindex value is :
```python
0 NaN
1 NaN
dtype: float64
```
which seems wrong.
However passing the dict value without the transformation done in the block beginning `if isinstance(value, (dict, ABCSeries))` seems to work fine :
```python
new_data = s._mgr.fillna(value=dict(a=0), downcast=None, inplace=None, limit=None)
s._constructor(new_data)
#returns
#0 {'a': 0}
#1 {'a': 1}
#dtype: object
```
(not saying that this approach should be pursue since I am not familiar with pandas internals and thus don't know what the consequences of doing like this could be)
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-122-generic
Version : #124-Ubuntu SMP Thu Oct 15 13:03:05 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.2
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2020-12-16T13:05:45Z | 2021-01-21T15:53:35Z | 2021-01-21T15:53:35Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Originally posted on [StackOverflow](https://stackoverflow.com/questions/65255166/interesting-results-with-duplicate-columns-in-pandas-dataframe).
Possibly related to #15695 (the traceback looks different though)
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(columns=['a', 'b', 'b'])
df.loc[:, 'a'] = list(range(5)) # raise ValueError
```
Traceback:
```python
Traceback (most recent call last):
File "c:\Users\leona\pandas\main.py", line 3, in <module>
df.loc[:, 'a'] = list(range(5))
File "c:\Users\leona\pandas\pandas\core\indexing.py", line 691, in __setitem__
iloc._setitem_with_indexer(indexer, value, self.name)
File "c:\Users\leona\pandas\pandas\core\indexing.py", line 1636, in _setitem_with_indexer
self._setitem_single_block(indexer, value, name)
File "c:\Users\leona\pandas\pandas\core\indexing.py", line 1862, in _setitem_single_block
self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
File "c:\Users\leona\pandas\pandas\core\internals\managers.py", line 565, in setitem
return self.apply("setitem", indexer=indexer, value=value)
File "c:\Users\leona\pandas\pandas\core\internals\managers.py", line 428, in apply
applied = getattr(b, f)(**kwargs)
File "c:\Users\leon\pandas\pandas\core\internals\blocks.py", line 1022, in setitem
values[indexer] = value
ValueError: cannot copy sequence with size 5 to array axis with dimension 0
```
#### Problem description
It works with no duplicated column:
```python
df = pd.DataFrame(columns=['a', 'b', 'c'])
df.loc[:, 'a'] = list(range(5))
```
These work even with duplicated column names:
```python
df = pd.DataFrame(columns=['a', 'b', 'b'])
df['a'] = list(range(5)) # Same as expected output below
df = pd.DataFrame(columns=['a', 'b', 'b'])
df.a = list(range(5)) # Same as expected output below
```
Setting on new column name is okay:
```python
df = pd.DataFrame(columns=['a', 'b', 'b'])
df.loc[:, 'c'] = list(range(5))
# a b b c
# 0 NaN NaN NaN 0
# 1 NaN NaN NaN 1
# 2 NaN NaN NaN 2
# 3 NaN NaN NaN 3
# 4 NaN NaN NaN 4
```
#### Expected Output
```
a b b
0 0 NaN NaN
1 1 NaN NaN
2 2 NaN NaN
3 3 NaN NaN
4 4 NaN NaN
```
#### Output of ``pd.show_versions()``
<details><summary>Output:</summary>
INSTALLED VERSIONS
------------------
commit : 122d50246bcffcf8c3f252146340ac02676a5bf6
python : 3.9.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_Singapore.1252
pandas : 1.3.0.dev0+83.g122d50246.dirty
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.2.1
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 22 | 2020-12-16T13:09:26Z | 2020-12-23T14:11:06Z | 2020-12-23T14:11:05Z | CONTRIBUTOR | null | See:
https://github.com/pandas-dev/pandas/issues/38424#issuecomment-744062773
https://github.com/pandas-dev/pandas/pull/38456
Discussion happening on #38424, code review happening here ;-) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38523/events | https://github.com/pandas-dev/pandas/issues/38523 | 768,940,754 | MDU6SXNzdWU3Njg5NDA3NTQ= | 38,523 | BUG: MultiIndex RollingGroupby returns only one level of index | {
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} | 6 | 2020-12-16T14:31:17Z | 2021-04-05T16:22:13Z | 2021-04-05T16:22:13Z | 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.
---
```python
import pandas as pd
print(pd.__version__)
arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
['Captive', 'Wild', 'Captive', 'Wild']]
index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
index=index)
print(df, '\n')
print(df.groupby(level=0)['Max Speed'].rolling(2).sum())
```
#### Problem description
Version 1.1.5 returned different result compared to earlier versions
#### Expected Output (0.22.0)
0.22.0
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
Animal Animal Type
Falcon Falcon Captive NaN
Wild 740.0
Parrot Parrot Captive NaN
Wild 50.0
Name: Max Speed, dtype: float64
#### Expected Output (1.1.3)
1.1.3
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
Animal Animal Type
Falcon Falcon Captive NaN
Wild 740.0
Parrot Parrot Captive NaN
Wild 50.0
Name: Max Speed, dtype: float64
#### Output (1.1.5)
1.1.5
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
Animal
Falcon NaN
Falcon 740.0
Parrot NaN
Parrot 50.0
Name: Max Speed, dtype: float64
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | 2020-12-16T15:39:41Z | 2021-05-04T16:11:15Z | 2021-05-04T16:10:42Z | CONTRIBUTOR | null | unless we merge the support indicated in https://github.com/pandas-dev/pandas/pull/38522
I think we need to pin xlrd so we can have a green ci. | {
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} | 1 | 2020-12-16T16:59:20Z | 2020-12-21T13:54:38Z | 2020-12-21T13:54:38Z | 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
from pyarrow import Table
df = pd.DataFrame({'int_na': [0, None, 2, 3, None, 5, 6, None, 8]}, dtype=pd.Int64Dtype())
print(df)
```
```python
int_na
0 0
1 <NA>
2 2
3 3
4 <NA>
5 5
6 6
7 <NA>
8 8
```
```python
Table.from_pandas(df).slice(2, None).to_pandas()
```
```python
int_na
0 2
1 <NA>
2 1
3 5
4 <NA>
5 1
6 8
```
#### Problem description
The pandas array does not accurately reflect the pyarrow array.
#### Expected Output
```python
Table.from_pandas(df).slice(2, None).to_pandas()
```
```python
int_na
0 2
1 3
2 <NA>
3 5
4 6
5 <NA>
6 8
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
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 : 3.0.0.dev391+g26aef88b5.d20201216
pytables : 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 | 2020-12-16T17:43:14Z | 2021-11-20T23:21:03Z | 2020-12-16T19:26:04Z | MEMBER | null | - [ ] closes #38524
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2020-12-16T18:50:27Z | 2020-12-23T14:08:08Z | 2020-12-23T14:08:08Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> pd.read_clipboard()
PyperclipException:
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit
https://pyperclip.readthedocs.io/en/latest/introduction.html#not-implemented-error
>>> pd.DataFrame().to_clipboard()
PyperclipException:
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit
https://pyperclip.readthedocs.io/en/latest/introduction.html#not-implemented-error
```
#### Problem description
`pd.read_clipboard` and `pd.DataFrame.to_clipboard` should work. For instance, I can run the following with no issues:
```python
>>> import pyperclip
>>> pyperclip.copy('foo')
>>> pyperclip.paste()
'foo'
```
#### Expected Output
N/A
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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} | 8 | 2020-12-16T19:51:12Z | 2021-05-07T20:04:50Z | 2021-05-07T20:04:50Z | NONE | null | Trying to improve the user experience and interaction with the webpage.
1.Edited button hover effect.
2. Make links open in new tabs instead of redirect in the current webpage because this may be not good for some people.
3. Added data manipulation note in the introduction section. | {
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@simonjayhawkins
This would fix the issue, but is pretty dirty. If we decide to merge this, we should remove this again with #38140 Also test would have to be adjusted (expected remove unused category) | {
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} | 10 | 2020-12-17T00:38:58Z | 2021-07-06T06:04:45Z | null | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
pd.DataFrame({"data" : [1,1], "value":[0,1]}).groupby("data").agg({"value":pd.Series.mode})
```
#### Problem description
This line of code crashes with `ValueError: Must produce aggregated value`
<details>
```python
Traceback (most recent call last):
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 261, in aggregate
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 1085, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 658, in agg_series
return self._aggregate_series_pure_python(obj, func)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 717, in _aggregate_series_pure_python
raise ValueError("Function does not reduce")
ValueError: Function does not reduce
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 951, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/base.py", line 416, in _aggregate
result = _agg(arg, _agg_1dim)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/base.py", line 383, in _agg
result[fname] = func(fname, agg_how)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/base.py", line 367, in _agg_1dim
return colg.aggregate(how)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 267, in aggregate
result = self._aggregate_named(func, *args, **kwargs)
File "/usr/local/Caskroom/miniconda/base/envs/rapids202012/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 482, in _aggregate_named
raise ValueError("Must produce aggregated value")
ValueError: Must produce aggregated value
```
#### Expected Output
`[0,1]`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 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
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Haven't yet worked on raising an error will work as advised
- [ ] closes #38000
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Hi, I am getting the below error while using pickle to load the files on kaggle. It has worked for everyone, but it is not working for me. The file path is correct. Thank you for your help.
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<timed exec> in <module>
/opt/conda/lib/python3.7/site-packages/pandas/io/pickle.py in read_pickle(filepath_or_buffer, compression)
167 if not isinstance(fp_or_buf, str) and compression == "infer":
168 compression = None
--> 169 f, fh = get_handle(fp_or_buf, "rb", compression=compression, is_text=False)
170
171 # 1) try standard library Pickle
/opt/conda/lib/python3.7/site-packages/pandas/io/common.py in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors)
497 else:
498 # Binary mode
--> 499 f = open(path_or_buf, mode)
500 handles.append(f)
501
FileNotFoundError: [Errno 2] No such file or directory: '../input/riiid-cross-validation-files/cv2_train.pickle'
``` | {
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"d... | closed | false | null | [] | null | 13 | 2020-12-17T13:49:35Z | 2021-03-23T01:55:59Z | 2021-02-11T01:31:34Z | CONTRIBUTOR | null | - [ ✔️] closes #34687
- [ ✔️] tests added / passed
Created test case for ttps://github.com/pandas-dev/pandas/issues/34687 | {
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- [Yes, it was already opened [here](https://github.com/pandas-dev/pandas/issues/21103). However the issue was closed even if the problem is still here]
- I have confirmed this bug exists on the latest version of pandas.
- [Yes, currently version 1.1.5]
- (optional) I have confirmed this bug exists on the master branch of pandas.
- [No, did not check this.]
---
#### Problem description
I am currently trying to push a dataframe to a MS SQL database using pandas' `to_sql()` (using sqlalchemy and pyodbc) method. My dataframe is rather large with a 14k x 72 shape and various types (strings, floats, integers.)
When trying to push the dataframe without any argument set to the `to_sql()` method (except the table name and DB connection), the data will push extremely slow (> 30min for this 5mb df).
To try to fasten this, I came accross the `method=` argument, that I set to `'multi'`. Without specifying a chunksize, I get the error
```('HY000', 'The driver did not supply an error!')```
So then I tried to set a chunksize, but whenever this latter one is bigger than ~20, I get the error ```The incoming request has too many parameters. The server supports a maximum of 2100 parameters. Reduce the number of parameters and resend the request.```
Setting the chunksize to 10 works, but the upload is ~3min, which is still too slow..
From my understanding, such an upload should be doable normally in a few seconds.
Am I the only one having this issue ?
## Sample code
```
# connection to the db
engine = sqlalchemy.create_engine("mssql+pyodbc://{},{}/{}?driver=SQL Server?Trusted_Connection=yes"
.format(server_ip, port_number, db_name))
connection = engine.connect()
# read the df
df_raw = pd.read_csv(files_path, index_col=0, low_memory=False)
# push to database
with connection.begin():
df_raw.to_sql('RawTestData', con=connection, if_exists='replace', index=False, chunksize=chunk_size, method='multi')
```
#### Expected Output
Using the `method = 'multi'` argument should speed the upload of the data. I guess in a few seconds maximum.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
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 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : French_Switzerland.1252
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
</details>
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"d... | closed | false | null | [] | null | 4 | 2020-12-17T15:06:32Z | 2021-02-11T01:42:30Z | 2021-02-11T01:42:30Z | CONTRIBUTOR | null | - [ ] closes #29687
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Create test cases for sort_index works with 'pd.set_option('mode.use_inf_as_na',True)
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```
import numpy as np
import pandas as pd
np.array([pd.Series({0:0})])
np.array([pd.Series({'a':'a'})])
np.array([pd.Series({1:1})])
```
Is that a bug? | {
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} | 6 | 2020-12-17T16:50:42Z | 2021-09-16T15:35:51Z | 2020-12-23T19:24:36Z | MEMBER | null | - [x] closes #24381
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Sits on top of #38535 | {
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} | 0 | 2020-12-17T17:30:47Z | 2020-12-17T23:17:29Z | 2020-12-17T22:45:03Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/38476#discussion_r543329205 | {
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} | 4 | 2020-12-17T17:52:57Z | 2020-12-23T14:08:11Z | 2020-12-23T14:08:08Z | CONTRIBUTOR | null | Change wsl check to something more universal and consistent
- [x] closes #38527
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [ ] 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
In[5]: df = pd.DataFrame(np.eye(2), columns=["a", "b"])
In[6]: df
Out[6]:
a b
0 1.0 0.0
1 0.0 1.0
In[7]: df.columns.values[0] = "x" # <---- HERE! Direct renaming "a" -> "x"
In[8]: df
Out[8]:
x b # Seems renamed (x instead of a)
0 1.0 0.0
1 0.0 1.0
In[9]: df.columns
Out[9]: Index(['x', 'b'], dtype='object') # Seems renamed ('x' instead of 'a')
In[10]: df["x"] # KeyError: 'x' # New name doesn't work
In[11]: df["a"] # OK # Old name indeed still exists
Out[11]:
0 1.0
1 0.0
Name: a, dtype: float64
```
#### Problem description
After direct renaming a column of a dataframe, all its properties *show* the new name.
But all attempts to *use* this new name fail with the `KeyError`, while the old, “hidden” name is still functional.
#### Expected Output
Raising an exception for such a direct renaming, or fully accept it as a new name (forgetting about the old one).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Slovak_Slovakia.1250
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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} | 5 | 2020-12-17T20:38:54Z | 2020-12-24T18:51:51Z | 2020-12-24T18:51:42Z | CONTRIBUTOR | null | - [x] closes #35076
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2020-12-17T21:26:24Z | 2020-12-18T22:50:04Z | null | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I was looking into #34765 which traced back to the parsers in general and isn't excel specific. For both the `c` and `python` engines, using a multiple line header and `index_col` which doesn't include only the leftmost columns, results are unexpected:
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import io
s = """
a,b,c,d
e,f,g,h
x,y,1,2
"""
df = pd.read_csv(io.StringIO(s), header=[0, 1], index_col=1)
print(df)
print(df.columns)
```
```
1 (c, g) (d, h)
y x 1 2
Index([1, ('c', 'g'), ('d', 'h')], dtype='object')
```
or
```python
print(pd.read_csv(io.StringIO(s), header=[0, 1], index_col=[1, 2]))
```
gives
```
1 (d, h)
y 1 x 2
Index([1, ('d', 'h')], dtype='object')
```
#### Problem description
I'd expect in the first example the output to match what happens with `index_col = 0`:
```python
df = pd.read_csv(io.StringIO(s), header=[0, 1], index_col=0)
print(df)
print(df.columns)
```
```
a b c d
e f g h
x y 1 2
MultiIndex([('b', 'f'),
('c', 'g'),
('d', 'h')],
names=['a', 'e'])
```
#### Expected Output
So I'd expect
```
b a c d
f e g h
y x 1 2
MultiIndex([('a', 'e'),
('c', 'g'),
('d', 'h')],
names=['b', 'f'])
```
The other possibility is that maybe this behavior just shouldn't be supported and if multiple headers lines are specified, passing an `index_col != range(n)` just raises.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d4b623361bf18b42c4074d7b5935101514cf128a
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0.dev0+90.gd4b623361.dirty
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.1
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.2.0
hypothesis : 5.43.3
sphinx : 3.3.1
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 : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
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... | closed | false | null | [] | null | 3 | 2020-12-17T22:51:47Z | 2021-11-11T16:25:33Z | 2021-03-12T03:49:55Z | NONE | null | - [x] closes #26131
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I have just tried to support the same behaviour as `datetime.datetime()`, without adding the additional `"nanoseconds"` option, but this could be handled as well. | {
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} | 16 | 2020-12-17T23:19:09Z | 2021-01-02T17:14:05Z | 2021-01-02T17:14:05Z | 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
students = [('Charlie', 'A'),
('Rich', 'B'),
('Katie', 'A'),
('Tommy', 'B'),
]
df = pd.DataFrame(students, columns =['Name', 'Grade'])
with open('file1.csv',mode='w') as f:
df.to_csv(path_or_buf=f)
df.to_csv(path_or_buf='file2.csv')
```
#### Problem description
Pandas currently seems inconsitent in terms of how it writes data to csv files using the to_csv method. I've looked at previous issues #20353 and #25048. Both are closed, but this issue seems to happen even right now in production Python 3
Above is a snippet of code, similar to #20353, that reproduces the issue. The result is that file1.csv has line endings that are \r\r\n and file2.csv has line endings that are as expected-- \r\n
#### Expected Output
In both cases, I'd expect:
,Name,Grade\r\n
0,Charlie,A\r\n
1,Rich,B\r\n
2,Katie,A\r\n
3,Tommy,B\r\n
Instead, in file1.csv, we see each row with a line ending of \r\r\n
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 23 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.7.5
pip : 20.3.3
setuptools : 50.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2020-12-18T00:53:45Z | 2020-12-23T15:52:12Z | 2020-12-23T14:17:46Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This also makes some MultiIndex constructor behavior consistent with Index behavior, with the side-effect of fixing the thorny cases in #36131 | {
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} | 4 | 2020-12-18T05:22:13Z | 2020-12-30T21:40:47Z | 2020-12-28T16:52:14Z | MEMBER | null | - [x] closes https://github.com/pandas-dev/pandas/issues/22993
- [x] tests added / passed
- [ ] ~~passes `black pandas`~~
- [ ] ~~passes `git diff upstream/master -u -- "*.py" | flake8 --diff`~~
- [ ] ~~whatsnew entry~~
## Background
I teach [a class on pandas for public policy students](https://github.com/afeld/python-public-policy/blob/master/syllabus.md#readme), and for many of them, spreadsheets are the only point of reference they have for working with tabular data. It would be very helpful to have (official) document comparing the two to point them to.
This is my first contribution to pandas and first time using reStructuredText, so feedback welcome. Thanks in advance!
## TODOs
Making a running checklist to show what I've done already, and what else I plan to do. Hoping for some preliminary feedback (like is there still interest in having this page) before spending too much more time on it.
Happy to continue in this pull request until complete with all of them, or get this merged sooner than later and take care of the others in follow-up pull requests. Slight preference for the latter (some documentation being better than none, less to review at once, etc.), but open to whatever.
- [x] start with what @rotuna did in https://github.com/pandas-dev/pandas/pull/23042
- [x] add links to canonical Excel documentation, as [previously requested](https://github.com/pandas-dev/pandas/pull/23042#discussion_r224289676)
- [x] fix for CI
- [x] link from Getting Started pages
- [ ] incorporate structure from [SAS](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sas.html)/[STATA](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_stata.html) comparison pages
- [x] Data structures
- [ ] Data input / output
- [x] Mention `read_excel()`
- [ ] Data operations
- [ ] String processing
- [ ] Merging
- [ ] Missing data
- [ ] GroupBy
- [ ] Other considerations
- [ ] convert examples to use [`tips` dataset](https://raw.github.com/pandas-dev/pandas/master/pandas/tests/io/data/csv/tips.csv)
- [ ] add info about charting
## Questions
- [x] Which whatsnew file should I add to?
- [x] I noticed that [`doc/source/_static` is in the `.gitignore`](https://github.com/pandas-dev/pandas/blob/54682234e3a3e89e246313bf8f9a53f98b199e7b/.gitignore#L113), but [there are files checked into that folder](https://github.com/pandas-dev/pandas/tree/master/doc/source/_static). Is that intentional? - https://github.com/pandas-dev/pandas/pull/38739
- [ ] Some of the comparison documentation refers to "columns", while other refer to "Series". Is there a preference, or can they be used interchangeably?
- [x] Since spreadsheet software is largely interchangeable/compatible, would it make sense to make the page more general as "Comparison to spreadsheets"?
- [ ] Thoughts about including [slightly more subjective content](https://colab.research.google.com/github/afeld/python-public-policy/blob/main/pandas_crash_course.ipynb#scrollTo=efp6dYYtL-Je), such as _why_ one might want to use spreadsheets vs. pandas? | {
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} | 5 | 2020-12-18T09:37:35Z | 2021-02-07T15:24:49Z | 2021-02-07T15:24:49Z | 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 (checked on 1.1.5, the latest Anaconda version)
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
pd.CategoricalIndex(['A', 'B', 'C', 'A', 'B']).get_indexer(pd.CategoricalIndex(['B', 'C', 'D', 'E']))
# returns array([ 1, 4, 2, -1, -1], dtype=int64)
pd.Index(['A', 'B', 'C', 'A', 'B']).get_indexer(pd.Index(['B', 'C', 'D', 'E']))
# InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
#### Problem description
`CategoricalIndex.get_indexer` should fail if the index contains duplicates, just like the regular index does. Instead, it returns a nonsense array that does not even have the same length as the target.
Note this problem also affects the case where dtypes are equal or the target is not categorical i.e.:
```python
# 1
dt = pd.CategoricalDtype(categories=['A', 'B', 'C', 'D', 'E'])
pd.CategoricalIndex(['A', 'B', 'C', 'A', 'B'], dtype=dt).get_indexer(pd.CategoricalIndex(['B', 'C', 'D', 'E'], dtype=dt))
# 2
pd.CategoricalIndex(['A', 'B', 'C', 'A', 'B']).get_indexer(pd.Index(['B', 'C', 'D', 'E']))
```
I fixed a related bug in Sep 2019 as #28257 but that fix was merged in v1.0.0 so this must be a seperate issue or a regression. Could also be related to #25459.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
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
pytables : 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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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Note sure what to do with the (smoke) tests here. I suppose an explicit test comparing input and output would be good to have.
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} | 4 | 2020-12-18T13:08:58Z | 2020-12-18T19:05:32Z | 2020-12-18T16:44:51Z | MEMBER | null | This reverts commit 0b90685f4df2024754748b992d5eaaa352e7caa5.
- [ ] closes #35804
- [ ] closes #35805
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
before after ratio
[54682234] [05c97adb]
<master> <revert-35330>
- 293±5ms 3.75±0.2μs 0.00 index_object.IndexEquals.time_non_object_equals_multiindex
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
```
before after ratio
[54682234] [05c97adb]
<master> <revert-35330>
- 1.44±0.1ms 800±20μs 0.56 reindex.LevelAlign.time_reindex_level
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | {
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} | 1 | 2020-12-18T15:55:35Z | 2020-12-23T06:47:41Z | 2020-12-22T23:21:10Z | CONTRIBUTOR | null | This is a pre-cursor PR for #32542 as requested by @jreback [here](https://github.com/pandas-dev/pandas/pull/32542#issuecomment-744043100).
It moves the docstring of `replace()` method(s) to `pandas.core.shared_docs` so that we can reuse most of it for index classes. | {
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} | 5 | 2020-12-18T18:30:09Z | 2021-05-10T11:06:10Z | 2020-12-21T16:38:25Z | MEMBER | null | Makes astype_nansafe for (td64|dt64) -> (object|str|string) match DTA/TDA/Series behavior.
Medium-term (weeks) the goal is to get rid of Block._astype altogether and just use astype_nansafe, which among other things will be helpful for ArrayManager.
This changes `Series[dt64].astype("string")` behavior in a way that causes a new xfail in test_astype_roundtrip, but as discussed in #36153 that test is already wrong for other reasons.
This also has a side-effect of changing Series(dt64, dtype="Sparse[object]") behavior, discussed in #38508 as possibly not-desirable. | {
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} | 0 | 2020-12-18T18:34:33Z | 2020-12-18T23:31:55Z | 2020-12-18T23:16:07Z | MEMBER | null | so we can rule them out later | {
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} | 1 | 2020-12-18T18:59:20Z | 2020-12-21T12:37:04Z | 2020-12-18T23:07:55Z | MEMBER | null | This pull request xref #30999 to remove bare pytest.raises. It doesn't close that issue as I have only addressed one file: pandas/tests/scalar/test_nat.py . In that file there was only one test that had a bare pytest.raises and I added a message to the two instances of pytest.raises in that test. It required modifications to the test parameters in order to populate the message.
