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MDU6SXNzdWU2NDYzODE4MTM=
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ENH: Delta Lake file format support
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| 2020-06-26T16:40:07Z
| 2021-10-13T05:23:51Z
| 2020-09-04T14:43:33Z
|
NONE
| null |
#### Is your feature request related to a problem?
No
#### Describe the solution you'd like
I'd love it if Pandas could support Databricks' Delta Lake file format (https://github.com/delta-io/delta). It's a type of versioned parquet file format that supports updates/inserts/deletions.
#### API breaking implications
None that I'm aware of
#### Describe alternatives you've considered
N/A
#### Additional context
N/A
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BUG-Fix: AssertionError when slicing MultiIndex and setting value of …
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CONTRIBUTOR
| null |
- [x] closes #34870
- [x] tests added / passed (1 / 1)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU2NDY0MTA0OTA=
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BUG: to_json not allowing uploads to S3
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| 2020-06-26T17:32:30Z
| 2020-08-07T12:45:37Z
| 2020-08-07T12:45:37Z
|
NONE
| null |
This issue exists on the latest Pandas version (v1.0.5). `read_json` works perfectly with S3 paths.
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MDU6SXNzdWU2NDY0MTg5ODI=
| 35,020
|
BUG: pandas.tseries.offsets.BusinessDay bug when using offset only
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| 2020-06-26T17:49:13Z
| 2021-08-08T16:43:11Z
| null |
NONE
| null |
Here `n` is by default set to 1. This is causing bug if using offset only to calculate timedelta.
```
from pandas.tseries.offsets import BDay
td = BDay(offset=dt.timedelta(minutes=240))
> td.n
> 1
> td.apply(pd.to_datetime('2020-07-22 08:00:00'))
> Timestamp('2020-07-23 12:00:00')
```
where to increment the datetime by 240 mins, the date should NOT change.
Ref: https://github.com/pandas-dev/pandas/blob/b687cd4d9e520666a956a60849568a98dd00c672/pandas/tseries/offsets.py#L552
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CI: add validate_unwanted_patterns to known_third_parties
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|
MEMBER
| null |
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
By using `seed-isort-config`, the `known_third_party` setting is automatically populated and alphabetically sorted. This is a cleaner solution which doesn't require manually adding third parties to `setup.cfg`.
Furthermore, it catches an existing inconsistency: currently, `validate_docstrings` is set as a known third party, while `validate_unwanted_patterns` isn't. Neither should be, IMO.
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|
TST: add test to ensure that df.groupby() returns the missing categories when grouping on 2 pd.Categoricals
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| 2
| 2020-06-26T21:24:55Z
| 2020-07-08T21:42:32Z
| 2020-07-08T15:35:26Z
|
MEMBER
| null |
- [x] closes #23865
- [x] closes #27075
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
1) _New Tests Addeded_
#23865 #27075 both reported that when df.groupby was called and `by` was set to more than one pd.Categorical column, that any missing categories were not returned, even when observed=False. This issue was fixed in #29690. This Pull Request adds tests to make sure that this correct behaviour is enforced by tests.
2) _New Bug Found_
Testing did reveal one further issue: DataFrameGroupBy.count() returns NaN for missing Categories, when it should return a count of 0. SeriesGroupBy.count() does return 0, which is the expected behaviour. I have raised an issue for this bug (#35028 ) and marked the test with an xfail. When the bug is fixed, the xfail will cause the tests to fail, and the xfail can be removed.
3) _Existing Test Changed as it had the Wrong Expected Result_
A similar issue was reported for .sum() in #31422: missing categories return a sum of NaN when they should return a sum of 0. There was a mistake on the existing test for SeriesGroupBy.sum(), as it said the expected output was NaN (see below) when it should have been 0.
https://github.com/pandas-dev/pandas/blob/0159cba6eb14983ab7eaf38ff138c3c397a6fe3b/pandas/tests/groupby/test_categorical.py#L1315
I have changed this so that the expected output is 0 (this is inline with the comment here: https://github.com/pandas-dev/pandas/issues/31422#issuecomment-579826193 ) and marked the tests for .sum() with xfail. When the bug is addressed, the xfail will cause the tests to fail, and the xfails can be removed.
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ASV: asvs for DataFrame.values, to_numpy
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| 15
| 2020-06-26T22:23:25Z
| 2020-09-19T19:55:09Z
| 2020-09-19T19:55:01Z
|
MEMBER
| null |
xref #34999
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TST: rename fixtures named 'indices' to 'index'
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| 1
| 2020-06-26T22:28:27Z
| 2020-06-27T00:16:04Z
| 2020-06-26T23:53:01Z
|
CONTRIBUTOR
| null |
The singular name makes more sense than the plural.
|
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PERF: conversion to M8[ns]/datetime64[ns] is slow from "M8[s]" and other time resolutions
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[] | null | 20
| 2020-06-27T01:37:47Z
| 2021-08-08T16:44:12Z
| 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.
---
import pandas as pd, numpy as np, time
x = np.repeat(int(time.time()), 100_000_000)
"these are slow"
pd.Series(x.astype("M8[s]"))
pd.Series(x).astype("M8[s]")
"these are an order of magnitude faster"
pd.Series(x.astype("M8[s]").astype("M8[ns]"))
pd.Series((x*1e9).astype("M8[ns]")
#### Problem description
As long as there is no value translation involved converting/creating a datetime64[ns] array is fast as expected. But it is an order of magnitude slower when there is a magnitude conversion. Converting from float64 is only slightly slower than int64.
I've tested with 0.25.3, 0.25.32 and 1.0.5. And they all seem roughly consistent for those examples. The older versions have added behavior that pd.Series((x*1e9).astype("M8[ns]"), dtype="datetime64[ns]") is also slow, but fast without the dtype arguement.
In all cases the result is the same series of datetime64[ns] values.
#### Output of ``pd.show_versions()``
python : 3.8.3.final.0
python-bits : 64
OS : Linux
machine : x86_64
processor : x86_64
byteorder : little
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
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MDExOlB1bGxSZXF1ZXN0NDQwODQ3NjMw
| 35,026
|
DOC: correction to "size" in the plotting.bootstrap_plot docstring
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| 1
| 2020-06-27T02:20:57Z
| 2020-06-29T21:38:27Z
| 2020-06-29T21:38:21Z
|
CONTRIBUTOR
| null |
The documentation for plotting.bootstrap_plot says that the size argument must be greater than or equal to the length of the series. Bootstrapping typically uses subsamples of a size less than or equal to the size of the data set, so I corrected the docs accordingly. Note that making size greater than the length of the series throws the following ValueError: Sample larger than population or is negative
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MDExOlB1bGxSZXF1ZXN0NDQwODY0Mjg4
| 35,027
|
PERF: pd.to_datetime, unit='s' much slower for float64 than for int64
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| 12
| 2020-06-27T05:33:39Z
| 2020-09-19T20:27:06Z
| 2020-09-19T20:24:19Z
|
MEMBER
| null |
- [x] closes #20445
- [x] tests passed
- [x] new benchmarks added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As per discussion in #20445 this PR addresses performance of `to_datetime` on a uniform type array of floats. The aim is to get a speed-up by implementing `astype`-ing, and avoid looping, for floats.
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MDU6SXNzdWU2NDY2Njg3NDU=
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|
BUG: df.groupby().count() returns NaN instead of Zero
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| 6
| 2020-06-27T12:04:41Z
| 2020-08-07T15:21:12Z
| 2020-08-07T15:21:12Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{
"cat_1": pd.Categorical(list("AABB"), categories=list("ABC")),
"cat_2": pd.Categorical(list("1111"), categories=list("12")),
"value": [0.1, 0.1, 0.1, 0.1],
}
)
# SeriesGroupBy on one pd.Categorical: unobserved categories have a count of 0
srg_grp = df.groupby(['cat_1'], observed=False)['value']
print(srg_grp.count())
# SeriesGroupBy on two pd.Categorical: unobserved categories have a count of 0
srs_grp = df.groupby(['cat_1', 'cat_2'], observed=False)['value']
print(srs_grp.count())
# DataFrameGroupBy on one pd.Categorical: unobserved categories have a count of 0
df_grp = df.groupby(['cat_1'], observed=False)
print(df_grp.count())
# DataFrameGroupBy on two pd.Categorical: unobserved categories have a count of NaN
df_grp = df.groupby(['cat_1', 'cat_2'], observed=False)
print(df_grp.count())
```
#### Problem description
When grouping by multiple pd.Categorical columns, DataFrameGroupBy.count() returns NaN for missing categories, and the dtype is float.
