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} | 5 | 2019-08-23T13:43:55Z | 2019-08-26T23:11:06Z | 2019-08-26T23:11:06Z | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/pull/28085#issuecomment-524066959
This is a regression in 0.25.2. It was partially fixed in #28085, but there was an issue with the first fix. | {
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- [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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} | 1 | 2019-08-23T14:47:35Z | 2020-01-06T13:30:42Z | 2020-01-06T13:30:42Z | CONTRIBUTOR | null | https://docs.python.org/3.8/whatsnew/3.8.html#datetime / https://docs.python.org/3.8/library/datetime.html#datetime.datetime.fromisocalendar
> Added new alternate constructors datetime.date.fromisocalendar() and datetime.datetime.fromisocalendar(), which construct date and datetime objects respectively from ISO year, week number and weekday; these are the inverse of each class’s isocalendar method. (Contributed by Paul Ganssle in bpo-36004.)
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} | [] | closed | false | null | [] | null | 2 | 2019-08-23T15:14:06Z | 2019-08-23T20:36:56Z | 2019-08-23T20:36:56Z | CONTRIBUTOR | null | I'm not sure whether supporting this is worth it. I don't know if it opens up complexities for operations like `df.eval("A[...]")`.
```python
In [4]: df = pd.DataFrame({"A": ['a', 'b']})
In [5]: df.eval("A == ...")
```
```pytb
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-1db591136780> in <module>
----> 1 df.eval("A == ...")
~/sandbox/pandas/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
3357 kwargs["target"] = self
3358 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
-> 3359 return _eval(expr, inplace=inplace, **kwargs)
3360
3361 def select_dtypes(self, include=None, exclude=None):
~/sandbox/pandas/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
320 )
321
--> 322 parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv)
323
324 # construct the engine and evaluate the parsed expression
~/sandbox/pandas/pandas/core/computation/expr.py in __init__(self, expr, engine, parser, env, truediv, level)
823 self.env.scope["truediv"] = truediv
824 self._visitor = _parsers[parser](self.env, self.engine, self.parser)
--> 825 self.terms = self.parse()
826
827 @property
~/sandbox/pandas/pandas/core/computation/expr.py in parse(self)
840 def parse(self):
841 """Parse an expression"""
--> 842 return self._visitor.visit(self.expr)
843
844 @property
~/sandbox/pandas/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/sandbox/pandas/pandas/core/computation/expr.py in visit_Module(self, node, **kwargs)
445 raise SyntaxError("only a single expression is allowed")
446 expr = node.body[0]
--> 447 return self.visit(expr, **kwargs)
448
449 def visit_Expr(self, node, **kwargs):
~/sandbox/pandas/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/sandbox/pandas/pandas/core/computation/expr.py in visit_Expr(self, node, **kwargs)
448
449 def visit_Expr(self, node, **kwargs):
--> 450 return self.visit(node.value, **kwargs)
451
452 def _rewrite_membership_op(self, node, left, right):
~/sandbox/pandas/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/sandbox/pandas/pandas/core/computation/expr.py in visit_Compare(self, node, **kwargs)
740 op = self.translate_In(ops[0])
741 binop = ast.BinOp(op=op, left=node.left, right=comps[0])
--> 742 return self.visit(binop)
743
744 # recursive case: we have a chained comparison, a CMP b CMP c, etc.
~/sandbox/pandas/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/sandbox/pandas/pandas/core/computation/expr.py in visit_BinOp(self, node, **kwargs)
561
562 def visit_BinOp(self, node, **kwargs):
--> 563 op, op_class, left, right = self._maybe_transform_eq_ne(node)
564 left, right = self._maybe_downcast_constants(left, right)
565 return self._maybe_evaluate_binop(op, op_class, left, right)
~/sandbox/pandas/pandas/core/computation/expr.py in _maybe_transform_eq_ne(self, node, left, right)
482 left = self.visit(node.left, side="left")
483 if right is None:
--> 484 right = self.visit(node.right, side="right")
485 op, op_class, left, right = self._rewrite_membership_op(node, left, right)
486 return op, op_class, left, right
~/sandbox/pandas/pandas/core/computation/expr.py in visit(self, node, **kwargs)
438
439 method = "visit_" + node.__class__.__name__
--> 440 visitor = getattr(self, method)
441 return visitor(node, **kwargs)
442
AttributeError: 'PandasExprVisitor' object has no attribute 'visit_Ellipsis'
```
Note that on 3.8 this actually seems to work, since it uses a different term. | {
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"description": "read_htm... | open | false | null | [] | null | 8 | 2019-08-23T17:17:30Z | 2020-05-07T21:42:33Z | null | NONE | null | #### Problem description
**This is an enhancement request**
In order to enable a pandas to provide a JSON schema representation of the dataframe utilizing ```application/vnd.dataresource+json```, ```pd.options.display.html.table_schema``` must be set to ```True```.
I'm just wondering if this could be set to True by default? That way front-ends that support it, can ingest it without having to enable it from the import. Not sure on the implications, but wanted to start the convo...
#### Code Sample
```
import pandas as pd
pd.options.display.html.table_schema = True
```
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} | 0 | 2019-08-23T19:07:37Z | 2019-09-17T20:21:24Z | 2019-09-17T20:21:24Z | CONTRIBUTOR | null | ```python
In [3]: t = pd.array(['a', 'b', 'c'])
In [4]: t[0] = 't'
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-7d1c8d6d2e6a> in <module>
----> 1 t[0] = 't'
~/sandbox/pandas/pandas/core/arrays/numpy_.py in __setitem__(self, key, value)
237
238 values = self._ndarray
--> 239 t = np.result_type(value, values)
240 if t != self._ndarray.dtype:
241 values = values.astype(t, casting="safe")
<__array_function__ internals> in result_type(*args, **kwargs)
TypeError: data type "t" not understood
``` | {
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} | 6 | 2019-08-23T19:08:53Z | 2019-09-17T20:21:29Z | 2019-09-17T20:21:25Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/28118
Closes https://github.com/pandas-dev/pandas/issues/28150 | {
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} | 6 | 2019-08-23T20:15:37Z | 2021-07-11T06:09:39Z | 2021-07-11T06:09:39Z | NONE | null | I'm trying to apply pd.to_datetime on an integer column to convert integers (including missing indicators) to datetimes. In order to preserve all the missing indicators, ideally, I would prefer setting the "errors" parameter to "ignore" to keep all the invalid parsings. But then I noticed that setting errors='ignore' not only preserved the missing indicators, but kept everything the same, even the dtype of the input column. See below for more details.
```python
print(dt_col)
```
<Outputs>
0 20181223
1 20161017
2 -99998
3 20171220
4 20170511
Name: dt_col, dtype: int64
</Outputs>
```python
pd.to_datetime(dt_col, format='%Y%m%d', errors='ignore')
```
<Outputs>
0 20181223
1 20161017
2 -99998
3 20171220
4 20170511
Name: dt_col, dtype: int64
</Outputs>
```python
pd.to_datetime(dt_col, format='%Y%m%d', errors='coerce')
```
<Outputs>
0 2018-12-23
1 2016-10-17
2 NaT
3 2017-12-20
4 2017-05-11
Name: dt_col, dtype: datetime64[ns]
</Outputs>
Pandas version:
- pandas==0.25.1
Is anyone else seeing the same behavior? | {
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} | 10 | 2019-08-23T23:05:51Z | 2021-06-08T20:49:51Z | 2021-06-08T20:49:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dft = pd.read_csv('pandas_gb_min_test.csv.gz', parse_dates=['split_lutevent_time_offset'])
# An unsigned dtype is slow (23 s) on 0.25.1, but fast on 0.24.2.
# Commenting out this line on 0.25.1 restores performance
dft['split_event_parent_event_id'] = dft.split_event_parent_event_id.astype('uint64')
for mdvn in dft.columns: print(mdvn, dft[mdvn].dtype, type(dft[mdvn]))
mindata = dft.groupby('xyidx').min()
```
#### Problem description
In pandas 0.25.1, the performance of `df.groupby().min()` is 30x slower than in 0.24.2 when one of the columns has a dtype of `uint64`.
I saved the exact dataframe `min_data_in` from my original code with `min_data_in.to_csv('pandas_gb_min_test.csv')`. The columns in the original code had the following data types:
```
>>> for mdvn in min_data_in.columns: print(mdvn, min_data_in[mdvn].dtype, type(min_data_in[mdvn]))
split_event_parent_event_id uint64 <class 'pandas.core.series.Series'>
split_lutevent_time_offset datetime64[ns] <class 'pandas.core.series.Series'>
split_event_mesh_x_idx int64 <class 'pandas.core.series.Series'>
split_event_mesh_y_idx int64 <class 'pandas.core.series.Series'>
split_lutevent_min_flash_area float64 <class 'pandas.core.series.Series'>
xyidx int64 <class 'pandas.core.series.Series'>
```
After loading with `read_csv` the dtype of `split_event_parent_event_id` was signed.
If I restore the `uint64` dtype, v0.25.1 runtime is 23 s. Runtime for v0.24.2 is 0.7 s.
A signed dtype in v0.25.1 has the same performance as 0.24.2.
The data file, cProfile capture, and snakeviz output are attached.
#### Output of ``pd.show_versions()``
<details>
0.24.2
and
0.25.1
</details>
[pd0242.pdf](https://github.com/pandas-dev/pandas/files/3536491/pd0242.pdf)
[pd0251.pdf](https://github.com/pandas-dev/pandas/files/3536492/pd0251.pdf)
[pd0251.prof.zip](https://github.com/pandas-dev/pandas/files/3536494/pd0251.prof.zip)
[pd0242.prof.zip](https://github.com/pandas-dev/pandas/files/3536495/pd0242.prof.zip)
[pandas_gb_min_test.csv.gz](https://github.com/pandas-dev/pandas/files/3536497/pandas_gb_min_test.csv.gz) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28123/events | https://github.com/pandas-dev/pandas/pull/28123 | 484,761,954 | MDExOlB1bGxSZXF1ZXN0MzEwNTcyMDg2 | 28,123 | DOC: Fix RangeIndex and other docstrings for missing period in summary | {
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} | 3 | 2019-08-24T01:12:35Z | 2019-08-26T09:40:22Z | 2019-08-26T02:19:38Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
@datapythonista
I added the period to the following cases:
pandas.Categorical.dtype: Summary does not end with a period
pandas.merge_ordered: Summary does not end with a period
pandas.bdate_range: Summary does not end with a period
pandas.period_range: Summary does not end with a period
pandas.timedelta_range: Summary does not end with a period
pandas.interval_range: Summary does not end with a period
pandas.util.hash_pandas_object: Summary does not end with a period
pandas.Grouper: Summary does not end with a period
pandas.Index.memory_usage: Summary does not end with a period
pandas.Index.fillna: Summary does not end with a period
pandas.Index.dropna: Summary does not end with a period
pandas.RangeIndex.start: Summary does not end with a period
pandas.RangeIndex.stop: Summary does not end with a period
pandas.RangeIndex.step: Summary does not end with a period | {
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"color": "AD7FA8"... | closed | false | null | [] | null | 7 | 2019-08-24T05:24:24Z | 2019-09-13T01:40:18Z | 2019-09-13T01:40:18Z | NONE | null | Sometimes data has object columns that contain both `NaN`s and `None` and we want to standardize them to `None`. Currently `fillna` doesn't allow `value=None` because `None` is used to mean "no value", and `method=None` to mean "fill with the value", so when both are none it's considered invalid. (On columns with dtypes that have their own NAs, like floats, timestamps, etc., filling with `None` leaves them as-is.)
To get around this, I added `method="constant"` which should also be taken to mean "fill with the value". `method="constant"` is required if filling with `None` (so that `value=None, method=None` still throws), otherwise it works the same as before.
As far as I can tell, doing it this way shouldn't break any existing code. I updated/added some tests for the new behaviour. If it looks like this change might be accepted, I'll update the docs as well.
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/28125 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28125/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28125/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28125/events | https://github.com/pandas-dev/pandas/issues/28125 | 484,787,922 | MDU6SXNzdWU0ODQ3ODc5MjI= | 28,125 | CI allowed failure - TestReadHtml.test_thousands_macau_index_col[bs4] | {
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... | closed | false | null | [] | null | 6 | 2019-08-24T07:10:48Z | 2019-11-15T15:11:21Z | 2019-11-15T15:11:09Z | MEMBER | null | ```
=================================== FAILURES ===================================
_______________ TestReadHtml.test_thousands_macau_index_col[bs4] _______________
[gw1] linux -- Python 3.6.9 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_html.TestReadHtml object at 0x7f11591bf5c0>
datapath = <function datapath.<locals>.deco at 0x7f1157bd3a60>
@pytest.mark.slow
def test_thousands_macau_index_col(self, datapath):
all_non_nan_table_index = -2
macau_data = datapath("io", "data", "macau.html")
> dfs = self.read_html(macau_data, index_col=0, header=0)
pandas/tests/io/test_html.py:389:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/io/html.py:1095: in read_html
displayed_only=displayed_only,
pandas/io/html.py:912: in _parse
raise_with_traceback(retained)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
exc = AttributeError("'NoneType' object has no attribute 'items'",)
traceback = None
def raise_with_traceback(exc, traceback=Ellipsis):
"""
Raise exception with existing traceback.
If traceback is not passed, uses sys.exc_info() to get traceback.
"""
if traceback == Ellipsis:
_, _, traceback = sys.exc_info()
> raise exc.with_traceback(traceback)
E AttributeError: 'NoneType' object has no attribute 'items'
pandas/compat/__init__.py:46: AttributeError
```
It appears that this started failing with #27962
cc @jbrockmendel
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#### Problem description
I want a prrogress bar when **read_csv** is used when the datasets is very large.
because it will be stuck with no response,although I understand that it is busy.
