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https://api.github.com/repos/pandas-dev/pandas/issues/35817
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MDU6SXNzdWU2ODI0ODExNjI=
| 35,817
|
BUG: error in reduction.pyx file while building pandas from source with setup.py file
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NONE
| null |
I am compiling pandas1.10 from source with the help of setup.py
It is showing me following error in reduction.pyx file
self.orig_data = self.buf.data
self.orig_len = self.buf.shape[0]
self.orig_stride = self.buf.strides[0]
self.buf.data = self.values.data
^
------------------------------------------------------------
pandas/_libs/reduction.pyx:313:16: Assignment to a read-only property
Error compiling Cython file:
enviornment : Linux opensuze
python version : 3.7.9
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MDU6SXNzdWU2ODI1MjEwNTI=
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|
BUG: Dataframe.apply(func, axis=1) when func returns a Series, apply replaces all the rows in the Dataframe for the result of func(first_row)
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| 2020-08-25T12:54:42Z
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|
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([[4, 9], [1, 2], [3, -1]], columns=['A', 'B'])
def add_columns(series):
series["C"] = series.A * series.B
series["D"] = series.A + series.B
return series
df.apply(add_columns, axis=1)
```
#### Problem description
In pandas '1.1.0' I encountered an issue when using `Dataframe.apply(func, axis=1)` to dynamically insert new columns into a dataframe. After calling `Dataframe.apply(func, axis=1)`, where `func` returns a `Series` (as shown in the Code Sample), every row in the will be a copy of the first row with the new columns added.
So the output of the Sample Code:
A B C D
0 4 9 36 13
1 4 9 36 13
2 4 9 36 13
#### Expected Output
Each row shouldn't be the same as the first row.
(Code Sample using pandas-1.0.5)
A B C D
0 4 9 36 13
1 1 2 2 3
2 3 -1 -3 2
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.10-201.fc32.x86_64
Version : #1 SMP Thu Jul 23 00:58:39 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
QST:Time index problem in version 1.1.0
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|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
I'm using pandas 1.1.0 on macos, python:3.7
As below it's weird, when I select time larger than 2020/03/01 it's rise error, but if it's 2019, all things go right
I'v tried make some random data, but can't reproduce
I don't konw it's something wrong with my data or a pandas bug with version 1.1.0(it's ok in version 1.0.1)
Very appreciate for someone can give help
```python
pd.__version__
'1.1.0'
df.index
DatetimeIndex(['2020-07-02 08:55:59', '2020-07-03 07:59:32',
'2020-07-06 06:57:38', '2020-07-11 09:25:35',
'2020-07-09 10:02:25', '2020-07-13 07:10:12',
'2020-07-16 07:58:52', '2020-07-17 10:46:43',
'2020-07-18 08:00:36', '2020-07-22 11:47:43',
...
'2020-08-01 07:58:25', '2020-08-01 07:59:05',
'2020-08-01 07:58:50', '2020-08-01 07:57:50',
'2020-07-29 02:50:06', '2020-08-01 07:58:20',
'2020-08-01 07:58:30', '2020-08-01 07:58:21',
'2020-08-01 07:59:08', '2020-08-01 07:58:53'],
dtype='datetime64[ns]', name='sub_time', length=4715513, freq=None)
df['20200701':]
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-54-0be901d6256c> in <module>
----> 1 df['20200701':]
/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
2880 # either we have a slice or we have a string that can be converted
2881 # to a slice for partial-string date indexing
-> 2882 return self._slice(indexer, axis=0)
2883
2884 # Do we have a (boolean) DataFrame?
/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _slice(self, slobj, axis)
3546 Slicing with this method is *always* positional.
3547 """
-> 3548 assert isinstance(slobj, slice), type(slobj)
3549 axis = self._get_block_manager_axis(axis)
3550 result = self._constructor(self._mgr.get_slice(slobj, axis=axis))
AssertionError: <class 'numpy.ndarray'>
df['20190701':]
A B ... C D
a_time ...
2020-07-02 08:55:59 aa cc ... 22 1.2
```
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QST: CSV read issue
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| 2021-01-23T21:36:53Z
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NONE
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suppose my csv has data
2 83 02
" 12 29
3 82 10
” 38 92
½ 29 39
and so on..
if I use pd.read_csv('file.csv')
then it gives EOF error.
I tried encoding UTF-8 but it doesn't worked.
The only way i figured out is by manually opening the file.
Please suggest an idea how to open this kind of Measurement kind of Data file.
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Backport PR #35809 on branch 1.1.x (CI: more xfail failing 32-bit tests)
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NONE
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Backport PR #35809: CI: more xfail failing 32-bit tests
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Backport PR #35801 on branch 1.1.x (DOC: another pass of v1.1.1 release notes)
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NONE
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Backport PR #35801: DOC: another pass of v1.1.1 release notes
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CI/DOC: unpin gitdb, use gitdb instead of gitdb2
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MEMBER
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The developers of gitdb have regained their original PyPi account. And they have moved back to the package name gitdb from gitdb2. See [gitpython-developers/gitdb/issues/59](https://github.com/gitpython-developers/gitdb/issues/59).
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MEMBER
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- [x] closes #35823
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DOC: Start 1.1.2
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BUG: Unable to build Pandas on z/OS when using xlc
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| 3
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CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
When building Pandas using xlc on z/OS, it errors out with the following message:
```
"pandas/_libs/window/aggregations.cpp", line 25295.13: CCN5888 (S) The current option settings do not allow the use of "long long". The suffix of the integer literal "1LL" is not valid.
```
This seems to be due to xlc++ needing specific options to be set when building the c++ code (need to swap the language standard to one that supports long long). However, after adding this via CFLAGS, it'll then error out with the following:
```
"pandas/_libs/window/aggregations.cpp", line 5461.18: CCN5130 (S) "std::__class" is not declared.
"pandas/_libs/window/aggregations.cpp", line 5548.18: CCN5130 (S) "std::__class" is not declared.
"pandas/_libs/window/aggregations.cpp", line 6783.16: CCN5130 (S) "std::__class" is not declared.
"pandas/_libs/window/aggregations.cpp", line 19752.16: CCN5130 (S) "std::__class" is not declared.
```
While the error messages are different and is on a different platform/OS, this issue might be related to https://github.com/pandas-dev/pandas/issues/35123. I haven't tested on AIX so cannot confirm this however. FYI - @aixtools - since this will need a fix for xlc on z/OS, it seems likely that it will apply to AIX as well. What it seems to be is that there is support for those functions within xlc, but they're not placed within the std:: namespace (macros from older standards).
Since there's two build errors here for z/OS, I'll be opening this issue rather than dup'ing the other issue.
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BUG: Same Directory copy.py incompatibility.
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NONE
| null |
#### Problem description
If you are importing pandas into a script which shares a folder with another python script named copy.py pandas fails to import and the script cannot execute.
I would recommend changing the duplicate name in the pandas code to something less accidentally callable (perhaps simply add a few numbers or symbols afterward), but overall this is a very minor issue that I suspect very few people will run into.
- [Yes] I have checked that this issue has not already been reported.
- [Yes] I have confirmed this bug exists on the latest version of pandas.
- [No] (optional) I have confirmed this bug exists on the master branch of pandas.
Apologies unsure how.
---
#### Code Sample
```python
import pandas as pd
print(yes)
```
#### Expected Output
```python
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/Christian/PycharmProjects/pythonProject/Workspace.py", line 1, in <module>
import pandas as pd
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "C:\Anaconda3\envs\pythonProject\lib\site-packages\pandas\__init__.py", line 52, in <module>
from pandas.core.api import (
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "C:\Anaconda3\envs\pythonProject\lib\site-packages\pandas\core\api.py", line 15, in <module>
from pandas.core.arrays import Categorical
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "C:\Anaconda3\envs\pythonProject\lib\site-packages\pandas\core\arrays\__init__.py", line 10, in <module>
from pandas.core.arrays.interval import IntervalArray
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "C:\Anaconda3\envs\pythonProject\lib\site-packages\pandas\core\arrays\interval.py", line 43, in <module>
from pandas.core.indexes.base import ensure_index
File "C:\Coding\JetBrains\PyCharm 2020.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_import_hook.py", line 21, in do_import
module = self._system_import(name, *args, **kwargs)
File "C:\Anaconda3\envs\pythonProject\lib\site-packages\pandas\core\indexes\base.py", line 1, in <module>
from copy import copy as copy_func
ImportError: cannot import name 'copy' from 'copy' (C:\Users\Christian\PycharmProjects\pythonProject\copy.py)
```
#### Output of ``pd.show_versions()``
<details>
Note: This is my first attempt at bug reporting, and am very new to this, and I don't have as much time as I would have liked to ensure I'm posting this in the correct place. If this should be reported to JetBrains instead or simply somewhere else on the site please let me know and I'll do my best to update/fix it promptly.
I am using Jetbrains Pycharm Professional IDE and found that having a python file named copy.py in the same project folder (windows folder) causes some kind of check/name confusion when pandas is importing. This happened by accident as I have been lately using copy.py as a dummy backup of working versions of code while working on improvements. I discovered the issue by accident.
A screenshot of the issue
A screenshot of the issue not occurring under identical circumstances but when copy.py is renamed to copy2.py
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Canada.1252
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CI: Pyarrow Min Version on Travis 3.7 Locale Build
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| 6
| 2020-08-20T18:18:33Z
| 2020-08-21T08:28:06Z
| 2020-08-20T21:26:05Z
|
CONTRIBUTOR
| null |
- [x] closes #35813
Conda seems to be installing pyarrow 0.11 for this env.
Note pyarrow tests will now run on Travis 3.7 Locale Build
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| 24
| 2020-08-20T19:10:41Z
| 2021-05-26T02:19:14Z
| 2021-05-26T02:19:09Z
|
CONTRIBUTOR
| null |
- [x] closes #35826
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is an attempt at being able to build Pandas using xlc on z/OS. There's two issues being fixed here:
1) Raise the default standard that being used with xlc++ (`-qlanglvl`) only when compiling c++ code, and fix other macros that otherwise cause errors when compiling with that option. I put the change directly above where it's used, but an alternate spot that it could go is directly within the declarations of each cpp file + it's respective macros. But since this would need to be added to any additional c++ code that's added, and all extra options are the same, I left it here.