A similar problem is reported for .sum() in #31422
#### Expected Output
.count() should return zero for missing categories with a dtype of int.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 248c191478aabacffec47105c3fe6caf22b0dcc1
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.1.0.dev0+1973.g248c19147.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.18.0
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : 0.6.2
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.2
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
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DEPR: Deprecate using `xlrd` engine for read_excel
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| 52
| 2020-06-27T14:26:43Z
| 2020-12-01T23:35:55Z
| 2020-12-01T23:27:29Z
|
CONTRIBUTOR
| null |
- [x] closes #28547
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is MR #29375 but rebased to master
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On traces of #33982
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NONE
| null |
To pay attention on https://github.com/pandas-dev/pandas/pull/33982#issuecomment-643246205. Sorry, I don't know another way now.
/cc @mproszewska , @jreback
Thanks.
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MDU6SXNzdWU2NDY3MjMzNTc=
| 35,031
|
DOC: text in pandas.Series.dt.day refers to month instead of day
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NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.day.html
#### Documentation problem
The text here is a duplicate of https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.month.html (`The month as January=1, December=12.`), when it should probably be something else.
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API: implement __array_function__ for ExtensionArray
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MEMBER
| null |
xref #26380
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Bug 31783
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CONTRIBUTOR
| null |
- [ ] closes #31783
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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PERF: avoid duplicate is_single_block check
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| 2020-06-29T18:12:50Z
| 2020-06-29T17:52:45Z
|
MEMBER
| null |
Small bump, make up some of the ground lost in #34999.
```
In [2]: df = pd.DataFrame([1])
In [3]: %timeit df.values
3.65 µs ± 75.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
3.84 µs ± 139 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
```
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MDExOlB1bGxSZXF1ZXN0NDQwOTYyNzg3
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BUG: fix read_excel error for header=None and index_col as list #31783
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| 4
| 2020-06-27T21:11:10Z
| 2020-07-10T13:54:04Z
| 2020-07-10T13:54:00Z
|
CONTRIBUTOR
| null |
- [x] closes #31783
- [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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MDExOlB1bGxSZXF1ZXN0NDQwOTgwMDIw
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PERF: put some Timetamp methods in _Timestamp
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| 0
| 2020-06-28T00:28:32Z
| 2020-06-30T13:49:40Z
| 2020-06-30T12:52:23Z
|
MEMBER
| null |
```
In [2]: ts = pd.Timestamp.now()
In [3]: %timeit ts.is_month_start
58.3 ns ± 1.5 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- PR
82.5 ns ± 2.16 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each) # <-- master
```
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| 35,037
|
ERR: could pandas suggest to use bitwise operators to filter instead of `and` and `or`, as on Kaggle?
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[] | null | 3
| 2020-06-28T08:30:02Z
| 2021-08-08T16:44:53Z
| null |
MEMBER
| null |
Currently, if one writes
```python
>>> import pandas as pd
>>> df = pd.DataFrame([1,2,3])
>>> df
0
0 1
1 2
2 3
>>> df[df[0].ge(0) and df[0].lt(0)]
```
then the traceback shows
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/marco/pandas-dev/pandas/core/generic.py", line 1326, in __nonzero__
raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
```
I've noticed that [Kaggle](https://www.kaggle.com) add their own warning when someone tries to do this:
```
To combine filtering conditions in Pandas, use bitwise operators ('&' and '|') not pure Python ones ('and' and 'or')
```
See below:
----
Is this something that could be in pandas as well? Sure, there's no way of reading users' minds - perhaps they reached this error by some other means - but would the error message be clearer if there was an extra line reading
```
If you were trying to combine filtering conditions in Pandas, use bitwise operators ('&' and '|') instead of pure Python ones ('and' and 'or')
```
?
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MDExOlB1bGxSZXF1ZXN0NDQxMDM5NjQ0
| 35,038
|
BUG: DataFrame.append with empty DataFrame and Series with tz-aware datetime value allocated object column
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| 2020-09-04T13:26:59Z
| 2020-07-16T22:50:39Z
|
MEMBER
| null |
broken off #35032
This PR fixes situation where appending a series consecutively to a empty dataframe produces different result from appending the series in one step.
on master
```
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1974.g0159cba6e'
>>>
>>> import dateutil
>>>
>>> date = pd.Timestamp("2018-10-24 07:30:00", tz=dateutil.tz.tzutc())
>>> date
Timestamp('2018-10-24 07:30:00+0000', tz='tzutc()')
>>>
>>> s = pd.Series({"date": date, "a": 1.0, "b": 2.0})
>>> s
date 2018-10-24 07:30:00+00:00
a 1
b 2
dtype: object
>>>
>>> df = pd.DataFrame(columns=["c", "d"])
>>> df
Empty DataFrame
Columns: [c, d]
Index: []
>>>
>>> result_a = df.append(s, ignore_index=True)
>>> result_a
c d a b date
0 NaN NaN 1.0 2.0 2018-10-24 07:30:00+00:00
>>>
>>> result_a.dtypes
c object
d object
a float64
b float64
date object
dtype: object
>>>
>>> result_b = result_a.append(s, ignore_index=True)
>>> result_b
c d a b date
0 NaN NaN 1.0 2.0 2018-10-24 07:30:00+00:00
1 NaN NaN 1.0 2.0 2018-10-24 07:30:00+00:00
>>>
>>> result_b.dtypes
c object
d object
a float64
b float64
date object
dtype: object
>>>
>>> result = df.append([s, s], ignore_index=True)
>>> result
c d date a b
0 NaN NaN 2018-10-24 07:30:00+00:00 1.0 2.0
1 NaN NaN 2018-10-24 07:30:00+00:00 1.0 2.0
>>>
>>> result.dtypes
c object
d object
date datetime64[ns, tzutc()]
a float64
b float64
dtype: object
>>>
```
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MDExOlB1bGxSZXF1ZXN0NDQxMDU2NzU4
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Fix issue #29837: added test case for aggregation with isnan
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| 4
| 2020-06-28T13:26:37Z
| 2020-10-13T17:32:19Z
| 2020-06-29T07:26:53Z
|
CONTRIBUTOR
| null |
- [x] closes #29837
- [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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Doc: Updated aggregate docstring
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| 2020-06-28T14:03:08Z
| 2020-06-28T14:04:00Z
| 2020-06-28T14:04:00Z
|
CONTRIBUTOR
| null |
Link to current `Series.agg`: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.agg.html
Original question asked in gitter:
Does `pd.Series.agg` with `func` parameter set to `'median'` uses `np.nanmedian`(Not only `median` including `mean`, `mode`)?
```python
s= pd.Series([np.nan, np.nan,1,1,1])
s.agg('median')
# 1
s.agg(np.median)
# 1
np.median(s.to_numpy())
# nan
np.nanmedian(s.to_numpy())
# 1
```
Whenever `NaN` is present does it fallback to using `np.nanmedian`?
---
Reply from @MarcoGorelli
if you look at `pandas/core/base.py` you'll see
```
np.median: "median",
np.nanmedian: "median",
```
in `_cython_table`. So, both resolve to the same internal cython function.
---
IMO it's better to mention this in the docs under `Note:` section.
Under note section saying:
*Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc. resolve to their corresponding internal cython function.*
<details>
```
################################################################################
######################## Docstring (pandas.Series.agg) ########################
################################################################################
Aggregate using one or more operations over the specified axis.
.. versionadded:: 0.20.0
Parameters
----------
func : function, str, list or dict
Function to use for aggregating the data. If a function, must either
work when passed a Series or when passed to Series.apply.
Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
- dict of axis labels -> functions, function names or list of such.
axis : {0 or 'index'}
Parameter needed for compatibility with DataFrame.
*args
Positional arguments to pass to `func`.
**kwargs
Keyword arguments to pass to `func`.
Returns
-------
scalar, Series or DataFrame
The return can be:
* scalar : when Series.agg is called with single function
* Series : when DataFrame.agg is called with a single function
* DataFrame : when DataFrame.agg is called with several functions
Return scalar, Series or DataFrame.
See Also
--------
Series.apply : Invoke function on a Series.
Series.transform : Transform function producing a Series with like indexes.
Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc.
resolve to their corresponding internal cython function.
A passed user-defined-function will be passed a Series for evaluation.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.agg('min')
1
>>> s.agg(['min', 'max'])
min 1
max 4
dtype: int64
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.agg" correct. :)
```
</details>
[Image to updated docs of `Series.agg`](https://i.stack.imgur.com/yx7N4.png)
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|
CI: Linux py37_np_dev Concatenation operation is not implemented for NumPy arrays, use np.concatenate() instead.
