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https://api.github.com/repos/pandas-dev/pandas/issues/26813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26813/events | https://github.com/pandas-dev/pandas/issues/26813 | 455,300,726 | MDU6SXNzdWU0NTUzMDA3MjY= | 26,813 | BadZipFile error when using read_excel on .xlsx | {
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} | [] | closed | false | null | [] | null | 12 | 2019-06-12T16:06:14Z | 2021-04-27T16:25:21Z | 2019-06-13T10:27:14Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
UKregions = pd.read_excel(r"K:\Sport\Sponsors\Lookups.xlsx",sheet_name="UKregions")
```
#### Problem description
```
Traceback (most recent call last):
File "<ipython-input-3-acf52be7bb80>", line 1, in <module>
UKregions0 = pd.read_excel(r"K:\Sport\Sponsors\Adidas\2018 - PSOV\Lookups.xlsx",sheet_name="UKregions")
File "E:\ANACONDA\lib\site-packages\pandas\util\_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "E:\ANACONDA\lib\site-packages\pandas\util\_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "E:\ANACONDA\lib\site-packages\pandas\io\excel.py", line 350, in read_excel
io = ExcelFile(io, engine=engine)
File "E:\ANACONDA\lib\site-packages\pandas\io\excel.py", line 653, in __init__
self._reader = self._engines[engine](self._io)
File "E:\ANACONDA\lib\site-packages\pandas\io\excel.py", line 424, in __init__
self.book = xlrd.open_workbook(filepath_or_buffer)
File "E:\ANACONDA\lib\site-packages\xlrd\__init__.py", line 117, in open_workbook
zf = zipfile.ZipFile(filename)
File "E:\ANACONDA\lib\zipfile.py", line 1131, in __init__
self._RealGetContents()
File "E:\ANACONDA\lib\zipfile.py", line 1198, in _RealGetContents
raise BadZipFile("File is not a zip file")
BadZipFile: File is not a zip file
```
#### Expected Output
A pandas DataFrame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: 4.6.2
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.10
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.4
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-06-12T18:08:02Z | 2019-06-21T01:48:37Z | 2019-06-21T01:48:37Z | MEMBER | null | ref this comment https://github.com/pandas-dev/pandas/pull/26802#discussion_r293045993 we currently duplicate code in managing versions between the pandas.compat._optional and pandas.util._print_versions modules which we should align | {
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} | 12 | 2019-06-12T18:12:48Z | 2019-10-07T12:36:50Z | 2019-10-07T12:36:49Z | MEMBER | null | - [ ] closes #23820
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-06-12T19:00:35Z | 2019-06-21T01:48:48Z | 2019-06-21T01:48:37Z | CONTRIBUTOR | null | Closes #26814
The old version had special cases for numpy, scipy, and pytz. All those now provide a standard `__version__`. xlrd and xlwt are the only special ones now (`__version__`)
I messed up the minimum xlwt version in the last PR. I copied openpyxl's rather than xlwt's. That's now fixed.
I prettied up the output format a bit, to align the version numbers. Hope that 's OK.
```
INSTALLED VERSIONS
------------------
commit : cbc6edf8bfad33c8bc698a3d035b593d921b0112
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0.dev0+729.gcbc6edf8b
pytest : 4.6.2
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
numpy : 1.17.0.dev0+5b06588
scipy : 1.3.0
pyarrow : 0.13.0
xarray : 0.12.1
IPython : 7.5.0
sphinx : 1.8.5
patsy : 0.5.1
dateutil : 2.7.3
pytz : 2018.5
blosc : 1.5.1
bottleneck : 1.2.1
tables : 3.5.1
numexpr : 2.6.8
feather : 0.4.0
matplotlib : 3.1.0
openpyxl : 2.5.11
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
bs4 : 4.7.1
html5lib : 1.0.1
sqlalchemy : 1.2.14
pymysql : 0.9.2
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
s3fs : 0.1.6
fastparquet : None
pandas_gbq : 0.10.0
pandas_datareader: None
gcsfs : 0.1.2
``` | {
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] | closed | false | null | [] | null | 30 | 2019-06-12T19:09:22Z | 2019-07-31T16:19:47Z | 2019-07-02T20:25:45Z | NONE | null | - [x] closes #26730
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [ ] Happy with approach
Note that the change to a `np.round(self.array)` call was chosen so that
- it works for numeric ndarrays
- it works for EA's which implements `__array__` and return a numeric ndarray
- once numpy 1.17 is out and NEP-18 support is enabled by default, it will enable
EA authors to implement round functionality via the `__array_function__` protocol.
**Update**:
This has become a test case on supporting a wider range of numpy functions for EA.
Here a some of the issues encounterd, several are indicative of the issues EA authors will encounter when trying to do the same:
1. Calling numpy function may require a roundtrip through an `object` dtype, so results need to be
cast to the extension type. Example: `np.repeat`
2. Converting EA to a numpy array, if using NEP-18, means inspecting all the arguments looking for EA passed in, once you find them you need to coerce them, they be an EA of a different class, so you only know about them what's available through the EA protocol. Example: `pd/np.concatenate`
3. ExtentionType discovery: discovering whether an EA has is essentially numeric, retrieving that representation, finding the best common dtype for mixed dtypes. Your EA may need to intelligently coerce an EA of another dtype. and cast back the result to the best dtype. Example: `pd/np.concatenate`
4. Once you use NEP-18, numpy ignores `__array__()` even if available, so there's no transparent casting to object.
5.`ExtensionArray` ABC doesn't offer a public API for requesting the backing numpy array of an EA, explicitly asking for numeric version (if applicable), or object dtype. [hgrecco/pint-pandas](https://github.com/hgrecco/pint-pandas) implements a [`_ndarray_values`](https://github.com/hgrecco/pint-pandas/blob/2cf891b63be778496e732f1ff9129eea6f38be73/pintpandas/pint_array.py#L51) method for `PintArray`, because you always need one (just as Series has `.values` and now `.array`), but it's not part of the EA base class and, for mixed-dtype cases, you need such a method when you dealing with a foreign EA.
5. The `Decimal` EA example defines a whole slew of synonyms for `self._data`, `data`, `_values`, `_items` for "pandas compatibility", these attributes have ill-defined or undefined semantics, leading to the same confusing situation () of `Series` having `.values` `_data` and ,`.get_values`, `.data`, and now `.array`. Seems like repeating old mistakes again.
6. If such aliases are **necessary** for pandas to recognize, they should defined and made part of the ABC.
7. aliases should not be attributes `self.foo=self._foo = values`, because then assigning to one does not update the other, and its not clear where the "actual" data is. Instead should use properties which lookup from the single, true, attribute which holds the data. I had a bug related to this because I set `_data` on a copy instead of calling a constructor. safety first.
8. another case: suppose you wish to convert the dtype of the EA's underlying numeric ndarray from float to int. there is no `transform` like method provided for the user to do that without reaching in and touching private attributes of the EA. Example, a `PintArray` with units, which you want to `round()` and convert to internal dtype. The ExtensionType doesn't change, but the underlying numeric subtype does. For example `pint[grams] (with float64 magnitudes) -> pint[grams] (with int64 magnitudes) ` .
Update:
1. EA authors are forces to implement 3 (!) separate dispatch function to handle common functions. `__array_ufunc__` for ops `np.floor`, '__array_function__` for ops like `round`, and `_reduce` for ops like sum/min/max, which pandas doesn't call numpy fo (instead calling `pd.core.nanops`), and has incompatible signatures with. | {
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} | 14 | 2019-06-12T20:05:26Z | 2019-06-21T16:09:46Z | 2019-06-21T14:19:43Z | CONTRIBUTOR | null | - [x] closes #11188
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2019-06-12T20:56:14Z | 2019-06-14T15:54:01Z | 2019-06-14T14:22:42Z | CONTRIBUTOR | null | ``util._decorators.make_signature`` is only used in ``core.groupby.base.whitelist_method_generator``.
However, in that function it is only used inside an if statement, that is conditional upon an attribute being a method. That however can not happen, as the function parameter only takes classes as an input, so that if-statement is never true and its content never executed. The net effect is that a nice chunk of code can just be removed, which is nice.
In the future, we should just use jus use ``inspect.signature`` or ``inspect.getfullargspecs`` for signature inspection. | {
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} | 4 | 2019-06-12T21:12:05Z | 2019-06-13T00:00:15Z | 2019-06-13T00:00:11Z | MEMBER | null | I must have been a little quick in merging #26802 as it looks like this is causing mypy failures in CI.
