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DOC: np.bool -> np.bool_
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MEMBER
| null |
xref #34848, #34835
a couple missed that are causing doc build failures with numpy-1.20.0rc2, https://github.com/pandas-dev/pandas/pull/36092/checks?check_run_id=1662818738
probably want to backport this to prevent possible future ci failures
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Backport PR #39019 on branch 1.2.x (DOC: np.bool -> np.bool_)
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NONE
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Backport PR #39019: DOC: np.bool -> np.bool_
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Backport PR #38997: REGR: errors='replace' when encoding/errors are not specified
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Deprecate DataFrame indexer for iloc setitem and getitem
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MEMBER
| null |
- [x] closes #39004
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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| 781,554,182
|
MDExOlB1bGxSZXF1ZXN0NTUxMjU3MTI5
| 39,023
|
Fix regression in setitem when expanding DataFrame with specific column name format
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| 2021-01-07T19:30:03Z
| 2021-01-08T08:41:05Z
| 2021-01-08T00:19:44Z
|
MEMBER
| null |
- [x] closes #39010
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
We obviously want cast to object if value is string.
|
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|
BUG: read_csv - file left open after UnicodeDecodeError when sep=None
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| 3
| 2021-01-07T19:40:37Z
| 2021-01-13T18:03:45Z
| 2021-01-13T18:03:45Z
|
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 tempfile
import textwrap
import pandas
import os
workspace_dir = tempfile.mkdtemp()
csv_file = os.path.join(workspace_dir, 'non-utf8.csv')
# encode with ISO Cyrillic, include a non-ASCII character to achieve UnicodeDecodeError
with open(csv_file, 'w', encoding='iso8859_5') as file_obj:
file_obj.write(textwrap.dedent(
"""
header,
fЮЮ,
bar
"""
).strip())
try:
dataframe = pandas.read_csv(csv_file, sep=None)
except UnicodeDecodeError as error:
os.remove(csv_file)
raise
```
#### Problem description
`os.remove` raises a PermissionError on Windows because apparently the file handle is still open. This only happens when the `sep=None` kwarg is used. Leaving out that kwarg gets the expected output.
#### Expected Output
```python
Traceback (most recent call last):
File "..\scratch\pandas_file_handle.py", line 19, in <module>
dataframe = pandas.read_csv(csv_file, sep=None)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 605, in read_csv
return _read(filepath_or_buffer, kwds)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 457, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 814, in __init__
self._engine = self._make_engine(self.engine)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 1045, in _make_engine
return mapping[engine](self.f, **self.options) # type: ignore[call-arg]
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 2291, in __init__
self._make_reader(self.handles.handle)
File "C:\Users\dmf\projects\invest\env\lib\site-packages\pandas\io\parsers.py", line 2412, in _make_reader
line = f.readline()
File "C:\Users\dmf\projects\invest\env\lib\codecs.py", line 322, in decode
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xce in position 10: invalid continuation byte
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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MDU6SXNzdWU3ODE1ODk4NTA=
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BUG: Missing keys using aggregation dictionary that are unsortable raise TypeError instead of SpecificationError
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| 2
| 2021-01-07T20:30:39Z
| 2021-01-08T23:17:59Z
| 2021-01-08T23:17:59Z
|
MEMBER
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.3.0.dev0+358.g68db2d26dd'
>>>
>>> df = pd.DataFrame(
... np.random.randn(1000, 3),
... index=pd.date_range("1/1/2012", freq="S", periods=1000),
... columns=[1, "foo", None],
... )
>>> r = df.resample("3T")
>>> r.agg({1: "mean", "foo": "sum"})
1 foo
2012-01-01 00:00:00 -0.070340 9.142790
2012-01-01 00:03:00 -0.048538 31.041777
2012-01-01 00:06:00 -0.058046 -0.394660
2012-01-01 00:09:00 0.014268 -8.947485
2012-01-01 00:12:00 0.040080 -6.869409
2012-01-01 00:15:00 0.020587 0.225159
>>>
>>> r.agg({2: "mean", "bar": "sum"})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\resample.py", line 298, in aggregate
result, how = aggregate(self, func, *args, **kwargs)
File "C:\Users\simon\pandas\pandas\core\aggregation.py", line 566, in aggregate
return agg_dict_like(obj, arg, _axis), True
File "C:\Users\simon\pandas\pandas\core\aggregation.py", line 741, in agg_dict_like
cols = sorted(set(keys) - set(selected_obj.columns.intersection(keys)))
TypeError: '<' not supported between instances of 'str' and 'int'
>>>
>>> df = pd.DataFrame(
... np.random.randn(1000, 3),
... index=pd.date_range("1/1/2012", freq="S", periods=1000),
... columns=["A", "B", "C"],
... )
>>>
>>> r = df.resample("3T")
>>> r.agg({"r1": "mean", "r2": "sum"})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\resample.py", line 298, in aggregate
result, how = aggregate(self, func, *args, **kwargs)
File "C:\Users\simon\pandas\pandas\core\aggregation.py", line 566, in aggregate
return agg_dict_like(obj, arg, _axis), True
File "C:\Users\simon\pandas\pandas\core\aggregation.py", line 742, in agg_dict_like
raise SpecificationError(f"Column(s) {cols} do not exist")
pandas.core.base.SpecificationError: Column(s) ['r1', 'r2'] do not exist
>>>
```
code sample based on test_agg_consistency in pandas/tests/resample/test_resample_api.py
#### Problem description
with mypy 0.790
pandas/core/aggregation.py:741: error: Value of type variable "_LT" of "sorted" cannot be "Optional[Hashable]" [type-var]
#### Expected Output
pandas.core.base.SpecificationError: Column(s) [2, 'bar'] do not exist
#### 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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MDExOlB1bGxSZXF1ZXN0NTUxMzE4MDIz
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TYP/CLN: Use futures annotations in apply
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- [ ] whatsnew entry
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REF: make FreqGroup an Enum
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BUG: Resample.aggregate raising TypeError instead of SpecificationError with missing keys dtypes
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BUG: read_csv does not close file during an error in _make_reader
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| 2021-01-07T22:14:19Z
| 2021-01-14T11:05:23Z
| 2021-01-13T18:03:45Z
|
CONTRIBUTOR
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- [x] closes #39024
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I don't understand why `td.check_file_leaks` doesn't complain about the left opened file (at least for me locally). I commented the close call on purpose to see whether the test fails at least for the CI.
@jbrockmendel I think you have debugged ResourceWarnings in the past. Do you know why the test doesn't fail? Even putting a `open("foo", mode="w")` in the test doesn't make it fail.
[The test case is different from #39024 but the symptoms are the same. Unless the except clause is narrowed down to specific exceptions, this PR will fix #39024]
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MDExOlB1bGxSZXF1ZXN0NTUxMzkwODMy
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CLN: add typing to dtype arg in core/internals, core/reshape and core (GH38808)
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| 0
| 2021-01-07T23:30:44Z
| 2021-01-08T23:09:19Z
| 2021-01-08T23:09:18Z
|
CONTRIBUTOR
| null |
incremental PR for issue #38808
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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MDU6SXNzdWU3ODE3MzIyNTg=
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|
pd.read_parquet causing Python to crash
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[] | null | 4
| 2021-01-07T23:57:21Z
| 2021-08-14T23:29:00Z
| null |
NONE
| null |
These are the commands I ran in IPython:
```
In [1]: import pandas as pd
In [2]: df = pd.DataFrame(data={'x': [1,2,3]})
In [3]: df.to_parquet('test.parquet')
In [4]: with open('test.parquet', 'rb') as f:
...: df2 = pd.read_parquet(f)
...:
```
At which point it exits IPython without displaying an error or stacktrace. Maybe this is some kind of segfault? It also fails when running from a script, and also when reading from a BytesIO instead of a file.
