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} | 6 | "2021-07-07T05:14:12Z" | "2021-07-11T19:15:04Z" | "2021-07-11T19:15:04Z" | CONTRIBUTOR | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/development/contributing_environment.html#installing-a-c-compiler
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
> You will need Build Tools for Visual Studio 2017.
>
> Warning
>
> You DO NOT need to install Visual Studio 2019. You only need “Build Tools for Visual Studio 2019”
#### Suggested fix for documentation
Someone should update. This is confusing for me because I'm trying to set one up. | {
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} | 4 | "2021-07-07T07:32:47Z" | "2021-08-21T04:12:38Z" | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
What I have:
```
pd.Timestamp('2021-07-05 08:10:11')
pd.Timestamp('2021-07-07 12:13:14')
```
What I'm looking for:
```
[pd.Timestamp('2021-07-05 09:00:00'),
pd.Timestamp('2021-07-06 09:00:00'),
pd.Timestamp('2021-07-07 09:00:00')]
```
#### Describe the solution you'd like
It feels like I should be able to do this with some combination of of `pd.date_range` and `pd.offsets`, but I can't find the right combination.
Given that 9am is the standard meteorological time point, I'm hoping someone has a nice solution for this already!
If it's possible and graceful, this is just a documentation issue I'm happy to put a PR in for...
#### alternatives / additional context
This is way more fiddly that I expected, I've started a gist to collect current possible implementations along with test cases to verify they work:
https://gist.github.com/cjw296/73af04f7a4dcbc39f2ec01384bf5aa90
I've also thrown a question up on StackOverflow in case I'm just missing something obvious:
https://stackoverflow.com/questions/68267555/pd-date-range-for-9am-each-day-between-two-ps-timestamp-objects
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} | 9 | "2021-07-07T12:40:11Z" | "2021-08-07T00:02:51Z" | "2021-07-10T14:26:58Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample, a copy-pastable example
#### Pandas 1.2
```python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([0.5, np.nan])
>>> df.where(pd.notnull(df), None)
0
0 0.5
1 None
```
#### Pandas 1.3
```python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([0.5, np.nan])
>>> df.where(pd.notnull(df), None)
0
0 0.5
1 NaN
```
#### Problem description
Replacing NaN values with None (or any other Python object) should work as in previous Pandas versions.
#### Expected Output
```python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([0.5, np.nan])
>>> df.where(pd.notnull(df), None)
0
0 0.5
1 None
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Darwin
OS-release : 21.0.0
Version : Darwin Kernel Version 21.0.0: Sun Jun 20 18:43:49 PDT 2021; root:xnu-8011.0.0.121.4~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : es_ES.UTF-8
LOCALE : es_ES.UTF-8
pandas : 1.3.0
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.3.3
sqlalchemy : 1.4.20
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 7 | "2021-07-07T12:46:37Z" | "2021-08-30T16:51:05Z" | "2021-08-30T16:50:38Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
ser = pd.Series(range(0,100))
ser1 = pd.cut(ser, 10).value_counts().head(5)
ser2 = pd.cut(ser, 10).value_counts().tail(5)
pd.DataFrame({'1': ser1, '2': ser2})
```
#### Problem description
as of 1.3.0, this raises the following error. previously, this would run and produce the expected output (see below).
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-ad7564e1a27b> in <module>
3 ser1 = pd.cut(ser, 10).value_counts().head(5)
4 ser2 = pd.cut(ser, 10).value_counts().tail(5)
----> 5 pd.DataFrame({'1': ser1, '2': ser2})
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
612 elif isinstance(data, dict):
613 # GH#38939 de facto copy defaults to False only in non-dict cases
--> 614 mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
615 elif isinstance(data, ma.MaskedArray):
616 import numpy.ma.mrecords as mrecords
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/internals/construction.py in dict_to_mgr(data, index, columns, dtype, typ, copy)
460 # TODO: can we get rid of the dt64tz special case above?
461
--> 462 return arrays_to_mgr(
463 arrays, data_names, index, columns, dtype=dtype, typ=typ, consolidate=copy
464 )
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype, verify_integrity, typ, consolidate)
120
121 # don't force copy because getting jammed in an ndarray anyway
--> 122 arrays = _homogenize(arrays, index, dtype)
123
124 else:
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/internals/construction.py in _homogenize(data, index, dtype)
566 # Forces alignment. No need to copy data since we
567 # are putting it into an ndarray later
--> 568 val = val.reindex(index, copy=False)
569
570 val = val._values
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/series.py in reindex(self, index, **kwargs)
4577 )
4578 def reindex(self, index=None, **kwargs):
-> 4579 return super().reindex(index=index, **kwargs)
4580
4581 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/generic.py in reindex(self, *args, **kwargs)
4807
4808 # perform the reindex on the axes
-> 4809 return self._reindex_axes(
4810 axes, level, limit, tolerance, method, fill_value, copy
4811 ).__finalize__(self, method="reindex")
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
4823
4824 ax = self._get_axis(a)
-> 4825 new_index, indexer = ax.reindex(
4826 labels, level=level, limit=limit, tolerance=tolerance, method=method
4827 )
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/indexes/category.py in reindex(self, target, method, level, limit, tolerance)
422 # coerce to a regular index here!