I did not add a whatsnew entry since it's only a tiny change to one test. Let me know if I should add one (and I am a bit unclear on how, i.e. what version this would end up in).
This is my first ever pull request to an open source project. I am expecting that I will have to make changes before it's accepted; thanks for your patience.
- [ ] xref #30999
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-12-18T19:05:46Z | 2020-12-18T20:54:56Z | 2020-12-18T20:54:56Z | NONE | null | Backport PR #38560: Revert "REF: remove special casing from Index.equals (always dispatchto subclass) (#35330)" | {
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} | 3 | 2020-12-18T19:07:55Z | 2021-01-11T14:11:58Z | 2021-01-11T14:00:35Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
df = pd.DataFrame({'value': [0, 1, 2, 3]},
index=[pd.Timestamp('2020-01-01 05:00:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 06:00:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 07:00:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 08:00:00+0000', tz='UTC')
]
)
new_index = pd.Series([pd.Timestamp('2020-01-01 5:30:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 6:30:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 7:30:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 8:30:00+0000', tz='UTC'),
pd.Timestamp('2020-01-01 9:30:00+0000', tz='UTC')]
)
new_df = df.reindex(new_index, method="ffill", tolerance=pd.Timedelta("1 hour"))
```
#### Problem description
The following exception is raised when `method` is `"ffill"` and `"bfill"` but not `"nearest"` (see #32740) **AND** `tolerance` is specified
```
TypeError: DatetimeArray subtraction must have the same timezones or no timezones
```
I found the timezone was dropped when reaching this function on lines 3024 and 3036
https://github.com/pandas-dev/pandas/blob/b5958ee1999e9aead1938c0bba2b674378807b3d/pandas/core/indexes/base.py#L3024-L3036
where `target` is the target index that's tz-aware. However once converted to `target_values`, the tz info disappears from the numpy array.
I found a working solution but unsure if this behavior affects any other parts functionalities
```
- self._filter_indexer_tolerance(target_values, indexer, tolerance)
+ self._filter_indexer_tolerance(target, indexer, tolerance)
```
#### Expected Output
```
value
2020-01-01 05:30:00+00:00 0.0
2020-01-01 06:30:00+00:00 1.0
2020-01-01 07:30:00+00:00 2.0
2020-01-01 08:30:00+00:00 3.0
2020-01-01 09:30:00+00:00 NaN
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-11-cloud-amd64
Version : #1 SMP Debian 4.19.146-1 (2020-09-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
```
</details>
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Added tests for some other column name types which raised before this change, if that's overkill I can just test the int case from the OP. | {
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Alternative to #38522. I've been testing this locally using both xlrd 1.2.0 and 2.0.1.
One test fails because we used to default to xlrd but now default to openpyxl, it's not clear to me if this test should be passing with openpyxl.
cc @cjw296, @jreback, @jorisvandenbossche
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This change fixes `UserWarning` being emitted in tests calling `autocorrelation_plot`, `andrews_curves`, and `radviz`.
`UserWarning` was caused by calling `plt.gca(xlim=..., ylim=...)` on a potentially already existing axis, so `matplotlib` warns about this forcing new axis creation. Solution was just to explicitly use `set_xlim` and `set_ylim` instead.
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry [N/A]
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
When looking into `matplotlib` warnings issued from `test_hist_method.py::TestSeriesPlots::test_hist_legacy` found a bunch of duplicate tests for `hist`. Also moved remaining `hist` tests from `test_series.py` to `test_hist_method.py`. | {
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} | 8 | 2020-12-19T08:08:08Z | 2020-12-31T22:13:44Z | 2020-12-31T22:13:39Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
#38370 adds a pyarrow engine to the csv reader. Only a fraction of the io/parser tests pass when pyarrow is used and the rest has to be xfailed/skipped, resulting in a large diff on the PR.
xref https://github.com/pandas-dev/pandas/pull/38370#discussion_r539579072 suggests reorganizing the tests into classes so groups of tests can be xfailed with a single mark.
I'm grouping the tests logically (not based on whether or not they pass with pyarrow) but merging this this _will_ reduce the diff in #38370 substantively.
Likely I will submit a follow-on with some further reorg. I'm happy to push that to this PR if that's preferred, though.
Verifying that total number of tests is unchanged:
```
(pandas-dev) andrewwieteska@Andrews-MacBook-Pro pandas % pytest pandas/tests/io/parser/test_dtypes.py pandas/tests/io/parser/test_usecols.py
========================================================================= test session starts =========================================================================
platform darwin -- Python 3.8.6, pytest-6.1.2, py-1.9.0, pluggy-0.13.1
rootdir: /Users/andrewwieteska/repos/pandas, configfile: setup.cfg
plugins: forked-1.2.0, xdist-2.1.0, cov-2.10.1, asyncio-0.14.0, hypothesis-5.41.2, instafail-0.4.1
collected 372 items
pandas/tests/io/parser/test_dtypes.py ......................................................................................................................... [ 32%]
............................................................................................ [ 57%]
pandas/tests/io/parser/test_usecols.py ........................................................................................................................ [ 89%]
....................................... [100%]
======================================================================== 372 passed in 32.49s =========================================================================
```
versus on master:
```
(pandas-dev) andrewwieteska@Andrews-MacBook-Pro pandas % pytest pandas/tests/io/parser/test_dtypes.py pandas/tests/io/parser/test_usecols.py
========================================================================= test session starts =========================================================================
platform darwin -- Python 3.8.6, pytest-6.1.2, py-1.9.0, pluggy-0.13.1
rootdir: /Users/andrewwieteska/repos/pandas, configfile: setup.cfg
plugins: forked-1.2.0, xdist-2.1.0, cov-2.10.1, asyncio-0.14.0, hypothesis-5.41.2, instafail-0.4.1
collected 372 items
pandas/tests/io/parser/test_dtypes.py ......................................................................................................................... [ 32%]
............................................................................................ [ 57%]
pandas/tests/io/parser/test_usecols.py ........................................................................................................................ [ 89%]
....................................... [100%]
======================================================================== 372 passed in 32.90s =========================================================================
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} | 2 | 2020-12-19T09:01:08Z | 2020-12-19T11:01:16Z | 2020-12-19T10:23:41Z | CONTRIBUTOR | null | - [ ] closes #38311
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ Timedelta.isoformat DOC had duplicates i have deleted one of them. And their was two lines with wrong output that i have also fixed.] whatsnew entry
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} | 2 | 2020-12-19T11:59:11Z | 2020-12-21T14:49:13Z | 2020-12-21T14:49:13Z | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
There was a recent PR to add `MultiIndex.dtypes` #37073 but this only works when each level is named.
https://github.com/pandas-dev/pandas/blob/03e1c899077d335a057a3f36c09645499638d417/pandas/core/indexes/multi.py#L703-L710
If `level` has no name then, `level.name` would be `None`.
Example where it fails:
```python3
idx_multitype = pd.MultiIndex.from_product(
[[1, 2, 3], ["a", "b", "c"], pd.date_range("20200101", periods=2, tz="UTC")],
)
{level.name: level.dtype for level in idx_multitype.levels}
# {None: datetime64[ns, UTC]}
```
When level is unamed i.e `None` may be we can add `level_0`, `level_1` etc. this would be consistent with API too(`reset_index` would add `level_x`).
@jreback
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} | 7 | 2020-12-19T13:52:47Z | 2021-06-25T17:38:24Z | 2021-06-25T17:38:24Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
When sampling a dataframe / series we get the original index back by default, which is good, but we should add an option to ignore the index to keep it consistent with other methods like `drop_duplicates` and `explode` for example.
#### Describe the solution you'd like
Add `ignore_index` argument to Series/DataFrame.sample.
#### API breaking implications
None as far as I can tell.
```python
df = pd.DataFrame(
{"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10}
)
df.sample(n=3, ignore_index=True)
col1 col2 colString
0 14 24 a
1 17 27 a
2 11 21 a
```
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} | 3 | 2020-12-19T14:00:12Z | 2020-12-24T18:37:22Z | 2020-12-21T14:49:13Z | CONTRIBUTOR | null | - [X] closes #38580
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- [X] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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This is the last set of ABCs to cast. Going out with a whimper, mypy didn't identify any new issues. | {
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@gfyoung
Raising ParserWarning now. Could change to FutureWarning, if we would like to deprecate for 2.0
As long as we are only raising a ParserWarning I am inclined to raise for trailing commas too. | {
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`test_abc_types` is a subset of the tests added and can be removed in a followup. | {
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} | [] | closed | false | null | [] | null | 2 | 2020-12-20T10:01:09Z | 2020-12-24T17:42:44Z | 2020-12-24T17:42:44Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/38591 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38591/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38591/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38591/events | https://github.com/pandas-dev/pandas/issues/38591 | 771,628,160 | MDU6SXNzdWU3NzE2MjgxNjA= | 38,591 | PERF: performance regressions in 1.2.0rc | {
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] | open | false | null | [] | null | 2 | 2020-12-20T16:39:36Z | 2021-02-01T14:36:05Z | null | MEMBER | null | I did a full benchmark run on a dedicated machine comparing v1.2.0rc0 with v1.1.5.
The top results:
```
[b5958ee1] [7688d3cf]
<v1.1.5^0> <v1.2.0rc0^0>
+ 4.25±0.05μs 366±9ms 86187.42 index_object.IndexEquals.time_non_object_equals_multiindex
+ 50.2±4μs 104±5ms 2075.23 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 120±8μs 102±5ms 848.38 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 268±20μs 103±5ms 383.87 indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 289±40μs 111±8ms 382.15 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 1000000)
+ 273±20μs 102±5ms 374.69 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 1.75±0.1ms 103±5ms 58.77 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 2.11±0.1ms 103±5ms 48.69 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 7.38±0.7ms 109±7ms 14.80 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 1000000)
+ 3.66±0.2μs 13.4±0.4μs 3.66 index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
+ 336±2ms 1.22±0s 3.65 groupby.TransformEngine.time_series_numba(True)
+ 287±2ms 1.02±0s 3.55 groupby.AggEngine.time_series_numba(True)
+ 289±2ms 1.02±0s 3.52 groupby.AggEngine.time_dataframe_numba(True)
+ 3.64±0.2μs 12.8±0.5μs 3.52 index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex')
+ 1.89±0.09μs 6.49±0.1μs 3.44 index_cached_properties.IndexCache.time_is_all_dates('PeriodIndex')
+ 3.80±0.2μs 12.7±0.4μs 3.35 index_cached_properties.IndexCache.time_is_all_dates('UInt64Index')
+ 3.27±0.1μs 10.9±0.2μs 3.33 index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
+ 728±30ns 2.39±0.06μs 3.28 index_cached_properties.IndexCache.time_is_all_dates('RangeIndex')
+ 2.02±0.09μs 6.49±0.1μs 3.21 index_cached_properties.IndexCache.time_is_all_dates('DatetimeIndex')
+ 719±30ns 2.27±0.06μs 3.15 index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
+ 4.11±0.2μs 12.4±0.3μs 3.02 index_cached_properties.IndexCache.time_is_all_dates('TimedeltaIndex')
+ 430±3ms 1.23±0s 2.85 groupby.TransformEngine.time_dataframe_numba(True)
+ 7.44±0.08ms 17.1±2ms 2.29 hash_functions.UniqueAndFactorizeArange.time_unique(6)
+ 75.3±1ms 172±1ms 2.28 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000000)
+ 7.64±0.04ms 17.1±2ms 2.24 hash_functions.UniqueAndFactorizeArange.time_unique(5)
+ 1.62±0.04ms 3.51±0.02ms 2.16 arithmetic.Timeseries.time_series_timestamp_compare(None)
+ 1.60±0.03ms 3.45±0.04ms 2.16 arithmetic.Timeseries.time_timestamp_series_compare(None)
+ 1.62±0.03ms 3.49±0.09ms 2.16 arithmetic.Timeseries.time_timestamp_series_compare('US/Eastern')
+ 10.9±0.7ms 23.2±1ms 2.14 hash_functions.UniqueAndFactorizeArange.time_factorize(6)
+ 11.1±0.5ms 23.2±1ms 2.10 hash_functions.UniqueAndFactorizeArange.time_factorize(5)
+ 1.63±0.04ms 3.41±0.03ms 2.09 arithmetic.Timeseries.time_series_timestamp_compare('US/Eastern')
+ 4.11±0.1ms 8.52±0.09ms 2.08 index_object.SetDisjoint.time_datetime_difference_disjoint
+ 74.5±0.3ms 153±1ms 2.05 replace.ReplaceList.time_replace_list_one_match(True)
+ 124±0.9ms 248±0.4ms 2.00 gil.ParallelDatetimeFields.time_datetime_to_period
```
The first, biggest regression in the `IndexEquals` benchmark is probably already fixed in https://github.com/pandas-dev/pandas/pull/38560
<details>
<summary>Full results:</summary>
```
before after ratio
[b5958ee1] [7688d3cf]
<v1.1.5^0> <v1.2.0rc0^0>
+ 4.25±0.05μs 366±9ms 86187.42 index_object.IndexEquals.time_non_object_equals_multiindex
+ 50.2±4μs 104±5ms 2075.23 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 120±8μs 102±5ms 848.38 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 268±20μs 103±5ms 383.87 indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 289±40μs 111±8ms 382.15 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 1000000)
+ 273±20μs 102±5ms 374.69 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 1.75±0.1ms 103±5ms 58.77 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 2.11±0.1ms 103±5ms 48.69 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 7.38±0.7ms 109±7ms 14.80 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 1000000)
+ 3.66±0.2μs 13.4±0.4μs 3.66 index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
+ 336±2ms 1.22±0s 3.65 groupby.TransformEngine.time_series_numba(True)
+ 287±2ms 1.02±0s 3.55 groupby.AggEngine.time_series_numba(True)
+ 289±2ms 1.02±0s 3.52 groupby.AggEngine.time_dataframe_numba(True)
+ 3.64±0.2μs 12.8±0.5μs 3.52 index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex')
+ 1.89±0.09μs 6.49±0.1μs 3.44 index_cached_properties.IndexCache.time_is_all_dates('PeriodIndex')
+ 3.80±0.2μs 12.7±0.4μs 3.35 index_cached_properties.IndexCache.time_is_all_dates('UInt64Index')
+ 3.27±0.1μs 10.9±0.2μs 3.33 index_cached_properties.IndexCache.time_is_all_dates('MultiIndex')
+ 728±30ns 2.39±0.06μs 3.28 index_cached_properties.IndexCache.time_is_all_dates('RangeIndex')
+ 2.02±0.09μs 6.49±0.1μs 3.21 index_cached_properties.IndexCache.time_is_all_dates('DatetimeIndex')
+ 719±30ns 2.27±0.06μs 3.15 index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
+ 4.11±0.2μs 12.4±0.3μs 3.02 index_cached_properties.IndexCache.time_is_all_dates('TimedeltaIndex')
+ 430±3ms 1.23±0s 2.85 groupby.TransformEngine.time_dataframe_numba(True)
+ 7.44±0.08ms 17.1±2ms 2.29 hash_functions.UniqueAndFactorizeArange.time_unique(6)
+ 75.3±1ms 172±1ms 2.28 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000000)
+ 7.64±0.04ms 17.1±2ms 2.24 hash_functions.UniqueAndFactorizeArange.time_unique(5)
+ 1.62±0.04ms 3.51±0.02ms 2.16 arithmetic.Timeseries.time_series_timestamp_compare(None)
+ 1.60±0.03ms 3.45±0.04ms 2.16 arithmetic.Timeseries.time_timestamp_series_compare(None)
+ 1.62±0.03ms 3.49±0.09ms 2.16 arithmetic.Timeseries.time_timestamp_series_compare('US/Eastern')
+ 10.9±0.7ms 23.2±1ms 2.14 hash_functions.UniqueAndFactorizeArange.time_factorize(6)
+ 11.1±0.5ms 23.2±1ms 2.10 hash_functions.UniqueAndFactorizeArange.time_factorize(5)
+ 1.63±0.04ms 3.41±0.03ms 2.09 arithmetic.Timeseries.time_series_timestamp_compare('US/Eastern')
+ 4.11±0.1ms 8.52±0.09ms 2.08 index_object.SetDisjoint.time_datetime_difference_disjoint
+ 74.5±0.3ms 153±1ms 2.05 replace.ReplaceList.time_replace_list_one_match(True)
+ 124±0.9ms 248±0.4ms 2.00 gil.ParallelDatetimeFields.time_datetime_to_period
+ 950±30μs 1.83±0.05ms 1.92 reindex.LevelAlign.time_reindex_level
+ 2.84±0ms 5.46±0.02ms 1.92 tslibs.normalize.Normalize.time_is_date_array_normalized(1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 283±2ms 535±1ms 1.89 groupby.AggEngine.time_series_numba(False)
+ 284±2ms 534±2ms 1.88 groupby.AggEngine.time_dataframe_numba(False)
+ 11.8±0.3ms 21.4±0.4ms 1.81 io.csv.ToCSVDatetime.time_frame_date_formatting
+ 331±3ms 599±2ms 1.81 groupby.TransformEngine.time_series_numba(False)
+ 9.45±0.5ms 17.1±2ms 1.81 hash_functions.UniqueAndFactorizeArange.time_unique(4)
+ 16.8±0.1ms 30.3±0.8ms 1.80 gil.ParallelDatetimeFields.time_datetime_field_normalize
+ 65.8±2ms 113±2ms 1.71 series_methods.IsInFloat64.time_isin_many_different
+ 192±20μs 326±20μs 1.70 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 500000)
+ 37.2±0.1μs 62.3±0.09μs 1.68 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 13.9±0.4ms 23.2±1ms 1.67 hash_functions.UniqueAndFactorizeArange.time_factorize(4)
+ 508±2ms 837±5ms 1.65 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew')
+ 11.7±0.1ms 18.5±0.2ms 1.58 period.PeriodIndexConstructor.time_from_ints('D', False)
+ 11.8±0.1ms 18.5±0.5ms 1.57 period.PeriodIndexConstructor.time_from_ints('D', True)
+ 4.24±0.03ms 6.58±0.03ms 1.55 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 100000)
+ 438±1ms 680±2ms 1.55 series_methods.IsInLongSeriesValuesDominate.time_isin('float64', 'monotone')
+ 94.0±0.2ms 143±0.8ms 1.52 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'transformation')
+ 94.2±0.1ms 143±0.5ms 1.52 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'direct')
+ 616±3μs 937±8μs 1.52 stat_ops.FrameOps.time_op('sum', 'int', 0)
+ 21.3±0.8ms 32.4±0.7ms 1.52 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 19)
+ 41.1±0.3ms 62.2±20ms 1.51 rolling.Apply.time_rolling('Series', 300, 'int', <built-in function sum>, False)
+ 1.87±0.02ms 2.80±0ms 1.49 series_methods.IsIn.time_isin('uint64')
+ 27.7±0.2μs 41.1±0.1μs 1.48 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, None)
+ 1.32±0s 1.95±0.01s 1.48 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mad')
+ 2.97±0.01ms 4.40±0.7ms 1.48 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'count')
+ 2.93±0.02ms 4.32±0.6ms 1.47 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'count')
+ 1.46±0ms 2.14±0.04ms 1.47 groupby.FillNA.time_srs_ffill
+ 1.47±0.01ms 2.14±0.01ms 1.46 groupby.FillNA.time_srs_bfill
+ 96.7±0.1ms 141±2ms 1.46 rolling.Groupby.time_rolling_int('min')
+ 28.0±0.2μs 40.8±0.2μs 1.46 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, datetime.timezone.utc)
+ 2.82±0.01ms 4.11±0.01ms 1.46 tslibs.normalize.Normalize.time_is_date_array_normalized(1000000, None)
+ 97.4±0.6ms 142±1ms 1.46 rolling.Groupby.time_rolling_int('kurt')
+ 18.5±0.1ms 27.0±0.2ms 1.46 frame_methods.Iteration.time_items
+ 2.81±0.1ms 4.09±0.01ms 1.46 tslibs.normalize.Normalize.time_is_date_array_normalized(1000000, datetime.timezone.utc)
+ 97.5±0.5ms 142±0.9ms 1.45 rolling.Groupby.time_rolling_int('mean')
+ 97.4±0.9ms 141±1ms 1.45 rolling.Groupby.time_rolling_int('max')
+ 751±4μs 1.09±0.01ms 1.45 stat_ops.FrameOps.time_op('mean', 'int', 0)
+ 99.9±0.7ms 144±1ms 1.44 rolling.Groupby.time_rolling_int('median')
+ 97.0±1ms 140±0.9ms 1.44 rolling.Groupby.time_rolling_int('sum')
+ 11.9±2ms 17.1±2ms 1.44 hash_functions.UniqueAndFactorizeArange.time_unique(15)
+ 2.98±0.02ms 4.27±0.7ms 1.43 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'count')
+ 9.98±0.4ms 14.3±0.5ms 1.43 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 18)
+ 8.77±0.02ms 12.5±0.03ms 1.43 frame_methods.ToString.time_to_string_floats
+ 4.25±0.03s 6.03±0.01s 1.42 replace.ReplaceDict.time_replace_series(False)
+ 786±10μs 1.11±0ms 1.41 stat_ops.FrameOps.time_op('prod', 'int', 0)
+ 122±0.5ms 173±0.8ms 1.41 frame_methods.Iteration.time_iteritems_indexing
+ 157±0.8ms 221±2ms 1.41 stat_ops.FrameMultiIndexOps.time_op(1, 'mad')
+ 134±0.8ms 188±20ms 1.41 gil.ParallelGroupbyMethods.time_loop(8, 'count')
+ 427±1ms 599±1ms 1.40 groupby.TransformEngine.time_dataframe_numba(False)
+ 284±3ms 398±8ms 1.40 frame_methods.GetDtypeCounts.time_info
+ 33.5±0.2ms 46.6±5ms 1.39 gil.ParallelGroupbyMethods.time_loop(2, 'count')
+ 74.5±0.4ms 103±0.9ms 1.38 stat_ops.FrameMultiIndexOps.time_op(1, 'skew')
+ 60.9±3ms 84.0±3ms 1.38 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 20)
+ 6.97±0.05ms 9.60±0.06ms 1.38 groupby.Apply.time_scalar_function_single_col
+ 223±5ms 304±4ms 1.36 groupby.MultiColumn.time_lambda_sum
+ 11.7±0.01ms 15.8±0.03ms 1.35 stat_ops.Correlation.time_corr_wide('pearson')
+ 25.4±0.4μs 34.2±0.2μs 1.35 indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 12.2±0.06μs 16.4±0.1μs 1.34 categoricals.CategoricalSlicing.time_getitem_slice('monotonic_decr')
+ 3.98±0.2ms 5.33±0.04ms 1.34 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+ 229±10ms 306±10ms 1.34 replace.ReplaceList.time_replace_list_one_match(False)
+ 26.1±0.4μs 34.9±0.7μs 1.34 indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+ 14.4±0.06ms 19.2±0.7ms 1.33 strings.Cat.time_cat(0, None, None, 0.0)
+ 122±3ms 162±2ms 1.33 groupby.MultiColumn.time_col_select_lambda_sum
+ 12.3±0.05μs 16.4±0.3μs 1.33 categoricals.CategoricalSlicing.time_getitem_slice('non_monotonic')
+ 12.3±0.08μs 16.3±0.3μs 1.32 categoricals.CategoricalSlicing.time_getitem_slice('monotonic_incr')
+ 25.9±0.4μs 34.3±0.4μs 1.32 indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+ 58.0±0.2μs 76.3±1μs 1.32 indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+ 67.3±0.3ms 88.6±9ms 1.32 gil.ParallelGroupbyMethods.time_loop(4, 'count')
+ 2.53±0.03ms 3.33±0.02ms 1.32 categoricals.Indexing.time_sort_values
+ 26.3±0.4μs 34.6±0.2μs 1.31 indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+ 20.7±0.03ms 27.1±0.03ms 1.31 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 8000, -2)
+ 25.9±0.1μs 33.9±0.2μs 1.31 indexing.NumericSeriesIndexing.time_iloc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 2.38±0.02ms 3.12±0.03ms 1.31 groupby.FillNA.time_df_ffill
+ 4.33±0.01ms 5.66±0.02ms 1.31 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 1000, -2)
+ 60.3±0.4μs 78.8±0.8μs 1.31 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 100000)
+ 3.92±0.02ms 5.12±0.2ms 1.31 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+ 90.4±2ms 118±0.7ms 1.30 timeseries.Iteration.time_iter_preexit(<function timedelta_range at 0x7f2782aecaf0>)
+ 3.94±0.01ms 5.12±0.02ms 1.30 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'kurt')