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| 2020-06-28T14:17:48Z
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MEMBER
| null |
https://github.com/numpy/numpy/commit/8ac7ac94ab868473e2276902be656946745c641f
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=38318&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=4d05ed0e-1ed3-5bff-dd63-1e957f2766a9
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DOC: Updated aggregate docstring
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| 2020-08-24T15:36:18Z
| 2020-08-24T15:36:18Z
|
CONTRIBUTOR
| null |
Link to current `Series.agg`: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.agg.html
Original question asked in gitter:
Does `pd.Series.agg` with `func` parameter set to `'median'` uses `np.nanmedian`(Not only `median` including `mean`, `mode`)?
```python
s= pd.Series([np.nan, np.nan,1,1,1])
s.agg('median')
# 1
s.agg(np.median)
# 1
np.median(s.to_numpy())
# nan
np.nanmedian(s.to_numpy())
# 1
```
Whenever `NaN` is present does it fallback to using `np.nanmedian`?
---
Reply from @MarcoGorelli
if you look at `pandas/core/base.py` you'll see
```
np.median: "median",
np.nanmedian: "median",
```
in `_cython_table`. So, both resolve to the same internal cython function.
---
IMO it's better to mention this in the docs under `Note:` section.
Under note section saying:
*Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc. resolve to their corresponding internal cython function.*
#### Output of `python validate_docstrings.py pandas.Series.agg`
<details>
```
################################################################################
######################## Docstring (pandas.Series.agg) ########################
################################################################################
Aggregate using one or more operations over the specified axis.
.. versionadded:: 0.20.0
Parameters
----------
func : function, str, list or dict
Function to use for aggregating the data. If a function, must either
work when passed a Series or when passed to Series.apply.
Accepted combinations are:
- function
- string function name
- list of functions and/or function names, e.g. ``[np.sum, 'mean']``
- dict of axis labels -> functions, function names or list of such.
axis : {0 or 'index'}
Parameter needed for compatibility with DataFrame.
*args
Positional arguments to pass to `func`.
**kwargs
Keyword arguments to pass to `func`.
Returns
-------
scalar, Series or DataFrame
The return can be:
* scalar : when Series.agg is called with single function
* Series : when DataFrame.agg is called with a single function
* DataFrame : when DataFrame.agg is called with several functions
Return scalar, Series or DataFrame.
See Also
--------
Series.apply : Invoke function on a Series.
Series.transform : Transform function producing a Series with like indexes.
Notes
-----
`agg` is an alias for `aggregate`. Use the alias.
Some NumPy functions such as `np.mean`, `np.nanmean`, `np.median` etc.
resolve to their corresponding internal cython function.
A passed user-defined-function will be passed a Series for evaluation.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.agg('min')
1
>>> s.agg(['min', 'max'])
min 1
max 4
dtype: int64
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.Series.agg" correct. :)
```
</details>
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MDU6SXNzdWU2NDY5NDEwNzU=
| 35,043
|
ENH: Add some features like R data.table's fread() function.
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| 2020-06-28T15:01:59Z
| 2021-08-08T16:47:13Z
| 2021-08-08T16:47:13Z
|
NONE
| null |
#### Is your feature request related to a problem?
The fread() function in R library data.table has many features which are very usefull.
#### Describe the solution you'd like
### 1. An argument treat tables with untidy columns
If we get a CSV file with shorter header line than table content, just like:
>A,B,C
>a,1,123,5,6
>b,123,3,3
>c,123,555,123
By using "fill = TRUE", we can easily read it into R:
```
library(data.table)
df1 <- fread("test.csv", sep = ",", header = TRUE, fill = TRUE)
print.data.frame(df1)
A B C V4 V5
1 a 1 123 5 6
2 b 123 3 3 NA
3 c 123 555 123 NA
```
### 2. An argument skip to one line with particular pattern
If we get an VCF file just like this:
>##fileformat=VCFv4.2
>##FILTER=<ID=Bias,Description="Strand Bias">
>##FILTER=<ID=Cluster0bp,Description="Two somatic variants are within 0 bp">
>##FILTER=<ID=DIFF0.05,Description="Non-somatic or LOH and allele frequency difference < 0.05">
>##FILTER=<ID=InDelLikely,Description="Likely Indels are not considered somatic">
>#CHROM POS ID REF ALT QUAL FILTER INFO
>chr2 79314684 . G C 154.0 PASS AC=3;AF=0.273;AN=11;DP=146;SOMATIC
>chr2 119750833 . G T 165.0 PASS AC=3;AF=0.273;AN=11;DP=149;SOMATIC
>chr2 203964409 . G A 89.0 PASS AC=2;AF=0.250;AN=8;DP=137
By using "skip = "#CHROM"", fread() can search the whole file, and jump to the first line with the pattern "#CHROM",
and read it into R:
```
library(data.table)
df2 <- fread("test.vcf", skip = "#CHROM")
print.data.frame(df2)
#CHROM POS ID REF ALT QUAL FILTER INFO
1 chr2 79314684 . G C 154 PASS AC=3;AF=0.273;AN=11;DP=146;SOMATIC
2 chr2 119750833 . G T 165 PASS AC=3;AF=0.273;AN=11;DP=149;SOMATIC
3 chr2 203964409 . G A 89 PASS AC=2;AF=0.250;AN=8;DP=137
```
#### API breaking implications
[this should provide a description of how this feature will affect the API]
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
# Your code here, if applicable
```
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| 2020-06-29T07:28:58Z
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|
MEMBER
| null |
- [ ] closes #35041
I don't think the message raised by Numpy is appropriate since AFAICT we are getting the message when using operator.add and not operator.concat
maybe better to skip tests and discuss with Numpy
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CLN: assorted tslibs cleanups, annotations
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BUG/API Should pd.NaT < "foo" raise?
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| 1
| 2020-06-29T00:43:57Z
| 2020-08-06T22:06:47Z
| 2020-08-06T22:06:47Z
|
MEMBER
| null |
If we treat NaT as a datetime, we would expect pd.NaT == "foo" to return False, != to return True, and for the inequalities to raise:
```
>>> np.datetime64("NaT", "ns") < "foo"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'numpy.ndarray' and 'str'
>>> np.nan < "foo"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'float' and 'str'
>>> pd.NaT < "foo"
False
```
It isn't obvious to me that the existing behavior is intentional. If we change this to raise, we can simplify `NaT.__richcmp__` a bit.
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| 2
| 2020-06-29T01:39:29Z
| 2020-07-08T12:40:46Z
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|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#frame_methods.Apply.time_apply_ref_by_name?commits=91802a9ae400830f9eaadd395f6a9b40cdd92ee5
https://github.com/pandas-dev/pandas/commit/91802a9ae400830f9eaadd395f6a9b40cdd92ee5 (cc @jbrockmendel)
Also affects other benchmarks that probably use apply: https://pandas.pydata.org/speed/pandas/index.html#frame_methods.Nunique.time_frame_nunique?commits=91802a9ae400830f9eaadd395f6a9b40cdd92ee5.
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Performance regression in arithmetic.ApplyIndex.time_apply_index
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| 2
| 2020-06-29T01:42:56Z
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|
CONTRIBUTOR
| null |
https://pandas.pydata.org/speed/pandas/index.html#arithmetic.ApplyIndex.time_apply_index?p-offset=%3CMonthBegin%3E&commits=55adcb8688582e3a3be66b1036d3604327ae5071-b05e6b1402124a062608b97ca02af0cfb13568d1, https://github.com/pandas-dev/pandas/compare/55adcb8688582e3a3be66b1036d3604327ae5071...b05e6b1402124a062608b97ca02af0cfb13568d1 (30 commits in there).
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CONTRIBUTOR
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https://pandas.pydata.org/speed/pandas/index.html#frame_methods.Quantile.time_frame_quantile?p-axis=0&commits=ce292485c781ec0df0a765869848b97ac3c88a54
https://github.com/pandas-dev/pandas/commit/ce292485c781ec0df0a765869848b97ac3c88a54 (an f-string PR). Most likely the formatting is somewhat expensive.
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Performance regression in stat_ops.FrameMultiIndexOps.time_op
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| 2020-11-25T21:37:30Z
| null |
CONTRIBUTOR
| null | ERROR: type should be string, got "https://pandas.pydata.org/speed/pandas/index.html#stat_ops.FrameMultiIndexOps.time_op?p-level=1&p-op=%27std%27&commits=c9144ca54dcc924995acae3d9dcb890a5802d7c0\r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nlevels = [np.arange(10), np.arange(100), np.arange(100)]\r\ncodes = [\r\n np.arange(10).repeat(10000),\r\n np.tile(np.arange(100).repeat(100), 10),\r\n np.tile(np.tile(np.arange(100), 100), 10),\r\n]\r\nindex = pd.MultiIndex(levels=levels, codes=codes)\r\ndf = pd.DataFrame(np.random.randn(len(index), 4), index=index)\r\n%timeit df.std(level=1)\r\n```\r\n\r\nPoints to https://github.com/pandas-dev/pandas/pull/34372 (cc @rhshadrach), but there was an earlier slowdown.\r\n"
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BUG: Segmentation faults in pd.read_csv for large files
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| 2020-06-29T02:51:20Z
| 2021-08-08T16:49:12Z
| 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 numpy as np
import pandas as pd
def from_file(filepath):
name_hints = []
name_hints.extend(['m1','m2','a0','e0'])
name_hints.extend(['weight','evolution_age','rejuvenation_age'])
df = pd.read_csv(filepath,
nrows=None,
names=name_hints,
sep=r'\s+',
engine='python',
dtype=np.float64)
return df
df = from_file('data/big_data_set.dat.gz')
```
#### Problem description
When loading sufficiently big files (mine are >800 MB on average), `pd.read_csv` fails with a Bus Error. I am loading in `.gz` files as I believe pandas is able to decompress these automatically.
