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} | 2 | 2019-12-03T21:31:00Z | 2019-12-04T15:18:23Z | 2019-12-04T13:48:44Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-12-03T21:32:15Z | 2019-12-04T07:11:52Z | 2019-12-03T23:22:49Z | NONE | null | #### Code Sample
```python
import pandas as pd
s = pd.Series(['foo'])
s.str.replace('$', 'bar', regex=True)
# 0 foo
# dtype: object
s.str.replace('^', 'bar', regex=True)
# 0 foo
# dtype: object
```
#### Problem description
When `regex=True`, `^` should match the beginning of the string, and `$` should match the end of the string, but this is not the case.
#### Expected Output
```python
import pandas as pd
s = pd.Series(['foo'])
s.str.replace('$', 'bar', regex=True)
# 0 foobar
# dtype: object
s.str.replace('^', 'bar', regex=True)
# 0 barfoo
# dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.10-134.current
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.15.4
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.5.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.1.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.2.14
tables : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
```
</details>
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} | 1 | 2019-12-03T21:40:48Z | 2019-12-04T16:42:39Z | 2019-12-04T13:46:07Z | MEMBER | null | making these classes less stateful bit by bit | {
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} | 4 | 2019-12-03T21:47:20Z | 2021-07-23T23:27:32Z | 2021-07-23T23:27:32Z | CONTRIBUTOR | null | > Given that this can have some impact on libraries (see how often we used it ourselves in pandas), meaning that their users will start to see warnings they cannot solve, should be start with making this a DeprecationWarning instead of FutureWarning?
>
> Given that this won't be actually changed in 1.0 (but only start raising a warning), we have some time to change it to a FutureWarning in a later release.
I guess we could have ``DeprecationWarning`` for a single version, then change to ``FutureWarning`` to give libraries a chance to change. I feel these are ignored as well by the downstream as they don't see them by-default (we in fact tend to ignore numpy ones :->)
@SaturnFromTitan let's change this and open an issue for the 1.1 milestone (let me create it) to change to a FutureWarning
_Originally posted by @jreback in https://github.com/pandas-dev/pandas/pull/29405#issuecomment-560383499_ | {
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} | 2 | 2019-12-03T22:55:06Z | 2019-12-04T19:16:26Z | 2019-12-04T18:50:04Z | MEMBER | null | removes preserve_UTC argument, which never did anything, and didnt have a clear purpose | {
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} | 1 | 2019-12-03T23:06:10Z | 2019-12-04T16:55:36Z | 2019-12-04T13:32:39Z | MEMBER | null | It takes a little bit of work to show, but `self.index_axes` is always length-1 on L4243, which means that nindexes == 1 (the assertion added in the code).
This ensures that the condition `if i > 0 and i < nindexes` on L4239 is is never satisfied, so `bindexes` is unnecessary. | {
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} | 5 | 2019-12-03T23:13:39Z | 2019-12-11T10:06:22Z | 2019-12-11T10:06:07Z | CONTRIBUTOR | null | - [x] reference #29886
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Issue: https://github.com/pandas-dev/pandas/issues/29886
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} | 6 | 2019-12-03T23:15:20Z | 2019-12-06T21:12:41Z | 2019-12-06T20:50:30Z | CONTRIBUTOR | null | replaced old formatting with fstrings where possible
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} | 2 | 2019-12-04T01:02:14Z | 2019-12-04T23:11:51Z | 2019-12-04T23:11:45Z | CONTRIBUTOR | null | - [ ] references #29547
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https://api.github.com/repos/pandas-dev/pandas/issues/30029 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30029/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30029/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30029/events | https://github.com/pandas-dev/pandas/pull/30029 | 532,386,825 | MDExOlB1bGxSZXF1ZXN0MzQ4NjQ2Njkw | 30,029 | REF: collect attribute-setting at the end of create_axes | {
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} | 2 | 2019-12-04T01:42:28Z | 2019-12-04T19:32:52Z | 2019-12-04T18:47:30Z | MEMBER | null | Ideally we wouldn't be altering `self` inplace at all, but we can make this a lot easier to reason about by collecting all of the attribute-setting at the end of the create_axes call.
`self.non_index_axes` I couldn't get all the way to the end of the call without a more invasive edit, which ill save for a separate pass to keep the diff smallish | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30030 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30030/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30030/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30030/events | https://github.com/pandas-dev/pandas/issues/30030 | 532,412,210 | MDU6SXNzdWU1MzI0MTIyMTA= | 30,030 | CI: numpy 1.18 deprecating automatic object dtype | {
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"description": ... | open | false | null | [] | null | 0 | 2019-12-04T02:46:54Z | 2021-07-23T05:25:04Z | null | MEMBER | null | The npdev build is failing again, the warnings produced are of the form:
```
> return PandasArray(np.array([np.nan, (1,)]))
E DeprecationWarning: Creating an ndarray with automatic object dtype is deprecated, use dtype=object if you intended it, otherwise specify an exact dtype
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30031 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30031/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30031/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30031/events | https://github.com/pandas-dev/pandas/issues/30031 | 532,414,158 | MDU6SXNzdWU1MzI0MTQxNTg= | 30,031 | CI: Flakey Test | {
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} | 0 | 2019-12-04T02:53:14Z | 2019-12-06T03:11:12Z | 2019-12-06T03:11:12Z | CONTRIBUTOR | null | https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=22330
Below test fails when ResourceWarnings are raised
```
______________ test_chunks_have_consistent_numerical_type[python] ______________
[gw1] linux -- Python 3.6.1 /home/vsts/miniconda3/envs/pandas-dev/bin/python
all_parsers = <pandas.tests.io.parser.conftest.PythonParser object at 0x7f204e3382b0>
def test_chunks_have_consistent_numerical_type(all_parsers):
parser = all_parsers
integers = [str(i) for i in range(499999)]
data = "a\n" + "\n".join(integers + ["1.0", "2.0"] + integers)
# Coercions should work without warnings.
with tm.assert_produces_warning(None):
> result = parser.read_csv(StringIO(data))
pandas/tests/io/parser/test_common.py:1204:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f2010677550>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('ResourceWarning', ResourceWarning("unclosed file <_io.BufferedReader name='testmultiindex.xlsm'>",), '/home/vsts/work/1/s/pandas/io/parsers.py', 2931), ('ResourceWarning', ResourceWarning("unclosed file <_io.BufferedReader name='testmultiindex.xlsx'>",), '/home/vsts/work/1/s/pandas/io/parsers.py', 2931), ('ResourceWarning', ResourceWarning("unclosed file <_io.BufferedReader name='test_index_name_pre17.xlsm'>",), '/home/vsts/work/1/s/pandas/io/parsers.py', 2931), ('ResourceWarning', ResourceWarning("unclosed file <_io.BufferedReader name='test1.xlsx'>",), '/home/vsts/work/1/s/pandas/io/parsers.py', 2931), ('ResourceWarning', ResourceWarning("unclosed file <_io.BufferedReader name='test4.xlsx'>",), '/home/vsts/work/1/s/pandas/io/parsers.py', 2931)]
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30032/events | https://github.com/pandas-dev/pandas/pull/30032 | 532,431,301 | MDExOlB1bGxSZXF1ZXN0MzQ4NjgwMDE1 | 30,032 | changed "fun !r" -> "repr(fun)" | {
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} | 3 | 2019-12-04T03:46:10Z | 2020-03-28T14:00:02Z | 2019-12-04T13:25:03Z | CONTRIBUTOR | null | As described in the following issue
, usage of !r is currently redundant and so changing to f strings in place of it.