423 result = Index(np.array(self), name=self.name)
--> 424 new_target, indexer, _ = result._reindex_non_unique(target)
425 else:
426
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/indexes/base.py in _reindex_non_unique(self, target)
3865 return self[:0], np.array([], dtype=np.intp), None
3866
-> 3867 indexer, missing = self.get_indexer_non_unique(target)
3868 check = indexer != -1
3869 new_labels = self.take(indexer[check])
~/workplace/ri/rime/python/rime/.venv/lib/python3.8/site-packages/pandas/core/indexes/interval.py in get_indexer_non_unique(self, target)
692 # because IntervalIndex does partial-int indexing
693 target = self._maybe_convert_i8(target)
--> 694 indexer, missing = self._engine.get_indexer_non_unique(target.values)
695
696 return ensure_platform_int(indexer), ensure_platform_int(missing)
pandas/_libs/intervaltree.pxi in pandas._libs.interval.IntervalTree.__pyx_fused_cpdef()
TypeError: No matching signature found
```
#### Expected Output
```
1 2
(-0.099, 9.9] 10.0 NaN
(9.9, 19.8] 10.0 NaN
(19.8, 29.7] 10.0 NaN
(29.7, 39.6] 10.0 NaN
(39.6, 49.5] 10.0 NaN
(49.5, 59.4] NaN 10.0
(59.4, 69.3] NaN 10.0
(69.3, 79.2] NaN 10.0
(79.2, 89.1] NaN 10.0
(89.1, 99.0] NaN 10.0
```
this expected output behavior is better imo because it is more consistent with how this would work for other types of categorical indices. for example:
```
ser1 = pd.Series([0,1,2,], index=pd.CategoricalIndex(['a', 'b', 'c'], categories = ['a', 'b', 'c', 'd', 'e']))
ser2 = pd.Series([2,4], index=pd.CategoricalIndex(['d', 'e'], categories = ['a', 'b', 'c', 'd', 'e']))
pd.DataFrame({'1': ser1, '2': ser2})
```
does NOT error. therefor it does not seem that the desired behavior is to error on non-overlapping categorical indices
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 57.0.0
Cython : 3.0a6
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 2021.06.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 2021.06.0
scipy : 1.6.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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] | open | false | null | [] | null | 0 | "2021-07-07T14:57:48Z" | "2021-08-21T04:15:19Z" | null | NONE | null | Hi,
Very often when manipulating pandas DataFrames I had a SettingWithCopyWarning and I did not understand why because the warning message was telling me to use the syntax .loc[row_indexer,col_indexer] = value , which I was already using !
The issue actually had nothing to do with the .loc syntax but with the fact that the dataframe I was trying to modify was created by applying a filter on another dataframe (something like : df1 = df0[df0.column_x == value_x]), therefore there was an ambiguity on whether the original dataframe should be modified or not. In my case I did not want to modify df0 so the clean way of creating df1 was df1 = df0[df0.column_x == value_x].copy(), which solved the issue and I did not get the warning anymore.
I think this a very common issue, this is why, in my opinion, it could be a nice idea to include in the warning message that if the issue is not coming from the .loc syntax, it is likely that it comes from the fact that the concerned dataframe is probably a filtered copy of another dataframe and that adding .copy() when defining this particular dataframe might solve the issue.
I hope my message was clear enough, otherwise I can try to explain it better if necessary.
Have a good day,
Wallerand | {
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} | 1 | "2021-07-07T15:07:11Z" | "2021-10-19T03:46:06Z" | "2021-07-08T13:09:36Z" | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42429 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42429/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42429/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42429/events | https://github.com/pandas-dev/pandas/issues/42429 | 939,038,433 | MDU6SXNzdWU5MzkwMzg0MzM= | 42,429 | BUG: .dt.isocalendar().week returns unexpected dtype (UInt32) | {
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] | closed | false | null | [] | null | 2 | "2021-07-07T15:58:16Z" | "2021-07-09T10:19:14Z" | "2021-07-09T10:19:14Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
data = {'date': pd.date_range('2021-07-01', freq='D', periods=7)}
df = pd.DataFrame(data)
weeknumbers = df.date.dt.isocalendar().week
weeknumbers
0 26
1 26
2 26
3 26
4 27
5 27
6 27
Name: week, dtype: UInt32
```
#### Problem description
This returns dtype UInt32, with `weeknumbers.values` being an "IntegerArray" from pandas.
I am not sure if this is intended, but I discovered this because I replaced the deprecated `dt.weekofyear` with `dt.isocalendar().week` and then had `weeknumbers.values.tolist()` at a later point in my code which results in an AttributeError.
#### Expected Output
I would have expected the dtype to be int64, or, more importanty, the the `.values` being a numpy array, so that `.values.tolist()` works as expected.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-59-generic
Version : #66-Ubuntu SMP Thu Jun 17 00:46:01 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 15 | "2021-07-07T16:39:42Z" | "2021-08-02T08:16:43Z" | "2021-07-31T01:01:48Z" | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
class LoggingStringArray(pd.arrays.StringArray):
"""Extend StringArray to log arguments to __getitem__"""
def __getitem__(self, item):
print(f"Argument to __getitem__ is '{item}'")
return super().__getitem__(item)
df = pd.DataFrame({"col1": LoggingStringArray(np.array(["hello", "world"], dtype=object))})
_ = df.iloc[:1]
```
Expected output:
```
Argument to __getitem__ is 'slice(None, 1, None)'
```
Actual output:
```
Argument to __getitem__ is '(Ellipsis, slice(None, 1, None))'
```
#### Problem description
If a DataFrame contains extension types, the block manager turns a 1D slice in the form `DataFrame.iloc[slice(x, y, z)]` into an N-dimensional slice over the ExtensionArray of the form `__getitem__((Ellipsis, slice(x, y, z))` . This problem breaks the `__getitem__()` method on one of our extension types in [Text Extensions for Pandas](https://github.com/CODAIT/text-extensions-for-pandas).
