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TST/REF: misplaced frame.indexing tests
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| 2020-10-21T17:59:06Z
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MEMBER
| null |
I've been saving the indexing tests for last-ish because they are some of the hardest to organize consistently. This starts in on that.
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ENH: enable Series.info()
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| 2021-10-05T15:39:35Z
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MEMBER
| null |
- [ ] closes #5167
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I took over https://github.com/pandas-dev/pandas/pull/31796 from @MarcoGorelli.
In this PR I took the tests and the docstring from https://github.com/pandas-dev/pandas/pull/31796, refactored tests (separated into dataframe and series-related only test classes).
Then on top of the recent changes (https://github.com/pandas-dev/pandas/pull/36752) I implemented series info.
New classes:
- ``SeriesInfo`` (store data, which will be used in the outputs)
- ``SeriesInfoPrinter`` (basically creator of the appropriate table builder)
- ``SeriesTableBuilder`` (both Verbose and NonVerbose)
- ``TableBuilderVerboseMixin`` (shared functionality for verbose info builders of both dataframe and series)
It seems to me that tests are not sufficient enough.
In particular, it seems that empty series info should be covered.
Currently there is a special empty dataframe info, but for series info there is just a generic verbose info with zero items.
If there is a need for a dedicated empty series info, then I would need to add method ``_fill_empty_info`` into ``SeriesTableBuilder``.
Static typing makes code quite verbose. In some cases we have the very same methods/properties, but with different type annotations to satisfy type checking (methods are small, but anyway). If somebody can suggest me a better way to handle it, then that would be great.
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BUG: Index._id
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| 3
| 2020-10-21T20:16:12Z
| 2020-10-24T08:05:43Z
| 2020-10-22T23:31:02Z
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CONTRIBUTOR
| null |
Fixes #37213.
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DOC: Add protocol value '5' to pickle #37316
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CONTRIBUTOR
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- [X] closes #37316
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df.groupby(by=["b"], dropna=False).sum() returns"groupby() got an unexpected keyword argument 'dropna'"
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NONE
| null |
Using the official documentation for groupby: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html
the dropna=False argument does not work even with given example.
l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
df = pd.DataFrame(l, columns=["a", "b", "c"])
df.groupby(by=["b"], dropna=False).sum()
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #37288 on branch 1.1.x (Regression in offsets caused offsets to be no longer hashable)
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Backport PR #37288: Regression in offsets caused offsets to be no longer hashable
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| 2020-10-22T00:19:07Z
| 2021-08-13T05:16:16Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In[42]: df = pd.DataFrame({'a':pd.Series([1, 2, 3], dtype="uint64")})
In[43]: df.to_parquet('a')
In[44]: pd.read_parquet('a').dtypes
Out[44]:
a uint64
dtype: object
In[45]: df = pd.DataFrame({'a':pd.Series([1, 2, 3], dtype="uint32")})
In[46]: df.to_parquet('a')
In[47]: pd.read_parquet('a').dtypes
Out[47]:
a int64
dtype: object
```
#### Problem description
It appears to be that `uint32` is not being preserved like `uint64` is being preserved while round-tripping through a parquet file.
#### Expected Output
Preserve the `uint32` dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-52-generic
Version : #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.3
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
Crosslinking to cudf fuzz-testing for tracking purpose: https://github.com/rapidsai/cudf/issues/6001
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| 1
| 2020-10-22T01:54:16Z
| 2020-10-28T12:04:57Z
| 2020-10-28T12:04:57Z
|
MEMBER
| null |
Ran into an unrelated failure I haven't seen before in #37227. Occurs in pandas/tests/computation/test_eval.py:211
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=45754&view=logs&j=89027c74-d1cd-5e5d-942f-e6a83ebe07fe&t=768a7acf-33bc-5b3d-16e1-da874611990b&l=92
```
E numpy array values are different (2.0 %)
E [left]: [[10.885282508088554, 24.01653292315993, 2.5334764666276754, -26.20850153209755, -9.292195139788854], [4.631505078070531, 0.43305142455418066, 9.796254287558813, -0.817344630073512, -0.22979400652647], [16.964677878827604, 3.69159983714683, 0.16736723603895715, -13.866779975183137, -0.6547978574323233], [0.9332007475405953, 7.326122317479498, 2.2912333245159338, -0.9957313583066364, -0.21968895568980523], [1.2901449066742292, 1.5178122458579082, 0.3101899430985658, -0.3600640963287845, -3.8622009358387115], [1.7324531378555383, 1.6717678316305915, 0.39248288379342006, -2.259323953594114, -13.215192982180358], [60.71781357885021, 13.08256705151808, 3.306197592158367, -2.603163236030203, -0.5783506355776388], [47.71284995565457, 39.508260846448245, 0.16695497225705538, -7.39400467084908, -0.9437156402277913], [0.40861584564047, 611.3601636482858, 0.8350779591410551, -3.034557944041943, -0.33953697203938915], [46.62807235120426, 6.308023182021756, 5.01671850424403, -0.23877079736974396, -0.5460348132251863]]
E [right]: [[10.885282508088554, 24.01653292315993, 2.5334764666276754, -26.20850153209755, -9.292195139788854], [4.631505078070531, 0.43305142455418066, 9.796254287558813, -0.817344630073512, -0.22979400652647], [16.964677878827608, 3.69159983714683, 0.16736723603895715, -13.866779975183137, -0.6547978574323233], [0.9332007475405953, 7.326122317479498, 2.2912333245159338, -0.9957313583066364, -0.21968895568980523], [1.2901449066742292, 1.5178122458579082, 0.3101899430985658, -0.3600640963287845, -3.8622009358387115], [1.7324531378555383, 1.6717678316305915, 0.39248288379342006, -2.259323953594114, -13.215192982180358], [60.71781357885021, 13.08256705151808, 3.306197592158367, -2.603163236030203, -0.5783506355776388], [47.71284995565457, 39.508260846448245, 0.16695497225705538, -7.39400467084908, -0.9437156402277913], [0.40861584564047, 611.3601636482858, 0.8350779591410551, -3.034557944041943, -0.33953697203938915], [46.62807235120426, 6.308023182021756, 5.01671850424403, -0.23877079736974396, -0.5460348132251863]]
```
where one entry is `16.964677878827604` and the other is `16.964677878827608`. Haven't looked into it, but guessing this is a numerical precision issue.
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|
#31793 Add support for subtracting datetime from Timestamp
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| 2021-11-01T23:56:16Z
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| null |
- [x] closes #31793
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Based on the discussion in the attached issue, i've made the following changes:
1. add support for subtracting when the two items have different timezones
2. raise an error if one (but not both) timezone is naive
The root cause of 31793 was that the tzinfo objects were from different libraries -- `Timestamp` makes a `pytz.UTC` whereas a datetime object might just have `datetime.timezone.utc`. These objects are not necessarily equal, even if they represent the same timezone.
In addition, this change *adds* support for time-deltas where the timezone of each date is not the same. This follows the behaviour of `datetime` more closely.
I reference the [python `datetime` module documentation](https://docs.python.org/3/library/datetime.html) which defines when a datetime is naive: when its `tzinfo` is `None` or a call to `item.tzinfo.utcoffset(item)` returns `None`.
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BUG: IntervalIndex.take without fill_value
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| 2020-10-22T02:56:01Z
| 2021-01-04T06:19:44Z
| 2020-10-31T18:56:21Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
`IntervalIndex.take([0, 1, -1])` without explicitly passing fill_value should not fill the -1 as NA.
Fixing this allows us to use the base class `take` for `IntervalIndex.take` (the original motivation)
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DOC: Remove confusing description from `core.DataFrame.iterrows`
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| 2020-10-22T06:36:01Z
| 2020-10-24T07:31:13Z
| 2020-10-24T07:31:01Z
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CONTRIBUTOR
| null |
Current [`core.DataFrame.iterrows`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html) is as follows
However, `core.DataFrame.iterrows` actually returns an iterator which yields `(index, data)`, not `(index, data, it)`.
This PRs remove such confusing description like [https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iteritems.html?highlight=yields]
- [ ] closes #xxxx (no issues found)
- [ ] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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REF: extract dialect validation
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Following https://github.com/pandas-dev/pandas/pull/36852, I extracted validation of dialect into a separate function.
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|
QST:bug or not? silent fail of df.iloc[].loc[] = newDataType
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[] | null | 1
| 2020-10-22T07:51:16Z
| 2021-08-13T05:17:49Z
| null |
NONE
| null |
If I use a loc chained to a iloc I can change a value if it is of the same dtype:
```python
df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
... index=[4, 5, 6], columns=['A', 'B', 'C'])
df.iloc[1].loc["B"] = 999
df
A B C
4 0 2 3
5 0 999 1
6 10 20 30
```
This changes the value to 999.
However, this does not work and fails silently if the dtype is different:
```python
df.iloc[1].loc["B"] = "888"
df
A B C
4 0 2 3
5 0 999 1
6 10 20 30
```
The value is not changed and there is no error raised.
This seems not consistent with the behavior of doing this:
```python
df.iloc[1,1] = "888"
df
A B C
4 0 2 3
5 0 888 1
6 10 20 30
```
the last code above changes the type of the column silently.
```
df.dtypes
A int64
B object
C int64
```
Envirnt:
```
Python 3.8.5
pd.__version__ : '1.1.3'
```
I am only a beginner in pandas so I am wondering if this should be reported as a bug or requesting a feature request or if this behavior is normal.
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|
CLN make namespace usage in tests consistent
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| 2020-10-22T08:38:06Z
| 2020-10-22T09:16:39Z
| 2020-10-22T09:15:39Z
|
MEMBER
| null |
Currently, there are many test files where we use the pandas namespace inconsistently: for example, we do
```
import pandas as pd
from pandas import DataFrame
```
and then one test function uses `pd.DataFrame`, the other `DataFrame`.
The goal here is to be consistent: each file should either just contain pd.DataFrame, or DataFrame. Same goes for, e.g. `to_timedelta`, `Series`, ...
Here are the files which have such inconsistencies. If you'd like to work on this, please choose 10-15 files at a time.
