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MDExOlB1bGxSZXF1ZXN0NDA5MzQzMDMx
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TST: Separate expanding_apply and rolling_apply out of consistency
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|
MEMBER
| null |
xref #30577 #30486
This is the second step for breaking up giant consistency test, this PR separates consistency tests about `expanding_apply` and `rolling_apply` out from their big `expanding_consistency` and `rolling_consistency` tests.
The followup after this would be removing `self.data` initiation from `init` and convert to either fixture or funcs with yields so as to gradually move tests out of the whole consistency class and easier for parametrization instead of current looping.
cc @jreback
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MDExOlB1bGxSZXF1ZXN0NDA5Mzk1NjAz
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CLN/TYP: update setup.cfg
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| 0
| 2020-04-27T10:11:09Z
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| 2020-04-27T12:57:39Z
|
MEMBER
| null |
```
@pytest.mark.xfail(
StrictVersion(dateutil.__version__.split(".dev")[0]) < StrictVersion("2.7.0"),
reason="Bug in dateutil < 2.7.0 when parsing old dates: Period('0001-01-07', 'D')",
strict=False,
)
```
removed in #33680
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MDU6SXNzdWU2MDc0NDg3OTg=
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BUG: Missing tick labels on twinned axes
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| 2020-04-27T10:56:58Z
| 2020-09-30T00:11:53Z
| 2020-09-30T00:11:53Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
#!/usr/bin/python3
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Create data
df = pd.DataFrame({'a': np.random.randn(1000),
'b': np.random.randn(1000)})
# Create figure
fig = plt.figure()
plots = fig.subplots(2, 3)
# Create *externally* shared axes
plots[0][0] = plt.subplot(231, sharex=plots[1][0])
# note: no plots[0][1] that's the twin only case
plots[0][2] = plt.subplot(233, sharex=plots[1][2])
# Create *internally* shared axes
# note: no plots[0][0] that's the external only case
twin_ax1 = plots[0][1].twinx()
twin_ax2 = plots[0][2].twinx()
# Plot data to primary axes
df['a'].plot(ax=plots[0][0], title="External share only").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][0])
df['a'].plot(ax=plots[0][1], title="Internal share (twin) only").set_xlabel("this label should always be visible")
df['a'].plot(ax=plots[1][1])
df['a'].plot(ax=plots[0][2], title="Both").set_xlabel("this label should never be visible")
df['a'].plot(ax=plots[1][2])
# Plot data to twinned axes
df['b'].plot(ax=twin_ax1, color='green')
df['b'].plot(ax=twin_ax2, color='yellow')
# Do it
plt.show()
```
#### Problem description
Multi-row and/or multi-column subplots can utilize shared axes.
An external share happens at axis creation when a sharex or sharey
parameter is specified.
An internal share, or twinning, occurs when an overlayed axis is created
by the Axes.twinx() or Axes.twiny() calls.
The two types of sharing can be distinguished after the fact in the
following manner. If two axes sharing an axis also have the same
position, they are not in an external axis share, they are twinned.
For externally shared axes Pandas automatically removes tick labels for
all but the last row and/or first column in
./pandas/plotting/_matplotlib/tools.py's function _handle_shared_axes().
_handle_shared_axes() should be interested in externally shared axes,
whether or not they are also twinned. It should, but doesn't, ignore
axes which are only twinned. Which means that twinned-only axes wrongly
also lose their tick labels.
The first image (produced by running the test code above) shows this, with the label on the axes in the middle spot of the top row (the one with the green plot) notably missing the "this label should always be visible" label:
#### Expected Output
With the proposed fix in PR #33767 the correct bevahiour is seen:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : caa5121ba510ad727b5b2b0284f61351612f8142
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-lowlatency
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.1.0
Cython : 0.29.10
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 5.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.1.2
numba : None
</details>
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ENH: Add na_value to DataFrame.to_numpy
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| 1
| 2020-04-27T11:39:12Z
| 2020-05-13T15:51:29Z
| 2020-05-13T15:51:29Z
|
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
Control the NA value used when converting a DataFrame to an ndarray.
#### Describe the solution you'd like
Add an `na_value` argument to `DataFrame.to_numpy()`. Has the same meaning as `na_value` in `Series.to_numpy`, except it applies to all the columns.
#### Additional context
```python
In [8]: df = pd.DataFrame({"A": pd.array([1, None])})
In [9]: df.to_numpy(na_value=np.nan)
```
This has come up in a few places (cc @jorisvandenbossche @dsaxton)
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BUG: repair 'style' kwd handling in DataFrame.plot (#21003)
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CONTRIBUTOR
| null |
- [x] closes #21003
- [x] tests added / passed
- [x] passes `black pandas`
=> `command not found`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
=> `command not found`
- [x] whatsnew entry
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REF: collect set_index tests
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MDU6SXNzdWU2MDc2NTc1MjE=
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|
API: copy(deep=False) -> view?
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| 2020-04-27T15:50:23Z
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MEMBER
| null |
These should behave the same, shouldn't they? If so, we should use the more standard usage (view).
Ditto for `_shallow_copy()` with no args.
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BUG: DataFrame[object] + Series[dt64], test parametrization
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| 2020-04-27T21:33:04Z
| 2020-04-27T20:24:43Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
In the course of parametrizing tests, found a failing case. The edit in ops.dispatch fixes it.
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| 607,723,000
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MDU6SXNzdWU2MDc3MjMwMDA=
| 33,825
|
BUG: ExtensionArray TestMethods.test_where_series
|
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open
| false
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[] | null | 3
| 2020-04-27T17:30:19Z
| 2020-09-04T13:06:03Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
I'm working on updating pint-pandas to 0.25.1 before the latest version...
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
<details>
```python
TypeError Traceback (most recent call last)
c:\users\a\repos\pint\pint\quantity.py in __getitem__(self, key)
1737 try:
-> 1738 return type(self)(self._magnitude[key], self._units)
1739 except PintTypeError:
TypeError: 'float' object is not subscriptable
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-14-67c12fe72115> in <module>
----> 1 self.test_where_series(data, na_value, as_frame)
c:\users\a\repos\pint-pandas\pintpandas\test_pandas_interface.py in test_where_series(self, data, na_value, as_frame)
266 cond = cond.reshape(-1, 1)
267
--> 268 result = ser.where(cond)
269 expected = pd.Series(
270 cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
~\anaconda3\envs\py37_pd251\lib\site-packages\pandas\core\generic.py in where(self, cond, other, inplace, axis, level, errors, try_cast)
9274 other = com.apply_if_callable(other, self)
9275 return self._where(
-> 9276 cond, other, inplace, axis, level, errors=errors, try_cast=try_cast
9277 )
9278
~\anaconda3\envs\py37_pd251\lib\site-packages\pandas\core\generic.py in _where(self, cond, other, inplace, axis, level, errors, try_cast)
9121 errors=errors,
9122 try_cast=try_cast,
-> 9123 axis=block_axis,
9124 )
9125
~\anaconda3\envs\py37_pd251\lib\site-packages\pandas\core\internals\managers.py in where(self, **kwargs)
555
556 def where(self, **kwargs):
--> 557 return self.apply("where", **kwargs)
558
559 def setitem(self, **kwargs):
~\anaconda3\envs\py37_pd251\lib\site-packages\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
~\anaconda3\envs\py37_pd251\lib\site-packages\pandas\core\internals\blocks.py in where(self, other, cond, align, errors, try_cast, axis)
2005 set_other = other
2006 else:
-> 2007 set_other = other[icond]
2008 try:
2009 result[icond] = set_other
c:\users\a\repos\pint\pint\quantity.py in __getitem__(self, key)
1742 raise TypeError(
1743 "Neither Quantity object nor its magnitude ({})"
-> 1744 "supports indexing".format(self._magnitude)
1745 )
1746
TypeError: Neither Quantity object nor its magnitude (nan)supports indexing
```
</details>
#### Problem description
I'm having trouble with this test as pandas is trying to index into a scalar. I believe this is because this line returns false:
https://github.com/pandas-dev/pandas/blob/185a654e3db9976d33e7ba10b9c464214e7bf280/pandas/core/internals/blocks.py#L1831
```python3
import pintpandas
import matplotlib.pyplot as plt
import numpy as np
import pint
import pandas as pd
ureg=pint.UnitRegistry()
PA_= pintpandas.PintArray
PT_ = pintpandas.PintType
PT_.ureg = ureg
Q_=ureg.Quantity
from pandas._libs import NaT, lib, tslib, tslibs
lib.is_scalar(Q_(1,'m'))
False
```
#### Expected Output
```python3
lib.is_scalar(Q_(1,'m'))
True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : AMD64 Family 23 Model 8 Stepping 2, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDU6SXNzdWU2MDc3NTM5NjQ=
| 33,826
|
ENH: add support for snake case in pandas.Series.str.title
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[] | null | 6
| 2020-04-27T18:20:59Z
| 2020-04-29T19:13:35Z
| 2020-04-28T19:13:08Z
|
NONE
| null |
add support for snake case in `pandas.Series.str.title`
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.title.html
so that :
s = pd.Series(['snake', 'CAPITALS', 'IdNumber'])
will be:
```
s
0 snake
1 capitals
2 id_number
dtype: object
```
This is very much needed when we want to create database columns and they can't have capital letters. lower() isn't good as well because `nextExecutionDateForYearlyBatch` will be impossible to read : `nextexecutiondateforyearlybatch`
Basically I'm looking to do this:
```
import pandas as pd
import janitor
df = pd.DataFrame(...).clean_names()
```
```
Columns before: First Name, Last Name, Employee Status, Subject
Columns after: first_name, last_name, employee_status, subject
```
https://pyjanitor.readthedocs.io/reference/janitor.functions/janitor.clean_names.html
within the `pandas` domain without the need for `janitor` package.
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| 33,827
|
BUG: pandas.io.common _stringify_path uses isinstance in a wrong way raising an exception
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[] | null | 3
| 2020-04-27T18:34:07Z
| 2020-04-28T11:49:36Z
| 2020-04-28T11:49:36Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# pandas.io.common._stringify_path (line 150) uses pathlib.Path in an
# isinstance check but misses a wrapping type()
# broken:
isinstance("dummyvalue", pahtlib.Path)
# working fix:
isinstance("dummyvalue", type(pathlib.Path)
```
#### Problem description
The current implementation raises following exception:
TypeError: isinstance() arg 2 must be a type or tuple of types
Tested in the latest python 3.7 and 3.8 releases
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
import pandas as pd
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.33-3-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.16
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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|
BUG: iloc.__setitem__ with a numpy array throws a TypeError expecting 1-D list
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| 2
| 2020-04-27T18:51:43Z
| 2020-05-01T20:02:41Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import numpy as np
import pandas as pd
dfx = pd.DataFrame({'a': np.ones(10)})
dfx.iloc[np.array([0]), np.array([0])] = np.array([[2]]) # works
dfx['a_I'] = dfx['a'].astype('Int64')
dfx = dfx[["a_I"]]
dfx.iloc[np.array([0]), np.array([0])] = np.array([[2]]) # doesn't work
```
#### Problem description
The second case should set the value to 2.