+ 31.0±0.2μs 40.2±0.2μs 1.30 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+ 3.09±0.3ms 4.00±0.06ms 1.30 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'kurt')
+ 2.42±0.01ms 3.13±0.02ms 1.29 groupby.FillNA.time_df_bfill
+ 8.73±0.3ms 11.3±0.05ms 1.29 timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
+ 105±1ms 135±0.6ms 1.29 groupby.Groups.time_series_groups('int64_large')
+ 29.4±0.2ms 37.8±0.2ms 1.29 rolling.Apply.time_rolling('Series', 3, 'int', <built-in function sum>, False)
+ 455±1ms 584±0.7ms 1.28 series_methods.IsInLongSeriesValuesDominate.time_isin('float32', 'monotone')
+ 8.95±0.03ms 11.5±0.1ms 1.28 index_object.IntervalIndexMethod.time_intersection(100000)
+ 2.60±0.02ms 3.33±0.09ms 1.28 stat_ops.SeriesMultiIndexOps.time_op(1, 'prod')
+ 3.20±0.02ms 4.10±0.03ms 1.28 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+ 29.9±0.2ms 38.2±0.2ms 1.28 rolling.Apply.time_rolling('Series', 3, 'float', <built-in function sum>, False)
+ 29.9±0.3μs 38.2±0.1μs 1.28 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'non_monotonic')
+ 18.3±1ms 23.2±1ms 1.27 hash_functions.UniqueAndFactorizeArange.time_factorize(14)
+ 30.2±0.6μs 38.4±0.4μs 1.27 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'unique_monotonic_inc')
+ 31.3±0.4μs 39.8±0.3μs 1.27 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+ 16.1±0.09ms 20.5±0.2ms 1.27 groupby.AggFunctions.time_different_str_functions
+ 30.3±0.5μs 38.3±0.4μs 1.27 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'nonunique_monotonic_inc')
+ 30.2±0.2ms 38.2±0.4ms 1.27 rolling.Apply.time_rolling('DataFrame', 3, 'float', <built-in function sum>, False)
+ 2.62±0.02ms 3.32±0.1ms 1.27 stat_ops.SeriesMultiIndexOps.time_op(1, 'sum')
+ 3.07±0.02ms 3.89±0.02ms 1.26 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+ 3.90±0.01ms 4.91±0.02ms 1.26 series_methods.IsInDatetime64.time_isin_cat_values
+ 115±0.5μs 145±1μs 1.26 tslibs.normalize.Normalize.time_is_date_array_normalized(10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 2.61±0.03ms 3.30±0.1ms 1.26 stat_ops.SeriesMultiIndexOps.time_op(0, 'sum')
+ 14.6±0.6ms 18.3±1ms 1.26 tslibs.normalize.Normalize.time_is_date_array_normalized(1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 21.1±0.04ms 26.4±0.07ms 1.25 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 8000, 2)
+ 4.50±0.03μs 5.64±0.05μs 1.25 categoricals.CategoricalSlicing.time_getitem_scalar('non_monotonic')
+ 16.1±0.08ms 20.2±0.09ms 1.25 groupby.AggFunctions.time_different_numpy_functions
+ 30.2±0.3ms 37.9±0.1ms 1.25 rolling.Apply.time_rolling('DataFrame', 3, 'int', <built-in function sum>, False)
+ 359±0.5ms 450±4ms 1.25 series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_misses')
+ 37.2±0.5μs 46.4±0.4μs 1.25 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'non_monotonic')
+ 142±0.4ms 177±0.3ms 1.24 hash_functions.IsinWithArange.time_isin(<class 'object'>, 2000, 2)
+ 21.7±0.1ms 26.9±0.1ms 1.24 hash_functions.IsinWithArange.time_isin(<class 'numpy.uint64'>, 8000, -2)
+ 271±2μs 335±3μs 1.24 reindex.Fillna.time_float_32('backfill')
+ 6.95±0.04ms 8.58±0.1ms 1.23 stat_ops.Correlation.time_corrwith_cols('pearson')
+ 48.1±0.4μs 59.3±0.6μs 1.23 frame_methods.GetNumericData.time_frame_get_numeric_data
+ 45.7±0.2μs 56.4±0.8μs 1.23 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 10000)
+ 6.06±0.4ms 7.47±0.4ms 1.23 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sum')
+ 6.22±0.07μs 7.67±0.04μs 1.23 dtypes.Dtypes.time_pandas_dtype('Int8')
+ 108±0.2ms 133±0.2ms 1.23 hash_functions.IsinWithArange.time_isin(<class 'object'>, 2000, -2)
+ 9.49±0.08μs 11.7±0.1μs 1.23 indexing.CategoricalIndexIndexing.time_getitem_scalar('monotonic_incr')
+ 4.53±0.04μs 5.57±0.04μs 1.23 categoricals.CategoricalSlicing.time_getitem_scalar('monotonic_decr')
+ 4.13±0.1ms 5.07±0.2ms 1.23 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, None)
+ 4.54±0.04μs 5.57±0.03μs 1.23 categoricals.CategoricalSlicing.time_getitem_scalar('monotonic_incr')
+ 137±1μs 168±2μs 1.23 hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 1000)
+ 56.4±0.3μs 68.9±1μs 1.22 indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 1.54±0.02ms 1.88±0.03ms 1.22 reindex.ReindexMethod.time_reindex_method('pad', <function period_range at 0x7f2782af4700>)
+ 3.76±0.2ms 4.59±0.03ms 1.22 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+ 25.7±0.3ms 31.4±0.4ms 1.22 groupby.ApplyDictReturn.time_groupby_apply_dict_return
+ 1.19±0.02μs 1.45±0.01μs 1.21 tslibs.normalize.Normalize.time_is_date_array_normalized(100, None)
+ 56.6±0.6μs 68.8±1μs 1.21 indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 62.8±0.1μs 76.0±1μs 1.21 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 100000)
+ 4.14±0.01ms 5.00±0.02ms 1.21 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone.utc)
+ 325±20ms 392±10ms 1.21 indexing.NumericSeriesIndexing.time_getitem_lists(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 28.4±0.2ms 34.3±0.2ms 1.21 groupby.AggFunctions.time_different_python_functions_multicol
+ 134±2μs 161±0.9μs 1.21 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'object'>, 10)
+ 58.9±2ms 71.2±3ms 1.21 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 20)
+ 5.19±0.04ms 6.27±0.04ms 1.21 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 17)
+ 9.55±0.1μs 11.5±0.09μs 1.20 indexing.CategoricalIndexIndexing.time_getitem_scalar('monotonic_decr')
+ 513±0.7μs 617±3μs 1.20 stat_ops.Correlation.time_corr('pearson')
+ 891±10μs 1.07±0.01ms 1.20 reindex.ReindexMethod.time_reindex_method('pad', <function date_range at 0x7f2782b204c0>)
+ 321±20ms 385±10ms 1.20 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 2.62±0.02ms 3.15±0.07ms 1.20 stat_ops.SeriesMultiIndexOps.time_op(0, 'prod')
+ 323±20ms 388±10ms 1.20 indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 86.7±0.6ms 104±0.5ms 1.20 frame_methods.ToHTML.time_to_html_mixed
+ 8.31±0.07ms 9.97±0.05ms 1.20 frame_methods.Apply.time_apply_pass_thru
+ 44.7±0.3μs 53.6±0.08μs 1.20 tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, None)
+ 182±0.4μs 218±2μs 1.20 hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 2000)
+ 4.64±0.02ms 5.56±0.05ms 1.20 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'kurt')
+ 5.25±0.03ms 6.28±0.1ms 1.20 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 2000, 0)
+ 653±2μs 782±4μs 1.20 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 8000)
+ 7.09±0.06μs 8.48±0.03μs 1.20 dtypes.Dtypes.time_pandas_dtype('Int16')
+ 609±4ms 728±2ms 1.20 frame_methods.Nunique.time_frame_nunique
+ 324±20ms 387±10ms 1.20 indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 22.0±0.07ms 26.3±0.04ms 1.20 hash_functions.IsinWithArange.time_isin(<class 'numpy.uint64'>, 8000, 2)
+ 50.4±0.2μs 60.2±0.3μs 1.20 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'nonunique_monotonic_inc')
+ 130±2μs 155±0.7μs 1.19 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'unique_monotonic_inc')
+ 595±2ms 710±1ms 1.19 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 5000000)
+ 44.9±0.08μs 53.5±0.1μs 1.19 tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone.utc)
+ 122±0.9μs 146±3μs 1.19 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'non_monotonic')
+ 603±5μs 719±4μs 1.19 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 7000)
+ 82.4±0.8μs 98.2±0.3μs 1.19 indexing.DataFrameNumericIndexing.time_iloc
+ 16.0±0.1ms 19.1±0.05ms 1.19 categoricals.Isin.time_isin_categorical('object')
+ 90.3±0.6μs 107±2μs 1.19 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+ 196±0.6ms 233±2ms 1.19 groupby.Groups.time_series_groups('object_large')
+ 47.3±0.2μs 56.2±0.7μs 1.19 hash_functions.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 10000)
+ 9.68±0.07μs 11.5±0.09μs 1.19 indexing.CategoricalIndexIndexing.time_getitem_scalar('non_monotonic')
+ 1.06±0ms 1.25±0ms 1.19 timeseries.DatetimeIndex.time_unique('repeated')
+ 4.66±0.02ms 5.52±0.02ms 1.18 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'kurt')
+ 4.54±0.03ms 5.38±0.05ms 1.18 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'kurt')
+ 2.59±0.01ms 3.07±0.01ms 1.18 series_methods.IsInForObjects.time_isin_long_series_short_values
+ 5.40±0.02ms 6.39±0.06ms 1.18 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 8000, 0)
+ 136±1ms 161±0.5ms 1.18 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 900000)
+ 326±20ms 385±20ms 1.18 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 66.1±1μs 78.1±0.4μs 1.18 indexing.DataFrameNumericIndexing.time_loc
+ 3.86±0.2ms 4.55±0.01ms 1.18 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint')
+ 667±10μs 785±5μs 1.18 groupby.GroupManyLabels.time_sum(1)
+ 3.38±0.04ms 3.98±0.08ms 1.18 stat_ops.SeriesMultiIndexOps.time_op(1, 'var')
+ 7.51±0.03μs 8.84±0.2μs 1.18 dtypes.Dtypes.time_pandas_dtype('interval')
+ 2.88±0.02ms 3.39±0.06ms 1.18 stat_ops.FrameMultiIndexOps.time_op(0, 'sum')
+ 2.84±0.04ms 3.34±0.05ms 1.18 stat_ops.FrameMultiIndexOps.time_op(0, 'mean')
+ 26.8±0.1ms 31.5±0.06ms 1.18 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 2000, -2)
+ 3.86±0.2ms 4.54±0.02ms 1.18 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'lower')
+ 145±1μs 170±0.8μs 1.18 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'object'>, 10)
+ 9.33±0.05μs 11.0±0.04μs 1.18 dtypes.Dtypes.time_pandas_dtype('UInt8')
+ 2.89±0.02ms 3.39±0.03ms 1.17 stat_ops.FrameMultiIndexOps.time_op(1, 'prod')
+ 5.21±0.02ms 6.12±0.01ms 1.17 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 1000, 0)
+ 2.24±0.03ms 2.63±0.04ms 1.17 stat_ops.FrameOps.time_op('var', 'int', 0)
+ 7.86±0.03μs 9.22±0.03μs 1.17 dtypes.Dtypes.time_pandas_dtype('Int32')
+ 5.03±0.03ms 5.90±0.03ms 1.17 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1000000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 26.4±0.03ms 30.9±0.04ms 1.17 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 2000, 2)
+ 4.72±0.06ms 5.53±0.06ms 1.17 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 80000)
+ 909±4μs 1.06±0.05ms 1.17 dtypes.SelectDtypes.time_select_dtype_int_include(<class 'float'>)
+ 2.86±0.04ms 3.35±0.02ms 1.17 stat_ops.FrameMultiIndexOps.time_op(1, 'mean')
+ 8.63±0.05μs 10.1±0.5μs 1.17 dtypes.Dtypes.time_pandas_dtype('Int64')
+ 90.9±0.8μs 106±1μs 1.17 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+ 199±0.3ms 232±1ms 1.17 frame_methods.Apply.time_apply_axis_1
+ 3.89±0.2ms 4.54±0.01ms 1.17 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest')
+ 76.3±1ms 89.0±0.6ms 1.17 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 750000)
+ 739±5μs 862±2μs 1.17 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 8000)
+ 4.64±0.03ms 5.41±0.03ms 1.17 index_object.Indexing.time_get_loc_non_unique('Float')
+ 3.39±0.03ms 3.96±0.05ms 1.17 stat_ops.SeriesMultiIndexOps.time_op(0, 'var')
+ 997±40ns 1.16±0.04μs 1.17 index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
+ 3.04±0.02ms 3.55±0.04ms 1.17 stat_ops.FrameMultiIndexOps.time_op(0, 'prod')
+ 2.94±0.02ms 3.42±0.05ms 1.17 groupby.TransformBools.time_transform_mean
+ 3.88±0.1ms 4.52±0.02ms 1.16 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'higher')
+ 2.22±0.06ms 2.58±0.02ms 1.16 frame_methods.Lookup.time_frame_fancy_lookup
+ 11.3±0.04ms 13.1±0.3ms 1.16 index_object.IntervalIndexMethod.time_intersection_one_duplicate(100000)
+ 1.41±0ms 1.63±0ms 1.16 timeseries.DatetimeIndex.time_normalize('tz_naive')
+ 81.8±0.2ms 95.0±0.2ms 1.16 series_methods.IsInFloat64.time_isin_nan_values
+ 3.90±0.2ms 4.53±0.03ms 1.16 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'lower')
+ 3.90±0.2ms 4.53±0.02ms 1.16 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest')
+ 90.6±1μs 105±1μs 1.16 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+ 41.0±0.3ms 47.6±0.1ms 1.16 rolling.Apply.time_rolling('Series', 300, 'float', <built-in function sum>, False)
+ 3.91±0.2ms 4.53±0.02ms 1.16 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear')
+ 95.6±0.2ms 111±0.9ms 1.16 series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 1000, 'random_misses')
+ 662±3μs 768±2μs 1.16 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 7000)
+ 90.6±1μs 105±2μs 1.16 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
+ 2.31±0.02ms 2.67±0.03ms 1.16 stat_ops.FrameOps.time_op('std', 'int', 0)
+ 78.7±0.4ms 91.1±1ms 1.16 period.PeriodIndexConstructor.time_from_ints_daily('D', True)
+ 3.91±0.1ms 4.53±0.01ms 1.16 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint')
+ 366±2μs 424±4μs 1.16 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct')
+ 129±1ms 149±1ms 1.16 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 900000)
+ 894±7ns 1.03±0.02μs 1.16 tslibs.normalize.Normalize.time_is_date_array_normalized(0, None)
+ 440±5μs 509±3μs 1.16 hash_functions.IsinWithArangeSorted.time_isin(<class 'object'>, 8000)
+ 3.75±0.04ms 4.33±0.04ms 1.16 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 70000)
+ 91.0±0.4μs 105±2μs 1.15 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
+ 10.7±0.2ms 12.4±0.4ms 1.15 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'median')
+ 40.5±0.8ms 46.7±1ms 1.15 stat_ops.FrameMultiIndexOps.time_op(0, 'mad')
+ 25.4±0.08ms 29.3±0.09ms 1.15 hash_functions.IsinWithArange.time_isin(<class 'numpy.int64'>, 1000, 2)
+ 91.4±1μs 105±0.9μs 1.15 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+ 1.42±0.01ms 1.63±0.01ms 1.15 timeseries.DatetimeIndex.time_normalize('repeated')
+ 177±1μs 204±0.9μs 1.15 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'object'>, 11)
+ 15.8±0.03ms 18.2±0.3ms 1.15 eval.Query.time_query_datetime_index
+ 10.1±0.04μs 11.6±0.03μs 1.15 dtypes.Dtypes.time_pandas_dtype('UInt16')
+ 41.6±0.2ms 47.9±0.3ms 1.15 rolling.Apply.time_rolling('DataFrame', 300, 'int', <built-in function sum>, False)
+ 6.71±0.3μs 7.72±0.3μs 1.15 index_cached_properties.IndexCache.time_shape('IntervalIndex')
+ 79.0±0.2ms 90.9±0.9ms 1.15 period.PeriodIndexConstructor.time_from_ints_daily('D', False)
+ 532±9ns 612±10ns 1.15 multiindex_object.Integer.time_is_monotonic
+ 141±3μs 162±0.4μs 1.15 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'nonunique_monotonic_inc')
+ 481±0.9ms 554±4ms 1.15 frame_methods.Iteration.time_iterrows
+ 27.4±0.3μs 31.5±0.8μs 1.15 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_decr')
+ 3.72±0.01ms 4.28±0.01ms 1.15 series_methods.IsInDatetime64.time_isin
+ 1.74±0ms 2.00±0.01ms 1.15 timeseries.DatetimeAccessor.time_dt_accessor_normalize(tzutc())
+ 453±3μs 520±2μs 1.15 reindex.DropDuplicates.time_series_drop_dups_int(False)
+ 31.8±0.6ms 36.5±0.8ms 1.15 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 19)
+ 2.94±0.04ms 3.38±0.02ms 1.15 stat_ops.FrameMultiIndexOps.time_op(1, 'sum')
+ 409±2μs 469±3μs 1.15 reindex.DropDuplicates.time_series_drop_dups_string(True)
+ 5.21±0.03ms 5.97±0.3ms 1.15 stat_ops.SeriesMultiIndexOps.time_op(1, 'median')
+ 3.94±0.2ms 4.52±0.02ms 1.15 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear')
+ 895±10ns 1.03±0.01μs 1.15 tslibs.normalize.Normalize.time_is_date_array_normalized(1, None)
+ 27.6±0.3μs 31.6±0.5μs 1.15 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_incr')
+ 47.7±0.4ms 54.7±0.7ms 1.15 gil.ParallelGroupbyMethods.time_parallel(4, 'max')
+ 73.3±0.4μs 84.0±0.9μs 1.15 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 10000)
+ 154±1μs 177±2μs 1.15 groupby.GroupByMethods.time_dtype_as_group('int', 'size', 'direct')
+ 258±0.4μs 295±1μs 1.14 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 2000)
+ 369±2μs 422±4μs 1.14 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'transformation')
+ 1.72±0ms 1.96±0.02ms 1.14 timeseries.DatetimeAccessor.time_dt_accessor_normalize(None)
+ 1.78±0.02ms 2.04±0.01ms 1.14 series_methods.IsIn.time_isin('int64')
+ 3.42±0.03ms 3.91±0.1ms 1.14 stat_ops.SeriesMultiIndexOps.time_op(0, 'std')
+ 74.6±0.7μs 85.2±0.4μs 1.14 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 10000)
+ 41.7±0.2ms 47.6±0.2ms 1.14 rolling.Apply.time_rolling('DataFrame', 300, 'float', <built-in function sum>, False)
+ 54.2±0.4μs 61.8±0.4μs 1.14 inference.ToNumeric.time_from_float('coerce')
+ 15.2±0.2ms 17.3±0.2ms 1.14 eval.Query.time_query_datetime_column
+ 279±2μs 318±0.8μs 1.14 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 2000)
+ 54.2±0.9μs 61.8±0.5μs 1.14 inference.ToNumeric.time_from_float('ignore')
+ 3.23±0.02ms 3.69±0.02ms 1.14 hash_functions.IsinWithRandomFloat.time_isin(<class 'numpy.float64'>, 70000)
+ 2.16±0.01μs 2.46±0.06μs 1.14 attrs_caching.SeriesArrayAttribute.time_array('object')
+ 1.74±0.01ms 1.98±0.01ms 1.14 timeseries.DatetimeAccessor.time_dt_accessor_normalize('UTC')
+ 2.16±0.02μs 2.45±0.05μs 1.14 attrs_caching.SeriesArrayAttribute.time_array('numeric')
+ 441±3μs 501±4μs 1.14 strings.Encode.time_encode_decode
+ 2.70±0.01ms 3.07±0.01ms 1.14 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 16)
+ 26.7±0.2ms 30.3±0.3ms 1.14 groupby.Groups.time_series_groups('int64_small')
+ 63.0±0.2μs 71.5±0.3μs 1.13 tslibs.normalize.Normalize.time_normalize_i8_timestamps(10000, datetime.timezone(datetime.timedelta(seconds=3600)))
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+ 27.6±0.3μs 31.3±0.5μs 1.13 indexing.CategoricalIndexIndexing.time_getitem_slice('non_monotonic')
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+ 127±0.1ms 144±0.3ms 1.13 series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_hits')
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+ 535±5μs 604±6μs 1.13 frame_methods.Iteration.time_itertuples_raw_read_first
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+ 1.15±0s 1.27±0s 1.10 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'transformation')
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+ 88.3±0.6μs 97.4±0.5μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'non_monotonic')
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+ 191±1μs 211±0.7μs 1.10 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'numpy.float64'>, 1300)
+ 100±2ms 110±1ms 1.10 io.json.ToJSON.time_to_json('values', 'df_date_idx')
+ 952±10μs 1.05±0.01ms 1.10 dtypes.SelectDtypes.time_select_dtype_int_include('datetime64[ns]')
+ 2.46±0.01ms 2.71±0.01ms 1.10 reindex.DropDuplicates.time_frame_drop_dups_bool(True)
+ 952±10μs 1.05±0.01ms 1.10 dtypes.SelectDtypes.time_select_dtype_int_include('bool')
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+ 257±2ms 282±0.9ms 1.10 series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 1000, 'random_hits')
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+ 2.46±0.02ms 2.69±0.03ms 1.10 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', 'high')
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+ 14.9±0.03μs 16.3±0.07μs 1.10 dtypes.DtypesInvalid.time_pandas_dtype_invalid('scalar-string')
+ 130±1ms 142±2ms 1.10 io.json.ToJSON.time_to_json('index', 'df')
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+ 138±2ms 151±2ms 1.10 io.json.ToJSON.time_to_json('index', 'df_date_idx')
+ 947±3μs 1.04±0.01ms 1.10 dtypes.SelectDtypes.time_select_dtype_float_include('M8[ns]')
+ 448±3μs 491±3μs 1.09 period.Algorithms.time_drop_duplicates('series')
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+ 1.46±0.01ms 1.60±0.01ms 1.09 dtypes.SelectDtypes.time_select_dtype_int_include('Int64')
+ 94.7±0.7ms 103±0.7ms 1.09 frame_methods.Repr.time_frame_repr_wide
+ 168±5ms 183±2ms 1.09 io.json.ToJSONLines.time_floats_with_int_idex_lines
+ 87.6±0.7μs 95.4±0.8μs 1.09 timeseries.SortIndex.time_get_slice(True)
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+ 1.49±0ms 1.62±0.02ms 1.09 frame_methods.Iteration.time_items_cached
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+ 22.0±0.05ms 23.9±0.07ms 1.09 groupby.MultiColumn.time_cython_sum
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+ 126±2μs 137±2μs 1.09 timeseries.AsOf.time_asof_single_early('DataFrame')
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+ 210±2ms 228±0.5ms 1.08 io.json.ToJSONLines.time_float_int_str_lines
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+ 699±40ns 757±30ns 1.08 index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
+ 2.38±0.02ms 2.58±0.02ms 1.08 groupby.TransformNaN.time_first
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+ 7.25±0.01ms 7.84±0.03ms 1.08 timeseries.ResampleSeries.time_resample('period', '5min', 'mean')
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+ 73.0±0.2ms 78.9±0.6ms 1.08 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct')
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+ 1.80±0.03ms 1.95±0.03ms 1.08 groupby.Datelike.time_sum('period_range')
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+ 3.46±0.02μs 3.73±0.1μs 1.08 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, None)
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+ 310±5μs 335±0.5μs 1.08 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.int64'>, 13)
+ 8.51±0.04ms 9.18±0.1ms 1.08 stat_ops.SeriesMultiIndexOps.time_op(0, 'skew')
+ 74.5±0.5ms 80.3±0.8ms 1.08 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'transformation')
+ 2.49±0.01ms 2.69±0.03ms 1.08 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', 'round_trip')
+ 28.2±0.4μs 30.4±0.09μs 1.08 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
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+ 756±5μs 814±10μs 1.08 series_methods.IsInForObjects.time_isin_nans
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+ 1.55±0.01ms 1.67±0.01ms 1.08 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex64')
+ 1.58±0.01ms 1.70±0.02ms 1.08 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint32')
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+ 1.54±0.01ms 1.66±0.01ms 1.08 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int16')
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+ 3.64±0.1μs 3.91±0.2μs 1.08 index_cached_properties.IndexCache.time_inferred_type('IntervalIndex')
+ 1.59±0.01ms 1.71±0.01ms 1.07 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint64')
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+ 886±8ms 952±7ms 1.07 gil.ParallelGroups.time_get_groups(8)
+ 18.3±0.2μs 19.6±0.2μs 1.07 series_methods.NanOps.time_func('prod', 1000, 'boolean')
+ 438±4μs 471±3μs 1.07 series_methods.Map.time_map('Series', 'int')
+ 1.53±0.01ms 1.65±0.01ms 1.07 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt64')
+ 1.57±0.01ms 1.69±0.02ms 1.07 dtypes.SelectDtypes.time_select_dtype_int_exclude('int8')
+ 160±0.4ms 172±2ms 1.07 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'transformation')
+ 287±0.5ms 308±0.4ms 1.07 join_merge.MergeCategoricals.time_merge_cat
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+ 5.87±0.2ms 6.30±0.02ms 1.07 io.csv.ReadCSVParseSpecialDate.time_read_special_date('mdY', 'c')
+ 1.58±0.02ms 1.70±0.01ms 1.07 dtypes.SelectDtypes.time_select_dtype_int_exclude('datetime64[ns]')
+ 16.2±0.05ms 17.4±0.7ms 1.07 stat_ops.SeriesMultiIndexOps.time_op(0, 'mad')
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+ 2.46±0.01ms 2.64±0.04ms 1.07 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', None)
+ 22.7±0.1μs 24.4±0.1μs 1.07 boolean.TimeLogicalOps.time_or_array
+ 1.44±0.01ms 1.54±0ms 1.07 groupby.SumMultiLevel.time_groupby_sum_multiindex
+ 39.0±0.5ms 41.8±0.3ms 1.07 indexing.ChainIndexing.time_chained_indexing('warn')
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+ 29.1±0.3ms 31.2±0.08ms 1.07 hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, 0)
+ 160±0.6ms 171±1ms 1.07 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+ 21.9±0.1μs 23.5±0.2μs 1.07 boolean.TimeLogicalOps.time_xor_array
+ 39.0±0.6ms 41.7±0.06ms 1.07 indexing.ChainIndexing.time_chained_indexing(None)
+ 45.2±0.1μs 48.4±0.5μs 1.07 boolean.TimeLogicalOps.time_xor_scalar