**There is no need to ask for permission to work on this.** Just open a pull request, and link to this issue: write `xref #` in the pull request description. I'll do my best to keep the checklist up-to-date.
List of files which may need changing (along with an example of an offending line):
- [ ] pandas/tests/api/test_api.py:238: assert datetime(2015, 1, 2, 0, 0) == pd.datetime(2015, 1, 2, 0, 0)
- [ ] pandas/tests/arithmetic/conftest.py:85: Float64Index([nan, inf, inf, inf, inf], dtype='float64')
- [ ] pandas/tests/arithmetic/test_datetime64.py:54: dti = date_range("20130101", periods=3, tz=tz)
- [ ] pandas/tests/arithmetic/test_interval.py:53:def array(left_right_dtypes):
- [ ] pandas/tests/arithmetic/test_numeric.py:138: expected = pd.TimedeltaIndex(["10s", "40s", "90s"])
- [ ] pandas/tests/arithmetic/test_object.py:132: [Timestamp("2011-01-01"), Timestamp("2011-01-02"), pd.NaT],
- [ ] pandas/tests/arithmetic/test_period.py:35: pi = pd.period_range("2000", periods=4)
- [ ] pandas/tests/arithmetic/test_timedelta64.py:57: tdi = pd.timedelta_range("2H", periods=4)
- [ ] pandas/tests/arrays/boolean/test_arithmetic.py:12: return pd.array(
- [ ] pandas/tests/arrays/boolean/test_astype.py:10: arr = pd.array([True, False, None], dtype="boolean")
- [ ] pandas/tests/arrays/boolean/test_comparison.py:12: return pd.array(
- [ ] pandas/tests/arrays/boolean/test_construction.py:11: values = np.array([True, False, True, False], dtype="bool")
- [ ] pandas/tests/arrays/boolean/test_function.py:13: a = pd.array([True, False, None], dtype="boolean")
- [ ] pandas/tests/arrays/boolean/test_logical.py:14: a = pd.array([True, False, None], dtype="boolean")
- [ ] pandas/tests/arrays/categorical/test_constructors.py:35: ordered = np.array([0, 1, 2])
- [ ] pandas/tests/arrays/categorical/test_indexing.py:37: expected = Categorical(["c", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
- [ ] pandas/tests/arrays/categorical/test_missing.py:24: tm.assert_numpy_array_equal(isna(cat), labels == -1)
- [ ] pandas/tests/arrays/categorical/test_operators.py:15: factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True)
- [ ] pandas/tests/arrays/floating/test_arithmetic.py:27: a = pd.array([1.0, 2.0, None, 4.0, 5.0], dtype=dtype)
- [ ] pandas/tests/arrays/floating/test_astype.py:10: arr = pd.array([0.1, 0.2, None], dtype="Float64")
- [ ] pandas/tests/arrays/floating/test_construction.py:11: a = pd.array([1, None], dtype=pd.Float64Dtype())
- [ ] pandas/tests/arrays/floating/test_function.py:12: a = pd.array([1, 2, -3, np.nan], dtype="Float64")
- [ ] pandas/tests/arrays/floating/test_to_numpy.py:16: expected = np.array([0.1, 0.2, 0.3], dtype="object")
- [ ] pandas/tests/arrays/integer/test_arithmetic.py:21: a = pd.array([0, 1, None, 3, 4], dtype=dtype)
- [ ] pandas/tests/arrays/integer/test_construction.py:12: a = pd.array([1, None], dtype=pd.Int64Dtype())
- [ ] pandas/tests/arrays/integer/test_dtypes.py:46: {"B": np.array([1.0, 3.0]), "C": integer_array([1, 3], dtype="Int64")},
- [ ] pandas/tests/arrays/integer/test_function.py:48: arr = np.array([1, 2, 3, 4])
- [ ] pandas/tests/arrays/interval/test_interval.py:25: (date_range("20170101", periods=3), date_range("20170102", periods=3)),
- [ ] pandas/tests/arrays/masked/test_arithmetic.py:11:arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_EA_INT_DTYPES]
- [ ] pandas/tests/arrays/masked/test_arrow_compat.py:8:arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_EA_INT_DTYPES]
- [ ] pandas/tests/arrays/sparse/test_array.py:19: self.arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
- [ ] pandas/tests/arrays/sparse/test_dtype.py:22: sparse_dtype = SparseDtype(dtype)
- [ ] pandas/tests/arrays/string_/test_string.py:13: df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype="string")})
- [ ] pandas/tests/arrays/test_array.py:33: ([1, 2], object, PandasArray(np.array([1, 2], dtype=object))),
- [ ] pandas/tests/arrays/test_datetimelike.py:677: arr.take([-1, 1], allow_fill=True, fill_value=pd.Period("2014Q1"))
- [ ] pandas/tests/arrays/test_datetimes.py:24: arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]")
- [ ] pandas/tests/base/test_conversion.py:126: i = CategoricalIndex([Timestamp("1999-12-31"), Timestamp("2000-12-31")])
- [ ] pandas/tests/base/test_misc.py:112: assert Index([1]).item() == 1
- [ ] pandas/tests/base/test_value_counts.py:36: expected.index = pd.Index(expected.index)
- [ ] pandas/tests/computation/test_eval.py:191: result = pd.eval(ex, engine=self.engine, parser=self.parser)
- [ ] pandas/tests/dtypes/test_common.py:170: assert com.is_object_dtype(np.array([], dtype=object))
- [ ] pandas/tests/dtypes/test_dtypes.py:313: dr = date_range("20130101", periods=3, tz="US/Eastern")
- [ ] pandas/tests/dtypes/test_inference.py:83: (Index([1]), True, "Index"),
- [ ] pandas/tests/dtypes/test_missing.py:67: assert isna(np.array(np.nan))
- [ ] pandas/tests/extension/arrow/arrays.py:119: pa.chunked_array([pa.array(result, mask=pd.isna(self._data.to_pandas()))])
- [ ] pandas/tests/extension/base/getitem.py:134: expected = data[np.array([], dtype="int64")]
- [ ] pandas/tests/extension/base/methods.py:33: data = data[:10].unique()
- [ ] pandas/tests/extension/base/missing.py:13: result = pd.isna(data_missing)
- [ ] pandas/tests/extension/base/setitem.py:47: data[np.array([], dtype=int)] = []
- [ ] pandas/tests/extension/decimal/test_decimal.py:72: # need to convert array([Decimal(NaN)], dtype='object') to np.NaN
- [ ] pandas/tests/extension/list/array.py:44: if not isinstance(val, self.dtype.type) and not pd.isna(val):
- [ ] pandas/tests/extension/test_boolean.py:36: return pd.array(make_data(), dtype=dtype)
- [ ] pandas/tests/extension/test_categorical.py:51: return Categorical(make_data())
- [ ] pandas/tests/extension/test_floating.py:44: return pd.array(make_data(), dtype=dtype)
- [ ] pandas/tests/extension/test_numpy.py:48: return PandasArray(np.array([np.nan, (1,)], dtype=object))
- [ ] pandas/tests/extension/test_sparse.py:29: return SparseDtype()
- [ ] pandas/tests/frame/apply/test_frame_apply.py:67: "A": date_range("20130101", periods=3),
- [ ] pandas/tests/frame/indexing/test_categorical.py:17: labels = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)])
- [ ] pandas/tests/frame/indexing/test_indexing.py:65: msg = "\"None of [Index(['baf'], dtype='object')] are in the [columns]\""
- [ ] pandas/tests/frame/indexing/test_sparse.py:50: expected = np.full(cols, SparseDtype(dtype, fill_value=0))
- [ ] pandas/tests/frame/indexing/test_xs.py:59: # no columns but Index(dtype=object)
- [ ] pandas/tests/frame/methods/test_align.py:89: tm.assert_index_equal(bf.index, Index([]))
- [ ] pandas/tests/frame/methods/test_append.py:132: df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(5))
- [ ] pandas/tests/frame/methods/test_cov_corr.py:73: "other_column", [pd.array([1, 2, 3]), np.array([1.0, 2.0, 3.0])]
- [ ] pandas/tests/frame/methods/test_describe.py:249: start = Timestamp(2018, 1, 1)
- [ ] pandas/tests/frame/methods/test_diff.py:78: dti = pd.date_range("2016-01-01", periods=4, tz=tz)
- [ ] pandas/tests/frame/methods/test_drop.py:90: expected = Index(["a", "b", "c"], name="first")
- [ ] pandas/tests/frame/methods/test_quantile.py:247: [Timestamp("2010-07-02 12:00:00"), 2.5], index=["a", "b"], name=0.5
- [ ] pandas/tests/frame/methods/test_replace.py:964: Timestamp("20130102", tz="US/Eastern"),
- [ ] pandas/tests/frame/methods/test_reset_index.py:42: expected["idx"] = expected["idx"].apply(lambda d: Timestamp(d, tz=tz))
- [ ] pandas/tests/frame/methods/test_sort_values.py:243: df = DataFrame({"x": pd.Categorical(np.repeat([1, 2, 3, 4], 5), ordered=True)})
- [ ] pandas/tests/frame/test_analytics.py:68: df = DataFrame({"b": date_range("1/1/2001", periods=2)})
- [ ] pandas/tests/frame/test_constructors.py:94: ([[]], RangeIndex(1), RangeIndex(0)),
- [ ] pandas/tests/frame/test_dtypes.py:62: "A": date_range("20130101", periods=3),
- [ ] pandas/tests/frame/test_query_eval.py:46: result = df.eval("A+1")
- [ ] pandas/tests/frame/test_reshape.py:64: tm.assert_index_equal(result.columns, Index(["A", "B"], name=0))
- [ ] pandas/tests/frame/test_to_csv.py:40: def read_csv(self, path, **kwargs):
- [ ] pandas/tests/generic/test_finalize.py:35: (np.array([0], dtype="float64")),
- [ ] pandas/tests/generic/test_generic.py:792: df1["start"] = date_range("2000-1-1", periods=10, freq="T")
