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https://api.github.com/repos/pandas-dev/pandas/issues/29714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29714/events | https://github.com/pandas-dev/pandas/issues/29714 | 525,145,798 | MDU6SXNzdWU1MjUxNDU3OTg= | 29,714 | After Anaconda upgrade to 0.25.3 can't import pandas | {
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} | [] | closed | false | null | [] | null | 3 | 2019-11-19T17:10:54Z | 2020-01-21T05:50:30Z | 2019-11-19T17:28:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
conda update pandas
```
#### Problem description
I did a conda update pandas at the anaconda prompt today to update from 0.23.x to 0.25.3 and now I get this error in Spyder simply importing pandas.
```
Traceback (most recent call last):
File "<ipython-input-4-7dd3504c366f>", line 1, in <module>
import pandas as pd
File "D:\Applications\Anaconda3\lib\site-packages\pandas\__init__.py", line 55, in <module>
from pandas.core.api import (
File "D:\Applications\Anaconda3\lib\site-packages\pandas\core\api.py", line 5, in <module>
from pandas.core.arrays.integer import (
File "D:\Applications\Anaconda3\lib\site-packages\pandas\core\arrays\__init__.py", line 7, in <module>
from .categorical import Categorical # noqa: F401
File "D:\Applications\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py", line 54, in <module>
from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
File "D:\Applications\Anaconda3\lib\site-packages\pandas\core\base.py", line 36, in <module>
import pandas.core.nanops as nanops
File "D:\Applications\Anaconda3\lib\site-packages\pandas\core\nanops.py", line 38, in <module>
bn = import_optional_dependency("bottleneck", raise_on_missing=False, on_version="warn")
File "D:\Applications\Anaconda3\lib\site-packages\pandas\compat\_optional.py", line 99, in import_optional_dependency
version = _get_version(module)
File "D:\Applications\Anaconda3\lib\site-packages\pandas\compat\_optional.py", line 48, in _get_version
raise ImportError("Can't determine version for {}".format(module.__name__))
ImportError: Can't determine version for bottleneck
```
[29181](https://github.com/pandas-dev/pandas/issues/29181) is similar but the iPython was already installed as part of Anaconda,
#### Expected Output
None. Should be able to import pandas as pd
#### Output of ``pd.show_versions()``
<details>
Traceback (most recent call last):
File "<ipython-input-5-3d232a07e144>", line 1, in <module>
pd.show_versions()
NameError: name 'pd' is not defined
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29715/events | https://github.com/pandas-dev/pandas/pull/29715 | 525,194,865 | MDExOlB1bGxSZXF1ZXN0MzQyODMyNTAz | 29,715 | WIP: All the CI in GitHub actions (DO NOT MERGE) | {
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"description": ... | closed | false | null | [] | null | 4 | 2019-11-19T18:47:30Z | 2019-12-30T18:53:23Z | 2019-12-30T18:53:22Z | MEMBER | null | Many things are incomplete or broken here, but opening this so people can see the progress.
This needs to be discussed, and if we move forward splitted in many PRs, before it can be merged.
This adds all our current builds to GitHub actions, with two new features:
- Creating an artifact for the built web and docs
- A `Finalize` job is triggered when all the others are finished, that should summarize the errors in a GitHub comment (WIP)
Some things that are broken:
- The builds with locale different than English
- The grep that checks if there are ipython errors in the sphinx logs
- The builds other than Linux (because the matrix tries to create the containers for MySQL and PostgreSQL for all builds now)
- The `Finalize` job is WIP, and not sure if even triggered
I'll probably work on #29685 before adding any test build to actions. I think we can do much better in terms of simplicity and coverage with the creation of the matrix (which builds we run, and what's tested in each). | {
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} | 3 | 2019-11-19T19:10:15Z | 2021-05-27T19:07:14Z | 2019-11-19T19:43:21Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'ind':[np.nan,np.nan,'a','a'],'col':[0,1,2,3]}).set_index('ind')
df.sum(level='ind')
```
results in
```
col
ind
a 5
```
#### Problem description
df.sum ignores rows where any index column contains a np.nan.
#### Expected Output
```
col
ind
NaN 1
1.0 5
```
#### 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:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 41.0.1
Cython: 0.28.4
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.14.0
xarray: None
IPython: 7.5.0
sphinx: 1.7.5
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.3.2
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-11-19T19:41:10Z | 2020-03-26T06:44:04Z | 2019-11-19T20:54:14Z | CONTRIBUTOR | null | Use https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna
docstring as a\ template.
MINOR DOCUMENTATION CHANGE ONLY (ignored below)
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "Concat, Merge/Join, Stac... | closed | false | null | [] | null | 4 | 2019-11-19T20:13:31Z | 2021-05-17T17:20:59Z | 2019-11-23T23:10:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(columns=[1, "string"])
pd.melt(df, id_vars=[1, "string"])
```
#### Problem description
The shown example fails with
```
Traceback (most recent call last):
File "test.py", line 5, in <module>
pd.melt(df, id_vars=[1, "string"])
File "/home/nils/projects/tsfresh/venv/lib/python3.6/site-packages/pandas/core/reshape/melt.py", line 52, in melt
"".format(missing=list(missing))
KeyError: "The following 'id_vars' are not present in the DataFrame: ['1']"
```
and I guess the reason is that the call of
```python
Index(np.ravel(id_vars))
```
in `pd.melt` somehow casts the numerical column name `1` to the string `"1"`.
I am not sure if this is intended behavior or if the case of numerical column names is just not supported, but at least in older pandas versions (e.g. 0.23.4) this still worked.
Thanks for looking into this! I am also fine if this is closed with "won't fix" :-)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-65-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: 0.8.1
bs4 : None
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
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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... | open | false | null | [] | null | 0 | 2019-11-19T20:30:40Z | 2021-07-23T04:52:35Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import matplotlib.pyplot as plt
x1 = pd.date_range(start='2018-03-02 12:00:00', end='2019-10-21 12:00:00', freq='1h')
df1 = pd.DataFrame([0]*len(x1), index=x1, columns=['x1'])
x2 = pd.date_range(start='2017-06-04 06:00:01', end='2019-11-10 06:00:01', freq='1h')
df2 = pd.DataFrame([0]*len(x2), index=x2, columns=['x2'])
fig, axes = plt.subplots(nrows=4, ncols=1, sharex=True)
df1.plot(ax=axes[0])
df2.plot(ax=axes[1])
axes[2].plot(df1)
axes[3].plot(df2)
#Autoscale doesn't affect this issue. Just used to make all lines visible from start.
plt.autoscale(enable=True, axis='both', tight=None)
for ax in axes:
ax.legend().remove()
# printouts
print(str(axes[0].get_xlim()))
print(str(axes[1].get_xlim()))
print(str(axes[2].get_xlim()))
print(str(axes[3].get_xlim()))
plt.show()
```
#### Problem description
The x-axes are misaligned when plotting two or more subplots and have different timestep base (in the example above, x1 starts with hour:minute:second 00:00:00 while x2 starts with 06:00:01.
UPDATE: Also, plotting df1 using axes[2].plot(df1) instead of df1.plot(ax=axes[0]) works perfectly.
If plt.autoscale is not used, the topmost plot, df1.plot(ax=axes[0]), is not visible unless one pan the axes manually.