I cannot reproduce this with a smaller dataset (have tried on files with ~364,000 lines). I have run this code with `gdb` and obtain the following stack trace:
<details>
Thread 0x00002aaab53d0700 (most recent call first):
File "/opt/nesi/CS400_centos7_bdw/Python/3.8.2-gimkl-2020a/lib/python3.8/threading.py", line 306 in wait
File "/opt/nesi/CS400_centos7_bdw/Python/3.8.2-gimkl-2020a/lib/python3.8/threading.py", line 558 in wait
File "/home/sric560/.local/lib/python3.8/site-packages/tqdm/_monitor.py", line 69 in run
File "/opt/nesi/CS400_centos7_bdw/Python/3.8.2-gimkl-2020a/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "/opt/nesi/CS400_centos7_bdw/Python/3.8.2-gimkl-2020a/lib/python3.8/threading.py", line 890 in _bootstrap
Current thread 0x00002aaaaaaea9c0 (most recent call first):
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 1763 in _infer_types
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 1708 in _convert_to_ndarrays
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 2528 in _convert_data
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 2464 in read
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 1133 in read
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 454 in _read
File "/home/sric560/.local/lib/python3.8/site-packages/pandas/io/parsers.py", line 676 in parser_f
File "/scale_wlg_persistent/filesets/home/sric560/masters/takahe/takahe/load.py", line 14 in from_file
File "/scale_wlg_persistent/filesets/home/sric560/masters/takahe/takahe/load.py", line 35 in from_directory
/var/spool/slurm/job13442044/slurm_script: line 10: 67748 Segmentation fault (core dumped) gdb -ex r --args python BigData.py
File "BigData.py", line 11 in <module>
</details>
#### Expected Output
I would expect the data to be read successfully.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.2.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.0.5
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 45.2.0
Cython : 0.29.15
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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CLN: move categorical tests from test_aggregate to test_categorical
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/35039#discussion_r446948974
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Performance regression in replace.Convert.time_replace
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| 2020-06-29T14:38:01Z
| 2020-07-07T13:37:52Z
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CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/pull/34999 caused a 2x slowdown in replace.Convert.time_replace (https://pandas.pydata.org/speed/pandas/index.html#replace.Convert.time_replace?p-constructor=%27Series%27&p-replace_data=%27Timestamp%27&commits=dbfbef7eaea7f23a42d9a65344b5745fc055f5b4)
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POC: make ccalendar.pyx valid python
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| 2021-11-20T23:22:37Z
| 2020-07-01T21:49:11Z
|
MEMBER
| null |
Proof of concept for making some of our cython files into valid python\*, which would allow us to lint and maybe (cc @simonjayhawkins thoughts?) use mypy
\* strictly speaking, we need to do a find-and-replace of "cimport" -> "import" to get all the way to valid python.
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TST: Adds CategoricalIndex DataFrame from_records test (GH32805)
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CONTRIBUTOR
| null |
- [x] closes #32805
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
The test checks to make sure `from_records` loads a `DataFrame` with a `CategorigalIndex` as shown in #32805.
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DOC: Alligned docstring for ignore_index in append
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ENH: Distinguish widths argument in read_fwf
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BUG: to_csv doesn't support file handles from ZipFiles
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| 3
| 2020-06-30T02:21:10Z
| 2020-08-07T11:53:10Z
| 2020-08-07T11:53:10Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
from zipfile import ZipFile
import numpy as np
import pandas as pd
with ZipFile("plots.zip", mode="w") as file:
# write some matplotlib plots into the zip file
# write CSV into zip file
data = pd.DataFrame(np.random.rand(10, 3))
with file.open("data.csv", mode="w") as csv_file:
data.to_csv(csv_file)
```
Last part of the stack trace:
```
~/miniforge3/envs/panama/lib/python3.8/site-packages/pandas/io/formats/csvs.py in _save_header(self)
278 if not has_mi_columns or has_aliases:
279 encoded_labels += list(write_cols)
--> 280 writer.writerow(encoded_labels)
281 else:
282 # write out the mi
~/miniforge3/envs/panama/lib/python3.8/zipfile.py in write(self, data)
1134 nbytes = len(data)
1135 self._file_size += nbytes
-> 1136 self._crc = crc32(data, self._crc)
1137 if self._compressor:
1138 data = self._compressor.compress(data)
TypeError: a bytes-like object is required, not 'str'
```
#### Problem description
I know that `to_csv` supports compression but I want to write a DataFrame into a zip file with multiple other files. I'm not sure whether the above error is an error in pandas or a missing feature/bug in zipfiles.
#### Expected Output
`to_csv` supports writing to a ZipFile's file handle.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.5.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.1.post20200529
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 647,866,421
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MDExOlB1bGxSZXF1ZXN0NDQxNzg2ODIy
| 35,059
|
REF: Rename NonFixedVariableWindowIndexer to VariableOffsetWindowIndexer
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| 1
| 2020-06-30T05:13:03Z
| 2020-06-30T16:33:31Z
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/34994#discussion_r447311723
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Fix issue #31708 Series.astype(str, skipna=True) vanished in the 1.0 release
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NONE
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- [ ] closes #31708
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Fixed #34859: Added support for '0' and '1' in BooleanArray._from_sequence_of_strings method
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CONTRIBUTOR
| null |
- [ ] closes #34859
- [ ] tests added / passed
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MDExOlB1bGxSZXF1ZXN0NDQyMDYzMzMz
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CLN: collect Timestamp methods
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MDExOlB1bGxSZXF1ZXN0NDQyMjA3NzY1
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ENH: support index=True/False keyword for io.sql.get_schema
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MEMBER
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- [x] closes #9084
- [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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MDExOlB1bGxSZXF1ZXN0NDQyMjA3OTIw
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Assorted ujson Cleanups
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MEMBER
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I think it would be great if we could move the block iteration out of JSON as it has generic functionality that we could use down in C. This isn't it, but a small set of cleanups I noticed while looking at that
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MDExOlB1bGxSZXF1ZXN0NDQyMjM3NDUy
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CLN: type get_resolution tz as tzinfo
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DOC: Remove mypy from pre commit
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| 2020-06-30T19:08:56Z
| 2020-09-01T11:42:05Z
| 2020-09-01T11:42:05Z
|
CONTRIBUTOR
| null |
- [x] closes #34902
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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CLN: type tz kwarg in create_timestamp_from_ts
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| 2020-06-30T20:17:05Z
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MEMBER
| null |
Perf is indistinguishable
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PERF: Timestamp.normalize
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|
MEMBER
| null |
The perf is actually a wash here, verging on slightly negative. The really worthwhile thing is the improved asvs.
The big perf gain is being split into a separate PR that optimizes _maybe_convert_value_to_local to the tune of 40%.
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|
ENH: Add option to keep index in `pd.melt`
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| 2020-06-30T21:26:07Z
| 2020-06-30T22:32:41Z
| 2020-06-30T22:29:46Z
|
CONTRIBUTOR
| null |
When melting a dataframe, there's no option to keep the original index. This can be beneficial for example when users want to do different kind `ravel` than the deault `F` (Fortran style) right now. To make this more clear, see example:
```python
df = pd.DataFrame({'A': [1, 4],
'B': [2, 5],
'C': [3, 6]})
print(df)
A B C
0 1 2 3
1 4 5 6
print(df.melt())
variable value
0 A 1
1 A 4
2 B 2
3 B 5
4 C 3
5 C 6
```
But the expected output can be row wise instead:
```python
variable value
0 A 1
1 B 2
2 C 3
3 A 4
4 B 5
5 C 6
```
**Solution proposal**
This could be solved by adding an `ignore_index` argument which is `True` by default, but if it is set to `False`, the result would come out like:
```python
variable value
0 A 1
1 A 4
0 B 2
1 B 5
0 C 3
1 C 6
```
This way the user can sort the index themself and achieve the same result:
```python
print(df.sort_index())
variable value
0 A 1
0 B 2
0 C 3
1 A 4
1 B 5
1 C 6
```
Also in `pandas/core/reshape/melt` there is an `# TODO: what about the existing index?` in the `melt` function. This might solve that TODO if I'm not mistaken.
