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} | 2 | 2019-06-28T22:51:23Z | 2019-06-30T03:46:39Z | 2019-06-30T03:46:34Z | MEMBER | null | - [X] closes #27111
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} | 1 | 2019-06-28T22:57:57Z | 2019-06-29T02:12:40Z | 2019-06-29T02:11:27Z | MEMBER | null | Suggested follow up by @Themanwithoutaplan here
https://github.com/pandas-dev/pandas/pull/25092/files#r298672802 | {
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} | 2 | 2019-06-28T23:15:36Z | 2020-01-16T00:34:54Z | 2019-06-30T17:15:17Z | MEMBER | null | Mentioned during sprint today some of the typing constructs we use were not added until Python 3.5.2, specifically typing.Type and typing.DefaultDict and maybe at some point typing.TYPE_CHECKING
https://docs.python.org/3/library/typing.html#typing.Type
This is a bump of the setup files and docs to 3.5.2. | {
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} | 6 | 2019-06-29T05:20:36Z | 2019-12-14T07:59:15Z | 2019-12-14T07:59:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Assuming `frame` is a pandas DataFrame which contains column `cal_dt`. If I want to write the DataFrame into a parquet partitioned by the column `cal_dt`, I have the following code without reading the doc carefully.
```python
frame.to_parquet('partitioned_parquet', partition_cols='cal_dt')
```
#### Problem description
The above code raises an issue of "KeyError: 'c'", which is not clear enough to users.
#### Expected Output
Of course, I know the right way is to pass a list of columns to `partition_cols` (see the code below).
```python
frame.to_parquet('partitioned_parquet', partition_cols=['cal_dt'])
```
However, as I mentioned that people will likely have the first example of code instead (expecting that passing a single column name would work) without reading the doc carefully. I think the method `to_parquet` should be enhanced to be either of the following.
1. Throws an exception with a clearer message saying that a list is required for `partition_cols` when a user passes a non-list object to it.
2. Support passing a single string to `partition_cols` in which it means to use that column as the partition column.
Either way, the implementation is simple but it does improve user experience.
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
How do I go about adding tests for that rely on an external module?
Would be something like
```python3
df = pd.DataFrame({
"length" : pd.Series([1,2],dtype = pd.Int16Dtype),
"width" : PA_([2,3], dtype="pint[m]"),
"distance" : PA_([2,3], dtype="in"),
"height" : PA_([2,3], dtype=ureg.m),
"depth" : PA_.from_1darray_quantity(Q_([2,3],ureg.mm)),
})
df.plot()
``` | {
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} | 3 | 2019-06-29T12:43:46Z | 2019-06-30T15:42:46Z | 2019-06-30T12:06:45Z | MEMBER | null | - [x] closes #16685
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The ``pytest.raises(AttributeError, ...)`` are somewhat superfluous in that they test a limit of the implementation, rather than a guarantee of the API... but they still make the test more robust. | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-06-29T15:07:56Z | 2019-06-29T17:02:44Z | 2019-06-29T17:02:41Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Removed:
- Using `Series` as list-like in `rename_categories`
- `DataFrame/Series.reindex_axis`
- Renaming labels using `rename_axis` | {
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} | 0 | 2019-06-29T15:21:49Z | 2019-06-30T15:56:40Z | 2019-06-30T15:56:36Z | MEMBER | null | As noticed by @jorisvandenbossche openpyxl was throwing some warnings for excel files in the test suite. The warnings did not appear relevant to the data within the files and are most likely historical artifacts of how the files were created. Simply resaving these removes the warning from the builds
```sh
pandas/tests/io/excel/test_readers.py::TestReaders::test_reader_special_dtypes[openpyxl-.xlsx]
pandas/tests/io/excel/test_readers.py::TestReaders::test_reader_special_dtypes[openpyxl-.xlsm]
pandas/tests/io/excel/test_readers.py::TestReaders::test_reading_all_sheets[openpyxl-.xlsx]
pandas/tests/io/excel/test_readers.py::TestReaders::test_reading_all_sheets[openpyxl-.xlsm]
pandas/tests/io/excel/test_readers.py::TestReaders::test_reading_multiple_specific_sheets[openpyxl-.xlsx]
pandas/tests/io/excel/test_readers.py::TestReaders::test_reading_multiple_specific_sheets[openpyxl-.xlsm]
pandas/tests/io/excel/test_readers.py::TestReaders::test_read_excel_squeeze[openpyxl-.xlsx]
pandas/tests/io/excel/test_readers.py::TestReaders::test_read_excel_squeeze[openpyxl-.xlsm]
pandas/tests/io/excel/test_readers.py::TestExcelFileRead::test_excel_passes_na[openpyxl-.xlsx]
pandas/tests/io/excel/test_readers.py::TestExcelFileRead::test_excel_passes_na[openpyxl-.xlsm]
/Users/williamayd/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/openpyxl/worksheet/_reader.py:296: UserWarning: Unknown extension is not supported and will be removed
``` | {
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"description": "Series/D... | open | false | null | [] | null | 2 | 2019-06-29T15:23:31Z | 2021-07-10T05:44:26Z | null | CONTRIBUTOR | null | From https://github.com/dask/dask/issues/5021,
suppose I have three ndarrays, different dtypes, and I'd like to place them in a DataFrame. However, one of more of the arrays is 2D.
Right now, I'd probably do something like
```python
In [18]: a = np.random.randn(10, 2)
In [19]: b = np.random.randn(10,)
In [20]: c = np.random.randint(0, 10, (10, 3))
In [21]: df1 = pd.DataFrame(a, columns=['a_1', 'a_2'])
In [22]: df2 = pd.DataFrame(b, columns=['b'])
In [23]: df3 = pd.DataFrame(c, columns=['c_1', 'c_2', 'c_3'])
In [24]: pd.concat([df1, df2, df2], axis=1)
Out[24]:
a_1 a_2 b b
0 -0.006595 0.255718 -1.085109 -1.085109
1 0.568423 -0.607054 -0.380154 -0.380154
2 0.589928 1.920212 -0.296742 -0.296742
...
```
Would we want to make that easier. Something like
```python
pd.DataFrame.from_dict({"a": a, "b": b, "c": c})
```
* The length of all the input arrays must match.
* For 2-d values in the dict, the output column names will be `<key>_<I>`.
There are a few issues (non-string keys, etc.), but this may be worth doing.
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} | 3 | 2019-06-29T15:48:34Z | 2019-07-03T12:08:08Z | 2019-07-03T12:08:08Z | MEMBER | null | The following tests fails from time to time on Python 3.5 builds
```
=================================== FAILURES ===================================
_____________________________ test_agg_consistency _____________________________
[gw0] darwin -- Python 3.5.6 /Users/vsts/miniconda3/envs/pandas-dev/bin/python
def test_agg_consistency():
# make sure that we are consistent across
# similar aggregations with and w/o selection list
df = DataFrame(np.random.randn(1000, 3),
index=pd.date_range('1/1/2012', freq='S', periods=1000),
columns=['A', 'B', 'C'])
r = df.resample('3T')
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
expected = r[['A', 'B', 'C']].agg({'r1': 'mean', 'r2': 'sum'})
result = r.agg({'r1': 'mean', 'r2': 'sum'})
> assert_frame_equal(result, expected)
pandas/tests/resample/test_resample_api.py:250:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/_libs/testing.pyx:65: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj)
E AssertionError: MultiIndex level [0] are different
E
E MultiIndex level [0] values are different (100.0 %)
E [left]: Index(['r2', 'r2', 'r2', 'r1', 'r1', 'r1'], dtype='object')
E [right]: Index(['r1', 'r1', 'r1', 'r2', 'r2', 'r2'], dtype='object')
pandas/_libs/testing.pyx:178: AssertionError
```
See eg https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=13625&view=logs&jobId=521b7dfd-2989-5ff8-bc8c-7481906480fa | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27125/events | https://github.com/pandas-dev/pandas/issues/27125 | 462,314,427 | MDU6SXNzdWU0NjIzMTQ0Mjc= | 27,125 | BUG: Index constructor should not allow an ndarray with ndim > 2 | {
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} | 2 | 2019-06-29T15:49:06Z | 2020-01-09T21:17:06Z | 2020-01-09T21:17:06Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
On master:
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+833.gad18ea35b'
In [2]: pd.Index(np.arange(8).reshape(2, 2, 2))
Out[2]: Int64Index([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dtype='int64')
```
If the first dimension is greater than 2 it appears to flatten but does not actually do so:
```python
In [3]: pd.Index(np.arange(12).reshape(3, 2, 2))
Out[3]: Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype='int64')
In [4]: _.values
Out[4]:
array([[[ 0, 1],
[ 2, 3]],
[[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11]]])
```
#### Problem description
The `Index` constructor accepts ndarrays with ndim > 2 and will even convert them to specialized subclasses, e.g. `Int64Index`.
#### Expected Output
I'd expect the operations above to raise, or at the very least should result in an `object` dtype `Index`, though I'd prefer to raise.
xref #17246
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ad18ea35ba461a92b1ea2204f4edc55bb42e9d71
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0.dev0+833.gad18ea35b
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.8.0
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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Removes:
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Raise a consistent error, regardless of the writing engine, when saving datetimes with timezones in Excel since they are unsupported. | {
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} | 0 | 2019-06-29T18:00:13Z | 2019-06-30T20:49:43Z | 2019-06-30T19:27:48Z | NONE | null | As discussed in #27059, the right-inclusive behavior of loc is surprising to many users. The docs include a section with an explanation (such as it is), but it is difficult to find, and not linked from the primary discussion of loc's behaviour. Add some links. | {
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} | 1 | 2019-06-29T18:01:04Z | 2019-07-31T16:20:11Z | 2019-06-30T21:41:56Z | NONE | null | I thought this paragraph could be improved, so I tried to.