2) Fix the compilation errors that occur - it's currently missing two functions that aren't in the std:: namespace. The first is that `signbit` is declared as a macro and not a function, so we cannot redeclare the function without changing the macro. The next, `isnan`, is declared as both a macro and a function. According to the [xlc docs](https://www.ibm.com/support/knowledgecenter/en/SSLTBW_2.4.0/com.ibm.zos.v2r4.bpxbd00/risnan.htm), to use the function you can simply undefine `isnan`, which is done here, and then it's also placed in the std namespace. To use the macro form we could take the same approach as signbit.
If we don't want to touch the std namespace, an alternative could be to swap the declarations (for z/OS only) within `pandas/_libs/window/aggregations.pyx` to not use std for those two functions.
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API: indexing DatetimeIndex with date object
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| 13
| 2020-08-20T20:41:21Z
| 2021-08-10T04:48:29Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
down load this file
[bug.xlsx](https://github.com/pandas-dev/pandas/files/5105345/bug.xlsx)
or create an excel file with the follwoing:
col1 | 8/21/2020 12:00:00 AM
-- | --
value | 10.1
then run the following code
```python
import pandas as pd
import datetime as dt
df = pd.read_excel("bug.xlsx", index_col=0)
toto = df[dt.date(2020, 8, 21)]
```
#### Problem description
I have this error on 1.1.0 and 1.1.1:
```python
File "C:/Users/snoof/PycharmProjects/palms_db_script/palms_db.py", line 58, in <module>
toto = df[dt.date(2020, 8, 21)]
File "C:\Users\snoof\PycharmProjects\palms_db_script\venv\lib\site-packages\pandas\core\frame.py", line 2899, in __getitem__
indexer = self.columns.get_loc(key)
File "C:\Users\snoof\PycharmProjects\palms_db_script\venv\lib\site-packages\pandas\core\indexes\datetimes.py", line 622, in get_loc
raise KeyError(key)
KeyError: datetime.date(2020, 8, 21)
```
#### Expected Output
no error, with pandas 1.0.5, it finds the element.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : Non
</details>
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RLS: 1.1.2
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| 17
| 2020-08-20T20:43:50Z
| 2020-09-09T09:07:11Z
| 2020-09-09T09:07:11Z
|
MEMBER
| null |
[](https://github.com/simonjayhawkins/pandas-release/actions?query=workflow%3A%22Manual+Release%22)
Tracking issue for the 1.1.2 release.
https://github.com/pandas-dev/pandas/milestone/76
Please do not remove/change milestones from these issues without a note explaining the reasoning (changing milestones doesn't trigger notification)
During issue triage, regressions from 1.0.5 or 1.1.0 to 1.1.0 or 1.1.1 should be milestoned 1.1.2 in the first instance.
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| 35,832
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Jsonlines append mode
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| 2020-08-20T21:10:35Z
| 2021-01-01T08:21:55Z
| 2021-01-01T07:18:35Z
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NONE
| null |
**Description**: Adding support to append mode for `to_json` when 'orient' is `records` and 'lines' is `True`
**Motivation**: [jsonlines](http://jsonlines.org/) is a format that is gaining some space (e.g. [Cloud Storage](https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-json)). In pandas It's achieved using `to_json` with `orient='records'` and `lines=True`. But `to_json` doesn't have the option to use `mode='a'` to append to a file, this PR aims to provide a simple append mode for jsonlines using pandas.
---
- [X] closes #35849
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #35757 on branch 1.1.x (CI: Unpin Pytest + Pytest Asyncio Min Version)
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NONE
| null |
Backport PR #35757: CI: Unpin Pytest + Pytest Asyncio Min Version
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Backport PR #35825 on branch 1.1.x (DOC: Start 1.1.2)
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NONE
| null |
Backport PR #35825: DOC: Start 1.1.2
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MAINT: Manual Backport PR #35825 on branch 1.1.x
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| 3
| 2020-08-20T23:28:05Z
| 2020-08-21T08:24:10Z
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|
CONTRIBUTOR
| null |
Manual backport of below
Ref: https://github.com/pandas-dev/pandas/pull/35828#issuecomment-677917854
This is because we dropped 3.6 support on master (https://github.com/pandas-dev/pandas/pull/35214)
cc @simonjayhawkins
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CI: avoid file leak from ipython tests
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| 1
| 2020-08-21T00:37:00Z
| 2020-08-21T21:16:45Z
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MEMBER
| null |
Broken off from #35711
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| 683,178,499
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MDExOlB1bGxSZXF1ZXN0NDcxMzEwMzU4
| 35,837
|
Update SparseDtype user guide doc
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| 3
| 2020-08-21T00:40:57Z
| 2020-08-31T18:47:20Z
| 2020-08-31T18:45:12Z
|
MEMBER
| null |
Tiny doc nit. The doc says you can pass both dtype and fill_value to SparseDtype then only passes one in the example.
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MDExOlB1bGxSZXF1ZXN0NDcxMzUwMjQy
| 35,838
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Fix Series construction from Sparse["datetime64[ns]"]
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| 4
| 2020-08-21T03:15:17Z
| 2020-08-27T02:37:46Z
| 2020-08-27T02:22:03Z
|
MEMBER
| null |
- [x] closes #35762
- [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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MDExOlB1bGxSZXF1ZXN0NDcxMzY3Nzcw
| 35,839
|
REF: remove unnecesary try/except
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| 2
| 2020-08-21T04:19:57Z
| 2020-08-22T03:17:42Z
| 2020-08-22T02:02:02Z
|
MEMBER
| null |
and make cast_result_block into cast_agg_result operate on values instead of blocks
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MDU6SXNzdWU2ODM1MzEzMDA=
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ENH: Select what to return when list is empty in explode
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| 1
| 2020-08-21T12:37:44Z
| 2020-08-21T13:02:37Z
| 2020-08-21T13:02:20Z
|
NONE
| null |
#### Is your feature request related to a problem?
I have used explode function over a df that has sometimes an empty list. The empty list return a `nan`, that raises problems in posteriors processing, because pandas detect them as floats.
#### Describe the solution you'd like
Explode could have a new argument called `return_value_when_empty` or something like that (probably a better and/or shorter name) that allows you to define what to return when a particular row has an empty list. For instance, it could be an empty dict, an empty list, a string, integer or whatever.
This way you don't have to use fillna (or more complicated things if fillna doesn't work i.e https://stackoverflow.com/questions/25898888/pandas-replace-all-nan-values-in-dataframe-with-empty-python-dict-objects)
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| 683,625,921
|
MDExOlB1bGxSZXF1ZXN0NDcxNjg3NTk0
| 35,841
|
REF: simplify _cython_agg_blocks
|
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| 2
| 2020-08-21T15:02:36Z
| 2020-08-21T21:22:26Z
| 2020-08-21T21:07:29Z
|
MEMBER
| null |
Orthogonal to #35839, though a rebase will be needed.
cc @TomAugspurger did you already try this in #31616? If so, we need to identify a test case in which this doesnt work
|
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MDU6SXNzdWU2ODM2NDI0NzQ=
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|
tidyr mutate equivalent without need for scoping
|
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| 2020-08-21T15:29:14Z
| 2020-08-21T17:55:02Z
| 2020-08-21T15:38:48Z
|
NONE
| null |
R equivalent of
mutate(df, c=a-b)
in pandas is this:
df.assign(c=df['a']-df['b'])
I wish it was like this:
df.assign(c=a-b)
In more complex examples,
df.assign(c=a-b, d=c**2, f=np.sin(c) )
it would make it easier to focus on math . It think implementation could be similar to df.query(...) syntax.
|
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| 683,683,726
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MDU6SXNzdWU2ODM2ODM3MjY=
| 35,843
|
REGR: Series.__repr__ is broken for SparseDtype("datetime64[ns]")
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| 17
| 2020-08-21T16:42:32Z
| 2020-12-07T09:44:49Z
| 2020-12-07T09:44:49Z
|
MEMBER
| null |
Regression due to https://github.com/pandas-dev/pandas/commit/1fa063552eef57ecec9c6a867ff2ae4bb3961e3d :
```python
import pandas as pd
print(pd.__version__)
values = [pd.Timestamp('2012-05-01T01:00:00.000000'), pd.Timestamp('2016-05-01T01:00:00.000000')]
arr = pd.arrays.SparseArray(values)
ser = pd.Series(arr)
ser
```
```python
1.2.0.dev0+147.g07983803b
Out[1]: ---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/opt/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/IPython/core/formatters.py in __call__(self, obj)
700 type_pprinters=self.type_printers,
701 deferred_pprinters=self.deferred_printers)
--> 702 printer.pretty(obj)
703 printer.flush()
704 return stream.getvalue()
~/opt/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/IPython/lib/pretty.py in pretty(self, obj)
392 if cls is not object \
393 and callable(cls.__dict__.get('__repr__')):
--> 394 return _repr_pprint(obj, self, cycle)
395
396 return _default_pprint(obj, self, cycle)
~/opt/miniconda3/envs/pandas-dev/lib/python3.8/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
698 """A pprint that just redirects to the normal repr function."""
699 # Find newlines and replace them with p.break_()
--> 700 output = repr(obj)
701 lines = output.splitlines()
702 with p.group():
~/pandas/pandas/core/series.py in __repr__(self)
1315 show_dimensions = get_option("display.show_dimensions")
1316
-> 1317 self.to_string(
1318 buf=buf,
1319 name=self.name,
~/pandas/pandas/core/series.py in to_string(self, buf, na_rep, float_format, header, index, length, dtype, name, max_rows, min_rows)
1386 max_rows=max_rows,
1387 )
-> 1388 result = formatter.to_string()
1389
1390 # catch contract violations
~/pandas/pandas/io/formats/format.py in to_string(self)
356
357 fmt_index, have_header = self._get_formatted_index()
--> 358 fmt_values = self._get_formatted_values()
359
360 if self.truncate_v:
~/pandas/pandas/io/formats/format.py in _get_formatted_values(self)
341
342 def _get_formatted_values(self) -> List[str]:
--> 343 return format_array(
344 self.tr_series._values,
345 None,
~/pandas/pandas/io/formats/format.py in format_array(values, formatter, float_format, na_rep, digits, space, justify, decimal, leading_space, quoting)
1179 )
1180
-> 1181 return fmt_obj.get_result()
1182
1183
~/pandas/pandas/io/formats/format.py in get_result(self)
1210
1211 def get_result(self) -> List[str]:
-> 1212 fmt_values = self._format_strings()
1213 return _make_fixed_width(fmt_values, self.justify)
1214
~/pandas/pandas/io/formats/format.py in _format_strings(self)
1467
1468 if not isinstance(values, DatetimeIndex):
-> 1469 values = DatetimeIndex(values)
1470
1471 if self.formatter is not None and callable(self.formatter):
~/pandas/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name)
269 name = maybe_extract_name(name, data, cls)
270
--> 271 dtarr = DatetimeArray._from_sequence(
272 data,
273 dtype=dtype,
~/pandas/pandas/core/arrays/datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous)
314 freq, freq_infer = dtl.maybe_infer_freq(freq)
315
--> 316 subarr, tz, inferred_freq = sequence_to_dt64ns(
317 data,
318 dtype=dtype,
~/pandas/pandas/core/arrays/datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous)
1957 if is_datetime64tz_dtype(data_dtype):
1958 # DatetimeArray -> ndarray
-> 1959 tz = _maybe_infer_tz(tz, data.tz)
1960 result = data._data
1961
AttributeError: 'SparseArray' object has no attribute 'tz'
```
xref https://github.com/pandas-dev/pandas/issues/35762
|
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|
BUG: UserWarning: Could not import the lzma module. Your installed Python is incomplete.