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+ 275±2μs 294±7μs 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'direct')
+ 430±5μs 460±3μs 1.07 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct')
+ 74.4±0.2ms 79.6±0.7ms 1.07 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct')
+ 1.56±0.02ms 1.66±0.01ms 1.07 dtypes.SelectDtypes.time_select_dtype_float_exclude('float32')
+ 30.0±0.03ms 32.0±0.1ms 1.07 reshape.Crosstab.time_crosstab_values
+ 1.59±0.01ms 1.70±0.01ms 1.07 dtypes.SelectDtypes.time_select_dtype_int_exclude('m8[ns]')
+ 1.42±0.08μs 1.52±0.05μs 1.07 index_cached_properties.IndexCache.time_is_monotonic_decreasing('Int64Index')
+ 16.3±0.2ms 17.5±0.05ms 1.07 frame_methods.Repr.time_repr_tall
+ 1.51±0.01ms 1.62±0.02ms 1.07 dtypes.SelectDtypes.time_select_dtype_int_include('int64')
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+ 12.6±0.2ms 13.5±0.07ms 1.07 frame_methods.Apply.time_apply_ref_by_name
+ 56.4±0.3μs 60.2±0.5μs 1.07 boolean.TimeLogicalOps.time_and_scalar
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+ 174±2μs 186±0.9μs 1.07 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 100000)
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+ 163±1ms 172±1ms 1.06 io.json.ReadJSON.time_read_json('split', 'int')
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+ 320±2ms 337±2ms 1.06 io.excel.ReadExcel.time_read_excel('openpyxl')
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+ 2.89±0.02ms 2.99±0.02ms 1.04 dtypes.SelectDtypes.time_select_dtype_string_exclude(<class 'int'>)
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+ 65.2±0.6ms 67.4±0.6ms 1.03 io.style.RenderApply.time_render(36, 12)
+ 162±0.5μs 168±0.9μs 1.03 timedelta.ToTimedelta.time_convert_int
+ 571±5ns 590±1ns 1.03 dtypes.Dtypes.time_pandas_dtype(dtype('uint64'))
+ 6.08±0.03ms 6.29±0.04ms 1.03 io.csv.ParseDateComparison.time_to_datetime_dayfirst(True)
+ 28.6±0.2ms 29.6±0.2ms 1.03 stat_ops.SeriesMultiIndexOps.time_op(1, 'skew')
+ 2.94±0.02ms 3.04±0.02ms 1.03 dtypes.SelectDtypes.time_select_dtype_string_exclude('int32')
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+ 36.5±0.09ms 37.7±0.08ms 1.03 groupby.AggEngine.time_dataframe_cython(False)
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+ 2.21±0.01ms 2.28±0.01ms 1.03 io.csv.ReadCSVDInferDatetimeFormat.time_read_csv(False, 'ymd')
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+ 2.98±0.04ms 3.07±0.01ms 1.03 dtypes.SelectDtypes.time_select_dtype_string_exclude('m8[ns]')
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+ 646±3μs 667±0.9μs 1.03 groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'transformation')
+ 47.3±0.4ms 48.8±0.4ms 1.03 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'datetime')
+ 572±6ns 590±2ns 1.03 dtypes.Dtypes.time_pandas_dtype(dtype('uint32'))
+ 714±2μs 736±2μs 1.03 groupby.GroupByMethods.time_dtype_as_field('datetime', 'nunique', 'direct')
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+ 1.00±0.01μs 1.03±0.01μs 1.03 dtypes.Dtypes.time_pandas_dtype(<class 'pandas.core.arrays.integer.UInt8Dtype'>)
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+ 91.1±0.3ms 93.6±0.4ms 1.03 plotting.SeriesPlotting.time_series_plot('bar')
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+ 1.06±0.01ms 1.09±0ms 1.03 groupby.GroupByMethods.time_dtype_as_group('object', 'value_counts', 'direct')
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+ 656±2μs 674±2μs 1.03 categoricals.Repr.time_rendering
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+ 92.2±0.4ms 94.5±0.8ms 1.02 plotting.TimeseriesPlotting.time_plot_regular
+ 5.25±0.02ms 5.38±0.03ms 1.02 dtypes.InferDtypes.time_infer('bytes')
+ 33.5±0.1ms 34.3±0.1ms 1.02 groupby.AggEngine.time_series_cython(False)
+ 1.06±0s 1.09±0s 1.02 join_merge.MergeAsof.time_multiby('forward', None)
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+ 256±0.3ms 262±6ms 1.02 io.stata.StataMissing.time_write_stata('th')
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+ 1.68±0ms 1.72±0.01ms 1.02 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<MonthEnd>)
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+ 5.65±0.07ms 5.77±0.07ms 1.02 index_cached_properties.IndexCache.time_is_all_dates('CategoricalIndex')
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- 118M 117M 0.99 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'count')
- 115M 113M 0.99 rolling.Methods.peakmem_rolling('DataFrame', 10, 'int', 'min')
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- 2.35±0s 2.31±0s 0.98 stat_ops.Correlation.time_corr_wide_nans('kendall')
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- 114M 112M 0.98 rolling.Methods.peakmem_rolling('DataFrame', 10, 'float', 'min')
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- 2.99±0.01ms 2.94±0ms 0.98 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 3.01±0ms 2.95±0ms 0.98 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 10000, None)
- 120M 118M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'median')
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- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'median')
- 502±4μs 493±2μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'direct')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'skew')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'skew')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'kurt')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'kurt')
- 21.6±0.09μs 21.2±0.5μs 0.98 tslibs.resolution.TimeResolution.time_get_resolution('ns', 100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'kurt')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'min')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'max')
- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'median')
- 119M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'median')
- 175±0.6ms 171±1ms 0.98 index_cached_properties.IndexCache.time_is_monotonic_decreasing('IntervalIndex')
- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'median')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'kurt')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'skew')
- 168±0.8ms 165±0.8ms 0.98 index_cached_properties.IndexCache.time_engine('IntervalIndex')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'median')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'skew')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'skew')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'kurt')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'kurt')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'std')
- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'std')
- 15.1±0.07ms 14.8±0.06ms 0.98 stat_ops.Correlation.time_corrwith_cols('spearman')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'std')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'skew')
- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'std')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'sum')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'std')
- 119M 117M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'std')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'mean')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'mean')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'sum')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'mean')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'sum')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'sum')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'mean')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'sum')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'min')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'mean')
- 8.24±0.03ms 8.07±0.02ms 0.98 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 10000, datetime.timezone.utc)
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'min')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'sum')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'min')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'int', 'max')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'float', 'mean')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'min')
- 118M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'float', 'max')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1d', 'int', 'max')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'max')
- 117M 115M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '50s', 'float', 'min')
- 118M 116M 0.98 rolling.VariableWindowMethods.peakmem_rolling('Series', '1h', 'int', 'max')
- 203M 199M 0.98 io.json.ToJSON.peakmem_to_json_wide('columns', 'df_int_float_str')
- 16.4±0.1ms 16.1±0.08ms 0.98 join_merge.Concat.time_concat_small_frames(1)
- 6.43±0.03ms 6.28±0.05ms 0.98 hash_functions.IsinWithArange.time_isin(<class 'numpy.uint64'>, 8000, 0)
- 18.2±0.1ms 17.8±0.03ms 0.98 join_merge.MergeAsof.time_on_uint64('forward', 5)
- 197M 193M 0.98 io.json.ToJSON.peakmem_to_json_wide('columns', 'df_td_int_ts')
- 318M 311M 0.98 frame_methods.Iteration.peakmem_itertuples_start
- 318M 311M 0.98 frame_methods.Iteration.peakmem_itertuples
- 870±3ns 850±3ns 0.98 tslibs.timestamp.TimestampOps.time_to_pydatetime(<UTC>)
- 169±0.5ms 165±0.9ms 0.98 index_cached_properties.IndexCache.time_is_monotonic('IntervalIndex')
- 9.48±0.05ms 9.26±0.02ms 0.98 stat_ops.Correlation.time_corrwith_cols('kendall')
- 55.9±0.4ms 54.5±0.1ms 0.98 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'nearest')
- 193M 189M 0.98 io.json.ToJSON.peakmem_to_json_wide('index', 'df_int_float_str')
- 193M 189M 0.98 io.json.ToJSON.peakmem_to_json_wide('records', 'df_int_float_str')
- 169±0.7ms 165±0.7ms 0.98 index_cached_properties.IndexCache.time_is_monotonic_increasing('IntervalIndex')
- 16.8±0.06ms 16.4±0.09ms 0.98 join_merge.MergeAsof.time_on_uint64('backward', 5)
- 187M 183M 0.98 io.json.ToJSON.peakmem_to_json_wide('records', 'df_td_int_ts')
- 187M 183M 0.98 io.json.ToJSON.peakmem_to_json_wide('index', 'df_td_int_ts')
- 140±1μs 137±1μs 0.97 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthEnd>)
- 193±0.5ms 188±0.4ms 0.97 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 2000)
- 434±4ns 423±0.7ns 0.97 tslibs.timestamp.TimestampProperties.time_dayofyear(tzfile('/usr/share/zoneinfo/US/Central'), 'B')
- 55.7±0.3ms 54.2±0.2ms 0.97 strings.Methods.time_match
- 181M 176M 0.97 io.json.ToJSON.peakmem_to_json_wide('split', 'df_int_float_str')
- 6.28±0.08ms 6.12±0.02ms 0.97 hash_functions.IsinWithArange.time_isin(<class 'numpy.uint64'>, 2000, 0)
- 196M 191M 0.97 io.json.ToJSON.peakmem_to_json_wide('columns', 'df_date_idx')
- 116M 113M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'median')
- 181M 176M 0.97 io.json.ToJSON.peakmem_to_json_wide('values', 'df_int_float_str')
- 188M 183M 0.97 io.json.ToJSON.peakmem_to_json_wide('index', 'df')
- 188M 183M 0.97 io.json.ToJSON.peakmem_to_json_wide('records', 'df')
- 188M 183M 0.97 io.json.ToJSON.peakmem_to_json_wide('index', 'df_date_idx')
- 188M 183M 0.97 io.json.ToJSON.peakmem_to_json_wide('records', 'df_date_idx')
- 187M 181M 0.97 io.json.ToJSON.peakmem_to_json_wide('columns', 'df')
- 175M 170M 0.97 io.json.ToJSON.peakmem_to_json_wide('split', 'df_td_int_ts')
- 175M 170M 0.97 io.json.ToJSON.peakmem_to_json_wide('values', 'df_td_int_ts')
- 22.3±0.09ms 21.7±0.1ms 0.97 join_merge.MergeAsof.time_on_uint64('nearest', None)
- 115M 112M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'median')
- 4.60±0.05μs 4.47±0.02μs 0.97 categoricals.Contains.time_categorical_index_contains
- 380±3ns 370±2ns 0.97 tslibs.timestamp.TimestampOps.time_tz_localize(tzutc())
- 194±0.9ms 188±0.1ms 0.97 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 2011)
- 6.08±0.02ms 5.89±0.03ms 0.97 tslibs.offsets.OnOffset.time_on_offset(<CustomBusinessMonthBegin>)
- 66.3±0.5ms 64.3±0.1ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'midpoint')
- 15.2±0.2μs 14.8±0.2μs 0.97 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BYearBegin: month=1>)
- 178±0.8μs 172±0.6μs 0.97 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthBegin>)
- 177M 172M 0.97 io.json.ToJSON.peakmem_to_json_wide('split', 'df')
- 177M 172M 0.97 io.json.ToJSON.peakmem_to_json_wide('values', 'df')
- 177M 172M 0.97 io.json.ToJSON.peakmem_to_json_wide('split', 'df_date_idx')
- 1.79±0.01s 1.73±0.01s 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'direct')
- 177M 172M 0.97 io.json.ToJSON.peakmem_to_json_wide('values', 'df_date_idx')
- 4.75±0.02ms 4.61±0.02ms 0.97 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '_', 'high')
- 310±4ms 301±1ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<CustomBusinessDay>)
- 317±4ms 307±2ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<CustomBusinessDay>)
- 196±2μs 189±0.9μs 0.97 tslibs.offsets.OffestDatetimeArithmetic.time_add_10(<CustomBusinessMonthBegin>)
- 5.05±0.7ms 4.89±0.03ms 0.97 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'skew')
- 20.7±0.08μs 20.0±0.06μs 0.97 tslibs.resolution.TimeResolution.time_get_resolution('h', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'median')
- 3.31±0.01ms 3.20±0.02ms 0.97 arithmetic.ApplyIndex.time_apply_index(<BusinessDay>)
- 98.8±0.5ms 95.6±0.6ms 0.97 rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'linear')
- 56.2±0.5ms 54.4±0.1ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0.5, 'lower')
- 4.78±0.02ms 4.62±0.02ms 0.97 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '_', None)
- 138±0.4ms 133±0.4ms 0.97 hash_functions.IsinWithArange.time_isin(<class 'object'>, 8000, 2)
- 10.2±0.2μs 9.82±0.1μs 0.97 tslibs.timestamp.TimestampProperties.time_weekday_name(datetime.timezone(datetime.timedelta(seconds=3600)), None)
- 91.6±0.4ms 88.5±0.4ms 0.97 rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'nearest')
- 117M 113M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'kurt')
- 116M 112M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'kurt')
- 204M 197M 0.97 io.json.ToJSON.peakmem_to_json_wide('columns', 'df_int_floats')
- 117M 113M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'median')
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'max')
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'min')
- 116M 112M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'mean')
- 116M 112M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'int', 'sum')
- 116M 112M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'skew')
- 116M 112M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'sum')
- 116M 112M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'median')
- 1.79±0.04s 1.73±0s 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'transformation')
- 117M 113M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'skew')
- 372±0.7ms 359±2ms 0.97 series_methods.IsInLongSeriesValuesDominate.time_isin('int64', 'monotone')
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'kurt')
- 2.01±0.01μs 1.94±0.02μs 0.97 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, None)
- 11.1±0.5μs 10.7±0.3μs 0.97 index_cached_properties.IndexCache.time_engine('Float64Index')
- 117M 113M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'std')
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'mean')
- 114M 110M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'max')
- 94.6±0.3ms 91.3±0.3ms 0.97 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'median')
- 29.2±0.1ms 28.2±0.2ms 0.97 io.csv.ToCSV.time_frame('mixed')
- 118M 114M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'median')
- 114M 110M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'min')
- 116M 112M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'kurt')
- 116M 112M 0.97 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'mean')
- 115M 111M 0.97 rolling.ForwardWindowMethods.peakmem_rolling('Series', 10, 'float', 'sum')
- 117M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'mean')
- 194M 187M 0.96 io.json.ToJSON.peakmem_to_json_wide('records', 'df_int_floats')
- 194M 187M 0.96 io.json.ToJSON.peakmem_to_json_wide('index', 'df_int_floats')
- 118M 114M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'std')
- 5.78±0.03μs 5.58±0.03μs 0.96 dtypes.InferDtypes.time_infer('np-floating')
- 11.4±0.1μs 11.0±0.08μs 0.96 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
- 4.80±0.02ms 4.63±0.01ms 0.96 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '_', 'high')
- 117M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'sum')
- 72.3±0.4ms 69.7±0.5ms 0.96 io.csv.ToCSVDatetimeBig.time_frame(10000)
- 2.06±0.01s 1.99±0.01s 0.96 frame_methods.Iteration.time_itertuples_to_list
- 15.4±0.1μs 14.8±0.05μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<YearBegin: month=1>)
- 117M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'min')
- 43.1±0.3ms 41.5±0.3ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'median')
- 98.7±0.3ms 95.0±0.5ms 0.96 rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'midpoint')
- 161±1μs 155±1μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_subtract_10(<CustomBusinessMonthBegin>)
- 117M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'max')
- 3.80±0.2μs 3.66±0.2μs 0.96 index_cached_properties.IndexCache.time_inferred_type('UInt64Index')
- 407±3ms 392±0.3ms 0.96 index_object.Indexing.time_get_loc('String')
- 129±2ms 124±0.6ms 0.96 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'median')
- 91.9±0.1ms 88.4±0.4ms 0.96 rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'lower')
- 2.96±0.8ms 2.85±0.02ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
- 44.6±0.2ms 42.9±0.5ms 0.96 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'median')
- 138M 132M 0.96 io.pickle.Pickle.peakmem_write_pickle
- 182M 175M 0.96 io.json.ToJSON.peakmem_to_json_wide('split', 'df_int_floats')
- 116M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'max')
- 116M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'min')
- 182M 175M 0.96 io.json.ToJSON.peakmem_to_json_wide('values', 'df_int_floats')
- 116M 111M 0.96 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'kurt')
- 56.6±0.4ms 54.4±0.5ms 0.96 reshape.Cut.time_cut_int(1000)
- 92.2±0.3ms 88.5±0.2ms 0.96 rolling.Quantile.time_quantile('Series', 1000, 'float', 0.5, 'higher')
- 15.2±0.06μs 14.6±0.3μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_add(<BusinessDay>)
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'median')
- 7.48±0.04μs 7.18±0.1μs 0.96 tslibs.normalize.Normalize.time_is_date_array_normalized(0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 179±1μs 171±1μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthBegin>)
- 9.99±0.04μs 9.58±0.09μs 0.96 tslibs.timestamp.TimestampProperties.time_month_name(<UTC>, None)
- 116±0.3μs 111±0.9μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessMonthEnd>)
- 10.2±0.1μs 9.81±0.08μs 0.96 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 6000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 2.96±0.8ms 2.84±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
- 1.61±0.01s 1.55±0.02s 0.96 frame_methods.Iteration.time_itertuples
- 186±1μs 179±1μs 0.96 stat_ops.SeriesOps.time_op('mean', 'int')
- 9.93±0.08μs 9.53±0.03μs 0.96 tslibs.timestamp.TimestampProperties.time_month_name(None, 'B')
- 10.3±0.1μs 9.86±0.08μs 0.96 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 145M 139M 0.96 io.pickle.Pickle.peakmem_read_pickle
- 117M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'skew')
- 117M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'sum')
- 3.70±0.2μs 3.55±0.2μs 0.96 index_cached_properties.IndexCache.time_values('UInt64Index')
- 166±0.4μs 159±0.4μs 0.96 series_methods.NanOps.time_func('median', 1000, 'Int64')
- 4.21±0.01ms 4.04±0.02ms 0.96 io.csv.ReadCSVCachedParseDates.time_read_csv_cached(True, 'python')
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'std')
- 117M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'mean')
- 117M 112M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'kurt')
- 164±0.8μs 157±0.7μs 0.96 period.DataFramePeriodColumn.time_setitem_period_column
- 119M 114M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'std')
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'kurt')
- 114M 109M 0.96 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'max')
- 185±3μs 177±1μs 0.96 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<CustomBusinessMonthEnd>)
- 250±2μs 239±1μs 0.96 series_methods.NanOps.time_func('sem', 1000, 'int32')
- 115M 110M 0.96 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'min')
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'skew')
- 115M 110M 0.96 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'max')
- 114M 109M 0.96 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'min')
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'mean')
- 252±1μs 241±3μs 0.96 series_methods.NanOps.time_func('sem', 1000, 'int8')
- 119M 114M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'median')
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'sum')
- 10.3±0.08μs 9.81±0.1μs 0.96 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 4006, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'min')
- 10.2±0.06μs 9.77±0.1μs 0.96 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 118M 113M 0.96 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'max')
- 10.2±0.08μs 9.76±0.2μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 73.9±0.2ms 70.6±0.3ms 0.95 index_object.SetOperations.time_operation('strings', 'intersection')
- 10.2±0.2μs 9.73±0.04μs 0.95 tslibs.timestamp.TimestampProperties.time_weekday_name(tzlocal(), None)
- 3.78±0.06μs 3.61±0.03μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, None)
- 4.95±0.04μs 4.72±0.03μs 0.95 tslibs.timedelta.TimedeltaConstructor.time_from_np_timedelta
- 10.5±0.06μs 10.0±0.09μs 0.95 tslibs.timestamp.TimestampProperties.time_month_name(tzfile('/usr/share/zoneinfo/US/Central'), 'B')
- 10.2±0.1μs 9.75±0.1μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 115M 110M 0.95 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'kurt')
- 3.49±0.02μs 3.32±0.09μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 10000, datetime.timezone.utc)
- 91.0±0.6ms 86.8±0.4ms 0.95 replace.Convert.time_replace('Series', 'Timedelta')
- 116M 110M 0.95 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'mean')
- 443±10ns 422±2ns 0.95 tslibs.timestamp.TimestampProperties.time_dayofyear(tzfile('/usr/share/zoneinfo/US/Central'), None)
- 116M 110M 0.95 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'int', 'sum')
- 115M 109M 0.95 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'mean')
- 101±0.6ms 95.9±0.3ms 0.95 rolling.Methods.time_rolling('Series', 1000, 'float', 'median')
- 10.2±0.1μs 9.76±0.05μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 5000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 5.71±0.05ms 5.44±0.03ms 0.95 rolling.Engine.time_rolling_apply('Series', 'float', <function sum at 0x7f2790e97430>, 'cython')
- 115M 109M 0.95 rolling.ForwardWindowMethods.peakmem_rolling('Series', 1000, 'float', 'sum')
- 3.00±0.8ms 2.86±0.02ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint')
- 95.6±0.3ms 91.0±0.2ms 0.95 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'median')
- 3.02±0.8ms 2.87±0.03ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
- 2.03±0.03μs 1.93±0.02μs 0.95 tslibs.resolution.TimeResolution.time_get_resolution('h', 1, None)
- 10.9±0.1μs 10.3±0.1μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 10.2±0.07μs 9.75±0.1μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 3000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 120M 114M 0.95 rolling.Methods.peakmem_rolling('Series', 1000, 'float', 'count')