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] | closed | false | null | [] | null | 1 | 2019-06-29T18:31:17Z | 2020-05-09T14:17:58Z | 2020-05-09T14:17:57Z | CONTRIBUTOR | null | We can probably move `Series.__array_ufunc__` to `core/base.py` (once it's in)
The primary difference is that `ufunc(Index, Series)` should defer to `Series` by returning NotImplemented. This may be as easy as excluding `Series` from `Index._HANDLED_TYPES` and including `Index` in `Series._HANDLED_TYPES`. | {
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} | 1 | 2019-06-29T18:40:41Z | 2019-06-30T21:41:41Z | 2019-06-30T21:41:28Z | NONE | null | xref #9309, though that's about consistency, not just missing info.
```python
In [7]: pd.Timedelta(nanoseconds=1)
Out[7]: Timedelta('0 days 00:00:00.000000')
In [8]: pd.Timestamp("2000-01-01")-pd.Timedelta(nanoseconds=1)
Out[8]: Timestamp('1999-12-31 23:59:59.999999999')
```
@jorisvandenbossche, recall we discussed using "roles" for repr? this might be a good use case, considering https://github.com/pandas-dev/pandas/issues/9309#issuecomment-70958154.
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} | 2 | 2019-06-29T20:55:19Z | 2020-06-29T21:42:19Z | 2020-06-29T21:42:19Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```bash
pwd
/home/guy/tmp
echo -e '{"a": 1, "b": 2}\n{"a": 3, "b": 4}' > test.json
cat test.json
{"a": 1, "b": 2}
{"a": 3, "b": 4}
```
```python
# Your code here
[ins] In [74]: ospath = '/home/guy/tmp/test.json'
[ins] In [75]: fileurl = 'file://localhost/home/guy/tmp/test.json'
[nav] In [76]: import pandas as pd
[ins] In [77]: pd.read_json(ospath, lines=True)
Out[77]:
a b
0 1 2
1 3 4
[ins] In [78]: pd.read_json(fileurl, lines=True)
Out[78]:
a b
0 1 2
1 3 4
[ins] In [79]: reader = pd.read_json(ospath, lines=True, chunksize=1)
[ins] In [80]: for chunk in reader:
...: print(chunk)
...:
a b
0 1 2
a b
1 3 4
[ins] In [81]: reader = pd.read_json(fileurl, lines=True, chunksize=1)
```
#### Problem description
Create a very simple two-line JSON file. Specify the location of the file two ways - using an OS path, and using a file URL. Both allow read_json() to read the JSON when read in one go. If using chunksize to create a reader, only the OS path specifier works. Trying to use the file path specifier produces a TypeError: sequence item 0: expected str instance, bytes found
The read_json doc says:
path_or_buf : a valid JSON string or file-like, default: None
The string could be a URL. Valid URL schemes include http, ftp, s3,
gcs, and file. For file URLs, a host is expected. For instance, a local
file could be ``file://localhost/path/to/table.json``
This leads to the expectation that a file URL should behave the same as a simple OS path under all use cases.
I think this is the root cause of the problem described under issue #27022
```
# output when using fileurl:
TypeError Traceback (most recent call last)
<ipython-input-82-605ab8a466fd> in <module>
----> 1 for chunk in reader:
2 print(chunk)
3
~/anaconda3/envs/test_latest_pandas_json/lib/python3.7/site-packages/pandas/io/json/json.py in __next__(self)
579 lines = list(islice(self.data, self.chunksize))
580 if lines:
--> 581 lines_json = self._combine_lines(lines)
582 obj = self._get_object_parser(lines_json)
583
~/anaconda3/envs/test_latest_pandas_json/lib/python3.7/site-packages/pandas/io/json/json.py in _combine_lines(self, lines)
520 """
521 lines = filter(None, map(lambda x: x.strip(), lines))
--> 522 return '[' + ','.join(lines) + ']'
523
524 def read(self):
TypeError: sequence item 0: expected str instance, bytes found
```
[ins] In [83]: pd.__version__
Out[83]: '0.24.2'
#### Expected Output
Behaviour of read_json to be the same regardless of the type of file-like.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
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: 3.1.0
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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} | 3 | 2019-06-30T00:13:47Z | 2019-07-06T22:11:52Z | 2019-07-06T22:11:47Z | CONTRIBUTOR | null | This PR fixes a number of issues involving non-unique non-numeric indexing:
- The `NonNumericSeriesIndexing` benchmark incorrectly assumes `tm.makeStringIndex()` returns a monotonic `Index`. It does not and unfortunately invalidates the existing results here.
- While modifying this, figured should add `PeriodIndex` and `non_monotonic` cases
- The `setup()` function creates an index but does not trigger the engine mapping to be populated. This leads to noisy and erroneous results.
- See here for examples and compare with below: https://pandas.pydata.org/speed/pandas/#indexing.NonNumericSeriesIndexing.time_getitem_scalar
- A `PeriodIndex` or `DatetimeIndex` with non-unique, monotonic data creates an entirely new `Index` when being sliced. This means the engine mapping gets recomputed on every list-like query.
- A `PeriodIndex` is actually a `PeriodEngine` plus a separate `Int64Index` (with associated `Int64Engine`). The former contains a mapping of `{Period: value}` while being a subclass of `Int64Engine`. This means calls to `_bin_search()` fail as the underlying array consists of `Period` objects. This only occurs when the index is non-unique, monotonic, *and* `>= 1M` elements long.
- We add an explicit test for this failure mode
- A number of index calls pass `value.ordinal` to the `PeriodEngine`; as that is a `{Period: value}` mapping, all such calls fail.
- We instead pass `value.ordinal` queries to the `Int64Engine`, where they succeed.
- Finally, `PeriodIndex.get_memory()` does not account for the memory of its associated `Int64Index`. This is fixed by simply adding it to the total.
```
before after ratio
[2811464a] [a68caa2a]
<import_bench~1> <period_fix>
failed 62.4±2μs n/a indexing.NonNumericSeriesIndexing.time_get_value('period', 'unique_monotonic_inc')
failed 216±20μs n/a indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'unique_monotonic_inc')
failed 2.74±0.05ms n/a indexing.NonNumericSeriesIndexing.time_getitem_list_like('period', 'unique_monotonic_inc')
failed 160±0.5μs n/a indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'unique_monotonic_inc')
failed 43.0±0.2μs n/a indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'unique_monotonic_inc')
- 718±10μs 207±10μs 0.29 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'non_monotonic')
- 2.53±0.05ms 701±10μs 0.28 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period', 'non_monotonic')
- 33.8±0.9ms 2.32±0.04ms 0.07 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period', 'nonunique_monotonic_inc')
- 62.7±0.5ms 532±3μs 0.01 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
before after ratio
[2811464a] [a68caa2a]
<import_bench~1> <period_fix>
+ 40.7±0.7μs 46.8±0.8μs 1.15 indexing.NonNumericSeriesIndexing.time_get_value('period', 'non_monotonic')
+ 27.0±2μs 30.4±0.6μs 1.12 indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'non_monotonic')
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 11 | 2019-06-30T05:21:11Z | 2019-07-03T20:57:39Z | 2019-07-03T20:40:31Z | CONTRIBUTOR | null | continue #26354
1. Categorical will be handled in a separate PR.
2. benchmark result:
```
before after ratio
[989f912e] [dc0ef701]
<master> <enh-21801-2>
+ 11.4±0.03μs 36.8±0.1μs 3.23 algorithms.EASorting.time_argsort(1000)
+ 785±0.5μs 1.26±0.03ms 1.60 algorithms.EASorting.time_argsort(100000)
```
xref https://github.com/pandas-dev/pandas/issues/21801 (doesn't close; Need to update existing EAs) | {
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... | open | false | null | [] | null | 3 | 2019-06-30T06:22:59Z | 2021-07-10T05:45:19Z | null | CONTRIBUTOR | null | I propose adding a ``MultiIndex._data`` that is of type ``List[Categorical]``, where all the underlying data of a MultiIndex would be stored. A ``multiIndex.array`` property would also be added, that accesses the ``_data``.
This has the advantage of collecting the data that is underlying MultiIndex into one data structure, that is human readable, and also makes access to zero-copy data very easy, e.g. would ``mi.array[1]`` return the data of the second level as a ``Categorical``, in a easy-to-read form.
A ``MultiIndex`` could with the above changes be explained as just "a container over a list of Categoricals", which is easier to explain than the current mode. The ``MultiIndex`` could also be related to ``CategoricalIndex``, which is "a container over a single Categorical".
This change means that ``MultiIndex.levels`` will become a property that returns a ``FrozenList(cat.categories for cat in self._data)``, and ``MultiIndex.codes`` will be a property that returns ``FrozenList(cat.codes for cat in self._data)``.
``MultiIndex.array`` will be added and will simply be a property that returns a FrozenList of ``self._data``.
Performance will not be affected, as most operations would still go through ``MultiIndex.codes`` and ``MultiIndex.levels``.