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[] | null | 2
| 2020-08-21T17:47:05Z
| 2020-08-23T10:28:49Z
| 2020-08-21T18:18:13Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
file = 'financial.csv'
df = pd.read_csv(file)
df.head(10)
```
#### Problem description
A simple csv import like the one above produce a user warning which arose since I had to move the HomeBrew directory into a custom location due to IT settings not letting me write anymore on the /usr/local.
I have since then reinstalled a fresh version of all the packages via pip as well as the zx module via homebrew.
Accordingly to this [user](https://superuser.com/a/1277306/229674), he was able to resolve by adding the following command into the .zshrc
export LDFLAGS="-L$HOME/homebrew/opt/xz/lib $LDFLAGS"
export CPPFLAGS="-I$HOME/homebrew/opt/xz/include $CPPFLAGS"
export PKG_CONFIG_PATH="$HOME/homebrew/opt/xz/lib/pkgconfig $PKG_CONFIG_PATH"
#### Expected Output
No User Warning
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : 1.0.6
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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CI: Revert 31323 for deprecation warning from Jedi
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| 2020-12-29T20:45:23Z
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|
MEMBER
| null |
- [x] closes #31407
ipython/ipython#12102 has been solved, and the new release came out a couple weeks ago, so revert should be fine according to https://github.com/ipython/ipython/issues/12102#issuecomment-670987484
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BUG: pd.read_parquet fails for wide parquet files and pyarrow 1.0.X
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| 2020-08-24T11:49:01Z
| 2020-08-24T11:48:54Z
|
NONE
| null |
I recently tried to update my pyarrow from 0.17.1 to 1.0.0 and I'm encountering a serious bug where wide DataFrames fail during read_parquet. Small parquet files (m=10000) read correctly, medium files (m=40000) fail with a "Bus Error: 10", and large files (m=100000) completely hang. I've tried python 3.8.5, pandas 1.0.5, pyarrow 1.0.0, and OSX 10.14.
The driver code and output is below:
```python
import pandas as pd
import numpy as np
import sys
filename = "test.parquet"
n = 10
m = int(sys.argv[1])
print(m)
x = np.zeros((n, m))
x = pd.DataFrame(x, columns=[f"A_{i}" for i in range(m)])
x.to_parquet(filename)
y = pd.read_parquet(filename, engine='pyarrow')
```
```bash
time python test_pyarrow.py 10000
10000
real 0m4.018s
user 0m5.286s
sys 0m0.514s
time python test_pyarrow.py 40000
40000
Bus error: 10
```
On a pyarrow 0.17.1 environment, the 40,000 case completes in 8 seconds.
If anyone is a pyarrow dev, it might make sense for them to cross-post this issue there as well.
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BUG: inconsistent naming when combining indices of various types
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| 3
| 2020-08-22T05:52:00Z
| 2020-10-07T11:33:29Z
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|
CONTRIBUTOR
| null |
This is building off of some issues seen in #13475 and subsequent PR. Essentially, the resulting index name when combining existing named indices with `union_indexes` is not consistent across index types. This primarily affects `concat` and the `DataFrame` constructor, which call `union_indexes`.
When combining named indices, there are three main name resolution rules I can think of:
- `ignore`: assign no name to output
- `unanimous`: assign name if all names agree
- `consensus`: assign name only if one unique non-null name
With some testing, below is my best understanding of what resolution rule the various index types use. Note that the behavior may differ depending on whether the indices are numerically equal or not, as with RangeIndex. For the not numerically equal case:
- `Index`: consensus
- `RangeIndex`: ignore
- `Int64Index`: unanimous
- `Float64Index`: unanimous
- `DateTimeIndex`: consensus
- `TimeDeltaIndex`: consensus
- `PeriodIndex`: unanimous
- `CategoricalIndex`: unanimous
- `MultiIndex`: unanimous (over all levels)
I'm not really taking a stand on the correct name resolution rule, but I think they should at least be consistent across index type! And of course MultiIndex is a bit more complicated. Seems possible things could be implemented in common for non-multi-indices in the higher level `union` function? I'm not really sure, but I'm happy to put some work into it.
The test is slightly different for each index type, but for the RangeIndex case, here's an MWE:
``` python
idx1 = pd.RangeIndex(0, 5, name='idx')
idx2 = pd.RangeIndex(2, 7, name='idx')
pd.core.indexes.api.union_indexes([idx1, idx2])
```
and the result will have no name (checked on master).
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MDExOlB1bGxSZXF1ZXN0NDcxOTQ4MzAx
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BUG: Index.get_slice_bounds does not accept datetime.date or tz naive datetime.datetimes
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| 4
| 2020-08-22T06:47:21Z
| 2020-11-12T05:15:24Z
| 2020-09-02T21:36:38Z
|
MEMBER
| null |
- [x] closes #35690
- [x] closes #34077
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This method appeared under tested so added some additional tests for numeric and object `Index`
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MDU6SXNzdWU2ODM5NTU0OTg=
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|
ENH: Enable `append mode` to JSON lines
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[] | null | 3
| 2020-08-22T08:18:37Z
| 2020-09-03T01:53:35Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
[JSON Lines](http://jsonlines.org/) format allow appending to an existing file in a easy way (new line with valid JSON string). Historically `DataFrame().to_json` didn't allow`mode="a"` because It would introduce complications of reading/parsing/changing pure JSON strings. But for JSON lines It's done in an elegant way, as easy as a CSV files.
The pandas way of using JSON lines is setting `orient='records'` together with `lines=True`, but It lacks a `mode="a"` for append mode. My feature proposal (PR already done, just need review) is simple: include the capability of append mode (`mode="a"`) on `to_json` IF `orient='records'` and `lines=True`
#### Describe the solution you'd like
The PR: https://github.com/pandas-dev/pandas/pull/35832
If I got It right, the solution is simple:
1. Include the [argument](https://github.com/pandas-dev/pandas/blob/dc5c1f5e3b85239bc8507cfb4b0d0a7f5f250bb5/pandas/io/json/_json.py#L53) `mode: str = "w"`on `to_json` this will NOT break anything as the default behavior continues as write mode
2. Include a [conditional](https://github.com/pandas-dev/pandas/blob/dc5c1f5e3b85239bc8507cfb4b0d0a7f5f250bb5/pandas/io/json/_json.py#L72) in case `mode="a"`, checking if `orient='records'` and `lines=True`, raising an Exception otherwise
3. In case `mode="a"` and the file already exists and is not empty, then add a new line to the [JSON string](https://github.com/pandas-dev/pandas/blob/dc5c1f5e3b85239bc8507cfb4b0d0a7f5f250bb5/pandas/io/json/_json.py#L103) (`s = convert_to_line_delimits(s)`) before sending to `handler`
4. Create the [handler using the correct mode](https://github.com/pandas-dev/pandas/blob/dc5c1f5e3b85239bc8507cfb4b0d0a7f5f250bb5/pandas/io/json/_json.py#L116) (`get_handle(path_or_buf, mode, compression=compression)` instead of the hardcoded `get_handle(path_or_buf, "w", compression=compression)` )
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To read CSV file which are not in UTF 8 format
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| 1
| 2020-08-22T10:21:21Z
| 2020-08-22T12:32:18Z
| 2020-08-22T12:32:18Z
|
NONE
| null |
hey, pandas family,
we are building a web app for this our teachers are going to upload CSV file which is not in UTF-8 format for this can you add a feature which read the CSV file which also supports non-UTF-8 format CSV file.
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How can i read the a non UTF-8 CSV file in pandas
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| 2020-08-22T10:24:10Z
| 2020-08-23T15:14:25Z
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|
NONE
| null |
hey guys, Can anyone tell me how to read a CSV which is not a UTF-8 CSV file format. I am doing a project for my school. help me.!
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API: replace dropna=False option with na_sentinel=None in factorize
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| 20
| 2020-08-22T10:50:31Z
| 2020-09-02T19:41:06Z
| 2020-09-02T15:00:50Z
|
MEMBER
| null |
- [x] closes #35667
- [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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DEPR: deprecate dtype param in Index.copy
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| 1
| 2020-08-22T15:47:59Z
| 2020-08-27T13:17:26Z
| 2020-08-24T22:55:43Z
|
CONTRIBUTOR
| null |
Deprecate ``dtype`` param in ``Index.copy`` and child methods. If users want to change dtype, they should use ``Index.astype``.
|
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|
REF: use Block.apply in cython_agg_blocks
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| 1
| 2020-08-22T16:26:36Z
| 2020-08-25T00:17:08Z
| 2020-08-24T23:32:54Z
|
MEMBER
| null |
Getting closer to making this use a BlockManager method
|
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|
DOC: code of conduct link doesn't work
|
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| 1
| 2020-08-22T17:14:00Z
| 2020-08-24T08:01:59Z
| 2020-08-23T07:40:30Z
|
MEMBER
| null |
#### Location of the documentation
https://pandas.pydata.org/about/team.html
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
> We have a code of conduct to ensure a friendly and welcoming environment.
