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https://api.github.com/repos/pandas-dev/pandas/issues/27813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27813/events | https://github.com/pandas-dev/pandas/issues/27813 | 478,212,024 | MDU6SXNzdWU0NzgyMTIwMjQ= | 27,813 | BUG: Series.rename raises error on values accepted by Series constructor. | {
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} | 0 | 2019-08-08T01:54:33Z | 2019-08-22T13:07:47Z | 2019-08-22T13:07:47Z | CONTRIBUTOR | null | #### Sample
```python
import pandas as pd
class MyIndexer:
pass
i1 = MyIndexer()
s = pd.Series([1, 2, 3], name=i1) # allowed
s.rename(i1) # raises error
```
The error stack trace is the following:
```python
Traceback (most recent call last):
File "test.py", line 8, in <module>
s.rename(i1) # raises error
File "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py", line 3736, in rename
return super(Series, self).rename(index=index, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py", line 1091, in rename
level=level)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 171, in rename_axis
obj.set_axis(axis, _transform_index(self.axes[axis], mapper, level))
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 2004, in _transform_index
items = [func(x) for x in index]
File "/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py", line 2004, in <listcomp>
items = [func(x) for x in index]
TypeError: 'MyIndexer' object is not callable
```
#### Description
Series.rename handle anything that isn't a scalar or list-like as a mapping.
#### Proposed change
Change the following code (from Series.rename):
```python
non_mapping = is_scalar(index) or (is_list_like(index) and not is_dict_like(index))
if non_mapping:
return self._set_name(index, inplace=kwargs.get("inplace"))
return super().rename(index=index, **kwargs)
```
to
```python
if callable(index) or is_dict_like(index):
return super().rename(index=index, **kwargs)
else:
return self._set_name(index, inplace=kwargs.get("inplace"))
````
so anything that isn't a dict or a callable will be treated the same way as a scalar or list-like.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: pt_BR.UTF-8
pandas: 0.24.2
pytest: 3.6.0
pip: 19.1.1
setuptools: 41.0.0
Cython: 0.26.1
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 5 | 2019-08-08T02:04:10Z | 2019-08-22T13:07:48Z | 2019-08-22T13:07:47Z | CONTRIBUTOR | null | - closes #27813 | {
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} | 3 | 2019-08-08T03:20:05Z | 2019-08-14T21:34:32Z | 2019-08-14T20:39:15Z | MEMBER | null | This uses should_extension_dispatch in _arith_method_SERIES. We'll be able to use it in _comp_method_SERIES after #27803. (this is orthogonal to 27803 so doesn't matter what order they go in) | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-08T08:30:11Z | 2019-08-09T14:27:49Z | 2019-08-09T14:27:48Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
dt = pd.date_range('2017-01-01', '2020-01-01', name='date', freq='D')
df = pd.DataFrame(pd.np.random.rand(dt.shape[0]), index=dt)
df.shift(-1, freq=pd.DateOffset(years=1))
```
#### Problem description
This code raise `ValueError: Inferred frequency None from passed values does not conform to passed frequency D` while date range from '2018-01-01' works perfectly fine.
#### Expected Output
Dataframe with properly shifted index.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 10.0.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: None
html5lib: None
sqlalchemy: 1.3.2
pymysql: None
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27817/events | https://github.com/pandas-dev/pandas/issues/27817 | 478,350,789 | MDU6SXNzdWU0NzgzNTA3ODk= | 27,817 | Conversion from Arrow to datetime not working since pandas 0.22.0 | {
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"description": "pandas ob... | closed | false | null | [] | null | 7 | 2019-08-08T09:23:23Z | 2020-04-02T05:02:02Z | 2020-04-02T05:02:01Z | NONE | null | #### Code Sample
```python
import arrow
import pandas as pd
date = arrow.Arrow(2019, 8, 8)
data = {'date': [date]}
df = pd.DataFrame(data)
df.astype('<M8[ns]')
```
#### Problem description
```
Traceback (most recent call last):
File "bug.py", line 7, in <module>
df.astype('<M8[ns]')
File "/lib/python3.5/site-packages/pandas/core/generic.py", line 5691, in astype
**kwargs)
File "/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 531, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 395, in apply
applied = getattr(b, f)(**kwargs)
File "/lib/python3.5/site-packages/pandas/core/internals/blocks.py", line 534, in astype
**kwargs)
File "/lib/python3.5/site-packages/pandas/core/internals/blocks.py", line 633, in _astype
values = astype_nansafe(values.ravel(), dtype, copy=True)
File "/lib/python3.5/site-packages/pandas/core/dtypes/cast.py", line 690, in astype_nansafe
return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
File "/lib/python3.5/site-packages/pandas/core/tools/datetimes.py", line 609, in to_datetime
result = convert_listlike(arg, box, format)
File "/lib/python3.5/site-packages/pandas/core/tools/datetimes.py", line 302, in _convert_listlike_datetimes
allow_object=True)
File "/lib/python3.5/site-packages/pandas/core/arrays/datetimes.py", line 1857, in objects_to_datetime64ns
require_iso8601=require_iso8601
File "pandas/_libs/tslib.pyx", line 460, in pandas._libs.tslib.array_to_datetime
File "pandas/_libs/tslib.pyx", line 685, in pandas._libs.tslib.array_to_datetime
File "pandas/_libs/tslib.pyx", line 813, in pandas._libs.tslib.array_to_datetime_object
File "pandas/_libs/tslib.pyx", line 659, in pandas._libs.tslib.array_to_datetime
TypeError: <class 'arrow.arrow.Arrow'> is not convertible to datetime
```
#### Expected Output
DataFrame with datetime data.
#### Output of ``pd.show_versions()``
Failing version:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.1
setuptools: 39.1.0
Cython: None
numpy: 1.17.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
Working version:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-55-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.22.0
pytest: None
pip: 19.2.1
setuptools: 39.1.0
Cython: None
numpy: 1.17.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-08-08T11:33:56Z | 2019-12-08T17:39:41Z | 2019-12-08T17:39:41Z | MEMBER | null | The current documentation (both docstring https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html and user guide https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#partitioning-parquet-files) don't make it very clear that the file path can also be a directory name that contains multiple (partitioned) parquet files (in addition to single file path). | {
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} | 1 | 2019-08-08T16:32:59Z | 2021-02-03T21:06:45Z | 2019-08-09T14:18:54Z | CONTRIBUTOR | null | Part of the tidelift setup. In https://github.com/numpy/numpy/issues/13475 / https://github.com/numpy/numpy/pull/13485, NumPy decided to just link to https://tidelift.com/docs/lifting/security. That should suffice to us.
Only thing to decide is who all gets notified with these reports. | {
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} | 0 | 2019-08-08T16:50:46Z | 2019-08-09T14:19:09Z | 2019-08-09T14:18:54Z | CONTRIBUTOR | null | Closes #27821 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27823 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27823/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27823/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27823/events | https://github.com/pandas-dev/pandas/pull/27823 | 478,583,609 | MDExOlB1bGxSZXF1ZXN0MzA1NjYzOTcz | 27,823 | Add tidelift sponsor to FUNDING | {
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} | 0 | 2019-08-08T16:51:00Z | 2019-08-09T14:19:06Z | 2019-08-09T14:19:03Z | CONTRIBUTOR | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27824/events | https://github.com/pandas-dev/pandas/issues/27824 | 478,619,603 | MDU6SXNzdWU0Nzg2MTk2MDM= | 27,824 | Document DataFrame._constructor_sliced-like property in the public API? | {
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] | open | false | null | [] | null | 3 | 2019-08-08T18:18:38Z | 2021-07-10T19:30:32Z | null | CONTRIBUTOR | null | Can we document something like `DataFrame._constructor_sliced` as the public way to say that slicing a column from a pandas.DataFrame will return a pandas.Series object?
Projects like Dask would like to work with any concrete DataFrame library (cudf being the second major dataframe implementation). Right now we resort to things like `type(dask.dataframe.DataFrame._meta.iloc[:, 0])` to get the concrete type of a slice. But that's fragile (e.g. a DataFrame with no columns).
I'm not sure how useful this would be to users, so using an underscore-prefixed name may be reasonable. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27825 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27825/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27825/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27825/events | https://github.com/pandas-dev/pandas/issues/27825 | 478,645,195 | MDU6SXNzdWU0Nzg2NDUxOTU= | 27,825 | Dispatch pd.is_na for scalar extension value | {
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"description": "np.nan, ... | open | false | null | [] | null | 6 | 2019-08-08T19:20:49Z | 2021-07-10T19:31:40Z | null | CONTRIBUTOR | null | Right now, I don't believe there's a way for an ExtensionDtype to declare a custom scalar NA value and have `pd.isna(scalar)` do the right thing.
<details>
```python
_nas = object()
class NaSType(str):
"""
NA for String type.