- [ ] pandas/tests/generic/test_series.py:65: o = Series(date_range("20130101", periods=3))
- [ ] pandas/tests/groupby/aggregate/test_aggregate.py:135: exp = Series([], dtype=np.float64, index=pd.Index([], dtype=np.float64))
- [ ] pandas/tests/groupby/aggregate/test_other.py:109: "time": date_range("1/1/2011", periods=8, freq="H"),
- [ ] pandas/tests/groupby/test_apply.py:43: exp_idx = pd.Index(
- [ ] pandas/tests/groupby/test_categorical.py:88: cats = Categorical(
- [ ] pandas/tests/groupby/test_counting.py:240: index=Index([0.0, 3.0, 4.0, 5.0], name="B"), data={"A": [1, 1, 1, 1]}
- [ ] pandas/tests/groupby/test_function.py:66: exp_df = DataFrame([exp] * 2, columns=["val"], index=Index(["a", "b"], name="key"))
- [ ] pandas/tests/groupby/test_groupby.py:19: result = repr(pd.Grouper(key="A", level="B"))
- [ ] pandas/tests/groupby/test_grouping.py:159: dates = date_range(d0, date.today())
- [ ] pandas/tests/groupby/test_missing.py:14: columns=pd.Index(["type", "a", "b"], name="idx"),
- [ ] pandas/tests/groupby/test_nth.py:14: expected.index = Index(["bar", "foo"], name="A")
- [ ] pandas/tests/groupby/test_pipe.py:35: index = Index(["bar", "foo"], dtype="object", name="A")
- [ ] pandas/tests/groupby/test_quantile.py:48: [a_expected, b_expected], columns=["val"], index=Index(["a", "b"], name="key")
- [ ] pandas/tests/groupby/test_timegrouper.py:54: index=date_range(
- [ ] pandas/tests/groupby/transform/test_transform.py:103: "d": pd.date_range("2014-1-1", "2014-1-4"),
- [ ] pandas/tests/indexes/base_class/test_reshape.py:14: index = pd.Index([1, 2, 3])
- [ ] pandas/tests/indexes/base_class/test_setops.py:15: idx1 = pd.Index(["a", "b"])
- [ ] pandas/tests/indexes/categorical/test_category.py:24: return CategoricalIndex(list("aabbca"), categories=categories, ordered=ordered)
- [ ] pandas/tests/indexes/categorical/test_formats.py:12: idx = pd.CategoricalIndex(["a", "bb", "ccc"])
- [ ] pandas/tests/indexes/categorical/test_indexing.py:14: idx = pd.CategoricalIndex([1, 2, 3], name="xxx")
- [ ] pandas/tests/indexes/categorical/test_map.py:20: index = CategoricalIndex(data, categories=categories, ordered=ordered)
- [ ] pandas/tests/indexes/common.py:96: result = pd.Index(expected)
- [ ] pandas/tests/indexes/datetimes/test_astype.py:24: idx = DatetimeIndex(["2016-05-16", "NaT", NaT, np.NaN], name="idx")
- [ ] pandas/tests/indexes/datetimes/test_constructors.py:30: dt_cls([pd.NaT, pd.Timestamp("2011-01-01")], freq="D")
- [ ] pandas/tests/indexes/datetimes/test_date_range.py:32: rng = date_range("20090415", "20090519", tz="US/Eastern")
- [ ] pandas/tests/indexes/datetimes/test_datetime.py:17: index = date_range("20130101", periods=3, tz="US/Eastern")
- [ ] pandas/tests/indexes/datetimes/test_indexing.py:20: idx = pd.date_range(
- [ ] pandas/tests/indexes/datetimes/test_misc.py:17: idx = pd.date_range(
- [ ] pandas/tests/indexes/datetimes/test_ops.py:44: rng = date_range("1/1/2000", "1/1/2001")
- [ ] pandas/tests/indexes/datetimes/test_setops.py:57: rng1 = pd.date_range("1/1/2000", freq="D", periods=5, tz=tz)
- [ ] pandas/tests/indexes/datetimes/test_shift.py:23: idx = pd.DatetimeIndex([], name="xxx", tz=tz)
- [ ] pandas/tests/indexes/datetimes/test_timezones.py:55: idx = DatetimeIndex(dates)
- [ ] pandas/tests/indexes/interval/test_interval.py:57: ivs = [Interval(l, r, closed) for l, r in zip(range(10), range(1, 11))]
- [ ] pandas/tests/indexes/multi/test_analytics.py:35: major_axis = Index(list(range(4)))
- [ ] pandas/tests/indexes/multi/test_constructors.py:21: expected = Index(["foo", "bar", "baz", "qux"], name="first")
- [ ] pandas/tests/indexes/multi/test_get_level_values.py:22: expected = Index(["foo", "foo", "bar", "baz", "qux", "qux"], name="first")
- [ ] pandas/tests/indexes/multi/test_get_set.py:289: expected = pd.MultiIndex(levels=[[0, 1]], codes=[[0, 1]], names=["first"])
- [ ] pandas/tests/indexes/multi/test_indexing.py:67: index = MultiIndex(
- [ ] pandas/tests/indexes/multi/test_integrity.py:161: # isna(MI)
- [ ] pandas/tests/indexes/multi/test_join.py:10: "other", [Index(["three", "one", "two"]), Index(["one"]), Index(["one", "three"])]
- [ ] pandas/tests/indexes/multi/test_missing.py:42: idx = MultiIndex(
- [ ] pandas/tests/indexes/multi/test_monotonic.py:37: i = MultiIndex(
- [ ] pandas/tests/indexes/multi/test_sorting.py:178: mi = MultiIndex(
- [ ] pandas/tests/indexes/period/test_formats.py:117: exp1 = """Series([], dtype: period[D])"""
- [ ] pandas/tests/indexes/period/test_indexing.py:28: idx = period_range("2011-01-01", "2011-01-31", freq="D", name="idx")
- [ ] pandas/tests/indexes/period/test_ops.py:32: idx = PeriodIndex(np.repeat(idx._values, range(1, len(idx) + 1)), freq="H")
- [ ] pandas/tests/indexes/ranges/test_indexing.py:11: index = RangeIndex(start=0, stop=20, step=2)
- [ ] pandas/tests/indexes/ranges/test_range.py:108: expected = pd.Index([0, pd.NaT, 1, 2, 3, 4], dtype=object)
- [ ] pandas/tests/indexes/test_base.py:50: return Index(list("abcde"))
- [ ] pandas/tests/indexes/test_numeric.py:42: expected = Float64Index(arr)
- [ ] pandas/tests/indexes/timedeltas/test_astype.py:21: idx = timedelta_range(start="1 days", periods=4, freq="D", name="idx")
- [ ] pandas/tests/indexes/timedeltas/test_constructors.py:17: TimedeltaIndex([1, 3, 7], unit)
- [ ] pandas/tests/indexes/timedeltas/test_formats.py:46: exp1 = """Series([], dtype: timedelta64[ns])"""
- [ ] pandas/tests/indexes/timedeltas/test_indexing.py:15: idx = timedelta_range("1 day", "31 day", freq="D", name="idx")
- [ ] pandas/tests/indexes/timedeltas/test_ops.py:16: idx = timedelta_range("1 days 09:00:00", freq="H", periods=10)
- [ ] pandas/tests/indexes/timedeltas/test_scalar_compat.py:19: rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
- [ ] pandas/tests/indexes/timedeltas/test_setops.py:14: i1 = timedelta_range("1day", periods=5)
- [ ] pandas/tests/indexes/timedeltas/test_shift.py:17: idx = pd.TimedeltaIndex([], name="xxx")
- [ ] pandas/tests/indexing/multiindex/test_multiindex.py:63: Index(["R1", "R2", np.nan, "R4"], name="a"),
- [ ] pandas/tests/indexing/test_categorical.py:101: exp = np.array([4, 3, 2, 1], dtype=np.int64)
- [ ] pandas/tests/indexing/test_check_indexer.py:13: ([1, 2], np.array([1, 2], dtype=np.intp)),
- [ ] pandas/tests/indexing/test_datetime.py:18: idx = date_range("20010101", periods=4, tz="UTC")
- [ ] pandas/tests/indexing/test_iloc.py:238: df = concat([df1, df2], axis=1)
- [ ] pandas/tests/indexing/test_indexing.py:39: df.loc[df.index[2:5], "bar"] = np.array([2.33j, 1.23 + 0.1j, 2.2, 1.0])
- [ ] pandas/tests/indexing/test_loc.py:332: expected = df.loc[np.array(mask)]
- [ ] pandas/tests/internals/test_internals.py:108: values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4])
- [ ] pandas/tests/io/excel/test_readers.py:842: si = Index(
- [ ] pandas/tests/io/excel/test_xlrd.py:30: with ExcelFile(book, engine=engine) as xl:
- [ ] pandas/tests/io/formats/test_format.py:185: with option_context(
- [ ] pandas/tests/io/formats/test_printing.py:112: with cf.option_context("display.unicode.ambiguous_as_wide", True):
- [ ] pandas/tests/io/formats/test_to_html.py:197: with option_context("display.multi_sparse", multi_sparse):
- [ ] pandas/tests/io/json/test_pandas.py:72: result = read_json(df.to_json(orient=orient), orient=orient)
- [ ] pandas/tests/io/json/test_readlines.py:21: result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True)
- [ ] pandas/tests/io/parser/test_c_parser_only.py:35: parser.read_csv(StringIO(malformed))
- [ ] pandas/tests/io/parser/test_parse_dates.py:448: Timestamp("05/31/2012, 15:30:00.029"),
- [ ] pandas/tests/io/parser/test_read_fwf.py:30: result = read_fwf(StringIO(data))
- [ ] pandas/tests/io/pytables/test_store.py:69: with HDFStore(path) as store:
- [ ] pandas/tests/io/pytables/test_timezones.py:47: Timestamp("20130102 2:00:00", tz=gettz("US/Eastern"))
- [ ] pandas/tests/io/test_clipboard.py:152: result = read_clipboard(sep=sep or "\t", index_col=0, encoding=encoding)
- [ ] pandas/tests/io/test_feather.py:40: result = read_feather(path, **read_kwargs)
- [ ] pandas/tests/io/test_parquet.py:191: actual = read_parquet(path, **read_kwargs)
- [ ] pandas/tests/io/test_sql.py:607: iris_frame = sql.read_sql_query("SELECT * FROM iris", self.conn)
- [ ] pandas/tests/io/test_stata.py:52: parsed_114 = read_stata(dta14_114, convert_dates=True)