Thanks
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"description": "Arithmet... | closed | false | null | [] | null | 6 | 2019-08-24T18:39:25Z | 2021-07-11T06:11:35Z | 2021-07-11T06:11:35Z | NONE | null | Hi, I would like to know if is possible to add to the cumsum method the option to make an operation (multiplication of a constant for example or a lambda function) while it is making every sum to get something like this: https://stackoverflow.com/questions/34182755/cumulative-addition-multiplication-in-numpy, I know this could look a little unnecesarry but this kind of cumulative sum appears in a lot of formulas of economic indexs and I think there are other cases where this can be useful. The reason of this request is that when the data is very big use a normal python loop is not a nice option due to the time it take, or even using the solution of the link is not a good idea due to the precision of the floats number.
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xref #27568
```
$ mypy pandas/util/_decorators.py --check-untyped-defs --follow-imports skip
pandas\util\_decorators.py:181: error: Item "function" of "Union[Dict[Any, Any], Callable[[Any], Any]]" has no attribute "get"
pandas\util\_decorators.py:183: error: "Dict[Any, Any]" not callable
pandas\util\_decorators.py:201: error: Argument 1 to "get" of "Mapping" has incompatible type "Optional[str]"; expected "str"
pandas\util\_decorators.py:207: error: Invalid index type "Optional[str]" for "Dict[str, Any]"; expected type "str"
pandas\util\_decorators.py:293: error: Item "Tuple[Any, ...]" of "Union[Tuple[Any, ...], Dict[str, Any]]" has no attribute "update"
```
```
$ mypy pandas/util/_decorators.py --disallow-any-generics --follow-imports skip
pandas\util\_decorators.py:12: error: Missing type parameters for generic type
pandas\util\_decorators.py:18: error: Missing type parameters for generic type
pandas\util\_decorators.py:93: error: Missing type parameters for generic type
pandas\util\_decorators.py:95: error: Missing type parameters for generic type
pandas\util\_decorators.py:217: error: Missing type parameters for generic type
pandas\util\_decorators.py:282: error: Missing type parameters for generic type
pandas\util\_decorators.py:323: error: Missing type parameters for generic type
```
also fixes following errors arising from more precise typing...
```
pandas\core\indexes\interval.py:790: error: Signature of "get_loc" incompatible with supertype "Index"
pandas\core\indexes\interval.py:985: error: Too many arguments for "get_indexer_non_unique" of "IntervalIndex"
pandas\core\groupby\generic.py:889: error: Incompatible types in assignment (expression has type "Callable[[DefaultArg(Any, 'func_or_funcs'), VarArg(Any), KwArg(Any)], Any]", base class "SelectionMixin" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
pandas\core\groupby\generic.py:1470: error: Incompatible types in assignment (expression has type "Callable[[DefaultArg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "NDFrameGroupBy" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
pandas\core\window\rolling.py:913: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "_Window" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
pandas\core\window\rolling.py:1794: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "_Window" defined the type as "Callable[[Arg(Any,
'func'), VarArg(Any), KwArg(Any)], Any]")
pandas\core\window\expanding.py:142: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "_Window" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
pandas\core\window\ewm.py:212: error: Incompatible types in assignment (expression has type "Callable[[Arg(Any, 'arg'), VarArg(Any), KwArg(Any)], Any]", base class "_Window" defined the type as "Callable[[Arg(Any, 'func'), VarArg(Any), KwArg(Any)], Any]")
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```
pandas\io\formats\console.py:68: error: Name '__IPYTHON__' is not defined
pandas\io\formats\console.py:78: error: Name 'get_ipython' is not defined
pandas\io\formats\csvs.py:230: error: Need type annotation for 'encoded_labels'
pandas\io\formats\css.py:92: error: Incompatible types in assignment (expression has type "None", variable has type "float")
pandas\io\formats\css.py:163: error: Item "None" of "Optional[Match[Any]]" has no attribute "groups"
pandas\io\formats\format.py:1710: error: Item "None" of "Optional[TextAdjustment]" has no attribute "len"
pandas\io\formats\excel.py:537: error: Incompatible types in assignment (expression has type "Generator[ExcelCell, None, None]", variable has type "Tuple[]")
pandas\io\formats\style.py:125: error: Need type annotation for 'ctx'
pandas\io\formats\style.py:126: error: Need type annotation for '_todo'
pandas\io\formats\style.py:147: error: Need type annotation for 'hidden_columns'
pandas\io\formats\style.py:158: error: Need type annotation for '_display_funcs'
pandas\io\formats\style.py:247: error: Need type annotation for 'cell_context'
pandas\io\formats\style.py:554: error: Cannot determine type of 'ctx'
pandas\io\formats\style.py:557: error: Cannot determine type of 'ctx'
pandas\io\formats\style.py:1335: error: Invalid type "cls"
pandas\io\formats\style.py:1335: error: Invalid base class``` | {
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} | 10 | 2019-08-24T22:33:50Z | 2020-01-28T13:12:43Z | 2019-09-18T15:10:07Z | MEMBER | null | - [X] closes #12004
- [X] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
After some of the recent refactor this should be relatively easy to do after vendoring a few updates | {
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} | 2 | 2019-08-25T03:07:38Z | 2019-08-29T11:57:25Z | 2019-08-28T23:01:47Z | CONTRIBUTOR | null | - [x] closes #26939
- [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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} | 1 | 2019-08-25T04:15:12Z | 2019-08-25T21:41:33Z | 2019-08-25T21:41:33Z | CONTRIBUTOR | null | Hi,
Why is there no support for `inplace = True` in `add_suffix('_some_suffix')` or `add_prefix('_some_prefix')`.
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] | open | false | null | [] | null | 3 | 2019-08-25T10:11:28Z | 2019-08-25T22:44:01Z | null | MEMBER | null | in setup.cfg we currently have
```
[mypy]
ignore_missing_imports=True
```
mypy states "We recommend using this approach only as a last resort: it’s equivalent to adding a # type: ignore to all unresolved imports in your codebase." https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
the number of instances of missing imports resolving to Any is already significant..
```
$ mypy pandas --disallow-any-unimported
pandas\util\_test_decorators.py:106: error: Return type becomes "Any" due to an unfollowed import
pandas\util\_test_decorators.py:120: error: Return type becomes "Any" due to an unfollowed import
pandas\_typing.py:19: error: Constraint 5 becomes "Any" due to an unfollowed import
pandas\_typing.py:22: error: Constraint 2 becomes "Any" due to an unfollowed import
pandas\_typing.py:23: error: Constraint 1 becomes "Any" due to an unfollowed import
pandas\_typing.py:23: error: Constraint 2 becomes "Any" due to an unfollowed import
pandas\_typing.py:23: error: Constraint 3 becomes "Any" due to an unfollowed import
pandas\io\formats\printing.py:252: error: Base type BaseFormatter becomes "Any" due to an unfollowed import
pandas\core\dtypes\dtypes.py:653: error: Type of variable becomes "Type[Any]" due to an unfollowed import
pandas\core\dtypes\dtypes.py:812: error: Type of variable becomes "Type[Any]" due to an unfollowed import
pandas\core\dtypes\common.py:144: error: Return type becomes "Any" due to an unfollowed import
pandas\core\dtypes\common.py:144: error: Argument 1 to "ensure_int_or_float" becomes "Any" due to an unfollowed import
pandas\core\dtypes\common.py:181: error: Argument 1 to "ensure_python_int" becomes "Union[int, Any]" due to an unfollowed import
pandas\core\indexers.py:39: error: Argument 2 to "is_empty_indexer" becomes "Any" due to an unfollowed import
pandas\core\indexers.py:115: error: Argument 1 to "validate_indices" becomes "Any" due to an unfollowed import
pandas\core\dtypes\cast.py:765: error: Argument 1 to "maybe_convert_objects" becomes "Any" due to an unfollowed import
pandas\core\dtypes\cast.py:814: error: Argument 1 to "soft_convert_objects" becomes "Any" due to an unfollowed import
pandas\core\nanops.py:194: error: Return type becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:194: error: Argument 1 to "_maybe_get_mask" becomes "Any" due to an unfollowed import
pandas\core\nanops.py:194: error: Argument 3 to "_maybe_get_mask" becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:237: error: Return type becomes "Tuple[Any, Optional[Any], Any, Any, Any]" due to an unfollowed import
pandas\core\nanops.py:237: error: Argument 1 to "_get_values" becomes "Any" due to an unfollowed import
pandas\core\nanops.py:237: error: Argument 5 to "_get_values" becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:634: error: Return type becomes "Tuple[Union[int, Any], Union[int, Any]]" due to an unfollowed import
pandas\core\nanops.py:634: error: Argument 2 to "_get_counts_nanvar" becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:672: error: Type of variable becomes "Any" due to an unfollowed import
pandas\core\nanops.py:1117: error: Return type becomes "Union[Any, int]" due to an unfollowed import
pandas\core\nanops.py:1117: error: Argument 1 to "_maybe_arg_null_out" becomes "Any" due to an unfollowed import
pandas\core\nanops.py:1117: error: Argument 3 to "_maybe_arg_null_out" becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:1141: error: Return type becomes "Union[int, Any]" due to an unfollowed import
pandas\core\nanops.py:1141: error: Argument 2 to "_get_counts" becomes "Optional[Any]" due to an unfollowed import
pandas\core\nanops.py:1185: error: Return type becomes "Any" due to an unfollowed import
pandas\core\nanops.py:1185: error: Argument 1 to "_maybe_null_out" becomes "Any" due to an unfollowed import
pandas\core\nanops.py:1185: error: Argument 3 to "_maybe_null_out" becomes "Optional[Any]" due to an unfollowed import
pandas\core\construction.py:51: error: Return type becomes "Any" due to an unfollowed import
pandas\core\construction.py:51: error: Argument 2 to "array" becomes "Union[str, Any, ExtensionDtype, None]" due to an unfollowed import
pandas\core\tools\datetimes.py:71: error: Argument 1 to "should_cache" becomes "Union[List[Any], Tuple[Any, ...], Any, Any]" due to an unfollowed import
pandas\core\tools\datetimes.py:161: error: Return type becomes "Any" due to an unfollowed import
pandas\core\tools\datetimes.py:161: error: Argument 1 to "_box_as_indexlike" becomes "Any" due to an unfollowed import
pandas\core\tools\datetimes.py:191: error: Return type becomes "Union[Any, Any]" due to an unfollowed import
pandas\core\tools\datetimes.py:191: error: Argument 1 to "_convert_and_box_cache" becomes "Union[Any, List[Any], Tuple[Any, ...], Any, Any]" due to an unfollowed import
pandas\core\tools\datetimes.py:191: error: Argument 2 to "_convert_and_box_cache" becomes "Any" due to an unfollowed import
pandas\tseries\offsets.py:157: error: Base type BaseOffset becomes "Any" due to an unfollowed import
pandas\tseries\offsets.py:2513: error: Base type _Tick becomes "Any" due to an unfollowed import
pandas\core\arrays\_ranges.py:15: error: Return type becomes "Tuple[Any, str]" due to an unfollowed import
pandas\core\arrays\_ranges.py:15: error: Argument 1 to "generate_regular_range" becomes "Any" due to an unfollowed import
pandas\core\arrays\_ranges.py:15: error: Argument 2 to "generate_regular_range" becomes "Any" due to an unfollowed import
pandas\core\ops\__init__.py:398: error: Argument 1 to "should_extension_dispatch" becomes "Any" due to an unfollowed import
pandas\core\ops\__init__.py:1152: error: Argument 1 to "maybe_dispatch_ufunc_to_dunder_op" becomes "Any" due to an unfollowed import
pandas\core\ops\__init__.py:1152: error: Argument 4 to "maybe_dispatch_ufunc_to_dunder_op" becomes "Any" due to an unfollowed import
pandas\core\arrays\period.py:230: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\period.py:230: error: Argument 2 to "_from_sequence" becomes "Sequence[Optional[Any]]" due to an unfollowed import
pandas\core\arrays\period.py:301: error: Argument 2 to "_unbox_scalar" becomes "Union[Any, Any]" due to an unfollowed import
pandas\core\arrays\period.py:313: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\period.py:655: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\period.py:655: error: Argument 2 to "_addsub_int_array" becomes "Union[Any, Any, Any, Any]" due to an unfollowed import
pandas\core\arrays\period.py:833: error: Argument 1 to "period_array" becomes "Sequence[Optional[Any]]" due to an unfollowed import
pandas\core\arrays\datetimes.py:330: error: Type of variable becomes "Union[Any, DatetimeTZDtype]" due to an unfollowed import
pandas\core\arrays\datetimes.py:604: error: Return type becomes "Union[Any, DatetimeTZDtype]" due to an unfollowed import
pandas\core\arrays\base.py:267: error: Argument 2 to "__setitem__" becomes "Union[int, Any]" due to an unfollowed import
pandas\core\arrays\base.py:392: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:414: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:525: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:637: error: Return type becomes "Tuple[Any, Any]" due to an unfollowed import
pandas\core\arrays\base.py:661: error: Return type becomes "Tuple[Any, Any]" due to an unfollowed import
pandas\core\arrays\base.py:764: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:855: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:865: error: Return type becomes "Union[Any, Any]" due to an unfollowed import
pandas\core\arrays\base.py:936: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:956: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\base.py:956: error: Argument 2 to "_concat_same_type" becomes "Sequence[Any]" due to an unfollowed import
pandas\core\arrays\base.py:980: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\numpy_.py:89: error: Base type NDArrayOperatorsMixin becomes "Any" due to an unfollowed import
pandas\core\arrays\datetimelike.py:58: error: Type of variable becomes "Any" due to an unfollowed import
pandas\core\arrays\datetimelike.py:65: error: Return type becomes "Type[Any]" due to an unfollowed import
pandas\core\arrays\datetimelike.py:74: error: Return type becomes "Union[Any, Any, Any, Any]" due to an unfollowed import
pandas\core\arrays\datetimelike.py:96: error: Argument 2 to "_unbox_scalar" becomes "Union[Any, Any, Any, Any]" due to an unfollowed import
pandas\core\arrays\datetimelike.py:115: error: Argument 2 to "_check_compatible_with" becomes "Union[Any, Any, Any, Any]" due to an unfollowed import
pandas\core\arrays\datetimelike.py:348: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\datetimelike.py:453: error: Argument 3 to "__setitem__" becomes "Union[Any, Any, Sequence[Any]]" due to an unfollowed import