#### Expected Output
I have narrowed the problem down to here:
https://github.com/pandas-dev/pandas/blob/dec736f3fcc3337bea0ee180ee28f73e5bf54939/pandas/core/indexing.py#L1768
The dimension of the `indexer` is being increased in that call.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2018.5
dateutil : 2.7.3
pip : 19.2.3
setuptools : 42.0.2
Cython : 0.29.10
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : 0.13.1
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDA5NzA5MDQx
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TST: ensure groupby get by index value #33439
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|
CONTRIBUTOR
| null |
- [x] closes #33439
- [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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API/BUG: freq retention in DatetimeIndex factorize, value_counts, MultiIndex, Categorical
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[] | null | 2
| 2020-04-27T21:20:32Z
| 2021-08-22T07:54:03Z
| null |
MEMBER
| null |
```
dti = pd.date_range('2016-01-01', periods=5)
dti.value_counts().index.freq # <-- None
dti.factorize()[1].freq # <-- None
mi = pd.MultiIndex.from_arrays([dti, dti])
mi.levels[0].freq # <-- None
```
There is a comment in tests.indexes.datetimes.test_datetime `test_factorize` suggesting that freq should be preserved by factorize, but that is not checked and would fail if it were
```
# freq must be preserved
idx3 = date_range("2000-01", periods=4, freq="M", tz="Asia/Tokyo")
exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
arr, idx = idx3.factorize()
tm.assert_numpy_array_equal(arr, exp_arr)
tm.assert_index_equal(idx, idx3)
```
So the question: do we want to try to preserve freq in factorize?
xref #33677 for the MultiIndex case
<b>Update</b> One more: Categorical:
```
dti = pd.date_range('2016-01-01', periods=5)
cat = pd.Categorical(dti)
cat.categories.freq # <-- None
```
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DOC/CLN: remove outdated warnings in enhancingperf.rst
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| 1
| 2020-04-27T21:41:58Z
| 2020-04-28T08:58:20Z
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MEMBER
| null |
xref #16732 and SyntaxError
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MDExOlB1bGxSZXF1ZXN0NDA5NzY2MTE4
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DEPR: Timestamp.freq
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MEMBER
| null |
- [x] closes #15146
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The motivation here is that I've concluded that DatetimeArray.freq _must_ be removed, so for internal consistency Timestamp.freq should be removed too.
The main user-facing behavior this will affect is Timestamp.is_month_start, is_month_end, is_quarter_start, is_quarter_end, is_year_start, is_year_end, for which I guess we'll offer users an alternative(?)
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MDExOlB1bGxSZXF1ZXN0NDA5NzY2NDUw
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DOC/CLN: Fix whatsnew typo
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| 1
| 2020-04-27T21:49:32Z
| 2020-04-27T23:51:43Z
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|
MEMBER
| null |
Should something like this cause a CI failure?
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MDExOlB1bGxSZXF1ZXN0NDA5NzczOTU1
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BUG: TimedeltaIndex[:] losing freq
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| 1
| 2020-04-27T22:08:46Z
| 2020-04-27T23:46:56Z
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MEMBER
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Reverts the freq-override I implemented a few days ago. That was made unnecessary by the more-recent change to _with_freq that ensures that self.freq and self._data.freq stay in sync.
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MDExOlB1bGxSZXF1ZXN0NDA5ODA4MzQx
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REF/CLN: Parametrize _isna
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| 2
| 2020-04-27T23:59:50Z
| 2020-04-29T12:38:28Z
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MEMBER
| null |
Replaces two nearly identical isna functions with one by parametrizing over the difference
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MDExOlB1bGxSZXF1ZXN0NDA5ODIzMjQ4
| 33,836
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BUG: preserve freq in DTI/TDI factorize
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| 2020-04-28T00:58:46Z
| 2020-06-11T17:27:14Z
| 2020-06-11T17:27:06Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #33830
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MDExOlB1bGxSZXF1ZXN0NDA5ODI5NDQx
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|
teaching someone this
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| 2020-04-28T01:23:36Z
| 2020-04-28T01:24:41Z
| 2020-04-28T01:24:24Z
|
NONE
| null |
sorry guys, i was teaching my friend... Dont ban me please.
|
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MDU6SXNzdWU2MDc5NzYwMzc=
| 33,838
|
API: EA/Index/Series.factorize should have same kwargs as pd.factorize
|
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[] | null | 3
| 2020-04-28T02:28:10Z
| 2021-07-31T04:49:11Z
| null |
MEMBER
| null |
This will allow pd.factorize to dispatch to the EA/Index/Series implementations instead of the other way around.
|
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| 608,002,609
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MDExOlB1bGxSZXF1ZXN0NDA5ODY0NjUy
| 33,839
|
CLN: freq inference in DTI/TDI set ops
|
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| 1
| 2020-04-28T03:46:04Z
| 2020-04-29T20:52:51Z
| 2020-04-29T20:10:26Z
|
MEMBER
| null |
These ops are a PITA for getting the freq check into assert_index_equal. I'm leaning towards ripping out the freq="infer" behavior in all the cases where we can't fast-infer it.
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MDExOlB1bGxSZXF1ZXN0NDA5OTA4NjMy
| 33,840
|
DOC: Fix groupby.agg/transform API reference and add numba arguments to documentation
|
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| 1
| 2020-04-28T06:22:51Z
| 2020-04-29T03:42:13Z
| 2020-04-29T03:42:04Z
|
MEMBER
| null |
- xref https://github.com/pandas-dev/pandas/pull/33388#issuecomment-618808443
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
<img width="927" alt="Screen Shot 2020-04-27 at 11 19 16 PM" src="https://user-images.githubusercontent.com/10647082/80453833-e20eeb80-88dd-11ea-9acc-ef322cb59df5.png">
<img width="934" alt="Screen Shot 2020-04-27 at 11 19 33 PM" src="https://user-images.githubusercontent.com/10647082/80453838-e4714580-88dd-11ea-85b2-735d327ee686.png">
<img width="922" alt="Screen Shot 2020-04-27 at 11 19 53 PM" src="https://user-images.githubusercontent.com/10647082/80453844-e6d39f80-88dd-11ea-8c49-a9d35fd80e0c.png">
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MDU6SXNzdWU2MDgxMDc0MzY=
| 33,841
|
BUG:RecursionError occur when running the script which belongs to other directories
|
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[] | null | 3
| 2020-04-28T07:59:09Z
| 2020-04-28T08:22:49Z
| 2020-04-28T08:22:48Z
|
NONE
| null |
This is my first issue. So, if I'm wrong, please teach me to be kind!
I cannot import pandas when running the script which belongs to other directories.
```python
# workdir/src/sample.py
import numpy as np
import pandas as pd
```
```bash
/workdir$ python src/sample.py
```
```
Traceback (most recent call last):
File "src/sample.py", line 2, in <module>
import pandas as pd
File "/usr/local/share/pip-global/pandas/__init__.py", line 55, in <module>
from pandas.core.api import (
File "/usr/local/share/pip-global/pandas/core/api.py", line 5, in <module>
from pandas.core.arrays.integer import (
File "/usr/local/share/pip-global/pandas/core/arrays/__init__.py", line 7, in <module>
from .categorical import Categorical # noqa: F401
File "/usr/local/share/pip-global/pandas/core/arrays/categorical.py", line 54, in <module>
from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
File "/usr/local/share/pip-global/pandas/core/base.py", line 36, in <module>
import pandas.core.nanops as nanops
File "/usr/local/share/pip-global/pandas/core/nanops.py", line 38, in <module>
bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
File "/usr/local/share/pip-global/pandas/compat/_optional.py", line 90, in import_optional_dependency
module = importlib.import_module(name)
File "/opt/conda/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/opt/conda/lib/python3.7/site-packages/bottleneck/__init__.py", line 41, in <module>
from numpy.testing import Tester
File "/usr/local/share/pip-global/numpy/testing/__init__.py", line 10, in <module>
from unittest import TestCase
File "/opt/conda/lib/python3.7/unittest/__init__.py", line 59, in <module>
from .case import (TestCase, FunctionTestCase, SkipTest, skip, skipIf,
File "/opt/conda/lib/python3.7/unittest/case.py", line 6, in <module>
import logging
File "/workdir/src/logging.py", line 5, in <module>
from lightgbm.callback import _format_eval_result
File "/usr/local/share/pip-global/lightgbm/__init__.py", line 8, in <module>
from .basic import Booster, Dataset
File "/usr/local/share/pip-global/lightgbm/basic.py", line 12, in <module>
import scipy.sparse
File "/usr/local/share/pip-global/scipy/sparse/__init__.py", line 229, in <module>
from .base import *
File "/usr/local/share/pip-global/scipy/sparse/base.py", line 7, in <module>
from scipy._lib._numpy_compat import broadcast_to
File "/usr/local/share/pip-global/scipy/_lib/_numpy_compat.py", line 16, in <module>
_assert_warns = np.testing.assert_warns
File "/usr/local/share/pip-global/numpy/__init__.py", line 213, in __getattr__
import numpy.testing as testing
File "/usr/local/share/pip-global/numpy/__init__.py", line 213, in __getattr__
import numpy.testing as testing
File "/usr/local/share/pip-global/numpy/__init__.py", line 213, in __getattr__
import numpy.testing as testing
[Previous line repeated 871 more times]
File "<frozen importlib._bootstrap>", line 200, in _lock_unlock_module
File "<frozen importlib._bootstrap>", line 163, in _get_module_lock
RecursionError: maximum recursion depth exceeded while calling a Python object
```
#### Output of ``pd.show_versions()``
'1.0.3'
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MDU6SXNzdWU2MDgxNjY2OTE=
| 33,842
|
BUG: groupby on columns does not work with Series
|
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[] | null | 4
| 2020-04-28T09:30:50Z
| 2021-07-31T04:49:41Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
No groups are found when using a pd.Series instead of a list in a groupby(axis=1)
```python
>>> df = pd.DataFrame(np.arange(16).reshape(4, 4), columns=['A', 'B', 'C', 'D'])
>>> grouped_list_row = df.groupby([1, 2, 1, 2])
>>> print(f"number of elem list_row: {len(grouped_list_row)}")
>>> grouped_series_row = df.groupby(pd.Series([1, 2, 1, 2]))
>>> print(f"number of elem series_row: {len(grouped_series_row)}")
>>> grouped_list_column = df.groupby([1, 2, 1, 2], axis=1)
>>> print(f"number of elem list_column: {len(grouped_list_column)}")
>>> grouped_series_column = df.groupby(pd.Series([1, 2, 1, 2]), axis=1)
>>> print(f"number of elem series_column: {len(grouped_series_column)}")
number of elem list_row: 2
number of elem series_row: 2
number of elem list_column: 2
number of elem series_column: 0
```
#### Problem description
I would expect to have the same result with a list and with a pd.Series(list).