The result from get_xlim() from the supplied code returns
```
(736484.2500115741, 737373.2500115741)
(736484.2500115741, 737373.2500115741)
(736484.2500115741, 737373.2500115741)
(736484.2500115741, 737373.2500115741)
```
If the df2.plot(ax=axes[1]) is plotted before the other three, the result of the get_xlim() is instead
```
(422220.0, 436572.0)
(422220.0, 436572.0)
(422220.0, 436572.0)
(422220.0, 436572.0)
```
This issue might be related to:
#11574, #18571 and #29705.
#### Expected Output
Plotting the data at the correct x axis coordinates.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-65-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : sv_SE.UTF-8
LOCALE : sv_SE.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : None
psycopg2 : 2.7.4 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-11-19T21:06:34Z | 2021-11-20T23:21:56Z | 2019-11-20T16:43:03Z | MEMBER | null | There are a handful of these PRs on the way and they're inevitably going to have whatsnew conflicts. I'm trying to group together related deprecations so as to have a single-digit number of PRs for these. | {
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} | 4 | 2019-11-19T21:08:43Z | 2019-11-20T21:12:26Z | 2019-11-20T21:07:00Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 3 | 2019-11-20T00:57:19Z | 2019-11-20T15:06:29Z | 2019-11-20T15:06:29Z | CONTRIBUTOR | null | #### Problem description
Hello all! Hope this is the right avenue to post this.
So Series has functions `to_list()` and `to_dict()`, with the underscore in the middle. There's also `tolist()` w/o the underscore, but not `todict()`. Don't suppose there's any harm in extending the API to allow the latter?
#### Code Sample
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.25.3'
In [3]: srs = pd.Series()
In [4]: srs.to_dict()
Out[4]: {}
In [5]: srs.to_list()
Out[5]: []
In [6]: srs.tolist()
Out[6]: []
In [7]: srs.todict()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-6908392d3852> in <module>
----> 1 srs.todict()
~/miniconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
5177 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5178 return self[name]
-> 5179 return object.__getattribute__(self, name)
5180
5181 def __setattr__(self, name, value):
AttributeError: 'Series' object has no attribute 'todict'
``` | {
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} | 1 | 2019-11-20T02:33:53Z | 2019-11-20T21:13:55Z | 2019-11-20T21:05:19Z | MEMBER | null | Looks like #29017 was supposed to do this and I just missed the reduce keyword | {
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} | 0 | 2019-11-20T03:22:20Z | 2019-11-20T15:23:20Z | 2019-11-20T12:26:25Z | MEMBER | null | We _also_ have a deprecation to remove the keyword altogether, but we can't enforce that until we give people time to stop passing `keep_tz=True` | {
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} | 1 | 2019-11-20T03:25:04Z | 2019-11-20T15:26:10Z | 2019-11-20T12:24:31Z | MEMBER | null | …achsorted, DataFrame.update, DatetimeIndex.shift, TimedeltaIndex.shift, PeriodIndex.shift
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} | 4 | 2019-11-20T05:46:52Z | 2020-01-02T01:35:32Z | 2020-01-02T01:35:32Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from collections import UserDict, Counter
class MissingDict(UserDict):
def __missing__(self, key):
return key
s = pd.Series(list("aab"))
s.map(Counter({"a": 1}))
# 0 1
# 1 1
# 2 0
# dtype: int64
s.map(MissingDict({"a": "c"}))
# <ipython-input-48-6cd4b0d364c9> in <module>
# ----> 1 s.map(MissingDict({"a": "c"}))
# /usr/local/lib/python3.7/site-packages/pandas/core/series.py in map(self, arg, na_action)
# 3826 dtype: object
# 3827 """
# -> 3828 new_values = super()._map_values(arg, na_action=na_action)
# 3829 return self._constructor(new_values, index=self.index).__finalize__(self)
# 3830
# /usr/local/lib/python3.7/site-packages/pandas/core/base.py in _map_values(self, mapper, na_action)
# 1298
# 1299 # mapper is a function
# -> 1300 new_values = map_f(values, mapper)
# 1301
# 1302 return new_values
# pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()
# TypeError: 'MissingDict' object is not callable
```
#### Problem description
`Series.map` can sometimes take Mapping subclasses other than `dict`, but not always. I think it would make sense if any object that satisfied `isinstance(x, collections.abc.Mapping)` could be used as a mapping. I came across this while trying to use map where I only wanted values present as keys in the mapping to change (I went for `s.map(lambda x: {"a": "b"}.get(x, x))` as a workaround).
`collections.Counter` and `collections.defaultdict` work fine, which made me expect other `Mapping`s would as well.
#### Expected Output
I expected `s.map(MissingDict({"a": "c"}))` to do something like:
```python
s.map(lambda x: MissingDict({"a": "c"})[x])
# 0 c
# 1 c
# 2 b
# dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.7.5
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 5.2.2
hypothesis : 4.44.1
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.0.3
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : 3.6.1
xarray : 0.14.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29734 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29734/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29734/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29734/events | https://github.com/pandas-dev/pandas/issues/29734 | 525,632,039 | MDU6SXNzdWU1MjU2MzIwMzk= | 29,734 | Pandas dropna throwing ValueError: "Cannot convert non-finite values (NA or inf) to integer" | {
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] | closed | false | null | [] | null | 3 | 2019-11-20T07:47:27Z | 2021-07-23T04:56:25Z | 2021-07-23T04:56:25Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
long_summary = long_summary.dropna(axis='columns', how='all')
```
#### Problem description
that simple line throws an exception:
> ValueError: Cannot convert non-finite values (NA or inf) to integer
I cannot see how calling dropna would lead to this exception. Why would dropna try to convert any values to an integer? Is this a bug or am I misunderstanding dropna?
#### Actual output
````
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-88-b4926abd4d81> in <module>
----> 1 long_summary = long_summary.dropna(axis='columns', how='all')
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\frame.py in dropna(self, axis, how, thresh, subset, inplace)
4860 agg_obj = self.take(indices, axis=agg_axis)
4861
-> 4862 count = agg_obj.count(axis=agg_axis)
4863
4864 if thresh is not None:
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\frame.py in count(self, axis, level, numeric_only)
7848 result = Series(counts, index=frame._get_agg_axis(axis))
7849
-> 7850 return result.astype("int64")
7851
7852 def _count_level(self, level, axis=0, numeric_only=False):
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors, **kwargs)
5880 # else, only a single dtype is given
5881 new_data = self._data.astype(
-> 5882 dtype=dtype, copy=copy, errors=errors, **kwargs
5883 )
5884 return self._constructor(new_data).__finalize__(self)
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, **kwargs)
579
580 def astype(self, dtype, **kwargs):
--> 581 return self.apply("astype", dtype=dtype, **kwargs)
582
583 def convert(self, **kwargs):
c:\users\timregan\appdata\local\programs\python\python37\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
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors, values, **kwargs)
557
558 def astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
--> 559 return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
560
561 def _astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\internals\blocks.py in _astype(self, dtype, copy, errors, values, **kwargs)
641 # _astype_nansafe works fine with 1-d only
642 vals1d = values.ravel()
--> 643 values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
644
645 # TODO(extension)
c:\users\timregan\appdata\local\programs\python\python37\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
698 if not np.isfinite(arr).all():
699 raise ValueError(
--> 700 "Cannot convert non-finite values (NA or inf) to " "integer"
701 )
702
ValueError: Cannot convert non-finite values (NA or inf) to integer
````
#### Expected Output
I expect columns containing only NaNs to be dropped, without any attempt at conversion to int64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
None
</details>
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} | 5 | 2019-11-20T12:24:32Z | 2020-09-12T21:23:21Z | 2020-09-12T21:23:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from scipy.sparse import csr_matrix
f = pd.DataFrame.sparse.from_spmatrix(csr_matrix((4, 4)), index=pd.MultiIndex.from_product([[1, 2], [1,2]]))
f
f.sort_index(level=0)
```
#### Problem description
When I create a MultiIndexed DataFrame with sparse columns, then call `sort_index()`, in the result `0.0` (the sparse fill value) has been replaced with `NaN` -- but it seems like only for columns which are all the fill value.