- [ ] pandas/tests/plotting/test_frame.py:508: idx = date_range(start="2014-07-01", freq="M", periods=10)
- [ ] pandas/tests/reductions/test_reductions.py:58: expected = pd.Period(ordinal=getattr(obj.asi8, opname)(), freq=obj.freq)
- [ ] pandas/tests/resample/test_datetime_index.py:44: b = Grouper(freq=Minute(5))
- [ ] pandas/tests/resample/test_deprecated.py:43: idx = pd.date_range("2001-01-01", periods=4, freq="T")
- [ ] pandas/tests/resample/test_period_index.py:45: new_index = date_range(start=start, end=end, freq=freq, closed="left")
- [ ] pandas/tests/resample/test_resample_api.py:11:dti = date_range(start=datetime(2005, 1, 1), end=datetime(2005, 1, 10), freq="Min")
- [ ] pandas/tests/resample/test_resampler_grouper.py:16: index=date_range("1/1/2000", freq="s", periods=40),
- [ ] pandas/tests/resample/test_time_grouper.py:13:test_series = Series(np.random.randn(1000), index=date_range("1/1/2000", periods=1000))
- [ ] pandas/tests/resample/test_timedelta.py:17: index=timedelta_range("0 day", periods=4, freq="1T"),
- [ ] pandas/tests/reshape/merge/test_join.py:412: ex_index = Index(index1.values).union(Index(index2.values))
- [ ] pandas/tests/reshape/merge/test_merge.py:127: result = pd.merge(df_empty, df_a, left_index=True, right_index=True)
- [ ] pandas/tests/reshape/merge/test_merge_asof.py:21: x.time = to_datetime(x.time)
- [ ] pandas/tests/reshape/merge/test_multi.py:110: expected = pd.merge(left, right.reset_index(), on=on_cols, how=join_type)
- [ ] pandas/tests/reshape/test_concat.py:50: pd.Timestamp("2011-01-01"),
- [ ] pandas/tests/reshape/test_cut.py:29: result = cut(data, 4, labels=False)
- [ ] pandas/tests/reshape/test_melt.py:29: result = melt(self.df)
- [ ] pandas/tests/reshape/test_pivot.py:31: return Categorical(pd.IntervalIndex.from_arrays(left, right, closed))
- [ ] pandas/tests/reshape/test_qcut.py:127: exp_levels = np.array(
- [ ] pandas/tests/reshape/test_union_categoricals.py:42: result = union_categoricals([box(Categorical(a)), box(Categorical(b))])
- [ ] pandas/tests/scalar/period/test_period.py:22: i1 = Period("1/1/2005", freq="M")
- [ ] pandas/tests/scalar/test_na_scalar.py:102: value = np.array([value])
- [ ] pandas/tests/scalar/timedelta/test_arithmetic.py:29: Timedelta(10, unit="s"),
- [ ] pandas/tests/scalar/timestamp/test_unary_ops.py:327: # datetime.timestamp() converts in the local timezone
- [ ] pandas/tests/series/apply/test_series_apply.py:28: s = Series(dtype=object, name="foo", index=pd.Index([], name="bar"))
- [ ] pandas/tests/series/indexing/test_datetime.py:29: assert s[Timestamp(datetime(2009, 1, 2))] == 48
- [ ] pandas/tests/series/indexing/test_indexing.py:73: pd.date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"]
- [ ] pandas/tests/series/indexing/test_where.py:182: [Timestamp("2017-01-01"), pd.NaT, Timestamp("2017-01-02")],
- [ ] pandas/tests/series/methods/test_interpolate.py:552: [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
- [ ] pandas/tests/series/methods/test_quantile.py:28: assert q == Timestamp("2000-01-10 19:12:00")
- [ ] pandas/tests/series/methods/test_shift.py:76: index = date_range("2000-01-01", periods=5)
- [ ] pandas/tests/series/methods/test_truncate.py:76: rng = pd.date_range("2011-01-01", "2012-01-01", freq="W")
- [ ] pandas/tests/series/methods/test_value_counts.py:88: values = pd.Categorical([1, 2, 3, 1, 1, 3], ordered=True)
- [ ] pandas/tests/series/test_api.py:158: [x[1] for x in _d], index=pd.Index([x[0] for x in _d], tupleize_cols=False)
- [ ] pandas/tests/series/test_arithmetic.py:244: dt = Series(date_range("2012-1-1", periods=3, freq="D"))
- [ ] pandas/tests/series/test_constructors.py:137: # these are Index() and RangeIndex() which don't compare type equal
- [ ] pandas/tests/series/test_datetime_values.py:85: Series(date_range("20130101", periods=5), name="xxx"),
- [ ] pandas/tests/series/test_io.py:15: def read_csv(self, path, **kwargs):
- [ ] pandas/tests/series/test_logical_ops.py:164: result = left & pd.Index(right)
- [ ] pandas/tests/series/test_missing.py:215: tm.assert_series_equal(result, ts[pd.notna(ts)])
- [ ] pandas/tests/series/test_period.py:19: [pd.Period("2011-01-01", freq="D"), pd.Period("2011-02-01", freq="D")]
- [ ] pandas/tests/series/test_repr.py:169: with pd.option_context("max_rows", None):
- [ ] pandas/tests/series/test_timeseries.py:19: rng = date_range("1/1/2000", "3/1/2000", freq="B")
- [ ] pandas/tests/series/test_ufunc.py:246: arr = np.array([Dummy(0), Dummy(1)])
- [ ] pandas/tests/test_algos.py:43: codes, uniques = algos.factorize(["a", "b", "b", "a", "a", "c", "c", "c"])
- [ ] pandas/tests/test_multilevel.py:307: index=pd.Index([2], name="ix1"),
- [ ] pandas/tests/test_strings.py:189: values = np.array(values, dtype=object) # object dtype to avoid casting
- [ ] pandas/tests/tools/test_to_datetime.py:43: results1 = [Timestamp("20000101"), Timestamp("20000201"), Timestamp("20000301")]
- [ ] pandas/tests/tools/test_to_numeric.py:58: result = to_numeric(ser, **input_kwargs)
- [ ] pandas/tests/tools/test_to_timedelta.py:14: result = to_timedelta(["", ""])
- [ ] pandas/tests/window/test_expanding.py:53: e = Expanding(Series([2, 4, 6]), window=2)
- [ ] pandas/tests/window/test_rolling.py:81: {"value": np.arange(n)}, index=pd.date_range("2015-12-24", periods=n, freq="D")
----
See #37335 for an example pull request
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CLN make namespace usage in tests consistent
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Example pull request for #37334
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[WIP] Arrow string array: Split array and dtype
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CI testing before considering including in #35259
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[WIP] Arrow string array: Common base class
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CI: test_binary_arith_ops failing in travis-38-slow
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```
=========================== short test summary info ============================
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_binary_arith_ops
= 1 failed, 14730 passed, 11 skipped, 1 xfailed, 37 warnings in 672.01s (0:11:12) =
```
e.g. https://travis-ci.org/github/pandas-dev/pandas/jobs/737965996
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TST: parametrize slow test
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| 2020-10-23T15:37:31Z
| 2020-10-23T15:37:15Z
|
MEMBER
| null |
Broken off from #37224
|
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1.1.x
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CONTRIBUTOR
| null |
- [X] closes #37316
|
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TST/REF: collect tests by method
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BUG: groupby __iter__ on pandas 1.1.x not propagating _metadata on DataFrame subclasses
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| 6
| 2020-10-22T17:43:41Z
| 2020-11-04T13:22:55Z
| 2020-11-04T13:22:55Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
...:
...: class SubclassedDataFrame2(pd.DataFrame):
...:
...: # temporary properties
...: _internal_names = pd.DataFrame._internal_names + ['internal_cache']
...: _internal_names_set = set(_internal_names)
...:
...: # normal properties
...: _metadata = ['added_property']
...:
...: @property
...: def _constructor(self):
...: return SubclassedDataFrame2
...:
In [2]: df = SubclassedDataFrame2({'A': [1, 2, 3], 'B': [1, 1, 2], 'C': [7, 8, 9]})
...: df.added_property = "hello"
...: for i, d in df.groupby("B"):
...: assert d.added_property == "hello"
...:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-bf64fc505324> in <module>
2 df.added_property = "hello"
3 for i, d in df.groupby("B"):
----> 4 assert d.added_property == "hello"
5
~\RMS-Python\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
5133 or name in self._accessors
5134 ):
-> 5135 return object.__getattribute__(self, name)
5136 else:
5137 if self._info_axis._can_hold_identifiers_and_holds_name(name):
AttributeError: 'SubclassedDataFrame2' object has no attribute 'added_property'
```
#### Problem description
Under pandas 1.0.x the above is working, on 1.1.x, the _metadata is not propagated to the split frames. I think this is a known issue with groupby and `__finalize__`, but this specific behavior with `__iter__` is a regression from 1.0.x behavior on DataFrame subclasses.