- [x] ref https://github.com/pandas-dev/pandas/issues/29886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30033/events | https://github.com/pandas-dev/pandas/pull/30033 | 532,444,208 | MDExOlB1bGxSZXF1ZXN0MzQ4Njg5NTE0 | 30,033 | revert openpyxl test changes | {
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} | 3 | 2019-12-04T04:33:56Z | 2019-12-04T13:24:26Z | 2019-12-04T13:24:23Z | MEMBER | null | follow up to #30009 - thanks @jbrockmendel for fixing it and sorry I wasn't able to review more. I *think* the root cause though is just defusedxml being installed alongside things, which is only prevalent in our 3.6 build.
pinning openpyxl version alone I think is fine, but let's see...note that this same defusedxml issue causes a lot of warnings to be captured for xlrd in the excel tests | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30034/events | https://github.com/pandas-dev/pandas/pull/30034 | 532,452,909 | MDExOlB1bGxSZXF1ZXN0MzQ4Njk2MjAy | 30,034 | BUG: unclosed file warning when passing invalid encoding | {
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} | 6 | 2019-12-04T05:05:52Z | 2019-12-05T16:08:07Z | 2019-12-05T12:57:15Z | MEMBER | null | This isn't directly related to #30031 but might solve it if the problem there is unclosed files piling up.
TL;DR: the actual problem is in the stdlib `codecs.open`:
```
def open(filename, mode='r', encoding=None, errors='strict', buffering=1):
if encoding is not None and 'b' not in mode:
mode = mode + 'b'
file = builtins.open(filename, mode, buffering)
if encoding is None:
return file
info = lookup(encoding)
srw = StreamReaderWriter(file, info.streamreader, info.streamwriter, errors)
# Add attributes to simplify introspection
srw.encoding = encoding
return srw
```
When we pass a bogus encoding "foo" in test_format, the `lookup` call raises, and leaves behind the file opened a few lines earlier. | {
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} | 19 | 2019-12-04T05:47:02Z | 2020-01-13T22:02:44Z | 2019-12-18T21:26:14Z | MEMBER | null | xref #30030, xref https://github.com/numpy/numpy/issues/15041, https://github.com/numpy/numpy/issues/15045
ATM we have 85 tests in the npdev build failing, this gets it down to 14 (at least for me locally). | {
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} | 2 | 2019-12-04T06:43:12Z | 2019-12-04T13:31:40Z | 2019-12-04T13:31:37Z | MEMBER | null | I think this is legacy stuff @gfyoung | {
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} | 1 | 2019-12-04T08:11:44Z | 2020-01-30T12:46:38Z | null | MEMBER | null | Now we start to have mask-based dtypes/arrays (integer, boolean), we should also look into making our algos work with such masked arrays. An example for which we could explore this is `factorize` / `unique`.
Currently, BooleanArray and IntegerArray need to convert their masked array into a single numpy array using a certain "NA sentinel" that is specified so the algo can recognize this sentinel. This happens through the `ExtensionArray._values_for_factorize`, which returns a (numpy array, NA sentinel) tuple.
In practice this means that the boolean array is converted to integer (with NA as -1), and IntegerArray is converted to float array with NA as NaN, so the algos can handle this.
We should look into:
- Can we adapt or make a specific version of the unique/factorize hashtable class that takes a mask instead of a NA sentinel
- We could then have a variant of `ExtensionArray._values_for_factorize` that then returns (array, mask) instead of (array, NA). | {
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} | 20 | 2019-12-04T08:31:24Z | 2020-04-15T09:57:57Z | 2020-01-08T04:36:33Z | MEMBER | null | In https://github.com/pandas-dev/pandas/pull/29964 and https://github.com/pandas-dev/pandas/pull/29961(using NA in IntegerArray and BooleanArray), the question comes up how to handle `pd.NA`'s in conversion to numpy arrays.
Such conversion occurs mainly in `__array__` (for `np.(as)array(..)`) and `.astype()`. For example:
```python
In [3]: arr = pd.array([1, 2, pd.NA], dtype="Int64")
In [4]: np.asarray(arr)
Out[4]: array([1, 2, None/pd.NA/..?], dtype=object)
In [5]: arr.astype(float)
Out[5]: array([ 1., 2., nan]) # <--- allow automatic NA to NaN conversion?
```
Questions that come up here:
- By default, when converting to object dtype, what "NA value" should be used? Before this was `NaN` or `None`, now it could logically be `pd.NA`.
A possible reason to choose None instead of pd.NA is that third party code that needs a numpy array will typically not be able to handle pd.NA while None is much more normal. On the other hand, there is also still time for such third party code to adapt. And it will probably be good to keep `list(arr)` (iteration/getitem) and `np.array(arr, dtype=object)` consisetnt.
- When converting to a float dtype, are we fine to automatically convert `pd.NA` to `np.nan` ? Or do we think the user should explicitly opt in for this?
We will probably want to add a `to_numpy` to those Integer/BooleanArray to be able to make those choices explicit, eg with following signature:
```
def to_numpy(self, dtype=object, na_value=...):
...
```
where you can explicitly say which value to use for the NAs in the final numpy array (and the `Series.numpy` can then forward such keyword).
That way, a user can do `arr.to_numpy(dtype=object, na_value=None`) to get a numpy array with None instead of pd.NA, or `arr.to_numpy(dtype=float, na_value=np.nan)` to get a float array with NaNs.
But even if we have that function (which I think we should), the above questions about the *defaults* are still to be answered (eg for `__array__` we cannot have such a `na_value` keyword, so we need to make a default choice).
cc @TomAugspurger @Dr-Irv | {
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} | 4 | 2019-12-04T09:03:20Z | 2019-12-04T13:54:58Z | 2019-12-04T13:10:25Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/29223, closes https://github.com/pandas-dev/pandas/issues/26716 | {
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} | 2 | 2019-12-04T09:17:05Z | 2019-12-04T13:21:58Z | 2019-12-04T13:21:45Z | MEMBER | null | I see this in the output on Azure:
```
========================== Starting Command Output ===========================
[command]"C:\Program Files\Git\bin\bash.exe" --noprofile --norc /d/a/_temp/4c06be83-acbe-437b-9d19-6bc54dadfbe7.sh
SKIPPED TESTS:
##[section]Finishing: Print skipped tests
```
while there definitely should be some skipped tests.
Also on Travis it is not working. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30041 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30041/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30041/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30041/events | https://github.com/pandas-dev/pandas/issues/30041 | 532,611,142 | MDU6SXNzdWU1MzI2MTExNDI= | 30,041 | arm64 CI takes too much time to complete . | {
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] | closed | false | null | [] | null | 13 | 2019-12-04T11:03:00Z | 2021-01-02T04:38:39Z | 2021-01-02T04:38:39Z | CONTRIBUTOR | null | Hi, I am trying to add support for arm64 in travis CI for Pandas. but unfortunately, all tests are taking more than 50min to complete and hence, travis shows up :
`The job exceeded the maximum time limit for jobs, and has been terminated.`
I have attached the logs here:
[travis-ci-test.txt](https://github.com/pandas-dev/pandas/files/3921219/travis-ci-test.txt)
Can anyone help me to identify the root cause for this ? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30042 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30042/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30042/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30042/events | https://github.com/pandas-dev/pandas/issues/30042 | 532,625,886 | MDU6SXNzdWU1MzI2MjU4ODY= | 30,042 | Possible bug in indexing | {
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} | 1 | 2019-12-04T11:31:46Z | 2019-12-04T11:46:56Z | 2019-12-04T11:46:43Z | NONE | null | #### Code Sample
```python
import pandas
x = pandas.Series(range(4))
x[::2][1]
```
This raises the following error:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-104-1e3011e95c0d> in <module>
1 import pandas
2 x = pandas.Series(range(4))
----> 3 x[::2][1]
/j/office/app/research-python/conda/envs/2019.10/lib/python3.7/site-packages/pandas/core/series.py in __getitem__(self, key)
1069 key = com.apply_if_callable(key, self)
1070 try:
-> 1071 result = self.index.get_value(self, key)
1072
1073 if not is_scalar(result):
/j/office/app/research-python/conda/envs/2019.10/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
4728 k = self._convert_scalar_indexer(k, kind="getitem")
4729 try:
-> 4730 return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
4731 except KeyError as e1:
4732 if len(self) > 0 and (self.holds_integer() or self.is_boolean()):
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1
```
#### Problem description
The expected output should be 2 in this case. I think this has to do with the way index values are calculated. But I'm not sure.