"""
# TODO: enforce singleton
def __new__(cls, value):
if value is not _nas:
raise ValueError("Cannot create NaS from '{}'".format(value))
return super().__new__(cls, value)
def __eq__(self, other):
# TODO: array comparisons, etc.
return False
def __str__(self):
return "NaS"
def __repr__(self):
return str(self)
NaS = NaSType(_nas)
@register_extension_dtype
class StringDtype(ExtensionDtype):
@property
def na_value(self):
return NaS
@property
def type(self) -> Type:
return str
@property
def name(self) -> str:
return "string"
@classmethod
def construct_from_string(cls, string: str):
if string in {"string", "str"}:
return cls()
return super().construct_from_string(string)
@classmethod
def construct_array_type(cls) -> "Type[StringArray]":
return StringArray
```
</details>
```python
In [18]: NaS
Out[18]: NaS
In [19]: pd.isna(NaS)
Out[19]: False
```
That should be `True`. In https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/missing.py#L131-L132 we go straight to `lib missing.checknull(obj)` for scalar values. | {
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} | 3 | 2019-08-08T19:49:31Z | 2019-10-31T01:19:37Z | 2019-08-12T17:56:47Z | CONTRIBUTOR | null | Validate that q is between 0 and 1.
Closes #27470 | {
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} | 5 | 2019-08-08T20:36:24Z | 2019-08-22T11:28:40Z | 2019-08-22T11:28:37Z | CONTRIBUTOR | null | Closes #27526
Just a WIP right now. This is under tested for
- [ ] `groupby(sort=False)`
- [ ] non-sorted `q`
- [ ] `groupby(multiple levels)` | {
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} | 2 | 2019-08-08T21:35:33Z | 2019-12-11T17:09:39Z | 2019-12-11T17:09:38Z | MEMBER | null | `IntegerArray._maybe_mask_result` starts with:
```
if is_float_dtype(result):
mask |= (result == np.inf) | (result == -np.inf)
```
but in many cases, `mask` will be `self._mask`, so it gets altered inplace. The intuitive solution is to add a check for `mask is self._mask` and make a copy, but that turns out to break 32 tests, so somewhere we're relying on this incorrect behavior.
cc @jreback | {
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} | 0 | 2019-08-08T21:47:05Z | 2019-08-13T13:59:35Z | 2019-08-13T12:13:17Z | MEMBER | null | Actual logic changes:
- return NotImplemented when operating with DataFrame (instead of raising NotImplementError)
- raise ValueError when operating with mismatched-length listlike
Everything else is just rearranging to use patterns that are more standard around the codebase, leading up to #23853 | {
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- [ X] closes #27802
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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```python
import pandas as pd
print(pd.__version__)
df = pd.DataFrame(1, index=range(1000), columns=range(1000))
%timeit df.sub(1);
%timeit df.sub(df.mean());
%timeit df - df.mean();
%timeit df.mean();
```
```
0.25.0
420 ms ± 6.46 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
390 ms ± 4.96 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
401 ms ± 2.82 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.48 ms ± 5.71 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
```
0.22.0
1.07 ms ± 40.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
11.4 ms ± 376 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
11.5 ms ± 291 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
1.64 ms ± 52.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
````
#### Problem description
DataFrame.add/sub is a lot slower after pandas upgrade.
I also tested other versions, 0.23.4 is the last version that works normally.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 3.16.35-031635-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL :
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.1.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27836/events | https://github.com/pandas-dev/pandas/pull/27836 | 478,843,275 | MDExOlB1bGxSZXF1ZXN0MzA1ODczMTQz | 27,836 | Backport PR #27818 on branch 0.25.x (COMPAT: restore shape for 'invalid' Index with nd array) | {
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} | 0 | 2019-08-09T07:37:36Z | 2019-08-09T08:20:19Z | 2019-08-09T08:20:19Z | NONE | null | Backport PR #27818: COMPAT: restore shape for 'invalid' Index with nd array | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27837/events | https://github.com/pandas-dev/pandas/issues/27837 | 478,849,657 | MDU6SXNzdWU0Nzg4NDk2NTc= | 27,837 | API: what should a 2D indexing operation into a 1D Index do? (eg idx[:, None]) | {
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} | 8 | 2019-08-09T07:53:50Z | 2020-07-06T23:25:28Z | 2020-07-06T23:25:28Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/issues/27775 and https://github.com/pandas-dev/pandas/pull/27818.
Short recap of what those issues were about:
Currently, indexing into an Index with a 2D (or multiple D) indexer results in an "invalid" Index with an underlying ndarray:
```
In [1]: idx = pd.Index([1, 2, 3])
In [2]: idx2 = idx[:, None]
In [3]: idx2
Out[3]: Int64Index([1, 2, 3], dtype='int64')
In [4]: idx2.values
Out[4]:
array([[1],
[2],
[3]])
```
So from the repr it *looks* like a proper index, but the underlying values of an Index should always be 1D (such an invalid index will also lead to errors once you do operations on them).
Before pandas 0.25.0, the `shape` attribute of the index "correctly" returned the shape of the underlying values: `(3, 1)`, but in 0.25.0 this was changed to `(3,)` (only checking the length). This caused a regression matplotlib (https://github.com/pandas-dev/pandas/issues/27775), and will be "fixed" in 0.25.1 returning again the 2D shape of the underlying values (https://github.com/pandas-dev/pandas/pull/27818). Of course, this is only about the `shape` attribute, while the root cause is this invalid Index.
I think it is clear that we should not allow such invalid Index object to exist.
I currently know of two ways to end up such situation:
* Passing a multidimensional array to the Index constructor (e.g. `pd.Index(np.random.randn(5, 5, 5))`. I think this is something we can deprecate and raise for later, and there is already an issue for this: https://github.com/pandas-dev/pandas/issues/27125
* Indexing into an Index (e.g. `idx[:, None] `) -> this issue
So let's use **this issue** to discuss what to do for this second way: **a 2D indexing operation on a 1D object.**
This is relevant for the Index, but we should probably try to have it consistent with Series as well. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27838 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27838/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27838/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27838/events | https://github.com/pandas-dev/pandas/pull/27838 | 478,876,195 | MDExOlB1bGxSZXF1ZXN0MzA1ODk5NDk4 | 27,838 | Parametrize JSON tests | {
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} | 18 | 2019-08-09T08:56:09Z | 2020-01-16T00:33:52Z | 2019-09-17T21:49:45Z | MEMBER | null | lot more to be done here but split these up as some of the large functions are difficult to highlight failures while working on extension module. Improved coverage and identified some inconsistencies in process (marked with TODOs) which can be tackled as follow ups | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27839/events | https://github.com/pandas-dev/pandas/issues/27839 | 478,877,754 | MDU6SXNzdWU0Nzg4Nzc3NTQ= | 27,839 | BUG/ERR: pd.DataFrame.to_hdf saving yields error message | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-09T08:59:27Z | 2019-08-09T13:32:53Z | 2019-08-09T10:54:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
testingthisout.to_hdf('test.h5',key='testme')
Traceback (most recent call last):
File "<ipython-input-20-37a68cfcdb3c>", line 1, in <module>
testingthisout.to_hdf('test.h5',key='testme')
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pandas\core\generic.py", line 2377, in to_hdf
return pytables.to_hdf(path_or_buf, key, self, **kwargs)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pandas\io\pytables.py", line 273, in to_hdf
complib=complib) as store:
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pandas\io\pytables.py", line 466, in __init__
import tables # noqa
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\tables\__init__.py", line 142, in <module>
from .tests import print_versions, test
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\tables\tests\__init__.py", line 22, in <module>
from tables.tests.common import print_versions
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\tables\tests\common.py", line 27, in <module>
from pkg_resources import resource_filename
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3241, in <module>
@_call_aside
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3225, in _call_aside
f(*args, **kwargs)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3254, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 574, in _build_master
ws = cls()
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 567, in __init__
self.add_entry(entry)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 623, in add_entry
for dist in find_distributions(entry, True):
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 1960, in find_distributions
importer = get_importer(path_item)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\pkgutil.py", line 419, in get_importer
importer = path_hook(path_item)
TypeError: expected str, bytes or os.PathLike object, not function
```
#### Problem description
Hi,
I've been trying to debug the above error message for hours now, and it seems I'm getting stuck here.
Whatever kind of data frame I'm producing (real data or simply just a few numbers), every time I try to save this to HDF5 (no matter the path or naming), I am receiving the above error message, whereas saving as .csv presents no problem. This error message occurs in pandas 0.25.0+138.g78c6843dc, 0.25, 0.24.2, 0.24.0, 0.23.0, but the above is from 0.24.2. I have also tried several versions of PyTables, but to no avail.
This is on Win10, conda 4.7.11, pytables 3.5.2, Python 3.7.3.
Thanks in advance, everyone!