Instead no groups are created
#### Expected Output
number of elem list_row: 2
number of elem series_row: 2
number of elem list_column: 2
number of elem series_column: **2**
#### 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 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : None.None
pandas : 1.0.3
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDA5OTk5MTIy
| 33,843
|
Roll quantile support multiple quantiles as per #12093: rough sketch for discussion
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| 2020-04-28T09:34:20Z
| 2020-11-26T01:25:19Z
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|
CONTRIBUTOR
| null |
- [ ] closes #12093
Putting this in a PR for discussion. It definitely needs more work on kicking the arguments through properly in the python interface but someone who has strong opinions about that can hopefully step in and then will refactor and test etc.
The "content" is really just the cython change.
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MDU6SXNzdWU2MDgyOTMzNzg=
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BUG: date is parsed wrong
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| 3
| 2020-04-28T12:53:00Z
| 2020-05-06T19:43:12Z
| 2020-04-29T01:03:45Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
#### Code Sample, a copy-pastable example
**1.** first I type in the date in Ms excel's B2 cell in local language format, as you can read from the picture  that it is been recognized as date(2020/12/20) without any problem. Then I save it as [`test.xlsx`](https://github.com/pandas-dev/pandas/files/4545801/test.xlsx) . Then I try to read the date info in pandas
```python
# I have tested both openpyxl and xlrd. All of them performs alike
# df = pd.read_excel('test.xlsx', engine='openpyxl')
df = pd.read_excel('test.xlsx', engine='xlrd')
print(df.loc[0, 'date']) # which outputs `44185`
```
#### Problem description
I can't fetch the right date in any of the following 3 methods
`pd.to_datetime(df['date'])` outputs
```
0 1970-01-01 00:00:00.000044185
Name: date, dtype: datetime64[ns]
```
`pd.to_datetime(df['date'], format='%Y/%m/%d')` outputs
```
0 1970-01-01 00:00:00.000044185
Name: date, dtype: datetime64[ns]
```
and
`pd.to_datetime(df['date'], unit='D')` outputs
```
0 2090-12-22
Name: date, dtype: datetime64[ns]
```
#### Expected Output
2020-12-20, i.e. Dec 20th, 2020
**2.** however If I change the cell format to `m/d/yyyy`, then change it back to custom-defined local language format `yyyy"年"m"月"d"日"` and save the file as [`test2.xlsx`](https://github.com/pandas-dev/pandas/files/4545802/test2.xlsx). Then
```python
import pandas as pd
# I have tested both openpyxl and xlrd. All of them performs alike
df = pd.read_excel('test2.xlsx', engine='openpyxl')
# df = pd.read_excel('test2.xlsx', engine='xlrd')
print(df.loc[0, 'date'])
```
outputs `2020-12-20 00:00:00` as expected
**3.** I can't tell is this a bug/feature in xlrd/openpyxl or pandas
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.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 : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : 0.27.3
pytest : 3.4.2
hypothesis : None
sphinx : 1.7.1
blosc : None
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.2.2
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.2
matplotlib : 2.2.2
numexpr : 2.6.4
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.4.2
pyxlsb : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.2.4
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
numba : 0.37.0
</details>
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|
TestDatetimeIndex.test_reindex_with_same_tz 32-bit CI failing in MacPython
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| 1
| 2020-04-28T13:13:30Z
| 2020-05-05T14:18:01Z
| 2020-05-05T14:18:01Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=34375&view=logs&j=517fe804-fa30-5dc2-1413-330699242c05&t=2e128ad5-2f7f-5333-3f34-c85b8fbc7250&l=1267
```
=================================== FAILURES ===================================
_________________ TestDatetimeIndex.test_reindex_with_same_tz __________________
[gw1] linux -- Python 3.7.0 /venv/bin/python
rng_a = date_range("2010-01-01", "2010-01-02", periods=24, tz="utc")
rng_b = date_range("2010-01-01", "2010-01-02", periods=23, tz="utc")
result1, result2 = rng_a.reindex(
rng_b, method="nearest", tolerance=timedelta(seconds=20)
)
expected_list1 = [
"2010-01-01 00:00:00",
"2010-01-01 01:05:27.272727272",
"2010-01-01 02:10:54.545454545",
"2010-01-01 03:16:21.818181818",
"2010-01-01 04:21:49.090909090",
"2010-01-01 05:27:16.363636363",
"2010-01-01 06:32:43.636363636",
"2010-01-01 07:38:10.909090909",
"2010-01-01 08:43:38.181818181",
"2010-01-01 09:49:05.454545454",
"2010-01-01 10:54:32.727272727",
"2010-01-01 12:00:00",
"2010-01-01 13:05:27.272727272",
"2010-01-01 14:10:54.545454545",
"2010-01-01 15:16:21.818181818",
"2010-01-01 16:21:49.090909090",
"2010-01-01 17:27:16.363636363",
"2010-01-01 18:32:43.636363636",
"2010-01-01 19:38:10.909090909",
"2010-01-01 20:43:38.181818181",
"2010-01-01 21:49:05.454545454",
"2010-01-01 22:54:32.727272727",
"2010-01-02 00:00:00",
]
expected1 = DatetimeIndex(
expected_list1, dtype="datetime64[ns, UTC]", freq=None,
)
expected2 = np.array([0] + [-1] * 21 + [23], dtype=np.int64,)
tm.assert_index_equal(result1, expected1)
> tm.assert_numpy_array_equal(result2, expected2)
E AssertionError: numpy array are different
E
E Attribute "dtype" are different
E [left]: int32
E [right]: int64
```
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MDExOlB1bGxSZXF1ZXN0NDEwMTE3MjM3
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BUG: Series construction with EA dtype and index but no data fails
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| 14
| 2020-04-28T13:25:19Z
| 2020-05-02T18:01:13Z
| 2020-05-02T17:49:04Z
|
MEMBER
| null |
- [ ] closes #26469
- [ ] closes #33559
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CI: Skip permissions test when running as sudo
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| 12
| 2020-04-28T13:28:18Z
| 2020-05-04T17:40:39Z
| 2020-04-28T22:54:21Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/33210
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ENH:
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| 15
| 2020-04-28T13:53:40Z
| 2020-05-06T19:45:05Z
| 2020-04-29T11:45:05Z
|
NONE
| null |
#### Is your feature request related to a problem?
I wish I could have a better way to mix loc index and iloc index.
#### Describe the solution you'd like
I think we can have a loc[...] like s_[...] in numpy.
The loc[] return a sepical type named " Locater " which wrap the loc index
And the DataFrame.iloc.__(get|set|del)item__ can recognize it and use its own indexer to get index.
So if we want the 0 row ,"A","B" column of Dataframe df.
We can use df.iloc[0, loc[["A","B"]] ] instead of df.iloc[0,df.columns.get_indexer(['A','B'])]
meanwhile, if labels out of array , will raise an error instead of get the last elements!
#### Additional context
i think the Locater can also have a __call__ which accept a df
so we can use like this:
```python
l = loc['a','b','c','d']
l(df,axis=0)
# equal to df.index.get_index(['a','b','c','d'])
l(df,axis=1)
# equal to df.columns.get_index(['a','b','c','d'])
l(axis=1)
# no df parameter
# return a new Locater instance with default parameter: axis = 1
```
and the __call__ can have parameter like:
df:Union[Series,DataFrame]
the indexer's provider
axis:int
determind use on wether ind or col of df.
only available if df is instance of DataFrame
default 0
if its called by df.__[...]item___ ,will change it by its position in df[...]
out_index:str
determind what to do if out of index
can be "warn" "error" "skip"
default "error"
endpoint:str
determind wether to contain the endpoint
can be "both","left","right","none"
method,limit,tolarence: pass to df.[...].get_index
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MDU6SXNzdWU2MDg0ODgwMzM=
| 33,849
|
BUG: weird behaviour of pivot_table when aggfunc tries to join string sequence containing None
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[] | null | 1
| 2020-04-28T17:27:14Z
| 2020-09-04T13:07:11Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", None],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"]})
df.pivot_table(index='A', columns='C', values='B', aggfunc=lambda x: ' '.join(x))
```
#### Problem description
`None` value in the `B` column should make the aggregation fail or, alternatively, the aggregation should ignore the `None` value. Instead, the output looks like this:
```
C large small
A
bar A B C A B C
foo A B C A B C
```
It looks like the names of all the pivot-ed columns were actually aggregated, which is a very surprising behaviour.
#### Expected Output
`TypeError: sequence item X: expected str instance, NoneType found`, just like `''.join([..., None])` would return
Alternatively, the `None` value could be ignored and then the output could look like this:
```
C large small
A
bar one one two
foo one one one two two
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.20.1
pytest: None
pip: 20.1
setuptools: 46.1.3
Cython: 0.28.5
numpy: 1.15.0
scipy: 1.1.0
xarray: None
IPython: 7.6.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.8.0
html5lib: None
sqlalchemy: 1.2.1
pymysql: None
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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ImportError: cannot import name 'TimeGrouper' from 'pandas' (F:\Anaconda\lib\site-packages\pandas\__init__.py)BUG:
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| 1
| 2020-04-28T18:17:29Z
| 2020-04-28T19:54:48Z
| 2020-04-28T19:54:47Z
|
NONE
| null |
ImportError Traceback (most recent call last)
<ipython-input-64-1268709bbfb0> in <module>
----> 1 from pandas import TimeGrouper
2 groups = series.groupby(TimeGrouper('A'))
3 years = DataFrame()
4 for name, group in groups:
5 years[name.year] = group.values
ImportError: cannot import name 'TimeGrouper' from 'pandas' (F:\Anaconda\lib\site-packages\pandas\__init__.py)
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DOC: Single Document For Code Guidelines
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| 17
| 2020-04-28T18:27:04Z
| 2021-08-29T10:08:29Z
| null |
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/docs/development/code_style.html
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#code-standards
#### Documentation problem
Most of the documentation for the coding style guidelines is limited and in multiple locations. Much of the coding style is in a script.