For example:
``` python
r = pandas.RangeIndex(0, 4, 2)
r.get_value(x, 1)
```
raises the following error:
```
KeyError Traceback (most recent call last)
<ipython-input-108-1f3c6d93e1c7> in <module>
1 r = pandas.RangeIndex(0, 4, 2)
----> 2 r.get_value(x, 1)
/j/office/app/research-python/conda/envs/2019.10/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
4728 k = self._convert_scalar_indexer(k, kind="getitem")
4729 try:
-> 4730 return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
4731 except KeyError as e1:
4732 if len(self) > 0 and (self.holds_integer() or self.is_boolean()):
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1
```
This should also be returning 2 instead of raising an error.
I understand that the preferred way of indexing in pandas is using `.iloc` but default indexing behavior using `x[...]` is necessary when I want to integrate pandas with some C code. The Pandas Series seems to follow the [PySequence](https://docs.python.org/3.8/c-api/sequence.html) protocol except in cases of a Series view that is obtained by slicing the series with a step > 1. For example by doing `x[::2]`. Because this view no longer follows the PySequence protocol, accessing members of this view using [PySequence_GetItem](https://docs.python.org/3/c-api/sequence.html#c.PySequence_GetItem) which is a way to do `x[i]` results in segfaults. Do you have any suggestions here?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.3.el7.jane1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191101
Cython : 0.29.14
pytest : 5.2.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : 0.4.0
xlsxwriter : 1.2.5
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : 0.11.0
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.10
tables : 3.6.1
xarray : 0.14.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.5
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30043/events | https://github.com/pandas-dev/pandas/issues/30043 | 532,635,047 | MDU6SXNzdWU1MzI2MzUwNDc= | 30,043 | CI: numpydev is failing | {
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==================================== ERRORS ====================================
_____ ERROR at setup of TestCasting.test_astype_object_series[data-object] _____
[gw1] linux -- Python 3.7.5 /home/vsts/miniconda3/envs/pandas-dev/bin/python
allow_in_pandas = None, dtype = PandasDtype('object')
@pytest.fixture
def data_missing(allow_in_pandas, dtype):
# For NumPy <1.16, np.array([np.nan, (1,)]) raises
# ValueError: setting an array element with a sequence.
if dtype.numpy_dtype == "object":
if _np_version_under1p16:
raise pytest.skip("Skipping for NumPy <1.16")
> return PandasArray(np.array([np.nan, (1,)]))
E DeprecationWarning: Creating an ndarray with automatic object dtype is deprecated, use dtype=object if you intended it, otherwise specify an exact dtype
```
from https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=22360.
So numpy has deprecated object-dtype inference. I'm not sure if something like `np.array([object(), object()])` warns now, or if it's just when the elements are sequences of difference lengths.
Anyway, this is a nice change, but will be a bit of work to get fixed. | {
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} | 2 | 2019-12-04T12:50:41Z | 2019-12-04T13:30:51Z | 2019-12-04T13:21:46Z | MEMBER | null | - [X] closes #30040
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Looks like the format of the junit file that pytest generates has changed, and now tests are inside a `testsuite` tag, which makes the script to not find any tests. Instead of looking for them as immediate children of `testcase`, I look for them in all the tree (including subchildren).
CC: @jorisvandenbossche | {
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"description": "read_csv... | open | false | null | [] | null | 6 | 2019-12-04T13:20:44Z | 2021-07-23T05:26:00Z | null | NONE | null | Pandas exposes a `thousands` optional parameter to `read_csv` used to specify a custom thousands separator, so that `1,000` or `1_000` can be successfully parsed to a numeral in the resulting DataFrame.
Unfortunately, Pandas is missing the very same parameter for `to_csv`, so that a DataFrame containing `1000` ends up serialized to csv as `1,000` or `1_000`.
I understand the general issue of custom float formatting in Pandas remains open (see https://github.com/pandas-dev/pandas/issues/4668) and may not even find a solution. However, this particular use case sounds a bit more accessible, since it has been done successfully for `read_csv`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30046 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30046/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30046/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30046/events | https://github.com/pandas-dev/pandas/issues/30046 | 532,682,538 | MDU6SXNzdWU1MzI2ODI1Mzg= | 30,046 | NO TABLES FOUND IN domain searchers | {
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} | [] | closed | false | null | [] | null | 4 | 2019-12-04T13:23:51Z | 2019-12-10T02:24:33Z | 2019-12-10T02:24:07Z | NONE | null | `import` `webbrowser`
`import` `urllib3`
`import` `urllib.parse`
`import` `lxml.html` `as` `lh`
`from` `bs4` `import` `BeautifulSoup`
`import` `requests`
`import` `pandas` `as pd`
`with requests.Session() as c:`
`url=("https://viewdns.info/reversewhois/?")`
` reg_name = input("enter registrant name")`
`data = urllib.parse.urlencode({'q': reg_name})`
` url_n = url + data`
`html = requests.get(url_n).content`
`df_list = pd.read_html(html)`
` df = df_list[1]`
` print(df)`
Been searching around for a while now,
This same program worked with other websites with tables, but hasn’t worked with any of the websites which provide domain search services etc. eg.-https://viewdns.info/reversewhois/?q=
thanks
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} | 1 | 2019-12-04T13:37:15Z | 2019-12-04T13:45:39Z | 2019-12-04T13:45:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
x = {"A": [1, 2, 3, 4, 5],
"B": [0, 1, 0, 1, 0],
"C": [0, 0, 0, 0, 0],
"Bsp": pd.SparseArray([0, 1, 0, 1, 0], fill_value=0),
"Csp": pd.SparseArray([0, 0, 0, 0, 0], fill_value=0)
}
xpd = pd.DataFrame(x)
print(xpd)
print("=== all rows")
print(xpd.loc[xpd.index])
print("=== even indices")
print(xpd.loc[[0, 2, 4]])
print("=== B=0s")
print(xpd[xpd.Bsp == 0])
print("=== B=1s")
print(xpd[xpd.Bsp == 1])
print("=== C=0s")
print(xpd[xpd.Csp == 0])
print("=== C=1s")
print(xpd[xpd.Csp == 1])
```
Output:
```
A B C Bsp Csp
0 1 0 0 0 0
1 2 1 0 1 0
2 3 0 0 0 0
3 4 1 0 1 0
4 5 0 0 0 0
=== all rows
A B C Bsp Csp
0 1 0 0 0 NaN
1 2 1 0 1 NaN
2 3 0 0 0 NaN
3 4 1 0 1 NaN
4 5 0 0 0 NaN
=== even indices
A B C Bsp Csp
0 1 0 0 0 NaN
2 3 0 0 0 NaN
4 5 0 0 0 NaN
=== B=0s
/home/daschm/.local/lib/python3.7/site-packages/pandas/core/indexing.py:2418: FutureWarning: DataFrame/Series.to_dense is deprecated and will be removed in a future version
result = result.to_dense()
A B C Bsp Csp
0 1 0 0 0 NaN
2 3 0 0 0 NaN
4 5 0 0 0 NaN
=== B=1s
A B C Bsp Csp
1 2 1 0 1 NaN
3 4 1 0 1 NaN
=== C=0s
A B C Bsp Csp
0 1 0 0 0 NaN
1 2 1 0 1 NaN
2 3 0 0 0 NaN
3 4 1 0 1 NaN
4 5 0 0 0 NaN
=== C=1s
Empty DataFrame
Columns: [A, B, C, Bsp, Csp]
Index: []
```
#### Problem description
I'd not expect the value of the Csp column to change if I take a subset of rows. Note that it doesn't seem to matter how I choose my subset of rows. As soon as I use .loc or [(condition)], my full-zero-column turns into a full-NaN-column.