## Moving names from MultiIndex.levels to MultiIndex._names
Currently the levels' names are stored at each level's ``name`` attribute. This is not very compatible with extracting the categories from ``_data``. (the ``.categories`` is actually part of the dtype, which ideally should be immutable, so we shouldn't set or change its name attribute).
To make my suggestion practically possible, the level names should be stored in ``MultiIndex._names`` instead, and ``MultiIndex.names`` will become a property that reads from/writes to ``MultiIndex._names``. I think this change simplifies the MultiIndex a bit, as data and names are dealt with separately. This is a small backward breaking change though.
So, I suggest making two PRs:
1. Separating the names from the levels (to be included in 0.25)
2. Add ``_data``, ``array`` and change ``levels`` and ``codes`` into properties.
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} | 0 | 2019-06-30T06:54:10Z | 2019-06-30T18:53:17Z | 2019-06-30T16:16:25Z | MEMBER | null | fixture for todos from #27114
use of safe_import in pytest.param #26735 (comment) #26784
cc @WillAyd
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} | 1 | 2019-06-30T13:19:17Z | 2019-07-11T15:47:41Z | 2019-07-01T21:56:48Z | CONTRIBUTOR | null | - [x] closes #26005 (#26940 and #26910 solves other issues presented in #26005)
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2019-06-30T14:23:43Z | 2019-06-30T18:57:05Z | 2019-06-30T18:57:02Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Removed:
- `cdate_range`
- `raise_on_error` keyword in `.where` and `.mask`
- `ordered` and `categories` keywords in `.astype` | {
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cc @jreback @jorisvandenbossche @TomAugspurger | {
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"description": "Un... | closed | false | null | [] | null | 11 | 2019-06-30T15:26:04Z | 2019-08-30T22:19:28Z | 2019-08-30T22:19:27Z | MEMBER | null | I'm frequently getting local test runs where nearly all tests/plotting tests will fail with something like
```
self = <pandas.tests.plotting.test_misc.TestSeriesPlots object at 0x12fc977b8>
@pytest.mark.slow
def test_autocorrelation_plot(self):
from pandas.plotting import autocorrelation_plot
> _check_plot_works(autocorrelation_plot, series=self.ts)
pandas/tests/plotting/test_misc.py:42:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/plotting/common.py:509: in _check_plot_works
ret = f(**kwargs)
pandas/plotting/_misc.py:374: in autocorrelation_plot
plot_backend = _get_plot_backend()
pandas/plotting/_core.py:630: in _get_plot_backend
return importlib.import_module(backend_str)
/usr/local/Cellar/python/3.7.3/Frameworks/Python.framework/Versions/3.7/lib/python3.7/importlib/__init__.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
<frozen importlib._bootstrap>:1006: in _gcd_import
???
<frozen importlib._bootstrap>:983: in _find_and_load
???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
name = 'not_an_existing_module', import_ = <function _gcd_import at 0x10927ae18>
> ???
E ModuleNotFoundError: No module named 'not_an_existing_module'
<frozen importlib._bootstrap>:965: ModuleNotFoundError
```
Re-running with `--lf` consistently sees all of these pass.
OSX, PY37 | {
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} | 2 | 2019-06-30T15:37:34Z | 2019-07-01T16:09:50Z | 2019-07-01T11:41:25Z | MEMBER | null | - [x] closes #7775
- [x] closes #21671
- [x] closes #14948
- [x] closes #11926
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Also adds 3 regression tests for fixed timezone issues. | {
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} | 3 | 2019-06-30T16:59:36Z | 2019-07-03T15:13:21Z | 2019-07-03T11:27:35Z | MEMBER | null | - [x] xref #18262
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Unfortunately, `.dtype.value_counts()` isn't a perfect replacement for `get_value_counts()` (IMO it's better), so the test replacements calls the internal representation:
```
In [2]: df = pd.DataFrame([[1, 1.5, pd.Timestamp.now(), True]])
In [3]: df
Out[3]:
0 1 2 3
0 1 1.5 2019-06-30 11:55:24.380126 True
In [7]: df.get_dtype_counts().index
Out[7]: Index(['float64', 'int64', 'datetime64[ns]', 'bool'], dtype='object')
In [8]: df.dtypes.value_counts().index
Out[8]: Index([datetime64[ns], bool, float64, int64], dtype='object')
``` | {
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} | 5 | 2019-06-30T17:09:13Z | 2019-07-24T16:42:51Z | 2019-07-24T16:42:51Z | MEMBER | null | Right now we use the ABC classes in pandas._typing to work around circular imports, but I think this has the effect of making our type checking practically non-existent. The ABCs are factory generated by an unannotated function, so they get replaced with `Any`
To illustrate, run mypy on a module with this content
```python
from pandas.core.dtypes.generic import ABCDataFrame
some_obj = None # type: ABCDataFrame
reveal_type(some_obj)
```
Mypy runs without issue yielding
```sh
test.py:4: note: Revealed type is 'Any'
```
By contrast, using the actual DataFrame class:
```python
import pandas as pd
some_obj = None # type: pd.DataFrame
reveal_type(some_obj)
```
Yields an error:
```sh
(pandas-dev) mypy test.py
test.py:3: error: Incompatible types in assignment (expression has type "None", variable has type "DataFrame")
test.py:4: note: Revealed type is 'pandas.core.frame.DataFrame'
```
I'm not sure the best approach here. Direct imports into pandas._typing should cause circular imports but the ABCs won't provide anything with type checking as long as they are factory generated. | {
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} | 4 | 2019-06-30T19:09:46Z | 2020-10-11T19:35:31Z | 2020-10-11T19:35:31Z | MEMBER | null | ```
from pandas.core.arrays import IntervalArray
import numpy as np
arr = np.random.randn(10)
a = IntervalArray.from_arrays(arr, arr+1)
b = a[:5]
b[0] = b[1]
assert a[0] == a[1] # <-fails
``` | {
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} | 0 | 2019-06-30T19:58:06Z | 2019-07-08T12:44:36Z | 2019-07-08T12:44:36Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [2]: mi = pd.MultiIndex.from_tuples([[1, 2, -1], [3,4, -2], [5, 6, -3]])
In [3]: s = pd.Series(1, mi)
In [4]: s.loc[[100, 101]]
Out[4]: Series([], dtype: int64)
In [5]: s.loc[[100]]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-7a5bc11c892d> in <module>
----> 1 s.loc[[100]]
~/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1427
1428 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1429 return self._getitem_axis(maybe_callable, axis=axis)
1430
1431 def _is_scalar_access(self, key):
~/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1829 raise ValueError('Cannot index with multidimensional key')
1830
-> 1831 return self._getitem_iterable(key, axis=axis)
1832
1833 # nested tuple slicing
~/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
1131 # A collection of keys
1132 keyarr, indexer = self._get_listlike_indexer(key, axis,
-> 1133 raise_missing=False)
1134 return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},
1135 copy=True, allow_dups=True)
~/nobackup/repo/pandas/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1068 # if it cannot handle:
1069 indexer, keyarr = ax._convert_listlike_indexer(key,
-> 1070 kind=self.name)
1071 # We only act on all found values:
1072 if indexer is not None and (indexer != -1).all():
~/nobackup/repo/pandas/pandas/core/indexes/multi.py in _convert_listlike_indexer(self, keyarr, kind)
2255 mask = check == -1
2256 if mask.any():
-> 2257 raise KeyError('%s not in index' % keyarr[mask])
2258
2259 return indexer, keyarr
KeyError: '[100] not in index'
```
#### Problem description
Because of a [trivial typo](https://github.com/pandas-dev/pandas/blob/af7f2ef73e449f01acc6de47463c9b1440c6b0fb/pandas/core/indexing.py#L1821), behavior depends on whether the list has length at least 2. The "one item" behavior is arguably seldomly used, and future incompatible (xref: #20916 ).
#### Expected Output
Like ``Out[4]:``.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b4913295ab549652d1714082a60ec730436ed612
python : 3.7.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 4.9.0-9-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0.dev0+849.gb4913295a
numpy : 1.16.4
pytz : 2016.7
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.4.9
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: 0.2.1
bs4 : 4.5.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
openpyxl : 2.3.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.0.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 0.9.6
</details>
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} | 6 | 2019-06-30T20:23:21Z | 2019-07-02T13:33:49Z | 2019-07-02T13:31:16Z | MEMBER | null | There is lots of code in core.internals inside try/except blocks, in particular calls to _try_coerce_args. It is tough to reason about these because it is often unclear what the raising cases are. This simplifies that problem by separating `values, args = self._try_coerce_args(values, args)` into `values = self._coerce_values(values)` and `args = self._try_coerce_args(args)` (Note the former doesnt have a "try" in the name because it never raises).
Also: removed a never-used `ndim` kwarg from `Block.make_block` and removed a now-redundant `DatetimeTZBlock.copy` method. | {
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"description": "Re... | open | false | null | [] | null | 2 | 2019-06-30T20:44:37Z | 2021-07-10T05:46:54Z | null | MEMBER | null | With the recent plotting backend refactor, the `pandas.plotting.register_matplotlib_converters` is now referring to the "general" `register` which calls the plotting backend, instead of just being the matplotlib register function.
It probably doesn't make much sense to expose this through the plotting backend?