It seems like this might also be an issue with `RangeIndex` as well, since if I make a simpler `DataFrame` and call `sort_index`, without any arguments it's ok but adding `level=0` (or in fact `level=(anything)` as a kwarg will cause the frame to be filled with `NaN` without raising any kind of warning
I would expect the following behaviour:
- Values preserved for `MultiIndex`
- For `RangeIndex`, the level keyword should either be totally ignored (no impact on output) or should not be valid to pass in.
I don't think this is a duplicate, I searched the tracker but the combination of sparse + multiindex seems to be rare
#### Expected Output
```python
>>> f.sort_index(level=0)
0 1 2 3
1 1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
2 1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
```
#### Actual Output
```python
>>> f.sort_index(level=0)
0 1 2 3
1 1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
2 1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-36-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.14
pytest : 3.10.1
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-11-20T12:54:01Z | 2019-11-20T14:09:54Z | 2019-11-20T14:09:51Z | CONTRIBUTOR | null | xref #29676, https://github.com/pandas-dev/pandas/pull/29712. | {
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} | 6 | 2019-11-20T13:54:39Z | 2020-11-19T00:34:28Z | null | MEMBER | null | We try to consistently return python objects (instead of numpy scalars) in certain functions like `tolist`, `to_dict`, `itertuples/items`, .. (we have had quite some issues fixing this in several cases).
However, currently we don't do that for extension dtypes (and don't have any mechanism to ask for this):
```
In [33]: type(pd.Series([1, 2], dtype='int64').tolist()[0])
Out[33]: int
In [34]: type(pd.Series([1, 2], dtype='Int64').tolist()[0])
Out[34]: numpy.int64
In [36]: type(pd.Series([1, 2], dtype='int64').to_dict()[0])
Out[36]: int
In [37]: type(pd.Series([1, 2], dtype='Int64').to_dict()[0])
Out[37]: numpy.int64
```
```
In [45]: s = pd.Series([1, 2], dtype='int64')
In [46]: type(list(s.iteritems())[0][1])
Out[46]: int
In [47]: s = pd.Series([1, 2], dtype='Int64')
In [48]: type(list(s.iteritems())[0][1])
Out[48]: numpy.int64
```
Should we add some API to ExtensionArray to provide this? Eg a method to iterate through the elements that returns "native" objects? | {
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} | 2 | 2019-11-20T15:09:31Z | 2019-11-20T16:40:39Z | 2019-11-20T16:31:34Z | CONTRIBUTOR | null | Changes only words in comments; function names not touched. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29740 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29740/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29740/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29740/events | https://github.com/pandas-dev/pandas/issues/29740 | 525,878,049 | MDU6SXNzdWU1MjU4NzgwNDk= | 29,740 | Different index when creating a DataFrame than when changing a column in the DataFrame | {
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```python
import pandas
from pandas.testing import assert_frame_equal
if __name__ == "__main__":
df_left = pandas.DataFrame(data={'number': [1, 2, 3]})
df_right = pandas.DataFrame(data={1: [2, 4, 6]})
df_left[1] = df_left['number'].map(lambda x: x * 2)
df_left.drop('number', axis=1, inplace=True)
assert_frame_equal(df_left, df_right)
```
The result of running this code is:
```
Traceback (most recent call last):
File "pandas_index_bug.py", line 10, in <module>
assert_frame_equal(df_left, df_right)
File "/home/terseus/.cache/pypoetry/virtualenvs/pandas-index-bug-py3.7/lib/python3.7/site-packages/pandas/util/testing.py", line 1372, in assert_frame_equal
obj="{obj}.columns".format(obj=obj),
File "/home/terseus/.cache/pypoetry/virtualenvs/pandas-index-bug-py3.7/lib/python3.7/site-packages/pandas/util/testing.py", line 635, in assert_index_equal
_check_types(left, right, obj=obj)
File "/home/terseus/.cache/pypoetry/virtualenvs/pandas-index-bug-py3.7/lib/python3.7/site-packages/pandas/util/testing.py", line 611, in _check_types
assert_class_equal(l, r, exact=exact, obj=obj)
File "/home/terseus/.cache/pypoetry/virtualenvs/pandas-index-bug-py3.7/lib/python3.7/site-packages/pandas/util/testing.py", line 728, in assert_class_equal
raise_assert_detail(obj, msg, repr_class(left), repr_class(right))
File "/home/terseus/.cache/pypoetry/virtualenvs/pandas-index-bug-py3.7/lib/python3.7/site-packages/pandas/util/testing.py", line 932, in raise_assert_detail
raise AssertionError(msg)
AssertionError: DataFrame.columns are different
DataFrame.columns classes are not equivalent
[left]: Index([1], dtype='object')
[right]: Int64Index([1], dtype='int64')
```
#### Problem description
Pandas assigns a different index when creating a dataframe with a given column than when adding that same column to an existing dataframe.
Please refer to the code example to see the behavior I'm trying to describe.
#### Expected Output
I expected that assert to *not* raise an `AssertionError`.
#### Output of ``pd.show_versions()``
<details>
```python
Python 3.7.4 (default, Oct 4 2019, 06:57:26)
[GCC 9.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.5-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
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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} | 0 | 2019-11-20T15:35:11Z | 2019-12-12T13:10:42Z | 2019-12-12T13:10:42Z | MEMBER | null | The argument is not used in the function.
xref #29627
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} | 5 | 2019-11-20T15:39:46Z | 2019-11-22T15:46:06Z | 2019-11-22T15:35:22Z | CONTRIBUTOR | null | - [x] closes #29650
- [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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... | open | false | null | [] | null | 0 | 2019-11-20T16:49:09Z | 2019-12-01T21:21:07Z | null | NONE | null | At the moment, the `subplots` parameter in `df.plot()` only supports boolean values: Generate either as many plots as there are columns or generate a single plot with all columns.
It would be nice if `subplots` understood level specifications and generate one plot for each unique value in the corresponding level(s) of a multi-index type `columns` index.