- 117M 111M 0.95 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'max')
- 8.15±0.02ms 7.75±0.03ms 0.95 io.csv.ToCSVDatetimeBig.time_frame(1000)
- 117M 111M 0.95 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'min')
- 10.1±0.08μs 9.60±0.04μs 0.95 tslibs.timestamp.TimestampProperties.time_weekday_name(tzfile('/usr/share/zoneinfo/US/Central'), 'B')
- 16.7±0.4ms 15.9±0.8ms 0.95 stat_ops.FrameOps.time_op('std', 'float', 1)
- 15.3±0.1μs 14.5±0.3μs 0.95 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<BusinessMonthBegin>)
- 11.6±0.2μs 11.0±0.1μs 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 5.69±0.04ms 5.41±0.03ms 0.95 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', None)
- 1.59±0.01ms 1.51±0.01ms 0.95 io.parsers.ConcatDateCols.time_check_concat(1234567890, 1)
- 38.4±0.3ms 36.5±0.5ms 0.95 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'median')
- 10.7±0.04μs 10.2±0.3μs 0.95 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
- 10.5±0.04μs 9.96±0.09μs 0.95 tslibs.timestamp.TimestampProperties.time_month_name(datetime.timezone(datetime.timedelta(seconds=3600)), 'B')
- 287±3μs 273±4μs 0.95 series_methods.NanOps.time_func('sem', 1000, 'boolean')
- 125±0.9μs 119±2μs 0.95 series_methods.NanOps.time_func('skew', 1000, 'float64')
- 8.96±0.05μs 8.50±0.2μs 0.95 tslibs.resolution.TimeResolution.time_get_resolution('ns', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.69±0.02μs 1.61±0.01μs 0.95 attrs_caching.SeriesArrayAttribute.time_array('datetime64')
- 4.32±0.01ms 4.10±0.06ms 0.95 rolling.Apply.time_rolling('Series', 300, 'int', <function sum at 0x7f2790e97430>, True)
- 2.43±0.01ms 2.31±0.02ms 0.95 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'mean')
- 3.03±0.8ms 2.88±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower')
- 11.0±0.05μs 10.4±0.1μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 69.6±0.1ms 66.0±0.3ms 0.95 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'median')
- 5.73±0.07ms 5.44±0.04ms 0.95 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', None)
- 5.73±0.04ms 5.44±0.03ms 0.95 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', 'high')
- 10.3±0.1μs 9.77±0.1μs 0.95 tslibs.normalize.Normalize.time_normalize_i8_timestamps(1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 33.2±0.2μs 31.4±0.2μs 0.95 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 10.8±0.1μs 10.2±0.06μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 5.73±0.04ms 5.43±0.02ms 0.95 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', 'high')
- 8.19±0.04μs 7.76±0.07μs 0.95 series_methods.SearchSorted.time_searchsorted('float64')
- 2.60±0.02ms 2.46±0.1ms 0.95 rolling.Apply.time_rolling('Series', 3, 'float', <built-in function sum>, True)
- 95.1±0.7μs 90.0±0.6μs 0.95 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
- 44.0±0.3ms 41.7±0.2ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'linear')
- 736±9ms 697±4ms 0.95 io.csv.ToCSVDatetimeBig.time_frame(100000)
- 15.2±0.3μs 14.4±0.3μs 0.95 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthEnd>)
- 120M 114M 0.95 rolling.Methods.peakmem_rolling('Series', 1000, 'int', 'count')
- 99.3±0.3ms 94.0±0.6ms 0.95 rolling.Methods.time_rolling('Series', 1000, 'int', 'median')
- 119±2μs 112±0.5μs 0.95 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64(<CustomBusinessMonthEnd>)
- 10.3±0.09μs 9.72±0.1μs 0.95 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 2.03±0.02μs 1.92±0.02μs 0.95 tslibs.resolution.TimeResolution.time_get_resolution('m', 1, None)
- 9.54±0.09μs 9.02±0.1μs 0.95 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 138±0.8μs 131±0.6μs 0.95 stat_ops.SeriesOps.time_op('sum', 'int')
- 9.73±0.2μs 9.20±0.2μs 0.95 tslibs.timestamp.TimestampProperties.time_weekday_name(<UTC>, None)
- 187±0.5μs 177±1μs 0.95 series_methods.NanOps.time_func('median', 1000, 'float64')
- 3.03±0.8ms 2.86±0.02ms 0.94 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'midpoint')
- 10.2±0.07μs 9.63±0.07μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 7000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 2.04±0.02μs 1.93±0.02μs 0.94 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, None)
- 9.69±0.1μs 9.15±0.05μs 0.94 tslibs.timestamp.TimestampProperties.time_weekday_name(<UTC>, 'B')
- 3.02±0.8ms 2.86±0.02ms 0.94 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher')
- 43.4±0.6ms 41.0±0.3ms 0.94 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'median')
- 121M 114M 0.94 rolling.Methods.peakmem_rolling('Series', 10, 'float', 'count')
- 10.9±0.06μs 10.3±0.09μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 5.05±0.7ms 4.77±0.02ms 0.94 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'skew')
- 37.9±0.3ms 35.7±0.3ms 0.94 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'linear')
- 15.5±0.2μs 14.6±0.3μs 0.94 tslibs.offsets.OffestDatetimeArithmetic.time_add(<MonthEnd>)
- 15.1±0.2μs 14.2±0.1μs 0.94 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<MonthBegin>)
- 40.8±0.4ms 38.5±0.3ms 0.94 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'nearest')
- 10.3±0.04μs 9.68±0.2μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2011, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 10.9±0.08μs 10.3±0.05μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 12000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 121M 114M 0.94 rolling.Methods.peakmem_rolling('Series', 10, 'int', 'count')
- 10.2±0.06μs 9.62±0.07μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 11000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 6.10±0.03ms 5.74±0.03ms 0.94 rolling.Engine.time_expanding_apply('Series', 'int', <function sum at 0x7f2790e97430>, 'cython')
- 9.64±0.06μs 9.08±0.2μs 0.94 tslibs.timestamp.TimestampProperties.time_weekday_name(None, None)
- 6.74±0.06ms 6.35±0.03ms 0.94 rolling.Engine.time_rolling_apply('Series', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'cython')
- 44.3±0.4ms 41.7±0.2ms 0.94 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'midpoint')
- 5.76±0.1ms 5.42±0.02ms 0.94 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(',', '.', 'round_trip')
- 3.26±0.02ms 3.07±0.02ms 0.94 io.csv.ReadCSVParseDates.time_multiple_date('python')
- 6.10±0.04ms 5.74±0.06ms 0.94 rolling.Engine.time_expanding_apply('Series', 'float', <function sum at 0x7f2790e97430>, 'cython')
- 38.0±0.4ms 35.8±1ms 0.94 stat_ops.FrameOps.time_op('median', 'float', 1)
- 21.1±0.3μs 19.9±0.3μs 0.94 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 5.10±0.06ms 4.80±0.09ms 0.94 ctors.SeriesConstructors.time_series_constructor(<function gen_of_str at 0x7f277fe8a310>, False, 'int')
- 117±0.9μs 110±1μs 0.94 series_methods.NanOps.time_func('var', 1000, 'float64')
- 1.84±0.05ms 1.73±0.01ms 0.94 reshape.Explode.time_explode(1000, 3)
- 3.86±0.02ms 3.63±0.03ms 0.94 index_cached_properties.IndexCache.time_is_unique('DatetimeIndex')
- 3.15±0.04μs 2.96±0.03μs 0.94 tslibs.timestamp.TimestampConstruction.time_parse_now
- 71.3±1ms 67.0±1ms 0.94 io.csv.ReadCSVCategorical.time_convert_post('c')
- 41.0±0.4ms 38.4±0.2ms 0.94 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'lower')
- 1.36±0.02ms 1.28±0.01ms 0.94 arithmetic.NumericInferOps.time_divide(<class 'numpy.uint8'>)
- 110±0.6μs 103±0.9μs 0.94 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
- 10.9±0.03μs 10.2±0.09μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 3000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 9.91±0.1μs 9.29±0.1μs 0.94 tslibs.timestamp.TimestampProperties.time_weekday_name(tzutc(), 'B')
- 9.88±0.1μs 9.26±0.2μs 0.94 tslibs.timestamp.TimestampProperties.time_weekday_name(tzutc(), None)
- 3.85±0.06μs 3.61±0.01μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1011, None)
- 2.57±0.04ms 2.41±0.1ms 0.94 rolling.Apply.time_rolling('Series', 3, 'int', <built-in function sum>, True)
- 11.0±0.05μs 10.3±0.2μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 2000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 41.6±0.5ms 38.9±0.3ms 0.94 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0.5, 'higher')
- 143±0.7ms 134±0.2ms 0.94 rolling.Apply.time_rolling('Series', 3, 'float', <function sum at 0x7f2790e97430>, False)
- 10.4±0.2μs 9.69±0.03μs 0.94 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 10.3±0.1μs 9.61±0.2μs 0.93 tslibs.timestamp.TimestampProperties.time_weekday_name(datetime.timezone(datetime.timedelta(seconds=3600)), 'B')
- 10.8±0.2μs 10.1±0.2μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 16.5±0.4ms 15.4±0.1ms 0.93 reshape.PivotTable.time_pivot_table_categorical
- 143±0.8ms 134±0.7ms 0.93 rolling.Apply.time_rolling('Series', 3, 'int', <function sum at 0x7f2790e97430>, False)
- 1.37±0.01ms 1.28±0.02ms 0.93 arithmetic.NumericInferOps.time_divide(<class 'numpy.int8'>)
- 146±0.6ms 136±1ms 0.93 rolling.Apply.time_rolling('DataFrame', 3, 'float', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 146±0.8ms 136±0.9ms 0.93 rolling.Apply.time_rolling('DataFrame', 3, 'int', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 145±0.3ms 135±1ms 0.93 rolling.Apply.time_rolling('Series', 3, 'int', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 1.62±0.01ms 1.51±0.01ms 0.93 series_methods.ValueCounts.time_value_counts('int')
- 10.8±0.1μs 10.1±0.2μs 0.93 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, datetime.timezone(datetime.timedelta(seconds=3600)))
- 37.9±0.6ms 35.4±0.2ms 0.93 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'midpoint')
- 184±3ms 172±1ms 0.93 io.json.ToJSON.time_to_json('index', 'df_int_floats')
- 1.81±0.01ms 1.69±0.03ms 0.93 frame_methods.GetDtypeCounts.time_frame_get_dtype_counts
- 9.19±0.2μs 8.57±0.09μs 0.93 tslibs.normalize.Normalize.time_is_date_array_normalized(1, datetime.timezone(datetime.timedelta(seconds=3600)))
- 29.2±0.3μs 27.2±0.3μs 0.93 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<CustomBusinessDay>)
- 44.3±0.1ms 41.3±0.2ms 0.93 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'median')
- 10.9±0.2μs 10.2±0.09μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 150±0.8ms 140±1ms 0.93 groupby.TransformEngine.time_dataframe_cython(True)
- 21.5±0.3μs 20.1±0.6μs 0.93 series_methods.SearchSorted.time_searchsorted('uint16')
- 16.4±0.1ms 15.2±0.3ms 0.93 reshape.Explode.time_explode(10000, 3)
- 9.54±0.05μs 8.89±0.1μs 0.93 tslibs.resolution.TimeResolution.time_get_resolution('D', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 10.1±0.07μs 9.41±0.2μs 0.93 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 11.0±0.09μs 10.2±0.2μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 1000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 9.04±0.09μs 8.41±0.1μs 0.93 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 7.06±0.2ms 6.56±0.03ms 0.93 stat_ops.FrameOps.time_op('median', 'Int64', 0)
- 8.97±0.07μs 8.34±0.2μs 0.93 tslibs.resolution.TimeResolution.time_get_resolution('us', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 3.15±0.02μs 2.93±0.08μs 0.93 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, datetime.timezone.utc)
- 5.34±0.05ms 4.96±0.08ms 0.93 ctors.SeriesConstructors.time_series_constructor(<function gen_of_str at 0x7f277fe8a310>, False, 'float')
- 103±1ms 96.0±0.5ms 0.93 rolling.Apply.time_rolling('Series', 300, 'int', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 35.4±0.2ms 32.9±0.2ms 0.93 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0.5, 'higher')
- 9.97±0.1ms 9.25±0.04ms 0.93 series_methods.NanOps.time_func('std', 1000000, 'Int64')
- 4.19±0.6ms 3.88±0.02ms 0.93 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'min')
- 11.0±0.09μs 10.2±0.2μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 1011, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 21.4±0.2μs 19.8±0.4μs 0.93 series_methods.SearchSorted.time_searchsorted('uint8')
- 10.4±0.1μs 9.67±0.1μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 9000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 58.3±0.2ms 54.0±0.3ms 0.93 rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'higher')
- 2.24±0.01ms 2.08±0.01ms 0.93 series_methods.NSort.time_nlargest('last')
- 11.0±0.2μs 10.2±0.1μs 0.93 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 4006, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 349±3μs 323±2μs 0.93 join_merge.Concat.time_concat_empty_left(0)
- 6.83±0.06ms 6.32±0.05ms 0.93 rolling.Apply.time_rolling('Series', 3, 'float', <function Apply.<lambda> at 0x7f277d8de160>, True)
- 1.62±0.01ms 1.50±0.01ms 0.93 series_methods.NSort.time_nsmallest('last')
- 10.6±0.1μs 9.78±0.1μs 0.93 tslibs.timestamp.TimestampOps.time_tz_convert(tzfile('/usr/share/zoneinfo/US/Central'))
- 105±0.5ms 96.7±1ms 0.93 rolling.Apply.time_rolling('DataFrame', 300, 'float', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 143±0.3ms 133±1ms 0.92 rolling.Apply.time_rolling('DataFrame', 3, 'int', <function sum at 0x7f2790e97430>, False)
- 25.4±0.09ms 23.4±0.1ms 0.92 io.csv.ReadCSVSkipRows.time_skipprows(None, 'c')
- 10.4±0.1μs 9.60±0.2μs 0.92 index_object.Indexing.time_get_loc_sorted('Float')
- 10.5±0.3μs 9.72±0.1μs 0.92 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 4.39±0.2ms 4.06±0.03ms 0.92 index_cached_properties.IndexCache.time_is_unique('IntervalIndex')
- 68.5±0.3ms 63.3±0.4ms 0.92 rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'linear')
- 150±0.8ms 138±1ms 0.92 groupby.TransformEngine.time_dataframe_cython(False)
- 50.2±0.4μs 46.3±0.5μs 0.92 array.IntegerArray.time_from_integer_array
- 103±0.6ms 95.1±1ms 0.92 rolling.Apply.time_rolling('DataFrame', 300, 'int', <function sum at 0x7f2790e97430>, False)
- 68.8±0.5ms 63.4±0.3ms 0.92 rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'midpoint')
- 97.8±0.8μs 90.2±1μs 0.92 series_methods.Any.time_any(1000000, 'slow', 'bool')
- 58.4±0.2ms 53.8±0.3ms 0.92 rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'nearest')
- 6.63±0.1μs 6.11±0.1μs 0.92 tslibs.timedelta.TimedeltaConstructor.time_from_datetime_timedelta
- 10.5±0.07μs 9.70±0.1μs 0.92 index_object.Indexing.time_get_loc('Float')
- 757±5μs 697±3μs 0.92 io.parsers.ConcatDateCols.time_check_concat('AAAA', 1)
- 15.4±0.4μs 14.2±0.1μs 0.92 tslibs.offsets.OffestDatetimeArithmetic.time_apply(<BusinessMonthBegin>)
- 44.3±0.5ms 40.7±0.2ms 0.92 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'median')
- 168±0.9ms 155±2ms 0.92 io.json.ToJSON.time_to_json_wide('columns', 'df_date_idx')
- 58.4±0.2ms 53.7±0.2ms 0.92 rolling.Quantile.time_quantile('Series', 1000, 'int', 0.5, 'lower')
- 49.7±2ms 45.6±1ms 0.92 stat_ops.FrameOps.time_op('mean', 'Int64', 1)
- 144±0.7ms 132±0.5ms 0.92 rolling.Apply.time_rolling('DataFrame', 3, 'float', <function sum at 0x7f2790e97430>, False)
- 9.29±0.1μs 8.52±0.05μs 0.92 tslibs.normalize.Normalize.time_is_date_array_normalized(0, datetime.timezone(datetime.timedelta(seconds=3600)))
- 56.0±0.1ms 51.3±0.4ms 0.92 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 1)
- 1.96±0.02μs 1.80±0.01μs 0.92 timedelta.TimedeltaIndexing.time_shallow_copy
- 3.13±0.01ms 2.86±0.04ms 0.92 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'mean')
- 3.11±0.05μs 2.85±0.1μs 0.91 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, datetime.timezone.utc)
- 21.0±0.3ms 19.2±0.3ms 0.91 timeseries.Iteration.time_iter_preexit(<function period_range at 0x7f2782af4700>)
- 10.9±0.2μs 9.96±0.2μs 0.91 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 170±0.5μs 155±0.9μs 0.91 period.Algorithms.time_drop_duplicates('index')
- 44.3±0.4ms 40.4±0.3ms 0.91 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'median')
- 106±0.8ms 96.2±0.4ms 0.91 rolling.Apply.time_rolling('DataFrame', 300, 'int', <function Apply.<lambda> at 0x7f277d8de160>, False)
- 353±2μs 321±0.9μs 0.91 join_merge.Concat.time_concat_empty_right(0)
- 1.92±0.01ms 1.75±0.02ms 0.91 series_methods.NSort.time_nsmallest('first')
- 20.5±0.06ms 18.7±0.07ms 0.91 groupby.MultiColumn.time_col_select_numpy_sum
- 202±4ms 183±3ms 0.91 io.json.ToJSON.time_to_json_wide('records', 'df_int_floats')
- 90.2±0.3ms 82.0±0.1ms 0.91 hash_functions.IsinWithArange.time_isin(<class 'object'>, 1000, 2)
- 22.0±0.2ms 19.9±0.1ms 0.91 rolling.Pairwise.time_pairwise(10, 'corr', True)
- 58.2±0.5μs 52.8±0.7μs 0.91 series_methods.NanOps.time_func('prod', 1000, 'float64')
- 2.98±0.01ms 2.71±0.02ms 0.91 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'mean')
- 57.5±0.08μs 52.1±0.6μs 0.91 array.BooleanArray.time_from_integer_array
- 15.2±0.2ms 13.8±0.1ms 0.91 frame_methods.Apply.time_apply_lambda_mean
- 45.9±0.4ms 41.6±0.3ms 0.91 rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'linear')
- 45.1±0.2ms 40.8±0.3ms 0.91 rolling.Methods.time_rolling('Series', 10, 'float', 'median')
- 4.28±0.6ms 3.87±0.02ms 0.90 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'max')
- 52.6±0.1ms 47.6±0.1ms 0.90 strings.Methods.time_title
- 7.28±0.1ms 6.59±0.1ms 0.90 rolling.Apply.time_rolling('DataFrame', 3, 'float', <function Apply.<lambda> at 0x7f277d8de160>, True)
- 45.5±0.3ms 41.1±0.5ms 0.90 rolling.Methods.time_rolling('Series', 10, 'int', 'median')
- 2.17±0.01ms 1.96±0ms 0.90 series_methods.NSort.time_nlargest('first')
- 7.21±0.1ms 6.51±0.07ms 0.90 rolling.Engine.time_rolling_apply('DataFrame', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'cython')
- 16.7±0.2ms 15.1±0.2ms 0.90 frame_methods.Apply.time_apply_np_mean
- 151±2ms 137±3ms 0.90 io.json.ToJSON.time_to_json_wide('values', 'df')
- 3.00±0.02ms 2.71±0.01ms 0.90 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'mean')
- 387±0.7μs 349±2μs 0.90 timedelta.TimedeltaIndexing.time_intersection
- 3.03±0.08ms 2.74±0.03ms 0.90 arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function add>)
- 7.56±0.06ms 6.82±0.03ms 0.90 rolling.Engine.time_expanding_apply('DataFrame', 'int', <function Engine.<lambda> at 0x7f277d8de310>, 'cython')
- 37.6±0.06ms 33.9±0.3ms 0.90 strings.Methods.time_lower
- 6.62±0.05ms 5.96±0.05ms 0.90 rolling.Engine.time_expanding_apply('DataFrame', 'float', <function sum at 0x7f2790e97430>, 'cython')
- 1.81±0.02ms 1.63±0.01ms 0.90 rolling.Engine.time_expanding_apply('Series', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'numba')
- 49.6±0.5ms 44.6±0.2ms 0.90 rolling.GroupbyLargeGroups.time_rolling_multiindex_creation
- 43.2±0.07ms 38.8±0.3ms 0.90 rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'higher')
- 1.97±0.01s 1.77±0s 0.90 timeseries.Iteration.time_iter(<function period_range at 0x7f2782af4700>)
- 218±10μs 196±1μs 0.90 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 42.6±0.2ms 38.3±0.3ms 0.90 rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'nearest')
- 6.59±0.04ms 5.91±0.05ms 0.90 rolling.Engine.time_expanding_apply('DataFrame', 'int', <function sum at 0x7f2790e97430>, 'cython')
- 6.24±0.05ms 5.59±0.03ms 0.90 rolling.Engine.time_rolling_apply('DataFrame', 'float', <function sum at 0x7f2790e97430>, 'cython')
- 46.2±0.05ms 41.4±0.3ms 0.90 rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'midpoint')
- 7.60±0.07ms 6.80±0.03ms 0.90 rolling.Engine.time_expanding_apply('DataFrame', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'cython')
- 2.82±0.01ms 2.52±0.01ms 0.89 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'sum')
- 42.7±0.1ms 38.1±0.3ms 0.89 rolling.Quantile.time_quantile('Series', 10, 'float', 0.5, 'lower')
- 6.24±0.1ms 5.57±0.05ms 0.89 rolling.Apply.time_rolling('DataFrame', 3, 'float', <function sum at 0x7f2790e97430>, True)
- 11.3±0.4μs 10.1±0.2μs 0.89 timeseries.TzLocalize.time_infer_dst('UTC')
- 7.69±0.08ms 6.84±0.02ms 0.89 rolling.Pairwise.time_pairwise(10, 'corr', False)
- 7.66±0.04ms 6.81±0.03ms 0.89 rolling.Pairwise.time_pairwise(1000, 'corr', False)
- 154±0.7μs 137±0.8μs 0.89 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 2.69±0.01ms 2.39±0.01ms 0.89 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'sum')
- 2.68±0.02ms 2.38±0.01ms 0.89 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'sum')
- 25.3±0.2ms 22.5±0.2ms 0.89 frame_methods.ToNumpy.time_to_numpy_mixed_tall
- 14.7±0.07ms 13.1±0.2ms 0.89 groupby.Categories.time_groupby_nosort
- 204±5ms 181±4ms 0.89 io.json.ToJSON.time_to_json_wide('index', 'df_int_floats')
- 177±3ms 157±1ms 0.89 io.csv.ToCSV.time_frame('wide')
- 39.7±0.2ms 35.2±0.4ms 0.89 rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'linear')
- 3.29±0.01ms 2.91±0.05ms 0.89 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'std')
- 1.83±0.05ms 1.62±0.04ms 0.89 rolling.Engine.time_expanding_apply('Series', 'float', <function sum at 0x7f2790e97430>, 'numba')
- 22.1±0.09ms 19.6±0.06ms 0.89 rolling.Pairwise.time_pairwise(None, 'corr', True)
- 39.8±0.3ms 35.2±0.2ms 0.88 rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'midpoint')
- 79.3±0.8μs 70.2±0.4μs 0.88 series_methods.NanOps.time_func('mean', 1000, 'int64')
- 227±2ms 201±4ms 0.88 io.json.ToJSON.time_to_json_wide('index', 'df_int_float_str')
- 7.33±0.08ms 6.48±0.03ms 0.88 rolling.Engine.time_rolling_apply('DataFrame', 'int', <function Engine.<lambda> at 0x7f277d8de310>, 'cython')
- 4.86±0.01ms 4.29±0.04ms 0.88 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'min')
- 525±6μs 464±10μs 0.88 join_merge.Append.time_append_homogenous
- 170±1ms 150±1ms 0.88 io.json.ToJSON.time_to_json_wide('columns', 'df')
- 3.71±0.01ms 3.27±0.01ms 0.88 series_methods.NanOps.time_func('mean', 1000000, 'boolean')
- 181±3ms 160±2ms 0.88 io.json.ToJSON.time_to_json_wide('split', 'df_int_floats')
- 19.5±0.07ms 17.2±0.1ms 0.88 rolling.Pairwise.time_pairwise(None, 'cov', True)
- 125±0.3ms 111±0.2ms 0.88 gil.ParallelKth.time_kth_smallest
- 24.1±0.4ms 21.2±0.2ms 0.88 categoricals.Indexing.time_reindex
- 1.85±0.01ms 1.63±0.03ms 0.88 rolling.Engine.time_expanding_apply('Series', 'int', <function sum at 0x7f2790e97430>, 'numba')
- 79.3±0.9μs 69.8±0.7μs 0.88 series_methods.NanOps.time_func('mean', 1000, 'int32')
- 7.38±0.1ms 6.49±0.04ms 0.88 rolling.Apply.time_rolling('DataFrame', 3, 'int', <function Apply.<lambda> at 0x7f277d8de160>, True)
- 972±3μs 855±4μs 0.88 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 14)
- 4.77±0.05ms 4.20±0.05ms 0.88 rolling.Apply.time_rolling('DataFrame', 300, 'float', <function sum at 0x7f2790e97430>, True)
- 182±2ms 160±3ms 0.88 io.json.ToJSON.time_to_json_wide('values', 'df_int_floats')
- 36.7±0.4ms 32.2±0.2ms 0.88 rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'nearest')
- 36.6±0.3ms 32.2±0.2ms 0.88 rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'lower')
- 78.9±0.5μs 69.2±0.6μs 0.88 series_methods.NanOps.time_func('mean', 1000, 'int8')
- 556±3μs 487±3μs 0.88 join_merge.Concat.time_concat_mixed_ndims(0)
- 4.94±0.01ms 4.33±0.02ms 0.88 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'min')
- 37.0±0.2ms 32.4±0.2ms 0.88 rolling.Quantile.time_quantile('Series', 10, 'int', 0.5, 'higher')
- 120±0.6ms 105±0.1ms 0.88 replace.Convert.time_replace('DataFrame', 'Timedelta')
- 120±1ms 105±0.7ms 0.87 replace.Convert.time_replace('DataFrame', 'Timestamp')
- 5.50±0.05ms 4.81±0.02ms 0.87 rolling.Apply.time_rolling('DataFrame', 300, 'float', <function Apply.<lambda> at 0x7f277d8de160>, True)
- 19.6±0.06ms 17.1±0.1ms 0.87 rolling.Pairwise.time_pairwise(1000, 'cov', True)
- 66.5±0.2μs 58.1±0.6μs 0.87 series_methods.NanOps.time_func('min', 1000, 'int32')
- 6.29±0.1ms 5.48±0.04ms 0.87 rolling.Engine.time_rolling_apply('DataFrame', 'int', <function sum at 0x7f2790e97430>, 'cython')
- 230±6ms 200±3ms 0.87 io.json.ToJSON.time_to_json_wide('records', 'df_int_float_str')
- 208±4ms 181±2ms 0.87 io.json.ToJSON.time_to_json_wide('values', 'df_int_float_str')
- 113±0.7ms 97.9±1ms 0.87 index_object.IndexAppend.time_append_obj_list
- 5.03±0.01ms 4.36±0.01ms 0.87 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'max')