Note that the above example looks artificial, but I ended up in this situation by performing one-hot-encoding via get_dummies() with sparse=True and ending up with problems as soon as I coincidentally chose a subset of my total data for which there is at least one column with full zeroes.
Small "real life" example:
```
import pandas as pd
x = {"A": [1, 2, 3, 4, 5],
"B": [0, 1, 2, 1, 0]
}
xpd = pd.DataFrame(x)
dummies_b = pd.get_dummies(xpd.B, prefix="B=", sparse=True)
xpd_ext = pd.concat([xpd, dummies_b], axis=1)
print(xpd_ext)
print(xpd_ext.dtypes)
print("=== even indices + dtypes")
xpd_ext_even = xpd_ext.loc[[0, 2, 4]]
print(xpd_ext_even)
print(xpd_ext_even.dtypes)
print("=== even indices and B=2 + dtypes")
print(xpd_ext_even[xpd_ext_even.B == 2])
print(xpd_ext_even[xpd_ext_even.B == 2].dtypes)
```
Output:
```
A B B=_0 B=_1 B=_2
0 1 0 1 0 0
1 2 1 0 1 0
2 3 2 0 0 1
3 4 1 0 1 0
4 5 0 1 0 0
A int64
B int64
B=_0 Sparse[uint8, 0]
B=_1 Sparse[uint8, 0]
B=_2 Sparse[uint8, 0]
dtype: object
=== even indices + dtypes
A B B=_0 B=_1 B=_2
0 1 0 1 0 0
2 3 2 0 0 1
4 5 0 1 0 0
A int64
B int64
B=_0 Sparse[int64, 0]
B=_1 Sparse[int64, 0]
B=_2 Sparse[int64, 0]
dtype: object
=== even indices and B=2 + dtypes
A B B=_0 B=_1 B=_2
2 3 2 0 NaN 1
A int64
B int64
B=_0 Sparse[int64, 0]
B=_1 Sparse[float64, 0]
B=_2 Sparse[int64, 0]
```
Note how NaNs start to appear after sub-indexing for exactly those sparse columns which were pure 0 before.
I also printed the dtypes here as I coindicentally noticed that these also change for the spurious columns (from int64 to float64). This might help identifying the problem.
I haven't figured out an efficient workaround yet. I get my code to run if I translate my sparse into dense data early enough, but clearly this is very inefficient. Applying "fillna" on potentially spurious columns also seems to be painfully slow.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.5.candidate.1
python-bits : 64
OS : Linux
OS-release : 5.3.0-23-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 : 0.25.3
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.7.3
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 5.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.2.19
tables : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-12-04T13:46:58Z | 2019-12-08T17:39:44Z | 2019-12-08T17:39:41Z | CONTRIBUTOR | null | - [x] closes #27820
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-12-04T14:03:52Z | 2019-12-04T16:51:43Z | 2019-12-04T16:41:35Z | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/issues/30043.
Will take some time to fix all these cases. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30050 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30050/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30050/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30050/events | https://github.com/pandas-dev/pandas/issues/30050 | 532,709,149 | MDU6SXNzdWU1MzI3MDkxNDk= | 30,050 | TypeError in to_datetime when passing Int64 column | {
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} | 7 | 2019-12-04T14:10:14Z | 2020-01-02T20:38:41Z | 2020-01-02T20:38:41Z | CONTRIBUTOR | null | #### Code Sample
```python
df = pd.DataFrame({'a':[1,2,3]})
df.a = df.a.astype("Int64")
pd.to_datetime(df.a, unit='ms')
```
#### Problem description
When a nullable int type is used, to_datetime gives the error:
`TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got IntegerArray)`
The error is unexpected and hard to track down (reliant on pandas internal storage formats).
#### Expected Output
Converted date times or NaT when input is NaN
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-36-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.0
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.0.1
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30051 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30051/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30051/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30051/events | https://github.com/pandas-dev/pandas/issues/30051 | 532,747,600 | MDU6SXNzdWU1MzI3NDc2MDA= | 30,051 | read_sas floating point error (SAS version 6) | {
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"id"... | closed | false | null | [] | null | 3 | 2019-12-04T15:11:01Z | 2021-11-06T00:54:49Z | 2021-07-23T05:31:15Z | NONE | null | I've found a bug in read_sas, where on certain fields, 0.0 (floats) are read in as 5.397605e-79 (lowest IBM float value).
>>> df1 = pd.read_sas('fname.xpt')
>>> df1['column_1']
0 5.397605e-79
1 5.397605e-79
2 5.397605e-79
3 5.397605e-79
4 5.397605e-79
...
423663 8.000000e+01
423664 8.000000e+01
423665 8.000000e+01
423666 8.000000e+01
423667 8.000000e+01
Name: column_1, Length: 423668, dtype: float64
Here is the same file read in with the xport (https://github.com/selik/xport) "to_dataframe"
>>> with open('fname.xpt', 'rb') as f:
df = xport.to_dataframe(f)
>>> df['column_1']
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
...
423663 80.0
423664 80.0
423665 80.0
423666 80.0
423667 80.0
Name: column_1, Length: 423668, dtype: float64
This only comes up occasionally, with a specific column in a specific file in a type of dataset that I work with. I probably will not be able to provide the example files, since these files always have proprietary information.
Let me know if there is any other information that would be useful to have.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 9.0.1
setuptools : 28.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://github.com/pandas-dev/pandas/runs/333145832
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} | 1 | 2019-12-04T16:15:08Z | 2020-10-20T02:08:30Z | 2020-10-20T02:08:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from pandas import MultiIndex
key = ('a', 7)
idx = MultiIndex(levels=[['a'], [0, 7], [1]],
codes=[[0, 0], [1, 0], [0, 0]],
names=['x', 'y', 'z'],
sortorder=0)
idx.get_loc(key)
```
#### Problem description
Executing the above code results in
```python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-19-26c5d4d0888a> in <module>
----> 1 idx.get_loc(key)
~/soft/miniconda3/envs/junk/lib/python3.7/site-packages/pandas/core/indexes/multi.py in get_loc(self, key, method)
2696
2697 if start == stop:
-> 2698 raise KeyError(key)
2699
2700 if not follow_key:
KeyError: ('a', 1, 2)
```
Note that the exception disappears if you do any of the following:
- change `sortorder` to `None`
- change the codes of level 1 to `[0, 1]`
- change the values of level 1 to `[0, 7]`
- completely remove level 2
#### Expected Output
I don't think there should be any exception here since the way I created the index appears to be valid, and the `key` is definitely in there.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-23-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 : 0.25.3
numpy : 1.15.4
pytz : 2018.9
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-12-04T16:25:51Z | 2019-12-07T09:09:18Z | 2019-12-06T21:40:49Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-12-04T17:25:38Z | 2019-12-05T15:14:49Z | 2019-12-04T18:50:43Z | MEMBER | null | - [x] ref #29886
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-12-04T17:35:00Z | 2019-12-12T11:55:02Z | 2019-12-12T11:55:02Z | CONTRIBUTOR | null | #### Problem description
The documentation for `MultiIndex.get_locs()` is missing in v0.25 docs (at least since v0.23?)