The `__getitem__` methods in Pandas' built-in extension types happens to work when fed this kind of n-dimensional slice
--- they just ignore the `Ellipsis` in the first position of the tuple. But I don't think they're supposed to accept that type of input.
The root cause is the new method `Block.getitem_block_index()`, added during some performance optimizations introduced in pandas-dev/pandas#40353:
```python
def getitem_block_index(self, slicer: slice) -> Block:
"""
Perform __getitem__-like specialized to slicing along index.
Assumes self.ndim == 2
"""
# error: Invalid index type "Tuple[ellipsis, slice]" for
# "Union[ndarray, ExtensionArray]"; expected type "Union[int, slice, ndarray]"
new_values = self.values[..., slicer] # type: ignore[index]
return type(self)(new_values, self._mgr_locs, ndim=self.ndim)
```
This method assumes that the contents of the block are 2-dimensional. The `ExtensionBlock` class inherits this method from `EABackedBlock`, which inherits it from `Block`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
```python
>>> from pandas import DataFrame
>>> df = DataFrame(dict(x=[1,2,3]))
>>> class MyList(list):
... ...
...
>>>
>>>
>>> key = MyList(['x'])
>>> key
['x']
>>>
>>> df[key]
Traceback (most recent call last):
File "<input>", line 1, in <module>
df[key]
File "/.../python3.7/site-packages/pandas/core/frame.py", line 3445, in __getitem__
if com.is_bool_indexer(key):
File "/...python3.7/site-packages/pandas/core/common.py", line 146, in is_bool_indexer
return len(key) > 0 and lib.is_bool_list(key)
TypeError: Argument 'obj' has incorrect type (expected list, got MyList)
```
This works in `v1.2.*`.
It is mentioned in the docstring of `is_bool_indexer` that
> with ``_is_boolean`` set are considered boolean indexers.
But I had no luck with that property being set with `MyList`.
Not sure if this is an intended change or not. I did find any documentation for this. | {
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} | 0 | "2021-07-07T17:07:16Z" | "2021-07-12T00:29:58Z" | "2021-07-12T00:29:58Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
from pandas import DataFrame
df = DataFrame(dict(x=[1,2,3]))
class MyList(list):
...
key = MyList(['x'])
print(df[key])
```
```
Traceback (most recent call last):
File "<input>", line 1, in <module>
df[key]
File "/.../python3.7/site-packages/pandas/core/frame.py", line 3445, in __getitem__
if com.is_bool_indexer(key):
File "/.../python3.7/site-packages/pandas/core/common.py", line 146, in is_bool_indexer
return len(key) > 0 and lib.is_bool_list(key)
TypeError: Argument 'obj' has incorrect type (expected list, got MyList)
```
#### Problem description
This works in `v1.2.*`.
This because:
https://github.com/pandas-dev/pandas/blob/f00ed8f47020034e752baf0250483053340971b0/pandas/core/frame.py#L3445-L3446
doesn't bypass list subclasses so that `MyList` objects are not treated as a collection of keys.
If we step in:
https://github.com/pandas-dev/pandas/blob/f00ed8f47020034e752baf0250483053340971b0/pandas/core/common.py#L144-L146
Compared to `v1.2.5`:
https://github.com/pandas-dev/pandas/blob/7c48ff4409c622c582c56a5702373f726de08e96/pandas/core/common.py#L119-L124
A possible solution would be:
```diff
elif isinstance(key, list):
# check if np.array(key).dtype would be bool
+ try:
- return len(key) > 0 and lib.is_bool_list(key)
+ return len(key) > 0 and lib.is_bool_list(key)
+ except TypeError:
+ return False
```
#### Expected Output
Print out the data frame `df`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #488-Microsoft Mon Sep 01 13:43:00 PST 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2020.4
dateutil : 2.8.1
pip : 19.3.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.3
sqlalchemy : 1.3.20
tables : None
tabulate : 0.8.6
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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See the next code (and `ascending` provided as a string, not boolean):
```python
>>> pandas.DataFrame({'col': [3, 1, 2]}).sort_values(by='col', ascending='False')
col
1 1
2 2
0 3
```
This should fail in the same way as in `Series`:
```python
>>> pandas.Series([3, 1, 2]).sort_values(ascending='False')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-f05e80474e4f> in <module>
----> 1 pandas.Series([3, 1, 2]).sort_values(ascending='False')
~/miniconda3/envs/ibis-dev/lib/python3.7/site-packages/pandas/core/series.py in sort_values(self, axis, ascending, inplace, kind, na_position, ignore_index, key)
3254
3255 if not is_bool(ascending):
-> 3256 raise ValueError("ascending must be boolean")
3257
3258 if na_position not in ["first", "last"]:
ValueError: ascending must be boolean
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42435 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42435/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42435/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42435/events | https://github.com/pandas-dev/pandas/pull/42435 | 939,146,880 | MDExOlB1bGxSZXF1ZXN0Njg1NDEzMjMx | 42,435 | BUG: .loc failing to drop first level | {
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} | 0 | "2021-07-07T18:21:01Z" | "2021-07-08T16:21:51Z" | "2021-07-08T12:58:05Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This does not fix any of the existing GH issues for dropping of levels. | {
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} | 7 | "2021-07-07T20:31:58Z" | "2021-10-16T19:04:53Z" | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
from pandas.api.extensions import ExtensionArray, ExtensionDtype
class StubDtype(ExtensionDtype):
"""Extension dtype whose elements are something that numpy.asarray()
will turn into an array (in this case a tuple)"""
def __init__(self):
pass
@property
def type(self):
return tuple
@property
def name(self) -> str:
return "StubDtype"
@classmethod
def construct_array_type(cls):
return StubExtensionArray()
class StubExtensionArray(ExtensionArray):
"""Just enough of an extension array to run the four lines of code
that follow."""