Most of these are pretty niche and I think a result of import machinery. Will try to dive deeper later but this should get things green for now | {
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} | [] | closed | false | null | [] | null | 4 | 2019-06-12T22:27:41Z | 2019-06-13T17:24:05Z | 2019-06-12T22:38:27Z | CONTRIBUTOR | null | ```python
df = pd.DataFrame([1,2,3],
pd.MultiIndex.from_arrays([range(3)]))
with pd.HDFStore('test.h5') as store:
store.put('df', df, 't')
```
```python
TypeError Traceback (most recent call last)
<ipython-input-46-1057478f5fa5> in <module>
2 pd.MultiIndex.from_arrays([[1,2,3]]))
3 with pd.HDFStore('test.h5') as store:
----> 4 store.put('df', df, 't')
~/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py in put(self, key, value, format, append, **kwargs)
887 format = get_option("io.hdf.default_format") or 'fixed'
888 kwargs = self._validate_format(format, kwargs)
--> 889 self._write_to_group(key, value, append=append, **kwargs)
890
891 def remove(self, key, where=None, start=None, stop=None):
~/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1413
1414 # write the object
-> 1415 s.write(obj=value, append=append, complib=complib, **kwargs)
1416
1417 if s.is_table and index:
~/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
4015 self.create_axes(axes=axes, obj=obj, validate=append,
4016 min_itemsize=min_itemsize,
-> 4017 **kwargs)
4018
4019 for a in self.axes:
~/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3558 name = obj._AXIS_NAMES[i]
3559 index_axes_map[i] = _convert_index(
-> 3560 a, self.encoding, self.errors, self.format_type
3561 ).set_name(name).set_axis(i)
3562 else:
~/Coding/Python/_venvs_/main/lib/python3.7/site-packages/pandas/io/pytables.py in _convert_index(index, encoding, errors, format_type)
4552
4553 if isinstance(index, MultiIndex):
-> 4554 raise TypeError('MultiIndex not supported here!')
4555
4556 inferred_type = lib.infer_dtype(index, skipna=False)
TypeError: MultiIndex not supported here!
```
Same error with `.from_product()` and `.from_tuples`. It is unexpected because at the same 2-level indexes (as example) works:
```python
df = pd.DataFrame([1,2,3],
pd.MultiIndex.from_arrays([range(3), range(3)]))
with pd.HDFStore('test.h5') as store:
store.put('df', df, 't')
```
Doesn't produce any error.
Suggestion: it rather not a problem of `HDFStore` but `.from_arrays` `.from_product`, `.from_tuples` with single array in input should produce simple index not multiindex:
```python
type(pd.MultiIndex.from_arrays([[1,2,3]]))
```
```
pandas.core.indexes.multi.MultiIndex
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-16-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: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26822/events | https://github.com/pandas-dev/pandas/issues/26822 | 455,465,806 | MDU6SXNzdWU0NTU0NjU4MDY= | 26,822 | pd.crosstab's handling of categorical variables | {
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] | closed | false | null | [] | null | 1 | 2019-06-12T23:20:20Z | 2019-06-13T02:01:33Z | 2019-06-13T02:01:28Z | NONE | null | #### Code Sample
```python
kyle = pd.DataFrame({'foo':np.random.randint(0,100,100),'boo':np.random.randint(0,100,100)})
kyle.loc[:,'fooCut'] = pd.qcut(kyle.loc[:,'foo'], np.arange(0,1.1,.1))
kyle.loc[:,'booCut'] = pd.qcut(kyle.loc[:,'boo'], np.arange(0,1.1,.1))
print(kyle.footCut.dtype)
pd.crosstab(kyle.fooCut,kyle.booCut)
```
#### Problem description
According to the documentation and previous versions of Pandas, `pd.Crosstab` handled categorical variables returned by `pd.qcut` but in version .24.2 pd.crosstab throws a TypeError even though both columns agree with the dtype presented in the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.crosstab.html)
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] | closed | false | null | [] | null | 3 | 2019-06-12T23:58:18Z | 2019-12-11T21:53:41Z | 2019-12-11T21:53:41Z | MEMBER | null | Turning this into a tracker since more are turning up;.
- [ ] api.test_api.test_api (much less frequently than the others) (OSX, Ubuntu)
- [x] frame.test_constructors.TestDataFrameConstructors.test_constructor_maskedrecarray_dtype (AttributeError: module 'numpy.ma' has no attribute 'mrecords', using numpy 1.16.4) (fixed in #26975)
- [x] frame.test_constructors.TestDataFrameConstructors.test_constructor_mrecarray (AttributeError: module 'numpy.ma' has no attribute 'mrecords', using numpy 1.16.4) (fixed in #26975)
- [x] series.indexing.test_datetime.test_index_unique (OSX) (fixed by #27004)
- [ ] resample.test_resample_api.test_select_bad_cols
- [ ] io.formats.test_format.TestDataFrameFormatting.test_repr_html_long_multiindex
- [ ] io.formats.test_format.TestDataFrameFormatting.test_info_repr_html
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} | 9 | 2019-06-13T01:29:25Z | 2021-07-10T04:23:05Z | 2021-07-10T04:23:05Z | NONE | null | #### Code
```python
import pandas as pd
df1 = pd.DataFrame([{'boro': 'B',
'from_st': 'EAST FORDHAM ROAD',
'main_st': 'DECATUR AVENUE',
'order_no': 'P-00097673',
'sos': 'W',
'to_st': 'EAST 193 STREET'}])
df2 = pd.DataFrame([{'SRP_Seq': 3,
'SR_Arrow': 'W ',
'SR_Distx': 138,
'SR_Mutcd_Code': 'PS-101BA ',
'Sign_description': 'NO PARKING (SANITATION BROOM SYMBOL) WEDNESDAY 8AM-9AM --> (SUPERSEDES R7-88-9RA)',
'Unnamed: 7': '',
'Unnamed: 8': '',
'boro': 'B',
'order_no': 'P-01287481'}])
pd.merge(df2, df1, how='left', left_on='A')
```
#### Problem description
Unexpected exception is raised when doing a left join without passing arguments for `right_on`.
#### Expected Output
```
ValueError: len(right_on) must equal len(left_on)
```
#### Actual Output
```
TypeError: object of type 'NoneType' has no len()
```
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.26-03278-g71dc68f9c9d0
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
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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} | 5 | 2019-06-13T04:10:08Z | 2019-06-27T20:40:20Z | 2019-06-27T20:39:25Z | MEMBER | null | This allows us to get rid of a bunch of xfails and FIXMEs in arithmetic tests. Changes in groupby are the most likely to need attention; this is not an area of the code I know well.
Not sure if TestTranspose belongs somewhere else. Suggestions welcome.
Split a couple of too-widely-scoped groupby tests.
Will follow-up with issues this closes, whatsnew entry, and GH references added to tests.
xref #23988
- [x] closes #13287
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26826/events | https://github.com/pandas-dev/pandas/pull/26826 | 455,580,376 | MDExOlB1bGxSZXF1ZXN0Mjg3Nzk2NjU2 | 26,826 | DOC: Fixing more doc warnings | {
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] | closed | false | null | [] | null | 2 | 2019-06-13T07:33:15Z | 2019-06-13T08:17:21Z | 2019-06-13T08:17:21Z | MEMBER | null | - [X] xref #24173
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Another set of doc warning fixes.
CC: @jorisvandenbossche | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26827 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26827/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26827/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26827/events | https://github.com/pandas-dev/pandas/issues/26827 | 455,660,322 | MDU6SXNzdWU0NTU2NjAzMjI= | 26,827 | Comparison of identical values found by LGTM.com | {
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] | closed | false | null | [] | null | 5 | 2019-06-13T10:26:22Z | 2021-06-26T02:25:31Z | 2021-06-26T02:25:31Z | CONTRIBUTOR | null | LGTM.com code analysis website found an interesting issue of comparison of identical values:
https://lgtm.com/projects/g/pandas-dev/pandas/snapshot/fe32cc08cf2e4dbdfa4aad33f3a74fada201600f/files/pandas/core/sparse/frame.py?sort=name&dir=ASC&mode=heatmap#L340
The line https://github.com/pandas-dev/pandas/blob/429078b01af47b356084c1da270b5b275fbf50f4/pandas/core/sparse/frame.py#L340
The `value == value` looks suspicious. It is there either by mistake or one was trying to check whether the value is float or not since as far as I know, the Boolean value of the comparison will always be the same, unless the value is the floating point value `float('nan')`.