It is referred to here:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.MultiIndex.get_loc.html
And it existed in v0.22:
https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.MultiIndex.get_locs.html
Mentioned in #29967 | {
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... | open | false | null | [] | null | 0 | 2019-12-04T17:58:35Z | 2020-05-11T19:19:47Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
This should be copy-pastable and self-contained:
```python
import pandas as pd
get_df = lambda : pd.DataFrame( {'DATETIME' : pd.to_datetime(['2018-01-01 11:25:00', '2018-01-01 11:50:00', '2018-01-03 10:30:00'
, '2018-01-04 10:25:00']*2),
'GROUP' : ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
'FILTER' : [True, True, True, True, False, False, True, True],
'X' : [1, 2, 3, 4, 5, 6, 7, 8]} )
df = get_df()
df = df.set_index('DATETIME')
df.groupby('GROUP').resample('D').X.sum() # <-- LINE A
# Returns
# -------
# GROUP DATETIME
# A 2018-01-01 3
# 2018-01-02 0
# 2018-01-03 3
# 2018-01-04 4
# B 2018-01-01 11
# 2018-01-02 0
# 2018-01-03 7
# 2018-01-04 8
# Name: X, dtype: int64
df = get_df()
df.groupby('GROUP').resample('D', on = 'DATETIME').X.sum() # <-- LINE B
# Returns
# -------
# GROUP DATETIME
# A 2018-01-01 10
# B 2018-01-03 11
# 2018-01-04 15
# Name: X, dtype: int64
df = get_df()
df = df.set_index('DATETIME')
df[df.FILTER].groupby('GROUP').resample('D').X.sum() # <-- LINE C
# Returns
# -------
# GROUP DATETIME
# A 2018-01-01 3
# 2018-01-02 0
# 2018-01-03 3
# 2018-01-04 4
# B 2018-01-03 7
# 2018-01-04 8
# Name: X, dtype: int64
df = get_df()
df[df.FILTER].groupby('GROUP').resample('D', on = 'DATETIME').X.sum() # <-- LINE D
# Error
# -----
# IndexError: index 6 is out of bounds for size 6
```
#### Problem description
Lines A and B are identical except that one does a resample on an index, and one does it on an identical column.
Lines C and D are identical except that one does a resample on an index, and one does it on an identical column.
Yet, they behave differently!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.16.2
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.0
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
Thanks!! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30058 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30058/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30058/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30058/events | https://github.com/pandas-dev/pandas/pull/30058 | 532,851,068 | MDExOlB1bGxSZXF1ZXN0MzQ5MDI1MjA4 | 30,058 | REF: pytables do string conversion early to set attributes in fewer places | {
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} | 2 | 2019-12-04T18:12:41Z | 2019-12-05T16:09:58Z | 2019-12-05T15:39:44Z | MEMBER | null | Next-step following #29979.
A lot of work is currently done in set_atom_string (a self-mutating method) that can be done in an earlier, not-mutating call. By making that move, we simplify set_atom quite a bit, and get closer to getting all the attribute-setting into the constructor | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30059/events | https://github.com/pandas-dev/pandas/issues/30059 | 532,888,448 | MDU6SXNzdWU1MzI4ODg0NDg= | 30,059 | setting value via iloc on a nd.datetime64[ns] column changes values on unrelated rows | {
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} | 3 | 2019-12-04T19:31:38Z | 2019-12-05T21:34:23Z | 2019-12-05T21:34:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
idf=pd.DataFrame([np.datetime64(x,'ns') for x in ['2019-09-23 21:12:34.789473126','2019-09-23 21:12:34.980507670']])
print(idf.iloc[:,0])
idf.iloc[1,0] = np.datetime64('now')
print(idf.iloc[:,0])
```
#### Problem description
I have a dataframe with two rows and a datetime64[ns] column. When I set the value for the second row using iloc (to now in the example but any datetime64 also has the same issue), the value from the first row gets changes. This issue is affecting all versions of pandas > 0.25.0. I confirmed that it works fine on v0.24.2
#### Actual Output
```
0 2019-09-23 21:12:34.789473126
1 2019-09-23 21:12:34.980507670
Name: 0, dtype: datetime64[ns]
0 2019-09-23 21:12:34.78947<b>3024</b>
1 2019-12-04 19:25:35.000000000
Name: 0, dtype: datetime64[ns]
```
#### Expected Output
0 2019-09-23 21:12:34.789473126
1 2019-09-23 21:12:34.980507670
Name: 0, dtype: datetime64[ns]
0 2019-09-23 21:12:34.78947<b>3126</b>
1 2019-12-04 19:25:35.000000000
Name: 0, dtype: datetime64[ns]
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.154-128.181.amzn2.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30060 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30060/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30060/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30060/events | https://github.com/pandas-dev/pandas/pull/30060 | 532,890,683 | MDExOlB1bGxSZXF1ZXN0MzQ5MDU3Njg1 | 30,060 | STY: fstrings doc/make.py | {
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} | 1 | 2019-12-04T19:36:19Z | 2019-12-05T20:32:43Z | 2019-12-05T15:33:51Z | MEMBER | null | - [x] ref #29547
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30061 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30061/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30061/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30061/events | https://github.com/pandas-dev/pandas/pull/30061 | 532,894,256 | MDExOlB1bGxSZXF1ZXN0MzQ5MDYwNzAy | 30,061 | Replace custom macro with stdlib | {
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} | 5 | 2019-12-04T19:43:43Z | 2019-12-06T17:16:28Z | 2019-12-06T17:16:14Z | MEMBER | null | This is part of the C99 standard so I think better to leverage builtin functionality rather than redefine | {
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} | 3 | 2019-12-04T19:53:01Z | 2019-12-12T13:24:29Z | 2019-12-12T13:24:24Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/29686
Implementation and tests for `any`/`all` with the updated logic as discussed in the linked issue. | {
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} | 3 | 2019-12-04T19:58:36Z | 2019-12-11T12:59:52Z | 2019-12-11T12:59:52Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
cols = pd.interval_range(0, 2)
data = np.random.rand(5, 2)
df = pd.DataFrame(data, columns=cols)
df.round() # works
cols2 = pd.CategoricalIndex(cols)
df2 = pd.DataFrame(data, columns=cols2)
df2.round() # fails
TypeError: No matching signature found
```
#### Problem description
The `round` method fails whenever the columns are CategoricalIndex made from an IntervalIndex. The `pd.cut` function creates these types of value and they can appear in columns when using `pivot_table`.
#### Expected Output
Should round as normal.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-12-04T20:23:46Z | 2019-12-05T16:06:34Z | 2019-12-05T14:46:33Z | MEMBER | null | A lot of this classmethod looks leftover from PanelND days. I think we can go even further towards getting rid of some of these attributes, but will save that for another time. | {
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} | 9 | 2019-12-04T20:35:15Z | 2019-12-05T17:44:02Z | 2019-12-05T17:43:57Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/30039
I put it in the wrong file, because the file names were swapped...
Now, here, I just renamed both files to the other name (the diff apparently cannot the rename, therefore you see a lot of changes, but I can ensure I didn't change anything except for pyarrow).
But, I could also leave the files as is and switch them in the posix.yml file ? (use the envs for the different build)
I don't know if there was something specific in either environment that was specifically added for the slow tests?
cc @datapythonista | {
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} | 1 | 2019-12-04T21:17:55Z | 2019-12-05T13:34:25Z | 2019-12-05T12:59:22Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-12-04T21:37:23Z | 2019-12-05T19:51:57Z | 2019-12-05T19:45:28Z | MEMBER | null | Follow-up to #30029 that handles the remaining attribute self.non_index_axes.
cc @WillAyd | {
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} | 1 | 2019-12-04T21:43:21Z | 2019-12-05T16:04:08Z | 2019-12-05T15:36:05Z | MEMBER | null | As mentioned in #30019, with Panel cases gone we can now deduce that `len(axes) == 1` in all cases in `create_axes`. From there we can simplify this method a lot. This PR does most of that; some of it is held back to avoid creating merge conflicts with #30067 (orthogonal) | {
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} | 0 | 2019-12-04T22:03:30Z | 2019-12-04T22:04:27Z | 2019-12-04T22:04:02Z | MEMBER | null | This is a failure from https://github.com/pandas-dev/pandas/pull/29961, sorry. In hindsight I should have updated that PR with master after the CI changes were merged, since I added pyarrow 0.15 to one of the CI builds earlier today exactly to catch those errors .. Sorry about that.