#### Expected Output
Assert statements above are passing, with metadata propagated to the split dataframe
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.17
pytest : 5.4.1
hypothesis : 5.36.1
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.49.0
</details>
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MDU6SXNzdWU3Mjc2MTI4Nzc=
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|
ENH: support different integer types in sql
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| 2020-10-22T18:11:29Z
| 2021-06-22T19:31:13Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Problem description
I ran across a situation where I needed to ensure that a PostgreSQL database table retained the same data types when being read using pd.read_sql() or pd.read_sql_table(). I found that reading a table with a column type of INTEGER (sqlalchemy.types.Integer), it was converted to dtype('int64') in pandas, which subsequently got written back to the database ( df.to_sql() ) as a BigInt (sqlalchemy.types.BigInteger). I saw that “refining integer size” was listed as a TODO item in the code, and would be happy to provide a PR if it would be accepted. I was able to resolve this with minor modifications to io/sql.py in _harmonize_columns() and _get_dtype():
```
def _harmonize_columns(self, parse_dates=None):
…
elif len(df_col) == df_col.count():
# No NA values, can convert ints and bools
if col_type is np.dtype("int64") or col_type is np.dtype("int32") or col_type is bool:
self.frame[col_name] = df_col.astype(col_type, copy=False)
def _get_dtype(self, sqltype):
from sqlalchemy.types import TIMESTAMP, Boolean, Date, DateTime, Float, Integer, BigInteger, SmallInteger
…
elif isinstance(sqltype, BigInteger):
return np.dtype("int64")
elif isinstance(sqltype, Integer):
return np.dtype("int32")
elif isinstance(sqltype, SmallInteger):
return np.dtype("int16")
…
````
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MDExOlB1bGxSZXF1ZXN0NTA4NDc3MzAw
| 37,345
|
Update sql.py to distinguish between BigInt, Integer, and SmallInt
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| 2020-10-22T18:24:12Z
| 2021-01-09T23:02:01Z
| 2021-01-09T23:01:54Z
|
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is to resolve #37344. I've never contributed to Pandas before, so may need guidance to proceed.
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BUG: .values upcasts dtype in non-mixed-dtype subset of mixed-dtype DataFrames
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| 2021-08-13T05:19:06Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = pd.DataFrame({"a": [1], "b": [0.1]})
print(type(data.loc[0, "a"])) # <class 'numpy.int64'>
print(data.loc[0, ["a"]].values.dtype) # float64
```
#### Problem description
`.values` seems to upcast dtype to a common dtype even when only a subset of columns (with the same dtype) are exported?
#### Expected Output
int64 for the second print.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.13.0-36-generic
Version : #40~16.04.1-Ubuntu SMP Fri Feb 16 23:25:58 UTC 2018
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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CI: xfail test_binary_arith_ops
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MEMBER
| null |
xref #37328, #37338
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BUG: comparison of int dataframe to string yield unpredictable error (ValueError: unknown type str128)
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| 3
| 2020-10-22T20:14:04Z
| 2021-10-16T14:46:40Z
| 2021-10-16T14:46:40Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
import numpy as np
pd.DataFrame(range(10**1), dtype=np.int64)=='' # OK, just a FutureWarning
pd.DataFrame(range(10**2), dtype=np.int64)=='' # OK, just a FutureWarning
pd.DataFrame(range(10**3), dtype=np.int64)=='' # OK, just a FutureWarning
pd.DataFrame(range(10**4), dtype=np.int64)=='' # OK, just a FutureWarning
# pd.DataFrame(range(10**5), dtype=np.int64)=='' # ValueError: unknown type str32
# pd.DataFrame(range(10**5), dtype=np.int64)==' ' # ValueError: unknown type str64
pd.DataFrame(range(10**5), dtype=np.int64)==' ' # ValueError: unknown type str128
/home/ubuntu/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py:68: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return op(a, b)
/home/ubuntu/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py:68: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return op(a, b)
/home/ubuntu/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py:68: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return op(a, b)
/home/ubuntu/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py:68: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
return op(a, b)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-104-1641071c93d2> in <module>
5 # pd.DataFrame(range(10**5), dtype=np.int64)=='' # ValueError: unknown type str32
6 # pd.DataFrame(range(10**5), dtype=np.int64)==' ' # ValueError: unknown type str64
----> 7 pd.DataFrame(range(10**5), dtype=np.int64)==' ' # ValueError: unknown type str128
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/ops/__init__.py in f(self, other)
702
703 # See GH#4537 for discussion of scalar op behavior
--> 704 new_data = dispatch_to_series(self, other, op, axis=axis)
705 return self._construct_result(new_data)
706
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/ops/__init__.py in dispatch_to_series(left, right, func, axis)
263 if not is_list_like(right):
264 # i.e. scalar, faster than checking np.ndim(right) == 0
--> 265 bm = left._mgr.apply(array_op, right=right)
266 return type(left)(bm)
267
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, **kwargs)
402
403 if callable(f):
--> 404 applied = b.apply(f, **kwargs)
405 else:
406 applied = getattr(b, f)(**kwargs)
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/internals/blocks.py in apply(self, func, **kwargs)
343 """
344 with np.errstate(all="ignore"):
--> 345 result = func(self.values, **kwargs)
346
347 return self._split_op_result(result)
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/ops/array_ops.py in comparison_op(left, right, op)
244 warnings.simplefilter("ignore", DeprecationWarning)
245 with np.errstate(all="ignore"):
--> 246 res_values = na_arithmetic_op(lvalues, rvalues, op, is_cmp=True)
247
248 return res_values
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/ops/array_ops.py in na_arithmetic_op(left, right, op, is_cmp)
140
141 try:
--> 142 result = expressions.evaluate(op, left, right)
143 except TypeError:
144 if is_cmp:
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py in evaluate(op, a, b, use_numexpr)
228 use_numexpr = use_numexpr and _bool_arith_check(op_str, a, b)
229 if use_numexpr:
--> 230 return _evaluate(op, op_str, a, b) # type: ignore
231 return _evaluate_standard(op, op_str, a, b)
232
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/pandas/core/computation/expressions.py in _evaluate_numexpr(op, op_str, a, b)
110 f"a_value {op_str} b_value",
111 local_dict={"a_value": a_value, "b_value": b_value},
--> 112 casting="safe",
113 )
114
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/numexpr/necompiler.py in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
820 # Create a signature
821 signature = [(name, getType(arg)) for (name, arg) in
--> 822 zip(names, arguments)]
823
824 # Look up numexpr if possible.
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/numexpr/necompiler.py in <listcomp>(.0)
819
820 # Create a signature
--> 821 signature = [(name, getType(arg)) for (name, arg) in
822 zip(names, arguments)]
823
~/miniconda3/envs/genomics.py3/lib/python3.7/site-packages/numexpr/necompiler.py in getType(a)
701 if kind == 'S':
702 return bytes
--> 703 raise ValueError("unknown type %s" % a.dtype.name)
704
705
ValueError: unknown type str128
```
#### Problem description
There is an error with an unclear message raised in unpredictable conditions (has to do with dataframe size). Either an informative error message should be raised (the error message itself seems buggy and somehow converts the string size to a supposed data type)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1028-aws
Version : #29~18.04.1-Ubuntu SMP Tue Oct 6 17:14:23 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : None
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTA4NTY1MDEy
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TST: fixturize/parametrize test_eval to the bone
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| 1
| 2020-10-22T21:19:39Z
| 2020-10-22T23:50:18Z
| 2020-10-22T23:30:07Z
|
MEMBER
| null |
Might help us track down the failing test there.
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DISC: Use of assertions in user-facing code
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MEMBER
| null |
There are a variety of places in the code base where we have some bare assertions that can be somewhat confusing when encountered by end-users (e.g., https://github.com/pandas-dev/pandas/issues/35509). As @WillAyd pointed out these are sometimes useful in convincing mypy of something's type, but is this nonetheless something we want to avoid as a general rule? If indeed the assertion is valid then it shouldn't actually raise, but if it can then probably we could come up with a more appropriate error type / message than an empty assertion.
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| 2020-10-23T01:00:41Z
| 2020-10-23T17:05:23Z
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MEMBER
| null |
Fixing use of pd.Index when Index is imported directly
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| 2020-10-23T02:12:31Z
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|
MEMBER
| null |
Should help fix red CI on master
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MEMBER
| null |
Looking to get rid of some more of the not-obviously-scoped test files
|
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PERF/ENH: add fast astyping for Categorical
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| 2020-10-23T04:14:28Z
| 2020-11-18T20:36:45Z
| 2020-11-18T18:21:51Z
|
MEMBER
| null |
- [x] closes #8628
- [ ] tests added / passed
- [x] benchmarks added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
To illustrate the speed-up, the set-up is (from OP):
```python
import numpy as np
import pandas as pd
rng = np.random.default_rng()
df = pd.DataFrame(
rng.choice(np.array(list("abcde")), 4_000_000).reshape(1_000_000, 4),
columns=list("ABCD"),
)
for col in df.columns:
df[col] = df[col].astype("category")
```
On master
``` python
In [5]: %timeit [df[col].astype('unicode') for col in df.columns]
250 ms ± 601 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
versus on this branch
``` python
In [5]: %timeit [df[col].astype('unicode') for col in df.columns]
5.38 ms ± 47 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
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API: require timezone match in DatetimeArray.take
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| 0
| 2020-10-23T04:23:07Z
| 2020-10-23T18:07:47Z
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|
MEMBER
| null |
Consistent with #37299
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DOC: update development documentation for link to built docs #37248
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| 2
| 2020-10-23T07:10:36Z
| 2020-10-24T07:28:07Z
| 2020-10-24T07:27:52Z
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CONTRIBUTOR
| null |
- [x] closes #37248
cc: @jreback
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MDU6SXNzdWU3Mjc5NzkxNjE=
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DOC mention pre-commit as a way to run (some) code checks
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| 2020-10-23T07:18:58Z
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MEMBER
| null |
xref this comment https://github.com/pandas-dev/pandas/pull/37240#discussion_r510518893
> as a followup we might want to update the contributing docs to put a pointer to these types of checks here (e.g. rather than just mentioning code_checks.sh) esp as we are using these a lot more now
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REGR: Notebook (html) repr of DataFrame no longer follows min_rows/max_rows settings
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| 10
| 2020-10-23T08:50:45Z
| 2020-11-13T05:45:48Z
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|
MEMBER
| null |
On master, the html repr in notebooks of a DataFrame is no longer following the min_rows/max_rows logic (see https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options).
If you have a DataFrame with many rows, it incorrectly shows 60 (max_rows) rows in the truncated repr intead of 10 (min_rows).
cc @ivanovmg (might be related to the refactoring you have been doing, but to be clear, I didn't check if this is actually the case!)