If the latter, then there is a better way to do it with the `cmath.isinf(value) and not cmath.isnan(value)` to be explicit in the intention. However, maybe there is some bug in `numpy` or similar that you are trying to work around? @rockg @jreback
I wanted to check with core developers before submitting a PR. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26828 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26828/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26828/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26828/events | https://github.com/pandas-dev/pandas/issues/26828 | 455,664,259 | MDU6SXNzdWU0NTU2NjQyNTk= | 26,828 | Unreachable statement found by LGTM.com | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-13T10:35:07Z | 2019-06-13T11:08:29Z | 2019-06-13T11:08:29Z | CONTRIBUTOR | null | I see an issue that was found by LGTM.com:
https://lgtm.com/projects/g/pandas-dev/pandas/snapshot/fe32cc08cf2e4dbdfa4aad33f3a74fada201600f/files/scripts/merge-pr.py?sort=name&dir=ASC&mode=heatmap#L54
The `merge-pr` utility script, https://github.com/pandas-dev/pandas/blob/429078b01af47b356084c1da270b5b275fbf50f4/scripts/merge-pr.py, has `auth_required` variable. My understanding that unless someone edits the source code before running the script, the GitHub authentication will never happen as `auth_required` will always be `False`. Am I wrong?
https://github.com/pandas-dev/pandas/blob/429078b01af47b356084c1da270b5b275fbf50f4/scripts/merge-pr.py#L51-L55
One could perhaps add the `auth_required` as a parameter to a script (either with plain `sys.argv` or using fancier `argparse`) so that one could call the script picking how the authorization should occur instead of editing the source code file if this is what people do.
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CC: @minggli | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26830/events | https://github.com/pandas-dev/pandas/issues/26830 | 455,693,686 | MDU6SXNzdWU0NTU2OTM2ODY= | 26,830 | DOC: Make section title capitalization consistent | {
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} | 12 | 2019-06-13T11:45:51Z | 2019-06-27T21:25:02Z | 2019-06-27T21:25:02Z | MEMBER | null | At the moment we've got a mix of capitalization formats in the title sections of our docs. For example, in the first level of the TOC tree in http://dev.pandas.io/ we've got (not the second word first letter, `s` lower case vs `G` upper case):
- `Getting started`
- `User Guide`
And this happens all across the documentation. I think it makes more sense to just capitalize the first letter of the first word, like in `Getting started`. More a personal preference than any objective reason, but looks simpler, more standard, and we avoid things like `pandas Ecosystem` which doesn't look really great to me.
CC: @TomAugspurger @jorisvandenbossche | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26831/events | https://github.com/pandas-dev/pandas/issues/26831 | 455,697,784 | MDU6SXNzdWU0NTU2OTc3ODQ= | 26,831 | DOC: Discussion on new getting started page (Numfocus Grant) | {
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] | closed | false | null | [] | null | 38 | 2019-06-13T11:55:27Z | 2020-02-17T11:49:43Z | 2020-02-17T11:49:43Z | CONTRIBUTOR | null | As currently prepared together with @jorisvandenbossche, the following document is a proposal on how to adjust the getting started page and section of the documentation: https://docs.google.com/document/d/1Rc_eql5KLrdf0c582KyWfs2ADVNxbJy4jfosnqdrVak/edit?usp=sharing
The general idea is to split the current 10 minutes into 10 x 1' topics of Pandas. Input is very welcome on:
- the topics to pick and how to introduce them
- the content of the introduction tutorial linked to each of the topics | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26832/events | https://github.com/pandas-dev/pandas/pull/26832 | 455,850,204 | MDExOlB1bGxSZXF1ZXN0Mjg4MDE0NjQy | 26,832 | Bump minimum verions | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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This just adds the "class_without_autosummary". Longer term, we could choose to add certain methods of the Array API to the html api pages, but for now just wanted to do this to get rid of the warnings. | {
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```python
In [9]: import numpy as np
...: import pandas as np
...: from pandas.tests.extension.decimal.array import to_decimal
...: ser = pd.Series(to_decimal([1,2,3]))
...: ser[0]=np.nan
...: ser.count()
3
```
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} | 22 | 2019-06-13T19:22:17Z | 2021-11-20T16:16:29Z | null | MEMBER | null | see https://github.com/pandas-dev/pandas/pull/26833#issuecomment-501838802
to make `format_array` more generic we need to push more functionality to the objects to be formatted themselves (and PandasArray) rather than encoding the conversions and special casing in `format_array`. | {
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} | 8 | 2019-06-13T19:55:27Z | 2019-08-07T21:09:54Z | 2019-08-07T21:09:54Z | CONTRIBUTOR | null | I followed the contributors guide and set up the dev environment without anaconda. Generally works, except for a small problem in
```
python -m pip install -r requirements-dev.txt
```
which fails with
```
Could not find a version that satisfies the requirement pyqt
```
`pyqt` is not known on PyPI and I resolved the problem by replacing it with `pyqt5`, i.e. with the following patch:
```
From a284ac89a43237784d663b2a475eead953e8a64d Mon Sep 17 00:00:00 2001
From: Jan-Philip Gehrcke <jgehrcke@googlemail.com>
Date: Wed, 12 Jun 2019 20:04:06 +0200
Subject: [PATCH] BUG: requirements-dev: Could not find ... pyqt
As of today 'pyqt' is not a valid PyPI project.
This results in the error message
Could not find a version that satisfies the requirement pyqt
when running
python -m pip install -r requirements-dev.txt
from the contributors guide.
---
requirements-dev.txt | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 46e857f2e9e..73b591ce8f6 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -44,7 +44,7 @@ html5lib
lxml
openpyxl
pyarrow>=0.9.0
-pyqt
+pyqt5
tables>=3.4.2
python-snappy
s3fs
@@ -52,4 +52,4 @@ sqlalchemy
xarray
xlrd
xlsxwriter
-xlwt
\ No newline at end of file
+xlwt
```
But that does not pass CI because of
```
requirements-dev.txt(,): error : `requirements-dev.txt` has to be generated with `ci/../scripts/generate_pip_deps_from_conda.py` after `environment.yml` is modified.
```
From here I am "giving up" but I would at least like to leave this report. | {
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} | 8 | 2019-06-13T23:00:13Z | 2019-06-17T17:06:27Z | 2019-06-17T11:44:40Z | MEMBER | null | - [x] closes #21344
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
AFAICT we don't have any tests specifically for Timedelta.resolution, so added one for reso_str. | {
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"following_url": "https://api.github.com/users/michael-michelotti/fo... | null | 8 | 2019-06-14T01:16:56Z | 2021-07-23T01:54:14Z | null | NONE | null | For Example, fillna requires `method` kwarg to be specified. the frame method
raises an exception if it is missing, but transform silently returns an empty frame
```python
In [22]: df=pd.DataFrame(dict(price=[10,10,20,20,30,30],color=[10,10,20,20,30,30],cost=(100,200,300,400,500,600)))
...: g=df.groupby(['price'])
...: try:
...: df.fillna()
...: except:
...: print("Raised: OK")
...: try:
...: g.fillna()
...: except:
...: print("Raised: OK")
...: else:
...: print("Raised: No, Failed")
...: try:
...: g.transform('fillna')
...: except:
...: print("Raised: OK")
...: else:
...: print("Raised: No, Failed")
Raised: OK
Raised: No, Failed
Raised: No, Failed
```
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Mozilla's (let's encrypt)[https://letsencrypt.org] makes it easy to get a free cert in 5 minutes.
cc @datapythonista, | {
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"description": ... | closed | false | null | [] | null | 1 | 2019-06-14T06:43:11Z | 2021-07-10T04:32:43Z | 2021-07-10T04:32:43Z | MEMBER | null | Regularly see a failure on one of the Windows builds on Azure:
```
Worker 'gw0' crashed while running 'pandas/tests/test_sorting.py::TestSafeSort::test_labels_out_of_bound[-1]'
```
Eg https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12703&view=logs or https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12839&view=logs&jobId=077026cf-93c0-54aa-45e0-9996ba75f6f7 | {
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} | 0 | 2019-06-14T06:48:03Z | 2019-11-12T21:25:42Z | 2019-11-12T21:25:42Z | MEMBER | null | In our quest to a warning free doc build, we added some temporary okexcept/okwarning in https://github.com/pandas-dev/pandas/pull/26810:
- [ ] okwarning for parquet/feather examples should be removed once the latest version of pyarrow is used in the doc build
- [x] the okexcept in missing_data.rst (https://github.com/pandas-dev/pandas/pull/26810/files#diff-239ec95d581257ed256954660663b277R461) should be removed once https://github.com/pandas-dev/pandas/issues/26778 is fixed
- [x] the okwarning in 10min for the plotting (added in https://github.com/pandas-dev/pandas/pull/26852), which should get fixed by https://github.com/pandas-dev/pandas/pull/26770 | {
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] | closed | false | null | [] | null | 3 | 2019-06-14T07:55:48Z | 2019-06-14T15:58:54Z | 2019-06-14T15:58:54Z | NONE | null | #### Original code in documentation now
```python
data = ('col1,col2,col3\n'
...: 'a,b,1\n'
...: 'a,b,2\n'
...: 'c,d,3')
...:
```
### Code that should be added
```python
data = ''.join(data)
```
#### Explanation
In the tutorial, I found that the code doesn't work in a python terminal. A StringIO object expects to receive one string only as an input. This is why i would add the join operation in the middle (for clearness, as one can also just type one string).
Found this in https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html .