Will merge this quickly to not have CI fail annoyingly, but will do a proper fix (for this and https://github.com/pandas-dev/pandas/issues/29976) tomorrow | {
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} | 7 | 2019-12-05T01:27:17Z | 2019-12-06T00:47:28Z | 2019-12-06T00:46:06Z | MEMBER | null | cc @WillAyd how do we annotate "this returns a subclass of Col"? Here i've used `Type["Col"]` but afaik that doesnt include subclasses | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30075 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30075/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30075/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30075/events | https://github.com/pandas-dev/pandas/pull/30075 | 533,118,870 | MDExOlB1bGxSZXF1ZXN0MzQ5MjQ5OTgz | 30,075 | Fix PR06 docstring errors in sql.py and gbq.py | {
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} | 1 | 2019-12-05T04:37:42Z | 2019-12-05T06:36:06Z | 2019-12-05T06:35:59Z | CONTRIBUTOR | null | - [x] xref #28724
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30076 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30076/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30076/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30076/events | https://github.com/pandas-dev/pandas/issues/30076 | 533,171,195 | MDU6SXNzdWU1MzMxNzExOTU= | 30,076 | issue is described in this link of stack overflow https://stackoverflow.com/questions/59189610/converting-a-pandas-datframe-column-to-date-type-with-a-particular-format-of-dat | {
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} | [] | closed | false | null | [] | null | 2 | 2019-12-05T07:20:04Z | 2019-12-05T08:14:10Z | 2019-12-05T08:11:07Z | 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/30077 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30077/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30077/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30077/events | https://github.com/pandas-dev/pandas/pull/30077 | 533,189,367 | MDExOlB1bGxSZXF1ZXN0MzQ5MzA3NTg4 | 30,077 | BUG/TST: fix arrow roundtrip / parquet tests for recent pyarrow | {
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} | 2 | 2019-12-05T08:07:23Z | 2019-12-18T16:56:43Z | 2019-12-18T16:56:42Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/29976 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30078 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30078/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30078/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30078/events | https://github.com/pandas-dev/pandas/issues/30078 | 533,202,684 | MDU6SXNzdWU1MzMyMDI2ODQ= | 30,078 | EWM functions fail with superclasses of pd.DataFrame | {
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] | closed | false | null | [] | null | 2 | 2019-12-05T08:39:11Z | 2020-05-08T20:07:32Z | 2020-05-08T20:07:32Z | NONE | null | I've built a class that is a superclass of pd.DataFrame and I am trying to apply an exponential weighted mean along the columns, but I get a TypeError saying: ```TypeError: __init__() got an unexpected keyword argument 'index'```
Here is a minimal reproducible example.
```
import pandas as pd
import numpy as np
class Foo(pd.DataFrame):
def __init__(self, df):
super().__init__(df)
f = Foo(pd.DataFrame(np.random.randn(10,10)))
f.ewm(alpha=1/7).mean()
```
The full exception goes like this:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-39-223b6d63e220> in <module>
6 f = Foo(pd.DataFrame(np.random.randn(10,10)))
7
----> 8 f.ewm(alpha=1/7).mean()
~\Anaconda3\lib\site-packages\pandas\core\window.py in mean(self, *args, **kwargs)
2323 """
2324 nv.validate_window_func('mean', args, kwargs)
-> 2325 return self._apply('ewma', **kwargs)
2326
2327 @Substitution(name='ewm')
~\Anaconda3\lib\site-packages\pandas\core\window.py in _apply(self, func, **kwargs)
2309 results.append(np.apply_along_axis(func, self.axis, values))
2310
-> 2311 return self._wrap_results(results, blocks, obj)
2312
2313 @Substitution(name='ewm')
~\Anaconda3\lib\site-packages\pandas\core\window.py in _wrap_results(self, results, blocks, obj)
262 for result, block in zip(results, blocks):
263
--> 264 result = self._wrap_result(result, block=block, obj=obj)
265 if result.ndim == 1:
266 return result
~\Anaconda3\lib\site-packages\pandas\core\window.py in _wrap_result(self, result, block, obj)
242 return Series(result, index, name=obj.name)
243
--> 244 return type(obj)(result, index=index, columns=block.columns)
245 return result
246
TypeError: __init__() got an unexpected keyword argument 'index'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.3.1
setuptools: 41.4.0
Cython: None
numpy: 1.15.4
scipy: 1.3.1
pyarrow: None
xarray: None
IPython: 7.8.0
sphinx: 2.2.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.4.1
bs4: 4.8.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.3
s3fs: 0.1.6
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
Any ideas? | {
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} | 3 | 2019-12-05T10:49:19Z | 2019-12-07T09:08:42Z | 2019-12-06T20:47:38Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-12-05T13:50:51Z | 2019-12-05T14:56:13Z | 2019-12-05T14:45:10Z | MEMBER | null | - [x] ref #29547
- [ ] tests added / passed
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Somehow missed it on #30071 | {
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The `pandas.read_parquet` and `pandas.to_parquet` methods defer operation to either `pyarrow` (first priority) or `fastparquet`. The issue is that these libraries have a slightly different interface:
For reading:
- `pyarrow` accepts `IOBase` and not `bytes`
- `fastparquet` accepts `bytes` and not `IOBase`
Pandas should support one or both and do the conversion automatically.
For writing:
- `pyarrow` accepts `IOBase`
- `fastparquet` doesn't really support writing to an ephemeral buffer because the stream is closed when using the `open_with` argument (see https://github.com/dask/fastparquet/issues/408) | {
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} | 4 | 2019-12-05T16:42:14Z | 2019-12-05T20:10:46Z | 2019-12-05T19:22:33Z | MEMBER | null | Along with #30074 this is a bite-sized-pieces part of a series of PRs that ends with IndexCol attributes being set in the constructor and not mutated elsewhere | {
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} | 8 | 2019-12-05T16:42:54Z | 2019-12-12T15:08:53Z | 2019-12-12T14:53:49Z | CONTRIBUTOR | null | - [ ] closes #30011
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry `bug fix of to_datetime for pandas versions >0.24. Now the change will avoid error even when value passed None to to_datetime`
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} | 1 | 2019-12-05T17:12:24Z | 2019-12-05T20:10:26Z | 2019-12-05T19:26:42Z | MEMBER | null | See that after this, the way we are calling `self.set_data` is always with just `block.values` within the `set_atom_foo` methods (except for `set_atom_string`, which needs a small edit coming up in another PR). Once set_atom_string joins the others, we can move these 7 calls to set_data up to just one place. | {
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} | 2 | 2019-12-05T17:15:19Z | 2019-12-05T23:51:17Z | 2019-12-05T23:13:50Z | MEMBER | null | This is the other PR mentioned in #30084. By doing this casting earlier, we'll be able to 7 calls to set_data into one place. | {
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} | 2 | 2019-12-05T18:40:29Z | 2020-03-31T18:24:23Z | 2020-03-31T18:24:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
`DatetimeIndex.searchsorted` works fine with mixed timezones:
```python
>>> series_dt = pd.date_range('2015-02-19', periods=2, freq='1d', tz='UTC')