Oliver
#### Expected Output
(a successfully saved .h5 file)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
Traceback (most recent call last):
File "<ipython-input-10-3d232a07e144>", line 1, in <module>
pd.show_versions()
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pandas-0.25.0+138.g78c6843dc-py3.7-win-amd64.egg\pandas\util\_print_versions.py", line 99, in show_versions
modname, raise_on_missing=False, on_version="ignore"
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pandas-0.25.0+138.g78c6843dc-py3.7-win-amd64.egg\pandas\compat\_optional.py", line 90, in import_optional_dependency
module = importlib.import_module(name)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\setuptools\__init__.py", line 18, in <module>
import setuptools.version
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\setuptools\version.py", line 1, in <module>
import pkg_resources
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3241, in <module>
@_call_aside
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3225, in _call_aside
f(*args, **kwargs)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 3254, in _initialize_master_working_set
working_set = WorkingSet._build_master()
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 574, in _build_master
ws = cls()
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 567, in __init__
self.add_entry(entry)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 623, in add_entry
for dist in find_distributions(entry, True):
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\site-packages\pkg_resources\__init__.py", line 1960, in find_distributions
importer = get_importer(path_item)
File "C:\Users\user-adm.BN-W-3015622\Anaconda3\envs\mouserun\lib\pkgutil.py", line 419, in get_importer
importer = path_hook(path_item)
TypeError: expected str, bytes or os.PathLike object, not function
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} | 3 | 2019-08-09T10:11:28Z | 2019-08-14T21:52:31Z | 2019-08-14T20:11:35Z | CONTRIBUTOR | null | - [x] xref #27585 & #27607
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Generalizes the test in #27607 to be used for all index types. This finds ref cycle issues for ``PeriodIndex`` and ``CategoricalIndex`` which are solved in this PR.
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} | 6 | 2019-08-09T10:40:51Z | 2019-08-13T01:04:21Z | 2019-08-13T01:04:21Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [2]: datum = '1'
In [3]: df = pd.DataFrame(datum, index=list('abcd'),
...: columns=pd.MultiIndex.from_product([['Main'], ('another', 'one')]))
In [4]: df['labels'] = 'a'
In [5]: df.loc[:, 'labels'] = df.index
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-5-fdece12e4959> in <module>
----> 1 df.loc[:, 'labels'] = df.index
~/nobackup/repo/pandas/pandas/core/indexing.py in __setitem__(self, key, value)
201 key = com.apply_if_callable(key, self.obj)
202 indexer = self._get_setitem_indexer(key)
--> 203 self._setitem_with_indexer(indexer, value)
204
205 def _validate_key(self, key, axis: int):
~/nobackup/repo/pandas/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
572 # actually do the set
573 self.obj._consolidate_inplace()
--> 574 self.obj._data = self.obj._data.setitem(indexer=indexer, value=value)
575 self.obj._maybe_update_cacher(clear=True)
576
~/nobackup/repo/pandas/pandas/core/internals/managers.py in setitem(self, **kwargs)
558
559 def setitem(self, **kwargs):
--> 560 return self.apply("setitem", **kwargs)
561
562 def putmask(self, **kwargs):
~/nobackup/repo/pandas/pandas/core/internals/managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436 kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438 applied = getattr(b, f)(**kwargs)
439 result_blocks = _extend_blocks(applied, result_blocks)
440
~/nobackup/repo/pandas/pandas/core/internals/blocks.py in setitem(self, indexer, value)
890 # set
891 else:
--> 892 values[indexer] = value
893
894 if transpose:
ValueError: could not broadcast input array from shape (4) into shape (4,1)
```
#### Problem description
Replace ``datum = '1'`` with ``datum = 1`` and everything works.
The problem might be related to the fact that
```python
In [5]: df._data.blocks
Out[5]:
(ObjectBlock: slice(0, 2, 1), 2 x 4, dtype: object,
ObjectBlock: slice(2, 3, 1), 1 x 4, dtype: object)
In [6]: df._data._is_consolidated
Out[6]: True
```
This is a regression, since it used to work in code of mine, but unfortunately I'm not sure which version this was working with.
Vaguely related to #27286 .
#### Expected Output
None, the assignment should just work as it does when data is numeric.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0+141.g0227e69bb
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 18.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.8.4
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: v0.7.0+52.gb1b54d6.dirty
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.4.9
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.18
tables : 3.4.4
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27842 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27842/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27842/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27842/events | https://github.com/pandas-dev/pandas/issues/27842 | 478,932,048 | MDU6SXNzdWU0Nzg5MzIwNDg= | 27,842 | invoking pivot_table results in unwanted row | {
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} | 9 | 2019-08-09T11:09:02Z | 2019-08-13T11:56:17Z | 2019-08-09T13:33:32Z | NONE | null | #### I have encounterd an issue with pd.pivot_table. this function adds unexpected row and I have no idea to remove it.
below is the code:
```python
import pandas as pd
import numpy as np
dates = {
'animal': ['cat', 'cat', 'wolf'],
'color': ['white', 'black', 'black'],
'age': np.random.randint(15, 30, 3)}
df = pd.DataFrame(dates)
dfp = pd.pivot_table(
df,
index='animal',
columns='color',
values='age',
aggfunc=[len, np.mean],
fill_value=0
)
excel_file = pd.ExcelWriter('result.xlsx', mode='w')
dfp.to_excel(
excel_writer=excel_file,
sheet_name='before'
)
```
### the result is:
| len | | mean|
-- | -- |-- |-- |--
color | black | white | black | white
animal | | | |
cat | 1 | 1 | 20 | 28
wolf | 1 | 0 | 24 | 0
#### Problem description
pivot_table adds unwanted row below the MultiIndex. this row starts with 'animal' which is the index name.
I have read though related docs and tried possible parameters.
Finallly I found there is no way to remove it.
#### Expected Output
| len | | mean |
-- | -- | -- | -- | --
color | black | white | black | white
cat | 1 | 1 | 20 | 28
wolf | 1 | 0 | 24 | 0
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27843 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27843/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27843/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27843/events | https://github.com/pandas-dev/pandas/issues/27843 | 479,009,538 | MDU6SXNzdWU0NzkwMDk1Mzg= | 27,843 | CI: GBQ tests failing on master | {
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```
_________ TestToGBQIntegrationWithServiceAccountKeyPath.test_roundtrip _________
2328[gw0] linux -- Python 3.6.6 /home/travis/miniconda3/envs/pandas-dev/bin/python
2329
2330self = <pandas.tests.io.test_gbq.TestToGBQIntegrationWithServiceAccountKeyPath object at 0x7fb10497cbe0>
2331
2332 def test_roundtrip(self):
2333 destination_table = DESTINATION_TABLE + "1"
2334
2335 test_size = 20001
2336 df = make_mixed_dataframe_v2(test_size)
2337
2338 df.to_gbq(
2339 destination_table,
2340 _get_project_id(),
2341 chunksize=None,
2342 credentials=_get_credentials(),
2343 )
2344
2345 result = pd.read_gbq(
2346 "SELECT COUNT(*) AS num_rows FROM {0}".format(destination_table),
2347 project_id=_get_project_id(),
2348 credentials=_get_credentials(),
2349 dialect="standard",
2350 )
2351> assert result["num_rows"][0] == test_size
2352E assert 0 == 20001
2353
2354pandas/tests/io/test_gbq.py:194: AssertionError
2355- generated xml file: /home/travis/build/pandas-dev/pandas/test-data-single.xml -
2356
```
https://travis-ci.org/pandas-dev/pandas/jobs/569818532
```
==================================== ERRORS ====================================
2907_ ERROR at teardown of TestToGBQIntegrationWithServiceAccountKeyPath.test_roundtrip _
2908[gw0] linux -- Python 3.6.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
2909
2910cls = <class 'pandas.tests.io.test_gbq.TestToGBQIntegrationWithServiceAccountKeyPath'>
2911
2912 @classmethod
2913 def teardown_class(cls):
2914 # - GLOBAL CLASS FIXTURES -
2915 # put here any instruction you want to execute only *ONCE* *AFTER*
2916 # executing all tests.
2917> cls.client.delete_dataset(cls.dataset, delete_contents=True)
2918
2919pandas/tests/io/test_gbq.py:173:
2920_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2921../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/google/cloud/bigquery/client.py:627: in delete_dataset
2922 retry, method="DELETE", path=dataset.path, query_params=params
2923../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/google/cloud/bigquery/client.py:400: in _call_api
2924 return call()
2925../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/google/api_core/retry.py:273: in retry_wrapped_func
2926 on_error=on_error,
2927../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/google/api_core/retry.py:182: in retry_target
2928 return target()
2929_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2930
2931self = <google.cloud.bigquery._http.Connection object at 0x7f0c41a5a160>
2932method = 'DELETE'
2933path = '/projects/pandas-travis/datasets/pydata_pandas_bq_testing_py31'
2934query_params = {'deleteContents': 'true'}, data = None, content_type = None
2935headers = None, api_base_url = None, api_version = None, expect_json = True
2936_target_object = None
2937
2938 def api_request(
2939 self,
2940 method,
2941 path,
2942 query_params=None,
2943 data=None,
2944 content_type=None,
2945 headers=None,
2946 api_base_url=None,
2947 api_version=None,
2948 expect_json=True,
2949 _target_object=None,
2950 ):
2951 """Make a request over the HTTP transport to the API.