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"color": "6138b... | closed | false | null | [] | null | 16 | 2019-06-14T08:17:02Z | 2019-06-14T13:01:35Z | 2019-06-14T12:51:06Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/26833#issuecomment-502012046
@simonjayhawkins this would be a test for the `_formatter` functionality (it is passing on master before the removal of ExtensionArrayFormatter
Adds an actual test for EA._formatter + reverts https://github.com/pandas-dev/pandas/pull/26833 until we have a more complete solution for simplifying the formatters that take the EA._formatter into account. | {
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```python
df = pd.DataFrame(dict(a=['a1', 'a2', 'a3', 'a3'], b=[np.nan, np.nan, np.nan, np.nan], d=['d1', 'd1', np.nan, 'd2'], c=['c1', 'c2', 'c3', 'c4']))
df = df.set_index(keys=['b', 'a']).sort_index()
print(df.index.levels)
print(len(df.index.intersection([(np.nan, 'a1')])))
[[], ['a1', 'a2', 'a3']]
0
```
#### Problem description
MultiIndex created from column with NaN only values has an empty level. While ```contains``` and ```loc``` still work, ```intersection``` method behaves incorrectly not returning empty results.
#### Expected Output
[[np.nan], ['a1', 'a2', 'a3']]
1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-21-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: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.3
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.10.0
pandas_datareader: None
gcsfs: 0.2.2
None
</details>
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import pandas as pd
meta = pd.DataFrame(([0],), columns=['status', ])
```
#### Problem description
Not sure what the desired behaviour is in this case but above code throws a
```
Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)
```
which is not very user friendly
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: 4.4.0
pip: 19.0.3
setuptools: 39.1.0
Cython: 0.29.7
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
Python 3.7.3
MAC OS Mojave 10.14.5 (18F132) | {
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} | 14 | 2019-06-14T10:14:08Z | 2019-06-21T07:05:12Z | 2019-06-21T07:05:08Z | MEMBER | null | - [x] closes https://github.com/pandas-dev/pandas/issues/26206
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
TODO: Need to add a whatsnew and test for DataFrame.
Question:
I am now letting this raise an error. But, an alternative could be to return an object array (by simply removing the `except OutOfBoundsDatetime: raise`, as the code below that fall backs to object array if no `dtype` is specified.
This would be more consistent with if it is not an array but list of scalars:
```
In [5]: a = np.array(['2262-04-12'], dtype='datetime64[s]')
In [6]: pd.Series(a) # now raising on this PR
...
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-04-12 00:00:00
In [7]: pd.Series([a[0]]) # already falling back to object array on master
Out[7]:
0 2262-04-12T00:00:00
dtype: object
```
That is somewhat the general logic of the `_try_cast`. Of course, if you specify the required dtype explicitly, you should still get an error (which is actually also a bug on master, I see now).
However, that would be inconsistent with the current `pd.Index` behaviour, which raises when passing out of bounds datetime64 (but also returns object dtype with list of datetime64 scalars)
cc @jbrockmendel @mroeschke
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Let's wait for the logs, tested locally only part of them, but this should fix most of the remaining warnings.
CC: @jorisvandenbossche | {
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] | closed | false | null | [] | null | 2 | 2019-06-14T14:14:23Z | 2019-06-14T15:40:33Z | 2019-06-14T15:29:53Z | NONE | null | #### Code demonstrating different tolist outputs
The following code demonstrate that a **values.tolist()** function return a different output if a dataframe has a single datetime column or more than one column
```
import pandas as pd
df = pd.DataFrame({'dt': [pd.datetime(2019, 1, 1), pd.datetime(2019, 1, 2)], 'value': [1, 2]})
df
Out[8]:
dt value
0 2019-01-01 1
1 2019-01-02 2
df.values.tolist()
[[Timestamp('2019-01-01 00:00:00'), 1], [Timestamp('2019-01-02 00:00:00'), 2]]
df[['dt', 'value']].values.tolist()
[[Timestamp('2019-01-01 00:00:00'), 1], [Timestamp('2019-01-02 00:00:00'), 2]]
df[['dt']].values.tolist()
[[1546300800000000000], [1546387200000000000]]
df['dt'].values.tolist()
[1546300800000000000, 1546387200000000000]
```
#### Problem description
The values.tolist() should be consistent regardless of the number of columns selected.
#### Expected Output
```
df[['dt']].values.tolist()
[[Timestamp('2019-01-01 00:00:00')], [Timestamp('2019-01-02 00:00:00')]]
df['dt'].values.tolist()
[Timestamp('2019-01-01 00:00:00')], [Timestamp('2019-01-02 00:00:00')]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.3.0
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 23 | 2019-06-14T15:05:23Z | 2019-06-17T09:13:13Z | 2019-06-17T07:24:30Z | MEMBER | null | - [X] closes #24661, xref #24173
- [ ] tests added / passed
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... | open | false | null | [] | null | 8 | 2019-06-14T15:29:06Z | 2021-07-10T04:38:04Z | null | MEMBER | null | Triggered by the regression reported in https://github.com/pandas-dev/pandas/issues/26206 and my attempt to fix it (https://github.com/pandas-dev/pandas/pull/26848), I looked a bit into how our different constructors handle different cases of invalid datetimes.
Out of bound datetimes (cases: a non-ns unit numpy datetime64 array, datetime.datetime objects, strings):
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>M8[D] out of bound</th>
<th>datetime.datetime out of bound</th>
<th>list of M8[D] scalars out of bound</th>
<th>string out of bound</th>
</tr>
</thead>
<tbody>
<tr>
<th>dateframe</th>
<td>datetime64[ns] 1842-11-22 0 ...</td>
<td>object</td>
<td>object</td>
<td>object</td>
</tr>
<tr>
<th>datetimeindex</th>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
</tr>
<tr>
<th>index</th>
<td>OutOfBoundsDatetime</td>
<td>object</td>
<td>object</td>
<td>object</td>
</tr>
<tr>
<th>index (explicit dtype)</th>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
</tr>
<tr>
<th>pd.to_datetime</th>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
<td>OutOfBoundsDatetime</td>
</tr>
<tr>
<th>series</th>
<td>datetime64[ns] 1842-11-22 0 ...</td>
<td>object</td>
<td>object</td>
<td>object</td>
</tr>
<tr>
<th>series (explicit dtype)</th>
<td>datetime64[ns] 1842-11-22 0 ...</td>
<td>datetime64[ns] 1842-11-22 0 ...</td>
<td>datetime64[ns] 1842-11-22 0 ...</td>
<td>datetime64[ns] 1842-11-22 0 ...</td>
</tr>
</tbody>
</table>
Some remarks here:
* The above table is for master (and the same for 0.24). However, on pandas 0.22, the case of out of bounds datetime64 array raised an error in all cases (so also for Series(..), that's https://github.com/pandas-dev/pandas/issues/26206).
* For `to_datetime`, all the OutOfBounds are expected, as the default is to error.
* For the ones with a specified dtype (either explicit dtype passed or DatetimeIndex), we also expect an error. So DatetimeIndex and Index(.., dtype='M8[ns]') are fine, but `Series(.., dtype='M8[ns])` is clearly broken (gives an incorrect date for all cases)
* The inconsistencies are mainly when no dtype is enforced. In some cases (eg for datetime.datetime objects) we return a object dtype Index/Series, in other cases we raise an error.
The last item is the **main question**: when inferring (no dtype enforced) and in case the data are clearly timestamps (either numpy datetime64 or datetime.datetime), should we raise an error or return object dtype.
Note that for datetime.datetime, returning object dtype might be more logical as it means returning the input as is. While for datetime64[non-ns], it actually means converting a numpy dtype to object data.
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} | 4 | 2019-06-14T15:31:23Z | 2019-06-26T13:28:17Z | 2019-06-26T13:28:16Z | CONTRIBUTOR | null | - closes #25439
- 1 test added and 1 test deleted
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
Presently, `nargsort` can handle EA too. Because `np.asanyarray` turns EA into `np.ndarray`.