>>> dt = pd.to_datetime('2015-02-19T12:34:56').tz_localize('America/New_York')
>>> series_dt.searchsorted(dt)
1
```
However, `Series.searchsorted` does not:
```python
>>> from io import StringIO
>>> df = pd.read_csv(StringIO("datetime\n2015-02-19T00:00:00Z\n2015-02-20T00:00:00Z"), parse_dates=['datetime'])
>>> df.datetime.searchsorted(dt)
Traceback (most recent call last):
File "/Users/kwilliams/Library/Application Support/IntelliJIdea2019.2/python/helpers/pydev/_pydevd_bundle/pydevd_exec2.py", line 3, in Exec
exec(exp, global_vars, local_vars)
File "<input>", line 1, in <module>
File "venv/lib/python3.7/site-packages/pandas/core/series.py", line 2694, in searchsorted
return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
File "venv/lib/python3.7/site-packages/pandas/core/algorithms.py", line 1887, in searchsorted
result = arr.searchsorted(value, side=side, sorter=sorter)
File "venv/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 666, in searchsorted
self._check_compatible_with(value)
File "venv/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 591, in _check_compatible_with
own=self.tz, other=other.tz
ValueError: Timezones don't match. 'UTC != America/New_York'
```
Ostensibly, the underlying data vector is the same in both cases:
```python
>>> series_dt.dtype
datetime64[ns, UTC]
>>> df.datetime.dtype
datetime64[ns, UTC]
```
#### Problem description
I believe the `DatetimeIndex` is correct (or at least more useful), because even if timezones don't agree, the underlying instants are well-ordered and compare fine. In fact, both versions compare fine using a simple `>` comparison:
```python
>>> dt > series_dt
array([ True, False])
>>> dt > df.datetime
0 True
1 False
Name: datetime, dtype: bool
```
#### Expected Output
```python
>>> df.datetime.searchsorted(dt)
1
```
A workaround is to wrap the column using `pd.DatetimeIndex()`:
```python
>>> pd.DatetimeIndex(df.datetime).searchsorted(dt)
1
```
#### Output of ``pd.show_versions()``
<details>
<pre>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
##teamcity[testStdOut timestamp='2019-12-05T12:38:05.901' flowId='test.test_driver.MyTestCase.test_driver' locationHint='python</Users/kwilliams/git/dispatcher/rush-springs-simulations>://test.test_driver.MyTestCase.test_driver' name='test_driver' nodeId='75' out='feather : None|n' parentNodeId='74']
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.3.5
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
##teamcity[testStdOut timestamp='2019-12-05T12:38:05.904' flowId='test.test_driver.MyTestCase.test_driver' locationHint='python</Users/kwilliams/git/dispatcher/rush-springs-simulations>://test.test_driver.MyTestCase.test_driver' name='test_driver' nodeId='75' out='xlrd : None|n' parentNodeId='74']
xlwt : None
xlsxwriter : None
</pre>
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30087 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30087/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30087/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30087/events | https://github.com/pandas-dev/pandas/issues/30087 | 533,543,166 | MDU6SXNzdWU1MzM1NDMxNjY= | 30,087 | pandas.read_sql_table switches double quotation wrapping keys and values to single quotation in JSON columns | {
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```
>>> df = pandas.read_sql_table('table_name', 'postgres:///db_name')
>>> df.head(1)
```
#### Problem description
I used `pandas.read_sql_table()` to fetch a table, in which there is a JSON column. In the returned dataframe, the double quotations wrapping the keys and values are all changed to single quotations.
Example of returned dataframe:
|id|name|
|-|-|
|1| {'first': 'Anna', 'last': 'Smith'}|
However, JSON standards requires double quotation, on top of that, other Python package like `json` doesn't recognize JSON with single quotations.
#### Expected Output
|id|name|
|-|-|
|1| {"first": "Anna", "last": "Smith"}|
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.1
hypothesis : None
sphinx : 2.2.2
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
```
</details>
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} | 12 | 2019-12-05T20:53:33Z | 2020-05-08T11:03:34Z | 2020-05-08T11:03:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from functools import partial
df = pd.DataFrame(
np.random.randn(1000, 3),
index=pd.date_range('1/1/2012', freq='S', periods=1000),
columns=['A', 'B', 'C']
)
dfg = df.resample('3T').agg(
{'A': [
partial(np.quantile, q=.9999),
partial(np.quantile, q=.90),
]}
)
```
#### Problem description
Resample, unlike groupby, has no ability to do named aggregation. This means if you use a function twice on the same column you get an error saying:
``pandas.core.base.SpecificationError: Function names must be unique, found multiple named quantile``.
It would be very useful to make the resample agg interface similar to the groupby agg interface. An example of the named aggregation is here: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#named-aggregation
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-12-05T20:58:57Z | 2019-12-08T21:09:27Z | 2019-12-08T17:37:06Z | MEMBER | null | - [x] ref #29547 #29886
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] pandas/_libs/tslibs/frequencies.pyx
- [x] pandas/_libs/tslibs/period.pyx
- [x] pandas/_libs/tslibs/strptime.pyx
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} | 9 | 2019-12-05T21:43:27Z | 2019-12-09T16:24:11Z | 2019-12-09T09:41:33Z | CONTRIBUTOR | null | - [x] closes pandas-dev#29838
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2019-12-05T22:08:50Z | 2019-12-06T03:13:25Z | 2019-12-06T03:11:12Z | MEMBER | null | I _think_ this
closes #30031
closes #29514
we'll probably want to wait a few days to make it official.
To make the new test decorator useful in the CI we'll need to add psutil to the environment (unless someone knows of a stdlib way of doing the same check?)
One more note, the check in check_no_file_leaks depends on `__del__` having been called, which in turn depends on the gc. I'm not sure, but that might induce another layer of flakiness. If so, I think that can be fixed by calling `gc.collect()` before doing the `flist == flist2` check | {
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} | 6 | 2019-12-05T22:16:06Z | 2019-12-08T17:35:48Z | 2019-12-08T17:35:45Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/29997
@jorisvandenbossche do you have thoughts on where to document this behavior? I added a bit to `reference/arrays.rst` for now, but perhaps we'll eventually have a `user_guide/scalar_na.rst`?
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} | 8 | 2019-12-05T23:21:51Z | 2019-12-25T22:33:01Z | 2019-12-25T22:10:45Z | MEMBER | null | - [x] ref #29886
- [x] tests added / passed
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---
Can merge only after merging:
- [x] #29948
- [x] #29950
- [x] #29953
- [x] #29959
- [x] #29963
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} | 2 | 2019-12-06T00:51:33Z | 2019-12-06T15:26:02Z | 2019-12-06T13:05:26Z | MEMBER | null | This is part 1 of 2 of the payoff promised from #30074. | {
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} | 2 | 2019-12-06T01:37:17Z | 2019-12-06T04:11:47Z | 2019-12-06T03:21:37Z | MEMBER | null | companion to #30101 with promised payoff (logically orthgonal, but will have merge conflict when the time comes)
This implements _get_atom and makes only one call to it on 3949. By making all the relevant methods into classmethods, we can call it before we've instantiated an instance. Then following #30100, we'll be able to pass `typ` to the constructor instead of pinning it. This in turn will let us pass `pos` instead of calling `set_pos`.