#### Code Sample
Let
```python
import numpy as np
import pandas as pd
df = pd.DataFrame([np.sin(np.linspace(0,y,10))+z for y in [1,2,3] for z in [10,20]]).T
df.columns = pd.MultiIndex.from_tuples([(y,z) for y in ['Y1','Y2','Y3'] for z in ['Z1','Z2']],names=['Y','Z'])
```
```
Y Y1 Y2 Y3
Z Z1 Z2 Z1 Z2 Z1 Z2
0 10.000000 20.000000 10.000000 20.000000 10.000000 20.000000
1 10.110883 20.110883 10.220398 20.220398 10.327195 20.327195
2 10.220398 20.220398 10.429956 20.429956 10.618370 20.618370
3 10.327195 20.327195 10.618370 20.618370 10.841471 20.841471
4 10.429956 20.429956 10.776372 20.776372 10.971938 20.971938
5 10.527415 20.527415 10.896192 20.896192 10.995408 20.995408
6 10.618370 20.618370 10.971938 20.971938 10.909297 20.909297
7 10.701698 20.701698 10.999884 20.999884 10.723086 20.723086
8 10.776372 20.776372 10.978656 20.978656 10.457273 20.457273
9 10.841471 20.841471 10.909297 20.909297 10.141120 20.141120
```
The proposed feature would allow
```
[df.xs(val,level='Z',axis=1).plot().set_title(val) for val in df.columns.levels[1]]
```
to be replaced by
```
df.plot(subplots='Z')
```
More generally, `df.plot(subplots=['Z','Y'])` would draw one plot for each combination of values Z and Y. For the particular dataframe defined above, where 'Z' and 'Y' are the only column levels, this would be equivalent to subplots=True, though it would be a nice bonus feature if the subplots were generated on a grid, with Y varying in the horizontal grid direction, and Z varying the vertical grid direction.
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"description": "Functionality t... | closed | false | null | [] | null | 3 | 2019-11-20T16:56:13Z | 2020-03-29T11:37:46Z | 2020-03-29T11:37:46Z | MEMBER | null | Seems to be an issue on master as this works on 0.25.3:
```python
>>> ser = pd.Series(pd.Categorical(["first", "second", "third", "fourth"], ordered=True))
>>> ser.groupby([1, 1, 1, 1]).first()
[first]
Categories (4, object): [first < fourth < second < third]
```
But fails on master:
```python
>>> ser = pd.Series(pd.Categorical(["first", "second", "third", "fourth"], ordered=True))
>>> ser.groupby([1, 1, 1, 1]).first()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/core/groupby/groupby.py", line 1368, in f
return self._cython_agg_general(alias, alt=npfunc, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/groupby/groupby.py", line 880, in _cython_agg_general
obj._values, how, min_count=min_count
File "/Users/williamayd/clones/pandas/pandas/core/groupby/ops.py", line 572, in aggregate
"aggregate", values, how, axis, min_count=min_count
File "/Users/williamayd/clones/pandas/pandas/core/groupby/ops.py", line 456, in _cython_operation
"{dtype} dtype not supported".format(dtype=values.dtype)
NotImplementedError: category dtype not supported
```
@jbrockmendel for visibility. Looking at this on my end | {
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} | 2 | 2019-11-20T17:33:13Z | 2019-11-20T21:22:43Z | 2019-11-20T21:03:52Z | MEMBER | null | `pandas\io\pytables.py:2048: error: "None" has no attribute "nrows"` seen locally after merging master and running mypy | {
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} | 1 | 2019-11-20T17:37:07Z | 2019-11-21T12:20:04Z | 2019-11-21T12:19:53Z | CONTRIBUTOR | null | also rename an inner function from ``f`` to ``func``, which is clearer. | {
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} | 0 | 2019-11-20T18:10:14Z | 2020-11-25T03:18:36Z | 2020-11-25T03:18:36Z | NONE | null | This works fine in pandas 0.22.0 but fails in 0.25.3 It appears a bug may have been introduced.
I have some old code that accidentally created a MultiIndex with a single level instead of a regular index, but everything worked fine with pandas 0.22.0. However, in new versions of pandas, that MultiIndex breaks pandas ability to select columns and gives an extremely misleading error message. In this case, I don't need the MultiIndex, so I can just remove that; but I think this code ought to work. (And it did work in the old 0.22.0 version).
```
import numpy as np
import pandas as pd
names = ['FirstColumn', 'SecondColumn']
data = np.array([[5,6],[7,8]])
df = pd.DataFrame(data, columns = [names]) #Bug: the brackets around "[names]" creates a
#multi-index but that was unintentional. (It's still legal, though).
#But "df.head()" and "df.describe()" both look normal so you can't see anything is wrong.
#All of the following work as expected in 0.22.0 but give misleading errors in 0.25.3.
#These all work with a regular index, but the 0.25.3 MultIndex doesn't like it.
df['FirstColumn'] #ERROR!
df.FirstColumn #ERROR!
df.loc[:,'FirstColumn'] #ERROR!
#This works, but it shouldn't be necessary to do this. The above syntax is more standard.
df.xs('FirstColumn', axis=1, level=0)
```
Those failing statements give misleading errors about ```only integer scalar arrays can be converted to a scalar index``` but that code looks like it ought to work, and it did work fine in the previous versions of pandas. Can we make the standard methods of selecting columns work with the MultiIndex? | {
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} | 1 | 2019-11-20T18:23:43Z | 2019-11-21T13:45:24Z | 2019-11-21T13:39:55Z | CONTRIBUTOR | null | I took a look at the code in core/category.py and found a usage of np.repeat for creating boolean arrays. np.repeat is much slower than np.zeros/np.ones for that purpose.
```python
>>> n = 1_000_000
>>> c = pd.Categorical(['a'] * n + ['b'] * n + ['c'] * n)
>>> %timeit c == 'x'
17 ms ± 270 µs per loop # master
82.6 µs ± 488 ns per loop # this PR
```
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} | 8 | 2019-11-20T18:31:52Z | 2021-01-07T13:58:08Z | 2021-01-07T13:58:08Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
a = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).set_index([0, 1])
b = pd.DataFrame([[1, np.nan, 3], [4, np.nan, 6], [7, np.nan, 9]]).set_index([0, 1])
a_idx = a.index[1]
b_idx = b.index[1]
```
```
>>> a.loc[a_idx:]
2
0 1
4 5 6
7 8 9
>>> a.loc[:a_idx]
2
0 1
1 2 3
4 5 6
>>> b.loc[b_idx:]
2
0 1
7 NaN 9
>>> b.loc[:b_idx]
2
0 1
1 NaN 3
4 NaN 6
```
#### Problem description
As above, slicing on MultiIndex has different behaviours with nans: it excludes the current index of selection from the slice. This is possibly because - rightly so - there is no match on NaNs when comparing the indices, but if that is the case, this should also not work:
```
>>> b.loc[b_idx]
2 6
Name: (4, nan), dtype: int64
```
Note: might be a duplicate of #25154, but OP does not slice directly on the MultiIndex with NaNs, so I am not sure if fixing that would also fix this issue.
#### Expected Output
```
>>> b.loc[b_idx:]
2
0 1
4 NaN 6
7 NaN 9
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
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} | 59 | 2019-11-20T18:44:23Z | 2021-03-25T18:46:31Z | null | CONTRIBUTOR | null | With the new dtypes (`IntegerArray`, `StringArray`, etc.), if you want to use them when reading in data, you have to specify the types for all of the columns. It would be nice to have the option to use the new dtypes for all columns as a keyword to `read_csv()`, `read_excel()`, etc.
(ref. discussion in pandas dev meeting on 11/20/19)
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} | [] | closed | false | null | [] | null | 10 | 2019-11-20T19:08:23Z | 2020-01-16T00:35:22Z | 2019-11-23T23:15:50Z | MEMBER | null | This isn't complete (2 tests fail) but this is arguably controversial, so putting up for review.
With #29124 we *could* get rid of the DataFrame aggregation methods that go by blocks. This would hopefully make our code easier to grok and move towards only "one way of doing things" in groupby.