- 815±1ms 706±3ms 0.87 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Float64Index'>, 5000000)
- 5.53±0.06ms 4.78±0.02ms 0.87 rolling.Apply.time_rolling('DataFrame', 300, 'int', <function Apply.<lambda> at 0x7f277d8de160>, True)
- 573±8μs 495±3μs 0.86 period.Indexing.time_intersection
- 5.49±0.04ms 4.75±0.03ms 0.86 stat_ops.FrameOps.time_op('std', 'Int64', 0)
- 4.81±0.07ms 4.15±0.04ms 0.86 rolling.Apply.time_rolling('DataFrame', 300, 'int', <function sum at 0x7f2790e97430>, True)
- 210±0.5ms 181±5ms 0.86 io.json.ToJSON.time_to_json_wide('columns', 'df_int_floats')
- 26.3±0.08ms 22.7±0.05ms 0.86 join_merge.Concat.time_concat_series(0)
- 66.6±0.4μs 57.3±0.4μs 0.86 series_methods.NanOps.time_func('min', 1000, 'int64')
- 5.98±0.7ms 5.14±0.03ms 0.86 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'kurt')
- 66.8±0.1μs 57.5±0.6μs 0.86 series_methods.NanOps.time_func('min', 1000, 'int8')
- 3.09±0.2ms 2.65±0.01ms 0.86 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'std')
- 5.03±0.02ms 4.31±0.03ms 0.86 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'max')
- 68.0±0.7μs 58.1±0.2μs 0.85 replace.ReplaceList.time_replace_list(True)
- 55.4±0.5μs 47.3±0.3μs 0.85 series_methods.NanOps.time_func('sum', 1000, 'int64')
- 10.6±0.2μs 9.05±0.04μs 0.85 timeseries.TzLocalize.time_infer_dst(tzutc())
- 38.1±0.5ms 32.5±0.1ms 0.85 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 1)
- 188±6ms 160±5ms 0.85 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
- 6.01±0.7ms 5.12±0.04ms 0.85 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'kurt')
- 3.50±0.02ms 2.98±0.02ms 0.85 series_methods.IsIn.time_isin('object')
- 2.61±0.02ms 2.22±0.02ms 0.85 stat_ops.FrameOps.time_op('mean', 'Int64', 0)
- 734±30μs 623±4μs 0.85 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'non_monotonic')
- 6.20±0.06ms 5.26±0.03ms 0.85 gil.ParallelRolling.time_rolling('std')
- 5.57±0.04ms 4.71±0.01ms 0.85 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'min')
- 5.62±0.02ms 4.75±0.02ms 0.85 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'min')
- 218±0.4ms 184±2ms 0.84 io.json.ToJSON.time_to_json_wide('records', 'df_td_int_ts')
- 5.57±0.01ms 4.70±0.02ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'min')
- 204±2ms 172±4ms 0.84 io.json.ToJSON.time_to_json_wide('columns', 'df_td_int_ts')
- 218±1ms 183±2ms 0.84 io.json.ToJSON.time_to_json_wide('index', 'df_td_int_ts')
- 5.89±0.7ms 4.96±0ms 0.84 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'kurt')
- 3.74±0.05ms 3.15±0.02ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'mean')
- 187±1ms 157±1ms 0.84 io.json.ToJSON.time_to_json_wide('values', 'df_td_int_ts')
- 5.76±0.09ms 4.84±0.01ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'min')
- 210±2ms 177±0.8ms 0.84 io.json.ToJSON.time_to_json_wide('split', 'df_int_float_str')
- 5.67±0.09ms 4.76±0.01ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'min')
- 5.82±0.05ms 4.89±0.01ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'max')
- 5.47±0.02ms 4.60±0.01ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'min')
- 3.70±0.03ms 3.10±0.01ms 0.84 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'mean')
- 227±2ms 190±4ms 0.84 io.json.ToJSON.time_to_json_wide('columns', 'df_int_float_str')
- 22.3±0.08ms 18.6±0.07ms 0.84 join_merge.Merge.time_merge_dataframe_integer_2key(True)
- 3.95±0.02ms 3.29±0.02ms 0.83 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 16)
- 6.32±0.7ms 5.26±0.03ms 0.83 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'kurt')
- 750±60μs 625±6μs 0.83 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'unique_monotonic_inc')
- 5.73±0.06ms 4.78±0.02ms 0.83 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'max')
- 188±3ms 157±1ms 0.83 io.json.ToJSON.time_to_json_wide('split', 'df_td_int_ts')
- 3.66±0.01ms 3.05±0.01ms 0.83 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'mean')
- 144±1μs 120±1μs 0.83 series_methods.NanOps.time_func('std', 1000, 'int32')
- 21.4±0.04ms 17.8±0.06ms 0.83 stat_ops.Correlation.time_corr_wide_nans('pearson')
- 3.35±0.02ms 2.78±0.01ms 0.83 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'sum')
- 145±0.9μs 119±1μs 0.83 series_methods.NanOps.time_func('std', 1000, 'int8')
- 5.74±0.03ms 4.74±0.06ms 0.83 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'min')
- 7.73±0.07ms 6.39±0.05ms 0.83 gil.ParallelRolling.time_rolling('kurt')
- 3.38±0.02ms 2.79±0.01ms 0.82 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'sum')
- 194±7ms 160±5ms 0.82 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
- 146±1μs 121±1μs 0.82 series_methods.NanOps.time_func('std', 1000, 'int64')
- 95.4±1μs 78.4±1μs 0.82 series_methods.NanOps.time_func('mean', 1000, 'boolean')
- 1.96±0.04ms 1.61±0.02ms 0.82 arithmetic.FrameWithFrameWide.time_op_same_blocks(<built-in function add>)
- 82.9±1μs 68.1±0.6μs 0.82 series_methods.NanOps.time_func('mean', 1000, 'Int64')
- 5.77±0.03ms 4.74±0.01ms 0.82 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'max')
- 58.8±0.5μs 48.3±0.6μs 0.82 series_methods.NanOps.time_func('sum', 1000, 'int8')
- 3.41±0.01ms 2.79±0.02ms 0.82 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'sum')
- 5.74±0.03ms 4.70±0.01ms 0.82 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'max')
- 5.71±0.01ms 4.66±0ms 0.82 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'max')
- 6.73±0.08ms 5.48±0.06ms 0.82 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f277fe8a160>, True, 'int')
- 7.95±0.05ms 6.48±0.03ms 0.82 rolling.Pairwise.time_pairwise(None, 'corr', False)
- 5.79±0.03ms 4.72±0.01ms 0.82 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'max')
- 6.54±0.07ms 5.32±0.07ms 0.81 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f277fe8a160>, False, 'int')
- 5.27±0.02ms 4.29±0.03ms 0.81 rolling.Pairwise.time_pairwise(10, 'cov', False)
- 48.9±3μs 39.7±0.7μs 0.81 series_methods.Any.time_any(1000000, 'fast', 'bool')
- 68.5±0.6ms 55.6±0.4ms 0.81 groupby.TransformEngine.time_series_cython(False)
- 192±4ms 156±6ms 0.81 indexing.CategoricalIndexIndexing.time_get_indexer_list('non_monotonic')
- 6.27±0.8ms 5.09±0.03ms 0.81 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'kurt')
- 68.7±0.6ms 55.7±0.4ms 0.81 groupby.TransformEngine.time_series_cython(True)
- 5.32±0.08ms 4.31±0.08ms 0.81 rolling.Pairwise.time_pairwise(1000, 'cov', False)
- 3.12±0.04ms 2.52±0.1ms 0.81 rolling.Apply.time_rolling('DataFrame', 3, 'int', <built-in function sum>, True)
- 5.65±0.03ms 4.57±0.01ms 0.81 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'max')
- 10.1±0.1ms 8.15±0.03ms 0.81 stat_ops.FrameOps.time_op('median', 'float', 0)
- 3.12±0.06ms 2.52±0.1ms 0.81 rolling.Apply.time_rolling('DataFrame', 3, 'float', <built-in function sum>, True)
- 6.21±0.01ms 5.01±0.01ms 0.81 rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
- 410±5μs 330±3μs 0.80 stat_ops.SeriesOps.time_op('mean', 'float')
- 49.0±0.9μs 39.4±0.3μs 0.80 series_methods.All.time_all(1000000, 'fast', 'bool')
- 66.6±0.6μs 53.2±0.7μs 0.80 series_methods.NanOps.time_func('max', 1000, 'int32')
- 100±0.9μs 79.8±1μs 0.79 series_methods.NanOps.time_func('mean', 1000, 'float64')
- 177±1ms 140±0.8ms 0.79 categoricals.Rank.time_rank_string
- 85.5±1ms 67.8±0.8ms 0.79 rolling.Groupby.time_rolling_offset('sum')
- 182±2μs 144±1μs 0.79 series_methods.NanOps.time_func('std', 1000, 'Int64')
- 66.6±0.6μs 52.8±0.1μs 0.79 series_methods.NanOps.time_func('max', 1000, 'int64')
- 177±0.7μs 140±1μs 0.79 series_methods.NanOps.time_func('std', 1000, 'float64')
- 1.03±0.02s 815±5ms 0.79 groupby.Apply.time_copy_overhead_single_col
- 66.6±0.7μs 52.7±0.3μs 0.79 series_methods.NanOps.time_func('max', 1000, 'int8')
- 85.5±0.3ms 67.6±0.1ms 0.79 rolling.Groupby.time_rolling_offset('kurt')
- 84.9±0.5ms 67.1±0.6ms 0.79 rolling.Groupby.time_rolling_offset('max')
- 9.05±0.02ms 7.14±0.03ms 0.79 join_merge.Merge.time_merge_dataframe_integer_2key(False)
- 6.64±0.7ms 5.24±0.03ms 0.79 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'kurt')
- 183±1μs 145±0.8μs 0.79 series_methods.NanOps.time_func('std', 1000, 'boolean')
- 2.31±0.04ms 1.82±0.07ms 0.79 rolling.Engine.time_expanding_apply('DataFrame', 'int', <function sum at 0x7f2790e97430>, 'numba')
- 2.11±0.01ms 1.66±0.03ms 0.79 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'mean')
- 85.0±1ms 67.0±0.6ms 0.79 rolling.Groupby.time_rolling_offset('mean')
- 5.58±0.7ms 4.40±0.02ms 0.79 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'skew')
- 2.24±0.02ms 1.76±0.02ms 0.79 rolling.EWMMethods.time_ewm_times('DataFrame', 10, 'int', 'std')
- 2.12±0.01ms 1.66±0.02ms 0.79 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 15)
- 5.59±0.7ms 4.39±0.02ms 0.79 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'skew')
- 88.9±1ms 69.7±0.6ms 0.78 rolling.Groupby.time_rolling_offset('median')
- 3.92±0.02ms 3.07±0.05ms 0.78 series_methods.NanOps.time_func('mean', 1000000, 'Int64')
- 3.27±0.4ms 2.56±0.01ms 0.78 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'min')
- 5.50±0.05ms 4.31±0.02ms 0.78 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'std')
- 86.0±0.6ms 67.4±0.4ms 0.78 rolling.Groupby.time_rolling_offset('min')
- 2.46±0.01ms 1.92±0.03ms 0.78 groupby.RankWithTies.time_rank_ties('int64', 'average')
- 2.43±0.01ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('datetime64', 'average')
- 2.25±0.01ms 1.76±0.03ms 0.78 rolling.EWMMethods.time_ewm_times('DataFrame', 1000, 'int', 'mean')
- 151±2μs 118±1μs 0.78 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'unique_monotonic_inc')
- 2.44±0.02ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('datetime64', 'dense')
- 2.44±0.02ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('datetime64', 'min')
- 2.30±0.09ms 1.79±0.02ms 0.78 rolling.Engine.time_expanding_apply('DataFrame', 'int', <function Engine.<lambda> at 0x7f277d8de310>, 'numba')
- 2.45±0.01ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('int64', 'min')
- 2.25±0.01ms 1.75±0.02ms 0.78 rolling.EWMMethods.time_ewm_times('DataFrame', 1000, 'int', 'std')
- 2.44±0.02ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('datetime64', 'first')
- 34.6±0.3μs 26.9±0.6μs 0.78 series_methods.Any.time_any(1000, 'slow', 'bool')
- 2.44±0.01ms 1.90±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('int64', 'dense')
- 2.24±0.02ms 1.74±0.01ms 0.78 rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'mean')
- 2.24±0.01ms 1.74±0.02ms 0.78 rolling.EWMMethods.time_ewm_times('DataFrame', 10, 'int', 'mean')
- 2.43±0.02ms 1.89±0.02ms 0.78 groupby.RankWithTies.time_rank_ties('float32', 'max')
- 2.41±0.02ms 1.86±0.01ms 0.78 groupby.RankWithTies.time_rank_ties('float64', 'dense')
- 2.12±0.03ms 1.64±0.01ms 0.78 rolling.EWMMethods.time_ewm_times('DataFrame', 10, 'float', 'mean')
- 2.44±0.03ms 1.89±0.02ms 0.77 groupby.RankWithTies.time_rank_ties('float32', 'min')
- 2.12±0.01ms 1.64±0.02ms 0.77 rolling.EWMMethods.time_ewm_times('DataFrame', 1000, 'float', 'std')
- 3.38±0.02ms 2.61±0.02ms 0.77 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'max')
- 2.45±0.01ms 1.90±0.01ms 0.77 groupby.RankWithTies.time_rank_ties('int64', 'first')
- 2.32±0.07ms 1.79±0.03ms 0.77 rolling.Engine.time_expanding_apply('DataFrame', 'float', <function sum at 0x7f2790e97430>, 'numba')
- 11.0±0.4μs 8.51±0.1μs 0.77 timeseries.TzLocalize.time_infer_dst(None)
- 75.4±1ms 58.3±0.2ms 0.77 reshape.Cut.time_qcut_timedelta(1000)
- 2.48±0.04ms 1.91±0.02ms 0.77 groupby.RankWithTies.time_rank_ties('datetime64', 'max')
- 2.46±0.02ms 1.89±0.02ms 0.77 groupby.RankWithTies.time_rank_ties('float32', 'dense')
- 2.12±0.01ms 1.63±0.01ms 0.77 rolling.EWMMethods.time_ewm_times('DataFrame', 10, 'float', 'std')
- 5.44±0.03ms 4.18±0.04ms 0.77 rolling.Pairwise.time_pairwise(None, 'cov', False)
- 2.32±0.05ms 1.78±0.03ms 0.77 rolling.Engine.time_expanding_apply('DataFrame', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'numba')
- 2.42±0.02ms 1.86±0.01ms 0.77 groupby.RankWithTies.time_rank_ties('float64', 'average')
- 11.5±0.06μs 8.86±0.1μs 0.77 indexing.CategoricalIndexIndexing.time_get_loc_scalar('monotonic_incr')
- 86.4±1ms 66.4±1ms 0.77 series_methods.SeriesConstructor.time_constructor('dict')
- 5.87±0.8ms 4.50±0.01ms 0.77 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'skew')
- 2.45±0.02ms 1.88±0.02ms 0.77 groupby.RankWithTies.time_rank_ties('float32', 'average')
- 2.42±0.02ms 1.86±0ms 0.77 groupby.RankWithTies.time_rank_ties('float64', 'min')
- 2.46±0.02ms 1.88±0.01ms 0.77 groupby.RankWithTies.time_rank_ties('float32', 'first')
- 1.03±0ms 789±4μs 0.77 period.Algorithms.time_value_counts('index')
- 6.71±0.01ms 5.13±0.01ms 0.77 rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
- 2.49±0.05ms 1.90±0.02ms 0.76 groupby.RankWithTies.time_rank_ties('int64', 'max')
- 2.43±0.02ms 1.86±0.01ms 0.76 groupby.RankWithTies.time_rank_ties('float64', 'max')
- 3.94±0.01ms 3.01±0.02ms 0.76 series_methods.NanOps.time_func('max', 1000000, 'float64')
- 2.45±0.01ms 1.87±0.01ms 0.76 groupby.RankWithTies.time_rank_ties('float64', 'first')
- 35.4±0.3μs 27.0±0.3μs 0.76 series_methods.All.time_all(1000, 'slow', 'bool')
- 3.39±0.4ms 2.58±0.02ms 0.76 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'max')
- 5.64±0.7ms 4.30±0.01ms 0.76 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'std')
- 34.6±0.5μs 26.4±0.2μs 0.76 series_methods.Any.time_any(1000, 'fast', 'bool')
- 4.42±0.1μs 3.36±0.1μs 0.76 index_cached_properties.IndexCache.time_shape('DatetimeIndex')
- 33.4±0.3ms 25.4±0.5ms 0.76 categoricals.Indexing.time_intersection
- 72.3±10ms 54.9±0.3ms 0.76 reshape.Cut.time_qcut_datetime(1000)
- 35.0±0.2μs 26.6±0.4μs 0.76 series_methods.All.time_all(1000, 'fast', 'bool')
- 8.87±0.2μs 6.73±0.1μs 0.76 index_cached_properties.IndexCache.time_engine('DatetimeIndex')
- 85.0±0.8μs 64.4±0.9μs 0.76 series_methods.NanOps.time_func('sum', 1000, 'float64')
- 5.94±0.8ms 4.50±0.01ms 0.76 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'skew')
- 3.41±0.02s 2.57±0.03s 0.76 groupby.Apply.time_copy_function_multi_col
- 3.35±0.8ms 2.53±0.01ms 0.76 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'sum')
- 3.92±0.02ms 2.95±0.04ms 0.75 series_methods.NanOps.time_func('mean', 1000000, 'float64')
- 5.84±0.7ms 4.39±0.01ms 0.75 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'skew')
- 6.66±0.01ms 5.01±0.01ms 0.75 rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
- 14.3±0.4μs 10.7±0.4μs 0.75 index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
- 958±9μs 717±3μs 0.75 period.Algorithms.time_value_counts('series')
- 8.93±0.5μs 6.68±0.2μs 0.75 index_cached_properties.IndexCache.time_engine('PeriodIndex')
- 5.81±0.7ms 4.34±0.02ms 0.75 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'std')
- 2.19±0.1ms 1.64±0.02ms 0.75 rolling.EWMMethods.time_ewm_times('DataFrame', 1000, 'float', 'mean')
- 5.52±0.7ms 4.12±0.01ms 0.75 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'float', 'std')
- 4.20±0.05ms 3.12±0.2ms 0.74 stat_ops.FrameOps.time_op('prod', 'float', 0)
- 4.67±0.1μs 3.46±0.2μs 0.74 index_cached_properties.IndexCache.time_shape('PeriodIndex')
- 134±2μs 99.4±0.4μs 0.74 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'non_monotonic')
- 303±2μs 223±1μs 0.74 index_cached_properties.IndexCache.time_is_monotonic('Float64Index')
- 4.86±0.07ms 3.58±0.06ms 0.74 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f277fe8a160>, True, 'float')
- 4.68±0.06ms 3.43±0.05ms 0.73 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f277fe8a160>, False, 'float')
- 5.87±0.7ms 4.30±0.02ms 0.73 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'skew')
- 666±3μs 487±0.8μs 0.73 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 13)
- 7.84±0.08μs 5.73±0.04μs 0.73 categoricals.SearchSorted.time_categorical_index_contains
- 57.8±0.7ms 42.2±0.4ms 0.73 reshape.Cut.time_cut_datetime(1000)
- 304±1μs 222±2μs 0.73 index_cached_properties.IndexCache.time_is_monotonic_increasing('Float64Index')
- 3.92±0.6ms 2.86±0.05ms 0.73 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'mean')
- 303±1μs 221±1μs 0.73 index_cached_properties.IndexCache.time_is_monotonic_decreasing('Float64Index')
- 5.39±0.2ms 3.92±0.2ms 0.73 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
- 5.51±0.1ms 4.00±0.03ms 0.73 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'max')
- 5.44±0.4ms 3.95±0.2ms 0.73 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'count')
- 87.0±0.5μs 63.2±0.6μs 0.73 series_methods.NanOps.time_func('max', 1000, 'float64')
- 4.45±0.7ms 3.23±0.01ms 0.73 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'mean')
- 7.14±0.2μs 5.18±0.02μs 0.72 categoricals.SearchSorted.time_categorical_contains
- 4.38±0.7ms 3.17±0.01ms 0.72 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'mean')
- 87.6±0.4μs 63.3±0.7μs 0.72 series_methods.NanOps.time_func('min', 1000, 'float64')
- 4.48±0.7ms 3.23±0.01ms 0.72 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'mean')
- 112±1μs 80.5±0.7μs 0.72 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'non_monotonic')
- 5.43±0.1ms 3.91±0.2ms 0.72 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'count')
- 299±1μs 215±1μs 0.72 index_cached_properties.IndexCache.time_is_monotonic_increasing('UInt64Index')
- 299±2μs 215±2μs 0.72 index_cached_properties.IndexCache.time_is_monotonic('UInt64Index')
- 112±2μs 80.2±0.5μs 0.72 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'nonunique_monotonic_inc')
- 299±2μs 214±1μs 0.72 index_cached_properties.IndexCache.time_is_monotonic_decreasing('UInt64Index')
- 7.18±0.01ms 5.14±0.02ms 0.72 rolling.Methods.time_rolling('Series', 10, 'int', 'kurt')
- 5.41±0.4ms 3.87±0.2ms 0.72 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'count')
- 111±2μs 79.3±0.2μs 0.71 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'unique_monotonic_inc')
- 63.6±1ms 45.3±0.1ms 0.71 reshape.Cut.time_cut_timedelta(1000)
- 4.12±0.7ms 2.92±0.01ms 0.71 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'sum')
- 196M 139M 0.71 rolling.PeakMemFixedWindowMinMax.peakmem_fixed('max')
- 196M 139M 0.71 rolling.PeakMemFixedWindowMinMax.peakmem_fixed('min')
- 6.04±0.02ms 4.28±0.02ms 0.71 rolling.Methods.time_rolling('Series', 1000, 'float', 'skew')
- 32.9±0.5ms 23.2±1ms 0.71 hash_functions.UniqueAndFactorizeArange.time_factorize(7)
- 4.10±0.7ms 2.90±0.02ms 0.71 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'sum')
- 6.75±0.02ms 4.75±0.01ms 0.70 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'kurt')
- 5.37±1μs 3.78±0.1μs 0.70 index_cached_properties.IndexCache.time_values('TimedeltaIndex')
- 6.76±0.03ms 4.75±0.01ms 0.70 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'kurt')
- 163±10μs 114±1μs 0.70 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'nonunique_monotonic_inc')
- 104±10ms 72.7±4ms 0.70 frame_methods.Equals.time_frame_object_equal
- 6.21±0.03ms 4.34±0.01ms 0.70 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'min')
- 33.4±1ms 23.3±1ms 0.70 hash_functions.UniqueAndFactorizeArange.time_factorize(12)
- 6.99±0.1ms 4.86±0.03ms 0.70 gil.ParallelRolling.time_rolling('var')
- 6.12±0.03ms 4.25±0.04ms 0.70 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'min')
- 5.65±0.4ms 3.93±0.02ms 0.69 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'min')
- 6.20±0.03ms 4.28±0.01ms 0.69 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'min')
- 4.32±0.7ms 2.95±0.03ms 0.68 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'sum')
- 6.35±0.03ms 4.33±0.01ms 0.68 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'max')
- 45.5±0.2ms 30.9±0.03ms 0.68 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 8000, -2)
- 5.79±0.03ms 3.92±0.03ms 0.68 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'max')
- 2.75±0ms 1.86±0.02ms 0.68 series_methods.NanOps.time_func('argmax', 1000000, 'float64')
- 7.78±0.2ms 5.25±0.7ms 0.67 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'std')
- 8.70±1μs 5.87±0.2μs 0.67 index_cached_properties.IndexCache.time_shape('TimedeltaIndex')
- 1.78±0.04ms 1.20±0.06ms 0.67 replace.FillNa.time_replace(True)
- 6.31±0.02ms 4.24±0.02ms 0.67 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'max')
- 519±3ms 348±2ms 0.67 series_methods.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'random_misses')
- 6.56±0.01ms 4.39±0.02ms 0.67 rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
- 4.67±0.03ms 3.11±0.01ms 0.67 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'mean')
- 5.72±0.03ms 3.79±0.01ms 0.66 rolling.Methods.time_rolling('Series', 1000, 'float', 'std')
- 40.9±1μs 27.1±0.2μs 0.66 series_methods.NanOps.time_func('argmax', 1000, 'int32')
- 73.1±0.6μs 48.3±0.5μs 0.66 series_methods.NanOps.time_func('argmax', 1000, 'float64')
- 41.0±0.8μs 27.1±0.2μs 0.66 series_methods.NanOps.time_func('argmax', 1000, 'int8')
- 17.4±0.2ms 11.5±2ms 0.66 stat_ops.FrameOps.time_op('skew', 'float', 0)
- 6.50±0.03ms 4.28±0.01ms 0.66 rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
- 1.09±0.05ms 717±60μs 0.66 rolling.Engine.time_rolling_apply('DataFrame', 'int', <function sum at 0x7f2790e97430>, 'numba')
- 2.67±0.05μs 1.75±0.03μs 0.66 period.Indexing.time_shallow_copy
- 2.75±0.01ms 1.80±0.01ms 0.65 series_methods.NanOps.time_func('prod', 1000000, 'int8')
- 1.35±0ms 879±3μs 0.65 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 14)
- 234±2μs 153±1μs 0.65 stat_ops.SeriesOps.time_op('sum', 'float')
- 41.4±0.4μs 27.0±0.1μs 0.65 series_methods.NanOps.time_func('argmax', 1000, 'int64')
- 6.50±0.02ms 4.19±0.02ms 0.64 rolling.Methods.time_rolling('Series', 1000, 'float', 'min')
- 13.3±1ms 8.57±0.05ms 0.64 series_methods.NanOps.time_func('std', 1000000, 'float64')
- 6.51±0.01ms 4.18±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'midpoint')
- 6.55±0.01ms 4.20±0.03ms 0.64 rolling.Methods.time_rolling('Series', 1000, 'float', 'max')
- 6.51±0.02ms 4.17±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'linear')
- 6.50±0.03ms 4.16±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'nearest')
- 6.50±0.03ms 4.16±0.02ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'higher')
- 15.7±0.9ms 10.1±0.9ms 0.64 stat_ops.FrameOps.time_op('sem', 'int', 0)
- 359±1μs 229±1μs 0.64 index_cached_properties.IndexCache.time_is_monotonic('CategoricalIndex')
- 359±0.9μs 229±1μs 0.64 index_cached_properties.IndexCache.time_is_monotonic_decreasing('CategoricalIndex')
- 482±2μs 308±2μs 0.64 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 12)
- 6.53±0.03ms 4.17±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'lower')
- 6.55±0.01ms 4.18±0.02ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'linear')
- 6.57±0.02ms 4.19±0.02ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'nearest')
- 359±1μs 229±1μs 0.64 index_cached_properties.IndexCache.time_is_monotonic_increasing('CategoricalIndex')
- 6.54±0.01ms 4.17±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'higher')
- 4.38±0.02ms 2.79±0.01ms 0.64 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'sum')
- 6.54±0.02ms 4.16±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'midpoint')
- 6.14±0.3ms 3.90±0.03ms 0.64 gil.ParallelRolling.time_rolling('mean')
- 6.57±0.01ms 4.18±0.01ms 0.64 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'lower')
- 7.69±0.5ms 4.87±0.02ms 0.63 stat_ops.FrameOps.time_op('mad', 'int', 0)
- 7.02±0.03ms 4.43±0.03ms 0.63 rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
- 4.49±0.01ms 2.82±0.01ms 0.63 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
- 6.94±0.7ms 4.36±0.01ms 0.63 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'min')
- 4.49±0.01ms 2.81±0.01ms 0.63 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower')
- 97.8±8ms 61.3±1ms 0.63 frame_methods.Equals.time_frame_object_unequal
- 4.50±0.01ms 2.82±0.01ms 0.63 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher')