"code of conduct" links to https://pandas.pydata.org/about/%7Bbase_url%7D/community/coc.html , which doesn't exist
#### Suggested fix for documentation
Link to correct page
|
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|
CI: specified bucket does not exist in TestParquetPyArrow.test_s3_roundtrip_explicit_fs
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| 2020-08-22T18:01:57Z
| 2020-08-26T02:21:41Z
| 2020-08-26T02:21:41Z
|
MEMBER
| null |
might be related to #35655
```
================================== FAILURES ===================================
______________ TestParquetPyArrow.test_s3_roundtrip_explicit_fs _______________
[gw2] win32 -- Python 3.8.5 C:\Miniconda\envs\pandas-dev\python.exe
self = <pandas.tests.io.test_parquet.TestParquetPyArrow object at 0x000002B24268BF10>
df_compat = A B
0 1 foo
1 2 foo
2 3 foo
s3_resource = s3.ServiceResource(), pa = 'pyarrow'
s3so = {'client_kwargs': {'endpoint_url': 'http://127.0.0.1:5555/'}}
def test_s3_roundtrip_explicit_fs(self, df_compat, s3_resource, pa, s3so):
s3fs = pytest.importorskip("s3fs")
if LooseVersion(pyarrow.__version__) <= LooseVersion("0.17.0"):
pytest.skip()
s3 = s3fs.S3FileSystem(**s3so)
kw = dict(filesystem=s3)
> check_round_trip(
df_compat,
pa,
path="pandas-test/pyarrow.parquet",
read_kwargs=kw,
write_kwargs=kw,
)
service_id = self._service_model.service_id.hyphenize()
handler, event_response = self.meta.events.emit_until_response(
'before-call.{service_id}.{operation_name}'.format(
service_id=service_id,
operation_name=operation_name),
model=operation_model, params=request_dict,
request_signer=self._request_signer, context=request_context)
if event_response is not None:
http, parsed_response = event_response
else:
http, parsed_response = self._make_request(
operation_model, request_dict, request_context)
self.meta.events.emit(
'after-call.{service_id}.{operation_name}'.format(
service_id=service_id,
operation_name=operation_name),
http_response=http, parsed=parsed_response,
model=operation_model, context=request_context
)
if http.status_code >= 300:
error_code = parsed_response.get("Error", {}).get("Code")
error_class = self.exceptions.from_code(error_code)
> raise error_class(parsed_response, operation_name)
E botocore.errorfactory.NoSuchBucket: An error occurred (NoSuchBucket) when calling the PutObject operation: The specified bucket does not exist
```
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|
DOC: Fix code of conduct link
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| 3
| 2020-08-22T18:35:39Z
| 2020-08-23T07:40:30Z
| 2020-08-23T07:40:30Z
|
NONE
| null |
closes #35855
|
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BUG: Datetime MultiIndex Regression
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| 5
| 2020-08-22T18:48:49Z
| 2020-10-02T23:02:46Z
| 2020-10-02T23:02:46Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas
date = pandas.Timestamp("2000")
x = pandas.DataFrame([
["a", date, 1],
], columns=["a", "b", "c"]).set_index(["a", "b"])["c"]
x.loc[:, [date]]
InvalidIndexError: [Timestamp('2000-01-01 00:00:00')]
```
#### Problem description
The problem stems from `pandas.DatetimeIndex.get_loc` raising a `InvalidIndexError` in `1.*` instead of a `TypeError` as in `0.25.3`
#### 0.25.3
```python
import pandas
level_index = pandas.DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='b', freq=None)
key = [pandas.Timestamp('2000-01-01 00:00:00')]
level_index.get_loc(key)
...
TypeError: Cannot convert input [[Timestamp('2000-01-01 00:00:00')]] of type <class 'list'> to Timestamp
```
#### 1.1.0
```python
import pandas
level_index = pandas.DatetimeIndex(['2000-01-01'], dtype='datetime64[ns]', name='b', freq=None)
key = [pandas.Timestamp('2000-01-01 00:00:00')]
level_index.get_loc(key)
...
InvalidIndexError: [Timestamp('2000-01-01 00:00:00')]
```
Previously the `TypeError` was handled by https://github.com/pandas-dev/pandas/blob/23b1717a14f185e3512607d33cdba17bfddfd064/pandas/core/indexing.py#L1078
#### Expected Output
I would expect this to behave similar to `0.25.3` which is valid indexing synatx.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.16.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 5.3.5
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.0-RAY
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : 1.3.19
tables : 3.5.1
tabulate : None
xarray : 0.15.0
xlrd : None
xlwt : None
numba : None
</details>
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|
BUG: Passing dataframe to a function creates an inplace update to the original dataframe
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| 2
| 2020-08-22T21:32:59Z
| 2020-08-22T22:43:37Z
| 2020-08-22T22:43:36Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from typing import Tuple
# sample data
data = {'Date': [pd.Timestamp('2019-01-31 00:00:00'), pd.Timestamp('2019-02-01 00:00:00'), pd.Timestamp('2019-02-04 00:00:00'), pd.Timestamp('2019-02-05 00:00:00'), pd.Timestamp('2019-02-06 00:00:00'), pd.Timestamp('2019-02-07 00:00:00'), pd.Timestamp('2019-02-08 00:00:00'), pd.Timestamp('2019-02-11 00:00:00'), pd.Timestamp('2019-02-12 00:00:00'), pd.Timestamp('2019-02-13 00:00:00'), pd.Timestamp('2019-02-14 00:00:00')],
'Close': [166.44000244140625, 166.52000427246094, 171.25, 174.17999267578125, 174.24000549316406, 170.94000244140625, 170.41000366210938, 169.42999267578125, 170.88999938964844, 170.17999267578125, 170.8000030517578]}
# create dataframe
aapl = pd.DataFrame(data)
def find_trend(data: pd.DataFrame, period: int) -> Tuple[pd.Series, pd.Series]:
data['sma'] = data['Close'].rolling(period).mean() # this creates an inplace update to aapl
diff = data['sma'] - data['sma'].shift(1) # calculates a series of values
greater_than_0 = diff > 0 # creates a series of bools
return diff, greater_than_0
aapl['value'], aapl['trend'] = find_trend(aapl, 4)
```
#### Current Output
- Note the creation of the `sma` column
- Is this the expected behavior?
```python
| | Date | Close | sma | value | trend |
|---:|:--------------------|--------:|--------:|------------:|:--------|
| 0 | 2019-01-31 00:00:00 | 166.44 | nan | nan | False |
| 1 | 2019-02-01 00:00:00 | 166.52 | nan | nan | False |
| 2 | 2019-02-04 00:00:00 | 171.25 | nan | nan | False |
| 3 | 2019-02-05 00:00:00 | 174.18 | 169.597 | nan | False |
| 4 | 2019-02-06 00:00:00 | 174.24 | 171.548 | 1.95 | True |
| 5 | 2019-02-07 00:00:00 | 170.94 | 172.653 | 1.105 | True |
| 6 | 2019-02-08 00:00:00 | 170.41 | 172.443 | -0.209999 | False |
| 7 | 2019-02-11 00:00:00 | 169.43 | 171.255 | -1.1875 | False |
| 8 | 2019-02-12 00:00:00 | 170.89 | 170.417 | -0.837502 | False |
| 9 | 2019-02-13 00:00:00 | 170.18 | 170.227 | -0.190002 | False |
| 10 | 2019-02-14 00:00:00 | 170.8 | 170.325 | 0.0974998 | True |
```
#### Problem description
- I do not expect `data['sma'] = data['Close'].rolling(period).mean()` to create an inplace update to `aapl`
- This seems related to:
- [BUG: DataFrame.apply with func altering row in-place #35633](https://github.com/pandas-dev/pandas/pull/35633)
- [Pandas 1.1.0 apply function is altering the row in place](https://stackoverflow.com/questions/63400773)
#### Expected Output
```python
| | Date | Close | value | trend |
|---:|:--------------------|--------:|------------:|:--------|
| 0 | 2019-01-31 00:00:00 | 166.44 | nan | False |
| 1 | 2019-02-01 00:00:00 | 166.52 | nan | False |
| 2 | 2019-02-04 00:00:00 | 171.25 | nan | False |
| 3 | 2019-02-05 00:00:00 | 174.18 | nan | False |
| 4 | 2019-02-06 00:00:00 | 174.24 | 1.95 | True |
| 5 | 2019-02-07 00:00:00 | 170.94 | 1.105 | True |
| 6 | 2019-02-08 00:00:00 | 170.41 | -0.209999 | False |
| 7 | 2019-02-11 00:00:00 | 169.43 | -1.1875 | False |
| 8 | 2019-02-12 00:00:00 | 170.89 | -0.837502 | False |
| 9 | 2019-02-13 00:00:00 | 170.18 | -0.190002 | False |
| 10 | 2019-02-14 00:00:00 | 170.8 | 0.0974998 | True |
```
#### Resolves Issue
- I know changing the function, as follows, will result in not creating an inplace update to `aapl`
```python
def find_trend(data: pd.DataFrame, period: int) -> Tuple[pd.Series, pd.Series]:
sma = data['Close'].rolling(period).mean() # does not create an inplace update to aapl
diff = sma - sma.shift(1) # calculates a series of values
greater_than_0 = diff > 0 # creates a series of bools
return diff, greater_than_0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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MDU6SXNzdWU2ODQwODA1NTU=
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DEPR: Deprecate as_index in groupby
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[] | null | 7
| 2020-08-22T22:53:40Z
| 2021-08-10T04:49:55Z
| null |
MEMBER
| null |
Should we deprecate using `as_index` in groupby? As the docs say, using `as_index=False` should only impact reductions. Here, my expectation is that a line such as
df.groupby(keys, as_index=False).op(...)
should always be equivalent to
df.groupby(keys, as_index=True).op(...).reset_index()
It seems quite easy for the user to do so if they desire, and this would simplify the groupby internals.
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MDExOlB1bGxSZXF1ZXN0NDcyMDY5MzU4
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REF: make window _apply_blockwise actually blockwise
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| 2020-08-23T03:43:55Z
| 2020-08-25T00:23:27Z
| 2020-08-24T23:36:51Z
|
MEMBER
| null |
There will be one more clean-up pass after this, kept separate to maintain targeted diff.
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BUG: value_counts() is non-reproducible from one run to another
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| 2020-08-23T12:25:47Z
| 2020-08-23T12:35:40Z
| 2020-08-23T12:35:40Z
|
NONE
| null |
- [X ] I have checked that this issue has not already been reported.