This is using pandas==1.2.0, pyarrow==2.0.0, and Python 3.7.3. Also this was run in Windows from Powershell.
If it helps, this is the output from `pd.show_versions()`:
```
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
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 45 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.4
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.0.post20201221
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.0.0
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.2
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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Backport PR #39023 on branch 1.2.x (Fix regression in setitem when expanding DataFrame with specific column name format)
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NONE
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Backport PR #39023: Fix regression in setitem when expanding DataFrame with specific column name format
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BUG: tm.assert_produces_warnings(None) failing to raise
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MEMBER
| null |
in tests.indexes.interval.test_constructors test_constructor_dtype if we disable the tm.assert_produces_warning and instead run pytest with `-W error`:
```
pytest pandas/tests/indexes/interval/test_constructors.py -k test_constructor_dtype -W error
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromArrays::test_constructor_dtype[breaks4-int64] - FutureWarning: casting datetime64[ns] values to int6...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromArrays::test_constructor_dtype[breaks5-int64] - FutureWarning: casting timedelta64[ns] values to int...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromBreaks::test_constructor_dtype[breaks4-int64] - FutureWarning: casting datetime64[ns] values to int6...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromBreaks::test_constructor_dtype[breaks5-int64] - FutureWarning: casting timedelta64[ns] values to int...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromTuples::test_constructor_dtype[breaks4-int64] - FutureWarning: casting datetime64[ns] values to int6...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestFromTuples::test_constructor_dtype[breaks5-int64] - FutureWarning: casting timedelta64[ns] values to int...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestClassConstructors::test_constructor_dtype[IntervalIndex-breaks4-int64] - FutureWarning: casting datetime...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestClassConstructors::test_constructor_dtype[IntervalIndex-breaks5-int64] - FutureWarning: casting timedelt...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestClassConstructors::test_constructor_dtype[Index-breaks4-int64] - FutureWarning: casting datetime64[ns] v...
FAILED pandas/tests/indexes/interval/test_constructors.py::TestClassConstructors::test_constructor_dtype[Index-breaks5-int64] - FutureWarning: casting timedelta64[ns] ...
```
we're passing kwargs to `pd.Index.__new__` which is deprecated.
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TST: flesh out EA setitem tests
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| 1
| 2021-01-08T04:44:42Z
| 2021-01-08T15:51:42Z
| 2021-01-08T14:12:27Z
|
MEMBER
| null |
Preliminary to fixing some behavior that these hit
|
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API: concatting DataFrames does not skip empty objects
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| 4
| 2021-01-08T09:28:29Z
| 2021-01-12T08:36:34Z
| 2021-01-08T15:58:27Z
|
MEMBER
| null |
See discussion in https://github.com/pandas-dev/pandas/pull/38843
To repeat the main argument: when not skipping empty objects, the resulting dtype of a concat-operation only depends on the input *dtypes*, and not on the exact content (the exact values, how many values (shape)). In general we want to get rid of value-dependent behaviour. In the past we discussed this in the context of the certain values (eg presence of NaNs or not), but I think also the shape should not matter (eg when slicing dataframes before contatting, you can get empties or not depending on *values*).
This starts with reverting the change for DataFrame. If we opt for this behaviour, in addition we should also discuss how to change the Series behaviour.
cc @jbrockmendel
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CLN, TYP use from __future__ import annotations
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| 3
| 2021-01-08T12:30:21Z
| 2021-01-10T13:59:02Z
| 2021-01-08T13:23:38Z
|
MEMBER
| null |
Seeing as pandas is Python3.7+ we can clean up some annotations. Not yet figured out how to write a good automated check for this, but here's a start
|
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REGR: concat of unaligned empty DataFrames failing
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| 5
| 2021-01-08T12:55:29Z
| 2021-05-28T19:05:48Z
| 2021-05-28T19:05:48Z
|
MEMBER
| null |
While trying to get dask's CI passing (https://github.com/dask/dask/pull/6996/), I noticed another error related to concat. Dask concatenates the empty "meta" dataframe to know the shape/dtypes of the resulting dataframe, and something is failing in there now.
Small reproducer without dask:
```python
>>> df1 = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
>>> df2 = pd.DataFrame({'b': [1, 2, 3], 'c': [4, 5, 6]})
>>> pd.concat([df1[0:0], df2[0:0], df1[0:0]])
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-151-b1206da866d9> in <module>
----> 1 pd.concat([df1[0:0], df2[0:0], df1[0:0]])
~/scipy/pandas/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
297 )
298
--> 299 return op.get_result()
300
301
~/scipy/pandas/pandas/core/reshape/concat.py in get_result(self)
518
519 new_data = concatenate_block_managers(
--> 520 mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
521 )
522 if not self.copy:
~/scipy/pandas/pandas/core/internals/concat.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
89 else:
90 b = make_block(
---> 91 _concatenate_join_units(join_units, concat_axis, copy=copy),
92 placement=placement,
93 ndim=len(axes),
~/scipy/pandas/pandas/core/internals/concat.py in _concatenate_join_units(join_units, concat_axis, copy)
325 join_units = nonempties
326
--> 327 empty_dtype, upcasted_na = _get_empty_dtype_and_na(join_units)
328
329 to_concat = [
~/scipy/pandas/pandas/core/internals/concat.py in _get_empty_dtype_and_na(join_units)
436
437 msg = "invalid dtype determination in get_concat_dtype"
--> 438 raise AssertionError(msg)
439
440
AssertionError: invalid dtype determination in get_concat_dtype
```
It occurs when reindexing happens (not fully aligned dataframes), and apparently at least 3 dataframes are needed to trigger it (the same example with only 2 dataframes passed to concat doesn't fail).
I suppose this might be related to https://github.com/pandas-dev/pandas/pull/38843 (change only on master, so not a target for 1.2.1) cc @jbrockmendel
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MDU6SXNzdWU3ODIxNTM0MzU=
| 39,038
|
BUG: Setting closed='neither' for rolling function of Series leads to mean() producing nan values
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[] | null | 2
| 2021-01-08T14:23:25Z
| 2021-01-11T07:31:23Z
| 2021-01-11T07:31:23Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
series = pd.Series(np.arange(20), index=pd.date_range("2018-01-01", periods=20, freq="M"))
series.rolling(window=5, closed='neither').mean()
```
#### Problem description
In the current version of pandas, the call to mean() produces a series of nan-values. For pandas 1.1.5, the output was different providing valid values starting from the fifth entry of the series.