2952
2953 You shouldn't need to use this method, but if you plan to
2954 interact with the API using these primitives, this is the
2955 correct one to use.
2956
2957 :type method: str
2958 :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).
2959 Required.
2960
2961 :type path: str
2962 :param path: The path to the resource (ie, ``'/b/bucket-name'``).
2963 Required.
2964
2965 :type query_params: dict or list
2966 :param query_params: A dictionary of keys and values (or list of
2967 key-value pairs) to insert into the query
2968 string of the URL.
2969
2970 :type data: str
2971 :param data: The data to send as the body of the request. Default is
2972 the empty string.
2973
2974 :type content_type: str
2975 :param content_type: The proper MIME type of the data provided. Default
2976 is None.
2977
2978 :type headers: dict
2979 :param headers: extra HTTP headers to be sent with the request.
2980
2981 :type api_base_url: str
2982 :param api_base_url: The base URL for the API endpoint.
2983 Typically you won't have to provide this.
2984 Default is the standard API base URL.
2985
2986 :type api_version: str
2987 :param api_version: The version of the API to call. Typically
2988 you shouldn't provide this and instead use
2989 the default for the library. Default is the
2990 latest API version supported by
2991 google-cloud-python.
2992
2993 :type expect_json: bool
2994 :param expect_json: If True, this method will try to parse the
2995 response as JSON and raise an exception if
2996 that cannot be done. Default is True.
2997
2998 :type _target_object: :class:`object`
2999 :param _target_object:
3000 (Optional) Protected argument to be used by library callers. This
3001 can allow custom behavior, for example, to defer an HTTP request
3002 and complete initialization of the object at a later time.
3003
3004 :raises ~google.cloud.exceptions.GoogleCloudError: if the response code
3005 is not 200 OK.
3006 :raises ValueError: if the response content type is not JSON.
3007 :rtype: dict or str
3008 :returns: The API response payload, either as a raw string or
3009 a dictionary if the response is valid JSON.
3010 """
3011 url = self.build_api_url(
3012 path=path,
3013 query_params=query_params,
3014 api_base_url=api_base_url,
3015 api_version=api_version,
3016 )
3017
3018 # Making the executive decision that any dictionary
3019 # data will be sent properly as JSON.
3020 if data and isinstance(data, dict):
3021 data = json.dumps(data)
3022 content_type = "application/json"
3023
3024 response = self._make_request(
3025 method=method,
3026 url=url,
3027 data=data,
3028 content_type=content_type,
3029 headers=headers,
3030 target_object=_target_object,
3031 )
3032
3033 if not 200 <= response.status_code < 300:
3034> raise exceptions.from_http_response(response)
3035E google.api_core.exceptions.NotFound: 404 DELETE https://www.googleapis.com/bigquery/v2/projects/pandas-travis/datasets/pydata_pandas_bq_testing_py31?deleteContents=true: Not found: Dataset pandas-travis:pydata_pandas_bq_testing_py31
3036
3037../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/google/cloud/_http.py:319: NotFound
3038=================================== FAILURES ===================================
3039_________ TestToGBQIntegrationWithServiceAccountKeyPath.test_roundtrip _________
3040[gw0] linux -- Python 3.6.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
3041
3042self = <pandas.tests.io.test_gbq.TestToGBQIntegrationWithServiceAccountKeyPath object at 0x7f0c41a5a400>
3043
3044 def test_roundtrip(self):
3045 destination_table = DESTINATION_TABLE + "1"
3046
3047 test_size = 20001
3048 df = make_mixed_dataframe_v2(test_size)
3049
3050 df.to_gbq(
3051 destination_table,
3052 _get_project_id(),
3053 chunksize=None,
3054> credentials=_get_credentials(),
3055 )
3056
3057pandas/tests/io/test_gbq.py:185:
3058_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
3059pandas/core/frame.py:1493: in to_gbq
3060 private_key=private_key,
3061pandas/io/gbq.py:209: in to_gbq
3062 private_key=private_key,
3063../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/pandas_gbq/gbq.py:1204: in to_gbq
3064 progress_bar=progress_bar,
3065../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/pandas_gbq/gbq.py:581: in load_data
3066 self.process_http_error(ex)
3067_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
3068
3069ex = NotFound('Not found: Dataset pandas-travis:pydata_pandas_bq_testing_py31',)
3070
3071 @staticmethod
3072 def process_http_error(ex):
3073 # See `BigQuery Troubleshooting Errors
3074 # <https://cloud.google.com/bigquery/troubleshooting-errors>`__
3075
3076> raise GenericGBQException("Reason: {0}".format(ex))
3077E pandas_gbq.gbq.GenericGBQException: Reason: 404 Not found: Dataset pandas-travis:pydata_pandas_bq_testing_py31
3078
3079../../../miniconda3/envs/pandas-dev/lib/python3.6/site-packages/pandas_gbq/gbq.py:437: GenericGBQException
3080=============================== warnings summary ===============================
``` | {
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} | 4 | 2019-08-09T18:57:21Z | 2019-08-23T08:06:04Z | 2019-08-23T08:06:04Z | CONTRIBUTOR | null | - [x] closes #6721
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Update DOC to make it consistent with actual behavior | {
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} | 1 | 2019-08-09T19:19:08Z | 2019-08-13T12:44:11Z | 2019-08-13T12:44:11Z | MEMBER | null | - [ ] closes #22799
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-08-10T00:27:36Z | 2019-08-12T14:24:36Z | 2019-08-12T14:24:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# extract certain part of the column names
df = pd.DataFrame({'a1': 1, 'b2':2, 'c3': 3}, index=[0])
df.columns = df.columns.str.extract(r'([0-9])')
df
# Out[]:
# (1,) (2,) (3,)
# 0 1 2 3
# Series object is returned instead of index
cols = pd.Index(list('abc'))
cols.str.extract(r'(.*)')
# Out[]:
# 0
# 0 a
# 1 b
# 2 c
pd.__version__
# Out[]: '0.25.0'
```
#### Problem description
I am dealing with files extracted from our Hive warehouse, and most of the time the column names follow this format `<table_name>.<col-name>`. To have a better workflow on downstream analysis, I would like to only keep the `<col_name>` part. So I use something like below this to replace the column names.
```python
df.colums = df.columns.str.extract(r'\.(.*)')
```
However, as the example shown above, the column names would be messed up again because `Series` is returned instead of `Index`.
#### Expected Output
When the Input data type for `str.extract` is `Index`, `Index` shoul be returned.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 40.4.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : 0.9.2
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-08-10T01:13:28Z | 2019-08-12T19:04:48Z | 2019-08-12T18:53:36Z | MEMBER | null | In general we use `op` to refer to operator.foo, and `opname` to refer to the string, which is what is passed in this case. | {
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] | closed | false | null | [] | null | 2 | 2019-08-10T02:23:39Z | 2019-08-12T18:46:45Z | 2019-08-12T18:46:45Z | NONE | null | I have a folder full of excel files if I go
for i in range(0,len(os.listdir())):
x=pd.read_excel(os.listdir()[i])
I get an xlrd error that reads "Unsupported format, or corrupt file: Expected BOF record; found b'\x00\x00\x00\x01Bud1'"
but if I go
x=pd.read_excel(os.listdir()[0])
I don't get an error | {
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} | 2 | 2019-08-10T06:07:12Z | 2019-08-19T17:17:27Z | 2019-08-19T17:17:20Z | NONE | null | #### Code Sample
```python
# This works fine
a = pd.DataFrame(np.random.randn(9999, 10))
b = a.divide(a.sum(axis=0), axis=1)
# This doesn't
a = pd.DataFrame(np.random.randn(10001, 10))
b = a.divide(a.sum(axis=0), axis=1)
# │AttributeError: 'numpy.dtype' object has no attribute 'value_counts'
```
#### Problem description
Should be able to divide the dataframe by the series and align on the axis.