pandas\core\base.py:990: error: Return type becomes "Any" due to an unfollowed import
pandas\core\indexes\frozen.py:123: error: Base type ndarray becomes "Any" due to an unfollowed import
pandas\core\indexes\base.py:3871: error: Return type becomes "Union[ExtensionArray, Any, Any]" due to an unfollowed import
pandas\core\arrays\sparse.py:109: error: Argument 2 to "__init__" becomes "Union[str, Any, ExtensionDtype]" due to an unfollowed import
pandas\core\arrays\sparse.py:393: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:393: error: Argument 1 to "_get_fill" becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:417: error: Argument 1 to "_sparse_array_op" becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:417: error: Argument 2 to "_sparse_array_op" becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:706: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:706: error: Argument 2 to "_simple_new" becomes "Any" due to an unfollowed import
pandas\core\arrays\sparse.py:706: error: Argument 3 to "_simple_new" becomes "Any" due to an unfollowed import
pandas\core\arrays\interval.py:143: error: Base type IntervalMixin becomes "Any" due to an unfollowed import
pandas\core\arrays\integer.py:519: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\integer.py:576: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\categorical.py:479: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\categorical.py:502: error: Argument 2 to "astype" becomes "Union[str, Any, ExtensionDtype]" due to an unfollowed import
pandas\core\arrays\categorical.py:502: error: Return type becomes "Any" due to an unfollowed import
pandas\core\arrays\categorical.py:2652: error: Argument 1 to "_recode_for_categories" becomes "Any" due to an unfollowed import
pandas\core\internals\blocks.py:1858: error: Argument 3 to "shift" becomes "Any" due to an unfollowed import
pandas\core\indexes\multi.py:64: error: Base type BaseMultiIndexCodesEngine becomes "Any" due to an unfollowed import
pandas\core\indexes\multi.py:64: error: Base type UInt64Engine becomes "Any" due to an unfollowed import
pandas\core\indexes\multi.py:103: error: Base type BaseMultiIndexCodesEngine becomes "Any" due to an unfollowed import
pandas\core\indexes\multi.py:103: error: Base type ObjectEngine becomes "Any" due to an unfollowed import
pandas\core\indexes\category.py:525: error: Argument 2 to "get_value" becomes "Any" due to an unfollowed import
pandas\core\indexes\range.py:79: error: Type of variable becomes "Any" due to an unfollowed import
pandas\core\indexes\range.py:363: error: Argument 2 to "__contains__" becomes "Union[int, Any]" due to an unfollowed import
pandas\core\indexes\interval.py:201: error: Base type IntervalMixin becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:629: error: Argument 2 to "_can_reindex" becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:790: error: Return type becomes "Union[int, slice, Any]" due to an unfollowed import
pandas\core\indexes\interval.py:861: error: Return type becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:861: error: Argument 2 to "get_indexer" becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:931: error: Return type becomes "Tuple[Any, Any]" due to an unfollowed import
pandas\core\indexes\interval.py:931: error: Argument 2 to "get_indexer_non_unique" becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:972: error: Return type becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:972: error: Argument 2 to "get_indexer_for" becomes "Any" due to an unfollowed import
pandas\core\indexes\interval.py:989: error: Argument 2 to "get_value" becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:123: error: Base type ConversionInterface becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:150: error: Base type Formatter becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:193: error: Base type DateConverter becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:252: error: Base type DateConverter becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:325: error: Base type AutoDateFormatter becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:330: error: Base type AutoDateLocator becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:355: error: Base type DateLocator becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:936: error: Base type Locator becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:1030: error: Base type Formatter becomes "Any" due to an unfollowed import
pandas\plotting\_matplotlib\converter.py:1098: error: Base type Formatter becomes "Any" due to an unfollowed import
pandas\io\formats\format.py:1294: error: Return type becomes "Any" due to an unfollowed import
pandas\io\formats\format.py:1400: error: Argument 2 to "__init__" becomes "Union[Any, Series, DatetimeIndex, DatetimeArray]" due to an unfollowed import
pandas\io\formats\format.py:1457: error: Argument 1 to "format_percentiles" becomes "Union[Any, List[Union[int, float]], List[float], List[Union[str, float]]]" due to an unfollowed import
pandas\io\formats\format.py:1527: error: Argument 1 to "_is_dates_only" becomes "Union[Any, DatetimeArray, Index, DatetimeIndex]" due to an unfollowed import
pandas\io\formats\format.py:1548: error: Argument 1 to "_format_datetime64" becomes "Union[Any, Any]" due to an unfollowed import
pandas\io\formats\format.py:1548: error: Argument 2 to "_format_datetime64" becomes "Union[Any, tzutc, None]" due to an unfollowed import
pandas\io\formats\format.py:1565: error: Argument 1 to "_format_datetime64_dateonly" becomes "Union[Any, Any]" due to an unfollowed import
pandas\io\formats\format.py:1592: error: Argument 1 to "_get_format_datetime64_from_values" becomes "Union[Any, DatetimeArray, DatetimeIndex]" due to an unfollowed import
pandas\io\formats\format.py:1623: error: Argument 2 to "__init__" becomes "Union[Any, TimedeltaIndex]" due to an unfollowed import
pandas\io\formats\format.py:1641: error: Argument 1 to "_get_format_timedelta64" becomes "Union[Any, TimedeltaIndex, TimedeltaArray]" due to an unfollowed import
pandas\io\formats\format.py:1719: error: Argument 1 to "_trim_zeros_complex" becomes "Any" due to an unfollowed import
pandas\io\formats\format.py:1730: error: Argument 1 to "_trim_zeros_float" becomes "Union[Any, List[str]]" due to an unfollowed import
pandas\io\formats\format.py:1872: error: Argument 1 to "_binify" becomes "List[Any]" due to an unfollowed import
pandas\io\formats\format.py:1872: error: Argument 2 to "_binify" becomes "Union[Any, int]" due to an unfollowed import
pandas\core\indexing.py:102: error: Base type _NDFrameIndexerBase becomes "Any" due to an unfollowed import
pandas\core\indexing.py:650: error: Argument 3 to "_align_series" becomes "Any" due to an unfollowed import
pandas\core\indexing.py:742: error: Argument 3 to "_align_frame" becomes "Any" due to an unfollowed import
pandas\core\indexing.py:2162: error: Base type _NDFrameIndexerBase becomes "Any" due to an unfollowed import
pandas\core\indexing.py:2371: error: Return type becomes "Any" due to an unfollowed import
pandas\core\internals\managers.py:1874: error: Return type becomes "Union[Any, ExtensionDtype, None]" due to an unfollowed import
pandas\core\generic.py:164: error: Argument 5 to "__init__" becomes "Union[str, Any, ExtensionDtype, None]" due to an unfollowed import
pandas\core\frame.py:6535: error: Return type becomes "Union[Series, Any]" due to an unfollowed import
pandas\core\frame.py:6535: error: Argument 4 to "_gotitem" becomes "Union[Series, Any, None]" due to an unfollowed import
pandas\core\groupby\groupby.py:1087: error: Return type becomes "Tuple[Any, Type[Any]]" due to an unfollowed import
pandas\core\groupby\groupby.py:1087: error: Argument 1 to "objs_to_bool" becomes "Any" due to an unfollowed import
pandas\core\groupby\groupby.py:1095: error: Return type becomes "Any" due to an unfollowed import
pandas\core\groupby\groupby.py:1095: error: Argument 1 to "result_to_bool" becomes "Any" due to an unfollowed import
pandas\core\groupby\groupby.py:1879: error: Return type becomes "Tuple[Any, Optional[Type[Any]]]" due to an unfollowed import
pandas\core\groupby\groupby.py:1879: error: Argument 1 to "pre_processor" becomes "Any" due to an unfollowed import
pandas\core\groupby\groupby.py:1894: error: Return type becomes "Any" due to an unfollowed import
pandas\core\groupby\groupby.py:1894: error: Argument 1 to "post_processor" becomes "Any" due to an unfollowed import
pandas\io\packers.py:800: error: Base type _Packer becomes "Any" due to an unfollowed import
pandas\io\packers.py:820: error: Base type _Unpacker becomes "Any" due to an unfollowed import
pandas\core\window\rolling.py:209: error: Return type becomes "Optional[Any]" due to an unfollowed import
pandas\core\window\rolling.py:222: error: Return type becomes "Any" due to an unfollowed import
pandas\core\window\rolling.py:222: error: Argument 2 to "_prep_values" becomes "Optional[Any]" due to an unfollowed import
pandas\core\window\rolling.py:335: error: Return type becomes "Any" due to an unfollowed import
pandas\core\window\rolling.py:352: error: Argument 4 to "_get_roll_func" becomes "Any" due to an unfollowed import
pandas\core\window\rolling.py:788: error: Return type becomes "Any" due to an unfollowed import
pandas\core\window\rolling.py:844: error: Argument 4 to "_get_roll_func" becomes "Any" due to an unfollowed import
```
Ideas for keeping this manageable?
cc @WillAyd | {
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```
pandas\util\_test_decorators.py:106: error: Return type becomes "Any" due to an unfollowed import
pandas\util\_test_decorators.py:120: error: Return type becomes "Any" due to an unfollowed import
``` | {
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```
pandas\io\packers.py:800: error: Base type _Packer becomes "Any" due to an unfollowed import
pandas\io\packers.py:820: error: Base type _Unpacker becomes "Any" due to an unfollowed import
``` | {
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This docstring https://github.com/pandas-dev/pandas/blob/master/pandas/compat/pickle_compat.py#L196-L201 no longer applies as the `compat` kwarg is no longer present
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} | 2 | 2019-08-25T13:09:35Z | 2019-08-25T15:58:05Z | 2019-08-25T15:56:16Z | CONTRIBUTOR | null | - [X] closes #28136
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
For a change this minor that affects only a comment, not sure if a `whatsnew entry` update is needed? | {
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"id": ... | closed | false | null | [] | null | 2 | 2019-08-25T13:34:22Z | 2019-08-25T15:48:21Z | 2019-08-25T15:48:21Z | NONE | null | I would like to request a method similar to `GroupBy.get_group(self, name, obj=None)` [(documentation)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.get_group.html), a method that can return a group by its index say call it as `GroupBy.get_nth_group(self, nth)`.
If there are 10 groups by calling `get_nth_group(4)` shall return the 4th group. | {
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} | 4 | 2019-08-25T15:55:59Z | 2019-09-10T18:21:23Z | 2019-09-07T17:59:26Z | MEMBER | null | ```python
import numpy as np
import pandas as pd
np.random.seed(1234)
df = pd.DataFrame(np.random.random((5000, 100)))
%timeit df.corr(method="spearman")
# 4.54 s ± 113 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit df.rank().corr(method="pearson")
# 105 ms ± 2.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
pd.testing.assert_frame_equal(df.corr(method="spearman"), df.rank().corr(method="pearson"))
# No error
pd.__version__
# '0.25.0'
```
#### Problem description
`DataFrame.corr(method="spearman")` seems to be an order of magnitude slower than `DataFrame.rank().corr(method="pearson")` even though the two are ultimately doing the same calculation. While probably not the cleanest option, I would think that `corr(method="spearman")` could at least be made an alias for `rank().corr(method="pearson")`, or maybe a change could be made in `algos.pyx`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-08-25T17:53:23Z | 2019-08-27T09:28:19Z | 2019-08-26T23:16:14Z | MEMBER | null | ```
pandas\io\formats\html.py:44: error: Expression type contains "Any" (has type "Union[str, List[Any], Tuple[Any, ...], None]")
pandas\io\formats\html.py:49: error: Expression type contains "Any" (has type "Dict[str, Any]")
pandas\io\formats\html.py:49: error: Expression has type "Any"
pandas\io\formats\html.py:50: error: Expression type contains "Any" (has type "Dict[str, Any]")
pandas\io\formats\html.py:50: error: Expression has type "Any"
pandas\io\formats\html.py:54: error: Expression type contains "Any" (has type "Union[int, Any]")
``` | {
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} | 2 | 2019-08-25T21:03:09Z | 2019-08-27T09:30:24Z | 2019-08-26T14:26:26Z | MEMBER | null | xref #27568
```
pandas\core\indexes\base.py:335: error: Incompatible types in assignment (expression has type "TimedeltaIndex", variable has type "DatetimeIndex")
pandas\core\indexes\base.py:338: error: Incompatible types in assignment (expression has type "TimedeltaIndex", variable has type "DatetimeIndex")
pandas\core\indexes\base.py:346: error: Incompatible types in assignment (expression has type "PeriodIndex", variable has type "DatetimeIndex")
pandas\core\indexes\base.py:408: error: Name 'PeriodIndex' already defined on line 74
pandas\core\indexes\base.py:408: error: Module 'pandas.core.indexes.period' has no attribute 'IncompatibleFrequency'
pandas\core\indexes\base.py:419: error: Name 'Float64Index' already defined on line 349
pandas\core\indexes\base.py:443: error: Name 'Float64Index' already defined on line 349
pandas\core\indexes\base.py:447: error: Name 'IntervalIndex' already defined on line 170
pandas\core\indexes\base.py:459: error: Name 'DatetimeIndex' already defined on line 88
pandas\core\indexes\base.py:470: error: Name 'TimedeltaIndex' already defined on line 57
```
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} | 45 | 2019-08-25T21:19:43Z | 2021-09-09T15:38:52Z | null | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/28135#issuecomment-524659775
do we want to make pandas PEP 561 compatible?
https://mypy.readthedocs.io/en/latest/installed_packages.html#making-pep-561-compatible-packages
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https://api.github.com/repos/pandas-dev/pandas/issues/28143 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28143/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28143/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28143/events | https://github.com/pandas-dev/pandas/issues/28143 | 484,984,100 | MDU6SXNzdWU0ODQ5ODQxMDA= | 28,143 | Use clang-format for Extension Modules | {
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"default... | closed | false | null | [] | null | 1 | 2019-08-25T22:34:32Z | 2020-02-16T21:49:36Z | 2020-02-16T21:49:36Z | MEMBER | null | Right now we use cpplint for header files only. Unfortunately this leaves some of our extension module implementations rather sloppy.