@property
def dtype(self):
return StubDtype()
def copy(self):
return StubExtensionArray()
def __len__(self):
return 5
def __getitem__(self, key):
# Every position in the array has the tuple (1, 2, 3)
return (1, 2, 3)
data = StubExtensionArray()
series1 = pd.Series(data, name="data")
series2 = pd.Series(index=series1.index, dtype=object, name="data")
series2.loc[series1.index] = data
```
Output:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-168-cfd127e7fa55> in <module>
41 series1 = pd.Series(data, name="data")
42 series2 = pd.Series(index=series1.index, dtype=object, name="data")
---> 43 series2.loc[series1.index] = data
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
721
722 iloc = self if self.name == "iloc" else self.obj.iloc
--> 723 iloc._setitem_with_indexer(indexer, value, self.name)
724
725 def _validate_key(self, key, axis: int):
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value, name)
1730 self._setitem_with_indexer_split_path(indexer, value, name)
1731 else:
-> 1732 self._setitem_single_block(indexer, value, name)
1733
1734 def _setitem_with_indexer_split_path(self, indexer, value, name: str):
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/indexing.py in _setitem_single_block(self, indexer, value, name)
1966
1967 # actually do the set
-> 1968 self.obj._mgr = self.obj._mgr.setitem(indexer=indexer, value=value)
1969 self.obj._maybe_update_cacher(clear=True)
1970
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/internals/managers.py in setitem(self, indexer, value)
353
354 def setitem(self: T, indexer, value) -> T:
--> 355 return self.apply("setitem", indexer=indexer, value=value)
356
357 def putmask(self, mask, new, align: bool = True):
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
325 applied = b.apply(f, **kwargs)
326 else:
--> 327 applied = getattr(b, f)(**kwargs)
328 except (TypeError, NotImplementedError):
329 if not ignore_failures:
~/opt/miniconda3/envs/pd/lib/python3.7/site-packages/pandas/core/internals/blocks.py in setitem(self, indexer, value)
965 values[indexer] = value.to_numpy(value.dtype.numpy_dtype)
966 else:
--> 967 values[indexer] = np.asarray(value)
968
969 # if we are an exact match (ex-broadcasting),
ValueError: shape mismatch: value array of shape (5,3) could not be broadcast to indexing result of shape (5,)
```
#### Problem description
If the user creates a Series of dtype `object` and attempts to set the value of that Series with an extension array, the block manager will first pass the extension array through `np.asarray()` and then assign the block's values to the ndarray returned by `np.asarray()` (See code [here](https://github.com/pandas-dev/pandas/blob/0a9cfcf8da8d64aeca2574aa5d7caa9dcd3f1779/pandas/core/internals/blocks.py#L967)).
This logic assumes that `np.asarray()` will always return a 1D array. However, `np.asarray()` is not guaranteed to return a 1D array; if the items of the argument to `np.asarray()` are array-like, `np.asarray()` will iterate over them and generate an array with 2 or more dimensions. This 2- or-more-dimensional array can't be assigned to the series, and Pandas throws the error "ValueError: shape mismatch...".
This problem is affecting the TensorArray extension type in [Text Extensions for Pandas](https://github.com/CODAIT/text-extensions-for-pandas), because the elements of a TensorArray are tensors. The example code above shows a simpler case where the items of the extension array are Python tuples. In general, any item type that `np.asarray()` converts to an array of one or more dimensions will have this problem.
#### Expected Output
The above code should fill `series2` with the individual objects at each of the positions in the extension array. In the case of the example code above, that means that each element of `series2` should contain the Python tuple `(1, 2, 3)`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 2 | "2021-07-08T08:50:58Z" | "2021-07-28T01:54:46Z" | "2021-07-28T01:54:46Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
####
```python
import pandas as pd
df = pd.DataFrame([1, 2, 3, 4, 5, 6, 7, 8])
df.groupby([1,2,1,2,1,2,1,2]).rolling(2, closed="left").var(0)
Output:
0
1 0 NaN
2 NaN
4 1.0
6 1.0
2 1 NaN
3 NaN
5 NaN
7 NaN
```
#### Problem description
It seems that the rolling variance is only calculated on the first group.
#### Expected Output
```python
0
1 0 NaN
2 NaN
4 1.0
6 1.0
2 1 NaN
3 NaN
5 1.0
7 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.0
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : 0.18.2
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-07-08T09:18:04Z" | "2021-07-11T16:10:57Z" | "2021-07-09T15:47:46Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
pd.DataFrame([np.random.randn(10)]).rolling(window=5,axis=1).mean().isna()
```
#### Problem description
The NaN cannot be detected by pandas, it is just a toy example above.