https://github.com/pandas-dev/pandas/blob/430f0fd04646a0ab44ccbfbad706dfe22e9fabfe/pandas/core/sorting.py#L264
In this PR, `nargsort` use `EA._values_for_argsort` instead to avoid the `np.ndarray` conversion. | {
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} | 8 | 2019-06-14T18:12:16Z | 2019-06-18T13:42:26Z | 2019-06-18T13:42:23Z | MEMBER | null | In #26648 we started building the documentation in azure pipelines. Seems to work fine, removing the travis build here. | {
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} | 15 | 2019-06-14T18:18:08Z | 2019-06-18T14:21:30Z | 2019-06-18T14:21:25Z | MEMBER | null | In #26648 we started building the documentation in https://dev.pandas.io . Updated the links to the development documentation to point to the new url. | {
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"description": "D... | closed | false | null | [] | null | 4 | 2019-06-14T19:43:05Z | 2019-06-15T20:00:59Z | 2019-06-15T20:00:59Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
import scipy.io
# Two lists
l1 = [1,2]
l2 = [4,5]
# Numpy arrays
a1 = np.array(l1)
a2 = np.array(l2)
# Dictionary of arrays
d = {'a':a1,'b':a2}
# Constructing DataFrame from dictionary
df = pd.DataFrame.from_dict(d,orient='index')
print(df)
```
The above code gives,
```python
0 1
a 1 2
b 4 5
```
If we change the length of the lists,
```python
# Two lists of unequal length
l1 = [1,2]
l2 = [4,5,6]
<rest code same as above>
```
Then we get,
```python
0 1 2
a 1 2 NaN
b 4 5 6.0
```
But now if we want to reshape the numpy arrays (for whatever processing needs)
```python
# Two lists of unequal length
l1 = [1,2]
l2 = [4,5,6]
# Numpy arrays with reshape
a1 = np.array(l1).reshape(1,-1)
a2 = np.array(l2).reshape(1,-1)
# Dictionary of arrays
d = {'a':a1,'b':a2}
# Constructing DataFrame from dictionary
df = pd.DataFrame.from_dict(d,orient='index')
print(df)
```
Here we get,
```python
0
a [[1, 2]]
b [[4, 5, 6]]
```
**This is ACCEPTABLE.**
But check what happens if the list lengths are equal,
```python
# Two lists of equal length
l1 = [1,2]
l2 = [4,5]
# Numpy arrays with reshape
a1 = np.array(l1).reshape(1,-1)
a2 = np.array(l2).reshape(1,-1)
# Dictionary of arrays
d = {'a':a1,'b':a2}
# Constructing DataFrame from dictionary
df = pd.DataFrame.from_dict(d,orient='index')
```
We get the error,
```python
ValueError Traceback (most recent call last)
<ipython-input-49-b17a697beafc> in <module>()
1 # Constructing DataFrame from dictionary
----> 2 df = pd.DataFrame.from_dict(d,orient='index')
c:\programdata\miniconda3\lib\site-packages\pandas\core\frame.py in from_dict(cls, data, orient, dtype)
898 raise ValueError('only recognize index or columns for orient')
899
--> 900 return cls(data, index=index, columns=columns, dtype=dtype)
901
902 def to_dict(self, orient='dict', into=dict):
c:\programdata\miniconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
383 else:
384 mgr = self._init_ndarray(data, index, columns, dtype=dtype,
--> 385 copy=copy)
386 else:
387 mgr = self._init_dict({}, index, columns, dtype=dtype)
c:\programdata\miniconda3\lib\site-packages\pandas\core\frame.py in _init_ndarray(self, values, index, columns, dtype, copy)
511 # by definition an array here
512 # the dtypes will be coerced to a single dtype
--> 513 values = _prep_ndarray(values, copy=copy)
514
515 if dtype is not None:
c:\programdata\miniconda3\lib\site-packages\pandas\core\frame.py in _prep_ndarray(values, copy)
6255 values = values.reshape((values.shape[0], 1))
6256 elif values.ndim != 2:
-> 6257 raise ValueError('Must pass 2-d input')
6258
6259 return values
ValueError: Must pass 2-d input
```
#### Problem description
The **`DataFrame.from_dict()` does not work properly when the dictionary contains Numpy arrays as values which have been reshaped**. If the arrays are of unequal length, the DataFrame is created (with a single column) with the arrays as an object. But if they are equal, it throws an unexpected error.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 6 | 2019-06-14T20:16:06Z | 2021-05-31T14:27:03Z | 2021-05-31T14:27:03Z | NONE | null | #### Code Sample
```python
df = pd.DataFrame({
'a': pd.Series(list('abc')),
'b': pd.Series(pd.to_datetime(['2018-01-01', '2018-02-01', '2018-03-01']), dtype='category'),
'c': pd.Categorical.from_codes([-1, 0, 1], categories=[0, 1])
})
df.groupby(['a', 'b']).indices
```
#### Problem description
Tossing an error. You can play around with difference choices of columns but this happens so long as you include `'b'` with one of the other columns. `'b'` on its own is okay.
```
>> df.groupby(['a', 'b']).indices
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-c4de90de974e> in <module>
1 gb = df.groupby(['a', 'b'])
----> 2 gb.indices
/opt/conda/lib/python3.6/site-packages/pandas/core/groupby/groupby.py in indices(self)
401 """
402 self._assure_grouper()
--> 403 return self.grouper.indices
404
405 def _get_indices(self, names):
pandas/_libs/properties.pyx in pandas._libs.properties.CachedProperty.__get__()
/opt/conda/lib/python3.6/site-packages/pandas/core/groupby/ops.py in indices(self)
204 keys = [com.values_from_object(ping.group_index)
205 for ping in self.groupings]
--> 206 return get_indexer_dict(label_list, keys)
207
208 @property
/opt/conda/lib/python3.6/site-packages/pandas/core/sorting.py in get_indexer_dict(label_list, keys)
331 group_index = group_index.take(sorter)
332
--> 333 return lib.indices_fast(sorter, group_index, keys, sorted_labels)
334
335
pandas/_libs/lib.pyx in pandas._libs.lib.indices_fast()
TypeError: Cannot convert DatetimeIndex to numpy.ndarray
```
#### Expected Output
Not an error.
#### Cause
If we inspect, `BaseGrouper.indices`, we see that `keys` gets passed to `get_indexer_dict` here:
https://github.com/pandas-dev/pandas/blob/430f0fd04646a0ab44ccbfbad706dfe22e9fabfe/pandas/core/groupby/ops.py#L227-L235
`get_indexer_dict` eventually passes the elements of `keys` to `get_value_at` found here:
https://github.com/pandas-dev/pandas/blob/2b32e4104779ae28698df45f0df058ec8050f763/pandas/_libs/util.pxd#L94-L99
The problem is that to build `keys`, the `get_values` method is called on each group index (you can see in `BaseGrouper.indices` how this isn't an issue when there's a single grouper). When grouping on a categorical-datetime column like `df['b']`, the `get_values` method on the underlying categorical array is called and within that method this branch of the if statement is triggered, causing a DatetimeIndex to be returned instead of a numpy array.
https://github.com/pandas-dev/pandas/blob/2b32e4104779ae28698df45f0df058ec8050f763/pandas/core/arrays/categorical.py#L1504
#### Solution
Now, it states in the `Categorical.get_values` doc string that an Index object could be return and not a numpy array. The simplest thing is to just introduce a line like this before `get_indexer_dict`
```
keys = [np.array(key) for key in keys]
```
A pull request for this will be created imminently.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-21-generic
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: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.14.3
scipy: 1.2.1
pyarrow: 0.13.0
xarray: None
IPython: 7.4.0
sphinx: 2.0.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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... | closed | false | null | [] | null | 15 | 2019-06-14T20:17:38Z | 2019-11-13T18:19:23Z | 2019-11-13T18:19:22Z | NONE | null | This is to fix a bug reported in #26859
- [x] closes #26859
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-06-14T20:45:01Z | 2021-07-23T23:22:00Z | 2021-07-23T23:22:00Z | CONTRIBUTOR | null | ```python
data = [pd.Period('2002-03','D')]
# 1
pd.DataFrame({'a': data}).a.dtype
# 2
df = pd.DataFrame([])
df['a'] = data
df.a.dtype
```
```python
period[D]
dtype('O')
```
Expected output is the same - `period[D]`. As it is the same for example for `data = [pd.Timestamp('2000-01-01')]` : both `dtype('<M8[ns]')`.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-16-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: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 19 | 2019-06-14T23:08:01Z | 2019-06-21T16:25:46Z | 2019-06-21T16:25:33Z | CONTRIBUTOR | null | - [x] closes #26536
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This change should have no effect on CPython, and may fix a crash in when trying to install using PyPy (see #26536).