#### Expected Output
2018-01-31 NaN
2018-02-28 NaN
2018-03-31 NaN
2018-04-30 NaN
2018-05-31 2.0
2018-06-30 3.0
2018-07-31 4.0
2018-08-31 5.0
2018-09-30 6.0
2018-10-31 7.0
2018-11-30 8.0
2018-12-31 9.0
2019-01-31 10.0
2019-02-28 11.0
2019-03-31 12.0
2019-04-30 13.0
2019-05-31 14.0
2019-06-30 15.0
2019-07-31 16.0
2019-08-31 17.0
Freq: M, dtype: float64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : 0.29.17
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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API/BUG: pd.array(float_ndarray_with_nans) casts np.nan to pd.NA
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MEMBER
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I was under the impression we meant to keep these as distinct concepts
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BUG: DataFrame.iloc.__setitem__ with IntegerArray values
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MEMBER
| null |
- [x] closes #38952
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG: pandas v1.2 read_csv crashes when reading a large CSV
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[] | null | 18
| 2021-01-08T19:45:49Z
| 2021-05-08T22:01:34Z
| 2021-05-08T22:01:34Z
|
NONE
| null |
#### Environment
OS: Windows 10
Python version: 3.7.8 (64 bit)
#### Problem description
Pandas 1.2 fails to read a large CSV (1.6GB), crashing the python process with exit code 0xC0000005. This issue is IDE agnostic, as it happens when running python from the console as well (no IDE).
- The CSV is quite simple, 7M rows x 100 columns with all integer values (1 or 0).
- Adding/changing/removing encoding doesn't change the crash behavior. Using engine='python' works fine, so it's just the C one that is breaking.
- Downgraded to pandas 1.1.4 and it works as expected, so the problem relies with pandas v1.2 only.
```python
df = pd.read_csv('large_file.csv')
or
df = pd.read_csv('large_file.csv', encoding='utf-8')
```
#### Expected Output:
It should be able to read a large CSV file. I have tried with a smaller CSV (400Mb) and it works fine. So the issue materializes only when the CSV is large (maybe > 1.5GB?).
#### Current Output
```
Process finished with exit code 0xC0000005
```
#### Area where the process exits and debugging can't continue:
File: parsers.py
```python
def read(self, nrows=None):
try:
data = self._reader.read(nrows) <---
except StopIteration:
```
#### pd.show_versions()
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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REF: Prep for moving Series.apply into apply
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| 2
| 2021-01-08T21:31:27Z
| 2021-01-09T01:57:25Z
| 2021-01-08T23:08:21Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Will be moving Series.apply into apply next. The plan is to have a base class Apply which is parent to both FrameApply and SeriesApply.
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MDExOlB1bGxSZXF1ZXN0NTUxOTg4NTA2
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CI: use conda incubator in benchmarks
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| 2021-01-08T22:17:36Z
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|
MEMBER
| null |
The benchmark ci currently has the longest runtime. Using conda incubator would save about 6-8 min.
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BUG/API: make setitem-inplace preserve dtype when possible with PandasArray, IntegerArray, FloatingArray
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| 6
| 2021-01-08T22:30:49Z
| 2021-07-16T16:08:40Z
| 2021-02-18T00:15:28Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
xref #38896 (doesn't close). In general `df[:, "A"] = foo` tries to operate in-place before falling back to casting. This makes sure we do that when `foo` is a `PandasArray`, `IntegerArray`, or `FloatingArray`
I guess could/should do the same for BooleanArray
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MDExOlB1bGxSZXF1ZXN0NTUyMDA0MTIw
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Update awkward phrasing
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| 1
| 2021-01-08T23:10:20Z
| 2021-01-09T21:21:54Z
| 2021-01-09T21:21:50Z
|
CONTRIBUTOR
| null |
Docs: change
"The builtin options available in each of the pandas plot functions that are worthwhile to have a look."
to
"The builtin options available in each of the pandas plot functions are worth reviewing."
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BUG: reindexing empty CategoricalIndex would fail if target had duplicates
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| 3
| 2021-01-08T23:44:42Z
| 2021-01-10T02:39:57Z
| 2021-01-10T02:39:54Z
|
CONTRIBUTOR
| null |
- [x] closes #38906
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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| 782,494,701
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MDExOlB1bGxSZXF1ZXN0NTUyMDQwOTk5
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TST: extend check for leaked files
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[] | null | 7
| 2021-01-09T02:31:51Z
| 2021-03-31T22:34:37Z
| 2021-03-31T22:34:37Z
|
CONTRIBUTOR
| null |
Use `ResourceWarning`s as indicators of leaked files. I left the `psutil`-based check for leaked files.
I enabled the check for leaked files for all io tests.
boto3 (#17058) is probably causing all of the unclosed ssl connections. This PR ignores ResourceWarnings that contain the string `'ssl'`. The fixture applied to all io tests further ignores all connections found by `psutils` for the same reason. Connections are checked for code that was previously covered by `check_file_leaks`.
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| 782,506,808
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MDU6SXNzdWU3ODI1MDY4MDg=
| 39,048
|
DOCUMENTATION BUG: DOC FOR `pandas.Index.resize` TALKS ABOUT `numpy`
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| 3
| 2021-01-09T04:02:16Z
| 2021-01-09T14:35:03Z
| 2021-01-09T14:34:46Z
|
NONE
| null |
I was reading the [documentation](https://pandas.pydata.org/pandas-docs/version/0.14/generated/pandas.Index.resize.html) for `pandas.Index.resize`
The doc for `pandas.index.resize` talks about `numpy`
All of the examples use the `numpy` library.
`pandas` and `numpy` are not the same library.
shouldn't the examples looks more like `pandas`?
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MDExOlB1bGxSZXF1ZXN0NTUyMDU0MTQx
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BUG: pd.to_numeric does not copy _mask for ExtensionArrays
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| 1
| 2021-01-09T04:42:40Z
| 2021-01-09T21:22:59Z
| 2021-01-09T21:22:56Z
|
MEMBER
| null |
- [x] closes #38974
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry (not needed as bug on master only)
Fixing a bug I introduced in #38746
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MDU6SXNzdWU3ODI1NjE1NDU=
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BUG: .diff() not working on int8 and int16 dtype
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| 1
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| 2021-01-10T00:42:30Z
| 2021-01-10T00:42:30Z
|
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.
---
```python
# Your code here
df=pd.DataFrame({'a':[1,2,3,3,2],'b':[0,1,1,0,0]})
df['b']=df['b'].astype(np.int8)
df.groupby('a')['b'].diff()
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-23-f6026edf0092> in <module>
1 df=pd.DataFrame({'a':[1,2,3,3,2],'b':[0,1,1,0,0]})
2 df['b']=df['b'].astype(np.int8)
----> 3 df.groupby('a')['b'].diff()
~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in wrapper(*args, **kwargs)
815 return self.apply(curried)
816
--> 817 return self._python_apply_general(curried, self._obj_with_exclusions)
818
819 wrapper.__name__ = name
~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in _python_apply_general(self, f, data)
926 data after applying f
927 """
--> 928 keys, values, mutated = self.grouper.apply(f, data, self.axis)
929
930 return self._wrap_applied_output(
~\Anaconda3\lib\site-packages\pandas\core\groupby\ops.py in apply(self, f, data, axis)
235 # group might be modified
236 group_axes = group.axes
--> 237 res = f(group)
238 if not _is_indexed_like(res, group_axes, axis):
239 mutated = True
~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in curried(x)
804
805 def curried(x):
--> 806 return f(x, *args, **kwargs)
807
808 # preserve the name so we can detect it when calling plot methods,
~\Anaconda3\lib\site-packages\pandas\core\series.py in diff(self, periods)
2436 {examples}
2437 """
-> 2438 result = algorithms.diff(self.array, periods)
2439 return self._constructor(result, index=self.index).__finalize__(
2440 self, method="diff"
~\Anaconda3\lib\site-packages\pandas\core\algorithms.py in diff(arr, n, axis, stacklevel)
2000 # TODO: can diff_2d dtype specialization troubles be fixed by defining
2001 # out_arr inside diff_2d?