This works for smaller dataframes but breaks suddenly for larger ones. Doesn't happen for me on 0.24.0 but occurs on 0.25.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.13.0-45-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.1
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.1.2
</details>
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```python
# import pandas_datareader
```
#### Problem description
Import error, probably because in version .25 pandas.compat. has been removed
#### Output
C:\PycharmProjects\Demo\venv\Scripts\python.exe C:/PycharmProjects/Demo/stock.py
Traceback (most recent call last):
File "C:/PycharmProjects/Demo/stock.py", line 3, in <module>
import pandas_datareader as wb
File "C:\PycharmProjects\Demo\venv\lib\site-packages\pandas_datareader\__init__.py", line 2, in <module>
from .data import (DataReader, Options, get_components_yahoo,
File "C:\PycharmProjects\Demo\venv\lib\site-packages\pandas_datareader\data.py", line 7, in <module>
from pandas_datareader.av.forex import AVForexReader
File "C:\PycharmProjects\Demo\venv\lib\site-packages\pandas_datareader\av\__init__.py", line 3, in <module>
from pandas_datareader.base import _BaseReader
File "C:\PycharmProjects\Demo\venv\lib\site-packages\pandas_datareader\base.py", line 11, in <module>
from pandas.compat import StringIO, bytes_to_str
ImportError: cannot import name 'StringIO'
<details>
</details>
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} | 5 | 2019-08-10T19:42:15Z | 2019-08-14T07:08:40Z | 2019-08-13T01:04:21Z | MEMBER | null | Closes #27841
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https://api.github.com/repos/pandas-dev/pandas/issues/27856 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27856/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27856/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27856/events | https://github.com/pandas-dev/pandas/issues/27856 | 479,357,589 | MDU6SXNzdWU0NzkzNTc1ODk= | 27,856 | CI fails? | {
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} | [] | closed | false | null | [] | null | 0 | 2019-08-11T10:59:35Z | 2019-08-11T11:22:48Z | 2019-08-11T11:22:48Z | MEMBER | null | Looks like Azure pipeline has failed for a while. Is it caused by version incompatibility? | {
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} | 1 | 2019-08-11T14:48:48Z | 2019-08-12T18:42:23Z | 2019-08-12T18:42:20Z | NONE | null | I was surprised by the behaviour of `groupby transform count`.
Versions:
```
np.__version__, pd.__version__
('1.16.4', '0.24.2')
```
Code:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': list(range(10))})
b = df.groupby('A').transform('count') # I was expecting all ones.
print(b) # empty dataframe
```
Results:
```
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
```
Question:
Is this the desired behaviour, I was expecting all values to be 1 ?
Also Another Question
====================
Code:
```
df = pd.DataFrame({'A': ['a']*5+['b']*4+list('ccde'),
'B': [1]*5 +[2]*4 +[3,3,4,5]})
df['xx'] = df.groupby('A').transform('count')
# df['yy'] = df.groupby('A').transform('count') # when I uncomment this it will fail
df
```
when I uncomment the line with `df['yy']` it will throw an error. Is that the desired behavior?
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2656 try:
-> 2657 return self._engine.get_loc(key)
2658 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'yy'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
9 frames
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 'yy'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py in __init__(self, values, placement, ndim)
85 raise ValueError(
86 'Wrong number of items passed {val}, placement implies '
---> 87 '{mgr}'.format(val=len(self.values), mgr=len(self.mgr_locs)))
88
89 def _check_ndim(self, values, ndim):
ValueError: Wrong number of items passed 2, placement implies 1
``` | {
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} | 17 | 2019-08-11T15:01:39Z | 2019-10-29T00:04:49Z | 2019-10-29T00:04:49Z | MEMBER | null | - [ ] closes #27472
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27859/events | https://github.com/pandas-dev/pandas/issues/27859 | 479,422,998 | MDU6SXNzdWU0Nzk0MjI5OTg= | 27,859 | DOC: Documentation says Series.items(), Series.iteritems() are equivalent to DataFrame.items(), but they're not. | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.items.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.iteritems.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.items.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html | {
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https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.items.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iteritems.html | {
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} | 1 | 2019-08-11T23:05:36Z | 2019-08-13T11:57:29Z | 2019-08-13T11:57:29Z | CONTRIBUTOR | null | Documentation for `DataFrame.items()` and `DataFrame.iteritems()` say
"Iterator over..."
while other iterators for `DataFrame` and `Series` say
"Iterate over..."
All of these methods return an iterator, so I think it would make sense to have them all say "Iterator over...", although the documentation for 4/6 of the iteration methods already use "Iterate over...".
I'm not sure if there are other methods that also create iterators that we can use to justify one over the other.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.items.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iteritems.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.itertuples.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.items.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.iteritems.html | {
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} | 3 | 2019-08-11T23:14:43Z | 2019-08-13T07:57:33Z | 2019-08-13T07:57:20Z | CONTRIBUTOR | null | See also for DataFrame.iterrows() currently has a "See also" section that lists
```
itertuples
items
```
while DataFrame.itertuples() shows
```
DataFrame.iterrows
DataFrame.items
```
We should change DataFrame.iterrows() to be like DataFrame.itertuples()
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.itertuples.html | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27863/events | https://github.com/pandas-dev/pandas/issues/27863 | 479,464,989 | MDU6SXNzdWU0Nzk0NjQ5ODk= | 27,863 | DataFrame Groupby Named Aggregation Using Lambda Results in a KeyError, Dict-of-Dicts Approach Works Fine | {
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```python
import numpy as np
import pandas as pd
data = [['tom', 10, 'blue', 1000, 'a'], ['nick', 15, 'blue', 2000, 'b'], ['julie', 14, 'green', 3000, 'a'], ['bob', 11, 'green', 4000, 'a'], ['cindy', 16, 'red', 5000, 'b']]
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Color', 'Num', 'Letter'])
# Dict-style renaming works fine:
df.groupby(by='Color').agg({'Num': {'SumNum' : np.sum, 'SumNumIfLetterA': lambda x: x[df.iloc[x.index].Letter=='a'].sum()}})
Out[2]:
Num
SumNum SumNumIfLetterA
Color
blue 3000 1000
green 7000 7000
red 5000 0
# Named Aggregation using function also works fine:
def simplefunc(x):
return x[df.iloc[x.index].Letter=='a'].sum()
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', simplefunc))
Out[3]:
SumNum SumNumIfLetterA
Color
blue 3000 1000
green 7000 7000
red 5000 0
# Named Aggregation using identical lambda throws a KeyError:
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', lambda x: x[df.iloc[x.index].Letter=='a'].sum()))
```
#### Problem description
A lambda function that previously worked fine (using a Dict-of-Dict) no longer works with Named Aggregation. Using a function with Named Aggregation works fine but replacing the function with an identical lambda does not work with Named Aggregation. Is this expected behavior or it is a bug?
#### Expected Output
~~~
SumNum SumNumIfLetterA
Color
blue 3000 1000
green 7000 7000
red 5000 0
~~~
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27864 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27864/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27864/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27864/events | https://github.com/pandas-dev/pandas/issues/27864 | 479,478,503 | MDU6SXNzdWU0Nzk0Nzg1MDM= | 27,864 | IntervalIndex | {
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```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.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 3 | 2019-08-12T07:14:02Z | 2019-08-19T19:26:21Z | 2019-08-19T19:26:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
x = pd.Series([-2.801298, -2.882724, -3.007899, -2.704554, -3.398761, -2.805034, -2.87554, -2.805034, -2.886459, -2.471618])
y= pd.Series([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
init_cut = pd.qcut(x, 5, duplicates='drop')
retbin = pd.Series(init_cut.values.categories).sort_values()
retbin.iloc[0] = pd.Interval(-np.inf, retbin.iloc[0].right)
retbin.iloc[-1] = pd.Interval(retbin.iloc[-1].left, np.inf)
init_cut = pd.cut(x, pd.IntervalIndex(retbin))
init_cut = init_cut.astype(object)
bin_df = pd.crosstab(index=init_cut, columns=y)
bin_df = bin_df.reindex(retbin)
bin_df = bin_df.sort_index()
bin_df = bin_df.fillna(0.0)
bin_df['nbin'] = np.nan
```
#### Problem description
bin_df =
col_0 0 nbin
(-inf, -2.911] 2 NaN
(-2.911, -2.878] 2 NaN
(-2.878, -2.805] 3 NaN
(-2.805, -2.782] 1 NaN
(-2.782, inf] 2 NaN
if I use bin_df.ix[0:2,0], I got an error like:
```pytb
Traceback (most recent call last):
File "D:\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2961, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-12-1ae8ba69565c>", line 1, in <module>
bin_df.ix[0:1,'nbin']
File "D:\PyTest\venv\lib\site-packages\pandas\core\indexing.py", line 125, in __getitem__
values = self.obj._get_value(*key)
File "D:\PyTest\venv\lib\site-packages\pandas\core\frame.py", line 2827, in _get_value
return engine.get_value(series._values, index)
AttributeError: 'pandas._libs.interval.IntervalTree' object has no attribute 'get_value'
```
the version is 0.25.0
but it works well in 0.24.x
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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```
$ ci/run_tests.sh
2333pytest -m "single and (not slow and not network)" -n 1 -s --strict --durations=10 --junitxml=test-data-single.xml pandas
2334ImportError while loading conftest '/home/travis/build/pandas-dev/pandas/pandas/conftest.py'.
2335pandas/__init__.py:38: in <module>
2336 "the C extensions first.".format(module)
2337E ImportError: C extension: No module named 'pandas._libs.tslibs.conversion' not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first.
2338The command "ci/run_tests.sh" exited with 4.
2339
```
Fixing in https://github.com/pandas-dev/pandas/pull/27868
Leaving this open to revert.