The result was:
```Python
0 1 2 3 4 5 6 7 8 9
0 False False False False False False False False False False
```
#### Expected Output
I believe that the desired result should be:
```Python
0 1 2 3 4 5 6 7 8 9
0 True True True True False False False False False False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:31 PDT 2021; root:xnu-7195.121.3~9/RELEASE_ARM64_T8101
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0
numpy : 1.18.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1
setuptools : 49.6.0.post20210108
Cython : 0.29.23
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 7 | "2021-07-08T13:55:35Z" | "2021-07-15T04:20:24Z" | "2021-07-15T04:20:24Z" | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas. 1.2.2
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
import pandas as pd
df= pd.DataFrame({
('A', 'int16'): pd.Series([1, 2, 3, 4], dtype='int16'),
('A', 'int32'): pd.Series([132, 232, 332, 432], dtype='int32'),
('B', 'float32'): pd.Series([1.01, 1.02, 1.03, 1.04], dtype='float32'),
('B', 'int16'): pd.Series([21, 22, 23, 24], dtype='int16')})
print(df)
df.to_csv('test_df.csv')
print(df.dtypes)
# full column name tuples with level 0/1 labels don't work
df_new= pd.read_csv(
'test_df.csv',
header=list(range(2)),
dtype = {
('A', 'int16'): 'int16',
('A', 'int32'): 'int32'
})
print(df_new.dtypes)
See my SO article for more detailed infos:
[https://stackoverflow.com/questions/54699527/dtype-is-ignored-when-using-multilevel-columns](url)
#### Problem description
Although the data types where passed in read_csv, they are not applied. dtype in read_csv only seems to work for column names that contain only one level. For Multilevel columns it generally does not seem to work.
#### Expected Output
Unnamed: 0_level_0 Unnamed: 0_level_1 int64
A int16 int16
int32 int32
B float32 float64
int16 int64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-77-generic
Version : #86-Ubuntu SMP Thu Jun 17 02:35:03 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.2
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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README.md file
#### Documentation problem
There are some unwanted commas and colons with grammatical error
#### Suggested fix for documentation
The fix is easy just have to grammatically correct the README file.
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} | 4 | "2021-07-08T14:33:11Z" | "2021-07-28T03:41:52Z" | "2021-07-28T01:01:42Z" | CONTRIBUTOR | null | - [x] closes #42185
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Creating a new PR. I had messed up the earlier one by mistake. | {
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} | 9 | "2021-07-08T18:16:08Z" | "2021-07-10T14:38:05Z" | "2021-07-09T13:09:08Z" | MEMBER | null | - [x] closes #42380
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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The issue in transpose was a bug that was introduced in 1.0.0, but induced a regression in 1.3.0 due to its use in `DataFrame.agg`. | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
idx = pd.IndexSlice
df = pd.DataFrame({'x':[1,2,3],
'y':['a','b','c'],
'z':['a*','b*','c*']
}).set_index(['y','z'])
new_y_order = ['c', 'b', 'a']
new_df = df.loc[idx[new_y_order,:],:].unstack('z') # <- this produces the unexpected behaviour.
print(new_df)
# x
# z a* b* c*
# y
# a 1.0 NaN NaN
# b NaN 2.0 NaN
# c NaN NaN 3.0
```
#### Problem description
Changing the order of `df`'s index using `loc` and calling `unstack` on the 2º index level, results in a dataframe whose index order unexpectedly remains the same as the initial index. However, the new index' order should follow `new_y_order`.
Additional notes:
- This behaviour happens only when all index elements are preserved when changing their order. In other words, If `new_y_order` contains fewer elements than `y`, then calling unstack would behave as expected.
- This unexpected behaviour happens even if we store the `.loc` reordering using `.copy()`, before applying `.unstack`. The DataFrame somehow remembers the initial index order it had.
#### Expected Output
```python
print(new_df)
# x
# z c* b* a*
# y
# c 3.0 NaN NaN
# b NaN 2.0 NaN
# a NaN NaN 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 20.2.0
Version : Darwin Kernel Version 20.2.0: Wed Dec 2 20:39:59 PST 2020; root:xnu-7195.60.75~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.3.0
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | "2021-07-08T18:54:12Z" | "2021-07-13T20:41:30Z" | "2021-07-09T13:58:43Z" | MEMBER | null | - [x] closes #37826
IIUC, all the 3.9 builds(except numpydev) should have all dependencies.
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} | 6 | "2021-07-09T00:33:20Z" | "2021-08-04T04:23:26Z" | "2021-08-04T04:23:26Z" | CONTRIBUTOR | null | - [X ] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
kk = pd.DataFrame(np.random.rand(20, 3))
kk = kk.astype(np.float32)
print(kk.ewm(alpha=0.5, axis=1).mean().shape)
# (20, 0)
kk = kk.astype(np.float64)
print(kk.ewm(alpha=0.5, axis=1).mean().shape)
# (20, 3)
```
#### Problem description
This should not depend on dtypes.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
commit : 7c48ff4409c622c582c56a5702373f726de08e96
python : 3.7.10.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.5
numpy : 1.17.4
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.23.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.53.1
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
1.
```
In [2]: pd.Index(['A', 'B', 'C', 'D'], dtype=pd.StringDtype()).dtype
Out[2]: dtype('O')
In [3]: pd.Index([1, 2, 3, 4], dtype=int).dtype
Out[3]: dtype('int64')
```
2.
<details>
<summary>Output pd.show_versions()</summary>
```
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 0, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.3.0
numpy : 1.19.5
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : 3.5.4
blosc : None
feather : None
xlsxwriter : 1.3.9
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.05.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.3
tables : None
tabulate : 0.8.9
xarray : 0.16.2
xlrd : None
xlwt : None
numba : 0.53.1
```
</details>
3.
I don't think the dtype should change here. Seems to be okay for other dtypes apart from strings. | {
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} | 4 | "2021-07-09T06:54:18Z" | "2021-07-26T22:29:04Z" | "2021-07-26T22:29:04Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
from numpy import empty, array
from pandas import DataFrame
structured_dtype = [('prop', int)]
# Does NOT work any more
result = empty((0, len(structured_dtype)))
structured_array = array(result, dtype=structured_dtype)
DataFrame.from_records(structured_array)
```
Triggers: `ValueError: If using all scalar values, you must pass an index`.