-> 2002 algos.diff_2d(arr, out_arr, n, axis, datetimelike=is_timedelta)
2003 else:
2004 # To keep mypy happy, _res_indexer is a list while res_indexer is
pandas\_libs\algos.pyx in pandas._libs.algos.diff_2d()
NotImplementedError:
```
#### Problem description
I try to take the difference of dataframe on columns with dtype int8 and int16 but this yields an error. This was working in previous version of pandas, 1.1.5, but not in current version, 1.2.0.
#### Expected Output
```python
0 NaN
1 NaN
2 NaN
3 -1.0
4 -1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 11, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTUyMDk3Mzky
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ENH: try to preserve the dtype on combine_first for the case where the two DataFrame objects have the same columns
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| 6
| 2021-01-09T08:27:59Z
| 2021-01-15T16:30:28Z
| 2021-01-15T16:30:23Z
|
CONTRIBUTOR
| null |
…e two DataFrame objects have the same columns
- [ ] closes #7509
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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TYP: remove ignores from set_testing_mode and reset_testing_mode
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| 0
| 2021-01-09T11:29:12Z
| 2021-01-10T09:26:01Z
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|
MEMBER
| null |
xref #37715
The error message is self-explanatory although the original code passing a tuple seemed to work also, although not documented.
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ENH: Please provide "universal2" wheels for macOS
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| 3
| 2021-01-09T12:08:52Z
| 2021-08-15T02:44:11Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
There is a, currently formally experimental, "universal2" installer for Python on www.python.org. This natively supports both x86_64 (macOS 10.9 or later) and arm64 (macOS 11.0 or later). Using pandas with this installer currently requires building pandas from source, not from a binary wheel.
#### Describe the solution you'd like
A binary wheel on PyPI for the "universal2" installer, next to the "x86_64" wheel there already is.
In the longer run I'd love to see only "universal2" wheels and no "x86_64" wheels, but that at the very least requires time for support in the packaging system to further mature (support for "universal2" in pip and wheel is pretty new at this point).
|
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TYP: remove ignore from all_timeseries_index_generator
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MEMBER
| null |
xref #37715
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QST: How to print specific element from dataframe?
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| 2021-01-09T13:46:14Z
| 2021-01-09T22:58:38Z
| 2021-01-09T14:00:57Z
|
NONE
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
# Your code here, if applicable
https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html
```
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD"))
print("Print the dataframe which is like a matrix")
print(df)
```
This works. But how do I print specific element of the dataframe? What is the syntax?
print(df[0:1] prints one row
print(df[0:2] prints two rows.
How do I print exact element (ex: row1, value of column A)?
Also length of a dataframe. Here, we know length=6 because we have created.
```
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TYP: remove ignore from makeMissingDataframe
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MEMBER
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xref #37715
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MEMBER
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xref #37715
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TYP: series apply method adds type hints
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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TYP: remove ignore in optional_args
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MEMBER
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xref #37715
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MDExOlB1bGxSZXF1ZXN0NTUyMTQ0Njk4
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BUG: in network decorator
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REF: Move Series.apply/agg into apply
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| 2021-01-09T15:22:44Z
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MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Will do follow up to share list/dict/str code in Apply for how=apply; they are essentially identical.
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TST: skips in extension.test_categorical
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| 2021-01-09T16:25:36Z
| 2021-01-12T01:28:00Z
| 2021-01-12T01:17:22Z
|
MEMBER
| null |
- [x] closes #38904
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
cc @simonjayhawkins
For the searchsorted test, the categorical data starts off ordered as "C" < "A" < "B" but then gets reordered in the test by these lines:
> b, c, a = data_for_sorting
arr = type(data_for_sorting)._from_sequence([a, b, c])
so the ordering becomes "A" < "B" < "C". This causes failure when "C" is said to be inserted at index 3 (new order) to preserve order rather than the expected 0 (original order).
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ENH: rolling for periodic data
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[] | null | 8
| 2021-01-09T16:48:26Z
| 2021-08-11T20:25:59Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I have a periodic DataFrame and I would like to compute a rolling mean. However, I struggled to do this with pandas nicely. What I ended up dooing was to copy the tail of the DataFrame to the top and the head to the bottom, and than used DataFrame.rolling().
#### Describe the solution you'd like
It would be nice to have somthing like `DataFrame.rolling(how='periodic')`
#### Describe alternatives you've considered
I am not sure how many users need this feature, so it might not be worth adding it for the few needing it.
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Added docs for the change of behavior of isin
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| 6
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| 2021-01-17T15:37:27Z
| 2021-01-17T15:36:02Z
|
CONTRIBUTOR
| null |
This is to close issue #38781, I added documentation to explain the change of behavior of the isin function.
- [x] closes #38781
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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Regression in replace raising ValueError for bytes object
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| 2
| 2021-01-09T19:42:51Z
| 2021-01-09T22:23:17Z
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|
MEMBER
| null |
- [x] closes #38900
- [x] tests added / passed
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- [x] whatsnew entry
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TYP: ALL_NUMPY_DTYPES mypy errors with numpy-1.20
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| 0
| 2021-01-09T20:03:59Z
| 2021-01-10T09:23:31Z
| 2021-01-09T21:34:55Z
|
MEMBER
| null |
prevents mypy errors
pandas/_testing/__init__.py:122: error: Unsupported operand types for + ("List[Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float], Type[int], Type[complex], Type[bool], Type[object]]]" and "List[object]") [operator]
pandas/_testing/__init__.py:123: error: Unsupported operand types for + ("List[Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float], Type[int], Type[complex], Type[bool], Type[object]]]" and "List[object]") [operator]
pandas/_testing/__init__.py:124: error: Unsupported operand types for + ("List[Union[ExtensionDtype, Union[str, dtype[Any]], Type[str], Type[float], Type[int], Type[complex], Type[bool], Type[object]]]" and "List[object]") [operator]
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MDExOlB1bGxSZXF1ZXN0NTUyMTgyNjg3
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TYP: prep _generate_range_overflow_safe for numpy 1.20
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| 1
| 2021-01-09T20:30:00Z
| 2021-02-02T09:00:19Z
| 2021-02-02T09:00:19Z
|
MEMBER
| null |
difficult call, will replace
pandas/core/arrays/_ranges.py:153: error: Incompatible return value type (got "signedinteger[_64Bit]", expected "int") [return-value]
pandas/core/arrays/_ranges.py:171: error: Incompatible return value type (got "unsignedinteger[_64Bit]", expected "int") [return-value]
with
pandas/core/arrays/_ranges.py:129: error: Argument 1 to "_generate_range_overflow_safe" has incompatible type "Union[int, number[Any]]"; expected "Union[int, integer[Any]]" [arg-type]
pandas/core/arrays/_ranges.py:129: error: Argument 2 to "_generate_range_overflow_safe" has incompatible type "Union[int, number[Any]]"; expected "Union[int, integer[Any]]" [arg-type]
pandas/core/arrays/_ranges.py:137: error: Argument 2 to "_generate_range_overflow_safe" has incompatible type "Union[int, number[Any]]"; expected "Union[int, integer[Any]]" [arg-type]
pandas/core/arrays/_ranges.py:138: error: Argument 2 to "_generate_range_overflow_safe" has incompatible type "Union[int, number[Any]]"; expected "Union[int, integer[Any]]" [arg-type]
these are from arithmetic operations on np.integer (including floordiv) which AFAICT should return np.integer and not np.number
can't add the ignores yet, since we will get unused ignore mypy messages - active branch with fixes https://github.com/pandas-dev/pandas/compare/master...simonjayhawkins:numpy-fixes?expand=1
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MDExOlB1bGxSZXF1ZXN0NTUyMTgyODky
| 39,068
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BUG/API: DTI/TDI/PI.insert cast to object on failure
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| 8
| 2021-01-09T20:31:41Z
| 2021-01-22T00:07:29Z
| 2021-01-21T23:58:35Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
ATM DTI/TDI/PI/insert are pretty idiosyncratic. This changes them to match the base Index.insert by a) always trying _validate_fill_value (this changes the behavior for strings that can be parsed to datetime/timedelta/period) and b) always fall back to object in cases where this validation fails (this changes behavior for non-strings where the validation fails).