This causes a definite regression for very wide DataFrames with a small number of rows. If you look at the new benchmark created, when testing Frames of N rows x 10_000 columns where N is one of 100, 1000 or 10_000 here are the results:
```sh
before after ratio
[6b3ba986] [124a135a]
<categorical-regression> <easier-groupby>
+ 3.94±0.2ms 2.55±0.1s 646.23 groupby.GroupByWide.time_wide(100)
+ 38.3±6ms 2.49±0.02s 64.91 groupby.GroupByWide.time_wide(1000)
```
So drastically slower at 100 rows, but we've already reached a break-even point by the time we get to 10,000 rows.
The rest of the benchmarks don't cover wide data frames that well but here are the results for posterity (I think these are noise)
```sh
before after ratio
[6b3ba986] [124a135a]
<categorical-regression> <easier-groupby>
+ 862±30μs 1.15±0.1ms 1.34 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct')
+ 412±7μs 533±50μs 1.29 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct')
+ 300±10μs 366±10μs 1.22 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct')
+ 375±10ms 455±6ms 1.21 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation')
+ 287±6μs 335±20μs 1.17 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct')
+ 252±2ms 290±10ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+ 414±3μs 469±10μs 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'transformation')
+ 398±9ms 450±8ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct')
```
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} | 6 | 2019-11-20T22:13:18Z | 2019-11-25T23:16:18Z | 2019-11-25T23:02:43Z | MEMBER | null | 1) Annotate return type for _create_storer
2) Annotate other functions that we can reason about given 1
3) Fixups to address mypy warnings caused by 1 and 2 | {
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} | 3 | 2019-11-20T23:26:32Z | 2019-11-23T23:33:08Z | 2019-11-23T23:10:38Z | MEMBER | null | honestly not sure why ive got such a bee in my bonnet about this file | {
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] | closed | false | null | [] | null | 2 | 2019-11-21T00:19:49Z | 2020-02-17T15:23:49Z | 2020-02-17T15:23:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
Reducer()
#!/usr/bin/env python3
# coding: utf-8
# In[7]:
import pandas as pd
import sys
import re
data=pd.read_csv(sys.stdin,names=["LOCATION","Value"],dtype=['str','float64'])
data["Value"]=data["Value"].str.strip()
#data1=pd.DataFrame(data,columns=['LOCATION','Value'],dtype=['str','float'])
#data["Value"]=data.Value.astype("float64")
#data["Value"]=data["Value"].str.astype("float64")
#data["Value"]=float(data["Value"])
#data["Value"]=data["Value"].astype(float)
#s=pd.Series(creditsa['Value'])
#pd.to_numeric(s)
#df1=pd.DataFrame(data=s,columns=['Value'])
#df1=data.groupby(["LOCATION"])["Value"].mean()
print(data.info())
```
#### Problem description
unable to convert datatype of column Value to float. I have tried several possible ways of doing it(commented in above code) but I am getting below error. Can you please let me know what is the problem here.
File "/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/common.py", line 2050, in pandas_dtype
npdtype = np.dtype(dtype)
TypeError: data type not understood
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/hadoop/reducer.py", line 13, in <module>
data=pd.read_csv(sys.stdin,names=["LOCATION","Value"],dtype=['str','float64'])
File "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py", line 685, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py", line 457, in _read
parser = TextFileReader(fp_or_buf, **kwds)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py", line 1135, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py", line 1917, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 493, in pandas._libs.parsers.TextReader.__cinit__
File "/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/common.py", line 2054, in pandas_dtype
raise TypeError("data type not understood")
TypeError: data type not understood
#### Expected Output
dtype of Value column should be float
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 1 | 2019-11-21T00:52:57Z | 2019-11-22T17:02:12Z | 2019-11-22T17:02:09Z | CONTRIBUTOR | null | - [x] closes #20410
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-11-21T01:05:23Z | 2019-11-30T01:14:42Z | 2019-11-25T22:50:54Z | CONTRIBUTOR | null | - [x] closes #21096
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"d... | closed | false | null | [] | null | 4 | 2019-11-21T01:16:05Z | 2020-01-22T13:25:02Z | 2020-01-22T13:25:02Z | CONTRIBUTOR | null | - [x] closes #19974
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-11-21T01:29:57Z | 2019-11-23T23:04:51Z | 2019-11-23T23:04:42Z | CONTRIBUTOR | null | - [x] closes #19437
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} | 2 | 2019-11-21T03:59:38Z | 2020-09-05T03:18:12Z | 2020-09-05T03:18:12Z | MEMBER | null | Just as a follow up to #29124 discovered during #29753 - looks like the column name is dropped for any/all and transformation functions in groupby
```python
>>> df = pd.DataFrame([[1]], columns=pd.Index(["a"], name="idx"))
>>> df.groupby([1]).sum()
idx a
1 1
>>> df.groupby([1]).any()
a
1 True
>>> df.groupby([1]).shift()
a
0 NaN
``` | {
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} | 1 | 2019-11-21T04:14:30Z | 2019-11-21T15:44:45Z | 2019-11-21T13:31:20Z | MEMBER | null | Make things in this file incrementally less stateful | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29767 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29767/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29767/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29767/events | https://github.com/pandas-dev/pandas/pull/29767 | 526,381,103 | MDExOlB1bGxSZXF1ZXN0MzQzODU3MDU0 | 29,767 | Reenabled no-unused-function in setup.py | {
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} | 2 | 2019-11-21T05:55:15Z | 2019-11-22T15:33:21Z | 2019-11-22T15:33:17Z | MEMBER | null | This enables a flag that was disabled 4 years ago in https://github.com/pandas-dev/pandas/commit/e9fde88c47aab023cafa6c98d5654db9dc89197f but maybe worth reconsidering, even though we don't fail on warnings for extensions in CI
This yielded the following on master:
```sh
pandas/_libs/hashing.c:25313:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
pandas/_libs/hashing.c:25318:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
pandas/_libs/hashtable.c:63342:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
pandas/_libs/hashtable.c:63347:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
pandas/_libs/parsers.c:21453:18: warning: unused function '__pyx_f_6pandas_5_libs_7parsers__string_box_factorize' [-Wunused-function]
pandas/_libs/tslibs/conversion.c:33247:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
pandas/_libs/tslibs/conversion.c:33252:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
pandas/_libs/tslibs/nattype.c:3496:18: warning: unused function '__pyx_pw_6pandas_5_libs_6tslibs_7nattype_4_NaT_1__hash__' [-Wunused-function]
pandas/_libs/src/ujson/python/objToJSON.c:403:14: warning: unused function 'PyIntToINT64' [-Wunused-function]
```
I'm not sure what the `memview_set_object` and `memview_get_object` complaints are coming from just yet, but the rest are cleaned up in this PR | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29768 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29768/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29768/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29768/events | https://github.com/pandas-dev/pandas/issues/29768 | 526,386,620 | MDU6SXNzdWU1MjYzODY2MjA= | 29,768 | CI: Action to assign issues fails when quotes are present | {
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... | null | 8 | 2019-11-21T06:12:16Z | 2020-01-25T03:17:39Z | 2020-01-25T03:17:39Z | CONTRIBUTOR | null | So, this is a little weird. Apparently I left such a bad comment on an issue, it failed CI 😄.