- [X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
ser = pd.Series(data=['n1', 'n2', 'n0', 'n4', 'n2', 'n1', 'n0', 'n3'], name='ser')
for _ in range(3):
np.random.seed(42)
print(ser.value_counts())
print(ser.value_counts(sort=False))
```
#### Problem description
The output of this file changes from one run to another. This creates a randomality which cannot be reproduced by np.random.seed(..).
Due to this randomality, the code cannot be made reproducible.
This non-reproducibility happens only from one run to the other; the loop in the code shows that the output is repeated exactly within the same run.
The code shows that the non-reproducibility happens both with value_counts(sort=True) (default value) and value_counts(sort=False).
#### Expected Output
Should be the same from one run to another.
#### Note
This could be related to #32514 or #32449 but I do not quite understand this.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-112-generic
Version : #113-Ubuntu SMP Thu Jul 9 23:41:39 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2ODQxNzU0NTE=
| 35,863
|
KeyError using custom agg function
|
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[] | null | 3
| 2020-08-23T12:34:38Z
| 2021-08-10T04:53:15Z
| 2021-08-10T04:53:14Z
|
NONE
| null |
Hi,
Suppose I have a dataframe of some stock price where the column 'Open' is the 0th column and 'Close' is the 3rd column. Suppose further that I want to find the maximum difference between Close and Open price. That can be done easily without using the agg method, but let me show what the error is when I use them.
```
!pip install quandl
import quandl
df = quandl.get("WIKI/AAPL", start_date="2006-12-25")
def daily_value(df):
df.iloc[:, 0] = df.iloc[:,3] - df.iloc[:, 0]
return df.max()
def daily_value(df):
df['Open'] = df['Close'] - df['Open']
return df.max()
display(daily_value(df))
df.agg(dailly_value)
```
Both work as to replace the 0th column, namely 'Open', and return the maximum difference between Open and Close.
This works fine when I have df1 and I type daily_value(df1).
However, when I try df1.agg(daily_value), both version fail. The first says IndexingError: Too many indexers while the latter say KeyError: Close.
How do I proceed if I indeed need to pass the function into *.agg method?
Thanks very much!
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PERF: Vectorized string operations are slower than for-loops
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| 2021-08-28T14:41:45Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
**In [1]:**
```python
import pandas as pd
import numpy as np
print(pd.__version__)
non_padded = pd.Series(np.random.randint(100, 99999, size=10000))
def for_loop(series):
return pd.Series([str(zipcode).zfill(5) for zipcode in series])
def vectorized(series):
return series.astype(str).str.zfill(5)
%timeit for_loop(non_padded)
%timeit vectorized(non_padded)
```
**Out [1]:**
```
0.25.1
3.32 ms ± 44.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
7.18 ms ± 60.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
#### Problem description
In most cases, using a `for-loop` in pandas is much slower than its vectorized equivalent. However, the above operations takes over twice as long when using vectorization. I have replicated this issue on MacOS & Ubuntu.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : 0.14.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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MDU6SXNzdWU2ODQyNzg1OTU=
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BUG: ser[:] = ["a", 1] casts 1 to "1"
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| 2
| 2020-08-23T23:41:40Z
| 2021-04-26T12:21:47Z
| 2021-04-26T12:21:47Z
|
MEMBER
| null |
```
ser = pd.Series(["a", 1])
ser[:] = list(ser.values))
>>> ser[1]
'1'
```
Looks like we call `np.array(ser.values)` and the cast happens in there.
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CLN/PERF: delay evaluation of get_day_of_month
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|
MEMBER
| null |
I don't expect a major perf improvement, but in some corner cases we can avoid evaluating get_day_of_month
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REF: implement Block.reduce for DataFrame._reduce
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| 1
| 2020-08-24T02:57:40Z
| 2020-08-25T00:08:39Z
| 2020-08-24T23:35:41Z
|
MEMBER
| null |
This lets us avoid reconstructing results from a dict, makes it feasible to use the same block-skipping code for DataFrame._reduce that we use for cython_agg_blocks and apply_blockwise.
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CI: TestParquetFastParquet::test_s3_roundtrip on Windows py37_np16/Windows py38_np18
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BUG: Groupby rolling count with datetime not working correctly
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| 8
| 2020-08-24T09:54:44Z
| 2020-10-09T20:02:09Z
| 2020-10-09T20:02:09Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({"column1": range(6),
"column2": range(6),
'group': 3*['A','B'],
'date':pd.date_range(end="20190101", periods=6)})
## Without Closed Parameter
df.groupby('group').rolling('3d',on='date')['column1'].count()
## Output
group
A 0 1.0
2 2.0
4 3.0
B 1 1.0
3 2.0
5 3.0
Name: column1, dtype: float64
## With Closed Parameter
df.groupby('group').rolling('3d',on='date',closed='left')['column1'].count()
## Output
ValueError: closed only implemented for datetimelike and offset based windows
```
#### Problem description
In Pandas 1.1.1, although .sum() and .mean() are working correctly, the rolling groupby .count() is not working correctly with datetime. Furthermore, when closed parameter is added, ValueError is produced.
#### Expected Output
```
## Without Closed Parameter
group
A 0 1.0
2 2.0
4 2.0
B 1 1.0
3 2.0
5 2.0
Name: column1, dtype: float64
## With Closed Parameter
group
A 0 NaN
2 1.0
4 1.0
B 1 NaN
3 1.0
5 1.0
Name: column1, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.1
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1.post20200802
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.48.0
</details>
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DOC: pandas.Series.transform
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| 1
| 2020-08-24T10:08:04Z
| 2020-08-25T10:36:09Z
| 2020-08-25T05:37:04Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.transform.html?highlight=pandas%20series%20transform#pandas.Series.transform
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
> list of functions and/or function names, e.g. [np.exp. 'sqrt']
#### Suggested fix for documentation
list of functions and/or function names, e.g. [np.exp, 'sqrt']
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| 35,871
|
BUG: Regression in pd.read_sql_query between 1.0.5 and 1.1.0
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[] | null | 7
| 2020-08-24T10:41:56Z
| 2020-09-10T20:56:06Z
| null |
NONE
| null |
- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import sqlalchemy
from config import credentials
engine = sqlalchemy.create_engine(
"mysql+mysqldb://%(user)s:%(password)s@%(host)s/%(default)s" % credentials,
echo=False,
)
sql_query = r"""
SELECT
time_string,
STR_TO_DATE(time_string, '%%d/%%m/%%Y'),
data
FROM
test_table
"""
data = pd.read_sql_query(sql_query, engine)
print(data)
```
#### Problem description
In Pandas 1.0.5, '%' in a sql query needed to be escaped as '%%'.
This changes in 1.1.0, but I couldn't see any relevant comment in the release notes. (The behaviour is the same in 1.1.1).
My feeling is that the new behaviour is actually better, but this still seems to be a regression (or an undocumented change).
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.8.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
Version : Darwin Kernel Version 17.7.0: Wed Feb 27 00:43:23 PST 2019; root:xnu-4570.71.35~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0
numpy : 1.18.5
pytz : 2018.9
dateutil : 2.8.0
pip : 20.2.2
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDcyNTYxMTQ3
| 35,872
|
REF: simplify latex formatting
|
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| 5
| 2020-08-24T14:32:13Z
| 2020-11-06T15:34:44Z
| 2020-09-07T18:58:51Z
|
MEMBER
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/35790
- [x] tests added ``pandas/tests/io/formats/test_latex.py``
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Refactor ``to_latex`` using polymorphism and the builder design pattern.
Polymorphism
--------------
Previously there was a complicated logic in multiple methods
for either longtable or regular table.
This PR implements various builders:
- ``RegularTableBuilder``
- ``LongTableBuilder``
- ``TabularBuilder``
Selection of the appropriate builder is carried out in ``LatexFormatter``,
depending on the kwargs provided.
Each builder must implement construction of all the table's components:
- Beginning and end of the environment,
- Separators (top, middle, bottom),
- Header,
- Body.
This allows one eliminate complex logic throughout multiple methods.
Separate Concerns
------------------
Separated concerns: row formatting into string (separate class ``RowStringConverter``)
is carried out independently of the table builder.
Derived from ``RowStringConverter`` are two classes used to iterate over headers or body of the tables:
- ``RowHeaderIterator``
- ``RowBodyIterator``
Extract Methods
----------------
Multiple methods were extracted to improve code readability.
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MDU6SXNzdWU2ODQ3NjAyNzg=
| 35,873
|
BUG: PythonParser does not use decimal separator when usecols and parse_date are specified
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| 3
| 2020-08-24T15:25:06Z
| 2021-01-03T23:26:00Z
| 2021-01-03T23:26:00Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Repl: https://repl.it/repls/TiredLightcyanNetworking#main.py
```python
import pandas as pd
import io
data = io.StringIO('"dump","-9,1",20101010')
read_options = {
'names': ['col', 'col1', 'col2'],
'usecols': ['col1', 'col2'],
'parse_dates': ["col2"],
'decimal': ",",
}
data.seek(0)
c_engine = pd.read_csv(
data,
engine="c",
**read_options,
)
print(c_engine['col1'].dtype) # float64
data.seek(0)
python_engine = pd.read_csv(
data,
engine="python",
**read_options,
)
print(python_engine['col1'].dtype) # object instead of float64
pd.testing.assert_frame_equal(c_engine, python_engine)
# Raises
# Attribute "dtype" are different
# [left]: float64
# [right]: object
```
#### Problem description
The PythonParser produces an invalid result when using a combination of options. It does not convert the comma correctly when the options 'usecols' and 'decimal' are used. The C engine does produce a correct result.
It seems that this check is wrong, the columns are shifted:
https://github.com/pandas-dev/pandas/blob/v1.1.1/pandas/io/parsers.py#L3089
The columns specified in that list are adjusted for the 'usecols' option (so shifted by 1 in the example), while the read line still has _all_ columns.