AFAICT the current behavior was implemented in #5819 and the only discussion I see about castable strings or non-castable non-strings is this [comment](https://github.com/pandas-dev/pandas/pull/5819#issuecomment-31454545), which I don't think is relevant any longer.
One corner case is mismatched tzs, which raise in master. In the PR they cast to object. They could also reasonably cast to the index's tz (xref #37605)
cc @rhshadrach IIRC you had a question about the insert tests in test_coercion. I think this addresses what you were asking about. Let me know if I missed something.
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MDExOlB1bGxSZXF1ZXN0NTUyMTgzMDMx
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REGR: diff_2d raising for int8, int16
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| 2021-01-09T20:32:57Z
| 2021-01-11T13:44:50Z
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MEMBER
| null |
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MDExOlB1bGxSZXF1ZXN0NTUyMTg2MjE1
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TYP: PeriodArray._unbox_scalar
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| 0
| 2021-01-09T21:02:08Z
| 2021-01-10T09:32:13Z
| 2021-01-09T22:06:02Z
|
MEMBER
| null |
mypy errors with numpy 1.20
pandas/core/arrays/period.py:273: error: Return type "int" of "_unbox_scalar" incompatible with return type "Union[signedinteger[_64Bit], datetime64, timedelta64]" in supertype "DatetimeLikeArrayMixin" [override]
pandas/core/arrays/period.py:277: error: Incompatible return value type (got "signedinteger[_64Bit]", expected "int") [return-value]
pandas/core/arrays/period.py:280: error: Incompatible return value type (got "signedinteger[_64Bit]", expected "int") [return-value]
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Regression in loc.setitem raising ValueError with unordered MultiIndex columns and scalar indexer
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| 10
| 2021-01-09T21:13:36Z
| 2021-01-11T17:06:34Z
| 2021-01-10T03:59:07Z
|
MEMBER
| null |
- [x] closes #38601
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
@jbrockmendel Would it be faster to use arange too in the line above of my fix instead of Index?
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TST GH26807 Break up pandas/tests/io/pytables/test_store.py
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| 6
| 2021-01-09T21:17:33Z
| 2021-01-13T18:04:39Z
| 2021-01-13T18:04:33Z
|
MEMBER
| null |
This PR addresses xref #26807 for pandas/tests/io/pytables/test_store.py, which was more than 5000 lines before this attempt. I made a new directory pandas/tests/io/pytables/store/ to put all the new modules in. I tried to 1) get all the new modules below 1000 lines and 2) break it up in a somewhat logical way, but let's see if I succeeded in the opinion of the reviewer.
There were 194 tests in pandas/tests/io/pytables/test_store.py, and now there are 194 tests in pandas/tests/io/pytables/store so I haven't gained or lost any. One is skipped on my dev machine.
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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|
DOC: Add STUMPY to pandas ecosystem
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| 0
| 2021-01-09T21:54:13Z
| 2021-01-10T20:25:46Z
| 2021-01-10T02:39:04Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[Pandas Ecosystem - Stats & ML](https://pandas.pydata.org/docs/ecosystem.html#statistics-and-machine-learning)
#### Documentation problem
This isn't a problem, just an addition to the current ecosystem listing.
#### Suggested fix for documentation
This is a simple addition to the `ecosystem.rst` file for adding [STUMPY](https://github.com/TDAmeritrade/stumpy) to the existing docs
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Fixed #39073 Add STUMPY to Pandas ecosystem docs
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| 4
| 2021-01-09T21:56:28Z
| 2021-01-10T02:39:08Z
| 2021-01-10T02:39:04Z
|
CONTRIBUTOR
| null |
- [ ] closes #39073
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
|
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Backport PR #39065 on branch 1.2.x (Regression in replace raising ValueError for bytes object)
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NONE
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Backport PR #39065: Regression in replace raising ValueError for bytes object
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CI: Benchmarks in CI are failing because xlrd was updated to 2.0.1
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| 0
| 2021-01-09T22:16:05Z
| 2021-01-10T00:41:50Z
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MEMBER
| null |
Raises in Benchmark run (for example https://github.com/pandas-dev/pandas/runs/1674714425) because it seems xlrd was updated on conda-forge
https://anaconda.org/conda-forge/xlrd
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Remove xlrd benchmark after xlrd was updated to 2.0.1 on conda-forge
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| 7
| 2021-01-09T22:19:47Z
| 2021-01-10T20:27:25Z
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|
MEMBER
| null |
- [x] closes #39076
- [x] tests added / passed
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- [x] whatsnew entry
Is pinning here the right thing to do in environment.yml?