#### Problem description
This previously worked on Pandas version 1.2.5.
#### Expected Output
No exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-77-generic
Version : #86-Ubuntu SMP Thu Jun 17 02:35:03 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 57.0.0
Cython : None
pytest : 6.2.4
hypothesis : 6.14.0
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 1 | "2021-07-09T09:52:38Z" | "2021-07-12T21:17:34Z" | "2021-07-12T21:17:34Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
records = {"col1": [1, 2], "col2": [3, 4]}
df = pd.DataFrame(data=records)
df.index = range(1, 3) # if this line is commented out it works as expected
xml = df.to_xml(index=False)
print(xml)
```
#### Problem description
This code snipped should not raise a `KeyError`. The index should be ignored regardless of its content.
Traceback:
```
Traceback (most recent call last):
File "/Users/xxx/xml.py", line 7, in <module>
xml = df.to_xml(index=False)
File "/Users/xxx/pandas/core/frame.py", line 2967, in to_xml
xml_formatter = TreeBuilder(
File "/Users/xxx/pandas/io/formats/xml.py", line 458, in __init__
self.handle_indexes()
File "/Users/xxx/pandas/io/formats/xml.py", line 199, in handle_indexes
x for x in self.frame_dicts[0].keys() if x not in self.orig_cols
KeyError: 0
```
#### Expected Output
```xml
<?xml version='1.0' encoding='utf-8'?>
<data>
<row>
<col1>1</col1>
<col2>3</col2>
</row>
<row>
<col1>2</col1>
<col2>4</col2>
</row>
</data>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : None
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.06.1
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.4.15
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
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} | 8 | "2021-07-09T12:51:27Z" | "2021-07-28T01:54:50Z" | "2021-07-28T01:54:46Z" | CONTRIBUTOR | null | - [ ] closes #42442
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I have some code that I used to extract the index columns as data frame, which worked in pandas 1.2.4 but not in 1.3.0. I have read what's new (https://pandas.pydata.org/pandas-docs/version/1.3.0/whatsnew/v1.3.0.html). To my best knowledge, this seems to be an undocumented change. I wonder if I can get some advice on how to make the code work for both 1.2 and 1.3 and potentially 1.4.
#### Code Sample, a copy-pastable example
```python
# To extract the index columns
col = df.reset_index()[df.index.names[:]]
```
#### Problem description
Say if we have a data frame.
```python
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index('col1')
>>> df
col2
col1
1 3
2 4
```
#### Expected Output
For pandas == 1.2.4,
```python
>>> df.reset_index()[df.index.names[:]]
col1
0 1
1 2
```
For pandas >= 1.3.0,
```python
>>> df.reset_index()[df.index.names[:]]
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "~/GitHub/pandas/pandas/core/frame.py", line 3444, in __getitem__
if com.is_bool_indexer(key):
File "~/GitHub/pandas/pandas/core/common.py", line 147, in is_bool_indexer
return len(key) > 0 and lib.is_bool_list(key)
TypeError: Argument 'obj' has incorrect type (expected list, got FrozenList)
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https://api.github.com/repos/pandas-dev/pandas/issues/42462 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42462/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42462/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42462/events | https://github.com/pandas-dev/pandas/issues/42462 | 940,847,448 | MDU6SXNzdWU5NDA4NDc0NDg= | 42,462 | read_excel() modifies provided types dict when accessing file with duplicate column | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
_test.xlsx :_
| a | a | b | c |
|---|---|---|---|
| 1 | 1 | b1| c1|
| 2 | 2 | b2| c2|
| 3 | 3 | b3| c3|
```
import pandas as pd
types_dict = {'a': str,
'b': str,
'c': str,
}
if __name__ == "__main__":
df = pd.read_excel('./test.xlsx', dtype=type_dict)
print(list(type_dict.keys()))
```
```
>> ['a', 'b', 'c', 'a.1']
```
**Bug/Issue description:**
When using dtype loading a .xlsx-file with a duplicate column into a dataframe modifies the provided types_dict / adds entries for duplicate columns.
It seems to me like the modification of the types_dict is an unwanted side effect. | {
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} | 2 | "2021-07-09T15:30:48Z" | "2021-07-28T12:37:39Z" | "2021-07-28T12:37:39Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> import numpy as np
>>> a = [[1], [2]]
>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)})
ValueError: If using all scalar values, you must pass an index
```
#### Problem description
Hey all! Thanks for the library, I use pandas on a daily basis.
When attempting to create a DataFrame with a 2-dimensional numpy array as input, a misleading `ValueError: If using all scalar values, you must pass an index` is raised instead of raising an expected `ValueError: Data must be 1-dimensional`.
This behaviour changes slightly if there is more than one array:
```python
>>> import pandas as pd
>>> import numpy as np
>>> a = [[1], [2]]
>>> pd.DataFrame({'a': a, 'b': a}) # No error, both nested lists pass validation and index is extracted
a b
0 [1] [1]
1 [2] [2]
>>> pd.DataFrame({'a': np.array(a), 'b': a}) # Expected behaviour, the nested list passes the check at pd.core.internals.construction._extract_index
ValueError: Data must be 1-dimensional
>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)})
ValueError: If using all scalar values, you must pass an index
```
I suspect this may have something to do with the check at https://github.com/pandas-dev/pandas/blob/386bad4c900ed09c1b664a341e3081851694ddbc/pandas/core/internals/construction.py#L620
not being exhaustive and/or that the shape validation only happens after this index creation step.