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"de... | closed | false | null | [] | null | 4 | 2019-08-12T15:39:59Z | 2019-10-07T00:15:11Z | 2019-08-15T13:42:18Z | CONTRIBUTOR | null | I found this behavior surprising:
```python
In [1]: pd.Series([1,2,3], dtype=np.uint8).unique()
Out[1]: array([1, 2, 3], dtype=uint64)
```
... because I did not expect a different dtype in the result. This PR adds a sentence to the docs to clarify this.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- (n/a) closes #xxxx
- (n/a) tests added / passed
- (n/a) whatsnew entry
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I've been thinking in more detail about the website, and I think it'll make things much simpler if we keep the pandas website and the documentation separate. My previous proposal was to unify everything, and use nginx rules to show the latest version (`master`) for pages that we want to show the latest version.
I still think that from the user perspective, there should be one single website, with one look and feel, a common header / navigation bar (no matter you're in the website or in the docs). It's easy to make it transparent that we're using different repos/hosts (with different domains or via nginx).
To keep things simple, I'd move from the docs (currently in this repo) to the website repo, everything in the website that doesn't make sense to show outdated (latest stable) versions, including:
- Install
- Roadmap
- Ecosystem
- Contributing documentation
I think this will make things much much simpler regarding Sphinx, will speed up a bit the documentation build, will avoid having to link users to the dev docs (like for contributing), and will simplify things in our workflow and the website set up. The only drawback I see is that we may have to repeat the main template, the css and the logo in both repositories. Those should change very rarely, and we can consider some CI check or automation (download them from one repo to another to build the docs).
For the documentation, I think with this approach will make more sense to create a custom (very simple) theme. This will make things easier and with the bootstrap theme I had some limitations that the maintainers don't seem so keen to address (dropdowns in the top navigation bar, customization in the levels of the sidebar...).
Another advantage is that I think we should be able to move much faster with this architecture when building the new website with the new design, compared to what would be with the Sphinx.
My proposed top-level structure is as follows:
- Home
- Install
- Documentation
- Tutorials
- User guide
- API reference
- What's new / Release notes
- Community
- Contributing
- Roadmap
- Ecosystem
- Discuss / Mailing list
- StackOverflow
- Blog
- Donate
I think the home page should be a visual and concise about us, so users new to pandas can very easily understand what the project is about.
For the blog, I'd mix the posts in the pandas blog, with a planet with the pandas related (tagged) posts of core devs, NumFOCUS, and anyone else we think it's worth.
For the Donate, NumFOCUS works with a new provider, that for what I've seen (I can be wrong), is able to handle the donations in an iframe, and not make users leave the page. I'd also include mentions to the institutional sponsors, comments from individual donors, and things like that.
Once there is agreement in the main structure, and we have a first draft, I'll open separate issues to discuss the exact details of the home, the community page, the blog and the donate page.
CC: @pandas-dev/pandas-core @stijnvanhoey | {
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closes #27861
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} | 10 | 2019-08-12T18:38:24Z | 2019-10-07T01:08:28Z | 2019-10-07T01:08:25Z | CONTRIBUTOR | null | The behavior of `pd.unique()` is surprising, because -- unlike `np.unique()` -- the result does not have the same `dtype` as the input:
```python
In [1]: pd.Series([1,2,3], dtype=np.uint8).unique()
Out[1]: array([1, 2, 3], dtype=uint64)
```
This PR just casts the output array to match the input dtype. Supercedes #27869.
**Update:** Augmented the tests to cover narrow dtypes.
~I added a new assertion in `test_value_counts_unique_nunique()`, but it may not be sufficient. From what I can see, there isn't good coverage of `Series` whose *data* is not `int`/`float`/ etc. There is only good coverage of various *index* types. Any advice concerning test coverage?~
- [x] closes #27869
- [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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closes #27860 | {
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} | 2 | 2019-08-12T20:05:22Z | 2019-08-15T21:14:43Z | 2019-08-15T20:27:18Z | MEMBER | null | …ssed
- [x] closes #27779
- [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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} | 1 | 2019-08-12T20:07:29Z | 2019-08-13T13:55:52Z | 2019-08-13T12:18:36Z | MEMBER | null | Only one test reaches this point, and we aren't currently checking for the output, just that a warning is emitted. This fixes the currently-incorrect output and updates the test to check for it. | {
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```>>> quotes
time ticker bid ask
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
1 2016-05-25 13:30:00.023 MSFT 51.95 51.96
2 2016-05-25 13:30:00.030 MSFT 51.97 51.98
3 2016-05-25 13:30:00.041 MSFT 51.99 52.00
4 2016-05-25 13:30:00.048 GOOG 720.50 720.93
5 2016-05-25 13:30:00.049 AAPL 97.99 98.01
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03
>>> trades
time ticker price quantity
0 2016-05-25 13:30:00.023 MSFT 51.95 75
1 2016-05-25 13:30:00.038 MSFT 51.95 155
2 2016-05-25 13:30:00.048 GOOG 720.77 100
3 2016-05-25 13:30:00.048 GOOG 720.92 100
4 2016-05-25 13:30:00.048 AAPL 98.00 100
>>> pd.merge_asof(trades, quotes,
... on='time',
... by='ticker',
... tolerance=pd.Timedelta('10ms'),
... allow_exact_matches=False)
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
2 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN
3 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN NaN
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
```
Above example merges only when the time gap is strictly less than '10ms', with direction='backward'.
I'm wondering if I can use a range of tolerance like between 10ms and 20ms?
The expected output would be such that it merges the closest one that happened more than 10 ms before, but not more than 20 ms(direction ='backward'). | {
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... | open | false | null | [] | null | 2 | 2019-08-12T20:24:27Z | 2020-05-03T21:47:27Z | null | MEMBER | null | When querying numbers from dataframe with `df.select_dtypes`, if i choose `include=np.number`, then timedelta will be also selected. But based on documentation, this should not happen. I checked the tests, it seems allow this to happen on purpose, then how should I only select numeric columns? should DOC be changed at least?
```python
df = pd.DataFrame({'a': pd.date_range("2012-01-01", periods=2),
'b': np.random.randn(2),
'd': pd.date_range("2012-01-01", periods=2).astype(str),
'e': pd.date_range("2012-01-01", periods=2, tz='UTC'),
'f': pd.timedelta_range("1 days", periods=2)
})
df.select_dtypes(include=['number'])
```
If diving to code, it seems because `timedelta` also is represented by `np.timedelta64` and is subclass of `np.number`.
Output:
the column b, f are selected
Expected Output:
only column b will be selected as a dataframe. Or a doc change instead?
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} | 19 | 2019-08-12T21:01:21Z | 2021-09-08T17:02:03Z | 2019-08-19T13:06:15Z | CONTRIBUTOR | null | Importing `lzma` when Python has been compiled without its support (usually due to a lack of `xz` in the system) will raise a warning. Substituted `import lzma` for helper function.
- [x] closes #27575
- [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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} | 1 | 2019-08-12T21:16:22Z | 2019-08-13T13:51:13Z | 2019-08-13T12:20:40Z | MEMBER | null | underlying goal is to share code between Index.__new__ and pd.array
Unrelated cleanup in DataFrame.select_dtypes because I found it cumbersome. | {
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} | 1 | 2019-08-12T21:26:55Z | 2019-08-19T11:43:34Z | 2019-08-19T11:43:34Z | CONTRIBUTOR | null |
#### Problem description
I Think that needs to appear on that table because is not clearly documented
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```python
import pandas
```
#### Problem description
When importing pandas on fresh install of jupyterlab 1.0.4 via Anaconda, I'm receiving a unboundlocal error. I've never seen this happen before until pandas 0.2.5, so a bit confused on why this would be occurring
```
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-4-38d4b0363d82> in <module>
----> 1 import pandas
/opt/conda/lib/python3.7/site-packages/pandas/__init__.py in <module>
40 import pandas.core.config_init
41
---> 42 from pandas.core.api import *
43 from pandas.core.sparse.api import *
44 from pandas.tseries.api import *
/opt/conda/lib/python3.7/site-packages/pandas/core/api.py in <module>
24 )
25 from pandas.core.arrays import Categorical, array
---> 26 from pandas.core.groupby import Grouper
27 from pandas.io.formats.format import set_eng_float_format
28 from pandas.core.index import (Index, CategoricalIndex, Int64Index,
/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/__init__.py in <module>
1 from pandas.core.groupby.groupby import GroupBy # noqa: F401
----> 2 from pandas.core.groupby.generic import ( # noqa: F401
3 SeriesGroupBy, DataFrameGroupBy, PanelGroupBy)
4 from pandas.core.groupby.grouper import Grouper # noqa: F401
/opt/conda/lib/python3.7/site-packages/pandas/core/groupby/generic.py in <module>
40 import pandas.core.indexes.base as ibase
41 from pandas.core.internals import BlockManager, make_block
---> 42 from pandas.core.panel import Panel
43 from pandas.core.series import Series
44
/opt/conda/lib/python3.7/site-packages/pandas/core/panel.py in <module>
1584 docs={})
1585
-> 1586 ops.add_special_arithmetic_methods(Panel)
1587 ops.add_flex_arithmetic_methods(Panel)
1588 Panel._add_numeric_operations()
/opt/conda/lib/python3.7/site-packages/pandas/core/ops/__init__.py in add_special_arithmetic_methods(cls)
810 special methods will be defined and pinned to this class
811 """
--> 812 _, _, arith_method, comp_method, bool_method = _get_method_wrappers(cls)
813 new_methods = _create_methods(
814 cls, arith_method, comp_method, bool_method, special=True
/opt/conda/lib/python3.7/site-packages/pandas/core/ops/__init__.py in _get_method_wrappers(cls)
717 comp_special = _comp_method_FRAME
718 bool_special = _arith_method_FRAME
--> 719 return arith_flex, comp_flex, arith_special, comp_special, bool_special
720
721
UnboundLocalError: local variable 'arith_flex' referenced before assignment
```
#### Expected Output
pandas would import properly
#### Output of ``pd.show_versions()``
N/A due to unable to import pandas properly
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} | 1 | 2019-08-13T01:14:57Z | 2019-08-13T07:50:48Z | 2019-08-13T07:50:40Z | CONTRIBUTOR | null | I changed the See also to:
DataFrame.items : Iterate over (column name, Series) pairs.