cc @jreback
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NONE
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Backport PR #39077: Remove xlrd benchmark after xlrd was updated to 2.0.1 on conda-forge
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BUG: read_csv with custom date parser and na_filter=True results in ValueError
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| 2
| 2021-01-10T00:44:25Z
| 2021-02-01T10:00:48Z
| 2021-01-31T00:11:56Z
|
CONTRIBUTOR
| null |
- [ ] closes #36111
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTUyMjA4NTQx
| 39,080
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Auto backport of pr 39069 on 1.2.x
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Backport PR #39069: REGR: diff_2d raising for int8, int16
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REF: Remove Apply.get_result
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MEMBER
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Slightly simpler and allows typing of the result that we get out of Apply. Having both apply and agg go through get_result would mean typing the result as `Union[FrameOrSeriesUnion, Tuple[bool, Optional[FrameOrSeriesUnion]]]`
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CLN: Simplify optional dependency
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- [ ] whatsnew entry
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Backport PR #39071 on branch 1.2.x (Regression in loc.setitem raising ValueError with unordered MultiIndex columns and scalar indexer)
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- [ ] closes #xxxx
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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BUG:
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| 2021-01-10T09:42:48Z
| 2021-01-22T14:23:45Z
| 2021-01-22T14:23:45Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### 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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MDU6SXNzdWU3ODI3ODI4NTQ=
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BUG:Dataframe assignment failure
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| 2021-01-10T20:25:42Z
| 2021-01-10T11:17:08Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
data = pd.DataFrame(np.array(range(12)).reshape(3,4))
data['1_xxx'] = 2
```
#### Problem description
When a column name consists of a number and a string. that triggers the bug.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : 0.29.21
pytest : 6.2.1
hypothesis : None
sphinx : 3.4.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.21
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.51.2
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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CI: Numpy changed dtype inference
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| 2021-01-10T12:37:38Z
| 2021-01-17T14:17:38Z
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MEMBER
| null |
Numpy changed the dtype inference (https://github.com/numpy/numpy/pull/17863), which causes tests to fail. Problem description as follows:
```
other = [pd.Interval(0, 1, closed="right"), 0, "foo"]
arr = np.asarray(other)
result = pd.array(arr)
```
On a regular Numpy version the numpy array has dtype object and this returns
```
<PandasArray>
[Interval(0, 1, closed='right'), 0, 'foo']
Length: 3, dtype: object
```
while on numpy dev the numpy array has dtype ``<U64`` and this returns
```
<StringArray>
['(0, 1]', '0', 'foo']
Length: 3, dtype: string
```
This leads to a different evaluation of ``is_object_dtype(other_dtype)``
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MDExOlB1bGxSZXF1ZXN0NTUyMjgyODQx
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|
CI: Skip numpy dev failing tests
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| 3
| 2021-01-10T13:23:02Z
| 2021-01-10T20:12:33Z
| 2021-01-10T19:00:37Z
|
MEMBER
| null |
- [x] xref #39089
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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|
DOC: Added reverse rolling window examples
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|
CONTRIBUTOR
| null |
- [x] closes #38627
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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CLN: Numpy compat functions namespace
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MEMBER
| null |
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Should we do something about the private variables ``_np_version_under1p19`` and ``_np_version_under1p20`` too?
cc @jreback
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TYP: NDFrame.pipe, GroupBy.pipe etc.
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CONTRIBUTOR
| null |
Type up `pipe` function and methods. This allows type checkers to understand the return value type from pipes, which is nice.
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REF: implement dtypes.cast.can_hold_element
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MEMBER
| null |
Passes the tests added by #39086, gets us close to collapsing the NumericBlock subclasses and just using NumericBlock directly.
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BUG: reindex raising TypeError with timezone aware index and tolerance for ffill and bfill
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| 2021-01-12T21:54:49Z
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MEMBER
| null |
- [x] closes #38566
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Is there a better place for these tests?
EDIT: Technically a regression, but since it persists since 1.0.0 is it worth backporting?
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|
TST/COMPAT: test memory usage failed on macOS after change pytest-xdist -dist to `no`
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[] | null | 1
| 2021-01-10T22:28:36Z
| 2021-08-15T02:46:02Z
| null |
MEMBER
| null |
In #38905, I changed the distribution algorithm of pytest-xdist to `no` from `loadfile`. And the test `pandas/tests/base/test_misc.py:test_memory_usage` failed on macOS.
```bash
index_or_series_obj = Series([], Name: a, dtype: float64)
@pytest.mark.skipif(PYPY, reason="not relevant for PyPy")
def test_memory_usage(index_or_series_obj):
obj = index_or_series_obj
res = obj.memory_usage()
res_deep = obj.memory_usage(deep=True)
is_object = is_object_dtype(obj) or (
isinstance(obj, Series) and is_object_dtype(obj.index)
)
is_categorical = is_categorical_dtype(obj.dtype) or (
isinstance(obj, Series) and is_categorical_dtype(obj.index.dtype)
)
if len(obj) == 0:
if isinstance(obj, Index):
expected = 0
else:
expected = 108 if IS64 else 64
> assert res_deep == res == expected
E assert 0 == 108
```
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BUG: DateOffset applied to DatetimeIndex losing freq starting in v1.1.0
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| 3
| 2021-01-11T00:01:58Z
| 2021-01-11T22:02:27Z
| 2021-01-11T22:02:19Z
|
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
dtsa = pd.date_range('1998', periods = 3, freq = 'A')
s1 = pd.Series([4,5,6], index=dtsa)
s1.index
# DatetimeIndex(['1998-12-31', '1999-12-31', '2000-12-31'], dtype='datetime64[ns]', freq='A-DEC')
i1 = s1.index + pd.DateOffset(years=-1, days=1)
i1
# DatetimeIndex(['1998-01-01', '1999-01-01', '2000-01-01'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
I expected **i1** to have the frequency "AS-JAN" instead of None. This behavior existed prior to version 1.1.0. For example, in 1.0.5 the output of the same commands is:
```python
> DatetimeIndex(['1998-01-01', '1999-01-01', '2000-01-01'], dtype='datetime64[ns]', freq='AS-JAN')
```
#### Expected Output
```python
> DatetimeIndex(['1998-01-01', '1999-01-01', '2000-01-01'], dtype='datetime64[ns]', freq='AS-JAN')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17134
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.0.0.post20201207
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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|
BUG: Groupby agg on decimal,json extension arrays changes dtype to object
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| 5
| 2021-01-11T05:30:39Z
| 2021-08-15T02:47:50Z
| null |
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 decimal
from pandas.tests.extension.decimal import DecimalArray
import pandas as pd
b = decimal.Decimal("1.0")
a = decimal.Decimal("0.0")
c = decimal.Decimal("2.0")
na = decimal.Decimal("NaN")
df = pd.DataFrame({
'key': [1, 1, 2, 2, 3, 3, 1, 4],
'val': DecimalArray([b, b, na, na, a, a, b, c])
})
result = df.groupby("key").agg({'val': 'first'})
print(result['val').dtype)
# object
```
#### Problem description
When performing a `groupby().agg()` on extension arrays `DecimalArray` or `JsonArray`, if a NULL value is present, the resulting aggregation will be converted to an `object` dtype.
#### Expected Output
Resulting series should be the same dtype as the input array, and any NULL values should be as defined in the extension dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 1c296b20f7ab9822dacaa894afecd960bb43fc47
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-58-generic
Version : #64-Ubuntu SMP Wed Dec 9 08:16:25 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+357.g1c296b20f.dirty
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.0.2
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.2
hypothesis : 5.41.5
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.52.0
</details>
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MDU6SXNzdWU3ODMxODEyNzc=
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|
BUG: DataFrame.to_csv with compression method xz does not consider compresslevel
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| 2021-01-11T13:03:02Z
| 2021-01-11T13:03:02Z
|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pdv
df = pd.DataFrame(np.random.randint(100,size=(10**5, 3)),columns=['A','B','C'])
for compresslevel in range(1, 10):
df.to_csv(f'/tmp/test.csv.{compresslevel}.xz', compression={'method': 'xz', 'compresslevel': compresslevel})
```
#### Output of checking file size
```
jore@mac /tmp % ls -l /tmp/test.csv.*
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.1.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.2.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.3.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.4.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.5.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.6.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.7.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:29 /tmp/test.csv.8.xz
-rw-r--r-- 1 jore wheel 419804 11 Jan 09:30 /tmp/test.csv.9.xz
```
#### Problem description
The `compresslevel` is not considered during the compression with `xz`. However for `gzip`and `bz2` the `compresslevel` is correctly considered during compression.