- 4.50±0.01ms 2.82±0.02ms 0.63 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
- 4.57±0.01ms 2.86±0.03ms 0.63 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
- 6.24±0.04ms 3.90±0.02ms 0.63 rolling.Methods.time_rolling('Series', 1000, 'int', 'std')
- 6.82±0.02ms 4.26±0.02ms 0.63 rolling.Methods.time_rolling('Series', 10, 'float', 'min')
- 2.04±0.02ms 1.27±0ms 0.62 series_methods.NanOps.time_func('sum', 1000000, 'int8')
- 7.19±0.8ms 4.49±0.03ms 0.62 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'min')
- 6.81±0.01ms 4.25±0.01ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'linear')
- 7.02±0.8ms 4.38±0.01ms 0.62 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'max')
- 7.04±0.8ms 4.38±0.03ms 0.62 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'min')
- 4.49±0.01ms 2.80±0.02ms 0.62 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear')
- 6.86±0.01ms 4.26±0.01ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'linear')
- 6.85±0.03ms 4.26±0.01ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'nearest')
- 6.88±0.02ms 4.27±0.02ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'lower')
- 6.83±0.02ms 4.24±0.02ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'midpoint')
- 6.84±0.02ms 4.24±0.02ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'nearest')
- 5.81±0.02ms 3.60±0.05ms 0.62 rolling.ExpandingMethods.time_expanding('Series', 'float', 'kurt')
- 4.57±0.01ms 2.83±0.02ms 0.62 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
- 4.59±0.02ms 2.84±0.01ms 0.62 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
- 6.90±0.03ms 4.27±0.02ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'midpoint')
- 6.85±0.02ms 4.24±0.01ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'lower')
- 6.89±0.02ms 4.27±0.01ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'higher')
- 6.85±0.03ms 4.24±0.02ms 0.62 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'higher')
- 6.64±0.09ms 4.10±0.03ms 0.62 period.DataFramePeriodColumn.time_set_index
- 6.91±0.08ms 4.26±0.01ms 0.62 rolling.Methods.time_rolling('Series', 10, 'float', 'max')
- 4.59±0.03ms 2.83±0.03ms 0.62 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
- 4.58±0.02ms 2.82±0.01ms 0.62 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
- 7.00±0.4ms 4.29±0ms 0.61 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'max')
- 7.38±0.8ms 4.51±0.01ms 0.61 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'max')
- 6.45±0.8ms 3.94±0.01ms 0.61 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'std')
- 6.96±0.02ms 4.25±0.02ms 0.61 rolling.Methods.time_rolling('Series', 1000, 'int', 'min')
- 18.0±0.4ms 10.9±1ms 0.61 stat_ops.FrameOps.time_op('kurt', 'float', 0)
- 6.46±0.8ms 3.92±0.01ms 0.61 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'std')
- 6.25±0.8ms 3.78±0.04ms 0.61 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'std')
- 7.21±0.8ms 4.36±0.01ms 0.60 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'max')
- 14.0±1ms 8.42±0.07ms 0.60 series_methods.NanOps.time_func('median', 1000000, 'float64')
- 1.30±0.01ms 779±7μs 0.60 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation')
- 1.04±0.02ms 625±20μs 0.60 rolling.Engine.time_rolling_apply('DataFrame', 'int', <function Engine.<lambda> at 0x7f277d8de310>, 'numba')
- 1.29±0.01ms 774±4μs 0.60 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'direct')
- 7.07±0.03ms 4.24±0.01ms 0.60 rolling.Methods.time_rolling('Series', 1000, 'int', 'max')
- 7.92±0.2ms 4.73±0.7ms 0.60 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'std')
- 1.29±0ms 773±3μs 0.60 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'transformation')
- 210±0.8ms 125±0.7ms 0.60 groupby.DateAttributes.time_len_groupby_object
- 1.30±0.01ms 775±2μs 0.59 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct')
- 1.31±0.01ms 780±5μs 0.59 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct')
- 7.33±0.03ms 4.35±0.01ms 0.59 rolling.Methods.time_rolling('Series', 10, 'int', 'min')
- 1.27±0.01ms 755±4μs 0.59 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'transformation')
- 7.40±0.03ms 4.38±0.01ms 0.59 rolling.Methods.time_rolling('Series', 10, 'int', 'max')
- 2.32±0.01ms 1.37±0.01ms 0.59 series_methods.NanOps.time_func('sum', 1000000, 'float64')
- 6.35±0.01ms 3.76±0.06ms 0.59 rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
- 7.98±0.2ms 4.72±0.7ms 0.59 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'std')
- 1.32±0.01ms 778±4μs 0.59 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation')
- 1.06±0.03ms 624±60μs 0.59 rolling.Engine.time_rolling_apply('DataFrame', 'float', <function sum at 0x7f2790e97430>, 'numba')
- 29.1±1ms 17.1±2ms 0.59 hash_functions.UniqueAndFactorizeArange.time_unique(7)
- 1.03±0.05ms 605±10μs 0.59 rolling.Engine.time_rolling_apply('DataFrame', 'float', <function Engine.<lambda> at 0x7f277d8de310>, 'numba')
- 1.28±0.01ms 752±2μs 0.59 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'direct')
- 29.4±1ms 17.2±2ms 0.58 hash_functions.UniqueAndFactorizeArange.time_unique(12)
- 4.45±0.01ms 2.59±0.02ms 0.58 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'std')
- 4.44±0.02ms 2.58±0.03ms 0.58 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'std')
- 6.53±0.02ms 3.79±0.01ms 0.58 rolling.Methods.time_rolling('Series', 10, 'float', 'std')
- 5.45±0.01ms 3.15±0.1ms 0.58 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'count')
- 1.33±0.01ms 765±3μs 0.58 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct')
- 1.33±0.01ms 762±2μs 0.57 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation')
- 384±2μs 218±2μs 0.57 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 11)
- 4.36±0.01ms 2.45±0.03ms 0.56 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'std')
- 4.36±0.01ms 2.44±0.01ms 0.56 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'std')
- 6.32±0.03ms 3.52±0.1ms 0.56 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'kurt')
- 6.32±0.01ms 3.53±0.03ms 0.56 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'kurt')
- 7.07±0.05ms 3.94±0.01ms 0.56 rolling.Methods.time_rolling('Series', 10, 'int', 'std')
- 6.31±0.01ms 3.50±0.02ms 0.55 rolling.ExpandingMethods.time_expanding('Series', 'float', 'min')
- 4.99±0.7ms 2.77±0.01ms 0.55 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'mean')
- 4.95±0.7ms 2.74±0.03ms 0.55 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'mean')
- 6.39±0.01ms 3.50±0.03ms 0.55 rolling.ExpandingMethods.time_expanding('Series', 'float', 'max')
- 4.94±0.7ms 2.70±0.03ms 0.55 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'mean')
- 5.24±0.8ms 2.87±0.02ms 0.55 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'mean')
- 3.94±0.05ms 2.16±0.01ms 0.55 series_methods.ValueCounts.time_value_counts('float')
- 56.0±0.5ms 30.6±0.05ms 0.55 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 2000, 2)
- 5.29±0.8ms 2.88±0.01ms 0.55 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'mean')
- 1.12±0ms 611±5μs 0.54 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'transformation')
- 1.13±0ms 612±3μs 0.54 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct')
- 5.21±0.8ms 2.81±0.01ms 0.54 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'mean')
- 43.0±0.1ms 23.0±0.1ms 0.54 join_merge.Concat.time_concat_small_frames(0)
- 88.6±1ms 47.4±0.09ms 0.54 frame_methods.Equals.time_frame_nonunique_equal
- 6.80±0.01ms 3.63±0.02ms 0.53 rolling.ExpandingMethods.time_expanding('Series', 'int', 'min')
- 89.8±1ms 48.0±0.2ms 0.53 frame_methods.Equals.time_frame_nonunique_unequal
- 5.89±0.03ms 3.15±0.04ms 0.53 rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
- 956±4μs 507±2μs 0.53 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 13)
- 6.89±0.02ms 3.63±0.02ms 0.53 rolling.ExpandingMethods.time_expanding('Series', 'int', 'max')
- 1.14±0.01ms 595±6μs 0.52 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'transformation')
- 67.4±0.3ms 35.3±0.3ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'random_hits')
- 68.9±0.4ms 36.1±0.3ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 5, 'random_hits')
- 68.3±0.3ms 35.7±0.4ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 5, 'monotone_hits')
- 1.14±0.01ms 594±5μs 0.52 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct')
- 1.11±0ms 580±2μs 0.52 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'transformation')
- 68.3±0.4ms 35.5±0.4ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 5, 'random_misses')
- 68.0±0.3ms 35.3±0.3ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 5, 'monotone_misses')
- 66.7±0.4ms 34.6±0.4ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'random_misses')
- 4.66±0.7ms 2.42±0.01ms 0.52 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'float', 'sum')
- 67.2±0.3ms 34.8±0.3ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'monotone_hits')
- 66.9±0.2ms 34.6±0.3ms 0.52 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'monotone_misses')
- 6.41±0.01ms 3.31±0.06ms 0.52 rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
- 1.13±0.03ms 582±8μs 0.52 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct')
- 4.95±0.8ms 2.55±0.02ms 0.51 rolling.VariableWindowMethods.time_rolling('Series', '1h', 'int', 'sum')
- 4.65±0.7ms 2.39±0.01ms 0.51 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'float', 'sum')
- 4.71±0.7ms 2.42±0.01ms 0.51 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'float', 'sum')
- 1.08±0.01ms 556±2μs 0.51 groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'transformation')
- 1.09±0.01ms 557±2μs 0.51 groupby.GroupByMethods.time_dtype_as_field('int', 'cumprod', 'direct')
- 7.78±0.02ms 3.97±0.01ms 0.51 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'min')
- 7.86±0.02ms 4.01±0.02ms 0.51 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'max')
- 5.89±0.2μs 2.93±0.03μs 0.50 categoricals.Indexing.time_get_loc
- 338±2μs 167±0.9μs 0.50 hash_functions.IsinAlmostFullWithRandomInt.time_isin_outside(<class 'numpy.float64'>, 10)
- 5.30±0.03ms 2.62±0.01ms 0.49 rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
- 5.53±0.01ms 2.73±0.02ms 0.49 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'count')
- 1.59±0ms 782±2μs 0.49 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 8000)
- 5.29±0.8ms 2.57±0.01ms 0.49 rolling.VariableWindowMethods.time_rolling('Series', '50s', 'int', 'sum')
- 621±1ms 302±0.3ms 0.49 series_methods.IsInLongSeriesLookUpDominates.time_isin('float64', 1000, 'monotone_misses')
- 4.64±0.3ms 2.23±0.01ms 0.48 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'min')
- 4.70±0.3ms 2.25±0.01ms 0.48 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'max')
- 5.82±0.02ms 2.74±0.04ms 0.47 rolling.Methods.time_rolling('Series', 1000, 'int', 'mean')
- 5.01±0.01ms 2.31±0.02ms 0.46 rolling.Methods.time_rolling('Series', 1000, 'float', 'sum')
- 5.73±0.01ms 2.63±0.01ms 0.46 rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
- 11.4±0.8ms 5.20±0.02ms 0.46 stat_ops.FrameOps.time_op('mad', 'int', 1)
- 1.58±0s 713±5ms 0.45 series_methods.IsInLongSeriesValuesDominate.time_isin('float64', 'random')
- 5.65±0.8ms 2.53±0.01ms 0.45 rolling.VariableWindowMethods.time_rolling('Series', '1d', 'int', 'sum')
- 5.72±0.02ms 2.54±0.03ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'midpoint')
- 5.71±0.02ms 2.53±0.02ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'linear')
- 6.24±0.01ms 2.77±0.02ms 0.44 rolling.Methods.time_rolling('Series', 10, 'int', 'mean')
- 5.69±0.02ms 2.52±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
- 41.1±0.3ms 18.1±0.05ms 0.44 index_object.Range.time_iter_dec
- 5.74±0.02ms 2.52±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
- 6.08±0.03ms 2.67±0.2ms 0.44 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'nearest')
- 5.69±0.04ms 2.50±0.04ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'midpoint')
- 5.74±0.03ms 2.51±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
- 5.74±0.02ms 2.51±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
- 5.70±0.02ms 2.49±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
- 5.72±0.01ms 2.50±0.02ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
- 41.5±0.9ms 18.1±0.1ms 0.44 index_object.Range.time_iter_inc
- 5.58±0.02ms 2.44±0.02ms 0.44 rolling.Methods.time_rolling('Series', 1000, 'int', 'sum')
- 6.08±0.02ms 2.65±0.2ms 0.44 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'higher')
- 5.71±0.04ms 2.48±0.01ms 0.44 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
- 639±0.5ms 278±0.7ms 0.44 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'monotone_misses')
- 915±5μs 394±3μs 0.43 groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'transformation')
- 920±9μs 393±4μs 0.43 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'transformation')
- 913±3μs 389±4μs 0.43 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'transformation')
- 939±4μs 400±4μs 0.43 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct')
- 914±4μs 389±2μs 0.43 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct')
- 912±8μs 388±3μs 0.43 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct')
- 925±9μs 393±4μs 0.42 groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'direct')
- 5.45±0.02ms 2.31±0ms 0.42 rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
- 4.40±0.04ms 1.87±0.02ms 0.42 stat_ops.FrameOps.time_op('mean', 'float', 0)
- 913±3μs 387±2μs 0.42 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'transformation')
- 909±4μs 383±4μs 0.42 groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'direct')
- 915±4μs 385±3μs 0.42 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'direct')
- 912±3μs 383±3μs 0.42 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct')
- 4.38±0.04ms 1.83±0.02ms 0.42 stat_ops.FrameOps.time_op('sum', 'float', 0)
- 943±7μs 395±4μs 0.42 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation')
- 931±4μs 390±1μs 0.42 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation')
- 759±5μs 318±4μs 0.42 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 12)
- 907±9μs 379±3μs 0.42 groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'transformation')
- 936±8μs 391±3μs 0.42 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct')
- 16.8±0.9ms 7.03±0.3ms 0.42 stat_ops.FrameOps.time_op('mad', 'float', 1)
- 932±20μs 388±9μs 0.42 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'transformation')
- 934±20μs 389±3μs 0.42 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation')
- 935±2μs 388±2μs 0.42 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct')
- 1.50±0s 621±2ms 0.42 series_methods.IsInLongSeriesValuesDominate.time_isin('int64', 'random')
- 1.61±0s 667±5ms 0.41 series_methods.IsInLongSeriesValuesDominate.time_isin('float32', 'random')
- 3.24±0.2ms 1.34±0.01ms 0.41 rolling.EWMMethods.time_ewm_times('Series', 1000, 'int', 'std')
- 6.17±0.03ms 2.53±0.02ms 0.41 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
- 6.07±0.01ms 2.48±0.02ms 0.41 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'lower')
- 3.28±0.2ms 1.34±0.01ms 0.41 rolling.EWMMethods.time_ewm_times('Series', 10, 'int', 'std')
- 3.28±0.2ms 1.34±0ms 0.41 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'mean')
- 5.99±0.02ms 2.45±0.01ms 0.41 rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
- 14.0±0.9ms 5.70±1ms 0.41 stat_ops.FrameOps.time_op('mad', 'float', 0)
- 6.06±0.02ms 2.47±0.01ms 0.41 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'linear')
- 3.28±0.2ms 1.34±0.01ms 0.41 rolling.EWMMethods.time_ewm_times('Series', 10, 'int', 'mean')
- 6.06±0.02ms 2.47±0.01ms 0.41 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'midpoint')
- 5.83±0.03ms 2.37±0.04ms 0.41 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'mean')
- 3.30±0.2ms 1.34±0.01ms 0.41 rolling.EWMMethods.time_ewm_times('Series', 1000, 'int', 'mean')
- 6.16±0.03ms 2.49±0.01ms 0.40 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'lower')
- 6.16±0.01ms 2.49±0.01ms 0.40 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
- 6.17±0.03ms 2.49±0.02ms 0.40 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
- 5.82±0.01ms 2.35±0.01ms 0.40 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'mean')
- 949±8μs 383±4μs 0.40 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation')
- 6.17±0.1ms 2.48±0.01ms 0.40 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
- 950±5μs 382±2μs 0.40 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct')
- 52.1±0.09ms 20.8±0.06ms 0.40 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 8000, 0)
- 6.34±0.01ms 2.49±0.01ms 0.39 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'mean')
- 859±3μs 336±3μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'transformation')
- 3.14±0.01ms 1.23±0.02ms 0.39 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'mean')
- 3.13±0.01ms 1.22±0.01ms 0.39 rolling.EWMMethods.time_ewm_times('Series', 10, 'float', 'std')
- 6.34±0.01ms 2.47±0ms 0.39 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'mean')
- 866±5μs 337±2μs 0.39 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'transformation')
- 3.14±0.01ms 1.22±0ms 0.39 rolling.EWMMethods.time_ewm_times('Series', 1000, 'float', 'mean')
- 859±2μs 335±2μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'direct')
- 3.14±0.02ms 1.22±0.01ms 0.39 rolling.EWMMethods.time_ewm_times('Series', 10, 'float', 'mean')
- 866±3μs 336±3μs 0.39 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'direct')
- 3.14±0.02ms 1.22±0.01ms 0.39 rolling.EWMMethods.time_ewm_times('Series', 1000, 'float', 'std')
- 867±5μs 336±4μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'transformation')
- 870±4μs 337±1μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'transformation')
- 871±7μs 337±0.9μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'direct')
- 858±7μs 332±2μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'transformation')
- 865±3μs 334±3μs 0.39 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'direct')
- 3.15±0.02ms 1.22±0.01ms 0.39 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'mean')
- 5.23±0.05ms 2.01±0ms 0.38 stat_ops.FrameOps.time_op('mean', 'float', 1)
- 768±2μs 294±7μs 0.38 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), 'monotonic_incr')
- 873±2μs 334±2μs 0.38 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'direct')
- 3.51±0.2ms 1.33±0ms 0.38 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'mean')
- 1.02±0ms 388±1μs 0.38 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 1300)
- 1.70±0.01ms 640±3μs 0.38 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 2000)
- 1.70±0.01ms 638±6μs 0.37 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 2000)
- 5.50±0.01ms 2.06±0.04ms 0.37 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'float', 'sum')
- 5.51±0.02ms 2.06±0.01ms 0.37 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'float', 'sum')
- 5.06±0.02ms 1.89±0.03ms 0.37 stat_ops.FrameOps.time_op('sum', 'float', 1)
- 27.2±2ms 10.0±3ms 0.37 stat_ops.FrameOps.time_op('sem', 'float', 0)
- 5.01±0.01ms 1.84±0.03ms 0.37 rolling.ExpandingMethods.time_expanding('Series', 'float', 'mean')
- 13.2±0.8ms 4.83±1ms 0.37 stat_ops.FrameOps.time_op('var', 'float', 0)
- 6.22±0.03ms 2.27±0.01ms 0.37 rolling.ExpandingMethods.time_expanding('Series', 'float', 'std')
- 1.12±0ms 409±4μs 0.36 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 1300)
- 6.03±0.02ms 2.19±0.01ms 0.36 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'sum')
- 6.05±0.05ms 2.18±0.01ms 0.36 rolling.ForwardWindowMethods.time_rolling('Series', 1000, 'int', 'sum')
- 4.72±0.02ms 1.69±0.02ms 0.36 rolling.ExpandingMethods.time_expanding('Series', 'float', 'sum')
- 6.75±0.03ms 2.41±0.05ms 0.36 rolling.ExpandingMethods.time_expanding('Series', 'int', 'std')
- 5.59±0.01ms 1.96±0.02ms 0.35 rolling.ExpandingMethods.time_expanding('Series', 'int', 'mean')
- 26.2±2ms 9.02±0.3ms 0.34 frame_methods.Equals.time_frame_float_unequal
- 5.26±0.03ms 1.81±0.04ms 0.34 rolling.ExpandingMethods.time_expanding('Series', 'int', 'sum')
- 5.93±0.02ms 2.03±0.02ms 0.34 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 7000)
- 6.51±0.02ms 2.21±0.01ms 0.34 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'min')
- 772±6μs 260±2μs 0.34 series_methods.IsInDatetime64.time_isin_empty
- 89.2±0.2ms 30.0±0.04ms 0.34 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 1000, 2)
- 6.60±0.01ms 2.22±0.01ms 0.34 rolling.ForwardWindowMethods.time_rolling('Series', 10, 'int', 'max')
- 9.09±0.02ms 3.02±0.1ms 0.33 rolling.Methods.time_rolling('Series', 1000, 'float', 'count')
- 646±7μs 214±2μs 0.33 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 11)
- 8.99±0.01ms 2.97±0.1ms 0.33 rolling.Methods.time_rolling('Series', 1000, 'int', 'count')
- 812±40μs 268±3μs 0.33 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), 'monotonic_incr')
- 7.00±0.03ms 2.30±0.02ms 0.33 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 8000)
- 1.51±0s 488±1ms 0.32 series_methods.IsInLongSeriesValuesDominate.time_isin('int32', 'random')
- 9.52±0.04ms 3.06±0.1ms 0.32 rolling.Methods.time_rolling('Series', 10, 'float', 'count')
- 9.45±0.07ms 2.99±0.1ms 0.32 rolling.Methods.time_rolling('Series', 10, 'int', 'count')
- 6.10±0.1ms 1.92±0ms 0.32 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 7000)
- 98.7±0.3ms 30.5±0.1ms 0.31 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 2000, -2)
- 586±1ms 179±0.2ms 0.31 series_methods.IsInLongSeriesLookUpDominates.time_isin('int64', 1000, 'monotone_misses')
- 529±6μs 162±1μs 0.31 hash_functions.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.float64'>, 10)
- 7.26±0.08ms 2.19±0.01ms 0.30 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 8000)
- 14.3±0.2ms 4.25±2ms 0.30 stat_ops.FrameOps.time_op('std', 'float', 0)
- 1.02±0.01ms 297±1μs 0.29 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 2000)
- 8.86±0.2ms 2.56±0.02ms 0.29 arithmetic.Timeseries.time_timestamp_ops_diff(None)
- 16.3±0.05ms 4.67±0.04ms 0.29 indexing.InsertColumns.time_assign_list_like_with_setitem
- 939±8μs 263±3μs 0.28 series_methods.IsInDatetime64.time_isin_mismatched_dtype
- 78.2±3ms 21.5±0.4ms 0.28 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 70000)
- 601±1ms 160±0.1ms 0.27 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 1000, 'monotone_misses')
- 1.71±0.01ms 437±2μs 0.26 series_methods.Dir.time_dir_strings
- 109±0.9ms 27.0±0.3ms 0.25 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 80000)
- 495±0.9ms 120±0.8ms 0.24 series_methods.IsInLongSeriesLookUpDominates.time_isin('float64', 1000, 'monotone_hits')
- 8.81±0.06ms 2.09±0.1ms 0.24 rolling.ExpandingMethods.time_expanding('Series', 'float', 'count')
- 864±10μs 205±0.8μs 0.24 hash_functions.IsinWithArangeSorted.time_isin(<class 'numpy.float64'>, 1000)
- 8.98±0.2ms 2.04±0.1ms 0.23 rolling.ExpandingMethods.time_expanding('Series', 'int', 'count')
- 3.55±0.02s 800±3ms 0.23 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 900000)
- 3.56±0.01s 733±4ms 0.21 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 900000)
- 513±1ms 104±0.2ms 0.20 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'monotone_hits')
- 99.5±4ms 19.8±0.3ms 0.20 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 70000)
- 165±0.4ms 30.8±0.03ms 0.19 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 1000, -2)
- 140±10ms 25.6±0.2ms 0.18 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 80000)
- 3.14±0.02s 539±4ms 0.17 hash_functions.IsinWithRandomFloat.time_isin(<class 'object'>, 750000)
- 118±0.1ms 19.9±0.04ms 0.17 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 2000, 0)
- 388±0.7ms 64.5±0.8ms 0.17 series_methods.IsInLongSeriesValuesDominate.time_isin('int32', 'monotone')