We could extend cpplint to those but from what I have seen it doesn't have an opinion on indentation, which coming from a Python perspective could be useful. It also not surprisingly focuses more on C++ which isn't applicable to our extensions
As an alternate I'd propose taking a look at the `clang-format` tool. I'll push up a PR for review to give an idea of diff. gnu indent is also an option though I'm not sure if that's available to Windows devs that may want to work on these
In either case a CI code check could be added simply using a rule like this:
https://stackoverflow.com/questions/22866609/can-clang-format-tell-me-if-formatting-changes-are-necessary#comment63404587_22866610 | {
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} | 5 | 2019-08-25T22:36:06Z | 2019-08-26T17:54:04Z | 2019-08-26T17:54:00Z | MEMBER | null | POC for #28143
@TomAugspurger may have interest | {
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} | 6 | 2019-08-26T01:20:22Z | 2021-05-22T01:24:08Z | 2021-05-22T01:24:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from pandas.core.internals.construction import _try_cast
arr = np.arange(0, 10, dtype = "int64")
# should return early
_try_cast(arr, None, False, False)
```
#### Problem description
During series construction, a function, `sanitize_array` attempts to use `_try_cast`, to cast the input to a better type. `_try_cast` is fairly slow to run, so it tries to avoid casting in common cases. However, due to a missing `not` keyword, it appears `_try_cast` **runs for the cases it wants to avoid (like the one above)**.
Here are the relevant lines of `_try_catch`:
https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py#L511-L513
Should this be `not maybe_castable(arr)`?
It is very surprising that lines like this would intentially create an array, and then try to cast it, even when the dtype option passed is None.
https://github.com/pandas-dev/pandas/blob/master/pandas/core/construction.py#L429-L432
#### Expected Output
`_try_cast` not run during `sanitize_array` for common types (e.g. int64). However, from looking at it with `%%prune`, and running pdb, I can see functions like `maybe_cast_to_datetime` are called.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.1
pytz : 2018.9
dateutil : 2.8.0
pip : 19.1.1
setuptools : 39.0.1
Cython : None
pytest : 4.4.2
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
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Ideally, we'd have 3.8 actually tested on CI before releasing 0.25.2. Hopefully once https://github.com/conda-forge/python-feedstock/pull/271 is up, we can do that (and maybe build our deps from source).
Once we're tested, we should add a note to the whatsnew saying that pandas 0.25.2 is compatible with Python 3.8 | {
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} | 3 | 2019-08-26T14:28:40Z | 2019-08-26T23:11:53Z | 2019-08-26T22:49:23Z | CONTRIBUTOR | null | Forgot this for 0.25.1 | {
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} | 8 | 2019-08-26T14:40:41Z | 2019-12-30T16:26:25Z | 2019-12-30T16:26:24Z | MEMBER | null | Discussed briefly in #28119, the ndarray backing a PandasArray can be swapped out by setitem, which can have surprising behavior for views
```
arr = pd.array([1, 2, 3])
view1 = arr.view()
view2 = arr[:]
view3 = np.asarray(arr)
arr[0] = 9
assert view1[0] == 9
assert view2[0] == 9
assert view3[0] == 9
arr[1] = 2.5
view1[-1] = 5
assert arr[-1] == 5 # FAIL
``` | {
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} | 22 | 2019-08-26T14:42:49Z | 2019-09-08T01:20:18Z | 2019-09-07T17:59:26Z | MEMBER | null | Saves off ranks for later reuse once they're calculated during the computation of `DataFrame.corr(method="spearman")` so as to speed up the calculation.
~~This PR ranks data outside the nested loop in `nancorr_spearman` so as to speed up the calculation~~ (closes #28139). The performance benchmarks show a nice improvement:
```
asv continuous -f 1.1 upstream/master corr-perf -b stat_ops.Correlation
before after ratio
[5d9fd7e3] [08d8e394]
<master> <corr-perf>
- 87.8±0.9ms 7.81±0.2ms 0.09 stat_ops.Correlation.time_corr('spearman', False)
- 88.0±1ms 7.82±0.1ms 0.09 stat_ops.Correlation.time_corr('spearman', True)
```
~~(One caveat here is that when two columns have different missing patterns we actually need to recompute ranks over the rows where both are non-missing, so in some situations this will actually perform slightly worse than the current implementation.)~~ | {
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} | 5 | 2019-08-26T15:07:58Z | 2021-06-19T03:47:25Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = pd.Timestamp("2019-2-2").date()
i = pd.MultiIndex.from_product([
[a, a],
[2, 3]
])
print("a={} ({}".format(a, type(a)))
print(i[0])
```
Output is :
```
a=2019-02-02 (<class 'datetime.date'>
(Timestamp('2019-02-02 00:00:00'), 2)
```
#### Problem description
When using from_product with python datetimes, the resulting MultiIndex level is converted to pandas datetimes (Timestamps). There is no way to keep the original python datetime which I want. I got the same behavior with from_tuples. But it was not the case with from_arrays.
#### Expected Output
I expect the output to respect the type I gave to from_product :
```
a=2019-02-02 (<class 'datetime.date'>
(datetime.date(2019, 2, 2), 2)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 4.6.5
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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"description": "to_sql, read_sql, read_s... | open | false | null | [] | null | 0 | 2019-08-26T19:24:58Z | 2020-05-16T04:00:35Z | null | NONE | null | I have a table containing about 28 million rows. I read the data in 1 million row chunks:
```python
params = urllib.parse.quote_plus("DRIVER={SQL Server Native Client 11.0};"
"SERVER=xxxx;"
"DATABASE=xxxx;"
"Trusted_Connection=yes")
engine = sa.create_engine("mssql+pyodbc:///?odbc_connect={}".format(params))
pd_frame_generator = pd.read_sql_table(table_name='myTable',
con=engine,
columns=['x', 'y', 'z'] ,
chunksize=1000000
)
```
But sometime one or two more rows are read. The following is the number of lines after exporting the read data into csv (one extra row is normal because of the header. Abnormal ones are morked in red):
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.15.1
pytz : 2018.5
dateutil : 2.7.3
pip : 18.0
setuptools : 39.0.1
Cython : None
pytest : 3.9.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.1
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 6 | 2019-08-26T20:41:13Z | 2020-04-05T00:12:40Z | 2020-04-05T00:12:39Z | NONE | null | #### Problem description
1. pandas statistics sometimes produce numpy data types and sometimes not. This is inconsistent.
2. json_dumps cannot serialize int64 (not a pandas issue but that's how I got here)
I created a dictionary containing pandas stats. The input is a pandas series, s, dtype float64.
The methods used are
```
s.count()
s.max()
s.min()
s.mean()
s.median()
s.mode()[0]
s.std()
```
Apparently, pandas returns numpy ints and numpy floats (sometimes).
```
count 564 <class 'numpy.int64'>
max 33.46 <class 'float'>
min 7.36 <class 'float'>
mean 24.727588652482286 <class 'float'>
median 26.28 <class 'float'>
mode 25.6 <class 'numpy.float64'>
std 5.857237135129123 <class 'float'>
````
Note:
This discrepancy matters because `json.dump` cannot currently serialize objects of type int64.
Apparently pandas DataFrame.to_json() method has no such issues. Converting the dictionary to a dataframe and then writing it out to JSON works without error.
Perhaps pandas could share the `to_json` code with the Python json library devs, resolving [Python issue 24313](https://bugs.python.org/issue24313)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 15.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28156 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28156/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28156/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28156/events | https://github.com/pandas-dev/pandas/issues/28156 | 485,478,948 | MDU6SXNzdWU0ODU0Nzg5NDg= | 28,156 | HDFStore: unable to create index, no error message | {
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} | 2 | 2019-08-26T22:12:39Z | 2020-06-30T13:01:24Z | 2020-06-30T13:01:24Z | CONTRIBUTOR | null | I was trying to follow the documentation at https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#indexing but ran into an unintuitive bug with HDFStore index creation. I thought I would report it in case someone else runs across this problem.
First, I create 2 dataframes and an HDFStore:
```python
>>> import pandas as pd
>>> import numpy as np
>>> df_1 = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))
>>> df_2 = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))
>>> st = pd.HDFStore('appends.h5', mode='w')
```
Now, when I append, if I do:
```python
>>> st.append('df', df_1, data_columns=['B'], index=False)
>>> st.append('df', df_2, data_columns=['B'], index=False)
```
I can successfully create an index:
```python
>>> st.create_table_index('df', columns=['B'], optlevel=9, kind='full')
>>> st.get_storer('df').table
/df/table (Table(20,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
"B": Float64Col(shape=(), dflt=0.0, pos=2)}
byteorder := 'little'
chunkshape := (2730,)
autoindex := True
colindexes := {
"B": Index(9, full, shuffle, zlib(1)).is_csi=True}
```
But if I instead leave out the `data_columns`:
```python
>>> st.append('df', df_1, index=False)
>>> st.append('df', df_2, index=False)
```
no index is created:
```python
>>> st.create_table_index('df', columns=['B'], optlevel=9, kind='full')
>>> st.get_storer('df').table
/df/table (Table(20,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(2,), dflt=0.0, pos=1)}
byteorder := 'little'
chunkshape := (2730,)
```
This is unintuitive for 2 reasons:
1. Why does HDFStore need to know the indexable columns during `append` _and_ during `create_table_index`?
2. Why doesn't `create_table_index` raise an error message when it isn't able to create an index?
I think fixing either 1 or 2 would make things much more intuitive. | {
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} | 5 | 2019-08-26T22:34:53Z | 2019-09-02T22:55:18Z | 2019-09-02T21:02:10Z | MEMBER | null | broken off from a branch fixing #28080. | {
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} | 5 | 2019-08-26T22:44:15Z | 2019-08-27T21:39:04Z | 2019-08-27T21:39:04Z | CONTRIBUTOR | null | Per #27353 changed ``isinstance`` checks listed in the @WillAyd script to use ABCs where it enabled removal of imports only.
Note there may be additional files which can be cleaned as the script does not parse multi-line statements.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-08-27T00:55:22Z | 2019-09-03T11:29:43Z | 2019-09-03T11:29:39Z | CONTRIBUTOR | null | https://pandas-docs.github.io/pandas-docs-travis/getting_started/comparison/comparison_with_sql.html
In section "SELECT", the important use case of adding a calculated column is omitted.
I suggest adding something like the following:
select *, tip/total_bill as tip_rate from tips
pd.concat([tips, pd.Series(tips.tip/tips.total_bill, name='tip_rate')], axis=1)
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} | 2 | 2019-08-27T02:12:23Z | 2019-09-03T19:01:09Z | 2019-09-03T19:01:09Z | CONTRIBUTOR | null | Right now a `pd.set_option('display.backend', name)` will
1. look for an entrypoint
2. fall back to importing the module `name` and setting that to the backend
In the second case, the backend's implementation isn't validated until a plot is actually requested. We should validate that `name.plot` is a thing when the backend is set.
```python
In [16]: module = types.ModuleType("foo")
In [17]: sys.modules['foo'] = module
In [18]: pd.set_option('plotting.backend', 'foo')
In [19]: df = pd.DataFrame({"A": [1, 2]})
In [20]: df.plot()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-20-848b80e64df8> in <module>
----> 1 df.plot()
~/sandbox/pandas/pandas/plotting/_core.py in __call__(self, *args, **kwargs)
794 data.columns = label_name
795
--> 796 return plot_backend.plot(data, kind=kind, **kwargs)
797
798 def line(self, x=None, y=None, **kwargs):
AttributeError: module 'foo' has no attribute 'plot'
```
Rather than raising an `AttributeError` on `df.plot`, we should raise a `ValueError` on `pd.set_option('plotting.backend', 'foo')`. | {
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} | 3 | 2019-08-27T02:40:09Z | 2019-09-03T19:01:13Z | 2019-09-03T19:01:09Z | CONTRIBUTOR | null | ```python
In [1]: import pandas as pd
In [2]: import sys
In [3]: import types
In [4]: module = types.ModuleType("foo")
In [5]: sys.modules['foo'] = module
In [6]: pd.set_option('plotting.backend', 'foo')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-123b6513deb2> in <module>
----> 1 pd.set_option('plotting.backend', 'foo')
...
~/sandbox/pandas/pandas/plotting/_core.py in _find_backend(backend)
1588 "top-level `.plot` method."
1589 )
-> 1590 raise ValueError(msg.format(name=backend))
1591
1592
ValueError: Could not find plotting backend 'foo'. Ensure that you've installed the package providing the 'foo' entrypoint, or that the package has atop-level `.plot` method.
```
Closes https://github.com/pandas-dev/pandas/issues/28163 | {
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} | 7 | 2019-08-27T03:21:29Z | 2019-10-10T22:46:33Z | 2019-10-08T12:43:11Z | MEMBER | null | The signatures match those in PandasArray.