It is maybe done for consistency (everything goes through the plotting backend, also internally), but personally I found this indirection confusing when looking at the source code (for this case).
cc @datapythonista
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} | 1 | 2019-06-30T21:03:58Z | 2019-07-08T12:44:41Z | 2019-07-08T12:44:37Z | MEMBER | null | - [x] closes #27148
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"id": ... | closed | false | null | [] | null | 1 | 2019-06-30T22:32:41Z | 2019-07-01T21:06:51Z | 2019-07-01T21:06:51Z | MEMBER | null | The docs for ``to_latex(float_format=)`` say
```
float_format : str, optional
Format string for floating point numbers.
```
and indeed the code contains a comment confirming this:
https://github.com/pandas-dev/pandas/blob/8b48f5c75a058c239a5d0eb9ee4f1593f1be1810/pandas/io/formats/format.py#L1010
... but it does accept a function:
```
In [2]: s = pd.Series(1/3, index=range(3))
In [3]: s.to_latex(float_format='{:0.2f}'.format)
Out[3]: '\\begin{tabular}{lr}\n\\toprule\n{} & 0 \\\\\n\\midrule\n0 & 0.33 \\\\\n1 & 0.33 \\\\\n2 & 0.33 \\\\\n\\bottomrule\n\\end{tabular}\n'
```
... and ``formatters`` (mentioned in the comment above) does not apply to ``Series`` (?), and is not as convenient for ``DataFrames`` (it requires to explicitly mention each column name).
So I guess we just want to document that it can be either a string, or a function.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f58a1fee78629ad158eca41c8f228e59aae311b0
python : 3.7.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 4.9.0-9-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0.dev0+854.gf58a1fee7.dirty
numpy : 1.16.4
pytz : 2016.7
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.4.9
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: 0.2.1
bs4 : 4.5.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
openpyxl : 2.3.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.0.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 0.9.6
</details>
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 4 | 2019-07-01T01:28:08Z | 2019-07-02T01:18:14Z | 2019-07-02T00:17:49Z | NONE | null | Cleans up a whole bunch. Remaining offenders are:
- Parsers.pyx and tokenizer. Signed/unsigned hell, made worse by python/cython/c mixing.
- interval.pyx, not touched because large PR in progress in #27100.
- np_datetime.c and np_datetime_strings.c. These are linked into 15+ cython modules as extra object files, but they '#include <datetime.h>`, which expects only standalone modules, who properly init to do it. Kinda painful to fix, though it means each function is compiled and kept in memory 15 times.
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Check out [this](https://blog.travis-ci.com/2018-11-19-required-linux-infrastructure-migration). | {
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RuntimeError: Python version >= 3.5 required.
this is because numpy has a release version 1.7 which requires 3.5 or above
[https://github.com/numpy/numpy/blob/maintenance/1.17.x/setup.py](url)
is it okay to add a dependency check in setup.py? i can do that and submit for review or let me know how do you suggest we address this? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27160 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27160/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27160/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27160/events | https://github.com/pandas-dev/pandas/issues/27160 | 462,701,960 | MDU6SXNzdWU0NjI3MDE5NjA= | 27,160 | Misleading error for pd.read_msgpack | {
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} | 5 | 2019-07-01T13:01:56Z | 2019-07-19T17:57:23Z | 2019-07-06T17:44:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.read_msgpack('this/path/does/not/exist')
```
#### Problem description
Such an error is misleading because it suggests that there is a problem with the datatype being passed, not that the path does not exist. The error raised is:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".local/anaconda3/lib/python3.7/site-packages/pandas/io/packers.py", line 226, in read_msgpack
raise ValueError('path_or_buf needs to be a string file path or file-like')
ValueError: path_or_buf needs to be a string file path or file-like
```
#### Expected Output
Raise an error indicating that the path was not found.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-24-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: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
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: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datarea
</details>
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} | 1 | 2019-07-01T13:13:44Z | 2019-07-01T15:36:25Z | 2019-07-01T15:36:25Z | CONTRIBUTOR | null | `conda env create -n travis-36-cov --force -f ci/deps/travis-36-cov.yaml `
is failing with
```
Downloading and Extracting Packages
psutil-5.6.2 | 318 KB | ######################################################################################################################################### | 100%
dask-2.0.0 | 11 KB | ######################################################################################################################################### | 100%
dask-core-2.0.0 | 576 KB | ######################################################################################################################################### | 100%
distributed-2.0.1 | 365 KB | ######################################################################################################################################### | 100%
gcsfs-0.2.3 | 26 KB | ######################################################################################################################################### | 100%
pytz-2019.1 | 236 KB | ######################################################################################################################################### | 100%
google-api-core-1.13 | 79 KB | ######################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Ran pip subprocess with arguments:
['/Users/taugspurger/miniconda3/envs/travis-36-cov/bin/python', '-m', 'pip', 'install', '-U', '-r', '/Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt']
Pip subprocess output:
Collecting brotlipy (from -r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 1))
Using cached https://files.pythonhosted.org/packages/7c/17/365232505a464b27b993f7fed99d235eb0a46a538a470d6d9a4b60b6055d/brotlipy-0.7.0-cp36-cp36m-macosx_10_6_intel.whl
Requirement already up-to-date: coverage in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from -r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 2)) (4.5.3)
Collecting moto (from -r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3))
Downloading https://files.pythonhosted.org/packages/68/43/c7afe0bfbd44f8a72981bd96c2c3b159333625e4aef24397a93870346b4d/moto-1.3.9.tar.gz (819kB)
Collecting pandas-datareader (from -r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 4))
Using cached https://files.pythonhosted.org/packages/cc/5c/ea5b6dcfd0f55c5fb1e37fb45335ec01cceca199b8a79339137f5ed269e0/pandas_datareader-0.7.0-py2.py3-none-any.whl
Requirement already up-to-date: python-dateutil in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from -r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 5)) (2.8.0)
Requirement already satisfied, skipping upgrade: cffi>=1.0.0 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from brotlipy->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 1)) (1.12.3)
Requirement already satisfied, skipping upgrade: Jinja2>=2.10.1 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (2.10.1)
Collecting boto>=2.36.0 (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3))
Using cached https://files.pythonhosted.org/packages/23/10/c0b78c27298029e4454a472a1919bde20cb182dab1662cec7f2ca1dcc523/boto-2.49.0-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: boto3>=1.9.86 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (1.9.162)
Requirement already satisfied, skipping upgrade: botocore>=1.12.86 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (1.12.163)
Requirement already satisfied, skipping upgrade: cryptography>=2.3.0 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (2.7)
Collecting datetime (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3))
Downloading https://files.pythonhosted.org/packages/73/22/a5297f3a1f92468cc737f8ce7ba6e5f245fcfafeae810ba37bd1039ea01c/DateTime-4.3-py2.py3-none-any.whl (60kB)
Requirement already satisfied, skipping upgrade: requests>=2.5 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (2.22.0)
Collecting xmltodict (from moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3))
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Requirement already satisfied, skipping upgrade: rsa in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from python-jose<4.0.0->moto->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 3)) (3.4.2)
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Requirement already satisfied, skipping upgrade: numpy>=1.12.0 in /Users/taugspurger/miniconda3/envs/travis-36-cov/lib/python3.6/site-packages (from pandas>=0.19.2->pandas-datareader->-r /Users/taugspurger/sandbox/pandas/ci/deps/condaenv.dhb14niq.requirements.txt (line 4)) (1.15.4)
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Building wheels for collected packages: moto, aws-sam-translator, pyrsistent
Building wheel for moto (setup.py): started
Building wheel for moto (setup.py): finished with status 'done'
Stored in directory: /Users/taugspurger/Library/Caches/pip/wheels/a6/60/88/3c5fb3de8983f06caab8d5343a20a59ca65e4cc0fb3b9d89c3
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Successfully built moto aws-sam-translator pyrsistent
Installing collected packages: brotlipy, boto, zope.interface, datetime, xmltodict, werkzeug, PyYAML, ecdsa, future, python-jose, websocket-client, docker, jsondiff, jsonpickle, wrapt, aws-xray-sdk, responses, jsonpointer, jsonpatch, pyrsistent, jsonschema, aws-sam-translator, cfn-lint, sshpubkeys, moto, lxml, pandas-datareader
Found existing installation: PyYAML 5.1
Pip subprocess error:
ERROR: Cannot uninstall 'PyYAML'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
```
Looking into it now. | {
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] | closed | false | null | [] | null | 3 | 2019-07-01T13:15:36Z | 2019-07-08T16:25:53Z | 2019-07-08T16:25:52Z | NONE | null | - [x] closes #27160
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 7 | 2019-07-01T13:26:48Z | 2019-07-01T15:36:28Z | 2019-07-01T15:36:25Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27161 | {
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} | 4 | 2019-07-01T14:34:06Z | 2021-04-26T07:51:28Z | 2021-04-26T07:51:28Z | MEMBER | null | PR https://github.com/pandas-dev/pandas/pull/26409 introduced a `get_block_values` to get the values of the Block, used in https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/src/ujson/python/objToJSON.c
Ideally, the JSON C code should not deal with blocks (eg by converting to a numpy array before passing the data to the C code).
So to do item for this issue: update the JSON code so the call from objToJSON.c to `get_block_values` can be removed. | {
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} | 6 | 2019-07-01T14:53:06Z | 2021-10-29T02:08:54Z | 2021-10-29T02:08:53Z | MEMBER | null | PR https://github.com/pandas-dev/pandas/pull/26409 introduced a `_internal_get_values` method for DataFrame, Series, Index, SparseArray and Categorical (in order to deprecate the public `get_values`).