#### Expected Output
Expect that decimals are correctly converted for all columns. However, the python engine does not when there are columns specified for date parsing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1019-gcp
Version : #19-Ubuntu SMP Tue Jun 23 15:46:40 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDcyNjI0MjIx
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BUG: to_dict_of_blocks failing to invalidate item_cache
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MEMBER
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- [ ] closes #xxxx
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| 4
| 2020-08-24T17:55:45Z
| 2020-10-28T15:55:27Z
| 2020-08-24T23:42:02Z
|
MEMBER
| null |
- [x] closes #35294
- [x] closes #35148
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Tested locally with @TomAugspurger docker container and the tests were working: https://github.com/pandas-dev/pandas/pull/35228#issuecomment-658833239
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groupby AssertionError with datetime column name
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| 2
| 2020-08-24T18:29:56Z
| 2020-08-24T23:45:46Z
| 2020-08-24T23:45:46Z
|
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
import datetime
column_names = ['dimension_1', 'dt', 'value']
data = [
('D1', datetime.date(2020, 1, 1), 2.1),
('D1', datetime.date(2020, 1, 2), 4.1),
('D1', datetime.date(2020, 1, 3), 1.7),
]
df_stack = pd.DataFrame(data=data, columns=column_names)
df_stack['dt'] = pd.to_datetime(df_stack['dt'])
df_stack.set_index(['dimension_1', 'dt'], inplace=True)
df_pivot = df_stack.unstack(level=['dt']).transpose().droplevel(None).transpose()
agg_map = {c: 'sum' for c in df_pivot.columns}
df_pivot.groupby(level=['dimension_1']).agg(agg_map)
```
#### Problem description
When executing groupby (on index) for dataframe whose column-names are of type datetime ('datetime64[ns]'), the groupby fails with AssertionError (result.name == res_name).
<details>
AssertionError Traceback (most recent call last)
<ipython-input-3-2655c5698d24> in <module>
1 # do groupby on index
2 agg_map = {c: 'sum' for c in df_pivot.columns}
----> 3 df_pivot.groupby(level=['dimension_1']).agg(agg_map)
c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, engine, engine_kwargs, *args, **kwargs)
947 )
948
--> 949 result, how = self._aggregate(func, *args, **kwargs)
950 if how is None:
951 return result
c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
349 keys = list(arg.keys())
350 if isinstance(obj, ABCDataFrame) and len(
--> 351 obj.columns.intersection(keys)
352 ) != len(keys):
353 cols = sorted(set(keys) - set(obj.columns.intersection(keys)))
c:\users\hli\appdata\local\programs\python\python38-32\lib\site-packages\pandas\core\indexes\datetimelike.py in intersection(self, other, sort)
701 # TODO: no tests rely on this; needed?
702 result = result._with_freq("infer")
--> 703 assert result.name == res_name
704 return result
705
</details>
#### Expected Output
The expected result in this example should be the dataframe itself, since it only has one row. This can be seen by changing the column data type to string:
```python
df_pivot.columns = df_pivot.columns.astype(str)
agg_map = {c: 'sum' for c in df_pivot.columns}
df_pivot.groupby(level=['dimension_1']).agg(agg_map)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 32
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.1.1
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 2
| 2020-08-24T21:39:04Z
| 2020-08-25T00:06:31Z
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|
MEMBER
| null |
- [x] closes #35876
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
@dsaxton this PR's test and whatsnew note reference the underlying issue you identified in #35876, but not the OP issue. Do you think we should add a test/note for that directly?
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| 2
| 2020-08-24T23:38:04Z
| 2020-09-05T21:18:51Z
| 2020-09-05T21:18:51Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
The behavior of assignment via `loc` seems to have changed as of `1.0.0`.
```python
import pandas
TS = pandas.Timestamp
s = pandas.DataFrame([
["A1", TS("2020-06-15"), -573.75],
["A2", TS("2020-06-15"), -198.75],
["A3", TS("2020-06-15"), -1.25],
], columns=["sid", "date", "price"]).set_index(["sid", "date"])["price"]
s.loc[[]] = 0
print(s)
```
```
sid date
A1 2020-06-15 0.0
A2 2020-06-15 0.0
A3 2020-06-15 0.0
Name: price, dtype: float64
```
I would expect this to behave the same as in in `0.25.3` which would assign nothing. I assume this is a bug because I can't find any discussion of the changed behavior in the [Changelog](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.0.html).
Note that selection or assignment without a `MultiIndex` behavior appears to be unchanged, i.e.
```python
print(s.loc[[]])
```
```
Series([], dtype: int64)
```
```python
s = pandas.DataFrame([
["A1", -573.75],
["A2", -198.75],
["A3", -1.25],
], columns=["sid", "price"]).set_index(["sid"])["price"]
s.loc[[]] = 0
print(s)
```
```
sid
A1 -573.75
A2 -198.75
A3 -1.25
Name: price, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 068e6542b0b28d80d8ea5953b6a49a2608f6a541
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-42-generic
Version : #46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+141.g068e6542b
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : 5.27.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
</details>
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Backport PR #35877 on branch 1.1.x (REGR: DatetimeIndex.intersection incorrectly raising AssertionError)
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| 0
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| 2020-08-25T06:27:56Z
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NONE
| null |
Backport PR #35877: REGR: DatetimeIndex.intersection incorrectly raising AssertionError
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QST: Date Overlapping Ranges
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| 2020-08-25T01:36:31Z
| 2020-08-25T00:42:29Z
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NONE
| null |
I've been trying to learn how to use Pandas, but I'm thoroughly confused about where in the API to find methods that can aggregate data conditionally based on sign across date ranges. I have a data frame like so:
```
Date Change
2010-08-25 0.08
2010-08-26 -0.22
2010-08-27 0.04
2010-08-30 -0.08
2010-08-31 -0.11
... ...
2020-08-18 0.96
2020-08-19 -1.79
2020-08-20 5.04
2020-08-21 -0.84
2020-08-24 -1.10
```
The Date column is an index of course. What I want to do is basically partition this data by year. Once partitioned by year, I want to group consecutive rows by the sign of the change column such that consecutive negatives and consecutive positives are grouped together. Once that is done, I want to get the overlap of date ranges with matching sign for all years. For example, if change is positive from 2010-08-25 to 2010-08-27 and from 2011-08-26 to 2011-08-29 the common overlap would be 08-26 to 08-27, obviously accounting for all years not just 2. At that point, once I have the common date ranges and their values I want to average all of the numbers in that range such that at the end I have ranges of dates for which change is consistently positive or negative and the average change for each range. How can I achieve this?
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REF: ignore_failures in BlockManager.reduce
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| 5
| 2020-08-25T00:54:54Z
| 2020-10-14T13:04:24Z
| 2020-10-10T18:36:03Z
|
MEMBER
| null |
Moving towards collecting all of the ignore_failures code in one place.
The case where we have object dtypes is kept separate in this PR, will be handled in the next pass.
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BUG: item_cache invalidation in get_numeric_data
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REF: use BlockManager.apply for Rolling.count
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REF: reuse _combine instead of reset_dropped_locs
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DOC: Fix documentation for pandas.Series.transform #35870
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QST:
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| 2020-08-25T04:18:23Z
| 2020-09-03T10:48:39Z
| 2020-08-26T08:27:53Z
|
NONE
| null |
import pandas as pd
import xlrd
book = xlrd.open_workbook("Excel_to_panda.xlsx")
getting below error:-
/usr/local/bin/python3.8 /Users/bhabanishankarkar/PycharmProjects/untitled/hello.py
Traceback (most recent call last):
File "/Users/bhabanishankarkar/PycharmProjects/untitled/hello.py", line 71, in <module>
book = xlrd.open_workbook("Excel_to_panda.xlsx")
File "/Users/bhabanishankarkar/Library/Python/3.8/lib/python/site-packages/xlrd/__init__.py", line 145, in open_workbook
raise XLRDError('ZIP file contents not a known type of workbook')
xlrd.biffh.XLRDError: ZIP file contents not a known type of workbook
Process finished with exit code 1
Can anyone help with this?
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Backport PR #35814: TST: Fix test_parquet failures for pyarrow 1.0
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| 1
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MEMBER
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Backport https://github.com/pandas-dev/pandas/pull/35814
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BUG: pd.to_datetime() raises InvalidIndexError with Null-like arguments
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| 8
| 2020-08-25T07:37:44Z
| 2021-10-07T08:56:10Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
s = pd.Series([pd.NaT] * 2000 + [None] * 2000, dtype='object')
pd.to_datetime(s)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/sbrochet/venvs/tmp-fa372ee62ee9bef/lib64/python3.8/site-packages/pandas/core/tools/datetimes.py", line 801, in to_datetime
result = arg.map(cache_array)
File "/home/sbrochet/venvs/tmp-fa372ee62ee9bef/lib64/python3.8/site-packages/pandas/core/series.py", line 3970, in map
new_values = super()._map_values(arg, na_action=na_action)
File "/home/sbrochet/venvs/tmp-fa372ee62ee9bef/lib64/python3.8/site-packages/pandas/core/base.py", line 1131, in _map_values
indexer = mapper.index.get_indexer(values)
File "/home/sbrochet/venvs/tmp-fa372ee62ee9bef/lib64/python3.8/site-packages/pandas/core/indexes/base.py", line 2980, in get_indexer
raise InvalidIndexError(
pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
#### Problem description
`pd.to_datetime()` crash if the input contains a mix of `NaT` and `None` with a `pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects` exception.
This issue is similar to https://github.com/pandas-dev/pandas/issues/22305 which was fixed a while ago. It only occurs if the input is large enough to force the caching mechanism.