I've given this a very quick check by installing locally with `pip install -e` (cpython 3.6 on mac). I dont have an easy way to check it on PyPy, so Im hoping the OP from #26536 can do this (@bhy) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26863 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26863/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26863/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26863/events | https://github.com/pandas-dev/pandas/issues/26863 | 456,486,400 | MDU6SXNzdWU0NTY0ODY0MDA= | 26,863 | REF: avoid using `concat` in __repr__ | {
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} | [] | closed | false | null | [] | null | 5 | 2019-06-15T01:25:21Z | 2019-06-16T18:05:50Z | 2019-06-16T18:05:49Z | MEMBER | null | ATM `DataFrame.__repr__` takes a path through `io.formats.format.DataFrameFormatter._chk_truncate` that can call `core.reshape.concat.concat`. Similar for `Series.__repr__`. It would be really nice if we could avoid that and find a simpler way to do this. | {
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"description": "pandas testing functions... | open | false | null | [] | null | 6 | 2019-06-15T03:05:42Z | 2020-05-22T03:35:08Z | null | MEMBER | null | from pandas/tests/groupby/aggregate/test_other.py test_agg_timezone_round_trip:
```
dates = [pd.Timestamp("2016-01-0%d 12:00:00" % i, tz='US/Pacific')
for i in range(1, 5)]
df = pd.DataFrame({'A': ['a', 'b'] * 2, 'B': dates})
grouped = df.groupby('A')
ts = df['B'].iloc[0]
assert ts == grouped.nth(0)['B'].iloc[0]
assert ts == grouped.head(1)['B'].iloc[0]
assert ts == grouped.first()['B'].iloc[0]
assert ts == grouped.apply(lambda x: x.iloc[0])[0]
```
The last assertion here is failing in a WIP branch, so I'm troubleshooting it on master and finding that it fails if I skip the first three assertions:
```
dates = [pd.Timestamp("2016-01-0%d 12:00:00" % i, tz='US/Pacific')
for i in range(1, 5)]
df = pd.DataFrame({'A': ['a', 'b'] * 2, 'B': dates})
grouped = df.groupby('A')
assert ts == grouped.apply(lambda x: x.iloc[0])[0] # <-- KeyError: 0
```
Current thought is that the assertion is wrong and the test along with it. Thoughts @jreback @jorisvandenbossche ? (I don't do much in this part of the code; anyone else I should be pinging for it?) | {
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"id": 7171206... | closed | false | null | [] | null | 12 | 2019-06-15T09:02:55Z | 2019-06-16T20:47:34Z | 2019-06-16T20:47:34Z | NONE | null | The page
https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#string-regular-expression-replacement
seems to have an example that can be improved. The page lists
```python
df.replace([r'\.', r'(a)'], ['dot', '\1stuff'], regex=True)
```
as an example. However '\1' is ignored because the replacement regex is not a raw string. I think what you mean is likely
```python
df.replace([r'\.', r'(a)'], ['dot', r'\1stuff'], regex=True)
```
if my understanding is correct please consider updating the page.
Regards,
Panos Karamertzanis
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https://api.github.com/repos/pandas-dev/pandas/issues/26866 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26866/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26866/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26866/events | https://github.com/pandas-dev/pandas/pull/26866 | 456,519,837 | MDExOlB1bGxSZXF1ZXN0Mjg4NTQ1MDI4 | 26,866 | NDFrame.filter -> NDFrame.select | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR moves ``NDFrame.filter`` over to ``NDFrame.select`` and deprecates ``NDFrame.filter``.
I'm not completely happy with the siganture from ``.filter``, and got some discussion points in #26642. The current PR can be seen as a first version, to be changed, if there's agreement, | {
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} | 10 | 2019-06-15T11:54:02Z | 2019-09-13T05:23:26Z | 2019-09-12T20:47:55Z | CONTRIBUTOR | null | - [x] Part of #26792
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-06-15T12:36:07Z | 2019-09-03T18:36:07Z | 2019-09-03T18:36:07Z | CONTRIBUTOR | null | Insertion Method section of the reference refers to "pd_table" in the function signature. This is the only place it is used in the whole documentation. Search engines results for "pd_table" are mostly examples where it's used to refer to pd.DataFrame, so it's rather confusing.
Not sure what's the best way to make this clear but the signature should refer to `pandas.io.SQLTable()`.
This was added in #21401. I came across this while working on #26795. | {
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} | 2 | 2019-06-15T13:56:51Z | 2019-06-18T15:59:39Z | 2019-06-18T15:59:35Z | CONTRIBUTOR | null | Follow up https://github.com/pandas-dev/pandas/pull/26225#pullrequestreview-242044547 | {
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} | 3 | 2019-06-15T18:49:38Z | 2019-06-16T14:12:08Z | 2019-06-16T14:05:21Z | CONTRIBUTOR | null | - [x] xref #26792
This PR add a type hint for ``(DataFrame|Series)._data`` and ``NDFrame.__init__``. This makes it easier to work with the ``BlockManager`` in Pandas.
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closes #26868
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- **What's new?** Some small fixes in documentation.
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Could use e.g. https://pypi.org/project/flake8-builtins/ to check for other cases like this, xref #22122 | {
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- [x] tests added / passed
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Updated some existing test codes because index order in some DataFrames constructed by from_dict have changed after this fix. | {
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- [x] closes #23861
- [x] tests added / passed
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https://api.github.com/repos/pandas-dev/pandas/issues/26877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26877/events | https://github.com/pandas-dev/pandas/issues/26877 | 456,603,472 | MDU6SXNzdWU0NTY2MDM0NzI= | 26,877 | TST: coverage config ignore e.g. _version.py | {
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} | 1 | 2019-06-16T05:18:11Z | 2019-11-06T19:27:15Z | 2019-11-06T19:27:15Z | MEMBER | null | It can probably be ignored for coverage purposes. util/_doctools.py and util/_depr_module.py can't be ignored while they're under pandas/ but they might not be needed there. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26878/events | https://github.com/pandas-dev/pandas/issues/26878 | 456,612,585 | MDU6SXNzdWU0NTY2MTI1ODU= | 26,878 | when i use ,like groupby('user_id')['repay_amt'].mean(), i find miss the index,become new number as index | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:38:21Z | 2019-06-16T08:27:19Z | 2019-06-16T08:27:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
user_repay_logs[['user_id','repay_amt']].groupby('user_id')['repay_amt'].mean().reset_index()
| user_id | repay_amt
-- | -- | --
1 | 120.875100
10 | 946.539729
1000 | 457.226195
10000 | 912.310300
100000 | 302.780817
loss the
but,this is i need
df_t.groupby('Q').agg(['mean','std','count','max'])
A
mean std count max
Q
LI 1.5 0.707107 2 2
WANG 2.0 NaN 1 2
ZHANG 1.0 0.000000 2 1
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:48:30Z | 2019-06-16T07:49:37Z | 2019-06-16T07:49:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26880/events | https://github.com/pandas-dev/pandas/issues/26880 | 456,613,355 | MDU6SXNzdWU0NTY2MTMzNTU= | 26,880 | groupby index | {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:48:31Z | 2019-06-16T07:50:43Z | 2019-06-16T07:50:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
| {
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:48:31Z | 2019-06-16T07:51:01Z | 2019-06-16T07:51:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:48:32Z | 2019-06-16T07:49:53Z | 2019-06-16T07:49:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
| {
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```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | [] | closed | false | null | [] | null | 0 | 2019-06-16T07:48:32Z | 2019-06-16T07:50:11Z | 2019-06-16T07:50:11Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26885 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26885/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26885/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26885/events | https://github.com/pandas-dev/pandas/issues/26885 | 456,636,849 | MDU6SXNzdWU0NTY2MzY4NDk= | 26,885 | BUG: no attribute when the column name contains an "ϵ" | {
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```python
df = pd.DataFrame({'col1': [0, 1]})
df['α'] = df['col1']
df['ϵ'] = df['col1']
```
This line is executed correctly:
```python
df.α
```
but this one yields `AttributeError: 'DataFrame' object has no attribute 'ε'`:
```python
df.ϵ
```
#### Problem description
With pandas 0.24.2, dataFrame columns can often be accessed as attributes, except when they contain specific unicode characters (at least "ε"). I managed to reproduce this issue when loading a CSV file with an ε inside the header, but _sometimes_ not when creating a DataFrame object from a dictionary:
This yields no exception when accessing `df.ϵ`:
```
df = pd.DataFrame(dict(α=[0, 1], ϵ=[0, 1]))
```
but this one does:
```
df = pd.DataFrame({'α': [0, 1], 'ϵ': [0, 1]})
```
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} | 3 | 2019-06-16T14:50:58Z | 2019-06-16T20:18:38Z | 2019-06-16T20:18:38Z | CONTRIBUTOR | null | - [x] closes #26865 | {
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] | closed | false | null | [] | null | 6 | 2019-06-16T17:15:32Z | 2020-07-06T15:40:56Z | 2020-07-06T15:40:56Z | MEMBER | null | The clipboard system does not work in Azure Pipelines. I tried the next things:
Installing `xsel` and `xclip` (separately) as suggested in the documentation
Setting up a step in the azure settings based on the travis docs:
```
- script: |
set -x
sudo apt-get install -y xsel xvfb
export DISPLAY=":99.0"
sh -e /etc/init.d/xvfb start
sleep 3
displayName: 'Setup clipboard'
```
But we still get the same paperclyp error. This is affecting the docs (see #26852) and also the tests (see #26428). | {
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} | 3 | 2019-06-16T18:12:58Z | 2019-06-19T13:34:50Z | 2019-06-19T00:19:16Z | CONTRIBUTOR | null | - [x] xref #26792 & #26871
This PR add a type hint for ``(BlockManager|SingleBlockManager).blocks`` and ``(BlockManager|SingleBlockManager).__init__``.