- 1.89±0s 309±0.3ms 0.16 series_methods.IsInLongSeriesLookUpDominates.time_isin('float64', 1000, 'random_misses')
- 4.28±0.01ms 678±6μs 0.16 index_cached_properties.IndexCache.time_is_unique('CategoricalIndex')
- 3.97±0.1s 600±2ms 0.15 hash_functions.IsinWithRandomFloat.time_isin_outside(<class 'object'>, 750000)
- 1.90±0s 284±0.2ms 0.15 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'random_misses')
- 4.57±0.08ms 670±4μs 0.15 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period', 'nonunique_monotonic_inc')
- 465±1ms 59.1±0.2ms 0.13 series_methods.IsInLongSeriesLookUpDominates.time_isin('int64', 1000, 'monotone_hits')
- 184±6ms 23.1±1ms 0.13 hash_functions.UniqueAndFactorizeArange.time_factorize(11)
- 194±10ms 23.2±1ms 0.12 hash_functions.UniqueAndFactorizeArange.time_factorize(8)
- 483±0.6ms 57.1±0.1ms 0.12 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 1000, 'monotone_hits')
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- 178±6ms 17.1±2ms 0.10 hash_functions.UniqueAndFactorizeArange.time_unique(11)
- 200±0.1ms 19.0±0.1ms 0.10 hash_functions.IsinWithArange.time_isin(<class 'numpy.float64'>, 1000, 0)
- 588±6ms 54.0±0.5ms 0.09 categoricals.Indexing.time_reindex_missing
- 187±9ms 17.1±2ms 0.09 hash_functions.UniqueAndFactorizeArange.time_unique(8)
- 27.8±3ms 2.50±0.06ms 0.09 frame_methods.Equals.time_frame_float_equal
- 1.90±0s 171±0.3ms 0.09 series_methods.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'random_hits')
- 10.6±0.3ms 924±10μs 0.09 frame_methods.Shift.time_shift(1)
- 128±0.8ms 5.47±0.05ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'sum')
- 129±0.7ms 5.49±0.06ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'sum')
- 129±0.9ms 5.45±0.05ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'min')
- 128±0.6ms 5.44±0.04ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'mean')
- 129±0.9ms 5.45±0.02ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'min')
- 129±1ms 5.44±0.03ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'mean')
- 132±1ms 5.54±0.06ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'median')
- 132±0.8ms 5.48±0.03ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'median')
- 129±0.7ms 5.30±0.04ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'max')
- 128±0.8ms 5.27±0.02ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'max')
- 137±0.7ms 5.53±0.02ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'std')
- 137±1ms 5.48±0.05ms 0.04 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'std')
- 182±0.6ms 5.79±0.05ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'median')
- 180±0.9ms 5.71±0.09ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'sum')
- 180±1ms 5.70±0.1ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'sum')
- 1.87±0.02s 59.1±0.5ms 0.03 series_methods.IsInLongSeriesLookUpDominates.time_isin('int64', 1000, 'random_hits')
- 183±0.9ms 5.78±0.05ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'median')
- 180±1ms 5.68±0.04ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'mean')
- 181±1ms 5.68±0.04ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'min')
- 181±1ms 5.66±0.05ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'mean')
- 181±1ms 5.66±0.08ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'max')
- 181±1ms 5.64±0.02ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'max')
- 181±1ms 5.62±0.06ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'min')
- 189±0.7ms 5.83±0.07ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'std')
- 189±0.9ms 5.81±0.07ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'std')
- 1.88±0s 57.4±0.1ms 0.03 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 1000, 'random_hits')
- 1.86±0.06s 54.8±0.1ms 0.03 series_methods.IsInLongSeriesLookUpDominates.time_isin('int64', 1000, 'random_misses')
- 205±2ms 5.76±0.02ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'count')
- 205±2ms 5.74±0.04ms 0.03 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'count')
- 869±40ms 23.0±1ms 0.03 hash_functions.UniqueAndFactorizeArange.time_factorize(10)
- 84.1±0.5μs 2.05±0.02μs 0.02 period.Indexing.time_unique
- 1.88±0s 42.8±0.1ms 0.02 series_methods.IsInLongSeriesLookUpDominates.time_isin('int32', 1000, 'random_misses')
- 114±0.4ms 2.56±0.5ms 0.02 timeseries.ToDatetimeFromIntsFloats.time_nanosec_float64
- 103±1μs 2.06±0.01μs 0.02 timedelta.TimedeltaIndexing.time_unique
- 862±50ms 17.1±2ms 0.02 hash_functions.UniqueAndFactorizeArange.time_unique(10)
- 348±2ms 6.14±0.05ms 0.02 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'count')
- 347±2ms 6.09±0.02ms 0.02 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'count')
- 2.30±0.1s 36.8±0.7ms 0.02 hash_functions.Float64GroupIndex.time_groupby
- 1.64±0.09s 23.5±1ms 0.01 hash_functions.UniqueAndFactorizeArange.time_factorize(9)
- 171±0.6μs 2.32±0.01μs 0.01 timeseries.DatetimeIndex.time_unique('dst')
- 1.63±0.09s 17.2±2ms 0.01 hash_functions.UniqueAndFactorizeArange.time_unique(9)
- 4.31±0.04s 9.19±0.7ms 0.00 timeseries.ToDatetimeFromIntsFloats.time_sec_float64
- 3.34±0.01ms 3.16±0.02μs 0.00 timeseries.DatetimeIndex.time_unique('tz_naive')
- 3.40±0.03ms 3.15±0.01μs 0.00 timeseries.DatetimeIndex.time_unique('tz_local')
- 3.43±0.03ms 3.13±0.06μs 0.00 timeseries.DatetimeIndex.time_unique('tz_aware')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
</details> | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38592 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38592/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38592/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38592/events | https://github.com/pandas-dev/pandas/issues/38592 | 771,632,413 | MDU6SXNzdWU3NzE2MzI0MTM= | 38,592 | Performance regression in DataFrame reduction ops | {
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} | 13 | 2020-12-20T17:00:48Z | 2021-07-13T21:18:24Z | 2021-07-13T21:18:24Z | MEMBER | null | The `stat_ops.FrameOps` are generally showing a slowdown since a few days, see eg https://pandas.pydata.org/speed/pandas/#stat_ops.FrameOps.time_op?python=3.8&Cython=0.29.21&p-op='mean'&p-dtype='int'&p-axis=0&commits=8dbb593d-7043f8fa
The indicated range is https://github.com/pandas-dev/pandas/compare/8dbb593d...7043f8fa, but don't directly see which commit might be the cause cc @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38593 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38593/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38593/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38593/events | https://github.com/pandas-dev/pandas/pull/38593 | 771,687,453 | MDExOlB1bGxSZXF1ZXN0NTQzMTI0NDg2 | 38,593 | DOC: Add datatest package to list of third-party extension accessors. | {
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} | 6 | 2020-12-20T20:03:46Z | 2020-12-21T16:33:20Z | 2020-12-21T16:33:14Z | CONTRIBUTOR | null | This change adds the `datatest` package to the list of third-party extension accessors. Since `datatest` supports more than `pandas`, I wasn't sure if it was appropriate to add it to the **Data cleaning and validation** section, so I only added it to the accessors list for now.
Unfortunately, the "Classes" column was not wide enough--so this patch affects all rows in the table. This shouldn't happen again as the column is now wide enough to accommodate all of the classes that support extension accessors. | {
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Redefined in more appropriate locations. | {
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} | 2 | 2020-12-21T00:31:14Z | 2020-12-21T02:59:21Z | 2020-12-21T02:59:11Z | CONTRIBUTOR | null | A recent change in Sphinx v3.4.0 is breaking pandas' CI *Build documentation* step.
Until this is resolved, we can temporarily pin Sphinx to the previous version (3.3.1).
_Submitted in response to @jreback in https://github.com/pandas-dev/pandas/issues/38593#issuecomment-748690637_
------
Here's some info that might help for getting this sorted, long term:
Sphinx Changelog: Release 3.4.0 (released Dec 20, 2020)
https://www.sphinx-doc.org/en/master/changes.html#release-3-4-0-released-dec-20-2020
CI build logs from some passing and failing builds:
* A successful build using Sphinx 3.3.1 - https://pastebin.com/raw/jLwVAJsP
* A failing build using Sphinx 3.4.0 - https://pastebin.com/raw/bpHTm2DD
* A here's a diff between these two logs - https://pastebin.com/psJ5hqP8
While passing and failing builds both emit warnings, the following warnings are exclusive to the failing builds under Sphinx 3.4.0:
```
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessDay.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessDay.rst:18: WARNING: autosummary: failed to import offset
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessDay.rst:40: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessDay.rst:40: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessDay.rst:40: WARNING: autosummary: failed to import rollforward
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthBegin.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthBegin.rst:36: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthBegin.rst:36: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthBegin.rst:36: WARNING: autosummary: failed to import rollforward
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthEnd.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthEnd.rst:36: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthEnd.rst:36: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.BusinessMonthEnd.rst:36: WARNING: autosummary: failed to import rollforward
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:18: WARNING: autosummary: failed to import cbday_roll
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:18: WARNING: autosummary: failed to import month_roll
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:18: WARNING: autosummary: failed to import offset
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:43: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:43: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthBegin.rst:43: WARNING: autosummary: failed to import rollforward
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:18: WARNING: autosummary: failed to import cbday_roll
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:18: WARNING: autosummary: failed to import month_roll
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:18: WARNING: autosummary: failed to import offset
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:43: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:43: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessMonthEnd.rst:43: WARNING: autosummary: failed to import rollforward
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessDay.rst:18: WARNING: autosummary: failed to import base
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessDay.rst:18: WARNING: autosummary: failed to import offset
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessDay.rst:40: WARNING: autosummary: failed to import __call__
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessDay.rst:40: WARNING: autosummary: failed to import rollback
/home/runner/work/pandas/pandas/doc/source/docstring of pandas._libs.tslibs.offsets.CustomBusinessDay.rst:40: WARNING: autosummary: failed to import rollforward
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] | closed | false | null | [] | null | 11 | 2020-12-21T00:47:25Z | 2021-01-13T17:26:54Z | 2021-01-13T17:26:54Z | NONE | null | I spent multiple hours wondering why my dataframe was not fitting well into my tf model but it was the fact that the shape of the numpy array was not inferred and thus left ambiguous. I suggest, having a warning or a strict option for what shape is allowed within the column.
https://stackoverflow.com/questions/65383794/tensorflow-fails-to-interpret-what-is-stored-in-numpy-ndarray/65384804?noredirect=1#comment115597094_65384804 | {
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} | 2 | 2020-12-21T01:43:20Z | 2020-12-23T22:37:05Z | 2020-12-23T22:37:05Z | CONTRIBUTOR | null | once warnings are fixed in updated sphinx can unpin #38598 | {
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} | 2 | 2020-12-21T02:03:49Z | 2021-01-10T03:59:06Z | 2021-01-10T03:59:06Z | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
# create a dataframe with non-lexsorted multilevel columns
df = pd.DataFrame(np.arange(12).reshape(3,4), columns=pd.MultiIndex.from_tuples([('B', 1), ('B', 2), ('A', '3'), ('A', '4')]))
df_sorted_columns = df.sort_index(1) # a copy with sorted columns
df_sorted_columns.loc[:, 'A'] = np.zeros((3, 2)) # works fine
df.loc[:, 'A'] = np.zeros((3, 2)) # ValueError: Must have equal len keys and value when setting with an ndarray
df.loc[:, ['A']] = np.zeros((3, 2)) # works fine
df.loc[:, ('A', slice(None))] = np.zeros((3, 2)) # works fine
```
#### Problem description
Assigning a numpy array to a subset of columns using 'loc' behaves inconsistently with multi-level columns. The behavior depends on the lexsort state of the column index. If the columns are not lexsorted, the assignment fails as shown in the code example. Otherwise the assignment succeeds. This issue affects pandas 1.1.5 and 1.1.0 but not 1.0.5.
#### Expected Output
'loc' assignment is expected to succeed regardless of the columns' lexsort state
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
#### Traceback
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-4dac1c2ec043> in <module>
----> 1 df.loc[:, 'A'] = np.zeros((3, 2))
2 df
~\AppData\Local\Continuum\miniconda3\envs\pandas110\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
668
669 iloc = self if self.name == "iloc" else self.obj.iloc
--> 670 iloc._setitem_with_indexer(indexer, value)
671
672 def _validate_key(self, key, axis: int):
~\AppData\Local\Continuum\miniconda3\envs\pandas110\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value)
1727 value = np.array(value, dtype=object)
1728 if len(ilocs) != value.shape[1]:
-> 1729 raise ValueError(
1730 "Must have equal len keys and value "
1731 "when setting with an ndarray"
ValueError: Must have equal len keys and value when setting with an ndarray
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38602 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38602/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38602/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38602/events | https://github.com/pandas-dev/pandas/issues/38602 | 771,792,659 | MDU6SXNzdWU3NzE3OTI2NTk= | 38,602 | BUG: Selection with Multiindex columns may not honor requested column order | {
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} | 2 | 2020-12-21T02:46:27Z | 2020-12-22T18:21:57Z | 2020-12-22T18:21:56Z | NONE | null | - [ x] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
data = np.broadcast_to(np.arange(2)[None, :], (3, 2))
df_sorted = pd.DataFrame(data, columns=pd.MultiIndex.from_product([['level_0'], ['a', 'b']]), dtype=int)
df_unsorted = df_sorted.reindex(pd.MultiIndex.from_product([['level_0'], ['b', 'a']]), axis=1)
df_unsorted
# level_0
# b a
# 0 1 0
# 1 1 0
# 2 1 0
df_unsorted.loc[:, ('level_0', ['a', 'b'])]
# level_0
# b a
# 0 1 0
# 1 1 0
# 2 1 0
df_unsorted.loc[:, (['level_0'], ['a', 'b'])]
# level_0
# a b
# 0 0 1
# 1 0 1
# 2 0 1
```
#### Problem description
Advanced indexing with hierarchical index [guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-indexing-with-hierarchical-index) documents a use case when a tuple of lists is passed to the indexer.
When the first tuple element is a column label (not wrapped into a list), the indexer ignores the order of labels specified in the second list (it appears to use the same order in which the labels are present in the column index). Understandably, this usage is not documented (as far as I know) but appears logical. Yet such subtle behavior difference is confusing.
Please consider making this behavior consistent or raise an error when the container passed to 'loc' does not match the documented structure (list of tuples/tuple of lists).
Note: the described behavior is the same for pandas versions all the way back to 0.23.2 (I checked 0.23.2, 0.25.3, 1.0.5, 1.0.0 and 1.1.5)
#### Expected Output
both examples are expected to output
```python
# level_0
# a b
# 0 0 1
# 1 0 1
# 2 0 1
```
OR
df_unsorted.loc[:, ('level_0', ['a', 'b'])] is expected to raise an error (unexpected label structure)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.5
numpy : 1.19.2
pytz : 2020.4
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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} | 0 | 2020-12-21T02:59:51Z | 2020-12-21T12:17:19Z | 2020-12-21T12:17:18Z | NONE | null | Backport PR #38598: BUG: Temporarily pin the version of Sphinx to 3.3.1 in requirements. | {
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} | 0 | 2020-12-21T03:07:03Z | 2021-01-24T22:01:02Z | 2021-01-24T22:01:02Z | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Assigning a multi-column DataFrame to a single column does not raise an error and simply assigns the first column of the DataFrame to the column. Below is an example:
```python
df1 = pd.DataFrame({'a': [0, 1, 2, 3], 'b': [4, 5, 6, 7]})
df2 = pd.DataFrame({'c': [8, 9, 10, 11], 'd': [12, 13, 14, 15]})
df1['b'] = df2
```
This seems like one of the instances where not raising an error might ease some short-term pain but could mask underlying issues with a user's code. This behavior seems to have been around for a while. It occurs in master and as far back as 1.0.3. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/38605 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/38605/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/38605/comments | https://api.github.com/repos/pandas-dev/pandas/issues/38605/events | https://github.com/pandas-dev/pandas/pull/38605 | 771,821,345 | MDExOlB1bGxSZXF1ZXN0NTQzMjA2OTEy | 38,605 | REF: simplify CategoricalIndex.__new__ | {
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} | 2 | 2020-12-21T04:15:14Z | 2020-12-28T16:04:23Z | 2020-12-22T14:09:21Z | MEMBER | null | Part of a larger goal of making `Index.__new__` share code with `Series.__init__` and `pd.array` | {
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} | [] | closed | false | null | [] | null | 0 | 2020-12-21T05:39:36Z | 2020-12-21T21:59:32Z | 2020-12-21T21:59:32Z | CONTRIBUTOR | null | - [x] closes #38509
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2020-12-21T08:55:23Z | 2020-12-21T17:27:18Z | 2020-12-21T17:27:18Z | MEMBER | null | On released version, we see this behaviour:
```
In [1]: idx = pd.Index(['あ', b'a'], dtype='object')
In [2]: idx
Out[2]: Index(['あ', b'a'], dtype='object')
In [4]: idx.astype(str)
Out[4]: Index(['あ', 'a'], dtype='object')
```
So where the `bytes` object `b"a"` gets converted to the string `"a"`.
On master (since a few days), however, we now get:
```
In [7]: idx.astype(str)
Out[7]: Index(['あ', 'b'a''], dtype='object')
```
so where the `bytes` object gets converted to the string `"b'a'"`
Possibly due to https://github.com/pandas-dev/pandas/pull/38518 cc @jbrockmendel
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... | open | false | null | [] | null | 1 | 2020-12-21T11:36:46Z | 2021-06-06T17:45:53Z | 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.
---
**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
dti = pd.date_range('2018-01-01', periods=12, freq='H')
df=pd.DataFrame(range(12),dti)
df.index=df.index.tz_localize(datetime.now().astimezone().tzinfo.tzname(None))
df.plot();plt.show()
```
#### Problem description
While using the plotting functionality of pandas, with timezone aware data/index, the plotted values are aligned with the date values, but not corrected to the timezone time differences.
Often data is stored in UTC, however when retrieved, if timezone is set, the expectation is that data will be plotted on the right timming.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 7 | 2020-12-21T12:52:25Z | 2020-12-24T17:14:23Z | 2020-12-22T23:17:29Z | MEMBER | null | This pull request xref #30999 to remove bare `pytest.raises`. It doesn't close that issue as I have only addressed one file: `pandas/tests/io/pytables/test_store.py`. In that file there were 80 instances of bare `pytest.raises`.
`test_append_to_multiple_dropna_false` is marked as failing because it isn't raising the `ValueError` that it should. Because of this, I didn't know what the message should be, and reading through the code didn't help me figure it out. I wrote a draft error message and added a `TODO` (line 3790) and I hope that's the best way to handle it.
In `test_multiple_open_close` there were several assertions that `ClosedFileError` is raised with the same error message, with one in the middle that asserts that an `AttributeError` is raised with a different error message. I moved the one for the `AttributeError` to the end of the list (line 4229) to organize the assertions a little better and make clear that the `msg` parameter is the same for all the `ClosedFileError`s.
I did not add a whatsnew entry since it only changes the tests. Let me know if I should add one (and I am a bit unclear on how, i.e. what version this would end up in).
- [ ] xref #30999
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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We're not testing databases on py38 slow currently. Keep the issue open until we figure out what to do with its SQL tests.
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} | 5 | 2020-12-21T15:46:02Z | 2021-01-04T13:29:49Z | 2020-12-23T15:53:26Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2020-12-21T15:48:10Z | 2020-12-22T19:05:58Z | 2020-12-22T18:36:20Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-12-21T16:04:58Z | 2020-12-22T19:22:19Z | 2020-12-22T19:22:19Z | MEMBER | null | From the dask test suite:
```
left = pd.DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
right = pd.DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
pd.merge(left, right, how="right", left_index=True, right_on="x")
```
This fails on master / 1.2.0rc:
```
In [18]: pd.merge(left, right, how="right", left_index=True, right_on="x")
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-94bf9fb575b1> in <module>
2 right = pd.DataFrame({"x": [1, 1], "z": ["foo", "foo"]})
3
----> 4 pd.merge(left, right, how="right", left_index=True, right_on="x")
~/scipy/pandas/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
93 validate=validate,
94 )
---> 95 return op.get_result()
96
97
~/scipy/pandas/pandas/core/reshape/merge.py in get_result(self)
710 result = self._indicator_post_merge(result)
711
--> 712 self._maybe_add_join_keys(result, left_indexer, right_indexer)
713
714 self._maybe_restore_index_levels(result)
~/scipy/pandas/pandas/core/reshape/merge.py in _maybe_add_join_keys(self, result, left_indexer, right_indexer)
866 if mask_left.all():
867 key_col = rvals
--> 868 elif mask_right.all():
869 key_col = lvals
870 else:
AttributeError: 'bool' object has no attribute 'all'
``` | {
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} | 3 | 2020-12-21T17:02:52Z | 2020-12-21T23:07:37Z | 2020-12-21T22:29:14Z | MEMBER | null | - [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Splitting up #38577, following @ivanovmg's comment. First step moves some `test_hist...` tests from `test_series.py` to `test_hist_method.py`, after this the remainder of `hist` tests in `test_series.py` will be duplicates to be deleted in a followup pr. | {
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