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PERF: _maybe_convert_value_to_local
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| 2020-06-30T21:34:39Z
| 2020-07-01T00:52:20Z
| 2020-06-30T23:26:29Z
|
MEMBER
| null |
Also fixes an incorrect asv.
```
In [2]: ts = pd.Timestamp.now("US/Pacific")
In [3]: %timeit ts.day_name()
9.13 µs ± 309 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
26.4 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
```
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MDExOlB1bGxSZXF1ZXN0NDQyMzIzMDE4
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CLN: remove unnecessary get_timezone calls
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MDExOlB1bGxSZXF1ZXN0NDQyMzI2NDA2
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Add test apply dtype
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| 2020-06-30T22:19:14Z
| 2020-07-01T21:11:57Z
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CONTRIBUTOR
| null |
- [x] closes #31466
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 648,535,777
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MDExOlB1bGxSZXF1ZXN0NDQyMzI5NTE5
| 35,073
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REF: de-duplicate month_offset in tslibs.fields
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| 648,537,441
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MDExOlB1bGxSZXF1ZXN0NDQyMzMwOTE2
| 35,074
|
TST: Test for groupby transform on categorical column
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| 2020-06-30T22:31:58Z
| 2020-07-01T18:25:49Z
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|
CONTRIBUTOR
| null |
Added test for groupby transform on categorical column
- [x] closes #29037
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BENCH: implement asvs for get_resolution
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| 2020-07-02T16:22:48Z
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BUG: Integer Capacity Higher Than Necessary in Mappings from Pandas to SQLAlchemy Types
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| 3
| 2020-07-01T00:59:55Z
| 2020-12-24T18:51:42Z
| 2020-12-24T18:51:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Problem description
Currently, to map the Pandas data type to a SQLAlchemy type: https://github.com/pandas-dev/pandas/blob/master/pandas/io/sql.py#L1066-L1069, the code reads:
```python
elif col_type == "integer":
if col.dtype == "int32":
return Integer
else:
return BigInteger
```
This means integers of a capacity less than 32-bit are written to the database as if they were 64-bit. An example where one might get smaller integers is through the use of [pd.to_numeric](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html) and `downcast=True`.
Since there is already a check for the `col_type == "integer"`, I think switching the `col.dtype` check to this could be a possible fix:
```python
elif col_type == "integer":
if col.dtype == "int64":
return BigInteger
else:
return Integer
```
But I'm not sure how to get started with an official PR or if this is a sane thing to do.
For context (this part is not an issue for Pandas, just explaining my interest in this issue), I discovered this when using the [df.to_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html) method to persist datasets to a Postgres database. From there, I use [Postgraphile](https://www.graphile.org/postgraphile/) to auto-generate a GraphQL endpoint. I found that the `BigInteger` type ends up resolving as a string, because Javascript can't represent such large numbers [safely](https://github.com/brianc/node-pg-types/issues/28). This would be fine if the source data warranted the high-capacity, but for me it often doesn't.
#### Expected Output
That the column types that get written to a new table in the database more tightly match their data types in Pandas.
#### Workarounds
If using [df.to_sql](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html), one could set the `dtype` argument but this can be unruly when there are many columns in the DataFrame.
For now, I have this helper method to downcast integer columns to their lowest capacity, but then back to 32-bit to persist them to the database with the desired column type:
```python
def cast_to_int32(df):
# Downcast to the lowest possible representation.
for col in df.select_dtypes(include=['int']).columns.values:
df[col] = pd.to_numeric(df[col], downcast='integer')
# Upcast back to 32-bit (since that's what gets persisted correctly)
for col in df.select_dtypes(include=['int8', 'int16']).columns.values:
df[col] = df[col].astype('int32')
return df
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-39-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.17
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDQyMzk4NDk2
| 35,077
|
REF: de-duplicate DST tzconversion code
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| 15
| 2020-07-01T02:18:50Z
| 2021-11-20T23:22:38Z
| 2020-07-08T17:36:47Z
|
MEMBER
| null |
This implements TZConvertInfo to de-duplicate a bunch of tzconversion code. This uses the new pattern on get_resolution. The other usages I intend to update in individual follow-ups in which I'll check that we have good asv coverage the affected functions.
Sits on top of #35075 (actually, that was broken off of this)
Perf-improving according to the newly implemented asvs:
```
$ asv continuous -E virtualenv -f 1.01 master HEAD -b resolution
[...]
before after ratio
[559189ad] [8114413b]
- 1.89±0.08μs 1.64±0.05μs 0.87 tslibs.resolution.TimeResolution.time_get_resolution('s', 1, None)
```
L50-L57 in libresolution is going to end up being repeated, should become a helper function. Doing that separately since it tentatively looks like that may have a perf hit if not done carefully.
xref https://github.com/pandas-dev/pandas/commit/652a3de6f4fd4e93ee84cd4b84741f3b1e713cea#r30437889
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BUG: DataFrameGroupBy.__getitem__ fails to propagate dropna
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| 2020-08-07T21:53:39Z
| 2020-08-07T21:33:06Z
|
MEMBER
| null |
- [x] closes #35014
- [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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TYP: stronger typing in libtimezones
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BUG: Strange behavior at testing test_ts_plot_with_tz
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| 7
| 2020-07-01T06:56:19Z
| 2020-11-12T16:23:38Z
| 2020-11-12T16:23:38Z
|
CONTRIBUTOR
| null |
I came across a rather strange and annoying behavior when running the plotting tests that leaves me completely clueless.
`TestTSPlot.test_ts_plot_with_tz['UTC']` fails when running the whole test suite but passes when run in isolation. In particular it fails when run **after all** `TestRegistration` tests and passes when run **on its own or after any** of the six test functions in `TestRegistration`:
### FAILURE:
```
(pandas-dev) C:\Daten\Python\pandas>pytest pandas\tests\plotting\ -k "TestRegistration | test_ts_plot_with_tz"
====================================================================== test session starts ======================================================================
platform win32 -- Python 3.8.2, pytest-5.4.3, py-1.9.0, pluggy-0.13.1
rootdir: C:\Daten\Python\pandas, inifile: setup.cfg
plugins: hypothesis-5.19.0, asyncio-0.14.0, cov-2.10.0, forked-1.2.0, xdist-1.32.0
collected 479 items / 460 deselected / 19 selected
pandas\tests\plotting\test_converter.py ...... [ 31%]
pandas\tests\plotting\test_datetimelike.py F............ [100%]
=========================================================================== FAILURES ============================================================================
____________________________________________________________ TestTSPlot.test_ts_plot_with_tz['UTC'] _____________________________________________________________
self = <pandas.tests.plotting.test_datetimelike.TestTSPlot object at 0x000002965C714E20>, tz_aware_fixture = 'UTC'
@pytest.mark.slow
def test_ts_plot_with_tz(self, tz_aware_fixture):
# GH2877, GH17173, GH31205, GH31580
tz = tz_aware_fixture
index = date_range("1/1/2011", periods=2, freq="H", tz=tz)
ts = Series([188.5, 328.25], index=index)
with tm.assert_produces_warning(None):
_check_plot_works(ts.plot)
ax = ts.plot()
xdata = list(ax.get_lines())[0].get_xdata()
# Check first and last points' labels are correct
> assert (xdata[0].hour, xdata[0].minute) == (0, 0)
E AttributeError: 'numpy.datetime64' object has no attribute 'hour'
pandas\tests\plotting\test_datetimelike.py:57: AttributeError
==================================================================== short test summary info ====================================================================
FAILED pandas/tests/plotting/test_datetimelike.py::TestTSPlot::test_ts_plot_with_tz['UTC'] - AttributeError: 'numpy.datetime64' object has no attribute 'hour'
========================================================= 1 failed, 18 passed, 460 deselected in 2.98s ==========================================================
```
### SUCCESS:
```
(pandas-dev) C:\Daten\Python\pandas>pytest pandas\tests\plotting\ -k test_ts_plot_with_tz
====================================================================== test session starts ======================================================================
platform win32 -- Python 3.8.2, pytest-5.4.3, py-1.9.0, pluggy-0.13.1
rootdir: C:\Daten\Python\pandas, inifile: setup.cfg
plugins: hypothesis-5.19.0, asyncio-0.14.0, cov-2.10.0, forked-1.2.0, xdist-1.32.0
collected 479 items / 466 deselected / 13 selected
pandas\tests\plotting\test_datetimelike.py ............. [100%]
============================================================== 13 passed, 466 deselected in 2.43s ===============================================================
```
As I said it passes when run after any of the individual functions in `TestRegistration`, i.e. with `-k "test_register_by_default | test_ts_plot_with_tz"`, `"test_registering_no_warning | test_ts_plot_with_tz"` etc.