I'm not sure how it got triggered, but looks like it's from a recently added workflow. The CI error has the contents of my comment, and I got an email that looks like this:
<details>
<summary> Notification email </summary>
------------
Run failed for master (6d11aa8)
Repository: pandas-dev/pandas
Workflow: Assign
Duration: 16.0 seconds
Finished: 2019-11-21 05:41:26 UTC
[View results](https://github.com/pandas-dev/pandas/commit/6d11aa89fc06ebc55519507f6652dcfcba35a302/checks)
Jobs:
[cb4db6dd-e658-55b7-b337-8761517bc1a3](https://github.com/pandas-dev/pandas/runs/313172123) failed (1 annotation)
—
You are receiving this because this workflow ran on your branch.
Manage your GitHub Actions notifications here.
-------------------
</details>
* [CI failure](https://github.com/pandas-dev/pandas/runs/313172123) - [Raw log](https://pipelines.actions.githubusercontent.com/xZyE9jtmkxWlfCAbyu1SHPJOlsa2huNFYcxohSTomy6EbdNZT9/_apis/pipelines/1/runs/857/signedlogcontent/3?urlExpires=2019-11-21T06%3A00%3A03.3232502Z&urlSigningMethod=HMACV1&urlSignature=vlq2JBJLcoJ3pFQnugH8M9AcGnopXrvuooUOaeU2eAA%3D)
* [The comment](https://github.com/pandas-dev/pandas/issues/14177)
Looks like this might be related to #29648, @datapythonista? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29769 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29769/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29769/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29769/events | https://github.com/pandas-dev/pandas/issues/29769 | 526,501,363 | MDU6SXNzdWU1MjY1MDEzNjM= | 29,769 | sort_values order ignored when sorting multiple columns | {
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} | [] | closed | false | null | [] | null | 2 | 2019-11-21T10:27:13Z | 2019-11-22T11:01:04Z | 2019-11-22T11:01:03Z | NONE | null | I am having issues sorting multiple columns in reverse order:
```
x_vals = [0, 0, 2, 0, 1, 0, 0, 1, 1, 1]
y_vals = [1, 2, 2, 2, 2, 0, 2, 1, 1, 2]
df = pd.DataFrame({'x': x_vals,
'y': y_vals})
df0 = df.sort_values(['y', 'x'])
df1 = df.sort_values(['y', 'x'], ascending=['False', 'True'])
df2 = df.sort_values(['y', 'x'], ascending=['False', 'False'])
df3 = df.sort_values(['y', 'x'], ascending=['True', 'False'])
print(np.all(df0 == df1),
np.all(df1 == df2),
np.all(df2 == df3))
```
returns `True True True`, e. g. I cannot manage to sort by `'y'` in reverse order. I would expect the comparisons to return `False False False`. The columns dataframes are always sorted in ascending order.
If I do `df.sort_values('y', ascending=False)` then it works as expected and the y values are sorted in descending order.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.6.0.post20191101
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : 0.13.0
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29770/events | https://github.com/pandas-dev/pandas/issues/29770 | 526,512,272 | MDU6SXNzdWU1MjY1MTIyNzI= | 29,770 | DataFrame.to_csv(quoting=csv.QUOTE_NONNUMERIC) quotes numeric values for dot separators | {
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} | [] | closed | false | null | [] | null | 1 | 2019-11-21T10:46:26Z | 2019-11-21T10:54:30Z | 2019-11-21T10:54:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
def test_pandas():
import csv
import pandas as pd
import numpy as np
df = pd.DataFrame.from_dict({'column': [1.0, 2.0]})
assert df['column'].dtype == np.dtype('float')
outpath = 'out.csv'
df.to_csv(outpath, quoting=csv.QUOTE_NONNUMERIC, sep=".", index=False) # This fails
# df.to_csv(outpath, quoting=csv.QUOTE_NONNUMERIC, sep="!", index=False) # This is fine
# df.to_csv(outpath, quoting=csv.QUOTE_NONNUMERIC, sep=":", index=False) # This is fine
# df.to_csv(outpath, quoting=csv.QUOTE_NONNUMERIC, sep=",", index=False) # This is fine
with open(outpath,"r") as f:
f.seek(0)
lines = f.read().splitlines()
assert lines[0] == '"column"'
assert not lines[1].startswith('"') # <--- 'sep="."' crashes here
assert [1, 2] == list(map(float, lines[1:]))
test_pandas()
```
#### Problem description
Using a "." as a separator causes QUOTE_NONNUMERIC to quote floats, even if there is only one column.
Explicitly stating other separators (I tried a couple) is fine.
#### Expected Output
The test above should pass.
#### 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 : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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"description": "read_csv... | open | false | null | [] | null | 11 | 2019-11-21T12:29:59Z | 2020-05-07T03:53:52Z | null | NONE | null | The two most common ways of creating a new dataframe use different parameters for taking a list of column names as input.
In pd.read_csv, the syntax is:
`pandas.read_csv(filepath, sep=',', delimiter=None, header='infer', names=None)`
In pd.DataFrame, the syntax is:
`pandas.DataFrame(data=None, index=None, columns=None)`
And then again, in df.to_csv, we use the name `columns`.
`df.to_csv(self, path_or_buf=None, sep=', ', na_rep='', float_format=None, columns=None)`
Can they both be changed to `columns`? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29772/events | https://github.com/pandas-dev/pandas/issues/29772 | 526,574,374 | MDU6SXNzdWU1MjY1NzQzNzQ= | 29,772 | Aggregation (sum) fails for a grouping along a multi-level column in pandas >=0.25.x | {
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} | 2 | 2019-11-21T12:49:33Z | 2019-11-27T18:39:48Z | 2019-11-27T18:39:48Z | NONE | null | #### Code Sample
Find the dataset [`bug.csv` here](https://github.com/pandas-dev/pandas/files/3874729/bug.csv.zip)
```python
import pandas as pd
df = pd.read_csv("bug.csv", header=[0,1], index_col=[0])
g = df.groupby(level="property", axis=1)
thisFails = g.sum()
print(thisFails)
```
The bug possibly applies to multi-level _indices_ as well (not just headers) - haven't checked it though.
#### Problem description
A groupby object `g` fails at aggregating the sum if `g` was created on a df with a multi-level header, with grouping along one of the column levels.
The code used to work for pandas 0.24.2 and 0.23.4. But it fails for pandas 0.25.x (0.25.3 as of writing this).