#### Expected Output
No crash
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.15-200.fc32.x86_64
Version : #1 SMP Tue Aug 11 16:36:14 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.10.0
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: groupby dropna=False with nan value in groupby causes ValueError when apply()
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| 5
| 2020-08-25T09:04:32Z
| 2020-09-05T03:15:04Z
| 2020-09-05T03:15:04Z
|
CONTRIBUTOR
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas, version `1.1.1`
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({
'groups': ['a', 'a', 'b', np.nan],
'tonnages': [10, 10, 20, 30]
})
dfg = df.groupby('groups', dropna=False)
rv = dfg.apply(lambda grp: pd.DataFrame({'values': list(range(len(grp)))}))
```
#### Problem description
`ValueError` raised on `dfg.apply(lambda grp: ...)`; with the following stacktrace:
<details>
```python
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:859: in apply
result = self._python_apply_general(f, self._selected_obj)
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:894: in _python_apply_general
return self._wrap_applied_output(
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/generic.py:1230: in _wrap_applied_output
return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
../../env3.8/lib/python3.8/site-packages/pandas/core/groupby/groupby.py:1145: in _concat_objects
result = concat(
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:274: in concat
op = _Concatenator(
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:454: in __init__
self.new_axes = self._get_new_axes()
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:519: in _get_new_axes
return [
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:520: in <listcomp>
self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:576: in _get_concat_axis
concat_axis = _make_concat_multiindex(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
indexes = [RangeIndex(start=0, stop=2, step=1), RangeIndex(start=0, stop=1, step=1), RangeIndex(start=0, stop=1, step=1)]
keys = Index(['a', 'b', nan], dtype='object', name='groups')
levels = [Index(['a', 'b', nan], dtype='object', name='groups')]
names = ['groups']
def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
if (levels is None and isinstance(keys[0], tuple)) or (
levels is not None and len(levels) > 1
):
zipped = list(zip(*keys))
if names is None:
names = [None] * len(zipped)
if levels is None:
_, levels = factorize_from_iterables(zipped)
else:
levels = [ensure_index(x) for x in levels]
else:
zipped = [keys]
if names is None:
names = [None]
if levels is None:
levels = [ensure_index(keys)]
else:
levels = [ensure_index(x) for x in levels]
if not all_indexes_same(indexes):
codes_list = []
# things are potentially different sizes, so compute the exact codes
# for each level and pass those to MultiIndex.from_arrays
for hlevel, level in zip(zipped, levels):
to_concat = []
for key, index in zip(hlevel, indexes):
mask = level == key
if not mask.any():
> raise ValueError(f"Key {key} not in level {level}")
E ValueError: Key nan not in level Index(['a', 'b', nan], dtype='object', name='groups')
../../env3.8/lib/python3.8/site-packages/pandas/core/reshape/concat.py:631: ValueError
```
</details>
#### Expected Output
No error should be raised. With the above, if i omit the `nan`:
```python
df = pd.DataFrame({
'groups': ['a', 'a', 'b'],
'tonnages': [10, 10, 20]
})
dfg = df.groupby('groups', dropna=False)
rv = dfg.apply(lambda grp: pd.DataFrame({'values': list(range(len(grp)))}))
```
Then it works successfully with `rv` being:
```python
values
groups
a 0 0
1 1
b 0 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.1
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : 0.29.17
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 1.8.6
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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|
BUG: ValueError: Of the four parameters: start, end, periods, and freq, exactly three must be specified
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| 2020-08-26T08:23:09Z
|
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
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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|
DOC: df.to_sql `chunksize` seems to be ignored by default.
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| 2020-10-15T13:56:02Z
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| null |
#### Location of the documentation
https://dev.pandas.io/docs/reference/api/pandas.DataFrame.to_sql.html
#### Documentation problem
Docs for `chunksize` state that "By default, all rows will be written at once". However, this seems to only be true if `method="multi"`, even though `method=None` by default.
Experimentally, I notice a substantial speedup if I set `method="multi"` and `chunksize` is either unset or large, but `chunksize` seems to have no effect if I don't set `method`.
#### Suggested fix for documentation
Documentation for `chunksize` should also reference the `method` argument. For example. we could revise to:
> chunksize : int, optional
> Specify the number of rows in each batch to be written at a time, **if batching is enabled via `method`**. By default, all rows will be written at once.
Also, are there many uses where users should avoid `method="multi"`? If not, would it make sense to change the default?
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DOC: pandas.Series.filter talks about dataframe not series
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[] | null | 1
| 2020-08-25T14:32:51Z
| 2020-08-25T16:22:46Z
| 2020-08-25T16:22:45Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.filter.html
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
Documentation talks about dataframe, not series
[this should provide a description of what documentation you believe needs to be fixed/improved]
#### Suggested fix for documentation
[this should explain the suggested fix and **why** it's better than the existing documentation]
Replace with the correct documentation for Series.
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Fix interpolate limit area and limit direction with pad
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MEMBER
| null |
fork of #31048
@jreback I don't seem to be able to push updates to #31048, so could close and carry on here
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DOC: avoid StorageOptions type alias in docstrings
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| 3
| 2020-08-25T14:42:06Z
| 2020-08-25T20:12:35Z
| 2020-08-25T20:12:32Z
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MEMBER
| null |
Small follow-up on https://github.com/pandas-dev/pandas/pull/35655, replacing the "StorageOptions" with a plain "dict" in the docstrings ("StorageOptions" is not something known to users, in the type annotations it will expand but not in the docstrings)
(cc @martindurant)
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CI: Mark s3 tests parallel safe
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| 7
| 2020-08-25T14:49:51Z
| 2020-09-10T16:30:56Z
| 2020-08-26T02:21:41Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/35856
I think we need to update the pytest pattern though, so this should
fail.
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BUG: DataFrame([]).squeeze() returns DataFrame instead of Series
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Regression in pandas 1.1.0: no longer allows adding a timestamp to timedelta range to create a datetime range.
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| 8
| 2020-08-25T16:35:54Z
| 2020-09-26T01:31:39Z
| 2020-09-26T01:31:38Z
|
NONE
| null |
Under pandas 1.0.5:
```python
>>> import pandas as pd
>>> pd.Timestamp.now() + pd.timedelta_range('0s','1s', periods=31)
DatetimeIndex([ '2020-08-25 12:27:57.690332',
'2020-08-25 12:27:57.723665333',
'2020-08-25 12:27:57.756998666',
'2020-08-25 12:27:57.790332',
'2020-08-25 12:27:57.823665333',
'2020-08-25 12:27:57.856998666',
'2020-08-25 12:27:57.890332',
'2020-08-25 12:27:57.923665333',
'2020-08-25 12:27:57.956998666',
'2020-08-25 12:27:57.990332',
'2020-08-25 12:27:58.023665333',
'2020-08-25 12:27:58.056998666',
'2020-08-25 12:27:58.090332',
'2020-08-25 12:27:58.123665333',
'2020-08-25 12:27:58.156998666',
'2020-08-25 12:27:58.190332',
'2020-08-25 12:27:58.223665333',
'2020-08-25 12:27:58.256998666',
'2020-08-25 12:27:58.290332',
'2020-08-25 12:27:58.323665333',
'2020-08-25 12:27:58.356998666',
'2020-08-25 12:27:58.390332',
'2020-08-25 12:27:58.423665333',
'2020-08-25 12:27:58.456998666',
'2020-08-25 12:27:58.490332',
'2020-08-25 12:27:58.523665333',
'2020-08-25 12:27:58.556998666',
'2020-08-25 12:27:58.590332',
'2020-08-25 12:27:58.623665333',
'2020-08-25 12:27:58.656998666',
'2020-08-25 12:27:58.690332'],
dtype='datetime64[ns]', freq=None)
```
Under pandas 1.1.0:
```python
>>> import pandas as pd
>>> pd.Timestamp.now() + pd.timedelta_range('0s','1s', periods=31)
Traceback (most recent call last):
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1157, in _validate_frequency
raise ValueError
ValueError
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/indexes/extension.py", line 147, in method
result = meth(_maybe_unwrap_index(other))
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1446, in __radd__
return self.__add__(other)
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/ops/common.py", line 65, in new_method
return method(self, other)
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1406, in __add__
result = self._add_datetimelike_scalar(other)
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/timedeltas.py", line 441, in _add_datetimelike_scalar
return DatetimeArray(result, dtype=dtype, freq=self.freq)
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/datetimes.py", line 284, in __init__
type(self)._validate_frequency(self, freq)
File "/Users/cbarber/miniconda3/envs/episodic-dev3/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1171, in _validate_frequency
) from e
ValueError: Inferred frequency None from passed values does not conform to passed frequency 33333333N
```
This error really makes no sense given the above code.
Obviously an unintended side-effect of some other fix.
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CI: docker 32-bit linux build #32709
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| 8
| 2020-08-25T22:09:26Z
| 2020-10-28T10:47:08Z
| 2020-10-28T03:15:11Z
|
MEMBER
| null |
- [x] closes #32709
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MDExOlB1bGxSZXF1ZXN0NDczNTU3NTE0
| 35,899
|
REF: handle axis=None case inside DataFrame.any/all to simplify _reduce
|
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| 1
| 2020-08-25T23:27:39Z
| 2020-09-02T02:30:58Z
| 2020-09-02T01:56:40Z
|
MEMBER
| null |
Between this, #35881, and the PR coming after 35881, we'll be able to simplify _reduce quite a bit.
|
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| 685,918,327
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MDExOlB1bGxSZXF1ZXN0NDczNTc0NjU1
| 35,900
|
REF: use BlockManager.apply for cython_agg_blocks, apply_blockwise
|
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| 1
| 2020-08-26T00:30:11Z
| 2020-09-03T02:57:42Z
| 2020-09-03T02:56:34Z
|
MEMBER
| null |
- [x] closes #34714
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 685,930,824
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MDU6SXNzdWU2ODU5MzA4MjQ=
| 35,901
|
BUG: Transformer Second Layer Access
|
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| 2020-08-26T01:11:16Z
| 2020-09-03T10:49:12Z
| 2020-08-26T01:11:23Z
|
NONE
| null |
pretrained_weights='bert-base-uncased'
tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
model = model_class.from_pretrained(pretrained_weights)
`features = []
input_ids = torch.tensor([self.tokenizer.encode(phrase, add_special_tokens=True)])
with torch.no_grad():
# Models outputs are tuples
outputs = self.model(input_ids)
last_hidden_states = outputs[0]
avg_pool_hidden_states = np.average(last_hidden_states[0], axis=0))
return avg_pool_hidden_states`
I am working on Sentence Similarity problem, given sentence S, find similar sentences W. So I want to encode sentence S and find the closest top-K sentences from W.
I have read the documentations, but I want to confirm few things how do I do. the following:
How do I get the second to last layer?