#### Expected Output
```python
>>> pd.DataFrame({'a': np.array(a), 'b': np.array(a)})
ValueError: Data must be 1-dimensional
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.104+
Version : #1 SMP Sat Jun 5 09:50:34 PDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.19.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 57.0.0
Cython : 0.29.23
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.3
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.20
tables : 3.4.4
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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} | 4 | "2021-07-09T15:45:30Z" | "2021-07-30T12:34:46Z" | "2021-07-12T21:17:34Z" | CONTRIBUTOR | null | Fixes #42458
It was assumed that the index contains the element `0`. This led to a
defect when the index of the input Dataframe has an offset, which is a
common use case when streaming Dataframes via generators.
This fix consists of not relying on accessing the `0` element of
`frame_dicts`.
- [x] closes #42458
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 2 | "2021-07-09T16:41:54Z" | "2021-07-16T04:48:57Z" | "2021-07-16T00:23:06Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
These are minimal examples which throw an exception. The same problem exist when you use `highlight_min` instead of `highlight_max`.
```python
import pandas as pd
# throws a TypeError("'>=' not supported between instances of 'int' and 'str'")
# pd.DataFrame({'col_0': [0, 'a']}).style.highlight_max().render()
# throws a TypeError("'>=' not supported between instances of 'Timestamp' and 'int'")
# pd.DataFrame({'col_0': [pd.to_datetime('1/1/2000'), 0]}).style.highlight_max().render()
# throws a ValueError('The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()')
pd.DataFrame({'col_0': [pd.period_range(start='2000-01-01', end='2000-01-02')]}).style.highlight_max().render()
```
#### Problem description
All examples worked in pandas 1.2.5 without throwing an exception.
#### Expected Output
The code doesn't throw an exception.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-77-generic
Version : #86-Ubuntu SMP Thu Jun 17 02:35:03 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-07-09T17:08:13Z" | "2021-07-19T16:51:58Z" | "2021-07-12T13:06:01Z" | MEMBER | null | - [ ] closes #42076
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
I haven't tested that this skips the cancel on master branch, only that it cancels already running workflows on PRs(e.g. https://github.com/lithomas1/pandas/pull/3).
So, be ready to revert if needed.
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- [x] closes #42433
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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>>> pd.read_json("""{"a": ["01", "02", "03"]}""")
a
0 1
1 2
2 3
```
These are well defined by JSON to be alphanumeric strings and not integers, so its a bit unfortunate that these get cast.
Expected Output:
```python
>>> pd.read_json("""{"a": ["01", "02", "03"]}""")
a
0 01
1 02
2 03
``` | {
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} | 1 | "2021-07-09T21:39:23Z" | "2021-07-12T08:58:54Z" | "2021-07-12T01:30:00Z" | CONTRIBUTOR | null | `Styler.bar` tests are refactored to avoid code duplication and enhance the testing process to cover a wider array of scenarios with simpler code.
Summary of removed tests and their replacement coverage:
Original Test | Replaced By | Parameters |
--- | --- | --- |
test_bar_align_left | test_align_positive_cases <br> test_numerics <br> test_colors_mixed | align=left |
test_bar_align_left_0points | test_align_axis | axis=[index,columns] <br> align=left |
test_bar_align_mid_pos_and_neg | test_align_mixed_cases <br> test_colors_mixed | align=mid |
test_bar_align_mid_all_pos | test_align_positive_cases | align=mid |
test_bar_align_mid_all_neg | test_align_negative_cases | align=mid |
test_bar_align_zero_pos_and_neg | test_align_mixed_cases <br> test_colors_mixed | align=zero |
test_bar_align_left_axis_none | test_align_axis | align=left <br> axis=None |
test_bar_align_zero_axis_none | test_align_axis | align=zero <br> axis=None |
test_bar_align_mid_axis_none | test_align_axis | align=mid <br> axis=None | | {
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} | 6 | "2021-07-09T22:12:40Z" | "2021-07-16T09:28:11Z" | "2021-07-14T18:19:05Z" | MEMBER | null | - [x] closes #39844
- [x] closes #42405
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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Didn't look like there was a consensus reached on NA propagation in #39844, this should maintain the behavior from 1.2.x of not propagating and just fix the regression | {
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`test_1d_object_array_does_not_copy` DIDN'T fail a little over a month ago (when I'd started writing the PR), so I'll be bisecting back to see when it happened.
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} | 5 | "2021-07-10T01:04:35Z" | "2021-07-14T00:36:35Z" | "2021-07-13T21:18:24Z" | MEMBER | null | - [x] closes #42396
- [x] closes #38592
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This just lets `numpy` stick with the default of `order="C"` for both `ravel` and `reshape` so no reordering occurs.