On failure, the following exception occurs:
```
Traceback (most recent call last):
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'a'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "bug.py", line 5, in <module>
thisFails = g.sum()
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/groupby/groupby.py", line 1382, in f
result[col] = self._try_cast(result[col], self.obj[col])
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/frame.py", line 2994, in __getitem__
return self._getitem_multilevel(key)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/frame.py", line 3043, in _getitem_multilevel
loc = self.columns.get_loc(key)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 2674, in get_loc
loc = self._get_level_indexer(key, level=0)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexes/multi.py", line 2939, in _get_level_indexer
code = level_index.get_loc(key)
File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/pandas/core/indexes/base.py", line 2899, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'a'
```
#### Expected Output
```
property a b c d e f
dataset
case 0 0.0 2.0 0.0 0.0 1.0 0.0
case 1 3.0 3.0 1.0 1.0 0.0 1.0
case 2 1.0 1.0 0.0 0.0 0.0 1.0
case 3 2.0 3.0 0.0 0.0 0.0 0.0
case 4 2.0 0.0 0.0 0.0 1.0 0.0
case 5 2.0 0.0 0.0 0.0 1.0 0.0
case 6 2.0 0.0 0.0 0.0 1.0 0.0
case 7 0.0 0.0 0.0 0.0 1.0 0.0
case 8 0.0 0.0 0.0 0.0 0.0 1.0
case 9 3.0 1.0 0.0 1.0 0.0 0.0
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.3
numpy : 1.17.2
pytz : 2018.4
dateutil : 2.7.2
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.1.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 7 | 2019-11-21T14:59:08Z | 2020-10-17T00:07:07Z | 2020-10-17T00:07:07Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Timedelta("P0DT1H")
Timedelta('0 days 00:00:00')
```
and
```python
pd.Timedelta("PT1H")
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_timedelta_unit()
KeyError: ''
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-5-35a9c7000b3e> in <module>()
----> 1 pd.Timedelta("PT1H")
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.Timedelta.__new__()
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_iso_format_string()
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.timedelta_from_spec()
pandas/_libs/tslibs/timedeltas.pyx in pandas._libs.tslibs.timedeltas.parse_timedelta_unit()
ValueError: invalid unit abbreviation:
```
#### Problem description
The ISO strings should be parsed correctly and provide valid output
as described [here](https://github.com/pandas-dev/pandas/pull/15136) and shown [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timedeltas.html)
#### Expected Output
First example and second example should have output
```
Timedelta('0 days 01:00:00')
```
#### Output of ``pd.show_versions()``
I had the same outputs locally and on google cola
<details><summary>Versions</summary>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 5.5.0
pandas_datareader: 0.7.4
bs4 : 4.6.3
bottleneck : 1.3.0
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.4.4
xarray : 0.11.3
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
```
</details>
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"description": "Extendin... | open | false | null | [] | null | 5 | 2019-11-21T15:22:52Z | 2021-07-23T04:58:14Z | null | CONTRIBUTOR | null | Right now, pandas let's you register your extension dtype. This does a few things, but the primary one is letting find your dtype given a string alias provided by the user.
An unfortunate consequence is that
```python
pd.array([1, 2], dtype="mydtype")
```
may fail, while
```python
import myarray # registers the dtype as a side-effect
pd.array([1, 2], dtype="mydtype")
```
succeeds. We might consider adding an entrypoint.
```python
# Key is the string alias, value is the path to your Dtype.
"pandas_extension_dtypes": []
```
and 3rd parties register to it with
```python
"pandas_extension_dtypes": ["mydtype = myarray:MyDtype"]
```
This avoids any awkard import-time side effects.
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ref https://github.com/pandas-dev/pandas/issues/29547 | {
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I searched for this among open issues but couldn't find it. Apologies in advance if I overlooked an existing issue, resolved or otherwise.
```python
Python 3.7.4 (default, Sep 7 2019, 18:29:04)
[Clang 10.0.0 (clang-1000.11.45.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import datetime
>>> import pandas as pd
>>> dt = datetime.datetime(1970, 1, 1)
>>> pdt = pd.Timestamp(1970, 1, 1)
>>> dt.timestamp() == pdt.timestamp() # I expected this to be True
False
>>> dt.timestamp() # I am in PST, hence the 8-hour offset from 0
28800.0
>>> pdt.timestamp()
0.0
```
[Python3 docs explain](https://docs.python.org/3/library/datetime.html#datetime.datetime.timestamp) that datetime.datetime.timestamp() assume local time if no tzinfo is specified: "Naive datetime instances are assumed to represent local time and this method relies on the platform C mktime() function to perform the conversion."
#### Problem description
I expected the behavior of pd.Timestamp.timestamp to match that of the stdlib (datetime.datetime.stdlib). Instead, pd.Timestamp.timestamp appears to treat naive timestamps differently.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 40.8.0
Cython : 0.29.13
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29780/events | https://github.com/pandas-dev/pandas/issues/29780 | 526,821,038 | MDU6SXNzdWU1MjY4MjEwMzg= | 29,780 | df.to_hdf(), HDF5 files containing strings generated by pandas are not usable by other software | {
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} | [] | closed | false | null | [] | null | 2 | 2019-11-21T20:22:02Z | 2019-11-22T01:13:49Z | 2019-11-22T01:11:56Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame()
df = pd.read_csv(csv_file)
df.to_hdf('example.h5', '/data', complib='zlib', complevel=9)
Example csv file:
| | col1 | col2 |
| 0 | hello | the |
| 1 | world | computer |
| 2 | lol | is |
```
#### Problem description
**HDF5 files containing strings generated by pandas are not usable by software other than pandas**
When writing a csv file containing strings to a hdf5 file, the output is unreadable by other software because it outputs python code representation of the strings.
#### Step to reproduce
* Make a .csv file with the table I provided
* run the code I have provided
* use hdfviewer to see the dataset
* copy and paste the output:
<details>
<summary>output</summary>
(128, 4, 149, 185, 0, 0, 0, 0, 0, 0, 0, 140, 21, 110, 117, 109, 112, 121, 46, 99, 111, 114, 101, 46, 109, 117, 108, 116, 105, 97, 114, 114, 97, 121, 148, 140, 12, 95, 114, 101, 99, 111, 110, 115, 116, 114, 117, 99, 116, 148, 147, 148, 140, 5, 110, 117, 109, 112, 121, 148, 140, 7, 110, 100, 97, 114, 114, 97, 121, 148, 147, 148, 75, 0, 133, 148, 67, 1, 98, 148, 135, 148, 82, 148, 40, 75, 1, 75, 3, 75, 2, 134, 148, 104, 3, 140, 5, 100, 116, 121, 112, 101, 148, 147, 148, 140, 2, 79, 52, 148, 75, 0, 75, 1, 135, 148, 82, 148, 40, 75, 3, 140, 1, 124, 148, 78, 78, 78, 74, 255, 255, 255, 255, 74, 255, 255, 255, 255, 75, 63, 116, 148, 98, 136, 93, 148, 40, 140, 5, 104, 101, 108, 108, 111, 148, 140, 3, 116, 104, 101, 148, 140, 5, 119, 111, 114, 108, 100, 148, 140, 8, 99, 111, 109, 112, 117, 116, 101, 114, 148, 140, 3, 108, 111, 108, 148, 140, 2, 105, 115, 148, 101, 116, 148, 98, 46)
</details>
* run the output through an ascii to text generator
* here's the final result: `¹numpy.core.multiarray _reconstructnumpyndarrayK
CbR(KKKhdtypeO4KKR(K|NNNJÿÿÿÿJÿÿÿÿK?tb](hellotheworldcomputerlolisetb`
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} | 1 | 2019-11-21T20:37:55Z | 2019-11-21T23:31:34Z | 2019-11-21T22:46:04Z | CONTRIBUTOR | null | Changed the string formatting in pandas/plotting/*.py to f-strings.