There are some remaining cases where `_internal_get_values` is used internally. Eventually, we should try to remove all those cases. For each of those usages, it will need to be inspected what object is actually being handled to choose the appropriate alternative. | {
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"color": "207de5... | closed | false | null | [] | null | 13 | 2019-07-01T15:01:15Z | 2020-01-16T00:35:05Z | 2019-07-26T00:53:00Z | MEMBER | null | - [X] closes #27164
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Getting one failure locally which will check later but pushing to see on other platforms | {
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} | 4 | 2019-07-01T15:17:20Z | 2021-10-29T16:05:29Z | 2021-10-29T16:05:29Z | MEMBER | null | We use this idiom quite a bit in the code base:
```
values = getattr(values, 'values', values)
```
One example:
https://github.com/pandas-dev/pandas/blob/ad2e98c3c59233dd8cd5234cead93010e0bd4b8a/pandas/core/algorithms.py#L123
Personally, I find that this makes the code harder to reason about. What kind of object is `values`? What does the `.values` attribute return? (which depends on the object and dtype).
I think a more explicit coding can make things clearer. Sometimes I would find it clearer to just do:
```
if isinstance(values, ABCSeries):
values = values.values
```
This is longer, yes, but then it is at least clear that it can potentially be a Series.
Although we should probably even try to avoid that algorithmic code is dealing with a mixture of Series, Index, EA, ndarray. I think, in general, we should nowadays be able to unpack containers in the beginning of / before algorithmic code so we know we only have either ndarray or EA.
Eg we could have an `ensure_array` that does such explicit checks and ensures the values are ndarray or EA.
cc @jreback @jbrockmendel @TomAugspurger
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} | 2 | 2019-07-01T16:22:02Z | 2019-07-01T22:48:58Z | 2019-07-01T21:54:18Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2019-07-01T20:03:19Z | 2020-01-01T02:45:55Z | 2020-01-01T02:45:55Z | NONE | null | The `scalar_t` fused type https://github.com/pandas-dev/pandas/blob/b115a6bf5553445fdf29824623ea2b7c3a424660/pandas/_libs/intervaltree.pxi.in#L9-L14 mixes signed and unsigned types. Later on, the code does comparisons with unsigned types, which creates a bunch of compiler warning.
xref http://docs.cython.org/en/latest/src/userguide/fusedtypes.html#type-checking-specializations
From comments in #27157 it appears there's no established guideline on how different cases for different types should look like in the templates. That's a hindrance to fixing this (and other warnings) as well. | {
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} | 11 | 2019-07-01T21:29:40Z | 2020-04-05T17:36:27Z | 2019-07-09T18:32:34Z | MEMBER | null | Introduced in #19434 Jan 2018. | {
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} | 2 | 2019-07-01T23:05:11Z | 2019-07-02T03:55:51Z | 2019-07-02T03:55:30Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2019-07-02T02:07:07Z | 2019-07-02T12:27:38Z | 2019-07-02T12:27:38Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python-traceback
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.25.0.dev0+865.g3b3b79157'
In [2]: ivs = [pd.Interval(0, 1, closed='left'),
...: pd.Interval(1, 2, closed='right')]
In [3]: pd.Index(ivs)
---------------------------------------------------------------------------
ValueError: intervals must all be closed on the same side
```
This propagates to other things failing with the same error, e.g. `ensure_index`.
#### Problem description
The `Index` constructor doesn't properly construct an `Index` from a list of mixed closed `Interval` objects. Note that this works if `dtype=object` is explicitly passed:
```python-traceback
In [4]: pd.Index(ivs, dtype=object)
Out[4]: Index([[0, 1), (1, 2]], dtype='object')
```
Note that an `object` dtype `Index` is successfully created from a list of mixed frequency `Period` objects, which is conceptually similar:
```python-traceback
In [5]: periods = [pd.Period('2018Q1', freq='Q'),
...: pd.Period('2018-01-01', freq='D')]
In [6]: pd.Index(periods)
Out[6]: Index([2018Q1, 2018-01-01], dtype='object')
```
#### Expected Output
I'd expect an `object` dtype `Index` to be successfully created and produce the same output as `Out[4]`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3b3b7915723e014331210e64dfb7a9574305c3e9
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0.dev0+865.g3b3b79157
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 2 | 2019-07-02T02:34:10Z | 2019-07-02T15:03:54Z | 2019-07-02T12:27:38Z | MEMBER | null | - [X] closes #27172
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
Note that this solution uses the same pattern that's used to support a list of mixed frequency `Period` objects:
https://github.com/pandas-dev/pandas/blob/527e714647a16ad31f80ea535af5a63156515861/pandas/core/indexes/base.py#L444-L448 | {
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} | 0 | 2019-07-02T03:54:22Z | 2019-07-12T14:26:54Z | 2019-07-12T14:26:54Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27171#issuecomment-507505074
An internal `._take` method was created to process a keyword argument deprecation.
Now that both `._take` and `.take` have the same signature, the code within `._take` can be collapsed into the public `.take` method.
https://github.com/pandas-dev/pandas/blob/527e714647a16ad31f80ea535af5a63156515861/pandas/core/generic.py#L3322 | {
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} | 5 | 2019-07-02T04:22:33Z | 2019-11-21T00:11:54Z | 2019-07-02T12:27:11Z | MEMBER | null | - [x] xref #6581
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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Use Case: Most of the times it's hard to determine the number of bins that would fit my data uniformly. Instead if I pass the bin size, based on bin size the function should determine how many bins must be created for the data.
Ex: If my data ranges from 0-100 and if I pass bin size as 2 the function must return a pandas series of 50 bins starting from 0. for ex: 0 and 1 would be in bin1 and so on. | {
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#### Problem description
When trying to insert data into oracle database through dataframe to_sql it is taking long time so i have made changes type(sqlalchemy types) of object to VARCHAR using below code
```python
dtyp = {c:types.VARCHAR(df[c].str.len().max())
for c in df.columns[df.dtypes == 'object'].tolist()}
df.to_sql(name='TEST_TABLE', con=engine, if_exists='append', index=False, chunksize=1000, dtype=dtyp)
```
Column of the dataframe contains text as utf-8, eg data is b'This is oracle'
b'This is oracle' is actual data present in the dataframe but the data is inserted in the oracle database is '415041432053656D69636F6E647563746F7' (example values) not the actual text 'This is oracle'. Seems like 'b' present before the text is causing the issue when converting it to VARCHAR if i trim out 'b' using decode UTF-8 i am getting UnicodeEncodeError: 'ascii' codec can't encode character error while inserting into database
Seems like to_sql having issue when utf-8 text is inserted as VARCHAR using dtypes
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.1.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
</details>
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"id": 7171206... | closed | false | null | [] | null | 4 | 2019-07-02T11:18:36Z | 2021-07-10T05:55:55Z | 2021-07-10T05:55:55Z | CONTRIBUTOR | null | #### Problem description
At https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html
the parameters *name* and *fastpath* are not explained.
*Edit*: See #20110 for *fastpath* - at least a short note "deprecated" or "to be deprecated" or "internal only" would help that users don't have to bother with this parameter.
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"description": n... | closed | false | null | [] | null | 1 | 2019-07-02T14:06:47Z | 2019-12-06T01:10:29Z | 2019-12-06T01:10:29Z | MEMBER | null | `isort` is a tool to validate the order of the imports in Python code. We are using it in the CI to make sure all pandas imports follow a defined standard. It is implemented in `ci/code_checks.sh`.
When `isort` detects imports incorrectly sorted, it finishes with an exit code different than 0, and that makes the CI red. Besides that, it'd be useful that in Azure Pipelines (the CI system where `isort` runs) the output messages of `isort` were detected as errors.
Some tools provide an option to specify the output format, like `flake8`, but `isort` doesn't (see https://github.com/timothycrosley/isort/issues/961).
What we can do in this case is to modify the output with bash pipelines. We are already doing that in the `invgrep` function in `ci/code_checks.sh`.
What needs to be done is:
- Change the format of `isort` output to be: `##vso[task.logissue type=error;sourcepath=%(path)s]%(text)s"
- Make sure that the resulting command still has an exit code of `0` if no issues are found, and the exit code of `isort` is there are. e.g. the exit code of `isort -c foo.py | awk -F" " '{ print $2 }'` is 0, the exit code of `awk` even if errors are found (the exit code of `isort` is lost in the pipeline).