#### Suggested fix for documentation
I propose to create a single document that contains all the coding guidelines.
This will allow new contributors to easily see all the required syntax and style for the project.
This also allows newer users to not have to guess against a script or have to keep submitting code over and over to find mistakes.
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|
DOC: Code Standards Broken Link
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| 1
| 2020-04-28T18:32:02Z
| 2020-04-29T12:15:02Z
| 2020-04-29T12:13:07Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#code-standards
#### Documentation problem
There is a broken link on Development -> Contributing To Pandas -> Contributing To The Code Base -> Code Standards -> "pandas code style guide"
Link location goes to 404 Error at
https://pandas.pydata.org/pandas-docs/stable/development/code_style
#### Suggested fix for documentation
Change link address to proper page/document
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MDU6SXNzdWU2MDg1Mjc3ODU=
| 33,853
|
BUG: pd.read_excel error when a cell contains string "INF"
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| 2020-04-28T18:34:18Z
| 2021-07-31T04:51:58Z
| 2021-07-31T04:51:58Z
|
NONE
| null |
Here is how to reproduce the bug: run this script:
import pandas as pd
pd.read_excel('test.xlsx')
# idem with pd.read_excel('test.xlsx', engine='openpyxl')
# so it's not specific to xlrd or openpyxl
with this [test.xlsx](https://gget.it/tuha/test.xlsx) file:
a | b
-- | --
12 | 1
14 | 1
15 | 1
16 | INF
Error:
> OverflowError: cannot convert float infinity to integer
Why does this happen / why is there a integer conversion? No automatic integer parsing has been asked.
___
PS: Congrats for this great project!
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MDExOlB1bGxSZXF1ZXN0NDEwMzUxNzg2
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TST: de-xfail, remove strict=False
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MEMBER
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- changed XXX comments to TODO (much more grep-friendly, since we sometimes use XXX for a `name` attribute.
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TST: stop skipping Categorical take tests
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MDExOlB1bGxSZXF1ZXN0NDEwNDc2MDg1
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DOC: Fix groupby.agg/transform rst reference and numba references
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DOC: Fix typo in inferred_freq docstring
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- [ ] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0NDEwNjI4OTM4
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DOC: timezone warning for DST beyond 2038-01-18
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| 6
| 2020-04-29T10:38:36Z
| 2020-05-10T18:09:58Z
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|
CONTRIBUTOR
| null |
Add warning to time_series user guide that after 2038-01-18 DST will not be respected in tz aware dates
- [x ] closes #33061
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TYP/CLN: remove #type: ignore from pandas\tests\base\test_conversion.py
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DOC: Code Standards Broken Link
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MEMBER
| null |
- [ ] closes #33852
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU2MDg5NzYxMzM=
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|
ENH: Support setting dataset name in to_hdf and datasets in hdf5 root node
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[] | null | 0
| 2020-04-29T11:19:53Z
| 2020-06-05T18:52:50Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Pandas always calls the dataset in the hdf5 file `"table"`.
This is very inconvenient, when there are outside requirements on how the file should
look like and basically prohibits the use of pandas to write hdf5 in a predefined schema.
#### Describe the solution you'd like
Add a keyword argument `name` or `dataset_name` to `DataFrame.to_hdf` and `HDFStore.put`
to allow setting this name.
Also allow writing datasets into the root of a hdf5 file.
#### API breaking implications
None. The current defaults can be kept and only additional keyword arguments be added.
#### Describe alternatives you've considered
Currently the only alternative would be directly using pytables or h5py and
loosing all the great conveniences that come with using pandas.
#### Additional context
[add any other context, code examples, or references to existing implementations about the feature request here]
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({
'x': np.random.normal(size=500),
'N': np.random.poisson(10, size=500)
})
df.to_hdf('test.hdf5', key='data', format='table', index=False, mode='w')
```
This creates a **group** "data" with a dataset "table" inside it.
What is the goal of this issue is to make it possible to create datasets of any name
and also without creating a group:
e.g.:
```python
df.to_hdf(
'test.hdf5',
key='/', # or even ""
name='data',
format='table',
index=False,
mode='w'
)
```
should create a dataset called "data" in the root of the hdf5 file.
Currently, using an empty or "/" key, results in an error about wanting to remove the root node:
```
File "create_data.py", line 10, in <module>
df.to_hdf('test.hdf5', key='/', format='table', index=False, mode='a')
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/pandas/core/generic.py", line 2505, in to_hdf
encoding=encoding,
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/pandas/io/pytables.py", line 282, in to_hdf
f(store)
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/pandas/io/pytables.py", line 274, in <lambda>
encoding=encoding,
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/pandas/io/pytables.py", line 1042, in put
errors=errors,
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/pandas/io/pytables.py", line 1657, in _write_to_group
self._handle.remove_node(group, recursive=True)
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/tables/file.py", line 1806, in remove_node
obj._f_remove(recursive)
File "/home/maxnoe/.local/anaconda3/envs/ctapipe/lib/python3.7/site-packages/tables/group.py", line 1223, in _f_remove
raise NodeError("the root node can not be removed")
tables.exceptions.NodeError: the root node can not be removed
```
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MDU6SXNzdWU2MDkwMzQ0MzA=
| 33,867
|
BUG: Max aggregator strange behavour
|
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[] | null | 2
| 2020-04-29T12:55:01Z
| 2020-05-07T13:28:44Z
| 2020-05-07T13:28:44Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
dataset_soft_cl = dataset_soft.loc[y_pred_gyr==0].abs()
max_y_g = dataset_soft_cl.groupby('event_id')[y_g_col].max().max(axis=1)
max_y_g_2=dataset_soft.loc[dataset_soft.event_id.isin(dataset_soft_cl.event_id)].abs().groupby('event_id')[y_g_col].max().max(axis=1)
```
#### Problem description
max_y_g is different from max_y_g_2 basically if the max per line is in the first line of the group max_y_g does not see it as opposed to max_y_g_2
#### Expected Output
max_y_g == max_y_g_2
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-46-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU2MDkwNDE1MTI=
| 33,868
|
BUG: RGBA-transparency issue in pandas boxplot
|
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[] | null | 1
| 2020-04-29T13:05:17Z
| 2020-07-03T17:01:58Z
| 2020-07-03T17:01:58Z
|
NONE
| null |
When setting color and transparency using rgba-values in pandas.boxplot(), the code doesn't seem to recognize the alpha-value. I raised a question on StackOverflow already (<https://stackoverflow.com/questions/61501795/set-boxplot-boxes-facecolor-transparency-in-python-matplotlib>) but this might be something to fix in the github. I have used rgba-values elsewhere and never had issues with it, only here it doesn't seem to work properly.
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MDExOlB1bGxSZXF1ZXN0NDEwNzQ5NzAz
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DOC: Remove deprecated Y and M from to_timedelta
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| 1
| 2020-04-29T14:29:15Z
| 2020-04-29T22:48:17Z
| 2020-04-29T22:48:13Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
[`to_timedelta()`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/tools/timedeltas.py#L84-L88) blocks those arguments anyway, so no reason to include them in the docstring.
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TST/REF: collect Index tests by method
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| 0
| 2020-04-29T15:03:43Z
| 2020-04-29T22:38:22Z
| 2020-04-29T22:37:11Z
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MEMBER
| null |
xfail tests in test_coercion rather than silently passing
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DOC/CLN: Fix to_numpy docstrings
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DOC: Remove TODO from `test_openpyxl`
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MEMBER
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xref #29862
Brought up by @jbrockmendel seems the `xfail` has been removed and version has been unpinned, just a tiny PR to remove this `TODO` from tests. probably people who fixed it forgot to remove this.
cc @jbrockmendel
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TST: parametrize test_partial_slicing tests
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TST: Fix xfailed offset test
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST/REF: Parametrize consistency data and de-private methods
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| 9
| 2020-04-29T18:04:43Z
| 2020-04-30T17:48:38Z
| 2020-04-30T17:48:05Z
|
MEMBER
| null |
xref #30486
I am sorry that this PR looks huge again. @jreback
But this seems a bit unavoidable, and please allow me to briefly explain this, and it is quite simple actually:
1. move `_create_consistency_data` method to `conftest.py` and fixturize it as `consistency_data`.
2. Use `consistency_data` as fixture, and parametrize it to replace the inner loops.
3. Now since consistency_data is fixture, we do not need `self.data = _create_consistency_data()` as part init in `ConsistencyBase`, so I remove it, also a bunch of tests can be moved out of the BIG ConsistencyBase class (which I think is the ultimate goal: avoid using big classes, but simply the functions).
4. I also move some tests in `test_moments_rolling`, `test_moments_ewm`, and `test_moment_expanding` out of their classes to be independent test functions
After this PR, we could start reducing the reliance on the giant Base/ConsistencyBase classes, and next step will be fixturize other data and then reduce more reliance on those classes and make tests look smaller.
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MDU6SXNzdWU2MDkyNjA2OTA=
| 33,876
|
BUG: Series.apply behaves incorrectly with timezone-aware data.
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[] | null | 5
| 2020-04-29T18:19:51Z
| 2021-03-26T05:48:56Z
| 2021-03-26T05:48:56Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# For a series with tz-unaware datetime, apply behaves correctly and passes each value to the function.
pd.Series([pd.Timestamp(1)]).apply(print)
# For tz-aware data, apply injects a DatetimeIndex containing the whole series as first argument
pd.Series([pd.Timestamp(1)]).astype("datetime64[ns, UTC]").apply(print)
```
Output:
```
# First, correct:
1970-01-01 00:00:00.000000001+00:00
# Second, incorrect:
DatetimeIndex(['1970-01-01 00:00:00.000000001+00:00'], dtype='datetime64[ns, UTC]', freq=None)
1970-01-01 00:00:00.000000001+00:00
```
#### Problem description
According to [the documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html#pandas.Series.apply), `apply` should call the provided function (`print` in the dummy example) to each value in the series.
For timezone-unaware data, this works as expected. In the example, `print` is called with the timestamp only.