Tests are run in an environment set up as described [here](https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#creating-a-python-environment-pip). Pytest and plugin versions see above. Pandas fresh snapshot as of yesterday `1.1.0.dev0+1977.g1706d830d`.
I'm pretty sure it's a question of my setup, so sorry for bothering you with it, but maybe someone has any idea or pointers of where to look for the reason of this behavior.
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MDU6SXNzdWU2NDg5OTM4NTE=
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|
BUG: idxmax returns erroneous index from grouped DataFrame
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| 2020-07-01T13:43:27Z
| 2020-07-02T14:20:01Z
| 2020-07-02T14:20:01Z
|
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.
---
The following code demonstrates the erroneous output:
```python
import numpy as np
import pandas as pd
N = 12
np.random.seed(1234)
price = np.random.uniform(0, 100, N)
color = np.repeat(list("ABCD"), 3)
df = pd.DataFrame(dict(price=price, color=color),
index=pd.date_range("2020-01-01", periods=N,
freq="100ms", tz="UTC"))
df["color"] = df["color"].astype("category")
```
```python
>>> print(df)
price color
2020-01-01 00:00:00+00:00 19.151945 A
2020-01-01 00:00:00.100000+00:00 62.210877 A
2020-01-01 00:00:00.200000+00:00 43.772774 A
2020-01-01 00:00:00.300000+00:00 78.535858 B
2020-01-01 00:00:00.400000+00:00 77.997581 B
2020-01-01 00:00:00.500000+00:00 27.259261 B
2020-01-01 00:00:00.600000+00:00 27.646426 C
2020-01-01 00:00:00.700000+00:00 80.187218 C
2020-01-01 00:00:00.800000+00:00 95.813935 C
2020-01-01 00:00:00.900000+00:00 87.593263 D
2020-01-01 00:00:01+00:00 35.781727 D
2020-01-01 00:00:01.100000+00:00 50.099513 D
```
```python
color_grp = df.groupby("color")
color_idxmax = color_grp.idxmax()
```
```python
>>> print(color_idxmax)
price
color
A 2020-01-01 00:00:00.100000+00:00
B 2020-01-01 00:00:00.300000+00:00
C 2020-01-01 00:00:00.800000+00:00
D 2020-01-01 00:00:00.900000+00:00
```
#### Problem description
Notice the erroneous value printed for the first row in column "price" of `color_idxmax`.
#### Expected Output
It should be: `2020-01-01 00:00:00.200000+00:00`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.0-2-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.0.5
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 44.0.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDQyNzQ2NzI4
| 35,082
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CLN: stronger typing in libtimezones
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MDExOlB1bGxSZXF1ZXN0NDQyNzQ5MDQy
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No fastcall attribute in POWER platform
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| 2020-07-15T18:05:17Z
| 2020-07-09T23:54:09Z
|
CONTRIBUTOR
| null |
Compiling the development branch of pandas fail in AIX.
pandas/_libs/src/ujson/lib/ultrajsonenc.c:397:1: error: ‘fastcall’ attribute directive ignored [-Werror=attributes]
Buffer_AppendShortHexUnchecked(char *outputOffset, unsigned short value) {
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/src/ujson/lib/ultrajsonenc.c:726:59: error: ‘fastcall’ attribute directive ignored [-Werror=attributes]
char *end) {
^~~~
cc1: all warnings being treated as errors
The fastcall attribute is not available in POWER platform also.
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TYP: annotations, typing for infer_tzinfo
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| null |
Keeping these orthogonal
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MDExOlB1bGxSZXF1ZXN0NDQyODExMTI1
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Fix numpy warning
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MDExOlB1bGxSZXF1ZXN0NDQyODI1NDg0
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CLN: Removed unreached else block GH33478
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| 2020-07-05T06:46:06Z
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|
CONTRIBUTOR
| null |
Unreached else block in method _wrap_applied_output was removed.
Local tests passed before and after change
- [ ] xref #33478 (Not fully closed, this addresses 1 part)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NDQyODI2ODA2
| 35,087
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PERF: tz_convert/tz_convert_single
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| 0
| 2020-07-01T17:04:08Z
| 2020-07-02T17:17:01Z
| 2020-07-02T16:59:10Z
|
MEMBER
| null |
Making these follow the same pattern we use elsewhere, we get a perf bump:
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, tz="US/Pacific")
In [3]: %timeit dti.tz_localize(None)
91.4 µs ± 3.06 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
102 µs ± 6.43 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
In [4]: ts = pd.Timestamp.now("US/Pacific")
In [5]: %timeit ts.tz_localize(None)
17.2 µs ± 307 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
19.6 µs ± 233 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
```
Next up is making sure we have full asv coverage for tz_convert/tz_convert_single, analogous to #35075. That can either be separate or added to this PR.
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MDExOlB1bGxSZXF1ZXN0NDQyODg2NzY5
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CLN: tighter typing in libconversion
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MDU6SXNzdWU2NDkyNzE5Nzc=
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QST: Zero copy access to the mask of a nullable array
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[] | null | 1
| 2020-07-01T19:46:40Z
| 2020-08-21T17:05:20Z
| 2020-07-01T20:09:59Z
|
NONE
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Hello Pandas devs,
I was wondering what's the best zero-copy way to access the mask of a nullable array, say a StringArray for example. I noticed these all inherit from BaseMaskedArray which provides `isna()` wrapping `_mask` however I want to make sure this is the recommended method, if I can always be sure this doesn't incur a copy, etc. Should I be using `isna()` or something else, if it exists?
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TST: update gbq service account key
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| 6
| 2020-07-01T20:06:54Z
| 2020-07-09T23:41:00Z
| 2020-07-09T23:41:00Z
|
CONTRIBUTOR
| null |
Re-enable gbq integration tests.
- [ ] closes #34779
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 4
| 2020-07-02T02:44:44Z
| 2020-07-14T20:30:48Z
| 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
>>> df = pd.DataFrame(
... {
... 0: [315.3324, 3243.32432, 3232.332, -100.32],
... 1: [0.3223, 0.32, 0.0000232, 0.32224],
... },
... index=[7, 20, 11, 9],
... )
>>> df
0 1
7 315.33240 0.322300
20 3243.32432 0.320000
11 3232.33200 0.000023
9 -100.32000 0.322240
>>> series = pd.Series([10, 11, 23, 234, 13], index=[11, 12, 13, 44, 33])
>>> series
11 10
12 11
13 23
44 234
33 13
dtype: int64
>>> df.append(series, ignore_index=True)
0 1 11 12 13 33 44
0 315.33240 0.322300 NaN NaN NaN NaN NaN
1 3243.32432 0.320000 NaN NaN NaN NaN NaN
2 3232.33200 0.000023 NaN NaN NaN NaN NaN
3 -100.32000 0.322240 NaN NaN NaN NaN NaN
4 NaN NaN 10.0 11.0 23.0 13.0 234.0
>>> df.append(series, ignore_index=True, sort=True)
0 1 11 12 13 33 44
0 315.33240 0.322300 NaN NaN NaN NaN NaN
1 3243.32432 0.320000 NaN NaN NaN NaN NaN
2 3232.33200 0.000023 NaN NaN NaN NaN NaN
3 -100.32000 0.322240 NaN NaN NaN NaN NaN
4 NaN NaN 10.0 11.0 23.0 13.0 234.0
>>> df.append(series, ignore_index=True, sort=False)
0 1 11 12 13 33 44
0 315.33240 0.322300 NaN NaN NaN NaN NaN
1 3243.32432 0.320000 NaN NaN NaN NaN NaN
2 3232.33200 0.000023 NaN NaN NaN NaN NaN
3 -100.32000 0.322240 NaN NaN NaN NaN NaN
4 NaN NaN 10.0 11.0 23.0 13.0 234.0
>>>
```
#### Problem description
The problem here is that even when `sort` is False, the output dataframe seems to have sorted columns(0, 1, 11, 12, 13, 33, 44), instead the desired order from what the docs say [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.append.html) should be (0, 1, 11, 12, 13, 44, 33).