- [x] closes #25335 | {
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} | 2 | 2019-08-27T05:40:41Z | 2019-11-08T16:47:37Z | 2019-11-08T16:47:37Z | CONTRIBUTOR | null | The documentation for merging categorical arrays ([here](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#merging)) states that calling `pd.concat` on two data frames with categorical columns will throw an error. This doesn't throw an error, and probably shouldn't following #13767.
Here's the example shown in the docs:
```python
import pandas as pd
cat = pd.Series(["a", "b"], dtype="category")
vals = [1, 2]
df = pd.DataFrame({"cats": cat, "vals": vals})
df_different = df.copy()
df_different["cats"].cat.categories = ["c", "d"]
try:
pd.concat([df, df_different])
except ValueError as e:
print("ValueError:", str(e))
``` | {
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In [11]: %prun -l 4 apply_integrate_f(df['a'].to_numpy(),
....: df['b'].to_numpy(),
....: df['N'].to_numpy())
....:
File "<ipython-input-11-613f5c6ec02d>", line 2
df['b'].to_numpy(),
^
IndentationError: unexpected indent
```
#### Problem description
Code example in documentation for [enhancing performance](http://pandas-docs.github.io/pandas-docs-travis/user_guide/enhancingperf.html#using-ndarray) is erroneous. Shows intendation error instead of %prun timing.
#### Expected Output
Expected to see %prun timing.
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] | open | false | null | [] | null | 15 | 2019-08-27T08:11:58Z | 2020-03-24T21:42:31Z | null | MEMBER | null | In #28014 we added a first draft of the new pandas website. It can be seen here:
https://datapythonista.github.io/pandas-web/ (will be at https://dev.pandas.io when #28497 is merged).
Things that are already in the home page, or that could be added:
- [X] Short sentence to summarize what pandas is about
- [X] Links to the main pages / section (feedback on things to add/remove/change welcome)
- [X] List of companies supporting pandas (do we need to formalize the criteria to be there?)
- [X] Info and links to the last version (download, docs...)
- [X] Follow us on twitter
- [X] Wes book
- [X] Links to previous versions
- [ ] Project highlights (See the home of Jupyter, Dask, Django or Spark for reference). How do we want it? What exact highlights do we want to list?
- [ ] Success stories. We have some in the current website (https://pandas.pydata.org/#what-do-our-users-have-to-say) but in my opinion we could do much better. Like some known faces/companies. I can ask one from the team of the black hole image for example.
- [ ] Summary of the ecosystem page. Like the basics, mentioning matplotlib for plotting, numpy for array computing, Jupyter as an interface, Dask for distributed...
- [ ] Anything else? | {
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} | 1 | 2019-08-27T09:41:52Z | 2019-08-27T17:36:03Z | 2019-08-27T17:36:03Z | NONE | null | The documentation of the merge of dataframes state that with the suffixes parameter you can set the columnname suffixes for the left and right dataframe respectively (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).
According to my example the suffixes are first sorted alphabetically.
```
A = pd.DataFrame({'prodID':[0,1,2,3,4],'units':[5,6,7,8,9]})
B = pd.DataFrame({'prodID':[0,1,2,3,4],'units':[10,11,12,13,14]})
merged = pd.merge(B,A,on='prodID',suffixes={'_b','_a'})
print(merged)
```
Actual result:
```
prodID units_a units_b
0 0 10 5
1 1 11 6
2 2 12 7
3 3 13 8
4 4 14 9
```
What I expected to get:
```
prodID units_b units_a
0 0 10 5
1 1 11 6
2 2 12 7
3 3 13 8
4 4 14 9
```
When I use the suffixes i want the left dataframe, in my example B, to get the left suffix, '_b'. And the right dataframe the right suffix. A lot of bugs can occur due to the selection of the wrong columns. | {
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} | 2 | 2019-08-27T10:55:13Z | 2019-08-27T17:46:21Z | 2019-08-27T17:46:18Z | NONE | null | Often when having difficulty solving a bug users resort to posting a question on a site such as stack overflow. Part of the question is inevitabley giving some background on their data. However in order to fix the issue other users occasionally have to try and recreate their dataset, this may be done incorrectly (misunderstanding column names or datatypes etc). As such it would be useful if users could run `df.export_text()` which would produce an ascii table that is both human readable and could be imported with `df.import_text(df.export_text())`. | {
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] | closed | false | null | [] | null | 2 | 2019-08-27T12:38:41Z | 2019-08-27T13:44:42Z | 2019-08-27T13:25:31Z | CONTRIBUTOR | null | #### Code Sample raising the error:
```python
df = pd.DataFrame(data=np.random.rand(15000, 5), columns=['x', 'a', 'ab', 'b', 'c'])
ser = pd.Series(data=[1.1, 9, 17.8], index=['a', 'b', 'c'])
df.multiply(ser, axis=1)
```
not raising the error if up to 10000 rows are taken:
```python
# NO error:
df.iloc[:10000].multiply(ser, axis=1)
# error:
df.iloc[:10001].multiply(ser, axis=1)
```
#### Problem description
Multiplying a DataFrame with more than 10k rows with a Series with the keyword `axis=1` raises the following error:
> AttributeError: 'numpy.dtype' object has no attribute 'value_counts'
It seems like another path is taken, if more than 10k rows are multiplied.
Version 0.24.2 did NOT reproduce the error. The error occurs since 0.25.0
Omitting the `axis` keyword seems to solve the problem, but is it safe to do so in all cases?
Any ideas for a workaround except for solving it in chunks?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : de
LOCALE : de_DE.ISO8859-1
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 2 | 2019-08-27T12:48:38Z | 2019-09-23T16:35:00Z | 2019-09-23T16:34:56Z | CONTRIBUTOR | null | - [x] closes #14119
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28173/events | https://github.com/pandas-dev/pandas/pull/28173 | 485,790,492 | MDExOlB1bGxSZXF1ZXN0MzExMzY3OTE4 | 28,173 | TYPING: change to FrameOrSeries Alias in pandas._typing | {
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} | 1 | 2019-08-27T12:50:17Z | 2019-08-28T11:34:26Z | 2019-08-27T21:50:23Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27646#pullrequestreview-279692079
cc @WillAyd | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28174 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28174/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28174/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28174/events | https://github.com/pandas-dev/pandas/issues/28174 | 485,815,410 | MDU6SXNzdWU0ODU4MTU0MTA= | 28,174 | Versions from 0.23.4 to 0.25.1 fail to install on Alpine Linux | {
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] | closed | false | null | [] | null | 8 | 2019-08-27T13:36:27Z | 2019-08-30T13:50:45Z | 2019-08-27T17:47:52Z | NONE | null |
#### Problem description
already have installed numpy==1.17.1
but installation of pandas > 0.23.4 fails with
```
ERROR: Command errored out with exit status 1:
command: /usr/local/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-hmmx65yg/pandas/setup.py'"'"'; __file__='"'"'/tmp/pip-install-hmmx65yg/pandas/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base pip-egg-info
cwd: /tmp/pip-install-hmmx65yg/pandas/
Complete output (18 lines):
Traceback (most recent call last):
File "/usr/local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 359, in get_provider
module = sys.modules[moduleOrReq]
KeyError: 'numpy'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-hmmx65yg/pandas/setup.py", line 732, in <module>
ext_modules=maybe_cythonize(extensions, compiler_directives=directives),
File "/tmp/pip-install-hmmx65yg/pandas/setup.py", line 475, in maybe_cythonize
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
File "/usr/local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 1144, in resource_filename
return get_provider(package_or_requirement).get_resource_filename(
File "/usr/local/lib/python3.6/site-packages/pkg_resources/__init__.py", line 361, in get_provider
__import__(moduleOrReq)
ModuleNotFoundError: No module named 'numpy'
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28175 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28175/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28175/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28175/events | https://github.com/pandas-dev/pandas/issues/28175 | 485,818,709 | MDU6SXNzdWU0ODU4MTg3MDk= | 28,175 | get_dummies ran for 20 minutes and ended without result | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-27T13:41:57Z | 2019-08-27T23:36:59Z | 2019-08-27T17:33:25Z | NONE | null | #### CODE
My Python file was only just this:
p_csv= pandas.read_csv(("/myDir/myFile.csv"), index_col=0)
pandas.get_dummies(p_csv, prefix="myDATA_", columns="myDATA")
p_csv.to_csv("/myDir/myNew.csv")
#### Problem description
The script ran in pycharm for over 20 minutes and ended with a SIGKILL. No result.
The Shape is about (450k, 150).
After trying to get it work for over an hour I decided to just do it myself. I knew there were 14 unique strings in that column (used pandas in console for that before). So I put in 14 blank columns after the "myDATA" column and ran this Script afterwards:
p_csv= pandas.read_csv(("/myDir/myFile.csv"), index_col=0)
myDataNames = sorted(p_csv["myDATA"].unique())
1
for i in range(0,14):
print(i)
col = myDataIndex+ 1 + i
p_csv.iloc[:,col] = p_csv["myData"].apply(lambda x: int(x==myDataNames[i]))
p_csv.to_csv("/myDir/myNew.csv")
It Ended in under 2 Minutes with the desired result. I am wondering why the get_dummies() method didn't work for me.
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https://api.github.com/repos/pandas-dev/pandas/issues/28176 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28176/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28176/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28176/events | https://github.com/pandas-dev/pandas/pull/28176 | 485,850,854 | MDExOlB1bGxSZXF1ZXN0MzExNDE3NDUx | 28,176 | Astype keeps nan when converting into string | {
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] | closed | false | null | [] | null | 22 | 2019-08-27T14:36:58Z | 2020-05-08T16:40:48Z | 2020-05-08T16:40:48Z | CONTRIBUTOR | null | - closes #25353
- 1 test added
- passes black pandas
- whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28177 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28177/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28177/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28177/events | https://github.com/pandas-dev/pandas/issues/28177 | 485,854,626 | MDU6SXNzdWU0ODU4NTQ2MjY= | 28,177 | DEPR: Clean up of pandas.plotting | {
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"description": "Fu... | open | false | null | [] | null | 9 | 2019-08-27T14:43:16Z | 2019-12-21T18:40:18Z | null | MEMBER | null | xref: #26747, #28159
The current plotting API feels inconsistent and I think it's the one we have for historical reasons, and not the one we want. I propose the next changes:
1. Leave the current API based on the `.plot` accessor as is (e.g. `Series.plot.hist`, `Series.plot(kind='box')`). In the future we should consider two things:
- Whether we want backend to be able to add plots that we don't define
- Move all the matplotlib specific parameters to `**kwargs`
2. Remove all duplicate functions:
- hist_series (`Series.hist` -> `Series.plot.hist`)
- hist_frame (`DataFrame.hist` -> `DataFrame.plot.hist`)
- boxplot (`pandas.plotting.boxplot` -> `DataFrame.plot.box`)
- boxplot_frame (`DataFrame.boxplot` -> `DataFrame.plot.box`)
3. Move the matplotlib backend to a separate project (`pandas.plotting._matplotlib` -> `pandas_matplotlib`)
4. Move to the matplotlib backend the non-accessor plotting functions:
- andrews_curves (`pandas.plotting.andrews_curves` -> `pandas_matplotlib.andrews_curves`)
- autocorrelation_plot
- bootstrap_plot
- lag_plot
- parallel_coordinates
- radviz
- scatter_matrix
- table
5. Move to the matplotlib backend register/unregister of the converters (pandas.plotting.register` -> `pandas_matplotlib.register`)
CC: @pandas-dev/pandas-core @jakevdp | {
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"descriptio... | closed | false | null | [] | null | 2 | 2019-08-27T15:19:29Z | 2019-09-28T11:37:50Z | 2019-09-28T11:37:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
pd.read_csv('../Data/features/data.csv')
```
#### Problem description
Trying to read a csv file with 300+ columns. The first two of them are string, 300 float numbers and the rest a boolean (0, 1). However, I get this error
```
Traceback (most recent call last):
File "<ipython-input-20-5031bb2145ff>", line 1, in <module>
pd.read_csv('../Data/features/ΔΙΚ4.csv')
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/io/parsers.py", line 685, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/io/parsers.py", line 463, in _read
data = parser.read(nrows)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/io/parsers.py", line 1169, in read
df = DataFrame(col_dict, columns=columns, index=index)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/frame.py", line 411, in __init__
mgr = init_dict(data, index, columns, dtype=dtype)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 224, in init_dict
arrays = Series(data, index=columns, dtype=object)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/series.py", line 259, in __init__
data, index = self._init_dict(data, index, dtype)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/series.py", line 353, in _init_dict
s = Series(values, index=keys, dtype=dtype)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/series.py", line 313, in __init__
data = SingleBlockManager(data, index, fastpath=True)
File "/Users/uuser/anaconda3/envs/news/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 1486, in __init__
self.axes = [axis]
TypeError: descriptor 'axes' for 'BlockManager' objects doesn't apply to 'SingleBlockManager' object
```
I checked the csv in online validators and it passes from all of them.
#### Expected Output
The expected output would be to read the csv in a dataframe variable
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-08-27T16:12:15Z | 2019-09-03T11:47:38Z | 2019-09-03T11:47:36Z | MEMBER | null | Some plotting public functions are missing from the documentation.
As proposed in #28177 I think those should be moved out of pandas or deprecated, but I guess it's worth having them documented as the rest of similar functions until that happens.
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https://api.github.com/repos/pandas-dev/pandas/issues/28180 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28180/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28180/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28180/events | https://github.com/pandas-dev/pandas/issues/28180 | 485,934,563 | MDU6SXNzdWU0ODU5MzQ1NjM= | 28,180 | to_iso methods for DatetimeLikeArray | {
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... | open | false | null | [] | null | 2 | 2019-08-27T17:20:38Z | 2020-04-02T05:04:59Z | null | MEMBER | null | to_json (and potentially others) handles the conversion of datetime-like objects to either epoch or isoformats depending on user input. All of this logic is currently housed in the objToJSON.c file and is a major reason why that extension module is rather complex / convoluted.