However, when the data is tz-aware, the function is first called with a `DatetimeIndex` containing the whole series. Only afterwards, the function is called with each value in the series.
#### Expected Output
Regardless of whether the data is tz-aware, apply should call the function only with each value in the series.
Or am I misunderstanding `apply`, and this output is actually intended?
In any case, thanks for your help!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 46.1.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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| 33,877
|
BUG: weird interaction between pyslurm, ujson that changes function signature of ujson.dumps
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| 2020-04-29T19:56:07Z
| 2020-08-02T17:38:54Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Hi maintainers, I'd like to start with saying I'm not entirely sure if this is a `pandas` bug, but seeing that [`pandas` bundles its own `ujson`](https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/src/ujson/python/version.h), figured I'd ask here.
#### Problem description
**TL;DR**: There seems to be a very strange behavior in `ujson` depending on how it is imported (in particular, depending on whether the `pyslurm` library is imported first). The observed behavior is that this modifies the function signature of `ujson.dumps` (by removing the `sort_keys` argument), as shown in the below examples. I'm not sure if this behavior is specific to importing `pyslurm` in the beginning or not, but this is just one specific example that can be used to reproduce the problem.
First, installation of these libraries via `pip` is needed:
```
pip install cython
pip install pyslurm pandas ujson
```
This is the script with the error:
```python
import pyslurm
import pandas
import ujson
print(f"pyslurm=={pyslurm.__version__}")
print(f"pandas=={pandas.__version__}")
print(f"ujson=={ujson.__version__}")
ujson.dumps({}, sort_keys=True)
```
If you run the script above, you'll see the output is:
```
pyslurm==18.08.1.1
pandas==1.0.3
ujson==2.0.3
Traceback (most recent call last):
File "<string>", line 1, in <module>
TypeError: 'sort_keys' is an invalid keyword argument for this function
```
However, if you remove the `pyslurm` import, the problem goes away!
```py
import pandas
import ujson
print(f"pandas=={pandas.__version__}")
print(f"ujson=={ujson.__version__}")
ujson.dumps({}, sort_keys=True)
```
Also, reordering the imports somehow fixes the problem:
```py
import pandas
import ujson
import pyslurm
ujson.dumps({}, sort_keys=True)
```
#### Expected Output
Importing `pyslurm` and `pandas` shouldn't affect the behavior of my `ujson` library. Also, import orders shouldn't cause this bug.
#### Additional Info
It seems that the very specific import order that causes this is `pyslurm -> pandas -> ujson`.
I suspect this has something to do with Python's mechanism for importing C extensions, but I don't understand the internals enough to be able to debug this further.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-43-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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|
BUG: read_feather doesn't work when columns are shuffle
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| 2
| 2020-04-29T20:54:57Z
| 2020-06-20T13:46:16Z
| 2020-06-20T13:46:15Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
df = pd.DataFrame({
"A": [1, 2],
"B": ["x", "y"],
"C": [True, False]
})
df.to_feather("./test_data.feather")
df2 = pd.read_feather("./test_data.feather", columns=['B', 'A'])
```
#### Error message
```python
ArrowInvalid Traceback (most recent call last)
<ipython-input-4-1e23cf201732> in <module>
15
16
---> 17 df2 = pd.read_feather("/misc/labshare/datasets3/rating/data/preprocessing/tests/test_data.feather", columns=['B', 'A'])
~/.conda/envs/venv/lib/python3.6/site-packages/pandas/io/feather_format.py in read_feather(path, columns, use_threads)
101 path = stringify_path(path)
102
--> 103 return feather.read_feather(path, columns=columns, use_threads=bool(use_threads))
~/.conda/envs/venv/lib/python3.6/site-packages/pyarrow/feather.py in read_feather(source, columns, use_threads, memory_map)
206 """
207 _check_pandas_version()
--> 208 return (read_table(source, columns=columns, memory_map=memory_map)
209 .to_pandas(use_threads=use_threads))
210
~/.conda/envs/venv/lib/python3.6/site-packages/pyarrow/feather.py in read_table(source, columns, memory_map)
237 return reader.read_indices(columns)
238 elif all(map(lambda t: t == str, column_types)):
--> 239 return reader.read_names(columns)
240
241 column_type_names = [t.__name__ for t in column_types]
~/.conda/envs/venv/lib/python3.6/site-packages/pyarrow/feather.pxi in pyarrow.lib.FeatherReader.read_names()
~/.conda/envs/venv/lib/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()
ArrowInvalid: Schema at index 0 was different:
B: string
A: int64
vs
A: int64
B: string
```
#### Problem description
We don't always know the order in which our columns are.
The issue is when we update pyarrow to 0.17.0
This line work fine:
```python
df2 = pd.read_feather("./test_data.feather", columns=['B', 'A'])
```
Should we apply a fix here or in the pyarrow repository ?
#### Expected Output
df2 = pd.DataFrame({
"A": [1, 2],
"B": ["x", "y"],
})
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
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LANG : en_US.UTF-8
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numexpr : None
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pytables : None
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s3fs : None
scipy : 1.2.3
sqlalchemy : 1.3.13
tables : None
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| 33,879
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DOC: Fix Dataframe.apply documentation to include note regarding calling function twice on first element
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| 0
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| 2020-08-21T17:05:35Z
| 2020-06-02T21:04:14Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html
#### Documentation problem
In commit f625730eb the documentation of `df.apply` was changed, and a note regarding `df.apply` behavior to call the applied function twice on the first column/row.
As far as i can tell from this [issue](https://github.com/pandas-dev/pandas/pull/24748#issuecomment-532148732) and from my own testing (See below) this behavior still exists.
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({'a': [1,2,3]})
In [3]: def mul2(x):
...: print('Hello')
...: return x*2
...:
In [4]: df.apply(mul2)
Hello
Hello
Out[4]:
a
0 2
1 4
2 6
In [5]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.3
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.13.1
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : 0.15.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.47.0
```
#### Suggested fix for documentation
Revert commit f625730eb in order to reflect the actual behavior of the apply function
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Fix Dataframe.apply documentation to include note regarding calling f…
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CONTRIBUTOR
| null |
…unction twice on first element
- [ ] closes #33879
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: address FIXME comments
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TST: stricter xfails, catch/suppress warnings
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MEMBER
| null |
Remove box_df_fail which is no longer used
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BUG: TimedeltaArray+Period, PeriodArray-TimedeltaArray
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motivated by the FIXME comment in test_timedelta64.py
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BUG: Preserve Series/DataFrame subclasses through groupby operations
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CONTRIBUTOR
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This pull request fixes a bug that caused subclassed Series/DataFrames to revert to `pd.Series` and `pd.DataFrame` after `groupby()` operations. This is a follow-up on a previously abandoned pull request (#28573)
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BUG: recognize M8[ns, tzstr]
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MEMBER
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ATM `dtype == "M8[ns, UTC]"` fails while `dtype == "datetime64[ns, UTC]"` succeeds
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CLN: address TODOs
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DOC: Fix Syntax Typo in Indexing
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| 1
| 2020-04-30T02:08:42Z
| 2020-04-30T07:02:59Z
| 2020-04-30T07:02:59Z
|
CONTRIBUTOR
| null |
Please let me know if anything else needs to accompany this change!
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DOC:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex
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| 2020-04-30T02:53:38Z
| 2021-11-10T09:41:39Z
| 2021-11-10T09:41:38Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex
#### Documentation problem
Returns
DataFrame with changed index.
#### Suggested fix for documentation
fix= a NEW DataFrame instance with changed index,NOT changing old instance .
why? I thought old instance has changed index.
|
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BUG: Concatenation of columns with tuples names is inconsistent
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| 1
| 2020-04-30T05:18:32Z
| 2020-09-04T13:11:29Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
def main():
col1 = [('A', 'a'), ('A', 'b')]
col2 = [('A', 'a'), ('A', 'b'), ('A', 'c')]
# 2 columns
df1 = pd.DataFrame([[1, 2], [3, 4]], columns=col1)
# 3 columns
df2 = pd.DataFrame([[5, 6, 7], [8, 9, 0]], columns=col2)
# DataFrames with matching indices
df_concat1 = pd.concat([df1, df1])
# DataFrames with different indices
df_concat2 = pd.concat([df1, df2])
# Passes
pd.testing.assert_index_equal(df1.columns, df_concat1.columns)
# Fails
pd.testing.assert_index_equal(df2.columns, df_concat2)
if __name__ == "__main__":
main()
```
Concatenated DataFrames:
```
df_concat1:
(A, a) (A, b)
0 1 2
1 3 4
0 1 2
1 3 4
df_concat2:
A
a b c
0 1 2 NaN
1 3 4 NaN
0 5 6 7.0
1 8 9 0.0
```
#### Problem description
When concatenating two dataframes which have tuples for column names, a length mismatch results in the output containing a MultiIndex.
I would expect the relative sizes of the inputs to not affect what type of index comes out - either always tuples or always MultiIndex would make sense.
#### Expected Output
Either neither assertion fails or they both fail and both `df_concat1` and `df_concat2` have a MultiIndex
#### Actual Output
```python-traceback
Traceback (most recent call last):
File "./test_mim.py", line 27, in <module>
main()
File "./test_mim.py", line 24, in main
pd.testing.assert_index_equal(df2.columns, df_concat2.columns)
File "/usr/lib/python3.8/site-packages/pandas/_testing.py", line 633, in assert_index_equal
_check_types(left, right, obj=obj)
File "/usr/lib/python3.8/site-packages/pandas/_testing.py", line 609, in _check_types
assert_class_equal(l, r, exact=exact, obj=obj)
File "/usr/lib/python3.8/site-packages/pandas/_testing.py", line 724, in assert_class_equal
raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
File "/usr/lib/python3.8/site-packages/pandas/_testing.py", line 915, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Index are different
Index classes are not equivalent
[left]: Index([('A', 'a'), ('A', 'b'), ('A', 'c')], dtype='object')
[right]: MultiIndex([('A', 'a'),
('A', 'b'),
('A', 'c')],
)
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.3-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.utf8
LOCALE : en_AU.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.16
pytest : None
hypothesis : None
sphinx : None
blosc : 1.9.0
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : 0.49.0
```
Also tested on the following system:
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.7.final.0
python-bits: 64
OS: Linux
OS-release: 5.5.10-100.fc30.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: en_AU.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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CI: failing timedelta64 test on Linux py37_locale build
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open
| false
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[] | null | 15
| 2020-04-30T07:06:39Z
| 2020-05-18T14:19:10Z
| null |
MEMBER
| null |
This is seen on a few PRs (eg https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=34508&view=logs&jobId=c4100758-acc4-5cd0-72d8-90828968cf00&j=c4100758-acc4-5cd0-72d8-90828968cf00)
```
=================================== FAILURES ===================================
___ TestSeriesDtypes.test_astype_generic_timestamp_no_frequency[timedelta64] ___
[gw0] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.series.test_dtypes.TestSeriesDtypes object at 0x7f3d21a6c050>
dtype = <class 'numpy.timedelta64'>
@pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
def test_astype_generic_timestamp_no_frequency(self, dtype):
# see gh-15524, gh-15987
data = [1]
s = Series(data)
msg = (
fr"The '{dtype.__name__}' dtype has no unit\. "
fr"Please pass in '{dtype.__name__}\[ns\]' instead."