Note that the error message of `isort` is not delimited in a way that can be easily parsed. But since the error message does not change, it should be possible to extract the name of the file removing the number of characters of the text around it (see https://github.com/timothycrosley/isort/blob/develop/isort/isort.py#L122). | {
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} | 4 | 2019-07-02T15:30:56Z | 2020-12-24T20:22:45Z | 2020-12-24T20:22:44Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
In [2]: index = pd.date_range('2001-01-01', periods=100)
In [3]: index.get_loc('2001-01')
Out[3]: slice(0, 31, None)
In [4]: index.get_indexer(['2001-01'])
Out[4]: array([-1])
In [5]: s = pd.Series(1, index=index)
In [6]: s.loc['2001-01']
Out[6]:
2001-01-01 1
2001-01-02 1
2001-01-03 1
2001-01-04 1
2001-01-05 1
2001-01-06 1
2001-01-07 1
2001-01-08 1
2001-01-09 1
2001-01-10 1
2001-01-11 1
2001-01-12 1
2001-01-13 1
2001-01-14 1
2001-01-15 1
2001-01-16 1
2001-01-17 1
2001-01-18 1
2001-01-19 1
2001-01-20 1
2001-01-21 1
2001-01-22 1
2001-01-23 1
2001-01-24 1
2001-01-25 1
2001-01-26 1
2001-01-27 1
2001-01-28 1
2001-01-29 1
2001-01-30 1
2001-01-31 1
Freq: D, dtype: int64
In [7]: s.loc[['2001-01']]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-f0d6dc35665a> in <module>
----> 1 s.loc[['2001-01']]
/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1427
1428 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1429 return self._getitem_axis(maybe_callable, axis=axis)
1430
1431 def _is_scalar_access(self, key):
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1829 raise ValueError('Cannot index with multidimensional key')
1830
-> 1831 return self._getitem_iterable(key, axis=axis)
1832
1833 # nested tuple slicing
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_iterable(self, key, axis)
1131 # A collection of keys
1132 keyarr, indexer = self._get_listlike_indexer(key, axis,
-> 1133 raise_missing=False)
1134 return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},
1135 copy=True, allow_dups=True)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1087 self._validate_read_indexer(keyarr, indexer,
1088 o._get_axis_number(axis),
-> 1089 raise_missing=raise_missing)
1090 return keyarr, indexer
1091
/home/nobackup/repo/pandas/pandas/core/indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1172 raise KeyError(
1173 "None of [{key}] are in the [{axis}]".format(
-> 1174 key=key, axis=self.obj._get_axis_name(axis)))
1175
1176 # We (temporarily) allow for some missing keys with .loc, except in
KeyError: "None of [Index(['2001-01'], dtype='object')] are in the [index]"
```
#### Problem description
If an indexing mechanism works for individual key, it would be expected to also work for lists of keys, and this is more or less the status quo for other indexes that allow some partial/smart selection.
``IntervalIndex`` works just fine:
```
In [2]: iidx = pd.IntervalIndex.from_breaks([0, 1, 4.6, 7])
In [3]: iidx.get_loc(5)
Out[3]: 2
In [4]: iidx.get_indexer([5])
Out[4]: array([2])
```
``MultiIndex`` doesn't (will open a bug now)
```
In [5]: midx = pd.MultiIndex.from_product([[0, 1], [2, 3]])
In [6]: midx.get_loc(0)
Out[6]: slice(0, 2, None)
In [7]: midx.get_indexer([0])
Out[7]: array([-1])
```
... but this is worked around in indexing code (which shouldn't happen):
```
In [8]: pd.Series(index=midx).loc[[0]]
Out[8]:
0 2 NaN
3 NaN
dtype: float64
```
#### Expected Output
The same as when passing a single key.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7ceefb3f2e1c1f6b0e4435ab4d10ffa4aef4ec36
python : 3.7.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 4.9.0-9-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0.dev0+861.g7ceefb3f2.dirty
numpy : 1.16.4
pytz : 2016.7
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.4.9
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: 0.2.1
bs4 : 4.5.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
openpyxl : 2.3.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.0.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 0.9.6
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27181/events | https://github.com/pandas-dev/pandas/issues/27181 | 463,299,995 | MDU6SXNzdWU0NjMyOTk5OTU= | 27,181 | Code for list of partial indexers should be in MultiIndex.get_indexer | {
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"de... | open | false | null | [] | null | 3 | 2019-07-02T15:37:39Z | 2021-06-10T01:38:02Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
From #27180
```python
In [5]: midx = pd.MultiIndex.from_product([[0, 1], [2, 3]])
In [6]: midx.get_loc(0)
Out[6]: slice(0, 2, None)
In [7]: midx.get_indexer([0])
Out[7]: array([-1])
```
#### Problem description
Indexing with lists of partial keys works (except for #18345 )
```
In [5]: midx = pd.MultiIndex.from_product([[0, 1], [2, 3]])
In [6]: midx.get_loc(0)
Out[6]: slice(0, 2, None)
In [7]: midx.get_indexer([0])
Out[7]: array([-1])
```
but it is not implemented in ``get_indexer()`` - where it should be.
#### Expected Output
``slice(0, 2, None)``
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7ceefb3f2e1c1f6b0e4435ab4d10ffa4aef4ec36
python : 3.7.3.candidate.1
python-bits : 64
OS : Linux
OS-release : 4.9.0-9-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : it_IT.UTF-8
LOCALE : it_IT.UTF-8
pandas : 0.25.0.dev0+861.g7ceefb3f2.dirty
numpy : 1.16.4
pytz : 2016.7
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.1
Cython : 0.29.2
pytest : 4.6.3
hypothesis : 3.71.11
sphinx : 1.4.9
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 4.3.2
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: 0.2.1
bs4 : 4.5.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.2
numexpr : 2.6.9
openpyxl : 2.3.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.0.15
tables : 3.4.4
xarray : None
xlrd : 1.0.0
xlwt : 1.3.0
xlsxwriter : 0.9.6
</details>
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} | 3 | 2019-07-02T16:02:16Z | 2019-07-03T13:17:22Z | 2019-07-03T13:17:22Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
ts = pd.Timestamp("2019-01-01", freq="M")
ts1 = ts + 1 * ts.freq
print(ts1.freq)
```
#### Problem description
There seem to be a problem when a new `pd.Timestamp` is constructed by adding a multiple of the frequency to an initial timestamp: when the frequency is `"M"` or `"W"`, then the frequency information is lost in the resulting timestamp.
I'm not sure if this is intended behaviour (for some reason), but it seems to be inconsistent across frequencies.
#### Expected Output
```
<MonthEnd>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.6.0
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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-07-02T16:07:30Z | 2019-07-03T11:29:53Z | 2019-07-03T11:29:53Z | CONTRIBUTOR | null | ```python
In [2]: df = pd.DataFrame({"A": [None, None]})
In [3]: df.describe()
Out[3]:
A
count 0.0
unique 0.0
top NaN
freq NaN
```
That way DataFrame[object].describe() is always object dtype. | {
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} | 1 | 2019-07-02T16:17:57Z | 2019-07-03T11:29:56Z | 2019-07-03T11:29:53Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27183 | {
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} | 3 | 2019-07-02T16:26:30Z | 2021-07-10T17:46:27Z | null | CONTRIBUTOR | null | ```python
In [43]: df = pd.DataFrame({"A": pd.to_timedelta([0, 1, None]), "B": pd.array([1, 2, None], dtype='Int32')})
In [44]: df.sum()
Out[44]:
A 00:00:00.000000
B NaT
dtype: timedelta64[ns]
```
cc @jbrockmendel
Not a blocker for the RC, but is a blocker for the release. | {
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} | 4 | 2019-07-02T16:55:46Z | 2019-07-03T13:18:54Z | 2019-07-03T13:18:54Z | CONTRIBUTOR | null | 0.24.2
```python
In [4]: s = pd.Series([1, 2, 3])
In [5]: o = np.array([0, 1 ,2])
In [6]: np.subtract.outer(s, o)
Out[6]:
array([[ 1, 0, -1],
[ 2, 1, 0],
[ 3, 2, 1]])
```
master
```pytb
In [7]: np.subtract.outer(s, o)
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
<ipython-input-7-a64bc670e48a> in <module>
----> 1 np.subtract.outer(s, o)
~/sandbox/pandas/pandas/core/series.py in __array_ufunc__(self, ufunc, method, *inputs, **kwargs)
793 return None
794 else:
--> 795 return construct_return(result)
796
797 def __array__(self, dtype=None):
~/sandbox/pandas/pandas/core/series.py in construct_return(result)
784 index=index,
785 name=name,
--> 786 copy=False)
787
788 if type(result) is tuple:
~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
255 else:
256 data = sanitize_array(data, index, dtype, copy,
--> 257 raise_cast_failure=True)
258
259 data = SingleBlockManager(data, index, fastpath=True)
~/sandbox/pandas/pandas/core/internals/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
654 elif subarr.ndim > 1:
655 if isinstance(data, np.ndarray):
--> 656 raise Exception('Data must be 1-dimensional')
657 else:
658 subarr = com.asarray_tuplesafe(data, dtype=dtype)
Exception: Data must be 1-dimensional
```
Probably not an RC blocker, but I may try to get a fix in quick.
Do we want to return an ndarray here? We don't want a DataFrame where the index & columns match?
cc @jorisvandenbossche @shoyer if you have thoughts on the ideal return type. | {
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} | 4 | 2019-07-02T18:02:31Z | 2019-07-04T07:26:39Z | 2019-07-04T01:38:36Z | CONTRIBUTOR | null | ``MultiIndex._shallow_copy`` doesnt pass on kwargs, unless ``values`` is passed also, but just silently drops the kwargs. This makes ``_shallow_copy`` currently a bit unreliable.