#### Expected Output
```python
>>> df.append(series, ignore_index=True, sort=True)
0 1 11 12 13 33 44
0 315.33240 0.322300 NaN NaN NaN NaN NaN
1 3243.32432 0.320000 NaN NaN NaN NaN NaN
2 3232.33200 0.000023 NaN NaN NaN NaN NaN
3 -100.32000 0.322240 NaN NaN NaN NaN NaN
4 NaN NaN 10.0 11.0 23.0 13.0 234.0
>>> df.append(series, ignore_index=True, sort=False)
0 1 11 12 13 44 33
0 315.33240 0.322300 NaN NaN NaN NaN NaN
1 3243.32432 0.320000 NaN NaN NaN NaN NaN
2 3232.33200 0.000023 NaN NaN NaN NaN NaN
3 -100.32000 0.322240 NaN NaN NaN NaN NaN
4 NaN NaN 10.0 11.0 23.0 234.0 13.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.5
numpy : 1.17.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.18.2
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.49.1
</details>
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| 1
| 2020-07-02T06:09:02Z
| 2020-07-02T06:32:50Z
| 2020-07-02T06:32:36Z
|
NONE
| null |
Simple loc() method does not work!
Here is the code to reproduce:
```python
# Your code here
letters = pd.Series(['A', 'B', 'C', 'D', 'E' ], index=[3, 4, 1, 2, 2])
letters.loc(2)
```
code above throws exception:
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-45-42a68f905e63> in <module>
----> 1 animals.loc(1)
/usr/local/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py in __call__(self, axis)
575
576 if axis is not None:
--> 577 axis = self.obj._get_axis_number(axis)
578 new_self.axis = axis
579 return new_self
/usr/local/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _get_axis_number(cls, axis)
405 except KeyError:
406 pass
--> 407 raise ValueError(f"No axis named {axis} for object type {cls}")
408
409 @classmethod
ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>
```
#### Problem description
Getting the same error on ```obj.iloc(1)```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDU6SXNzdWU2NDk5OTc4MDg=
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BUG: reset_index is passing a bad dtype to NumPy
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| 4
| 2020-07-02T14:58:09Z
| 2020-07-06T20:55:03Z
| 2020-07-06T20:55:03Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug **DOES NOT ** 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
index = pd.period_range("12-1-2000", periods=2, freq="Q-DEC")
df = pd.DataFrame([[1],[2]],index=index)
df = df.reset_index()
```
#### Problem description
Should reset the index.
This is happening on the nightly:
pandas: 1.1.0.dev0+2004.g8d10bfb6f
Full Travis CI log with 4 identical failures:
https://travis-ci.org/github/statsmodels/statsmodels/jobs/704310975
The relevant part of the traceback:
```
statsmodels/tsa/statespace/tests/test_news.py:617:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
statsmodels/tsa/statespace/tests/test_news.py:242: in check_news
details_by_update = news.details_by_update
statsmodels/base/wrapper.py:36: in __getattribute__
obj = getattr(results, attr)
statsmodels/tsa/statespace/news.py:392: in details_by_update
df = df.reset_index()
venv/lib/python3.8/site-packages/pandas/core/frame.py:4800: in reset_index
level_values = _maybe_casted_values(lev, lab)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
index = PeriodIndex(['2009Q2', '2009Q3', '2009Q4', '2010Q1'], dtype='period[Q-DEC]', name='impact date', freq='Q-DEC')
labels = array([], dtype=int8)
def _maybe_casted_values(index, labels=None):
values = index._values
if not isinstance(index, (PeriodIndex, DatetimeIndex)):
if values.dtype == np.object_:
values = lib.maybe_convert_objects(values)
# if we have the labels, extract the values with a mask
if labels is not None:
mask = labels == -1
# we can have situations where the whole mask is -1,
# meaning there is nothing found in labels, so make all nan's
if mask.all():
> values = np.empty(len(mask), dtype=index.dtype)
E TypeError: Cannot interpret 'period[Q-DEC]' as a data type
```
#### Expected Output
```
index 0
0 2000Q4 1
1 2001Q1 2
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
Python: 3.8.1.final.0
OS: Linux 5.0.0-1031-gcp #32-Ubuntu SMP Tue Feb 11 03:55:48 UTC 2020 x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
statsmodels
===========
Installed: v0.12.0.dev0+519.g502132468 (/home/travis/build/statsmodels/statsmodels/statsmodels)
Required Dependencies
=====================
cython: 3.0a5 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/Cython)
numpy: 1.20.0.dev0+4d5b255 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/numpy)
scipy: 1.6.0.dev0+fd6f6d6 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/scipy)
pandas: 1.1.0.dev0+2004.g8d10bfb6f (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/pandas)
dateutil: 2.8.1 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/dateutil)
patsy: 0.5.1 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/patsy)
Optional Dependencies
=====================
matplotlib: 3.3.0rc1+128.g5e11a2312 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/matplotlib)
backend: TkAgg
cvxopt: 1.2.5 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/cvxopt)
joblib: 0.16.0 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/joblib)
Developer Tools
================
IPython: Not installed
jinja2: Not installed
sphinx: Not installed
pygments: Not installed
pytest: 5.4.3 (/home/travis/build/statsmodels/statsmodels/venv/lib/python3.8/site-packages/pytest)
virtualenv: Not installed
```
</details>
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MDExOlB1bGxSZXF1ZXN0NDQzNjM3OTIx
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TYP: type for get_timezone
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| 1
| 2020-07-02T16:55:11Z
| 2020-07-02T17:41:02Z
| 2020-07-02T17:34:52Z
|
MEMBER
| null |
Nearly done with these
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MDU6SXNzdWU2NTAxMDQ1OTM=
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QST: Does the following method have circular dependencies? hash_pandas_object
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| 2020-08-06T19:31:27Z
| 2020-08-06T17:49:53Z
|
NONE
| null |
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Hi Team, does the method `hash_pandas_object` have circular dependencies?
I'm not able to serialize class that contains the method in my code with `dill`.
```python
# Your code here, if applicable
import numpy as np
import pandas as pd
from pandas.core.util.hashing import hash_pandas_object
# Same result using the following library
# from pandas.util import hash_pandas_object
class T:
def __init__(self):
self.dataset_hash = ""
def main(self):
df = pd.DataFrame(np.random.randint(0, 100, size=(100, 4)), columns=list('ABCD'))
print(df)
self.dataset_hash = hash_pandas_object(df).sum()
import sys
import dill
sys.setrecursionlimit(10 ** 4)
t = T()
with open("test.dill", "wb") as f:
dill.dump(t, f, recurse=True)
t.main()
```
NOTE:
I've opened an issue to the `dill` repository to [https://github.com/uqfoundation/dill/issues/374]
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MDExOlB1bGxSZXF1ZXN0NDQzNjY5NTA4
| 35,098
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BUG: fix union_indexes not supporting sort=False for Index subclasses
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| 11
| 2020-07-02T18:05:31Z
| 2020-07-09T15:11:45Z
| 2020-07-09T13:02:33Z
|
MEMBER
| null |
- [X] closes #35092
- [X] 1 tests added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
# Scope of PR
The reason for `DataFrame.append` unnecessarily sorting columns turned out to be that when merging indexes we were not passing the `sort` argument for an `Index` subclass. This PR fixes this bug.
# Details
At some point down the call stack, `DataFrame.append` calls `union_indexes` in `core.indexes.api`. In it we detect the type of our indexes:
```python
indexes, kind = _sanitize_and_check(indexes)
```
We don't pass `sort` for `kind == 'special'` in `union_indexes`.
I had some other ideas, but what I ended up doing is to simply pass the `sort` argument to `Index.union` for `kind == 'special'`. We do have to ignore the `sort` argument for `[MultiIndex, RangeIndex, DatetimeIndex, CategoricalIndex]` to avoid breaking tests and backward compatibility, but it doesn't make sense for these ones to not be sorted when joined anyway, in my opinion.
# Performance
There are no changes to algorithms called. I still did some benchmarking to compare with my previous approach. The current solution didn't have any negative performance impact.
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Document Tips for Debugging C Extensions
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MEMBER
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Quick notes in case they are helpful to others. Can move to the Wiki as well if we don't want in the standard docs
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PERF: Fix quantile perf regression
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|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35049
```pyhon
591 µs ± 29.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
440 µs ± 22.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
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MDExOlB1bGxSZXF1ZXN0NDQzNzQ2NjYy
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REF: standardize tz_convert_single usage
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| 2020-07-02T21:19:33Z
| 2020-07-07T01:06:17Z
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|
MEMBER
| null |
I'm increasingly convinced that we can get all tz_convert/tz_convert_single usages to have tz1=UTC, and everything else should go through tz_localize_to_utc.
|
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MDExOlB1bGxSZXF1ZXN0NDQzNzY3ODUx
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TYP: maybe_get_tz
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MDExOlB1bGxSZXF1ZXN0NDQzNzcxOTc1
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|
CLN: tz_convert is always from UTC
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| 0
| 2020-07-02T22:42:52Z
| 2020-07-07T01:05:26Z
| 2020-07-07T00:12:00Z
|
MEMBER
| null |
Follow-up can remove the unnecessary arg from tz_convert, possibly rename.