Also after both this and #30101, we can get to rip out a bunch of no-longer-needed state-altering methods. | {
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} | 8 | 2019-12-06T01:44:13Z | 2019-12-08T19:07:59Z | 2019-12-08T17:30:41Z | MEMBER | null | DatetimeIndex.offset I thought was removed in #29801, but apparently slipped through the cracks. | {
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} | 1 | 2019-12-06T05:25:45Z | 2019-12-06T15:26:50Z | 2019-12-06T13:03:31Z | MEMBER | null | xref #27198 | {
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} | 3 | 2019-12-06T05:45:09Z | 2019-12-06T08:50:02Z | 2019-12-06T08:49:41Z | NONE | null | I'd like more flexibility in the stringification of a pandas dataframe. Consider a trivial dataframe:
```python
import pandas as pd
df = pd.DataFrame([[1.2454235445,2],
[3,4.452455]],
columns = ['A','B'])
print(df.to_string())
```
This outputs:
````
A B
0 1.245424 2.000000
1 3.000000 4.452455
````
My desired output is:
```
| | A | B |
+---+----------+----------+
| 0 | 1.245424 | 2.000000 |
| 1 | 3.000000 | 4.452455 |
```
I suggest adding the following options to `to_string()` to support the above output:
- `output_column_separator` -- The vertical bar in the above example
- `output_row_separator` -- The hyphen in the above example
- `output_cross_separator` -- The plus sign in the above example
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] | closed | false | null | [] | null | 4 | 2019-12-06T10:31:55Z | 2019-12-21T05:52:01Z | 2019-12-21T05:52:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.read_csv('example')
df.to_excel('Excel_Sample.xlsx',sheet_name='Sheet1')
```
where output of ``df`` is:
```
a b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
3 12 13 14 15
```
and file ***example*** is:
```
a,b,c,d
0,1,2,3
4,5,6,7
8,9,10,11
12,13,14,15
```
#### Problem description
The to_excel function of class dataframe must write the dataframe in the specified excel file instead it returns error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py", line 2256, in to_excel
engine=engine,
File "/usr/local/lib/python3.6/dist-packages/pandas/io/formats/excel.py", line 742, in write
writer.save()
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_openpyxl.py", line 44, in save
return self.book.save(self.path)
File "/usr/local/lib/python3.6/dist-packages/openpyxl/workbook/workbook.py", line 408, in save
save_workbook(self, filename)
File "/usr/local/lib/python3.6/dist-packages/openpyxl/writer/excel.py", line 293, in save_workbook
writer.save()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/writer/excel.py", line 275, in save
self.write_data()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/writer/excel.py", line 75, in write_data
self._write_worksheets()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/writer/excel.py", line 215, in _write_worksheets
self.write_worksheet(ws)
File "/usr/local/lib/python3.6/dist-packages/openpyxl/writer/excel.py", line 200, in write_worksheet
writer.write()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/worksheet/_writer.py", line 354, in write
self.write_top()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/worksheet/_writer.py", line 98, in write_top
self.write_properties()
File "/usr/local/lib/python3.6/dist-packages/openpyxl/worksheet/_writer.py", line 60, in write_properties
self.xf.send(props.to_tree())
File "/usr/local/lib/python3.6/dist-packages/openpyxl/worksheet/_writer.py", line 294, in get_stream
xf.write(el)
File "src/lxml/serializer.pxi", line 1230, in lxml.etree._IncrementalFileWriter.write
TypeError: got invalid input value of type <class 'xml.etree.ElementTree.Element'>, expected string or Element
```
trying to open written file using ``df = pd.read_excel('Excel_Sample.xlsx',sheet_name='Sheet1')``
returns error:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/pandas/util/_decorators.py", line 208, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py", line 310, in read_excel
io = ExcelFile(io, engine=engine)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py", line 819, in __init__
self._reader = self._engines[engine](self._io)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_xlrd.py", line 21, in __init__
super().__init__(filepath_or_buffer)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_base.py", line 359, in __init__
self.book = self.load_workbook(filepath_or_buffer)
File "/usr/local/lib/python3.6/dist-packages/pandas/io/excel/_xlrd.py", line 36, in load_workbook
return open_workbook(filepath_or_buffer)
File "/usr/local/lib/python3.6/dist-packages/xlrd/__init__.py", line 145, in open_workbook
raise XLRDError('ZIP file contents not a known type of workbook')
xlrd.biffh.XLRDError: ZIP file contents not a known type of workbook
```
#### Expected Output
The excel file should be written correctly with the dataframe's data.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-70-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 0.25.3
numpy : 1.16.3
pytz : 2018.7
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.0.1
Cython : None
pytest : 3.9.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.3
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30107 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30107/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30107/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30107/events | https://github.com/pandas-dev/pandas/issues/30107 | 533,886,628 | MDU6SXNzdWU1MzM4ODY2Mjg= | 30,107 | When applied as style in to_excel(), 'vertical-align: middle' works for OpenPyXL, but not for XlsxWriter engine | {
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... | open | false | null | [] | null | 0 | 2019-12-06T10:47:20Z | 2020-05-08T00:44:59Z | null | NONE | null | #### Code sample, a copy-pastable example if possible
```python
import pandas as pd
def align(data):
return pd.DataFrame('text-align: center', index=data.index, columns=data.columns)
def valign(data):
return pd.DataFrame('vertical-align: middle', index=data.index, columns=data.columns)
def whitespace(data):
return pd.DataFrame('white-space: normal', index=data.index, columns=data.columns)
d = {'col1': ['first\nsecond', 'only one'], 'col2': ['only one', 'first\nsecond']}
df = pd.DataFrame(data=d)
df = df.style.apply(align, axis=None).apply(valign, axis=None).apply(whitespace, axis=None)
with pd.ExcelWriter('test.xlsx', engine='xlsxwriter') as writer:
df.to_excel(writer, sheet_name='Test sheet')
```
#### Problem description
When using the XlsxWriter engine, the vertical alignment is ignored, the text is aligned to the bottom. The horizontal alignment as well as the whitespace/wrapping are correctly applied.

#### Expected output
This doesn't occur, when the OpenPyXL engine is chosen, i.e. when using:
```python
with pd.ExcelWriter('test.xlsx', engine='openpyxl') as writer:
df.to_excel(writer, sheet_name='Test sheet')
```
The expected output is correctly generated:

#### Some observations
The problem doesn't occur resp. the expected output can be generated solely using the XlsxWriter engine **without** using pandas styling:
```python
import pandas as pd
d = {'col1': ['first\nsecond', 'only one'], 'col2': ['only one', 'first\nsecond']}
df = pd.DataFrame(data=d)
with pd.ExcelWriter('test.xlsx', engine='xlsxwriter') as writer:
df.to_excel(writer, sheet_name='Test sheet')
workbook = writer.book
worksheet = writer.sheets['Test sheet']
fmt = workbook.add_format({'align': 'center', 'valign': 'vcenter', 'text_wrap': True})
worksheet.set_column('B:C', None, fmt)
```
From [pandas/io/formats/excel.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/formats/excel.py#L109), I assume, that the vertical alignment setting `middle` is mapped to `center`, such that `'vertical-align': 'center'`. Furthermore, from [pandas/io/excel/_xlsxwriter.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_xlsxwriter.py#L8), I guess, that the corresponding settings from the OpenPyXL engine are used and (partially) mapped to the correct names used in the XlsxWriter engine, cf. the correct mapping to `valign`. Now, my assumption is, that the (OpenPyXL's) `center` setting isn't properly translated to the XlsxWriter's `vcenter` setting, because, for example, `'vertical-align: top'` generates the proper output also for the XlsxWriter engine.
#### Output of ``pd.show_versions()``
<details>
```lang-none
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.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 : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 39.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0rc1
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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"default... | closed | false | null | [] | null | 4 | 2019-12-06T11:33:11Z | 2019-12-24T22:25:48Z | 2019-12-24T22:25:37Z | CONTRIBUTOR | null | ERROR: type should be string, got "https://github.com/pandas-dev/pandas/issues/29547#issuecomment-562164065\r\nhttps://github.com/pandas-dev/pandas/issues/29547\r\n\r\n- [x] pandas/_libs/tslibs/frequencies.pyx\r\n- [x] pandas/_libs/tslibs/period.pyx\r\n- [x] pandas/_libs/tslibs/strptime.pyx\r\n\r\nchanged %s to f strings and f ' ' to f \" \".\r\nstr.format() remained unchanged due to:\r\nhttps://github.com/pandas-dev/pandas/issues/29547#issuecomment-562227987 " | {
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} | 2 | 2019-12-06T15:52:59Z | 2019-12-08T17:30:52Z | 2019-12-08T17:29:44Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I think there is a bug in the docs. This Jupyter notebook shows that the code does not work as expected from the docs https://gist.github.com/bessarabov/5e42bbb359fb86aaa87aed6c12a95bc9
So I've fixed the doc to match behaviour.
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} | 0 | 2019-12-06T16:12:57Z | 2019-12-06T18:47:58Z | 2019-12-06T18:47:58Z | CONTRIBUTOR | null | part of #23877
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 9 | 2019-12-06T16:28:32Z | 2019-12-12T16:23:02Z | 2019-12-12T16:17:01Z | MEMBER | null | there are two msgpack files in LICENSES, should those go? | {
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} | 1 | 2019-12-06T16:32:49Z | 2019-12-07T00:39:21Z | 2019-12-06T22:34:52Z | MEMBER | null | im looking forward to being done with these | {
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