This follows up to the type hints added in #26871 and makes it easier to work with the ``BlockManager`` and blocks in Pandas. | {
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... | null | 9 | 2019-06-16T18:51:08Z | 2021-08-19T19:02:58Z | null | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
from string import ascii_uppercase
df = pd.DataFrame(columns=list(ascii_uppercase),
data=pd.util.testing.rands_array(10, (10 ** 6, 26)))
arr = np.random.randint(1, 10, 10 ** 6)
```
```python
%%time
df['I'] = arr
# Wall time: 2.46 s
```
```python
%%time
df['J'] = arr.astype(object)
# Wall time: 69 ms
```
#### Problem description
For some reason, replacing string columns with integers in large dataframes seems extremely slow.
#### Expected Output
Same for both cases, e.g. `Wall time: 69 ms`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.5.1
pip: 19.0.2
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.15.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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"d... | open | false | null | [] | null | 4 | 2019-06-16T19:26:56Z | 2021-07-10T04:49:01Z | null | CONTRIBUTOR | null | We have a recurring problem where tests are skipped without us realizing. See the discussion on https://github.com/pandas-dev/pandas/pull/26852 and elsewhere.
Can we think of a way to make this more robust? Perhaps by adding an additional command line option to our pytest calls
```
pytest pandas --no-skip-xclip
```
Then we would update our calls to `pytest.importorskip` to use a pandas' wrapper that checks the config options. By default, it'll still skip, but if the `--no-skip-{dep}` flag is passed we would fail.
This would complicate the actual `pytest` call in the CI scripts, though it's already quite complicated. | {
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} | 2 | 2019-06-16T20:06:33Z | 2019-06-17T11:21:38Z | 2019-06-16T20:47:34Z | CONTRIBUTOR | null | - [x] closes #26865
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] | closed | false | null | [] | null | 13 | 2019-06-16T21:19:40Z | 2019-08-02T15:45:04Z | 2019-07-08T01:51:58Z | NONE | null | ```python
In [19]: import pandas as pd
...: s=pd.Series([1,2])
...: s.floor()
AttributeError: 'Series' object has no attribute 'floor'
``` | {
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... | open | false | null | [] | null | 8 | 2019-06-16T21:26:20Z | 2020-04-27T00:14:50Z | null | NONE | null | The special case where `left=right` is not checked properly by the constructor, only `closed='both'` (a point) or `closed='neither'` (an empty interval) makes sense but '[0,0)' or `(0,0]` are nonsensical. The `overlaps` method returns inconsistent results when encountering such Intervals.
```python
import pandas as pd
pd.Interval(0,0,'left')
```
**Update:**
Examples (from below):
```python
In [4]:
...: import pandas as pd
...: from pandas import Interval
...:
...: a=Interval(0,1,'both')
...: b=Interval(0,0,'right')
...: print(a.overlaps(b))
...: b=Interval(0,0,'left')
...: print(a.overlaps(b))
True
False
``` | {
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- [] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
**Update:**
[ ] - Following https://github.com/pandas-dev/pandas/pull/26894#pullrequestreview-250742121, needs tests about creating an `Intervalindex` with '(x,x]', also checking that the repr doesn't blowup. | {
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"description": "Continuo... | open | false | null | [] | null | 3 | 2019-06-16T21:51:16Z | 2021-07-10T04:55:42Z | null | MEMBER | null | When we are in a PR, we got the list of all CI checks:
If we click in travis, it takes us to the travis log build, which seems reasonable. But when we click in one of the azure-pipelines builds, it takes us to this "Checks" tab, that how we have configured azire-pipelines, it provides literally no value, at least to me (never seen any useful information there):
Then what afaik we all do, is immediately click on the `View more details on Azure Pipelines `, so we go to azure-pipelines, and we can see the errors (if we have them highlighted, like for the linting), or we can click in the step and see the log:
I guess we have something configured incorrectly, because the second step (the "Checks" tab) doesn't add any value, it's just making things more complex for no reason.
@davidstaheli @vtbassmatt do you mind helping us here? What are we missing? It's not only us, checked numba and scikit-learn, and they've got the same as us. Thanks! | {
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} | 2 | 2019-06-16T22:19:23Z | 2019-06-27T02:17:02Z | 2019-06-27T02:17:02Z | MEMBER | null | Codecov is adding two comments to our PRs after the tests are successfully run. Which makes us receive two messages. Personally I don't find these useful. I see that other projects have codecov in the checks, next to the builds (expand the "All checks have passed" in https://github.com/xhochy/fletcher/pull/74 to see an example).
Is other people finding those comments/messages useful? Would be enough for you to just have the info in the list of checks and no emails?
xref #12634 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26897/events | https://github.com/pandas-dev/pandas/issues/26897 | 456,800,580 | MDU6SXNzdWU0NTY4MDA1ODA= | 26,897 | TimedeltaIndex should have a method to help to convert to string (with a custom format with microsecond resolution) | {
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"description": "Timedelt... | open | false | null | [] | null | 3 | 2019-06-17T08:15:17Z | 2021-07-10T04:57:07Z | null | CONTRIBUTOR | null | Hello,
I noticed in doc https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.strftime.html that `DatetimeIndex` have a `dt` attribute which can help to convert a `Timestamp` to string.
```python
>>> rng = pd.date_range(pd.Timestamp("2018-03-10 09:00"),
... periods=3, freq='s')
>>> rng.strftime('%B %d, %Y, %r')
Index(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',
'March 10, 2018, 09:00:02 AM'],
dtype='object')
```
(or `rng.to_series().dt.strftime('%B %d, %Y, %r')`)
A similar method should exist for `TimedeltaIndex` to help to convert it to string (it's especially useful to output an Excel files without "0 days" for each index when each timedelta is below 1 day)
```python
>>> rng = pd.timedelta_range(start="0h", freq="15min", periods=3)
TimedeltaIndex(['00:00:00', '00:15:00', '00:30:00'], dtype='timedelta64[ns]', freq='15T')
>>> rng.dt.strftime("%M:%S")
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-48-1f408f75ecf6> in <module>
----> 1 rng.dt.strftime("%m:%s")
AttributeError: 'TimedeltaIndex' object has no attribute 'dt'
```
I have found a bad method to do this:
```python
rng.map(lambda td: time.strftime("%M:%S", time.gmtime((td.to_pytimedelta().total_seconds()))))
```
unfortunatelly it doesn't support the "f" specifier for microsecond (I only need in fact millisecond)
```python
>>> rng = pd.to_timedelta(["00:01:02.345", "00:02:03.456"])
TimedeltaIndex(['00:01:02.345000', '00:02:03.456000'], dtype='timedelta64[ns]', freq=None)
>>> rng.map(lambda td: time.strftime("%M:%S.%f", time.gmtime((td.to_pytimedelta().total_seconds()))))
Index(['01:02.f', '02:03.f'], dtype='object')
```
it could be nice to simply be able to do
```python
rng.to_series().dt.strftime("%M:%S.%f")
```
or naming attribute `td` (instead of `dt`) to avoid confusion between `DatetimeIndex` and `TimedeltaIndex`
```python
rng.to_series().td.strftime("%M:%S.%f")
```
or even simplier
```python
rng.strftime("%M:%S.%f")
```
I think a workaround to achieve this is (with positive `Timedelta`) to add epoch and so manage `Timestamp` instead.
```python
>>> rng = pd.to_timedelta(["00:01:02.345", "00:02:03.456"])
TimedeltaIndex(['00:01:02.345000', '00:02:03.456000'], dtype='timedelta64[ns]', freq=None)
>>> rng = rng + pd.to_datetime(0)
DatetimeIndex(['1970-01-01 00:01:02.345000', '1970-01-01 00:02:03.456000'], dtype='datetime64[ns]', freq=None)
rng = rng.strftime("%H:%M.%f")
Index(['00:01.345000', '00:02.456000'], dtype='object')
>>> rng.str[:-3]
Index(['00:01.345', '00:02.456'], dtype='object')
```
But I'm still wondering how to manage negative `Timedelta` such as
```python
>>> rng = pd.to_timedelta(["00:01:02.345", "-00:02:03.456"])
TimedeltaIndex(['00:01:02.345000', '-1 days +23:57:56.544000'], dtype='timedelta64[ns]', freq=None)
```
Any idea?