This PR fixes that problem by calling ``.copy`` directly and passing kwargs to ``copy``and not passing the call through ``.view``. Also, ``_shallow_copy`` doesn't call ``.view`` on other index types, so this PR makes ``MultiIndex._shallow_copy`` conform with other index types. | {
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} | 4 | 2019-07-02T18:21:27Z | 2019-07-06T11:38:10Z | 2019-07-06T11:38:10Z | CONTRIBUTOR | null | ```python
In [2]: s = pd.Series([1, 2, 3])
In [3]: np.i0(s)
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-3-0fe1635dc55e> in <module>
----> 1 np.i0(s)
<__array_function__ internals> in i0(*args, **kwargs)
~/Envs/dask-dev/lib/python3.7/site-packages/numpy/lib/function_base.py in i0(x)
3104 """
3105 x = np.abs(x)
-> 3106 return piecewise(x, [x <= 8.0], [_i0_1, _i0_2])
3107
3108 ## End of cephes code for i0
<__array_function__ internals> in piecewise(*args, **kwargs)
~/Envs/dask-dev/lib/python3.7/site-packages/numpy/lib/function_base.py in piecewise(x, condlist, funclist, *args, **kw)
621 y[condlist[k]] = item
622 else:
--> 623 vals = x[condlist[k]]
624 if vals.size > 0:
625 y[condlist[k]] = item(vals, *args, **kw)
IndexError: too many indices for array
``` | {
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} | 8 | 2019-07-02T19:44:48Z | 2019-07-03T13:16:48Z | 2019-07-03T13:16:48Z | CONTRIBUTOR | null | Pandas 0.24.2
```python
In [14]: s = pd.Series([])
In [15]: s.groupby([])
Out[15]: <pandas.core.groupby.groupby.SeriesGroupBy object at 0x11f4d8748>
```
master
```pytb
In [8]: s = pd.Series([])
In [9]: s.groupby([])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-cd9ba1ec9bdd> in <module>
----> 1 s.groupby([])
~/sandbox/pandas/pandas/core/generic.py in groupby(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, **kwargs)
7496 return groupby(self, by=by, axis=axis, level=level, as_index=as_index,
7497 sort=sort, group_keys=group_keys, squeeze=squeeze,
-> 7498 observed=observed, **kwargs)
7499
7500 def asfreq(self, freq, method=None, how=None, normalize=False,
~/sandbox/pandas/pandas/core/groupby/groupby.py in groupby(obj, by, **kwds)
2402 raise TypeError('invalid type: {}'.format(obj))
2403
-> 2404 return klass(obj, by, **kwds)
~/sandbox/pandas/pandas/core/groupby/groupby.py in __init__(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, **kwargs)
362 sort=sort,
363 observed=observed,
--> 364 mutated=self.mutated)
365
366 self.obj = obj
~/sandbox/pandas/pandas/core/groupby/grouper.py in _get_grouper(obj, key, axis, level, sort, observed, mutated, validate)
608
609 if len(groupings) == 0:
--> 610 raise ValueError('No group keys passed!')
611
612 # create the internals grouper
ValueError: No group keys passed!
```
I think this should work. If not, we'll want to deprecate it properly. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27192 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27192/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27192/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27192/events | https://github.com/pandas-dev/pandas/pull/27192 | 463,421,746 | MDExOlB1bGxSZXF1ZXN0MjkzOTA0NTM2 | 27,192 | improve test for timestamp addition/subtraction | {
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} | 1 | 2019-07-02T20:30:34Z | 2019-07-03T13:18:44Z | 2019-07-03T13:17:23Z | CONTRIBUTOR | null | This PR adds regression test cases for the addition/subtraction of time deltas to timestamps. In fact, this is causing issues in 0.24.2 when `freq="M"` and `freq="W"`, and the problem is solved in `master`.
- [ ] closes #27182
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-07-02T21:27:18Z | 2019-07-03T17:22:22Z | 2019-07-03T11:26:31Z | NONE | null | ```
warning: pandas/_libs/tslib.pyx:724:49: Buffer unpacking not optimized away.
warning: pandas/_libs/tslib.pyx:724:49: Buffer unpacking not optimized away.
```
the error is due to a cdef inline function which uses ndarray[object]. The function has one caller, and it isn't in any perf-critical inner-loop, so the inline isn't important. Remove it and avoid warning during cythonization.
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} | 2 | 2019-07-02T21:33:24Z | 2019-07-03T13:16:53Z | 2019-07-03T13:16:48Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27190
Whether or not we do a warning is still TBD. | {
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- towards https://github.com/pandas-dev/pandas/issues/26344
As per travis logs:
https://travis-ci.org/pandas-dev/pandas/jobs/552444580
```
ccache: /usr/bin/ccache
source deactivate
DeprecationWarning: 'source deactivate' is deprecated. Use 'conda deactivate'.
```
As of [conda 4.6 the preferred](https://www.anaconda.com/conda-4-6-release/) way of using an environment is to use `conda activate (env)`.
`source activate` will eventually be deprecated
(only remaining place to do this is in `code_checks.yml` -> I will do this in a follow up for ease of review)
* This make the steps in the `windows.yml` and `posix.yml` file very similar and we can potentially merge these.
* Use `run_tests.sh` on windows this will mean we now tests with `pytest -n 2` and `pytest -n 1` on windows - now consistent with the linux builds
cc. @TomAugspurger @datapythonista | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27197/events | https://github.com/pandas-dev/pandas/issues/27197 | 463,474,614 | MDU6SXNzdWU0NjM0NzQ2MTQ= | 27,197 | BUG: Dataframe fill_na with series | {
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```python
import pandas as pd
pdf = pd.DataFrame({'a': [1, 2, None], 'b': [None, None, 5]})
fill_value_ser = pd.Series([3,4,5])
filled_df = pdf.fillna(fill_value_ser)
print(filled_df)
print("\n")
filled_ser = pdf['a'].fillna(fill_value_ser)
print(filled_ser)
```
Output:
```
a b
0 1.0 NaN
1 2.0 NaN
2 NaN 5.0
0 1.0
1 2.0
2 5.0
Name: a, dtype: float64
```
#### Problem description
Currently filling a dataframe with a pandas series does not seem to be working as no values are filled.
Filling a single series seems to be working though.
#### Expected Output
```
a b
0 1.0 3.0
1 2.0 4.0
2 5.0 5.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-50-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.11
numpy: 1.16.4
scipy: None
pyarrow: 0.12.1
xarray: None
IPython: 7.6.0
sphinx: 2.1.2
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: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-07-03T02:56:09Z | 2019-07-03T13:18:58Z | 2019-07-03T13:18:54Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/27186 | {
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} | 5 | 2019-07-03T03:02:24Z | 2019-07-03T21:57:17Z | 2019-07-03T20:42:39Z | MEMBER | null | Discussed in #27142.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry | {
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} | 6 | 2019-07-03T05:29:48Z | 2019-07-07T02:25:28Z | 2019-07-06T17:44:56Z | CONTRIBUTOR | null | - [x] closes #27160
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2019-07-03T08:02:36Z | 2019-07-13T05:42:06Z | 2019-07-06T14:37:11Z | NONE | null | Why does pandas dataframe.var() method return "unbiased variance" by default?
I think variance implies "sample variance" in general.
Off course, I know this method can return "sample variance" if we provide ddof=0 option.
However, this setting option is very confusing.
Why don't you add new methods sample_var() and unbiased_var() or return "sample variance" by default?
Other data analysis OSS such as numpy, R and so on, their method return "sample variance" by default.
I think pandas is outstanding library for data analysis with Python! Therefore, I would like to know about this confusing specification. | {
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} | 2 | 2019-07-03T08:27:15Z | 2019-12-09T09:10:56Z | 2019-12-09T09:10:56Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/26403#discussion_r299144624
This will need to be done as part of the process for changing the default value of `CategoricalDtype.ordered` from `None` to `False`, which should be part of the 1.0 release. Opening this issue to serve as a reminder.
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} | 2 | 2019-07-03T11:38:30Z | 2019-07-03T15:50:35Z | 2019-07-03T15:50:35Z | CONTRIBUTOR | null | This is from a PR recently rebased; I don't think the asv's were run when we merged the ordered deprecation changes
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=13903
cc @jschendel
```
[ 4.36%] ··· categoricals.Rank.time_rank_string_cat failed
[ 4.36%] ···· Traceback (most recent call last):
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 1184, in main_run_server
main_run(run_args)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 1052, in main_run
skip = benchmark.do_setup()
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 583, in do_setup
result = Benchmark.do_setup(self)
File "/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/asv/benchmark.py", line 515, in do_setup
setup(*self._current_params)
File "/home/vsts/work/1/s/asv_bench/benchmarks/categoricals.py", line 138, in setup
ordered=True)
File "/home/vsts/work/1/s/pandas/core/generic.py", line 5600, in astype
**kwargs)
File "/home/vsts/work/1/s/pandas/core/internals/managers.py", line 529, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/home/vsts/work/1/s/pandas/core/internals/managers.py", line 393, in apply
applied = getattr(b, f)(**kwargs)
File "/home/vsts/work/1/s/pandas/core/internals/blocks.py", line 514, in astype
**kwargs)
File "/home/vsts/work/1/s/pandas/core/internals/blocks.py", line 554, in _astype
deprecated_arg))
ValueError: Got an unexpected argument: ordered
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27206/events | https://github.com/pandas-dev/pandas/issues/27206 | 463,731,589 | MDU6SXNzdWU0NjM3MzE1ODk= | 27,206 | Older Pandas dep pulling in newest Numpy that is py2 incompat | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-03T12:44:16Z | 2019-07-03T13:01:14Z | 2019-07-03T13:01:14Z | NONE | null | ## Problem description
I have the following dep listed in my py2 project on a LINUX machine:
> pandas==0.17.1
Since Pypi has no linux wheels until pandas v0.18.1 ( https://pypi.org/simple/pandas/ ),
pip install -r <reqs>
Will download and attempt to build pandas from source.
I have numpy 1.16.4 installed, but get this error:
> run_setup
> ...
> File "/tmp/easy_install-cXiZ1Y/numpy-1.17.0rc1/setup.py", line 31, in <module>
> from setuptools import setup, Command
> RuntimeError: Python version >= 3.5 required.