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
ref #29547 | {
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} | 3 | 2019-11-21T22:02:34Z | 2019-11-22T15:26:53Z | 2019-11-22T15:26:49Z | MEMBER | null | Follow up of #29678
@jreback I think `show_versions` gets the version by importing the module. Correct me if I'm wrong, but I didn't see anything to update there. | {
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} | 2 | 2019-11-21T22:04:20Z | 2019-11-22T15:25:56Z | 2019-11-22T15:25:53Z | MEMBER | null | We use `pytest-xdist>=1.21` in all builds except this one. Using the same for the 3.8 build, which was introduced in parallel as the standardization of dependencies.
xref: https://github.com/pandas-dev/pandas/pull/29678#discussion_r348873095 | {
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} | 10 | 2019-11-22T07:58:44Z | 2020-01-02T01:35:39Z | 2020-01-02T01:35:32Z | CONTRIBUTOR | null | - [x] closes #29733 (partly, look at my issue comment for details on why it won't exactly work on the issue's example)
- [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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"default"... | open | false | null | [] | null | 0 | 2019-11-22T10:46:31Z | 2021-07-23T04:59:03Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import time
df_time = pd.DataFrame(np.random.randint(low=0, high=20, size=(1000, 4)), pd.date_range(start='2000-01-01', end='2000-01-02', periods=1000), columns=['a', 'b', 'c', 'd'])
time0 = time.time()
df_time.resample('1s').last()
print('resampling the datetime64[ns] data frame took {} seconds'.format(time.time()-time0))
df_time.loc[:,'a'] = df_time['a'].astype('category')
time0 = time.time()
df_time.resample('1s').last()
print('resampling the datetime64[ns] data frame took {} seconds'.format(time.time()-time0))
```
resampling the df containing integers took 0.007719516754150391 seconds
resampling the df containing categoricals took 38.36969518661499 seconds
#### Problem description
As code above shows resampling df containing categoricals is super slow for aggregation .last(). Same behaviour occurs for .first(), whereas .mean() is performing fine.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1052-aws
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : 5.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-11-22T11:41:03Z | 2019-11-22T12:03:31Z | 2019-11-22T12:03:13Z | NONE | null | `to_pickle` supports only file names, not file handles. See: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_pickle.html
Is there some particular reason for this? `to_csv` does support file handles so I would have expected `to_pickle` to support too.
To reproduce:
```
>>> with open("tmp.p", 'w') as fp:
... pd.Series([1,2,3]).to_pickle(fp)
ValueError: Unrecognized compression type: infer
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} | 2 | 2019-11-22T14:40:07Z | 2020-12-21T10:08:06Z | 2019-12-02T17:38:49Z | CONTRIBUTOR | null | Currently `pd.array` sometimes requires an explicit `dtype=...` to get one of our extension arrays (we'll infer for Period, Datetime, and Interval).
This proposal is to have it infer the extension type for
* strings -> StringArray
* boolean -> BooleanArray
* integer -> IntegerArray
All of these currently return PandasArray.
Concretely, we'll need to teach `infer_dtype` how not to infer `mixed` for a mix of strings / booleans and NA values, similar to how it handles integer-na
```python
In [27]: lib.infer_dtype([True, None], skipna=False)
Out[27]: 'mixed'
In [28]: lib.infer_dtype(['a', None], skipna=False)
Out[28]: 'mixed'
In [29]: lib.infer_dtype([0, np.nan], skipna=False)
Out[29]: 'integer-na'
```
and then handle those in `array`. | {
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} | 3 | 2019-11-22T16:13:16Z | 2019-12-20T01:03:10Z | 2019-11-23T23:10:01Z | MEMBER | null | - [X] closes #29718
- [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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} | 13 | 2019-11-22T19:40:57Z | 2020-11-17T18:07:53Z | 2019-12-02T17:38:49Z | CONTRIBUTOR | null | * string
* integer
Closes #29791 (though we'll want to add BooleanArray). | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-11-22T20:44:36Z | 2019-11-25T23:17:16Z | 2019-11-25T23:13:59Z | MEMBER | null | I think asobject should have been done as part of #29720. | {
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} | 2 | 2019-11-22T20:53:42Z | 2019-11-29T23:35:38Z | 2019-11-29T23:31:46Z | MEMBER | null | I'm less confident in this one than in others. Some of the affected tests seem to involve non-missing labels in duplicate-containing indexes. Did I conflate multiple issues/deprecations? | {
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... | open | false | null | [] | null | 7 | 2019-11-22T21:09:08Z | 2021-02-18T13:10:07Z | null | NONE | null | ```python
import pandas as pd
data = pd.read_excel('excel_file.xlsx', engine='openpyxl')
```
#### Problem description:
After running the above code in console, I switch to Excel and try to save 'excel_file.xlsx'. Excel raises an access error.
I tried to figure out if the file remains open using file descriptor. As output shows, it does:
```python
import os
import pandas as pd
xl_file_stat = os.stat(os.path.relpath('excel_file.xlsx'))
try:
os.fstat(3)
except OSError:
print('File is not open')
print('Reading excel file...')
data = pd.read_excel('excel_file.xlsx', engine='openpyxl')
print('File has been read')
if os.fstat(3) == xl_file_stat:
print('File is open')
```
##### Output:
```
File is not open
Reading excel file...
File has been read
File is open
```
The problem may arise from the openpyxl's class `ExcelReader`. Its' `read()` method closes the archive if read-only attribute is set to False (openpyxl/reader/excel.py, rows 281-282):
```
if not self.read_only:
self.archive.close()
```
I'm not sure if it's a normal behavior of `ExcelReader`, but I expect `pd.read_excel()` to close the file in this case.
<details>
pandas: 0.25.3<br>
openpyxl: 3.0.0
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} | [] | closed | false | null | [] | null | 4 | 2019-11-22T23:20:52Z | 2019-11-27T08:55:31Z | 2019-11-27T08:55:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.DataFrame([
{'A':'a', 'B':'b'}
]).groupby(
['A','B']
).apply(
lambda x: x.to_dict(orient='records')
).to_json()
```
produces an invalid JSON string:
```python
'{"["a","b"]":[{"A":"a","B":"b"}]}'
```
#### Problem description
For the given transformation, the output of `to_json` of a `pandas.core.series.Series` is not a valid JSON object.
Would be nice if it was.
#### Expected Output
```python
'{"[\'a\',\'b\']":[{"A":"a","B":"b"}]}'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.84
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : None
setuptools : None
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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} | 1 | 2019-11-23T00:11:15Z | 2019-11-24T16:43:54Z | 2019-11-24T08:24:05Z | MEMBER | null | Broken off from #29725 since that has tricky mypy errors | {
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} | 1 | 2019-11-23T01:38:34Z | 2019-11-25T23:16:43Z | 2019-11-25T23:08:22Z | MEMBER | null | also removed the `inferred_type == "unicode"` branch, as `lib.infer_dtype` never returns unicode anymore
- [x] closes #19475
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https://api.github.com/repos/pandas-dev/pandas/issues/29813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29813/events | https://github.com/pandas-dev/pandas/issues/29813 | 527,525,211 | MDU6SXNzdWU1Mjc1MjUyMTE= | 29,813 | Series.replace not working on slices of heterogenoues types | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 4 | 2019-11-23T07:51:32Z | 2021-04-20T14:11:35Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
c = pd.Series([
np.nan,
1,
"hello",
])
c_replaced_1 = c[0:3].replace({np.nan: None})
c_replaced_2 = c[0:2].replace({np.nan: None})
print(c_replaced_1)
print(c_replaced_2)
```
#### Problem description
`Series.replace` does not work in the second case. In both cases one would expect that the `np.nan` gets replaced by `None`. However, the replace fails in the second case.
The actual output is:
```
0 None
1 1
2 hello
dtype: object
0 NaN
1 1.0
dtype: float64
```
#### Expected Output
```
0 None
1 1
2 hello
dtype: object
0 None
1 1.0
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.15.0-70-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.3
setuptools: 41.2.0
Cython: None
numpy: 1.15.2
scipy: 1.2.2
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: 1.1.2
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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