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
No assertions about the equivalence of DataFrame.items and Series.items
closes #27859
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} | 1 | 2019-08-13T01:20:48Z | 2019-08-13T13:50:42Z | 2019-08-13T12:22:41Z | MEMBER | null | these aren't accomplishing anything and are very non-performant.
Also, remove an unnecessary values_from_object call and use operator.foo instead of `"__foo__"` in categorical ops. | {
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} | 3 | 2019-08-13T01:52:58Z | 2019-08-13T16:23:38Z | 2019-08-13T16:15:37Z | MEMBER | null | - [x] closes #27080
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 10 | 2019-08-13T02:22:42Z | 2020-07-16T22:48:30Z | 2020-07-16T22:48:30Z | CONTRIBUTOR | null | In pandas 0.24.x, we had
```python
In [1]: import pandas as pd
In [2]: pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()
Out[2]:
Empty DataFrame
Columns: []
Index: []
```
In 0.25.0, we have
```python
In [3]: pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-8152ffc0932b> in <module>
----> 1 pd.DataFrame({"A": ['a', 'b']}).groupby([0, 0]).quantile()
~/sandbox/pandas/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1908 post_processing=post_processor,
1909 q=q,
-> 1910 interpolation=interpolation,
1911 )
1912
~/sandbox/pandas/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, grouper, aggregate, cython_dtype, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2236 vals = obj.values
2237 if pre_processing:
-> 2238 vals, inferences = pre_processing(vals)
2239 func = partial(func, vals)
2240
~/sandbox/pandas/pandas/core/groupby/groupby.py in pre_processor(vals)
1875 if is_object_dtype(vals):
1876 raise TypeError(
-> 1877 "'quantile' cannot be performed against 'object' dtypes!"
1878 )
1879
TypeError: 'quantile' cannot be performed against 'object' dtypes!
```
This is most relevant for mixed dataframes
```python
In [6]: df = pd.DataFrame({"A": [0, 1], 'B': ['a', 'b']})
In [7]: df.groupby([0, 1]).quantile(0.5)
...
TypeError: 'quantile' cannot be performed against 'object' dtypes!
``` | {
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} | 1 | 2019-08-13T02:57:17Z | 2019-08-13T07:57:21Z | 2019-08-13T07:57:20Z | CONTRIBUTOR | null | - [x] closes #27862
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/27894 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27894/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27894/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27894/events | https://github.com/pandas-dev/pandas/issues/27894 | 479,935,746 | MDU6SXNzdWU0Nzk5MzU3NDY= | 27,894 | remove index column in SeriesGroupBy.nlargest | {
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} | 1 | 2019-08-13T02:57:30Z | 2019-08-14T21:15:16Z | 2019-08-14T21:15:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
pdf = pd.DataFrame({
'a': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'b': [1, 2, 2, 2, 3, 3, 3, 4, 4]}, columns=['a', 'b'])
pdf.groupby(['a'])['b'].nlargest(1)
a
1 1 2
2 4 3
3 7 4
Name: b, dtype: int64
```
#### Problem description
It raise the index to column(1, 4, 7) above. But we dont need it most time.
#### Expected Output
```python
pdf.groupby(['a'])['b'].nlargest(1)
a
1 2
2 3
3 4
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.5.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: zh_CN.utf8
LANG: None
LOCALE: zh_CN.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 18.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.17.0
scipy: 1.1.0
pyarrow: 0.14.1
xarray: None
IPython: 7.2.0
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.1.1
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.2.5
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/27895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27895/events | https://github.com/pandas-dev/pandas/pull/27895 | 479,936,377 | MDExOlB1bGxSZXF1ZXN0MzA2NzI4MDMz | 27,895 | Infer names in MultiIndex.from_product | {
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} | 4 | 2019-08-13T03:00:46Z | 2019-09-12T16:00:17Z | 2019-09-12T16:00:06Z | CONTRIBUTOR | null | - [x] closes #27292
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/27896 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27896/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27896/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27896/events | https://github.com/pandas-dev/pandas/pull/27896 | 479,943,653 | MDExOlB1bGxSZXF1ZXN0MzA2NzMzNjEx | 27,896 | CLN: remove _maybe_update_attributes | {
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} | 9 | 2019-08-13T03:37:31Z | 2019-10-19T12:11:51Z | 2019-08-15T12:42:38Z | MEMBER | null | It is only non-trivial for DTI and TDI, and only actually needed in `__array_wrap__`. It is cleaner to implement the relevant patch in `DatetimeIndexOpsMixin.__array_wrap__` and avoid the need for maybe_update_attributes altogether.
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} | 2 | 2019-08-13T08:09:31Z | 2019-08-13T16:14:27Z | 2019-08-13T16:14:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({
'A': [1, 1, 2, 2],
'B': [2, 4, None, 3],
'C': [None, None, None, 1],
'D': [0, 1, 5, 4]
},
columns=['A', 'B', 'C', 'D'])
df.groupby(['A']).fillna(method='bfill')
B C D
0 2.0 NaN 0
1 4.0 NaN 1
2 3.0 1.0 5
3 3.0 1.0 4
df.groupby(['A']).bfill()
A B C D
0 1 2.0 NaN 0
1 1 4.0 NaN 1
2 2 3.0 1.0 5
3 2 3.0 1.0 4
```
#### Problem description
`DataFrame.groupby.bfill()` contains groupby col ['A'] while DataFrame.groupby.fillna(method='bfill) dont have it . Same problem in `ffill`.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.5.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: zh_CN.utf8
LANG: None
LOCALE: zh_CN.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 18.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.17.0
scipy: 1.1.0
pyarrow: 0.14.1
xarray: None
IPython: 7.2.0
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.1.1
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.2.5
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27898 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27898/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27898/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27898/events | https://github.com/pandas-dev/pandas/issues/27898 | 480,032,649 | MDU6SXNzdWU0ODAwMzI2NDk= | 27,898 | Issue with str.contains output | {
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] | closed | false | null | [] | null | 2 | 2019-08-13T08:44:27Z | 2019-08-14T05:29:30Z | 2019-08-14T05:29:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import pandas as pd
import numpy as np
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
csv1="multiple_responses.csv"
df1 = pd.read_csv(csv1, index_col='id' , na_values = [' '] , low_memory=False)
## creating a list containing 8 family planning method names
method_names = ['female_condoms', 'emergency', 'male_condoms', 'pill', 'injectables', 'iud', 'male_sterilization', 'female_sterilization']
for method in method_names:
print(method)
for method in method_names:
df1[method]=df1["methods_discussed"].str.contains(pat = method)
df1.head(10)
## output
id | methods_discussed | female_condoms | emergency | male_condoms | pill | injectables | iud | male_sterilization | female_sterilization
1 | emergency | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE
2 | female_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | _**TRUE**_ | TRUE
3 | male_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE
4 | iud | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE
5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN
6 | injectables male_condoms | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE
7 | male_condoms | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE
8 | female_sterilization male_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE
9 | injectables | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE
10 | iud male_condoms | FALSE | FALSE | TRUE | FALSE | FALSE | TRUE | FALSE | FALSE
#### Problem description
I used CSV file
[multiple_responses.zip](https://github.com/pandas-dev/pandas/files/3496001/multiple_responses.zip)
which contains two columns "id" and "methods_discussed". After running above code the ouput shown is wrong as at index [2] column male_sterilization shows TRUE (I have made it bold and italic. It should be FALSE as "methods_discussed" contains only female_sterilization.
#### Expected Output
id | methods_discussed | female_condoms | emergency | male_condoms | pill | injectables | iud | male_sterilization | female_sterilization
1 | emergency | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE
2 | female_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | **_FALSE_** | TRUE
3 | male_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE
4 | iud | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE
5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN
6 | injectables male_condoms | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE
7 | male_condoms | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE
8 | female_sterilization male_sterilization | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE
9 | injectables | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE
10 | iud male_condoms | FALSE | FALSE | TRUE | FALSE | FALSE | TRUE | FALSE | FALSE
I have also used str.match but it did not work for me.