I think these methods may be general purpose enough to define in a vectorized manner on DatetimeLikeArrays. This wouldn't fully eliminate the need for the objToJSON.c extension module to define serialization for datetimes, timedelta and period object (as items stuck within an object array still need to be serialized), but it would definitely allow it to be more performant and move some of the logic out of the C layer back into Python
@jbrockmendel this is potentially also a precursor to #19486 | {
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} | 7 | 2019-08-27T18:53:33Z | 2019-09-02T23:52:21Z | 2019-09-02T23:52:10Z | CONTRIBUTOR | null |
- [x] closes #10065
- [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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} | 1 | 2019-08-27T21:24:23Z | 2019-08-29T12:31:38Z | 2019-08-29T12:31:32Z | CONTRIBUTOR | null | - [ ] closes https://github.com/pandas-dev/pandas/issues/28162
- [ ] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
@hugoecarl
@guipleite
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} | 0 | 2019-08-28T01:11:12Z | 2019-08-30T14:58:22Z | 2019-08-30T14:28:37Z | MEMBER | null | Make it easier to grep for the places where it is used. Broken off from a WIP branch. | {
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} | 8 | 2019-08-28T07:35:14Z | 2019-11-08T16:47:44Z | 2019-11-08T16:47:38Z | CONTRIBUTOR | null | Fixes #28166.
Updated docs about merging categorical to reflect current behavior. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28186 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28186/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28186/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28186/events | https://github.com/pandas-dev/pandas/issues/28186 | 486,246,939 | MDU6SXNzdWU0ODYyNDY5Mzk= | 28,186 | pandas.DataFrame.astype casts numerical nulls to string 'nan' | {
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] | closed | false | null | [] | null | 1 | 2019-08-28T08:58:49Z | 2019-08-28T15:22:29Z | 2019-08-28T15:22:29Z | NONE | null |
```python
import pandas as pd
import numpy as np
s = pd.Series([1, 2, np.nan])
print str(s.isnull().any())
s = s.astype(str)
print str(s.isnull().any())
##output
True
False
```
#### Problem description
When casting a series from float to string, the nulls in float should also be nulls in string. But, astype method converts the nulls to string 'nan', and so it does not reflect as null.
#### Expected Output
True
True
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-154-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
LOCALE: None.None
pandas: 0.24.1
pytest: 2.8.7
pip: 19.0.1
setuptools: 40.6.2
Cython: None
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
feather: None
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml.etree: 3.5.0
bs4: 4.4.1
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28187 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28187/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28187/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28187/events | https://github.com/pandas-dev/pandas/issues/28187 | 486,314,399 | MDU6SXNzdWU0ODYzMTQzOTk= | 28,187 | DOC: Fix PR08 in docstrings | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-28T11:12:53Z | 2019-08-30T21:53:40Z | 2019-08-30T21:53:40Z | CONTRIBUTOR | null | Hi all,
This is my very first time contributing to an open source project and have tried to follow all guidelines. Apologies if I missed something.
This relates to [#28031](https://github.com/pandas-dev/pandas/pull/28031)
I have ran `python ./scripts/validate_docstrings.py --errors=PR08` and recieved the following errors regarding capital letters. There are quite a few in here that where the parameter descriptions start with a integer i.e. in pandas.tseries.offsets.WeekOfMonth:
```
class WeekOfMonth(_WeekOfMonthMixin, DateOffset):
"""
Describes monthly dates like "the Tuesday of the 2nd week of each month".
Parameters
----------
n : int
week : {0, 1, 2, 3, ...}, default 0
0 is 1st week of month, 1 2nd week, etc.
```
Could I please get some advice on how to handle those?
#### Current Errors for PR08
```
pandas.tseries.offsets.CustomBusinessDay: Parameter "weekmask" description should start with a capital letter
pandas.tseries.offsets.CustomBusinessDay: Parameter "holidays" description should start with a capital letter
pandas.tseries.offsets.WeekOfMonth: Parameter "week" description should start with a capital letter
pandas.tseries.offsets.WeekOfMonth: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.LastWeekOfMonth: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253: Parameter "variation" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "qtr_with_extra_week" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "variation" description should start with a capital letter
pandas.tseries.offsets.CDay: Parameter "weekmask" description should start with a capital letter
pandas.tseries.offsets.CDay: Parameter "holidays" description should start with a capital letter
pandas.Series.pipe: Parameter "func" description should start with a capital letter
pandas.Series.pipe: Parameter "args" description should start with a capital letter
pandas.Series.pipe: Parameter "kwargs" description should start with a capital letter
pandas.Series.corr: Parameter "method" description should start with a capital letter
pandas.Series.quantile: Parameter "q" description should start with a capital letter
pandas.Series.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.Series.duplicated: Parameter "keep" description should start with a capital letter
pandas.Series.rename: Parameter "index" description should start with a capital letter
pandas.Series.reorder_levels: Parameter "order" description should start with a capital letter
pandas.Series.dt.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Series.str.match: Parameter "flags" description should start with a capital letter
pandas.Series.str.replace: Parameter "case" description should start with a capital letter
pandas.Series.str.replace: Parameter "flags" description should start with a capital letter
pandas.Series.str.replace: Parameter "regex" description should start with a capital letter
pandas.Series.str.translate: Parameter "table" description should start with a capital letter
pandas.Series.cat.rename_categories: Parameter "new_categories" description should start with a capital letter
pandas.Series.plot: Parameter "kind" description should start with a capital letter
pandas.Series.plot: Parameter "style" description should start with a capital letter
pandas.Series.hist: Parameter "xrot" description should start with a capital letter
pandas.Series.hist: Parameter "yrot" description should start with a capital letter
pandas.Series.hist: Parameter "figsize" description should start with a capital letter
pandas.Series.to_msgpack: Parameter "path" description should start with a capital letter
pandas.Series.to_msgpack: Parameter "append" description should start with a capital letter
pandas.Series.to_msgpack: Parameter "compress" description should start with a capital letter
pandas.Series.to_clipboard: Parameter "excel" description should start with a capital letter
pandas.Timestamp.ceil: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.day_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.floor: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.fromordinal: Parameter "ordinal" description should start with a capital letter
pandas.Timestamp.month_name: Parameter "locale" description should start with a capital letter
pandas.Timestamp.round: Parameter "ambiguous" description should start with a capital letter
pandas.Timestamp.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Period.to_timestamp: Parameter "how" description should start with a capital letter
pandas.arrays.IntervalArray.from_tuples: Parameter "copy" description should start with a capital letter
pandas.arrays.IntervalArray.from_breaks: Parameter "copy" description should start with a capital letter
pandas.read_msgpack: Parameter "iterator" description should start with a capital letter
pandas.io.json.build_table_schema: Parameter "primary_key" description should start with a capital letter
pandas.read_html: Parameter "skiprows" description should start with a capital letter
pandas.HDFStore.put: Parameter "format" description should start with a capital letter
pandas.HDFStore.append: Parameter "format" description should start with a capital letter
pandas.HDFStore.append: Parameter "append " description should start with a capital letter
pandas.HDFStore.append: Parameter "dropna " description should start with a capital letter
pandas.read_feather: Parameter "nthreads" description should start with a capital letter
pandas.read_feather: Parameter "use_threads" description should start with a capital letter
pandas.read_sql_table: Parameter "parse_dates" description should start with a capital letter
pandas.read_sql_query: Parameter "con" description should start with a capital letter
pandas.read_sql_query: Parameter "parse_dates" description should start with a capital letter
pandas.read_sql: Parameter "con" description should start with a capital letter
pandas.read_sql: Parameter "parse_dates" description should start with a capital letter
pandas.core.resample.Resampler.get_group: Parameter "name" description should start with a capital letter
pandas.core.resample.Resampler.get_group: Parameter "obj" description should start with a capital letter
pandas.core.resample.Resampler.pipe: Parameter "args" description should start with a capital letter
pandas.core.resample.Resampler.pipe: Parameter "kwargs" description should start with a capital letter
pandas.core.resample.Resampler.ffill: Parameter "limit" description should start with a capital letter
pandas.core.resample.Resampler.pad: Parameter "limit" description should start with a capital letter
pandas.core.resample.Resampler.sem: Parameter "ddof" description should start with a capital letter
pandas.core.resample.Resampler.var: Parameter "ddof" description should start with a capital letter
pandas.plotting.parallel_coordinates: Parameter "ax" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "alpha" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "figsize" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "grid" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "diagonal" description should start with a capital letter
pandas.plotting.scatter_matrix: Parameter "range_padding" description should start with a capital letter
pandas.api.types.union_categoricals: Parameter "to_union" description should start with a capital letter
pandas.merge_ordered: Parameter "how" description should start with a capital letter
pandas.merge_asof: Parameter "allow_exact_matches" description should start with a capital letter
pandas.to_numeric: Parameter "errors" description should start with a capital letter
pandas.to_datetime: Parameter "arg" description should start with a capital letter
pandas.to_datetime: Parameter "errors" description should start with a capital letter
pandas.to_datetime: Parameter "box" description should start with a capital letter
pandas.to_datetime: Parameter "format" description should start with a capital letter
pandas.to_datetime: Parameter "exact" description should start with a capital letter
pandas.to_datetime: Parameter "unit" description should start with a capital letter
pandas.to_timedelta: Parameter "box" description should start with a capital letter
pandas.to_timedelta: Parameter "errors" description should start with a capital letter
pandas.infer_freq: Parameter "index" description should start with a capital letter
pandas.util.hash_array: Parameter "encoding" description should start with a capital letter
pandas.util.hash_pandas_object: Parameter "index" description should start with a capital letter
pandas.util.hash_pandas_object: Parameter "encoding" description should start with a capital letter
pandas.Index.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.Index.is_: Parameter "other" description should start with a capital letter
pandas.Index.fillna: Parameter "downcast" description should start with a capital letter
pandas.Index.to_series: Parameter "index" description should start with a capital letter
pandas.Index.to_series: Parameter "name" description should start with a capital letter
pandas.Index.get_indexer: Parameter "method" description should start with a capital letter
pandas.Index.get_loc: Parameter "method" description should start with a capital letter
pandas.CategoricalIndex.rename_categories: Parameter "new_categories" description should start with a capital letter
pandas.IntervalIndex.from_tuples: Parameter "copy" description should start with a capital letter
pandas.IntervalIndex.from_breaks: Parameter "copy" description should start with a capital letter
pandas.IntervalIndex.get_loc: Parameter "method" description should start with a capital letter
pandas.IntervalIndex.get_indexer: Parameter "method" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "levels" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "level" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "inplace" description should start with a capital letter
pandas.MultiIndex.set_levels: Parameter "verify_integrity" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "codes" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "level" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "inplace" description should start with a capital letter
pandas.MultiIndex.set_codes: Parameter "verify_integrity" description should start with a capital letter
pandas.MultiIndex.get_loc_level: Parameter "drop_level" description should start with a capital letter
pandas.MultiIndex.get_indexer: Parameter "method" description should start with a capital letter
pandas.MultiIndex.get_level_values: Parameter "level" description should start with a capital letter
pandas.DatetimeIndex.indexer_at_time: Parameter "time" description should start with a capital letter
pandas.DatetimeIndex.indexer_between_time: Parameter "start_time, end_time" description should start with a capital letter
pandas.DatetimeIndex.tz_localize: Parameter "errors" description should start with a capital letter
pandas.TimedeltaIndex.to_series: Parameter "index" description should start with a capital letter
pandas.TimedeltaIndex.to_series: Parameter "name" description should start with a capital letter
pandas.PeriodIndex.asfreq: Parameter "freq" description should start with a capital letter
pandas.PeriodIndex.asfreq: Parameter "how" description should start with a capital letter
pandas.core.window.rolling.Rolling.apply: Parameter "raw" description should start with a capital letter
pandas.core.window.expanding.Expanding.apply: Parameter "raw" description should start with a capital letter
pandas.core.groupby.GroupBy.get_group: Parameter "name" description should start with a capital letter
pandas.core.groupby.GroupBy.get_group: Parameter "obj" description should start with a capital letter
pandas.Grouper: Parameter "key" description should start with a capital letter
pandas.Grouper: Parameter "level" description should start with a capital letter
pandas.Grouper: Parameter "sort" description should start with a capital letter
pandas.core.groupby.GroupBy.pipe: Parameter "args" description should start with a capital letter
pandas.core.groupby.GroupBy.pipe: Parameter "kwargs" description should start with a capital letter
pandas.core.groupby.GroupBy.bfill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.GroupBy.ffill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.GroupBy.nth: Parameter "n" description should start with a capital letter
pandas.core.groupby.GroupBy.nth: Parameter "dropna" description should start with a capital letter
pandas.core.groupby.GroupBy.rank: Parameter "method" description should start with a capital letter
pandas.core.groupby.GroupBy.rank: Parameter "na_option" description should start with a capital letter
pandas.core.groupby.GroupBy.sem: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.GroupBy.std: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.GroupBy.var: Parameter "ddof" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.bfill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.corr: Parameter "method" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.ffill: Parameter "limit" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.filter: Parameter "dropna" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.idxmax: Parameter "axis" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.idxmin: Parameter "axis" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.rank: Parameter "method" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.rank: Parameter "na_option" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.shift: Parameter "periods" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.corrwith: Parameter "axis" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.corrwith: Parameter "method" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.boxplot: Parameter "subplots" description should start with a capital letter
pandas.core.groupby.DataFrameGroupBy.boxplot: Parameter "layout" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "precision" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "table_styles" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "uuid" description should start with a capital letter
pandas.io.formats.style.Styler: Parameter "caption" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "cond" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "value" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "other" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "cmap" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "low, high" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "text_color_threshold" description should start with a capital letter
pandas.io.formats.style.Styler.bar: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.use: Parameter "styles" description should start with a capital letter
pandas.api.extensions.ExtensionArray.argsort: Parameter "*args, **kwargs:" description should start with a capital letter
pandas.DataFrame.insert: Parameter "column" description should start with a capital letter
pandas.DataFrame.apply: Parameter "raw" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "func" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "args" description should start with a capital letter
pandas.DataFrame.pipe: Parameter "kwargs" description should start with a capital letter
pandas.DataFrame.corr: Parameter "method" description should start with a capital letter
pandas.DataFrame.corrwith: Parameter "axis" description should start with a capital letter
pandas.DataFrame.corrwith: Parameter "method" description should start with a capital letter
pandas.DataFrame.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.DataFrame.duplicated: Parameter "keep" description should start with a capital letter
pandas.DataFrame.idxmax: Parameter "axis" description should start with a capital letter
pandas.DataFrame.idxmin: Parameter "axis" description should start with a capital letter
pandas.DataFrame.unstack: Parameter "fill_value" description should start with a capital letter
pandas.DataFrame.plot: Parameter "kind" description should start with a capital letter
pandas.DataFrame.plot: Parameter "style" description should start with a capital letter
pandas.DataFrame.to_feather: Parameter "fname" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "path" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "append" description should start with a capital letter
pandas.DataFrame.to_msgpack: Parameter "compress" description should start with a capital letter
pandas.DataFrame.to_clipboard: Parameter "excel" description should start with a capital letter
```
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"default"... | closed | false | null | [] | null | 5 | 2019-08-28T11:31:07Z | 2020-06-05T17:26:58Z | 2020-06-05T17:26:43Z | NONE | null | I measured the creation of an empty dataframe with 3 similar arguments:
```python
import pandas as pd
cols = np.arange(100)
index = np.arange(1000)
%timeit pd.DataFrame(columns=cols, index=index)
# 100 loops, best of 3: 18.8 ms per loop
%timeit pd.DataFrame({}, columns=cols, index=index)
100 loops, best of 3: 18.7 ms per loop
%timeit pd.DataFrame(np.nan ,columns=cols, index=index)
1000 loops, best of 3: 434 µs per loop
z1 = pd.DataFrame(columns=cols,index=index) # dtype -> object
z2 = pd.DataFrame({},columns=cols,index=index) # dtype -> object
z3 = pd.DataFrame(np.nan,columns=cols,index=index) # dtype -> float
```
If I understand correctly the code in here:
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/core/frame.py#L399
the first 2 constructions are the same: the default value of data is an empty dictionary.