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TST: moved file test_concat.py to folder ./concat/ (#37243)
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| 2020-10-23T10:05:18Z
| 2020-10-23T15:27:44Z
| 2020-10-23T15:27:14Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN Apply style to Jupyter notebook
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[] | null | 11
| 2020-10-23T10:26:29Z
| 2021-01-03T18:06:48Z
| 2020-11-26T15:08:02Z
|
MEMBER
| null |
We apply many style checks to our source code files / Python snippets in documentation, so I've applied them to the (lone) Jupyter notebook.
Not adding anything to CI as it's arguably not worth it for just a single file
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CI autoupdate pre-commit versions
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| 2020-10-23T10:57:19Z
| 2020-11-01T10:32:02Z
| 2020-10-31T23:06:13Z
|
MEMBER
| null |
Rather than manually updating versions of hooks when new ones come out, we can make a GitHub action to open a PR to do this automatically every Monday.
For this to work, we will need whoever owns the repo to [create an access token](https://github.com/technote-space/create-pr-action#github_token) and add it as secret `ACTION_TRIGGER_TOKEN` to the repo - see screenshot below for an example
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REGR: Notebook (html) repr of DataFrame no longer follows min_rows/max_rows settings
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| 2020-10-23T11:16:39Z
| 2020-11-13T05:53:19Z
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MEMBER
| null |
- [ ] closes #37359
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fix display logic in number of rows,
explained here https://pandas.pydata.org/docs/dev/user_guide/options.html#frequently-used-options
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Backport PR #37302 on branch 1.1.x (BUG: Allow empty chunksize in stata reader when using iterator)
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| 2
| 2020-10-23T12:36:24Z
| 2020-10-24T16:11:09Z
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NONE
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Backport PR #37302: BUG: Allow empty chunksize in stata reader when using iterator
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TYP: DatetimeIndex, TimedeltaIndex
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| 0
| 2020-10-23T16:08:47Z
| 2020-10-29T02:00:11Z
| 2020-10-29T00:57:47Z
|
MEMBER
| null |
Better alternative to parts of #37224
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| 37,366
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Column lookups using str subclasses fail on DataFrames with DateTime indexes
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| 1
| 2020-10-23T18:31:15Z
| 2021-05-11T20:06:13Z
| 2021-05-11T20:06:12Z
|
NONE
| null |
Consider the following code:
```python
x = pd.DataFrame({"a": [1]}, index=pd.DatetimeIndex(["2020-10-22 01:21:00+00:00"]))
x["b"] = 2
class mystring(str):
pass
x[mystring("b")] # works
x[mystring("c")] = 3 # error!
```
We are specifically interested in this because we've been trying to move towards using `enum.Enum` classes to remove column-name string constants from our code, which works well in many cases, but fails badly when there's a datetime index. Just as an example, we might do something like
```python
import enum
@enum.unique
class ColNames(str, enum.Enum):
NAME = "name"
POWER_W = "power (watts)"
```
As far as I can tell, what's going on is:
1. When indexing, pandas (for reasons I'm not sure I understand) [first tries to interpret the key as a row slice](https://github.com/pandas-dev/pandas/blob/f0ecfd87b10d95eb1d328655da9cee1bb9118876/pandas/core/frame.py#L3069)
2. That function will [happily accept str subclasses](https://github.com/pandas-dev/pandas/blob/f0ecfd87b10d95eb1d328655da9cee1bb9118876/pandas/core/indexing.py#L2174) (and works as expected if the column already exists)
3. But with a DateTime index, it eventually [gets here](https://github.com/pandas-dev/pandas/blob/f0ecfd87b10d95eb1d328655da9cee1bb9118876/pandas/_libs/tslibs/parsing.pyx#L281) where apparently str subclasses are no longer accepted and a `TypeError` is raised.
I see two easy solutions (i.e. that I could easily submit a PR for):
1. Convert the key to a normal string either [here](https://github.com/pandas-dev/pandas/blob/f0ecfd87b10d95eb1d328655da9cee1bb9118876/pandas/core/indexing.py#L2184) or in the subclass implementations thereof
2. Watch for `TypeError`s [here](https://github.com/pandas-dev/pandas/blob/f0ecfd87b10d95eb1d328655da9cee1bb9118876/pandas/core/indexing.py#L2193) in addition to the other known types there, allowing it to gracefully continue on and try the key as a column name (which will then succeed)
I can also envision fixing this by
3. extending the low-level parsing function to work better with this case, or
4. finishing the [deprecation of row lookups](https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexing.py#L2186) using regular `frame[indx]` notation
However solution 3 is outside my comfort zone, and 4 seems like it might be more involved anyway.
It feels to me like solution 3 is the "correct" resolution to this problem, and that 1 is almost as good (if applied in all the right places), but since it's not my project, I wanted to get feedback before trying to jump in to any of these.
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BUG/TST: Fix infer_dtype for Period array-likes and general ExtensionArrays
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| 32
| 2020-10-23T19:07:37Z
| 2021-02-12T17:47:22Z
| 2021-02-12T17:45:26Z
|
MEMBER
| null |
Closes #23553
In addition, I also changed to let infer_dtype fall back to inferring from the scalar elements if the array-like is not recognized directly (now it raises an error, which doesn't seem very useful?)
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TST: split up test_concat.py #37243 - follows up
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| 2
| 2020-10-23T19:31:48Z
| 2020-10-23T20:42:21Z
| 2020-10-23T20:41:13Z
|
CONTRIBUTOR
| null |
* created test_datetime.py and
split datetime/timezone/period related tests
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: Call finalize in DataFrame.unstack
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This commit adds a step to call `__finalize__` in `DataFrame.unstack`, as motivated by #28283. Adding this step also prevents needing a step to call `__finalize__` in `DataFrame.pivot` since `DataFrame.unstack` is called before returning, so the xfail for that test was removed as well.
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DOC: add searchsorted examples #36411
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PERF: ensure_string_array with non-numpy input array
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|
CONTRIBUTOR
| null |
Currently, if the input array to `ensure_string_array` is a python object (e.g. an `ExtensionArray`), the function will constantly switch between python and cython level code, which is slow.
This PR fixes that by ensuring we always have a numpy array, avoiding the trips to python level code.
``` python
>>> n = 50_000
>>> cat = pd.Categorical([*['a', pd.NA] * n])
>>> %timeit cat.astype("string")
447 ms ± 11.9 ms per loop # master
5.43 ms ± 80.5 µs per loop # this PR
```
xref #35519, #36464.
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ENH/BUG: implement __iter__ for IntegerArray so conversions (to_dict, tolist, etc.) return python native types
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[] | null | 32
| 2020-10-24T10:11:08Z
| 2020-10-31T21:39:52Z
| 2020-10-31T21:39:52Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate
[this should provide the location of the documentation, e.g. "pandas.read_csv" or the URL of the documentation, e.g. "https://dev.pandas.io/docs/reference/api/pandas.read_csv.html"]
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
It is strange that even on stackoverflow reverse explode has only a single answer(that too is asked very recently). So adding a few more examples should not be bad.
[this should provide a description of what documentation you believe needs to be fixed/improved]
#### Suggested fix for documentation
The following example tells how to perform k-hot encoding with agg.
```
maximum = df.groupby("ImageId").agg({"ClassId":lambda x:x.tolist()})["ClassId"].max()[0]
df = df.groupby("ImageId").agg({"ClassId":lambda x:x.tolist()})
df["ClassId"] = df["ClassId"].apply(lambda x:[1 if i+1 in x else 0 for i in range(maximum)] )
```
[this should explain the suggested fix and **why** it's better than the existing documentation]
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| 728,770,012
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MDExOlB1bGxSZXF1ZXN0NTA5NDE3Njg2
| 37,379
|
CI move validate unwanted patterns over to pre-commit
|
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| 1
| 2020-10-24T11:29:40Z
| 2020-10-26T12:51:10Z
| 2020-10-26T12:07:18Z
|
MEMBER
| null |
This becomes much simpler with pre-commit :smile: As with other PRs of this kind, the motivation for moving checks like these over to pre-commit is that they become cross-platform and that they provide faster feedback to devs
----
example: if I apply
```diff
diff --git a/pandas/io/formats/info.py b/pandas/io/formats/info.py
index 891b3ea7a..6c1075910 100644
--- a/pandas/io/formats/info.py
+++ b/pandas/io/formats/info.py
@@ -479,7 +479,7 @@ class DataFrameTableBuilder(TableBuilderAbstract):
collected_dtypes = [
f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
]
- self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
+ self._lines.append(" " f"dtypes: {', '.join(collected_dtypes)}")
def add_memory_usage_line(self) -> None:
"""Add line containing memory usage."""
```
then I get
```console
$ pre-commit run unwanted-patterns-strings-to-concatenate --all
Check for use of not concatenated strings................................Failed
- hook id: unwanted-patterns-strings-to-concatenate
- exit code: 1
pandas/io/formats/info.py:482:String unnecessarily split in two by black. Please merge them manually.
```
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TST: Update unreliable num precision component of test_binary_arith_ops (GH37328)
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| 12
| 2020-10-24T14:11:12Z
| 2020-10-28T12:05:00Z
| 2020-10-28T12:04:57Z
|
CONTRIBUTOR
| null |
- [x] closes #37328
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
Fixed Metadata Propogation in DataFrame
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| 7
| 2020-10-24T15:25:35Z
| 2021-06-06T19:22:15Z
| 2020-11-04T16:52:45Z
|
CONTRIBUTOR
| null |
In reference to #28283
Added finalize reference to append, merge, eval, diff, applymap, and update. Removed the ignore marks from the associated tests.