Am I average pooling the last hidden states correctly?
|
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MDU6SXNzdWU2ODYwNzMxODc=
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|
BUG: to_parquet does not accept pathlib.PosixPath if partition_cols are defined
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| 2
| 2020-08-26T07:12:35Z
| 2020-09-23T11:20:13Z
| 2020-09-23T11:20:13Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pathlib
df = pd.DataFrame({'A':[1,2,3,4], 'B':'C'})
df.to_parquet('tmp_path1.parquet') # OK
df.to_parquet(pathlib.Path('tmp_path2.parquet')) # OK
df.to_parquet('tmp_path3.parquet', partition_cols=['B']) # OK
df.to_parquet(pathlib.Path('tmp_path4.parquet'), partition_cols=['B']) # TypeError
```
#### Problem description
`to_parquet` method raises TypeError when using `pathlib.Path()` as an argument in case when `partition_cols` argument is not None. If no partition cols are provided, then `pathlib.Path()` is properly accepted
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-53-cae5a944d982> in <module>
3
4 df.to_parquet('tmp_path3.parquet', partition_cols=['B']) # OK
----> 5 df.to_parquet(pathlib.Path('tmp_path4.parquet'), partition_cols=['B']) # TypeError
~/miniconda3/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
197 else:
198 kwargs[new_arg_name] = new_arg_value
--> 199 return func(*args, **kwargs)
200
201 return cast(F, wrapper)
~/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py in to_parquet(self, path, engine, compression, index, partition_cols, **kwargs)
2370 index=index,
2371 partition_cols=partition_cols,
-> 2372 **kwargs,
2373 )
2374
~/miniconda3/lib/python3.7/site-packages/pandas/io/parquet.py in to_parquet(df, path, engine, compression, index, partition_cols, **kwargs)
274 index=index,
275 partition_cols=partition_cols,
--> 276 **kwargs,
277 )
278
~/miniconda3/lib/python3.7/site-packages/pandas/io/parquet.py in write(self, df, path, compression, index, partition_cols, **kwargs)
117 compression=compression,
118 partition_cols=partition_cols,
--> 119 **kwargs,
120 )
121 else:
~/miniconda3/lib/python3.7/site-packages/pyarrow/parquet.py in write_to_dataset(table, root_path, partition_cols, partition_filename_cb, filesystem, **kwargs)
1790 subtable = pa.Table.from_pandas(subgroup, schema=subschema,
1791 safe=False)
-> 1792 _mkdir_if_not_exists(fs, '/'.join([root_path, subdir]))
1793 if partition_filename_cb:
1794 outfile = partition_filename_cb(keys)
TypeError: sequence item 0: expected str instance, PosixPath found
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.2
setuptools : 42.0.1.post20191125
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.1
pymysql : 0.9.3
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : None
fsspec : 0.6.0
fastparquet : 0.3.2
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : None
numba : 0.46.0
</details>
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MDU6SXNzdWU2ODYwODUxNTY=
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Datetime error
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| 2020-08-26T07:34:02Z
| 2020-08-26T12:27:12Z
| 2020-08-26T08:58:01Z
|
NONE
| null |
### Can you please let me know how I can correct the following error
**CODE:**
tx_data['InvoiceDate'] = pd.to_datetime(tx_data['InvoiceDate'])
tx_uk = tx_data.query("Country=='United Kingdom'").reset_index(drop=True)
#create 3m and 6m dataframes
tx_3m = tx_uk[(tx_uk.InvoiceDate < date(2011,6,1)) & (tx_uk.InvoiceDate >= date(2011,3,1))].reset_index(drop=True)
tx_6m = tx_uk[(tx_uk.InvoiceDate >= date(2011,6,1)) & (tx_uk.InvoiceDate < date(2011,12,1))].reset_index(drop=True)
**ISSUE:**
32 else:
33 typ = type(right).__name__
---> 34 raise TypeError(f"Invalid comparison between dtype={left.dtype} and {typ}")
35 return res_values
36
**TypeError: Invalid comparison between dtype=datetime64[ns] and date**
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Backport PR #35712: PERF: RangeIndex.format performance
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MEMBER
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#35712
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Backport PR #35777: BUG: DataFrame.apply with result_type=reduce incorrect index
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#35777
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TST/API: test column indexing copy/view semantics
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| 2020-08-26T12:10:57Z
| 2021-06-16T13:52:32Z
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MEMBER
| null |
This is adding some tests for current behaviour related to the discussions in #33457 and #35417 (and also adds the tests from https://github.com/pandas-dev/pandas/pull/35266 which got never merged). I think it is good to have some more explicit tests of current behaviour, while we are considering changing things.
Note: some semantics I am testing are certainly debatable, but I was experimenting a bit with what is happening on current master.
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Truncate columns list to match tr_frame for correct dict formatters lookup
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NONE
| null |
When using a dictionary of formatters and a truncated DataFrame is being displayed, the column list used for looking up the formatter in the dict are incorrect. The columns attribute of the DataFrameFormatter needs to be truncated as well for the lookup to be correct.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2ODYzOTcxOTY=
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ENH: Arithmetic with Timestamp-based intervals
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| 6
| 2020-08-26T15:05:29Z
| 2020-09-09T18:47:59Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
It is currently possible to perform arithmetic on an `Interval` iff it is an `Interval` of numbers. For `Interval` `iv`, `iv - c == Interval(iv.left-c, iv.right-c)`. This does not work for intervals of `Timestamp`s:
```
In [90]: iv = pd.Interval(3, 4)
In [91]: pd.Interval(iv.left-2, iv.right-2)
Out[91]: Interval(1, 2, closed='right')
In [92]: iv - 2
Out[92]: Interval(1, 2, closed='right')
In [93]: iv = pd.Interval(pd.Timestamp("1900-01-01"), pd.Timestamp("1900-01-02"))
In [94]: pd.Interval(iv.left - pd.Timestamp("1900-01-01"), iv.right - pd.Timestamp("1900-01-0
...: 1"))
Out[94]: Interval(Timedelta('0 days 00:00:00'), Timedelta('1 days 00:00:00'), closed='right')
In [95]: iv - pd.Timestamp("1900-01-01")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-95-b61ec78c0d66> in <module>
----> 1 iv - pd.Timestamp("1900-01-01")
TypeError: unsupported operand type(s) for -: 'pandas._libs.interval.Interval' and 'Timestamp'
```
#### Describe the solution you'd like
I would like that the behaviour that `iv - b == Interval(iv.left-b, iv.right-b)` holds for all types that `Interval` can hold, including `Timestamp`s and `Timedelta`s.
#### API breaking implications
No backward compatibility issues expected.
#### Describe alternatives you've considered
Users can currently resort to `Interval(iv.left-b, iv.right-b)`. This is more typing and required repeating the expression `b`.
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MDU6SXNzdWU2ODY0Mzc2NjA=
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|
BUG: sorting dataframe increases memory usage
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[] | null | 1
| 2020-08-26T16:00:39Z
| 2020-08-26T16:29:07Z
| 2020-08-26T16:08:00Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,4000,size=(1000, 4)), columns=list('ABCD'))
print(df.memory_usage(index=False).sum())
df = df.sort_values(['A', 'B']).reset_index(drop=True)
print(df.memory_usage(index=False).sum())
```
Output:
32000
32000
#### Problem description
The dataframe is essentially same rows and same dtypes, just sorted. Why should the sorted df be bigger?
I understand that during sorting, a copy is created so the memory consumption increases but once sorting is finished, that should be back to where it was. Here, the memory usage of the dataframe increased from 31KB to 40 KB for this toy example.
#### Expected Output
The memory usage of sorted pandas dataframe should be similar to what it was before sorting.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0
Cython : None
pytest : 6.0.0
hypothesis : 5.16.1
sphinx : 1.6.7
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CI: Attempt to unpin pytest-xdist
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| 4
| 2020-08-26T19:48:33Z
| 2020-08-27T16:06:38Z
| 2020-08-27T16:06:26Z
|
CONTRIBUTOR
| null |
- [x] closes #35756
2.1.0 was released yesterday: https://pypi.org/project/pytest-xdist/#history
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| 2
| 2020-08-26T20:23:32Z
| 2020-08-26T21:16:05Z
| 2020-08-26T21:16:05Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"ints":[2**x for x in range(50)]})
df["ints"].astype(int)
```
#### Problem description
`astype(int)` converts values to negative at 2**31 power and 0 afterwards.
#### Expected Output
`[int(x) for x in df["ints"].values]`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.2
setuptools : 42.0.2.post20191201
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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| 686,611,587
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MDU6SXNzdWU2ODY2MTE1ODc=
| 35,912
|
DOC: Aggregate and rename is undocumented
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| 1
| 2020-08-26T20:57:51Z
| 2020-08-31T18:43:09Z
| 2020-08-31T18:40:12Z
|
MEMBER
| null |
The function `DataFrame.agg` allows for kwargs to aggregate and rename the results.
df = pd.DataFrame({'a': [1, 2, 3]})
print(df.agg(a_new=('a', 'sum')))
results in
a
a_new 6
Perhaps I'm missing it, but I can't find it in both the API reference and the User Guide.
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TYP: annotate tseries.holiday
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| 1
| 2020-08-27T02:08:26Z
| 2020-08-27T18:50:01Z
| 2020-08-27T18:25:03Z
|
MEMBER
| null |
cc @simonjayhawkins
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|
Make MultiIndex.get_loc raise for unhashable type
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| 1
| 2020-08-27T02:25:38Z
| 2020-09-06T13:01:22Z
| 2020-09-05T21:18:52Z
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MEMBER
| null |
- [x] closes #35878
- [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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"Backport PR #35838 on branch 1.1.x"
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| 2020-08-27T17:11:02Z
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MEMBER
| null |
xref #35838
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| 686,901,351
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MDU6SXNzdWU2ODY5MDEzNTE=
| 35,916
|
BUG: Dataframe slicing incosistent behavior when using & operator
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| null |
[] | null | 4
| 2020-08-27T03:05:40Z
| 2020-08-28T01:08:42Z
| 2020-08-28T01:08:42Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
Not familiar enough with pandas to know what to search for to verify this
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
There seems to be a bug with indexing into DataFrames using logical and. When I do the operations in series, like this, it works, giving two rows:
```
slidescan_db = pd.read_csv('slidescan_db.csv')
db = slidescan_db[slidescan_db['Stain'].str.contains('unstained').fillna(False)]
db[db['Batch'] == batch_index]
```
But when I do it with a one-liner, it gives a different result--no rows
```
slidescan_db = pd.read_csv('slidescan_db.csv')
slidescan_db[slidescan_db['Batch'] == batch_index & slidescan_db['Stain'].str.contains(ch_name).fillna(False)]
```
slidescan_db.csv is attached, but renamed to slidescan.txt because GitHub wouldn't let me upload a csv
[slidescan_db.txt](https://github.com/pandas-dev/pandas/files/5133799/slidescan_db.txt)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.2.0.post20200511
Cython : 0.29.17
pytest : 5.4.2
hypothesis : 5.11.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fsspec : 0.7.1
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.0
</details>
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