The output is a dataframe of type object.
The 3rd, with np.nan as initial value, is about 40 times faster, and the dtype is float, as expected.
So I would change:
1. the default value of data to np.nan. If one creates an empty dataframe with no specific dtype, then the best pandas can do is to return the cheapest and fastest dataframe that can contain "empty" values. A dataframe of type float filled with np.nan is so a good candidate.
2. the documentation to report the current behavior.
What is your opinion?
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} | 2 | 2019-08-28T11:43:09Z | 2020-02-27T23:07:26Z | 2020-02-27T23:07:26Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
pdf = pd.DataFrame({
"idx": pd.Categorical(["1"] * 4),
"value": [1, 2, 3, 4]
})
pdf = pdf.set_index("idx")
pdf
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>value</th>
</tr>
<tr>
<th>idx</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
</tr>
<tr>
<th>1</th>
<td>3</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
</tr>
</tbody>
</table>
</div>
```python
agg = pdf.groupby("idx").agg(np.sum)["value"]
agg
```
idx
1 10
Name: value, dtype: int64
```python
merged = pd.merge(pdf, agg, how="left", left_index=True, right_index=True)
merged
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-89-5347bee83336> in <module>
----> 1 merged = pd.merge(pdf, agg, how="left", left_index=True, right_index=True)
2 merged
/usr/local/lib/python3.7/site-packages/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
46 copy=copy, indicator=indicator,
47 validate=validate)
---> 48 return op.get_result()
49
50
/usr/local/lib/python3.7/site-packages/pandas/core/reshape/merge.py in get_result(self)
544 self.left, self.right)
545
--> 546 join_index, left_indexer, right_indexer = self._get_join_info()
547
548 ldata, rdata = self.left._data, self.right._data
/usr/local/lib/python3.7/site-packages/pandas/core/reshape/merge.py in _get_join_info(self)
742 join_index, left_indexer, right_indexer = \
743 left_ax.join(right_ax, how=self.how, return_indexers=True,
--> 744 sort=self.sort)
745 elif self.right_index and self.how == 'left':
746 join_index, left_indexer, right_indexer = \
/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in join(self, other, how, level, return_indexers, sort)
3291 if self.is_monotonic and other.is_monotonic:
3292 return self._join_monotonic(other, how=how,
-> 3293 return_indexers=return_indexers)
3294 else:
3295 return self._join_non_unique(other, how=how,
/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in _join_monotonic(self, other, how, return_indexers)
3583 else:
3584 if how == 'left':
-> 3585 join_index, lidx, ridx = self._left_indexer(sv, ov)
3586 elif how == 'right':
3587 join_index, ridx, lidx = self._left_indexer(ov, sv)
/usr/local/lib/python3.7/site-packages/pandas/core/indexes/base.py in _left_indexer(self, left, right)
217
218 def _left_indexer(self, left, right):
--> 219 return libjoin.left_join_indexer(left, right)
220
221 def _inner_indexer(self, left, right):
pandas/_libs/join.pyx in pandas._libs.join.__pyx_fused_cpdef()
TypeError: No matching signature found
#### Problem description
The problem is triggered in `libjoin.left_join_indexer(left, right)`, where both `left` and `right` have `dtype(int8)`, which raises the `TypeError: No matching signature found`.
Interestingly enough, if you change from
`pd.merge(pdf, agg, how="left", left_index=True, right_index=True)`
to
`pd.merge(pdf, agg, how="left", on="idx")`
everything works fine as demonstrated below.
#### Expected Output
```python
merged = pd.merge(pdf, agg, how="left", on="idx")
merged
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>value_x</th>
<th>value_y</th>
</tr>
<tr>
<th>idx</th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>1</th>
<td>1</td>
<td>10</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>10</td>
</tr>
<tr>
<th>1</th>
<td>3</td>
<td>10</td>
</tr>
<tr>
<th>1</th>
<td>4</td>
<td>10</td>
</tr>
</tbody>
</table>
</div>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.7.5
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 18 | 2019-08-28T12:32:31Z | 2021-07-11T06:18:12Z | 2021-07-11T06:18:12Z | NONE | null | Currently I am not able to use groupby on columns to produce groupings that contain combinations of same columns. Let's say I have a dataframe with columns A, B , C and D. I would like to create groups that contain [A,B], [A,C] and [B,D].
One obvious way to implement this would be to allow as argument a dictionary of lists, where the keys are output labels and the lists are the column labels to be grouped.
I am aware that currently grouping via dictionary works the opposite way, i.e. the key is the input label and the value is the output label, but at least to me this seems somewhat less useful than the reverse. | {
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a daraframe have valida shape number, but its index is a blank list, which start from 0,end at 0. it is so weird!!! | {
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} | 3 | 2019-08-28T13:13:59Z | 2019-09-04T17:07:17Z | 2019-09-04T17:07:17Z | NONE | null | #### Code Sample
Here I have a DataFrame with a DatetimeIndex for the columns:
```python
cols = pd.date_range('2019-8-01', '2019-08-07', freq='D')
data = np.ones((5, len(cols)))
df = pd.DataFrame(data, columns=cols)
df.rolling('1d', axis=1).sum()
```
The above gives 'ValueError: window must be an integer'
Here I have a DataFrame with a DatetimeIndex for the rows:
```python
df2 = df.transpose()
df2.rolling('2d', axis=0).sum()
```
However this works totally fine.
#### Problem description
I believe there may be a bug: If I have a DatetimeIndex in the columns, specifying an offset for df.rolling does not work but gives a value error (that the window must be an integer). It works however as expected if I transpose the dataframe.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28193 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28193/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28193/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28193/events | https://github.com/pandas-dev/pandas/issues/28193 | 486,384,784 | MDU6SXNzdWU0ODYzODQ3ODQ= | 28,193 | pd.to_datetime() does not respect exact format string ,when I use '%Y-%m-%d' | {
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] | closed | false | null | [] | null | 3 | 2019-08-28T13:30:41Z | 2019-12-17T06:15:04Z | 2019-12-17T06:15:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.to_datetime('xx 2019-8-1 xxx',format='%Y-%m-%d',exact=False)
```
#### Problem description
ValueError: time data xx 2019-8-1 xxx doesn't match format specified
#### Expected Output
Timestamp('2019-08-01 00:00:00')
####
Normal when executing the following code：
```python
pd.to_datetime('xxa2019-8-1axxx',format='a%Y-%m-%da',exact=False)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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"description": "Duplicate issue... | closed | false | null | [] | null | 10 | 2019-08-28T14:18:45Z | 2019-10-30T19:43:10Z | 2019-10-30T19:43:09Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(data = {
"x": [1, 1, 1],
"y": [np.nan, np.nan, np.nan],
"z": [1, 2, 3]
})
# Works on SeriesGroupBy
df.groupby(["x", "y"])["z"].quantile(0.5)
# Segfault on DataFrameGroupBy
df.groupby(["x", "y"])[["z"]].quantile(0.5)
```
#### Problem description
Hello all,
I just noticed that there is still an issue with the `quantile` function when used on a `DataFrameGroupBy` object with Pandas 0.25.1. When the groupby operation is done with an empty column, a segfault occurs. Above is a code sample to reproduce the bug. This issue didn't occur with Pandas 0.24.2.
#### Expected Output
Pandas 0.24.2 gives this output :
```
Empty DataFrame
Columns: []
Index: []
```
#### Output of ``pd.show_versions()``
My computer is running Ubuntu 18.04. Below are the installed packages (in a clean virtual env). The bug doesn't seem to be related to numpy as it also occured with version `1.16.4`.
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28195 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28195/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28195/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28195/events | https://github.com/pandas-dev/pandas/pull/28195 | 486,449,011 | MDExOlB1bGxSZXF1ZXN0MzExOTA0OTIz | 28,195 | TYPING: add skeleton stub for Timestamp | {
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} | 12 | 2019-08-28T15:19:45Z | 2021-01-12T18:20:27Z | 2019-08-30T15:21:59Z | MEMBER | null | xref #28133
This PR adds just the skeleton so that the attributes, methods and arguments available are known.
in this PR, the methods starting with an underscore have been omitted and the attributes typed with Any. This follows default stubgen output. https://mypy.readthedocs.io/en/latest/stubgen.html#automatic-stub-generation-stubgen
fix for `pandas\core\arrays\datetimes.py:275: error: Incompatible types in assignment (expression has type "Type[Timestamp]", base class "AttributesMixin" defined the type as "Type[DatetimeLikeScalar]")` arising from adding type information for Timestamp.
relevant diff for `mypy pandas --disallow-any-unimported`...
```
< pandas\_typing.py:23: error: Constraint 2 becomes "Any" due to an unfollowed import
7d5
< pandas\core\dtypes\dtypes.py:653: error: Type of variable becomes "Type[Any]" due to an unfollowed import
44,45d41
< pandas\core\arrays\_ranges.py:15: error: Argument 1 to "generate_regular_range" becomes "Any" due to an unfollowed import
< pandas\core\arrays\_ranges.py:15: error: Argument 2 to "generate_regular_range" becomes "Any" due to an unfollowed import
129c125
< pandas\io\formats\format.py:1550: error: Argument 1 to "_format_datetime64" becomes "Union[Any, Any]" due to an unfollowed import
---
> pandas\io\formats\format.py:1550: error: Argument 1 to "_format_datetime64" becomes "Union[Any, Timestamp]" due to an unfollowed import
131c127
< pandas\io\formats\format.py:1567: error: Argument 1 to "_format_datetime64_dateonly" becomes "Union[Any, Any]" due to an unfollowed import
---
> pandas\io\formats\format.py:1567: error: Argument 1 to "_format_datetime64_dateonly" becomes "Union[Any, Timestamp]" due to an unfollowed import
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28196 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28196/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28196/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28196/events | https://github.com/pandas-dev/pandas/issues/28196 | 486,509,884 | MDU6SXNzdWU0ODY1MDk4ODQ= | 28,196 | Fix GL01,GL02 errors in docstrings | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-28T17:23:12Z | 2019-08-28T20:34:57Z | 2019-08-28T20:34:57Z | CONTRIBUTOR | null | Since I can't see an open issue to fix GL01 and GL02 errors specifically, creating one. Referencing the methodology laid out in #27977.