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TST: Different tests were collected between gw0 and gw1
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| 4
| 2020-10-24T15:58:59Z
| 2020-10-26T09:40:48Z
| 2020-10-25T21:56:55Z
|
MEMBER
| null |
not sure if anyone else if having issue running tests locally. have created a clean pandas-dev env on a different machine so maybe a transient thing.
https://github.com/pytest-dev/pytest-xdist/issues/432
<details>
```
INSTALLED VERSIONS
------------------
commit : dce547ea2fe7f8c5c51c04d0a87704a2b4abf476
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.2.0.dev0+908.gdce547ea2f
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.4
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.0
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
```
</details>
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CI fix failing codecheck
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MEMBER
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replace Appender decorator with doc
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|
CONTRIBUTOR
| null |
- [x] ref #31942
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] replaces Appender with doc for NDFrame.compare()
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BUG: pandas 1.1.3 fails to install using pip and clang due to unreachable code
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|
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pip3 install -U --user pandas
```
#### Problem description
The installation fails with the following output:-
```
clang -pthread -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -DNPY_NO_DEPRECATED_API=0 -I./pandas/_libs -Ipandas/_libs/src/klib -I/tmp/
pip-build-env-ikhpgen8/overlay/lib/python3.9/site-packages/numpy/core/include -I/home/chaz/.local/local/python-3.9.0/include/python3.9 -c pandas/_libs/algos.c -o build/temp.linux-x
86_64-3.9/pandas/_libs/algos.o -Werror
pandas/_libs/algos.c:81785:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:83402:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:84302:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85202:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85385:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:86285:3: error: code will never be executed [-Werror,-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
6 errors generated.
error: command '/home/chaz/.local/local/clang+llvm/bin/clang' failed with exit code 1
----------------------------------------
ERROR: Failed building wheel for pandas
```
#### Expected Output
The module should be installed successfully.
#### Output of ``pd.show_versions()``
N/A
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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DOC: Improve clarity and fix grammar
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| 1
| 2020-10-24T17:38:39Z
| 2020-10-26T14:38:14Z
| 2020-10-25T18:38:23Z
|
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR improves the "Performance considerations" section of the IO tools user guide:
https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#performance-considerations
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TST: split up test_concat.py #37243 - more follows up
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| 9
| 2020-10-24T18:27:03Z
| 2020-10-28T12:31:53Z
| 2020-10-28T12:04:17Z
|
CONTRIBUTOR
| null |
* created test_categorical.py
- [x] closes #37243
- [ ] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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PERF: release gil for ewmc_time
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|
MEMBER
| null |
no perf change for `rolling.EWMMethods`
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| 4
| 2020-10-24T18:50:04Z
| 2020-11-04T02:57:07Z
| 2020-11-04T02:57:03Z
|
MEMBER
| null |
The performance changes for `rolling.EWMMethods` are not stable. I've seen those and no changes in separate runs.
```
+ 1.35±0.02ms 2.17±0.5ms 1.61 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'std')
```
```
- 2.58±0.2ms 1.36±0.01ms 0.53 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'std')
```
```
+ 1.39±0.01ms 2.17±0.5ms 1.56 rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'std')
- 1.71±0.3ms 1.35±0.01ms 0.79 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'std')
```
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CLN: Deprecate dayofweek/hello day_of_week (#9606)
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| 7
| 2020-10-24T22:39:20Z
| 2020-10-29T01:19:03Z
| 2020-10-29T01:18:57Z
|
CONTRIBUTOR
| null |
Rename dayofweek properties in Period, Datetime, and PeriodArray classes.
Add day_of_week property to respective tests for each class.
- [x] closes #9606
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: nanstd with td64 and skipna=False
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| 2020-10-24T23:53:22Z
| 2020-10-24T23:55:40Z
| 2020-10-24T23:54:24Z
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- [ ] closes #xxxx
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The actual bugfix is the one line changed in nanops; the test is rolled in to the existing TDA.std test. Everything else is cleanup that was involved in tracking down the bug.
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BUG: DataFrame.std(skipna=False) with td64 dtype
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DOC: make RST files conform to pandas token usage
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| 2020-12-29T17:17:34Z
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CONTRIBUTOR
| null |
See also: #32316
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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In #36845 the RST files were updated to normalize usage of the word "pandas". The following tokens were replaced:
+ \`pandas\`
+ \`\`pandas\`\`
+ \*pandas\*
+ \*\*pandas\*\*
+ Pandas
In that PR, I wrongly said that I caught all patterns. I was wrong, and this PR address that (my apologies).
#### Misc Note
I'm also working on a rule for `code_checks.sh` to catch this in the future. I have the logic of the rule, but I'm struggling to get it to output nicely in such a way that it conforms with the rest of the rules in `code_checks.sh`. I want to leave this information here in case someone has any ideas I can bounce of off, or in case anyone else wants to try their hand at this. I could be spinning my wheels there for awhile and don't want it to hold up getting these changes in.
I used the following rule to find the remaining cases:
```shell
MSG='Check doc/source RST files which format pandas incorrectly'; echo $MSG
invgrep -R --include=*.rst -Poz '((\*|`)+)pandas\2|((((\.\. code-block:: ipython\s+)(\s +[^\n]*)+)|.\.\ [^\n]+https?[^\n]+|(`+).*\s+<https?[^\n]+>\8(__)?|`.*`_+\s+~+|.*:ref:`.*`)(*SKIP)(?!)|\bPandas\b(?!-))' doc/source/ | sed 's/\x0/\n/g'
RET=$(($RET + $?)) ; echo $MSG "DONE"
```
This finds easily identifiable formatting errors (\`\`pandas\`\`, for example), but it also respects article titles and code examples in an RST `code-block`s. It uses `grep -v` to get all text on a single line, but that causes error messages to lose the line number of the file that the error occurred on (everything in file `foo.rst` is reported to have happened on line 1). Because of this I'm not including the regex in this PR. If anyone is interested in giving me pointers on getting that bit fixed, or even taking my idea and running with it themselves, please do so.
Edit:
Because its desired to retain \`\`pandas\`\` in some places where it contextually make sense, regex won't be able to capture such uses. The above regex can instead be:
```shell
MSG='Check doc/source RST files which format pandas incorrectly'; echo $MSG
invgrep -R --include=*.rst -Poz '(\*+|`)pandas\1|((((\.\. code-block:: ipython\s+)(\s +[^\n]*)+)|.\.\ [^\n]+https?[^\n]+|(`+).*\s+<https?[^\n]+>\7(__)?|`.*`_+\s+~+|.*:ref:`.*`)(*SKIP)(?!)|\bPandas\b(?!-))' doc/source/ | sed 's/\x0/\n/g'
RET=$(($RET + $?)) ; echo $MSG "DONE"
```
which will ignore \`\`pandas\`\` but catch \`pandas\`
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BUG: Series[td64].sum() wrong on empty series, closes #37151
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| 2020-10-25T02:32:00Z
| 2020-10-26T15:19:15Z
| 2020-10-26T12:06:46Z
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MEMBER
| null |
- [x] closes #37151
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Will handle min_count on the next pass.
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CLN: nanops
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| 1
| 2020-10-25T03:19:21Z
| 2020-10-26T15:11:01Z
| 2020-10-26T12:13:15Z
|
MEMBER
| null |
This sits on top of #37394. The cleanup in _maybe_get_mask means that we end up getting a correctly-dtyped result instead of object-dtype, which becomes important in the upcoming nanmean PR.
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RLS: 1.1.4
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| 39
| 2020-10-25T08:44:41Z
| 2020-10-31T17:46:52Z
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|
MEMBER
| null |
[](https://github.com/simonjayhawkins/pandas-release/actions?query=workflow%3A%22Tag+Release%22)
Tracking issue for the 1.1.4 release. Scheduled for release October 30, 2020
https://github.com/pandas-dev/pandas/milestone/78
Please do not remove/change milestones from these issues without a note explaining the reasoning (changing milestones doesn't trigger notification)
During issue triage, regressions from 1.0.5 onwards should be milestoned 1.1.4 in the first instance. cc @pandas-dev/pandas-triage
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|
test_rolling_var_numerical_issues on linux py_3.8_32 failing on MacPython.pandas-wheels
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| 2021-08-13T05:20:55Z
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MEMBER
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test_rolling_var_numerical_issues added in #37055 cc @phofl
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CI: enable 32bit build on CI
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| 2020-10-25T09:50:24Z
| 2020-10-25T20:08:49Z
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|
MEMBER
| null |
the number of failures on the 32bit build on https://github.com/MacPython/pandas-wheels is starting to accumulate
we should consider having a 32bit build on CI to catch these earlier (and can be addressed by the people more familiar with the changes)
a snapshot of the failures on linux py_3.8_32
```
=========================== short test summary info ============================
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/arrays/floating/test_function.py::test_stat_method[var-kwargs1]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/arrays/floating/test_function.py::test_stat_method[var-kwargs0]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/arrays/floating/test_function.py::test_stat_method[kurtosis-kwargs2]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/arrays/floating/test_function.py::test_stat_method[sem-kwargs4]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/base/test_misc.py::test_memory_usage[series-with-empty-index]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/groupby/test_groupby.py::test_groupby_nat_exclude
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/formats/test_info.py::test_info_int_columns
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_high]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/io/parser/test_c_parser_only.py::test_float_precision_options[c_low]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/reshape/test_pivot.py::TestPivot::test_pivot_empty
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/window/test_rolling.py::test_rolling_var_numerical_issues[var-1-values0]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/window/test_rolling.py::test_rolling_var_numerical_issues[std-1-values1]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/window/test_rolling.py::test_rolling_var_numerical_issues[var-2-values2]
FAILED ../../venv/lib/python3.8/site-packages/pandas/tests/window/test_rolling.py::test_rolling_var_numerical_issues[std-2-values3]
= 14 failed, 80215 passed, 16918 skipped, 1380 xfailed, 13 warnings in 528.07s (0:08:48) =
```
#37398, #36662 and #36429 address some of these.
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CONTRIBUTOR
| null |
Fixes https://github.com/pandas-dev/pandas/pull/37321#issuecomment-715878012
The offender is actually #37321. Apparantly is's a bit slow to do the check using a generator like `com.any_none`.
```python
In [1]: from pandas import *
In [2]: idx_large_fast = RangeIndex(100000)
...: idx_small_slow = date_range(start="1/1/2012", periods=1)
...: mi_large_slow = MultiIndex.from_product([idx_large_fast, idx_small_slow])
...:
...: idx_non_object = RangeIndex(1)
In[3]: %timeit mi_large_slow.equals(idx_non_object)
3.13 µs ± 79.4 ns per loop # master
2.09 µs ± 31.1 ns per loop # This PR
```
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- [x] closes #23930
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
MEMBER
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Introduced with #37090
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DEPR: Add FutureWarning when matching NaN with NaN in merge/join
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| 2021-02-12T22:48:25Z
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|
MEMBER
| null |
- [x] xref #32306 (and a few others, will add them later
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @WillAyd
I started with raising a FutureWarning when merging nan on nan. This works, if ``left_index`` and ``right_index`` are not both True.