Any idea if I don't want to generate values if methods_discussed contains NaN.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/27899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27899/events | https://github.com/pandas-dev/pandas/pull/27899 | 480,108,865 | MDExOlB1bGxSZXF1ZXN0MzA2ODY3MzEx | 27,899 | EHN: Add encoding_errors option in pandas.DataFrame.to_csv (#27750) | {
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"description": "read_csv... | closed | false | null | [] | null | 24 | 2019-08-13T11:39:49Z | 2019-09-10T21:05:20Z | 2019-09-10T21:05:20Z | NONE | null | - [x] closes #27750
- [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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} | 5 | 2019-08-13T11:43:16Z | 2019-09-20T12:43:53Z | 2019-09-20T12:43:53Z | MEMBER | null | I reported it at Jedi: https://github.com/davidhalter/jedi/issues/1383
But opening an issue here as well in case there are workarounds we need to do in pandas itself (last time we did do something: https://github.com/pandas-dev/pandas/pull/16414) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27901 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27901/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27901/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27901/events | https://github.com/pandas-dev/pandas/issues/27901 | 480,173,941 | MDU6SXNzdWU0ODAxNzM5NDE= | 27,901 | Move `.github` files to the organization level | {
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] | open | false | null | [] | null | 2 | 2019-08-13T14:00:21Z | 2020-04-03T03:55:30Z | null | CONTRIBUTOR | null | Described in https://help.github.com/en/articles/creating-a-default-community-health-file-for-your-organization, we can make a `.github` repo under pandas-dev. Then all the "community" files (code of conduct, funding, security, etc) will apply to every repo in the organization. This makes it clear that these policies apply everywhere, but we don't have to worry about copying / updating the files to every repo. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27902 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27902/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27902/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27902/events | https://github.com/pandas-dev/pandas/issues/27902 | 480,177,739 | MDU6SXNzdWU0ODAxNzc3Mzk= | 27,902 | CI: master failing windows py37_np141 builds | {
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```python
================================== FAILURES ===================================
______________________________ test_apply[True] _______________________________
[gw1] win32 -- Python 3.7.4 C:\Miniconda\envs\pandas-dev\python.exe
ordered = True
@pytest.mark.parametrize("ordered", [True, False])
def test_apply(ordered):
# GH 10138
dense = Categorical(list("abc"), ordered=ordered)
# 'b' is in the categories but not in the list
missing = Categorical(list("aaa"), categories=["a", "b"], ordered=ordered)
values = np.arange(len(dense))
df = DataFrame({"missing": missing, "dense": dense, "values": values})
grouped = df.groupby(["missing", "dense"], observed=True)
# missing category 'b' should still exist in the output index
idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"])
expected = DataFrame([0, 1, 2.0], index=idx, columns=["values"])
result = grouped.apply(lambda x: np.mean(x))
> assert_frame_equal(result, expected)
E AssertionError: DataFrame are different
E
E DataFrame shape mismatch
E [left]: (3, 3)
E [right]: (3, 1)
pandas\tests\groupby\test_categorical.py:228: AssertionError
______________________________ test_apply[False] ______________________________
[gw1] win32 -- Python 3.7.4 C:\Miniconda\envs\pandas-dev\python.exe
ordered = False
@pytest.mark.parametrize("ordered", [True, False])
def test_apply(ordered):
# GH 10138
dense = Categorical(list("abc"), ordered=ordered)
# 'b' is in the categories but not in the list
missing = Categorical(list("aaa"), categories=["a", "b"], ordered=ordered)
values = np.arange(len(dense))
df = DataFrame({"missing": missing, "dense": dense, "values": values})
grouped = df.groupby(["missing", "dense"], observed=True)
# missing category 'b' should still exist in the output index
idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"])
expected = DataFrame([0, 1, 2.0], index=idx, columns=["values"])
result = grouped.apply(lambda x: np.mean(x))
> assert_frame_equal(result, expected)
E AssertionError: DataFrame are different
E
E DataFrame shape mismatch
E [left]: (3, 3)
E [right]: (3, 1)
pandas\tests\groupby\test_categorical.py:228: AssertionError
``` | {
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] | open | false | null | [] | null | 6 | 2019-08-13T14:45:09Z | 2021-07-10T19:36:44Z | null | MEMBER | null | As the project and the community grows, I think it'd be good to keep some consistency among the different channels to communicate. Currently we have:
- pydata@googlegroups.com (broader than pandas, but it's kind of our users list)
- pandas-dev@python.org
- pandas-core-team@googlegroups.com
- pydata/pandas on Gitter for IM (I guess it's more intended for devs, but a mix of dev/users in practice)
I don't have a preference on the platform (google groups, mailman, discourse if it's free for open source projects,...), but I think it'd make things simpler and better if we had:
- pandas-users@...
- pandas-contributors@...
- pandas-maintainers@...
And same channels in Gitter. The pandas-maintainers ones should be private for the active maintainers (both the list and the gitter channel).
I think the names would make it clear what's expected to be discussed, and should help keep each of them focused (I'd avoid pandas-dev, since I think for beginners it can be ambiguous if a pandas dev is a person working with pandas or on pandas).
CC: @pandas-dev/pandas-core | {
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} | 3 | 2019-08-13T19:08:00Z | 2019-09-07T11:29:41Z | 2019-09-07T11:29:41Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
import datetime
df = pd.DataFrame({'a': ['x','x','x','x','x'], 'b': [datetime.date(2019,1,1), pd.NaT, datetime.date(2019,1,1), pd.NaT, datetime.date(2019,1,1)]})
print(df)
# unique() returns only 1 value (plus the NaT) as expected
print('unique() with dates')
print(df.groupby(['a'])['b'].unique())
# nunique() returns 3 instead of the expected 1 (NaTs are dropped). Seems to be due to the NaTs
print('nunique() with dates')
print(df.groupby(['a'])['b'].nunique())
# convert date column to datetime
df['b'] = df.b.astype(np.datetime64)
# with datetime, unique() still returns 1 value (plus the NaT) as expected
print('unique() with datetimes')
print(df.groupby(['a'])['b'].unique())
# with datetime, nunique() returns 1 as expected
print('nunique() with datetimes')
print(df.groupby(['a'])['b'].nunique())
```
#### Problem description
`Series.groupby().nunique()` seems to have a different behavior with dates vs datetimes when NaTs are in the Series. The behavior with `date` seems to return more distinct values than there are.
This is not the same issue as https://github.com/pandas-dev/pandas/issues/14149
#### Expected Output
For nunique() with dates the expected output is:
```
a
x 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-25-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
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 : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 5 | 2019-08-13T19:47:11Z | 2020-03-03T15:48:41Z | 2020-03-03T15:45:44Z | MEMBER | null | e.g. `fillna`, `tshift`and `corrwith` are all allowed in `group.transform()`, but looking at `pandas/tests/groupby/test_transform.py`, they are not tested. There are more, and I will add to here and make a PR to tests these cases. | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-08-13T21:23:55Z | 2019-08-14T19:46:11Z | 2019-08-14T19:34:11Z | CONTRIBUTOR | null | ~second commit is temporary.~
Closes #27907
cc @jjhelmus | {
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} | 3 | 2019-08-13T23:33:04Z | 2019-08-19T18:07:22Z | 2019-08-19T16:45:42Z | MEMBER | null | After this I think we'll be ready for #23853 | {
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In many arithmetic/comparison ops we do something like
```
if is_list_like(other) and not hasattr(other, "dtype"):
other = np.asarray(other)
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
```
but we are not entirely consistent about this (see below for summary of what we do where). AFAICT the only case where we wouldn't want to do both of these consistently is if we have an object-dtype, the relevant test case being something like:
```
ser = pd.Series([["A"], ("A", "B"), frozenset("C")], dtype=np.object_)
ser == ("A", "B")
```
(note in this example ATM `ser == ser[0]` will raise `ValueError`).
We should be consistent in what we are doing for these, but need to decide what that consistent behavior should be.
Summary of current behavior:
- Series comparison
- wraps list in ndarray (not listlike)
- checks length matching for list, ndarray, Index, Series (not tuple, set, ...)
- Series arithmetic
- doesn't wrap listlike at all
- length checks are left implicit
- Categorical comparisons do the "full" checks
- What if we have list-like categories?
- DatetimeArray/TimedeltaArray/PeriodArray arithmetic - no wrapping, no explicit checks
- DatetimeArray comparisons
- wrap only list
- Checks length match for all listlikes, but not at the beginning
- TimedeltaArray comparisons
- wrap any listlike (though indirectly)
- Checks length match for all listlikes, but not at the beginning
- PeriodArray comparisons
- dont wrap _any_ listlikes
- check all list-likes up-front
Not yet reviewed: IntegerArray, PandasArray, SparseArray, Index, FooIndex | {
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} | 2 | 2019-08-14T00:50:52Z | 2019-08-19T21:57:36Z | 2019-08-19T21:48:39Z | MEMBER | null | This sits on top of #27815
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