#### Expected Output
The file size should decrease when the compresslevel is increased.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.9.1.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : de_DE.UTF-8
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20201009
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : Nonev
```
</details>
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BUG: merge by MultiIndex containing tuples doesn't work correctly
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| 3
| 2021-01-11T12:20:29Z
| 2021-01-22T14:14:28Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
a_dict = {'val-single-trial-LitPrettyBoringMNIST_f1': {(0,
'Train/Val',
('train', 'val')): 0.1189,
(1, 'Train/Val', ('train', 'val')): 0.1373,
(2, 'Train/Val', ('train', 'val')): 0.3173,
(2, 'Reval', ('reval',)): 0.3173,
(2, 'Test', ('test',)): 0.3173}}
a_df = pd.DataFrame(a_dict)
a_df.index = a_df.index.set_names(['epoch', 'stage', 'stage-list'])
a_df = a_df.iloc[[0,1,4,2, 3],:]
b_dict = {'reval-hyper-opt-LitPrettyBoringMNIST_f1': {(0, 'Reval', ('reval',)): 0.0317,
(0, 'Test', ('test',)): np.nan}}
b_df = pd.DataFrame(b_dict)
b_df.index = b_df.index.set_names(['epoch', 'stage', 'stage-list'])
bad_merge_df = a_df.merge(b_df, how='outer', left_index=True, right_index=True)
good_merge_df = a_df.merge(b_df, how='outer', on=['epoch', 'stage', 'stage-list'])
```
Same example on Colab
https://colab.research.google.com/drive/1kz2ddulSuC4o3w580wk1iiiU8KJPll1U#scrollTo=2ZCn944CXDRG
#### Problem description
bad_merge_df contains a lot of nans:
<img width="810" alt="image" src="https://user-images.githubusercontent.com/4868370/104181719-5368dd00-5420-11eb-8fdd-6de500c02d25.png">
#### Expected Output
<img width="840" alt="image" src="https://user-images.githubusercontent.com/4868370/104181775-68457080-5420-11eb-9c34-45af8797f218.png">
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.85-1-MANJARO
Version : #1 SMP PREEMPT Mon Dec 21 21:38:53 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en.UTF-8
LOCALE : None.None
pandas : 1.2.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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TST: add test for describe include/exclude
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| 1
| 2021-01-11T12:33:50Z
| 2021-01-11T14:15:30Z
| 2021-01-11T14:15:27Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Added test for checking that if include is 'all' then setting exclude will raise ValueError.
This case was not covered.
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MOVE: describe to pandas/core/describe.py
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Move ``describe`` functionality to a separate module and prepare for the further refactor/simplification (xref #36833).
- Created new module ``pandas/core/describe.py``
- Delegate the functionality of ``NDFrame.describe`` to function ``describe_ndframe`` in the new module
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BUG: GroupBy dictionary aggregation raises ValueError when 'as_index=False'
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| 0
| 2021-01-11T13:06:46Z
| 2021-08-15T02:50:42Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas
# Could be any data with MultiIndex columns
data = {(f"col{i}", f"level-{i % 2}"): np.arange(5) for i in range(5)}
df = pandas.DataFrame(data)
by = df.columns[0]
agg_col = df.columns[1]
agg_dict = {agg_col: ["min", "max"]}
df.groupby(by, as_index=False).agg(func=agg_dict) # ValueError
```
<details><summary>Traceback</summary>
```
Traceback (most recent call last):
File "../exp.py", line 12, in <module>
df.groupby(by, as_index=False).agg(func=agg_dict) # ValueError
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 990, in aggregate
self._insert_inaxis_grouper_inplace(result)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 1614, in _insert_inaxis_grouper_inplace
result.insert(0, name, lev)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/frame.py", line 3760, in insert
self._mgr.insert(loc, column, value, allow_duplicates=allow_duplicates)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 1197, in insert
new_axis = self.items.insert(loc, item)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 3801, in insert
item = self._validate_fill_value(item)
File "/localdisk/dchigare/miniconda3/envs/modin_tests/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 3784, in _validate_fill_value
raise ValueError("Item must have length equal to number of levels.")
ValueError: Item must have length equal to number of levels.
```
</details>
#### Problem description
When we specify an aggregation function via list, pandas adds a new level with function name to the columns of the resulted frame:
```python
>>> df
col0 col1
0 0 0
>>> df.groupby("col0").agg({"col1": ["max"]})
col1
max
col0
0 0
```
In the result, the number of levels in the index which represents grouper and columns is different, which causes some problems in [`_insert_inaxis_grouper_inplace`](https://github.com/pandas-dev/pandas/blob/3bd3d1ec253a8a18ac951511820eb970f62baaed/pandas/core/groupby/generic.py#L1610-L1621). When we're trying to insert MultiIndex grouper inside the resulted frame, but their `nlevels` is different, `pandas.DataFrame.insert` raises a `ValueError` because of it.
Filling mismatched levels with an empty string (like pandas already does when inserted value don't have MultiIndex columns) will probably solve this issue
```python
>>> df.groupby("col0", as_index=False).agg({"col1": ["max"]}).columns
MultiIndex([('col0', ''),
('col1', 'max')],
)
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-50-generic
Version : #54-Ubuntu SMP Mon May 6 18:46:08 UTC 2019
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
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.5.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : 0.13.2
pyarrow : 1.0.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : None
```
</details>
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MEMBER
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- [ ] xref #37715
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Handle ignored mypy errors in ``pandas/core/arraylike.py``.
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PR direct against 1.2.x
syncing release notes between 1.2.x and master
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| 2021-01-11T13:44:27Z
| 2021-01-11T14:52:28Z
| 2021-01-11T14:52:28Z
|
MEMBER
| null |
- [ ] xref #37715
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Handle ignored mypy error in ``pandas/core/common.py``.
|
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MDExOlB1bGxSZXF1ZXN0NTUyNzY5NjIz
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TYP: replace Label alias from pandas._typing with Hashable
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| 1
| 2021-01-11T14:13:46Z
| 2021-01-11T18:12:15Z
| 2021-01-11T17:07:01Z
|
MEMBER
| null |
- [ ] closes #37134
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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MDU6SXNzdWU3ODM0MTM4NDc=
| 39,108
|
TYP: clean notations in the wrong place
|
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| null |
CONTRIBUTOR
| null |
https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/groupby.py#L1294
```
# To track operations that expand dimensions, like ohlc
OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame)
```
at the very least this should be in `pandas._typing`. puzzled why we need this at *all*, as this is the same as `FrameOrSeries`
|
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MDExOlB1bGxSZXF1ZXN0NTUyODM4OTU1
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|
DOC: NDFrame fillna method add use case
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| 2021-01-25T16:40:42Z
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|
CONTRIBUTOR
| null |
- [x] closes #38917
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
cc @MarcoGorelli
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|
BUG: `get_dummies` works on a single integer value column, but fails silently when applied to multiple columns.