If both are True, we are dispatching to the Index methods. Should we deprecate this for indexes too? Additionally, should we remove this for ``intersection`` of index in the future too? Will continue after receiving feedback :)
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BUG: Fix inconsistent ordering between left and right in merge
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| 6
| 2020-10-25T22:18:48Z
| 2020-11-21T23:50:46Z
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|
MEMBER
| null |
- [x] closes #35382
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Passed sort through for right join too. Did not see a reason why not.
On a related note: I think outer joins return a inconsistent order too.
```
df = pd.DataFrame({'a': [1, 0, 1]})
print(df.merge(df, on='a', how='outer', sort=False))
a
0 1
1 1
2 1
3 1
4 0
```
instead of
```
a
0 1
1 1
2 0
3 1
4 1
```
like ``left`` and ``right`` now. Would have to add a ``sort`` keyword to outer join. Should I open an issue for that?
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TST: Set timeoffset as the window parameter
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| 3
| 2020-10-25T22:51:15Z
| 2020-10-27T15:45:14Z
| 2020-10-26T03:18:19Z
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CONTRIBUTOR
| null |
- [X] closes #28266
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Hi guys, just added tests for issue #28266. I'm participating in the Hacktoberfest event and would much appreciate it if someone put the label ‘hacktoberfest-accepted’ on this PR. Any modification needed just hit me up :)
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| 2
| 2020-10-25T23:59:09Z
| 2020-10-26T07:23:47Z
| 2020-10-26T07:23:42Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.DataFrame({1: [0]}).info()
```
#### Problem description
Stack trace:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2894 try:
-> 2895 return self._engine.get_loc(casted_key)
2896 except KeyError as err:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 0
The above exception was the direct cause of the following exception:
KeyError Traceback (most recent call last)
<ipython-input-21-b154f24c816b> in <module>
----> 1 pd.DataFrame({1: [0]}).info()
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/core/frame.py in info(self, verbose, buf, max_cols, memory_usage, null_counts)
2588 ) -> None:
2589 return DataFrameInfo(
-> 2590 self, verbose, buf, max_cols, memory_usage, null_counts
2591 ).info()
2592
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/io/formats/info.py in info(self)
248 self._non_verbose_repr(lines, ids)
249 else:
--> 250 self._verbose_repr(lines, ids, dtypes, show_counts)
251
252 # groupby dtype.name to collect e.g. Categorical columns
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/io/formats/info.py in _verbose_repr(self, lines, ids, dtypes, show_counts)
333
334 for i, col in enumerate(ids):
--> 335 dtype = dtypes[i]
336 col = pprint_thing(col)
337
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/core/series.py in __getitem__(self, key)
880
881 elif key_is_scalar:
--> 882 return self._get_value(key)
883
884 if is_hashable(key):
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/core/series.py in _get_value(self, label, takeable)
987
988 # Similar to Index.get_value, but we do not fall back to positional
--> 989 loc = self.index.get_loc(label)
990 return self.index._get_values_for_loc(self, loc, label)
991
~/.pyenv/versions/3.7.7/envs/ga/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2895 return self._engine.get_loc(casted_key)
2896 except KeyError as err:
-> 2897 raise KeyError(key) from err
2898
2899 if tolerance is not None:
KeyError: 0
```
It seems that this error arises when calling `.info()` on any DataFrame that has at least one integer column name unless the sorted column names form a sequence of consecutive integers starting at 0.
#### Expected Output
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
1 1 non-null int64
dtypes: int64(1)
memory usage: 136.0 bytes
```
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.utf-8
LANG : en_US.utf-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.1
setuptools : 41.2.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.48.0
```
</details>
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TST: rename generic/, base/ test directories
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base -> index_or_series
generic -> frame_or_series
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| 1
| 2020-10-26T04:02:40Z
| 2020-10-26T13:47:42Z
| 2020-10-26T13:47:37Z
|
CONTRIBUTOR
| null |
small clean ups
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDU6SXNzdWU3MjkzNTI5MzE=
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List indexing works for .tail() but not .head()
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| 5
| 2020-10-26T08:14:27Z
| 2020-10-26T16:29:40Z
| 2020-10-26T16:29:24Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df.close.head(1)[0]
df.close.tail(1)[0]
```
df.close.head(1)[0] <-- Works just fine
df.close.tail(1)[0] <-- Returns KeyError: 0
Current fix:
list(df.close.tail(1))[0]
[df.close.tail(1)][0] does not work.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
The last number by itself without its index. The current output just returns an error, so, it would be great to have it more consistent with the return of its .head() counterpart.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200622
Cython : 0.29.17
pytest : 5.4.3
hypothesis : 5.18.3
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.4.3
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.49.1
</details>
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MDU6SXNzdWU3Mjk0MzY4OTg=
| 37,414
|
BUG: UnicodeDecodeError: 'unicodeescape' codec can't decode byte 0x5c at end of string
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| 2020-10-26T10:21:25Z
| 2021-10-14T10:11:29Z
| 2021-10-14T10:11:29Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas. Checked 8da70fc36705e9dcf0de28227f4ad4c914d84f78
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas
import numpy as np
row_size = 2*10**5
test_csv_name = "test_df.csv"
dates = pandas.date_range("2000", freq="s", periods=row_size)
df = pandas.DataFrame(
{
"col1": np.arange(row_size),
"col2": [str(x.date()) for x in dates],
"col3": np.arange(row_size),
"col4": [str(x.time()) for x in dates],
}
)
df.to_csv(test_csv_name, sep=",", encoding="unicode_escape")
pandas_df = pandas.read_csv(test_csv_name, encoding="unicode_escape")
print(pandas_df)
```
#### Problem description
This reproducer works well with `row_size = 2*10**4` so I expect similar behaviour for `row_size = 2*10**5`.
<details>
<summary> Traceback for case when row_size = 2*10**5</summary>
```sh
File "reproducer.py", line 18, in <module>
pandas_df = pandas.read_csv(test_csv_name, encoding="unicode_escape")
File "...\pandas\io\parsers.py", line 686, in read_csv
return _read(filepath_or_buffer, kwds)
File "...\pandas\io\parsers.py", line 458, in _read
data = parser.read(nrows)
File "...\pandas\io\parsers.py", line 1196, in read
ret = self._engine.read(nrows)
File "...\pandas\io\parsers.py", line 2155, in read
data = self._reader.read(nrows)
File "pandas\_libs\parsers.pyx", line 847, in pandas._libs.parsers.TextReader.read
File "pandas\_libs\parsers.pyx", line 862, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas\_libs\parsers.pyx", line 918, in pandas._libs.parsers.TextReader._read_rows
File "pandas\_libs\parsers.pyx", line 905, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas\_libs\parsers.pyx", line 2034, in pandas._libs.parsers.raise_parser_error
UnicodeDecodeError: 'unicodeescape' codec can't decode byte 0x5c in position 8191: \ at end of string
```
</details>
#### Expected Output
Expected output can be computed using workaround:
```python
# workaround
from io import StringIO
with open(test_csv_name, mode="rb") as f:
pandas_df = pandas.read_csv(StringIO(f.read().decode("unicode_escape")))
```
```python
# expected output
Unnamed: 0 col1 col2 col3 col4
0 0 0 2000-01-01 0 00:00:00
1 1 1 2000-01-01 1 00:00:01
2 2 2 2000-01-01 2 00:00:02
3 3 3 2000-01-01 3 00:00:03
4 4 4 2000-01-01 4 00:00:04
... ... ... ... ... ...
199995 199995 199995 2000-01-03 199995 07:33:15
199996 199996 199996 2000-01-03 199996 07:33:16
199997 199997 199997 2000-01-03 199997 07:33:17
199998 199998 199998 2000-01-03 199998 07:33:18
199999 199999 199999 2000-01-03 199999 07:33:19
[200000 rows x 5 columns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : 0.14.0
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3Mjk0NzE4OTY=
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BUG: groupby with std aggregation of pandas integer dtype throws exception: 'IntegerArray' object has no attribute 'reshape'
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| 5
| 2020-10-26T11:11:43Z
| 2020-10-27T12:38:14Z
| 2020-10-27T12:38:14Z
|
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({'a': [1,1,1,2,2],'b': [1,2,3,4,5]})
df['b'] = df['b'].astype(pd.Int32Dtype())
df.groupby('a').agg('std') # Error 'IntegerArray' object has no attribute 'reshape'. The same error occurs with all pandas integer types
```
#### Problem description
For all integer pandas dtypes the code expects an IntegerArray to have the reshape function as is the case with numpy arrays.
It is possible that the error will also occur with other operations performed after a groupby, this has only been checked for 'std'.
The error occurs at
pandas\core\groupby\groupby.py", line 2491, in _get_cythonized_result
vals = vals.reshape((-1, 1))
#### Expected Output
The same output as with a numpy data type, for example above this would be:
a b
1 1.000000
2 0.707107
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.112+
Version : #1 SMP Thu Jul 23 08:00:38 PDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 50.3.0
Cython : 0.29.21
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.2
pyarrow : 0.14.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTA5OTgwMTc4
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Backport PR #35792 on branch 1.1.x: CLN/BUG: Clean/Simplify _wrap_applied_output
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MEMBER
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Backport PR #35792
https://github.com/pandas-dev/pandas/pull/37305#issuecomment-716498455 cc @jreback
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DOC: move release note for 35792
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follow-on from #37416
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Backport PR #37023 on branch 1.1.x (REGR: fix bug in DatetimeIndex slicing with irregular or unsorted indices)
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| 2020-10-26T15:46:48Z
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NONE
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Backport PR #37023: REGR: fix bug in DatetimeIndex slicing with irregular or unsorted indices
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