)
with pytest.raises(ValueError, match=msg):
> s.astype(dtype)
E Failed: DID NOT RAISE <class 'ValueError'>
pandas/tests/series/test_dtypes.py:395: Failed
```
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pandas interpolate inconsistent results with axis and method ffill
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| 2020-04-30T08:12:05Z
| 2020-05-07T20:00:20Z
| 2020-05-07T20:00:19Z
|
NONE
| null |
**Problem:**
I am trying to interpolate a dataframe of mxn with na values. The default axis is 0 (index), so if I were to interpolate at axis 0, df should fill missing values (say at index n) for each column with values from index n-1 and n+1 (or n+p, where n+p is the closest index with a valid value). This holds for default linear method but not for ffill method.
**Code:**
`data = np.array([[1,2,3,4, np.nan, 5], [2,4,6,np.nan, 8, 10], [3, 6, 9, np.nan, np.nan, 30]]).T`
`d = pd.DataFrame(data, columns=['A', 'B', 'C'])`
`d`
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 NaN NaN
4 NaN 8.0 NaN
5 5.0 10.0 30.0
`d.interpolate(method='ffill', axis=1)`
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 6.0 9.0
4 4.0 8.0 9.0
5 5.0 10.0 30.0
`d.interpolate(method='ffill')`
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 4.0 4.0
4 NaN 8.0 8.0
5 5.0 10.0 30.0
`d.interpolate()`
A B C
0 1.0 2.0 3.0
1 2.0 4.0 6.0
2 3.0 6.0 9.0
3 4.0 7.0 16.0
4 4.5 8.0 23.0
5 5.0 10.0 30.0
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COMPAT: add back block manager constructors to pandas.core.internals API
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| 12
| 2020-04-30T08:44:04Z
| 2020-05-23T06:45:16Z
| 2020-05-01T06:09:22Z
|
MEMBER
| null |
Those were removed in a for the rest unrelated PR (https://github.com/pandas-dev/pandas/pull/33100, cc @jbrockmendel)
But at least `create_block_manager_from_blocks` is used by dask (in partd: https://github.com/dask/partd/blob/master/partd/pandas.py), so dask's tests are failing with pandas master (cc @TomAugspurger dask isn't testing with pandas master?). But so to be safe just added back both.
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QST: Conversion of series to Dataframe Issue
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| 1
| 2020-04-30T09:10:11Z
| 2020-05-06T19:42:25Z
| 2020-04-30T10:56:01Z
|
NONE
| null |
#### Question about pandas
After using groupby on a Dataframe to group and sum column data into a series I then converted the result back to a Dataframe using .to_frame method which I then converted to html for output to a file. This appears to work well except that the header row has a zero in the last column which I am unable to delete - any ideas? - see here
0
Board Type NE Type Hardware Version Software Version
# Your code here, if applicable
NE_3 = NE_2.groupby(NE_2.columns.tolist(), as_index=False).size()
NE_3 = NE_3.to_frame()
NE_2 = NE_2.drop_duplicates()
NE_3 = NE_3.drop(columns='NE Type') # This doesn't work due to the '0' corrupting the header row
html_txt = NE_3.to_html()
tfile.write(html_txt)
tfile.write('<br/>')
```
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ENH: Construct pandas dataframe from function
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| 10
| 2020-04-30T10:14:14Z
| 2020-08-21T17:05:35Z
| 2020-04-30T16:03:53Z
|
NONE
| null |
It would be great if we could construct a `pd.DataFrame` from a function.
#### Describe the solution you'd like
```python
import pandas as pd
import numpy as np
def my_random():
return np.random.rand() + 1
df = pd.DataFrame(my_random, index=range(10), columns=range(15))
```
The output would be equivalent to
```python
col_len = 10
index_len = 15
data = np.array([[my_random() for i in range(col_len)] for j in range(index_len)])
df = pd.DataFrame(data, index=range(index_len), columns=range(columns_len))
```
Alternatively, we could do the same with something like
```python
df = pd.DataFrame.from_function(my_random, *args, **kwargs)
```
`args` and `kwargs` are passed to the df constructor.
## EDIT
I just realized I can easily obtain what I want with
```python
df = pd.DataFrame(index=range(10), columns=range(15)).applymap(
lambda x: my_random()
)
```
It's quite compact and easy to read. It's slowish but I don't expect it to be used in high performance tasks.
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BUG: resample().nearest() throws an exception if tz is provided for the DatetimeIndex
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| 3
| 2020-04-30T10:48:56Z
| 2020-05-06T17:58:20Z
| 2020-05-06T17:58:20Z
|
NONE
| null |
Here a simple create of a pandas DataFrame:
```python
import pandas as pd
df = pd.DataFrame(range(5), index=pd.date_range('1/1/2000', periods=5, freq='H', tz='UTC'))
print(df) gives:
2000-01-01 00:00:00+00:00 0
2000-01-01 01:00:00+00:00 1
2000-01-01 02:00:00+00:00 2
2000-01-01 03:00:00+00:00 3
2000-01-01 04:00:00+00:00 4
```
When trying to resample like this
```python
df.resample('15Min').nearest()
```
this throws an exception:
```python
File "C:\Users\user\anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-41-9b9242799057>", line 1, in <module>
df.resample('15Min').nearest()
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\resample.py", line 517, in nearest
return self._upsample("nearest", limit=limit)
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\resample.py", line 1095, in _upsample
res_index, method=method, limit=limit, fill_value=fill_value
File "C:\Users\user\anaconda3\lib\site-packages\pandas\util\_decorators.py", line 227, in wrapper
return func(*args, **kwargs)
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\frame.py", line 3856, in reindex
return self._ensure_type(super().reindex(**kwargs))
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\generic.py", line 4544, in reindex
axes, level, limit, tolerance, method, fill_value, copy
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\frame.py", line 3744, in _reindex_axes
index, method, copy, level, fill_value, limit, tolerance
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\frame.py", line 3760, in _reindex_index
new_index, method=method, level=level, limit=limit, tolerance=tolerance
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3145, in reindex
target, method=method, limit=limit, tolerance=tolerance
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 2740, in get_indexer
indexer = self._get_nearest_indexer(target, limit, tolerance)
File "C:\Users\user\anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 2821, in _get_nearest_indexer
left_distances = abs(self.values[left_indexer] - target)
numpy.core._exceptions.UFuncTypeError: ufunc 'subtract' cannot use operands with types dtype('<M8[ns]') and dtype('O')
```
However, if we remove the **tz** filed it works:
```python
import pandas as pd
df = pd.DataFrame(range(5), index=pd.date_range('1/1/2000', periods=5, freq='H'))
df.resample('15Min').nearest()
OutPut:
0
2000-01-01 00:00:00 0
2000-01-01 00:15:00 0
2000-01-01 00:30:00 1
2000-01-01 00:45:00 1
2000-01-01 01:00:00 1
2000-01-01 01:15:00 1
2000-01-01 01:30:00 2
2000-01-01 01:45:00 2
2000-01-01 02:00:00 2
2000-01-01 02:15:00 2
2000-01-01 02:30:00 3
2000-01-01 02:45:00 3
2000-01-01 03:00:00 3
2000-01-01 03:15:00 3
2000-01-01 03:30:00 4
2000-01-01 03:45:00 4
2000-01-01 04:00:00 4
```
The used pandas version is:
```
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
```
</details>
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MDU6SXNzdWU2MTAwMzg3NDE=
| 33,896
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FEATURE: Type hints for DataFrame, Series, etc.
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[] | null | 1
| 2020-04-30T14:07:02Z
| 2020-04-30T16:01:38Z
| 2020-04-30T16:01:23Z
|
NONE
| null |
#### Is your feature request related to a problem?
Type hinting with `pd.DataFrame` and `pd.Series` (`def data_science_etl_function(input_data_frame: pd.DataFrame) -> pd.DataFrame:`) does not document ETL APIs build on top of `pandas` w.r.t. `DataFrame` and/or `Series` meta-data like shape, indices, etc.
#### Describe the solution you'd like
It would be great to have something like [`nptyping`](https://github.com/ramonhagenaars/nptyping) (type hints for numpy data types) for `pd.DataFrames` and `pd.Series`.
#### API breaking implications
Should not break the API.