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|
NONE
| null |
- [x] I could not find this issue under open issues when searching: "bug get_dummies"
- [X] I have confirmed this bug exists on the latest version of pandas (1.2.0)
---
#### Code Sample, a copy-pastable example
```python
# A sample dataframe. Last column has integer values.
df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': ['1', '2', '3'], 'C': [1, 2, 3]})
# Encoding the integer values fine.
df2 = pd.get_dummies(df['C'], prefix='C')
# Encoding the string values works fine.:
df3 = pd.get_dummies(df[['A', 'B']], prefix=['a', 'b'])
# Encoding both this fails silently:
df4 = pd.get_dummies(df)
# Supplying prefixes makes the issue apparent:
df5 = pd.get_dummies(df, prefix=['A', 'B', 'C'])
```
#### Problem description
Pandas `get_dummies` will one-hot encode a single integer valued categorical column. **However, it silently ignores any further columns containing numeric categories.**
The last line of the minimal working example (with prefix list argument) results in:
```
ValueError: Length of 'prefix' (3) did not match the length of the columns being encoded (2).
```
#### Why the current behavior a problem and why the expected output is a better solution:
The expected output matches what the documentation advertises.
#### Expected Output
All columns passed to `get_dummies` should be considered categorical and encoded, including those containing integers.
**or**
No integer columns should be allowed. Produce a warning/error/update the docs.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.0
numpy : 1.19.2
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 51.1.2.post20210110
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
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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| 783,539,531
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MDU6SXNzdWU3ODM1Mzk1MzE=
| 39,111
|
BUG: pd.apply returns a dataframe when empty dataframe instead of a series
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| 2021-01-11T16:58:17Z
| 2021-01-19T10:59:55Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> pd.DataFrame({'a': [1,2], 'b': [3, 0]})
a b
0 1 3
1 2 0
>>> df = pd.DataFrame({'a': [1,2], 'b': [3, 0]})
>>> df
a b
0 1 3
1 2 0
>>> df.apply(lambda x: max(x['a'], x['b']), axis = 1)
0 3
1 2
dtype: int64
>>> df.head(0).apply(lambda x: max(x['a'], x['b']), axis = 1)
Empty DataFrame
Columns: [a, b]
Index: []
>>>
```
#### Problem description
When running `.apply()` to a dataframe, I'm expecting to get a pd.Series back to add as an additional column. If the dataframe is empty and I run `.apply()` on it, it will return a dataframe and I cannot add it as a column
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.5
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.50.1
>>>
```
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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MDExOlB1bGxSZXF1ZXN0NTUyOTM2MDYx
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DOC: minor cleanup of 1.2.1 release notes
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| 2
| 2021-01-11T18:36:11Z
| 2021-01-11T20:03:50Z
| 2021-01-11T19:59:39Z
|
MEMBER
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MDU6SXNzdWU3ODM2MDk3NTQ=
| 39,113
|
BUILD: openSUSE: Can't import IncompatibleFrequency in Pandas
|
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[] | null | 4
| 2021-01-11T18:45:03Z
| 2021-08-15T02:59:04Z
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NONE
| null |
Hi, I am trying to install Pandas for a project for our Devs. After installing Pandas with pip3 and trying to run the project, we get this stack trace:
```Traceback (most recent call last):
File "src/bin/app.py", line 17, in <module>
from bin.generator_dispatcher import GeneratorDispatcher
File "/opt/yana/python/report-engine-dev/src/bin/generator_dispatcher.py", line 13, in <module>
from athlete_baseline_report.bin.athlete_baseline_report import (
File "/opt/yana/python/report-engine-dev/src/athlete_baseline_report/bin/athlete_baseline_report.py", line 3, in <module>
from athlete_baseline_report.data_builder.data_builder import DataBuilder
File "/opt/yana/python/report-engine-dev/src/athlete_baseline_report/data_builder/data_builder.py", line 12, in <module>
from shared.algorithm.cc.cc_calculation import CcCalculation
File "/opt/yana/python/report-engine-dev/src/shared/algorithm/cc/cc_calculation.py", line 9, in <module>
from shared.algorithm.cc.CalculateCC import calculateCC_list
File "/opt/yana/python/report-engine-dev/src/shared/algorithm/cc/CalculateCC.py", line 16, in <module>
import pandas as pd
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/__init__.py", line 42, in <module>
from pandas.core.api import *
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/api.py", line 10, in <module>
from pandas.core.groupby.groupby import Grouper
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/groupby/__init__.py", line 2, in <module>
from pandas.core.groupby.groupby import (
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 46, in <module>
from pandas.core.index import (Index, MultiIndex,
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/index.py", line 2, in <module>
from pandas.core.indexes.api import *
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/indexes/api.py", line 4, in <module>
from pandas.core.indexes.base import (Index,
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 53, in <module>
from pandas.core import ops
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 21, in <module>
from pandas.core.construction import extract_array
File "/home/hzhang/.local/lib/python3.6/site-packages/pandas/core/construction.py", line 15, in <module>
from pandas._libs.tslibs import IncompatibleFrequency, OutOfBoundsDatetime
ImportError: cannot import name 'IncompatibleFrequency'
```
Does Pandas require something else to be installed? The project uses Pandas 0.23.4. I get the same error if I install latest version.
Pandas was installed with pip3 like this:
`sudo -H pip3 install pandas==0.23.4`
The host is Ubuntu 18.04.5 LTS with all installed packages completely up to date.
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|
Backport PR #39112 on branch 1.2.x (DOC: minor cleanup of 1.2.1 release notes)
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| 0
| 2021-01-11T20:02:12Z
| 2021-01-12T00:00:27Z
| 2021-01-12T00:00:27Z
|
NONE
| null |
Backport PR #39112: DOC: minor cleanup of 1.2.1 release notes
|
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MDU6SXNzdWU3ODM2NjY0ODU=
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|
Documentation showing placeholders instead of text
|
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| 7
| 2021-01-11T20:03:48Z
| 2021-01-17T15:59:39Z
| 2021-01-17T15:59:39Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[pandas.DataFrame.mad](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.mad.html)
#### Documentation problem
The documentation is showing placeholders instead of the actual text.
`{desc}`, `{axis_descr}`, `{name1}`, `{name2}`, `{see_also}`, `{examples}`
https://github.com/pandas-dev/pandas/blob/3e89b4c4b1580aa890023fc550774e63d499da25/pandas/core/generic.py#L10890-L10901
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MDU6SXNzdWU3ODM3NTMxNDg=
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DataFrame.apply doesn't handle numpy ops or DataFrame properties
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| 0
| 2021-01-11T22:30:14Z
| 2021-01-12T01:15:07Z
| 2021-01-12T01:15:07Z
|
MEMBER
| null |
```
s = pd.Series([1, 2])
df = pd.DataFrame([1, 2])
print(s.agg("sqrt"))
print(df.agg("sqrt"))
print(s.apply("sqrt"))
print(df.apply("sqrt"))
print(s.agg("size"))
print(df.agg("size"))
print(s.apply("size"))
print(df.apply("size"))
```
In each of the two blocks, first three lines all give the expected output and the last raises.
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API: pd.array(index_or_series[object]) should infer like Series and Index constructors
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[] | null | 11
| 2021-01-11T22:57:12Z
| 2021-01-19T11:01:19Z
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NTUzMDg0Njkx
| 39,118
|
ENH: Allow numpy ops and DataFrame properties as str arguments to DataFrame.apply
|
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| 1
| 2021-01-11T23:35:03Z
| 2021-01-12T01:27:48Z
| 2021-01-12T01:15:07Z
|
MEMBER
| null |
- [x] closes #39116
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
|
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