I believe that the pandas build is pulling in numpy itself in order to build numpy/pandas binaries from source (correct?), rather than use the installed numpy version.
HOWEVER, it pulls in the latest numpy, which ASOF is numpy-1.17.0r, where 1.17 is the first numpy to drop support for py27.
If my assumptions are correct, is it possible to set a numpy "max_version" to prevent the py27 incompatible numpys from being pulled in for older pandas being built on py27? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27207/events | https://github.com/pandas-dev/pandas/pull/27207 | 463,743,407 | MDExOlB1bGxSZXF1ZXN0Mjk0MTYxMDI1 | 27,207 | BUG: Fixup bench for deprecation | {
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} | 3 | 2019-07-03T13:08:21Z | 2019-07-03T15:50:35Z | 2019-07-03T15:50:35Z | CONTRIBUTOR | null | Closes #27205
Haven't reproduced the tz one yet. Will see if it shows up here.
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https://api.github.com/repos/pandas-dev/pandas/issues/27208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27208/events | https://github.com/pandas-dev/pandas/issues/27208 | 463,785,170 | MDU6SXNzdWU0NjM3ODUxNzA= | 27,208 | Proper way to slice and modify a dataframe | {
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] | closed | false | null | [] | null | 1 | 2019-07-03T14:27:09Z | 2019-07-03T15:15:03Z | 2019-07-03T15:14:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
1.
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df
def f(df_par):
df_par.iloc[0, 0] = 8
mask = df.iloc[:, 0] > 1
mask = mask.tolist()
df = df.loc[mask,:]
f(df)
2.
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
print(df)
def f(df_par):
df_par.iloc[0, 0] = 8
mask = df.iloc[:, 0] > 1
mask = mask.tolist()
df = df.loc[mask,:]
f(df)
```
#### Problem description
Hello, I am using pandas 0.24.2. I would like to know what is the correct way to slice and modify a dataframe and not a view. In particular I am using the loc as suggested in the documentation and if I execute the code 1 I get the warning: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
Is it possible to do that kind of slice and update of the dataframe and be sure that I am updating correctly the dataframe?
If I execute f(df.copy(deep=True)) I do not get the waning but it is not very efficient.
Why if I execute the version 2 of the code I do not get the warning?
Thank you.
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https://api.github.com/repos/pandas-dev/pandas/issues/27209 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27209/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27209/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27209/events | https://github.com/pandas-dev/pandas/pull/27209 | 463,810,430 | MDExOlB1bGxSZXF1ZXN0Mjk0MjE1Njg0 | 27,209 | POC: add closed argument to IndexSlice | {
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"color": "AD... | closed | false | null | [] | null | 6 | 2019-07-03T15:13:17Z | 2019-08-28T16:46:30Z | 2019-08-28T16:46:29Z | MEMBER | null | alternative to #27060 for consideration.
implementation is far from complete.
only the following cases have been implemented to start..
```python
>>> import pandas as pd
>>>
... dates=[
... "2001-01-01 23:50",
... "2001-01-02 00:00",
... "2001-01-03 00:08"]
>>>
>>> ser=pd.Series(range(3),pd.DatetimeIndex(dates))
>>> ser
2001-01-01 23:50:00 0
2001-01-02 00:00:00 1
2001-01-03 00:08:00 2
dtype: int64
>>>
>>> ser.loc[:pd.Timestamp("2001-01-02 00:00")]
2001-01-01 23:50:00 0
2001-01-02 00:00:00 1
dtype: int64
>>>
>>> idx = pd.IndexSlice
>>> ser.loc[idx[:pd.Timestamp("2001-01-02 00:00")]]
2001-01-01 23:50:00 0
2001-01-02 00:00:00 1
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='left')
>>> ser.loc[idx[:pd.Timestamp("2001-01-02 00:00")]]
2001-01-01 23:50:00 0
dtype: int64
>>>
>>> ser.loc[:"2001-01-02"]
2001-01-01 23:50:00 0
2001-01-02 00:00:00 1
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='left')
>>> ser.loc[idx[:"2001-01-02"]]
2001-01-01 23:50:00 0
dtype: int64
>>>
>>> ser2=pd.Series(range(3),index=[50,70,80])
>>>
>>> ser2.loc[:70]
50 0
70 1
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='left')
>>> ser2.loc[idx[50:70]]
50 0
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='right')
>>> ser2.loc[idx[50:70]]
70 1
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='both')
>>> ser2.loc[idx[50:70]]
50 0
70 1
dtype: int64
>>>
>>> idx = pd.IndexSlice(closed='neither')
>>> ser2.loc[idx[50:70]]
Series([], dtype: int64)
>>>
>>> idx = pd.IndexSlice(closed='foo')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 97, in __call__
return _IndexSlice(closed=closed)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\indexing.py", line 93, in __init__
raise ValueError(msg)
ValueError: invalid option for 'closed': foo
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27210 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27210/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27210/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27210/events | https://github.com/pandas-dev/pandas/issues/27210 | 463,812,386 | MDU6SXNzdWU0NjM4MTIzODY= | 27,210 | Segfault when erroneously passing non-utf8 bytes object to pandas.Index | {
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} | [] | closed | false | null | [] | null | 2 | 2019-07-03T15:17:02Z | 2019-07-03T17:21:45Z | 2019-07-03T15:20:25Z | NONE | null | *Root cause* appears to lie in numpy, raising this here just for completeness
```
In [0]: import pandas; pandas.Index(b'\xc7')
Segmentation fault
```
#### Problem description
Calling `pandas.Index` and passing in a bytes object that is not valid UTF8 results in a segfault.
This call pattern is not valid and, for example, calling:
```
pandas.Index(b'foo')
```
raises the expected exception)
TypeError: Index(...) must be called with a collection of some kind, b'foo' was passed
But it is not ideal that pandas generates a segfault in this case.
#### Analysis
This appears to *not* be directly a pandas issue.
Down the stack, pandas ends up calling numpy.dtype() (pandas/core/dtypes/common.py:2084)
and passing in the bytes object. This causes the segfault.
I'm still raising this just so there's a record, but the fix looks like it lives somewhere else.
#### Expected Output
TypeError: Index(...) must be called with a collection of some kind, b'\xc7' was passed
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.0
pip: 19.1.1
setuptools: 40.8.0
Cython: 0.29.11
numpy: 1.16.2
scipy: 1.1.0
pyarrow: 0.13.0
xarray: None
IPython: 7.5.0
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27211 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27211/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27211/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27211/events | https://github.com/pandas-dev/pandas/issues/27211 | 463,826,328 | MDU6SXNzdWU0NjM4MjYzMjg= | 27,211 | Question: Guaranteed zero-copy round-trip from numpy? | {
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} | [] | closed | false | null | [] | null | 7 | 2019-07-03T15:44:19Z | 2019-07-03T17:50:56Z | 2019-07-03T17:39:15Z | NONE | null | This is for informing a scikit-learn design decision, I had briefly talked with @jorisvandenbossche about this a bit ago.
The question is whether we can rely on having zero-copy wrapping and unwrapping of numpy arrays into pandas dataframes, i.e. is it future proof to assume something like
```python
X = np.array(...)
X_df = pd.DataFrame(X)
X_again = np.asarray(X_df)
```
doesn't result in a copy of the data and ``X_again`` shares the memory of ``X``?
Context: We want to attach some meta-data to our numpy arrays, in particular I'm interested in column names. Pandas is an obvious candidate for doing that, but core sklearn works on numpy arrays.
So if we want to use pandas, we need to make sure that there's no overhead in wrapping and unwrapping.
And this is a design decision that's very hard to undo, so I want to make sure that it's reasonably future-proof.
@jorisvandenbossche had mentioned that there were thoughts about making pandas a column store, which sounds like it would break the zero copy requirement.
Thanks! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27212 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27212/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27212/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27212/events | https://github.com/pandas-dev/pandas/issues/27212 | 463,833,049 | MDU6SXNzdWU0NjM4MzMwNDk= | 27,212 | groupby/apply behaves different if a column is timezone-aware or not | {
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"id": 233160,
"name": "Groupby",
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"description": null,
"id": 7... | open | false | null | [] | null | 5 | 2019-07-03T15:57:57Z | 2021-06-11T13:35:05Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dates = ['2001-01-01'] * 2 + ['2001-01-02'] * 2 + ['2001-01-03'] * 2
index_no_tz = pd.DatetimeIndex(dates)
index_tz = pd.DatetimeIndex(dates, tz='UTC')
df1 = pd.DataFrame({'x': list(range(2))*3, 'y': range(6), 't': index_no_tz})
df2 = pd.DataFrame({'x': list(range(2))*3, 'y': range(6), 't': index_tz})
result1 = df1.groupby('x', group_keys=False).apply(lambda df: df[['x','y']].copy())
result2 = df2.groupby('x', group_keys=False).apply(lambda df: df[['x','y']].copy())
try:
pd.testing.assert_frame_equal(result1, result2)
except:
print("does not work")
```
#### Problem description
It appears that groupby/apply appears to sort differently for the two dataframes in the example; the only difference between them is the timezone-awareness of the `t` column.
(Note: I posted this in the pydata/pandas gitter, and @jorisvandenbossche suggested I open an issue here for it.)
#### Expected Output
We would expect the assertion to pass, i.e. the `does not work` message would not appear.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-24-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: 4.6.3
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: 0.13.0
xarray: None
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.4
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: 0.2.1
fastparquet: 0.3.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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