#### Describe alternatives you've considered
There is no real workaround. Adding `docstrings` everywhere is anoying and people tend to write them in an incomplete manner or miss them completly. Extensive unit testing of functionality :dizzy_face:
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ENH:New API of indexing
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| 4
| 2020-04-30T15:30:27Z
| 2020-08-21T17:05:36Z
| 2020-04-30T16:26:15Z
|
NONE
| null |
#### Is your feature request related to a problem?
the old indexing API some time is lack of function
so I design this indexing API with more functions and easy rules
#### loc mg cc and supporting equipment
```python
1) #directly indexing, will treat the index as ".iloc" index
eg. df[1,2] <==> df.iloc[1,2] # old indexing
2) #if we want some "loc" index, we use loc[] wrap it
eg. df[loc[a],:] <==> df[df.index.get_loc(a),:]
eg. df[loc[a]:b:c,:] <==> df[df.index.get_silce_bound(a,'left','loc'):b:c,:]
eg. df[loc[[a,b,c,d]],:] <==> df[df.index.get_indexer_for([a,b,c,d]),:]
3) #loc expand rule : the df.loc[r,c] is complitetly equalivent to df[loc[r],loc[c]]
eg. df.loc[ a,:] <==> df[ loc[a],loc[:]]
eg. df[loc[a:b:c],:] <==> df[ loc[a]:loc[b]:loc[c], : ]
eg. df[loc[a,b,c],:] <==> df[[loc[a],loc[b],loc[c]], : ]
4) #for slice, the slice.step choose how to slice
4.1) #if slice.step is wraped by loc
#the index dtype should be "strongly ordered"
#I get element which label meet conditions
eg. df[loc[start]:loc[end]:loc[step]] <==> df[(start <= x) & (x <= end) & ((x-start) % step == 0),:]
#if start or step is NOT wraped by loc
#use x[start] or x[stop] instead in condition expretions
eg. df[start:loc[end]:loc[step]] <==> df[(x[start] <= x) & (x <= end) & ((x-x[start]) % step == 0),:]
4.2) #if slice.step is NOT wraped by loc
#I get element like old slice
eg. df[start:end:step,:] <==> df.iloc[start:end:step,:] # old indexing
if start or step is wraped by loc
use index.get_slice_bound to get postions
eg. df[start:loc[end]:step,:] <==> df[start:index.get_slice_bound(end,'right','loc'):step,:]
4.3) #if step < 0
#swap start and stop, and set step to its opposite
#and reverse the result
eg. df[loc[start]:loc[end]:loc[-1]] <==> df[rev(loc[end]:loc[start]:loc[1]),:] # rev just for this pseudocode
4.4) by passing arg 'closed' to loc # default is 'both'
can decide the interval of slice
eg.
lloc = loc(closed='left')
rloc = loc(closed='right')
nloc = loc(closed='neither')
I = index
gsb = index.get_slice_bound
df[ loc[2]: loc[10]:,:] <==> df[gsb(start, 'left','loc'):gsb(end,'right','loc'):],:]
df[lloc[2]:lloc[10]:,:] <==> df[gsb(start, 'left','loc'):gsb(end, 'left','loc'):],:]
df[rloc[2]:rloc[10]:,:] <==> df[gsb(start,'right','loc'):gsb(end,'right','loc'):],:]
df[nloc[2]:nloc[10]:,:] <==> df[gsb(start,'right','loc'):gsb(end, 'left','loc'):],:]
df[ loc[2]: loc[10]: loc[None],:] <==> df[ loc[2:10:],:] <==> df[(2<=I)&(I<=10),:]
df[lloc[2]:lloc[10]:lloc[None],:] <==> df[lloc[2:10:],:] <==> df[(2<=I)&(I< 10),:]
df[rloc[2]:rloc[10]:rloc[None],:] <==> df[rloc[2:10:],:] <==> df[(2< I)&(I<=10),:]
df[nloc[2]:nloc[10]:nloc[None],:] <==> df[nloc[2:10:],:] <==> df[(2< I)&(I< 10),:]
# if loc is different
# will use the 'closed' arg of step's loc and raise warning
5) #for multi index
#use the default directly tuple indexing
eg. df[[[1,0],[1,1],[2,0]],:] <==> df[df.index.isin([1,0],[1,1],[2,0]),:]
eg. df[loc[[1,0],[1,1],[2,0]],:] <==> df[df.index.get_locs([1,0],[1,1],[2,0]),:]
using cc to index from index like pandas.MultiIndex.fromArray() make
# Aka cc:concat
eg. df[cc[[1,2,3],[1,2,3]], :] <==> df[[[1,1],[2,2],[3,3]], :]
eg. df[cc.loc[[1,2,3],[1,2,3]], :] <==> df[loc[[1,1],[2,2],[3,3]], :]
using mg to index from index like pandas.MultiIndex.fromProduct make
# Aka mg:meshgrid
eg. df[mg[[1,2],[1,2]], :] <==> df[[[1,1],[1,2],[2,1],[2,2]], :]
eg. df[mg.loc[[1,2],[1,2]], :] <==> df[loc[[1,1],[1,2],[2,1],[2,2]], :]
#if you want to index with level
#can pass less indexer than multiindex have
#or use : to take up postions
eg. df[mg[[1,2]], :] <==> df[[index.get_loc_level([1,2],0)]]
eg. df[mg[:,[1,2]], :] <==> df[[index.get_loc_level([1,2],1)]]
#you can pass level directly to 'cc' or 'mg'
eg. df[mg({0})[[1,2]], :]
#you can also use slice on levels
eg. TODO
#if you use through cc
cc will check the result of level indexing by slice is equal
and raise error
```
#### API breaking implications
pd.DataFrame used index as columns
df[['A','B']] seems will be broken
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CI: troubleshoot CI
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BUG: Series construction with dtype=ArrowBoolDtype() and index but no data fails
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MEMBER
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- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1436.g6016b9841'
>>>
>>> from pandas.tests.extension.arrow.arrays import ArrowBoolDtype
>>>
>>> pd.Series(index=[1, 2, 3], dtype=ArrowBoolDtype())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\series.py", line 283, in __init__
data, index = self._init_dict(data, index, dtype)
File "C:\Users\simon\pandas\pandas\core\series.py", line 372, in _init_dict
s = create_series_with_explicit_dtype(
File "C:\Users\simon\pandas\pandas\core\construction.py", line 624, in create_series_with_explicit_dtype
return Series(
File "C:\Users\simon\pandas\pandas\core\series.py", line 329, in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
File "C:\Users\simon\pandas\pandas\core\construction.py", line 454, in sanitize_array
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
File "C:\Users\simon\pandas\pandas\core\construction.py", line 537, in _try_cast
subarr = array_type(arr, dtype=dtype, copy=copy)
File "C:\Users\simon\pandas\pandas\tests\extension\arrow\arrays.py", line 82, in _from_sequence
return cls.from_scalars(scalars)
File "C:\Users\simon\pandas\pandas\tests\extension\arrow\arrays.py", line 72, in from_scalars
arr = pa.chunked_array([pa.array(np.asarray(values))])
File "pyarrow\array.pxi", line 265, in pyarrow.lib.array
File "pyarrow\array.pxi", line 80, in pyarrow.lib._ndarray_to_array
File "pyarrow\error.pxi", line 84, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: only handle 1-dimensional arrays
>>>
```
#### Problem description
raises pyarrow.lib.ArrowInvalid
#### Expected Output
1 pa.NULL
2 pa.NULL
3 pa.NULL
dtype: arrow_bool
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
|
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MDU6SXNzdWU2MTAzMDk3Njg=
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BUG: Series construction with dtype=JSONDtype() and index but no data RecursionError
|
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[] | null | 0
| 2020-04-30T18:54:54Z
| 2021-08-07T05:26:53Z
| null |
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1436.g6016b9841'
>>>
>>> from pandas.tests.extension.json.array import JSONDtype
>>>
>>> pd.Series(index=[1, 2, 3], dtype=JSONDtype())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\series.py", line 283, in __init__
data, index = self._init_dict(data, index, dtype)
File "C:\Users\simon\pandas\pandas\core\series.py", line 372, in _init_dict
s = create_series_with_explicit_dtype(
File "C:\Users\simon\pandas\pandas\core\construction.py", line 624, in create_series_with_explicit_dtype
return Series(
File "C:\Users\simon\pandas\pandas\core\series.py", line 283, in __init__
data, index = self._init_dict(data, index, dtype)
File "C:\Users\simon\pandas\pandas\core\series.py", line 372, in _init_dict
s = create_series_with_explicit_dtype(
File "C:\Users\simon\pandas\pandas\core\construction.py", line 624, in create_series_with_explicit_dtype
...
RecursionError: maximum recursion depth exceeded in comparison
```
#### Problem description
raises RecursionError
#### Expected Output
1 {}
2 {}
3 {}
dtype: json
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
|
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MDU6SXNzdWU2MTAzMjM5MjE=
| 33,901
|
Series construction with dtype=JSONDtype() and index with collection as 'scalar'
|
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[] | null | 1
| 2020-04-30T19:17:59Z
| 2021-08-07T05:27:41Z
| null |
MEMBER
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1436.g6016b9841'
>>>
>>> from pandas.tests.extension.json.array import JSONDtype
>>>
>>> pd.Series({"g": 63}, index=[1, 2, 3], dtype=JSONDtype())
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\series.py", line 283, in __init__
data, index = self._init_dict(data, index, dtype)
File "C:\Users\simon\pandas\pandas\core\series.py", line 372, in _init_dict
s = create_series_with_explicit_dtype(
File "C:\Users\simon\pandas\pandas\core\construction.py", line 624, in create_series_with_explicit_dtype
return Series(
File "C:\Users\simon\pandas\pandas\core\series.py", line 329, in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
File "C:\Users\simon\pandas\pandas\core\construction.py", line 441, in sanitize_array
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
File "C:\Users\simon\pandas\pandas\core\construction.py", line 537, in _try_cast
subarr = array_type(arr, dtype=dtype, copy=copy)
File "C:\Users\simon\pandas\pandas\tests\extension\json\array.py", line 64, in _from_sequence
return cls(scalars)
File "C:\Users\simon\pandas\pandas\tests\extension\json\array.py", line 52, in __init__
raise TypeError("All values must be of type " + str(self.dtype.type))
TypeError: All values must be of type <class 'collections.abc.Mapping'>
```
#### Problem description
Extension arrays may be able to hold a collection as a scalar value. should this be allowed?
#### Expected Output
same as
```
>>> pd.Series([{"g": 63}, {"g": 63}, {"g": 63}], index=[1, 2, 3], dtype=JSONDtype())
1 {'g': 63}
2 {'g': 63}
3 {'g': 63}
dtype: json
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
|
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| 610,339,589
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MDExOlB1bGxSZXF1ZXN0NDExNzk4ODM0
| 33,902
|
DEPR: Deprecate try_sort
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| 2020-04-30T19:45:14Z
| 2020-05-05T12:23:21Z
| 2020-05-05T12:23:18Z
|
MEMBER
| null |
xref #33336
While working on another PR, and @WillAyd brought up `try_sort`, and after a quick look, I don't think `try_sort` is needed in that check method in `indexes/api.py`. (I also dont think it is right to have try_sort there), so deprecate it.