Kind regards
PS : related issue https://github.com/pandas-dev/pandas/issues/17232 (about display of negative Timedelta) | {
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] | closed | false | null | [] | null | 2 | 2019-06-17T09:00:44Z | 2019-06-19T10:34:39Z | 2019-06-17T09:36:43Z | NONE | null | #### Code Sample,
try to use DataFrame.inset() and get error pd.__version__ = 0.24.2
```python
# Your code here
df = pd.DataFrame(data = np.random.randn(6,2), index=['a', 'b','c','d','e','f'], columns=['X', 'Y'])
df.insert(1, 'Z', np.random.randn(6), allow_duplicates=True) # works good
df.insert(loc=1, 'Z', np.random.randn(6), allow_duplicates=True) # Error here
```
#### Problem description
when add 'loc=' before 1, it shows error
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26899 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26899/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26899/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26899/events | https://github.com/pandas-dev/pandas/issues/26899 | 456,847,573 | MDU6SXNzdWU0NTY4NDc1NzM= | 26,899 | Usability: TAB completion no longer works with accessors | {
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} | [] | closed | false | null | [] | null | 7 | 2019-06-17T10:00:23Z | 2020-01-26T06:16:48Z | 2020-01-26T06:16:48Z | MEMBER | null | When doing the following in the IPython console:
```
In [1]: pd.Series.str.<TAB>
```
or
```
In [2]: s = pd.Series(['a', 'b'])
In [3]: s.str.<TAB>
```
this used to give the list of available methods on the `str` accessor (the same for eg `dt`). But with latest master / latest IPython this no longer works. This is quite a usability regression, I typically used it when giving workshows to show a way to interactively check what methods are available.
The non working case is with pandas 0.24.2 + IPython 7.5.0. I checked it with another environment with pandas 0.22.0 + IPython 5.8.0 where it is working.
So it could be either due to a change in pandas or a change in IPython (or one of its dependencies for this). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26900 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26900/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26900/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26900/events | https://github.com/pandas-dev/pandas/pull/26900 | 456,916,342 | MDExOlB1bGxSZXF1ZXN0Mjg4ODMxNzA4 | 26,900 | CLN: In DecimalArray implement aliases as properties | {
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- [ ] tests passed (as long as it doesn't break existing)
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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1. Test behavior when installed, skip if not installed
2. Test behavior when not installed, skip if installed
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} | 1 | 2019-06-17T17:47:03Z | 2019-06-17T18:09:03Z | 2019-06-17T18:08:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
##Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def read_data():
file_name = 'temperature_2017.csv'
data = pd.read_csv(file_name)
df = pd.DataFrame(data,columns=['timestamp', 'temperature'])
#print(df) #display data
return df
data = read_data()
def plot_data(df):
fig, axes = plt.subplots()
x_data = df.loc['timestamp']
y_data = df.loc['temperature']
plt.plot(x_data,y_data,'--r')
plt.show()
plot_data(data)
Issue Description: I get an error whenever I try to index a dataframe using df.loc[] - stating that "index_class_helper.pxi' file does not exist. Here is the error:
Traceback (most recent call last):
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\indexes\base.py", line 2657, in get_loc
return self._engine.get_loc(key)
File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 129, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index_class_helper.pxi", line 91, in pandas._libs.index.Int64Engine._check_type
KeyError: 'timestamp'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\Users\liamw\.vscode\extensions\ms-python.python-2019.5.18875\pythonFiles\ptvsd_launcher.py", line 43, in <module>
main(ptvsdArgs)
File "c:\Users\liamw\.vscode\extensions\ms-python.python-2019.5.18875\pythonFiles\lib\python\ptvsd\__main__.py", line 434, in main
run()
File "c:\Users\liamw\.vscode\extensions\ms-python.python-2019.5.18875\pythonFiles\lib\python\ptvsd\__main__.py", line 312, in run_file
runpy.run_path(target, run_name='__main__')
File "C:\Users\liamw\Python\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\liamw\Python\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\liamw\Python\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "c:\Users\liamw\OneDrive\McMasterUniversity\2019-2020 School Year\SpringSummerProjects\DataScience\bee_temp.py", line 28, in <module>
plot_data(data)
File "c:\Users\liamw\OneDrive\McMasterUniversity\2019-2020 School Year\SpringSummerProjects\DataScience\bee_temp.py", line 23, in plot_data
x_data = df.loc['timestamp']
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\indexing.py", line 1500, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\indexing.py", line 1913, in _getitem_axis
return self._get_label(key, axis=axis)
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\indexing.py", line 141, in _get_label
return self.obj._xs(label, axis=axis)
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\generic.py", line 3585, in xs
loc = self.index.get_loc(key)
File "C:\Users\liamw\Python\lib\site-packages\pandas\core\indexes\base.py", line 2659, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 129, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index_class_helper.pxi", line 91, in pandas._libs.index.Int64Engine._check_type
KeyError: 'timestamp'
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
Should output a series based on my selection from the dataframe
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26908 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26908/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26908/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26908/events | https://github.com/pandas-dev/pandas/issues/26908 | 457,081,774 | MDU6SXNzdWU0NTcwODE3NzQ= | 26,908 | Difference in time of MultiIndex init | {
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"default"... | closed | false | null | [] | null | 17 | 2019-06-17T18:17:40Z | 2019-07-09T19:08:21Z | 2019-06-27T12:05:46Z | CONTRIBUTOR | null | Test script
```python
import pandas as pd
print(pd.__version__)
size_mult = 8
d1 = [1]*10**size_mult
d2 = [2]*10**size_mult
start = pd.Timestamp.now()
index_arr = pd.MultiIndex.from_arrays([d1, d2], names=['a', 'b']) #1
print('ARR done in %f' % (pd.Timestamp.now()-start).total_seconds())
start = pd.Timestamp.now()
index_tup = pd.MultiIndex.from_tuples(zip(d1, d2), names=['a', 'b']) #2
print('TUP done in %f' % (pd.Timestamp.now()-start).total_seconds())
```
`from_arrays` is significantly slower than `from_tuples` though produce same `MI`:
```python
0.24.2
ARR done in 76.987541
TUP done in 29.744813
```
For 0.25 pandas `from_arrays` is even _slower_:
```python
0.25.0.dev0+693.g22dc14ae4
ARR done in 119.278725
TUP done in 31.643452
```
Expected is nearly the same time, because methods `#1` and `#2` are interchangeable here. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26909 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26909/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26909/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26909/events | https://github.com/pandas-dev/pandas/pull/26909 | 457,099,588 | MDExOlB1bGxSZXF1ZXN0Mjg4OTgwNTYx | 26,909 | Update requirements-dev.txt | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26910 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26910/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26910/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26910/events | https://github.com/pandas-dev/pandas/pull/26910 | 457,139,846 | MDExOlB1bGxSZXF1ZXN0Mjg5MDEzMjEw | 26,910 | BUG: Fix rolling median and quantile with closed='left' and closed='neither' (#26005) | {
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} | 7 | 2019-06-17T20:44:10Z | 2019-07-11T15:47:48Z | 2019-06-21T17:01:35Z | CONTRIBUTOR | null | - [x] Fixes issues with rolling `median` (and also `quantile`) `closed='left'` and `closed='neither'` in #26005
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Here is my understanding of how it should work:
*Same as test cases, there is one day between each element and window size is 3 days.
For `closed='both'` (**()** denotes Nth window and **-** is just to state window is out of ):
```
N=0 | (-, -, - [0), 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=1 | -, (-, - [0, 1), 2, 3, 4, 5, 6, 7, 8, 9]
N=2 | -, -, (- [0, 1, 2), 3, 4, 5, 6, 7, 8, 9]
N=3 | -, -, - [(0, 1, 2, 3), 4, 5, 6, 7, 8, 9]
...
```
For `closed='right'`:
```
N=0 | -, (-, - [0), 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=1 | -, -, (- [0, 1), 2, 3, 4, 5, 6, 7, 8, 9]
N=2 | -, -, - [(0, 1, 2), 3, 4, 5, 6, 7, 8, 9]
N=3 | -, -, - [0, (1, 2, 3), 4, 5, 6, 7, 8, 9]
...
```
For `closed='left'`:
```
N=0 | (-, -, -) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=1 | -, (-, - [0), 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=2 | -, -, (- [0, 1), 2, 3, 4, 5, 6, 7, 8, 9]
N=3 | -, -, - [(0, 1, 2), 3, 4, 5, 6, 7, 8, 9]
...
```
For `closed='neither'`:
```
N=0 | -, (-, -) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=1 | -, -, (- [0), 1, 2, 3, 4, 5, 6, 7, 8, 9]
N=2 | -, -, - [(0, 1), 2, 3, 4, 5, 6, 7, 8, 9]
N=3 | -, -, - [0, (1, 2), 3, 4, 5, 6, 7, 8, 9]
...
``` | {
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"color": "ff... | closed | false | null | [] | null | 6 | 2019-06-17T21:44:44Z | 2019-06-26T19:48:49Z | 2019-06-26T19:48:44Z | CONTRIBUTOR | null | - [x] closes #26658
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/26912 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26912/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26912/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26912/events | https://github.com/pandas-dev/pandas/pull/26912 | 457,185,215 | MDExOlB1bGxSZXF1ZXN0Mjg5MDUwMTYx | 26,912 | Fix miscellaneous small holiday errors | {
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} | 3 | 2019-06-17T22:59:38Z | 2019-06-18T16:00:45Z | 2019-06-18T16:00:42Z | CONTRIBUTOR | null | Modified an incorrect comment on US Memorial Day and corrected some holiday names.
Nothing affects the code, only trying to contribute because I noticed some issues while reading through. | {
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