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MDExOlB1bGxSZXF1ZXN0NDQzNzg1MjA1
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|
Extension base test
|
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| 2020-07-02T23:40:19Z
| 2020-07-02T23:43:04Z
| 2020-07-02T23:42:47Z
|
CONTRIBUTOR
| null |
- [x] closes #28488
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The style is not consistent with guidelines etc, and not at all ready to be merged.
|
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MDExOlB1bGxSZXF1ZXN0NDQzNzkxODAx
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|
PERF: tz_localize(None) from dst, asvs
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| 0
| 2020-07-03T00:12:40Z
| 2020-07-07T01:06:58Z
| 2020-07-07T00:13:27Z
|
MEMBER
| null |
This makes tz_convert_dst follow the same pattern we use elsewhere, and brings a perf improvement along with it.
```
In [2]: dti = pd.date_range("2016-01-01", periods=10000, freq="S", tz="US/Pacific")
In [3]: %timeit dti.tz_localize(None)
89.3 µs ± 627 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
100 µs ± 6.36 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
```
The boost comes from not doing a copy, so makes a bigger difference in the scalar case than the array case.
```
In [5]: ts = dti[0]
In [6]: %timeit ts.tz_localize(None)
12.4 µs ± 188 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
16.1 µs ± 215 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
```
The improvement is much bigger for fixed offsets:
```
In [9]: tz = pytz.FixedOffset(-60)
In [10]: dti2 = dti.tz_convert(tz)
In [11]: %timeit dti2.tz_localize(None)
34.8 µs ± 1.09 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
71.8 µs ± 2.99 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
```
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TYP: get_utcoffset
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MDExOlB1bGxSZXF1ZXN0NDQzODM4NzIz
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REF: implement tz_localize_to_utc_single
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| 0
| 2020-07-03T03:59:31Z
| 2020-07-07T01:08:21Z
| 2020-07-07T00:14:26Z
|
MEMBER
| null |
In addition to implementing `tz_localize_to_utc_single`, this replaces all (but one, xref #35102) usages of tz_convert_single that have UTC as the target timezone. In conjunction with 35102, this will allow us to remove the second kwarg altogether, similar to #35104.
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BUG: Groupby.transform on datetime with nunique giving incorrect result
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| 1
| 2020-07-03T11:09:44Z
| 2020-07-10T22:46:42Z
| 2020-07-10T22:46:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (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
>>> import pandas as pd
>>> bdates = pd.bdate_range('2008-12-31', '2011-12-30')
>>> df = pd.DataFrame(bdates, columns=['date'])
>>> df['year'] = df['date'].dt.year
>>> df.groupby('year')['date'].transform('nunique')
0 1970-01-01 00:00:00.000000001
1 1970-01-01 00:00:00.000000261
2 1970-01-01 00:00:00.000000261
3 1970-01-01 00:00:00.000000261
4 1970-01-01 00:00:00.000000261
...
778 1970-01-01 00:00:00.000000260
779 1970-01-01 00:00:00.000000260
780 1970-01-01 00:00:00.000000260
781 1970-01-01 00:00:00.000000260
782 1970-01-01 00:00:00.000000260
Name: date, Length: 783, dtype: datetime64[ns]
>>>
```
#### Problem description
The current behaviour returns the sum of default timestamp with the number of unique count of dates in each year. The expected output should have been the number of unique counts for the date in each year which is consistent with the behavior of other Groupby.transform functions.
#### Expected Output
```python
# Your code here
>>> import pandas as pd
>>> bdates = pd.bdate_range('2008-12-31', '2011-12-30')
>>> df = pd.DataFrame(bdates, columns=['date'])
>>> df['year'] = df['date'].dt.year
>>> df.groupby('year')['date'].transform('nunique')
0 1
1 261
2 261
3 261
4 261
...
778 260
779 260
780 260
781 260
782 260
Name: date, Length: 783, dtype: int
>>>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.181-140.257.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 47.1.1.post20200604
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 : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
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BUG: reset_index is passing a bad dtype to NumPy
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BUG: Mixed DataFrame with Extension Array incorrect aggregation
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| 2020-07-10T09:14:03Z
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MEMBER
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| 3
| 2020-07-03T17:18:38Z
| 2020-07-07T00:15:07Z
| 2020-07-06T22:59:08Z
|
MEMBER
| null |
This refactors ints_to_pydatetime to use a much more concise pattern (similar to #35077 but without the helper function refactored out) that I intend to move all of the ~7 functions using get_dst_info to use. In the process, we are slower on very small arrays and faster on bigger arrays, which I think is a good trade:
```
% asv continuous -E virtualenv -f 1.01 master HEAD -b time_ints_to_pydatetime
[...]
before after ratio
[a4e19fa5] [2492aeb4]
<master> <ref-ints_to_pydatetime-3>
+ 3.95±0.09μs 8.22±0.5μs 2.08 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 4.80±0.2μs 9.53±0.5μs 1.99 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.45±0.3μs 9.01±0.2μs 1.65 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 5.31±0.6μs 8.31±0.3μs 1.57 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 6.08±1μs 8.84±0.2μs 1.45 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 0, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 7.00±0.3μs 10.0±0.3μs 1.44 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
+ 7.17±0.1μs 10.3±0.3μs 1.43 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 7.47±0.1μs 9.77±0.2μs 1.31 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
+ 7.08±0.2μs 8.46±0.5μs 1.20 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 10.7±0.6μs 9.48±0.3μs 0.88 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 0, datetime.timezone(datetime.timedelta(seconds=3600)))
- 3.38±0.02μs 2.89±0.06μs 0.85 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('date', 0, None)
- 74.3±1μs 56.3±1μs 0.76 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 100, datetime.timezone.utc)
- 73.4±4μs 54.7±2μs 0.74 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 100, None)
- 20.7±3ms 9.31±2ms 0.45 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 15.4±0.2ms 6.23±1ms 0.40 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.75±0.03s 699±5ms 0.40 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.56±0.05s 614±20ms 0.39 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.65±0.01s 650±10ms 0.39 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 156±10μs 57.3±9μs 0.37 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 16.8±1ms 5.95±0.2ms 0.36 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 164±7μs 58.3±0.8μs 0.36 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 14.8±0.1ms 5.14±0.5ms 0.35 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 1.42±0.02s 491±20ms 0.34 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 169±6μs 56.0±3μs 0.33 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 193±30μs 63.1±0.9μs 0.33 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 229±30μs 73.7±3μs 0.32 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('timestamp', 100, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 1.65±0.06s 505±20ms 0.31 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1000000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 159±3μs 46.1±2μs 0.29 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 100, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 16.0±1ms 4.64±0.1ms 0.29 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 10000, <DstTzInfo 'US/Pacific' LMT-1 day, 16:07:00 STD>)
- 15.2±0.2ms 3.78±0.2ms 0.25 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 10000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
- 1.65±0.1s 408±20ms 0.25 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1000000, tzfile('/usr/share/zoneinfo/Asia/Tokyo'))
```
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MDExOlB1bGxSZXF1ZXN0NDQ0MTYxOTQ3
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|
BUG: get_loc with time object matching NaT micros
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| 3
| 2020-07-03T18:37:22Z
| 2020-07-07T14:30:16Z
| 2020-07-07T13:14:29Z
|
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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CI: suppress external warning
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| 2020-07-04T17:06:43Z
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TST: base test for ExtensionArray.astype to its own type + copy keyword
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"labels_url": "https://api.github.com/repos/pandas-dev/pandas/milestones/73/labels",
"node_id": "MDk6TWlsZXN0b25lNTQ3OTgxOQ==",
"number": 73,
"open_issues": 0,
"state": "closed",
"title": "1.2",
"updated_at": "2021-04-13T15:46:43Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/73"
}
| 14
| 2020-07-03T19:58:22Z
| 2020-09-22T13:41:46Z
| 2020-09-22T13:41:25Z
|
CONTRIBUTOR
| null |
- [] closes #28488
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
n.b. I'm not certain. I get no output on the command line when I run this command.
- [x] whatsnew entry
|
{
"+1": 0,
"-1": 0,
"confused": 0,
"eyes": 0,
"heart": 0,
"hooray": 0,
"laugh": 0,
"rocket": 0,
"total_count": 0,
"url": "https://api.github.com/repos/pandas-dev/pandas/issues/35116/reactions"
}
|
https://api.github.com/repos/pandas-dev/pandas/issues/35116/timeline
| null | 0
|
{
"diff_url": "https://github.com/pandas-dev/pandas/pull/35116.diff",
"html_url": "https://github.com/pandas-dev/pandas/pull/35116",
"merged_at": "2020-09-22T13:41:25Z",
"patch_url": "https://github.com/pandas-dev/pandas/pull/35116.patch",
"url": "https://api.github.com/repos/pandas-dev/pandas/pulls/35116"
}
|
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