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| 610,372,767
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MDU6SXNzdWU2MTAzNzI3Njc=
| 33,903
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API: Revert changes to describe
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| 2
| 2020-04-30T20:43:11Z
| 2020-07-14T20:21:57Z
| 2020-07-14T20:21:57Z
|
CONTRIBUTOR
| null | ERROR: type should be string, got "https://github.com/pandas-dev/pandas/pull/30209/ has some changes to describe for datelike data. I think the changes count as API breaking and should wait until a 2.0, possibly with a deprecation warning.\r\n\r\ncc @david-cortes, @jreback.\r\n\r\nmaster:\r\n\r\n```python\r\nIn [1]: import pandas as pd\r\n\r\nIn [2]: pd.Series(pd.date_range('2000', periods=10)).describe()\r\nOut[2]:\r\ncount 10\r\nmean 2000-01-05 12:00:00\r\nmin 2000-01-01 00:00:00\r\n25% 2000-01-03 06:00:00\r\n50% 2000-01-05 12:00:00\r\n75% 2000-01-07 18:00:00\r\nmax 2000-01-10 00:00:00\r\ndtype: object\r\n\r\nIn [3]: pd.__version__\r\nOut[3]: '1.1.0.dev0+1417.g12c9c626d'\r\n```\r\n\r\n1.0.2:\r\n\r\n```python\r\nIn [1]: import pandas as pd\r\n\r\nIn [2]: pd.Series(pd.date_range('2000', periods=10)).describe()\r\nOut[2]:\r\ncount 10\r\nunique 10\r\ntop 2000-01-08 00:00:00\r\nfreq 1\r\nfirst 2000-01-01 00:00:00\r\nlast 2000-01-10 00:00:00\r\ndtype: object\r\n\r\nIn [3]: pd.__version__\r\nOut[3]: '1.0.2'\r\n```\r\n\r\n\r\n"
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MDExOlB1bGxSZXF1ZXN0NDExODM1ODI1
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BUG: DTI/TDI intersection result names
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| 1
| 2020-04-30T21:06:56Z
| 2020-05-01T20:29:54Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Also improve _can_fast_intersect to catch the missing case where we can fast-intersect. Identified that _most_ of the remaining places where we do freq inference are not tested, so may be removeable/deprecateable
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Fix #28552 Add test for DataFrame min/max preserve timezone
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| 3
| 2020-04-30T21:15:40Z
| 2020-05-12T17:04:20Z
| 2020-05-12T13:32:09Z
|
CONTRIBUTOR
| null |
- [x] closes #28552
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry _(not applicable - no functionality added)_
Here's a test discussed in #28552. `mean` and other aggregates except for `min` and `max` are not covered since they don't support datetimes - it's a nan being returned.
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REF: implement _unbox to de-duplicate unwrapping
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| 0
| 2020-04-30T23:35:27Z
| 2020-05-01T20:16:19Z
| 2020-05-01T19:34:23Z
|
MEMBER
| null |
Ultimately we want these _validate_foo methods to be whittled down to only one or two methods.
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DEPR: get_value
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| 2020-05-01T02:20:09Z
| 2020-05-01T14:30:44Z
| 2020-05-01T12:54:54Z
|
MEMBER
| null |
- [x] closes #19728
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
No longer used internally.
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REF: de-duplicate listlike validation in DTA._validate_foo
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| 2020-05-07T01:31:47Z
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|
MEMBER
| null |
Implements a helper `_validate_listlike` that is re-used across 4 of the _validate_foo methods. We can then smooth out the remaining differences by passing the same flags in each of the four usages.
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BUG: Cannot save categorical index to HDF5
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[] | null | 0
| 2020-05-01T03:26:09Z
| 2020-06-05T20:36:00Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
cat = pd.Categorical(['A', 'B'])
df = pd.DataFrame([[1, 2], [3, 4]], index=cat)
df.to_hdf("test.h5", "/test")
```
#### Problem description
Attempting to save a dataframe with a categorical index fails with an AssertionError:
```python-traceback
Traceback (most recent call last):
File "pandas_bug.py", line 4, in <module>
df.to_hdf("test.h5", "/test")
File "/usr/lib/python3.8/site-packages/pandas/core/generic.py", line 2490, in to_hdf
pytables.to_hdf(
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 282, in to_hdf
f(store)
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 265, in <lambda>
f = lambda store: store.put(
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 1030, in put
self._write_to_group(
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 1697, in _write_to_group
s.write(
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 3094, in write
self.write_index(f"axis{i}", ax)
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 2770, in write_index
converted = _convert_index("index", index, self.encoding, self.errors)
File "/usr/lib/python3.8/site-packages/pandas/io/pytables.py", line 4732, in _convert_index
assert isinstance(converted, np.ndarray) and converted.dtype == object
AssertionError
```
It is expected that this would work and it has worked in previous Pandas versions.
#### Expected Output
A HDF5 file containing the dataframe.
#### Output of ``pd.show_versions()``
The following system produces the bug:
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.8-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.utf8
LOCALE : en_AU.UTF-8
pandas : 1.0.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : 1.9.0
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
numba : 0.49.0
```
</details>
The following system works as expected (downgrade to Pandas 0.25.3):
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.6.8-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_AU.utf8
LOCALE : en_AU.UTF-8
pandas : 0.25.3
numpy : 1.18.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.17
pytest : None
hypothesis : None
sphinx : None
blosc : 1.9.0
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.8
```
</details>
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REF: Create numba helper function for jitting + generating cache key
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API: make min/max on empty datetime df consistent with datetime serie…
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| 2020-05-09T20:31:32Z
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| null |
…s (#33704)
- [x] closes #33704
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixes the following issue:
```python
import pandas as pd
df = pd.DataFrame(dict(x=pd.to_datetime([])))
df.max()
```
throws a `ValueError` but
```python
pd.Series(pd.to_datetime([])).max()
```
results in `NaT`.
With this change, calling an DataFrame on en empty `pd.to_datetime([])`, results in
```
In[1]: df = pd.DataFrame(dict(x=pd.to_datetime([])))
In[2]: df.max()
Out[5]:
x NaT
dtype: datetime64[ns]
```
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MDU6SXNzdWU2MTA2NTcwNDQ=
| 33,912
|
BUG: cut() precision at the left end does not appear as specified (3 digits by default)
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[] | null | 7
| 2020-05-01T09:14:16Z
| 2020-11-25T10:53:16Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
...
bins = 10
df_bins = pd.cut(fatalities_asc.fatality_rate, bins, include_lowest=True)
fatalities_asc['fatality_bin'] = df_bins.values
df_counts = fatalities_asc.groupby('fatality_bin', as_index = False).count()
df_counts
```
You can find the complete code here:
https://github.com/nnworkspace/covid19-insight/blob/master/covid19-insight.ipynb
#### Problem description
I expected all bounds of the bins appear to be a rounded float with 3 digits of precision. But the output of above code (please pay attention to the first interval, the left bound):
fatality_bin Confirmed Deaths Recovered Active fatality_rate
0 (0.057999999999999996, 1.632] 39 39 39 39 39
1 (1.632, 3.19] 33 33 33 33 33
2 (3.19, 4.748] 27 27 27 27 27
3 (4.748, 6.305] 18 18 18 18 18
4 (6.305, 7.863] 12 12 12 12 12
5 (7.863, 9.421] 2 2 2 2 2
6 (9.421, 10.978] 3 3 3 3 3
7 (10.978, 12.536] 6 6 6 6 6
8 (12.536, 14.094] 2 2 2 2 2
9 (14.094, 15.652] 3 3 3 3 3
When the lower and upper bounds used as labels of a plot, it looks like this (pay attention to the first x-tick label )
#### Expected Output
(0.058, 1.632] should be the lower bound of the first interval, not an infinite number.
#### Output of ``pd.show_versions()``
pandas version: 1.0.3
</details>
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MDU6SXNzdWU2MTA3MTUyNTU=
| 33,913
|
Performance regression in rolling.Quantile.time_quantile
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| 8
| 2020-05-01T11:54:35Z
| 2021-08-07T05:32:26Z
| null |
CONTRIBUTOR
| null | ERROR: type should be string, got "https://pandas.pydata.org/speed/pandas/index.html#rolling.Quantile.time_quantile?p-constructor=%27Series%27&p-window=1000&p-dtype=%27int%27&p-percentile=1&p-param5=%27linear%27&commits=df5eee60d60a604969bdc3e36a0da20ea36af3f6.\r\n\r\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nconstructor = \"Series\"\r\nwindow = 1000\r\ndtype = \"int\"\r\npercentile = 1\r\ninterpolation = \"linear\"\r\n\r\n\r\nN = 10 ** 5\r\narr = np.random.random(N).astype(dtype)\r\nroll = getattr(pd, constructor)(arr).rolling(window)\r\n\r\n%timeit roll.quantile(percentile, interpolation=interpolation)\r\n```\r\n\r\nSeems to be from https://github.com/pandas-dev/pandas/pull/33693 (cc @s-scherrer).\r\n\r\nInterestingly, I'm not able to reproduce this locally. Is anyone else able to?\r\n\r\n```\r\n# 1.0.2\r\n3.16 ms ± 295 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\r\n\r\n# master\r\n2.27 ms ± 56.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\r\n```\r\n\r\n"
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ENH: print DataFrame columns names at the end
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| 8
| 2020-05-01T12:04:59Z
| 2020-07-22T09:20:46Z
| 2020-07-22T09:20:44Z
|
CONTRIBUTOR
| null |
- [ ] closes #32296
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I've added the possibility of printing columns names again at the end of DataFrame. I'm not sure whether the number of rows should be checked again (max number of rows could be exceeded with that extra row).
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MDU6SXNzdWU2MTA3Mzk0Mjc=
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BUG: Timedelta.microseconds is rounded up if nanos>500
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| 2
| 2020-05-01T13:01:19Z
| 2020-05-06T19:41:18Z
| 2020-05-02T03:34:34Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
t = pd.Timedelta(501, unit='ns')
t.components
print("sec=%d, micro=%d, nano=%d" % (t.seconds, t.microseconds, t.nanoseconds))
assert t.microseconds == 0, "This should be zero, right?"
```
#### Problem description
Timedelta.microseconds is rounded up based on nanos
#### Expected Output
t.microseconds == 0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.138+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.16
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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ENH: Add nrows parameter to pd.read_json
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| 1
| 2020-05-01T13:29:07Z
| 2020-08-21T17:05:36Z
| 2020-06-04T20:44:21Z
|
NONE
| null |
#### Is your feature request related to a problem?
Let's say I have a huge `jsonlines` file and I want to read only the first `n` lines of the file.
#### Describe the solution you'd like
This problem could be fixed by adding `nrows` parameter in method `pd.read_json`. This parameter should be applicable if and only if `lines=True`
#### API breaking implications
Simply add and implement `nrows` parameter in `pd.read_json` (like `pd.read_csv`).
#### Additional context
How this enhancement could work:
```python
import pandas as pd
df = pd.read_json('/path/to/file.jsonlines', lines=True, nrows=1000)
```
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