Currently running `./scripts/validate_docstrings.py --errors=GL01,GL02` yields:
```
pandas.tseries.offsets.DateOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.DateOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessHour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessHour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessHour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessHour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.MonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.MonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BusinessMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BusinessMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.SemiMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.SemiMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Week.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Week.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.WeekOfMonth.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.WeekOfMonth.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.LastWeekOfMonth.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.LastWeekOfMonth.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BQuarterEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BQuarterEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BQuarterBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BQuarterBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.QuarterBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.QuarterBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearOffset.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearOffset.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BYearEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BYearEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BYearBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BYearBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.YearBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.YearBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.FY5253.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.FY5253.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.FY5253Quarter.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.FY5253Quarter.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Easter.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Easter.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Tick.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Tick.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Day.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Day.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Hour.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Hour.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Minute.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Minute.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Second.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Second.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Milli.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Milli.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Micro.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Micro.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.Nano.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.Nano.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.BMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.BMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CBMonthEnd.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CBMonthEnd.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CBMonthBegin.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CBMonthBegin.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.tseries.offsets.CDay.normalize: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.tseries.offsets.CDay.normalize: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Series.index: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Series.index: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.ctime: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.ctime: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.date: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.date: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.dst: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.dst: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.isocalendar: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.isocalendar: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.isoweekday: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.isoweekday: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.strftime: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.strftime: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.time: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.time: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.timestamp: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.timestamp: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.timetuple: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.timetuple: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.timetz: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.timetz: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.toordinal: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.toordinal: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.tzname: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.tzname: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.utcoffset: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.utcoffset: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.utctimetuple: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.utctimetuple: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timestamp.weekday: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timestamp.weekday: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timedelta.days: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timedelta.days: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timedelta.microseconds: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timedelta.microseconds: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Timedelta.seconds: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Timedelta.seconds: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Interval.left: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Interval.left: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Interval.length: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Interval.length: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.Interval.right: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.Interval.right: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.read_sql_query: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.io.stata.StataReader.data: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.plotting.lag_plot: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.plotting.parallel_coordinates: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.testing.assert_series_equal: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.testing.assert_index_equal: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.IntervalIndex.to_tuples: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.io.formats.style.Styler.env: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.io.formats.style.Styler.template: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.io.formats.style.Styler.loader: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.api.extensions.ExtensionArray._formatter: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.api.extensions.ExtensionArray._from_sequence_of_strings: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.DataFrame.index: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.DataFrame.index: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.DataFrame.columns: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.DataFrame.columns: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28197 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28197/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28197/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28197/events | https://github.com/pandas-dev/pandas/pull/28197 | 486,514,836 | MDExOlB1bGxSZXF1ZXN0MzExOTU4NTc4 | 28,197 | Solving GL01,GL02 in pandas.Interval and a few mentioned in the comments | {
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} | 1 | 2019-08-28T17:33:52Z | 2019-08-28T20:35:01Z | 2019-08-28T20:34:57Z | CONTRIBUTOR | null | - [1] closes #28196
- [0] tests added / passed
- [0] passes `black pandas`
- [0] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [0] whatsnew entry
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} | 1 | 2019-08-28T18:16:12Z | 2019-09-07T21:25:30Z | 2019-09-07T19:43:43Z | MEMBER | null | Lots of these to whittle down. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28199/events | https://github.com/pandas-dev/pandas/pull/28199 | 486,543,640 | MDExOlB1bGxSZXF1ZXN0MzExOTgyMjg5 | 28,199 | 0.24.x | {
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- [ ] passes `black pandas`
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- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28200/events | https://github.com/pandas-dev/pandas/pull/28200 | 486,552,348 | MDExOlB1bGxSZXF1ZXN0MzExOTg5NDY5 | 28,200 | Update frame.py | {
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/28201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28201/events | https://github.com/pandas-dev/pandas/pull/28201 | 486,556,893 | MDExOlB1bGxSZXF1ZXN0MzExOTkzMTk3 | 28,201 | 0.24.x | MyPy for Python2 Compatability - Patch "misplaced type annotation" | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-28T19:05:31Z | 2019-08-28T20:13:26Z | 2019-08-28T20:13:26Z | NONE | null | This PR fixes misplaced annotations error, after checking with **MyPy**.
**What was fixed?**
For Python 2 you need place mypy annotations `# type: ...` before docstrings. | {
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} | 3 | 2019-08-28T19:09:12Z | 2019-09-20T03:36:06Z | 2019-09-20T03:36:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Example A:
```python
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]})
>>> assert df.nunique().tolist() == [df[col].nunique() for col in df.columns]
# Both equal [2, 2, 2]
```
Example B:
```python
>>> df = pd.DataFrame(columns=['a', 'b', 'c'])
>>> df.nunique()
# Empty DataFrame
# Columns: [a, b, c]
# Index: []
>>> [df[col].nunique() for col in df.columns]
# [0, 0, 0]
```
#### Problem description
In Example A, when a DataFrame isn't empty, getting nunique is consistent between the DataFrame and Series approaches; however, when a DataFrame is empty (Example B), DataFrame.nunique returns itself, while the Series approach returns 0.
#### Expected Output
I would expect df.nunique to return 0 for each column, consistent with how a Series behaves. An empty object, by definition, has 0 unique elements in my mind.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 41.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
Thank you! | {
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} | 4 | 2019-08-28T21:25:23Z | 2019-09-10T18:52:02Z | 2019-08-30T17:06:50Z | NONE | null | While using **Jupyter notebooks**, I like to change the **color of header cells** in Pandas dataframes (all index cells, up and left) for more **contrast between the indexes and the data itself** (especially when I'm dealing with multiple indexes after using `.groupby()` like in the picture below) :
I used to achieve this using this CSS code :
```html
%%HTML
<style>
.dataframe th {
background: #3f577c ;
font-family: monospace ;
color: white ;
border: 3px solid white ;
text-align: left !important ; }
</style>
```
This worked because every index cell (in all indexes, up and left) used to be represented by a `<th> </th>` HTML tag.
However, after updating to Pandas version **`0.25.1`** from version **`0.21.x`**, the CSS stopped working because the HTML changed so only the upper index cells (column names) are wrapped in `<th> <th>` tags. The left index simply uses `<td> </td>` tags, just like the data cells (See image below. Note that both Genre and MediaTypeId are indexes):
Was this intentional? I tried using `first-child` like below but it only worked with dataframes having only 2 indexes (up and left). For the dataframes with more than one index on the left, only the first index is affected :
```html
%%HTML
<style>
.dataframe th, td:first-child {
background: #3f577c ;
font-family: monospace ;
color: white ;
border: 3px solid white ;
text-align: left !important ; }
</style>
```
Any ideas? | {
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} | 3 | 2019-08-28T22:06:33Z | 2019-09-02T22:54:10Z | 2019-09-02T21:23:19Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
| {
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https://api.github.com/repos/pandas-dev/pandas/issues/28206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28206/events | https://github.com/pandas-dev/pandas/issues/28206 | 486,648,076 | MDU6SXNzdWU0ODY2NDgwNzY= | 28,206 | EOFError on Gzipped CSV read from S3 | {
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} | [] | closed | false | null | [] | null | 4 | 2019-08-28T22:56:17Z | 2019-08-29T00:00:51Z | 2019-08-29T00:00:51Z | NONE | null | Not sure if this is a Pandas issue or s3fs issue:
```python
import pandas as pd
data = pd.read_csv("s3://bucketname/file.csv.gz")
```
Gives the following error:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/site-packages/pandas/io/parsers.py", line 685, in parser_f
return _read(filepath_or_buffer, kwds)
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/site-packages/pandas/io/parsers.py", line 463, in _read
data = parser.read(nrows)
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/site-packages/pandas/io/parsers.py", line 1154, in read
ret = self._engine.read(nrows)
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/site-packages/pandas/io/parsers.py", line 2059, in read
data = self._reader.read(nrows)
File "pandas/_libs/parsers.pyx", line 881, in pandas._libs.parsers.TextReader.read
File "pandas/_libs/parsers.pyx", line 896, in pandas._libs.parsers.TextReader._read_low_memory
File "pandas/_libs/parsers.pyx", line 950, in pandas._libs.parsers.TextReader._read_rows
File "pandas/_libs/parsers.pyx", line 937, in pandas._libs.parsers.TextReader._tokenize_rows
File "pandas/_libs/parsers.pyx", line 2124, in pandas._libs.parsers.raise_parser_error
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/_compression.py", line 68, in readinto
data = self.read(len(byte_view))
File "/home/ubuntu/miniconda3/envs/test/lib/python3.7/gzip.py", line 482, in read
raise EOFError("Compressed file ended before the "
EOFError: Compressed file ended before the end-of-stream marker was reached
```
### Setup
Installed Pandas and s3fs via pip:
```bash
pip install pandas s3fs
```
**Pandas version:** 0.25.1
**s3fs version:** 0.3.3 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28207 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28207/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28207/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28207/events | https://github.com/pandas-dev/pandas/issues/28207 | 486,658,656 | MDU6SXNzdWU0ODY2NTg2NTY= | 28,207 | Localizing dates to a timezone with dls after 2038 does not work (2038 Problem) | {
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] | closed | false | null | [] | null | 4 | 2019-08-28T23:34:09Z | 2019-08-30T17:39:01Z | 2019-08-30T17:39:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pandas version 0.24.2
dates = pd.date_range(start='1/1/2037 00:00:00', end='12/31/2038 23:00:00', freq='h')
test = pd.to_datetime(dates, infer_datetime_format=True)
test = test.tz_localize('Etc/GMT+8').tz_convert('America/Los_Angeles')
```
#### Problem description
[Current behavior correctly dls corrects years before 2038. It is necessary for forecasting applications (and other applications) to have dls corrections after 2038]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: 2.6.1
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28208 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28208/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28208/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28208/events | https://github.com/pandas-dev/pandas/issues/28208 | 486,658,779 | MDU6SXNzdWU0ODY2NTg3Nzk= | 28,208 | Requiring default index for to_feather() is unintuitive | {
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} | 6 | 2019-08-28T23:34:35Z | 2021-10-03T20:54:43Z | 2019-08-29T03:09:23Z | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'a': [1,2,3,4], 'b': [5,6,7,8]} )
df = df.drop(1)
df.to_feather("test.feather")
```
#### Problem description
I saw documentation in the code requiring default index at: https://github.com/pandas-dev/pandas/blob/794be8c7ab6897b7206f2c6ec60d22fea2e440a3/pandas/io/feather_format.py#L46-L52
However, I think it is unintuitive to require the user to have a default index before writing to feather (for example this is not a requirement to writing to csv). Why is this a requirement? What are your thoughts about reindexing being the default?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : 5.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 3.8.0
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.8.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 3.8.0
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-08-29T02:20:39Z | 2019-08-29T13:57:58Z | 2019-08-29T12:17:04Z | MEMBER | null | all occurrences of `'class.*:\n\n( )+"""'`
@datapythonista would it make sense to add this to the checks or docstring validation? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28210 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28210/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28210/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28210/events | https://github.com/pandas-dev/pandas/issues/28210 | 486,714,981 | MDU6SXNzdWU0ODY3MTQ5ODE= | 28,210 | cannot output csv with IntervalIndex | {
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} | 5 | 2019-08-29T03:26:31Z | 2020-11-18T08:51:39Z | 2019-08-30T14:32:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Using `pd.interval_range`:
```python
pd.DataFrame({'a': [1, 2, 3]}, index=pd.interval_range(0, 1, periods=3)).to_csv('hello.csv')
```
Using `pd.IntervalIndex.from_arrays`:
```python
pd.DataFrame({'a': [1, 2, 3]}, index=pd.IntervalIndex.from_arrays(np.array([0, 1, 2]), np.array([1, 2, 3]))).to_csv('hello.csv')
```
#### Problem description
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-6f913f6c0211> in <module>
----> 1 pd.DataFrame({'a': [1, 2, 3]}, index=pd.IntervalIndex.from_arrays(np.array([0, 1, 2]), np.array([1, 2, 3]))).to_csv('hello.csv')
~/Development/propensity-to-subscribe-modeling/.venv/lib/python3.7/site-packages/pandas/core/generic.py in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal)
3226 decimal=decimal,
3227 )
-> 3228 formatter.save()
3229
3230 if path_or_buf is None:
~/Development/propensity-to-subscribe-modeling/.venv/lib/python3.7/site-packages/pandas/io/formats/csvs.py in save(self)
200 self.writer = UnicodeWriter(f, **writer_kwargs)
201
--> 202 self._save()
203
204 finally:
~/Development/propensity-to-subscribe-modeling/.venv/lib/python3.7/site-packages/pandas/io/formats/csvs.py in _save(self)
322 break
323
--> 324 self._save_chunk(start_i, end_i)
325
326 def _save_chunk(self, start_i, end_i):
~/Development/propensity-to-subscribe-modeling/.venv/lib/python3.7/site-packages/pandas/io/formats/csvs.py in _save_chunk(self, start_i, end_i)
354 )
355
--> 356 libwriters.write_csv_rows(self.data, ix, self.nlevels, self.cols, self.writer)
TypeError: Argument 'data_index' has incorrect type (expected numpy.ndarray, got list)
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28211 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28211/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28211/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28211/events | https://github.com/pandas-dev/pandas/issues/28211 | 486,715,537 | MDU6SXNzdWU0ODY3MTU1Mzc= | 28,211 | 使用pivot_table传入values报错KeyError: 0.0 | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-29T03:28:38Z | 2019-08-29T10:00:00Z | 2019-08-29T10:00:00Z | NONE | null | #### Code Sample, a copy-pastable example if possible
#发现了一个问题：
位置pandas\core\reshape\pivot.py 58行
源代码：
values_passed = values is not None
if values_passed:
if is_list_like(values):
values_multi = True
values = list(values)
else:
values_multi = False
values = [values]
# GH14938 Make sure value labels are in data
for i in values:
if i not in data:
raise KeyError(i)
在经过测试：
import numpy as np
import pandas as pd
tmp_df = pd.DataFrame(np.zeros(shape=(5,5),dtype='int8'),columns=['A','B','C','D','E'])
print(tmp_df)
for i in tmp_df['A']:
print(i in tmp_df,end=' ')
print()
for i in tmp_df['A']:
print(i in tmp_df.values,end=' ')
A B C D E
0 0 0 0 0 0
1 0 0 0 0 0
2 0 0 0 0 0
3 0 0 0 0 0
4 0 0 0 0 0
False False False False False
True True True True True
希望可以得到修改
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https://api.github.com/repos/pandas-dev/pandas/issues/28212 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28212/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28212/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28212/events | https://github.com/pandas-dev/pandas/issues/28212 | 486,722,802 | MDU6SXNzdWU0ODY3MjI4MDI= | 28,212 | I think I submitted a pull request to this effect a few months ago, but it looks like you are still using the old donation link from Flipcause. The new donation link to salsa is here: https://numfocus.salsalabs.org/donate-to-pandas | {
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} | [] | closed | false | null | [] | null | 3 | 2019-08-29T03:57:47Z | 2019-08-30T17:12:44Z | 2019-08-30T17:12:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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