Benchmarks:
```
before after ratio
[87d78559] [0c9d5496]
<master> <regr_astype>
- 9.34±0.7ms 4.95±0.9ms 0.53 stat_ops.FrameOps.time_op('kurt', 'int', 1)
- 5.08±0.8ms 2.63±0.1ms 0.52 stat_ops.FrameOps.time_op('skew', 'Int64', 0)
- 4.46±0.2ms 2.25±0.3ms 0.50 stat_ops.FrameOps.time_op('std', 'int', 1)
- 5.76±0.2ms 2.73±0.6ms 0.47 stat_ops.FrameOps.time_op('var', 'int', 1)
- 11.5±0.7ms 5.09±0.3ms 0.44 stat_ops.FrameOps.time_op('sem', 'int', 0)
- 1.36±0.2ms 600±50μs 0.44 stat_ops.FrameOps.time_op('prod', 'int', 0)
- 17.5±5ms 7.60±0.7ms 0.43 stat_ops.FrameOps.time_op('skew', 'int', 1)
- 8.64±0.7ms 3.37±0.4ms 0.39 stat_ops.FrameOps.time_op('kurt', 'int', 0)
- 9.03±0.6ms 3.21±0.2ms 0.36 stat_ops.FrameOps.time_op('skew', 'int', 0)
- 9.49±0.6ms 3.27±0.4ms 0.34 stat_ops.FrameOps.time_op('mad', 'int', 0)
- 1.31±0.08ms 420±20μs 0.32 stat_ops.FrameOps.time_op('sum', 'int', 1)
- 5.87±0.5ms 1.87±0.5ms 0.32 stat_ops.FrameOps.time_op('var', 'int', 0)
- 1.49±0.09ms 467±20μs 0.31 stat_ops.FrameOps.time_op('sum', 'int', 0)
- 4.44±0.3ms 1.39±0.1ms 0.31 stat_ops.FrameOps.time_op('std', 'int', 0)
- 1.90±0.06ms 569±70μs 0.30 stat_ops.FrameOps.time_op('mean', 'int', 0)
- 1.22±0.1ms 346±4μs 0.28 stat_ops.FrameOps.time_op('prod', 'int', 1)
- 1.86±0.1ms 519±60μs 0.28 stat_ops.FrameOps.time_op('mean', 'int', 1)
```
Will look to come up with a benchmark hitting this regression more squarely (which would help test if a more clever contiguity approach than always defaulting to "C" can improve performance).
EDIT: after regression is fixed, might be worth looking into reinstating something like the original approach. Some timings show performance benefit we lose here:
```
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: data = np.ones((2000, 2000), order="F")
In [4]: df = pd.DataFrame(data)
In [5]: %timeit df.astype(np.int32)
5.63 ms ± 114 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # this pr
5.7 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # master
In [6]: data = np.ones((2000, 2000), order="C")
In [7]: df = pd.DataFrame(data)
In [8]: %timeit df.astype(np.int32)
26.8 ms ± 313 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # this pr
5.63 ms ± 77.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # master
```
EDIT: This slowdown comes down to the ravel copying because of contiguity that doesn't match. Let me know if it's better to try to fix the slowdown shown above in this pr or to leave as a followup for 1.4 | {
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Original motivation was to get rid of _get_partial_string_timestamp_match_key. | {
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} | 3 | "2021-07-10T06:38:11Z" | "2021-07-23T01:02:26Z" | "2021-07-23T01:02:26Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Minimal sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'a': np.random.randint(0, 100, size=55), 'b': np.random.randint(0, 100, size=55)})
# Assign > 100 new columns to the dataframe
for i in range(0, 100):
df.loc[:, f'n_{i}'] = np.random.randint(0, 100, size=55)
# Alternative assignment - triggers Performancewarnings here already.
# df[f'n_{i}'] = np.random.randint(0, 100, size=55)
df1 = df.copy()
# Triggers performance warning again
df1['c'] = np.random.randint(0, 100, size=55)
# Visualize blocks
print(df._data.nblocks)
print(df1._data.nblocks)
```
#### Problem description
Since pandas 1.3.0, the above minimal sample code produces the output of a Performance warning.
While i think i understand the warning - i don't understand how to mitigate it (the docs don't contain help i could find for this - and the proposed solution (`copy()` does not seem to work.
```
PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider using pd.concat instead. To get a de-fragmented frame, use `newframe = frame.copy()`
```
While this for sure isn't an ideal scenario (assignment of single columns one after the other), i also don't see how this can be changed in our usecase.
The proposed `df.copy()` does not mitigate the warning - and the block count remains the same.
Based on my understanding, using `df.loc[:, 'colname'] = ` is the recommended way to assign new columns.
This does create a new block for every insert - and `df.copy()` (which is proposed in the error) does not consolidate the blocks into 1 block - which means the error can't really be mitigated.
Strangely enough - the behaviour of `df['colname] =` and `df.loc[:, 'colname'] = ` is not identical - with the first triggering the PerformanceWarning - and the 2nd not triggering the warning (although the problem is still there in the background).
So this leaves me with a few questions
* How should the above scenario correctly handle inserts to keep performance and avoid this error?
* how can the dataframe be effectively consolidated (the proposed `frame.copy()` in the error does not do that)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.12.11-arch1-1
Version : #1 SMP PREEMPT Wed, 16 Jun 2021 15:25:28 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.3
setuptools : 57.0.0
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : 1.10.4
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : 1.4.20
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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] | closed | false | null | [] | null | 6 | "2021-07-10T07:41:43Z" | "2021-07-12T20:27:08Z" | "2021-07-11T18:33:07Z" | CONTRIBUTOR | null | - [x] closes #26247
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Inserted test to verify that np.ravel can be used on DataFrames with specified column names. | {
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] | closed | false | null | [] | null | 7 | "2021-07-10T17:46:07Z" | "2021-10-17T19:36:17Z" | "2021-10-17T19:15:45Z" | NONE | null | #### Is your feature request related to a problem?
the [csv_reader](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) has many features, but getting the settings right is quite a pain.
I look after a corporate data analysis platform and found that the largest waste of time over a due course of 76000 engineering hours came from figuring out how to import the data.
To back this with some numbers I find from 1.5Tb of zipped csv data (billions of rows of data):
- Reattempts for engineers to upload the same file with different encoding: 4.1x (Poisson distributed).
- CSV imports that are accepted by the engineer: 95/105
- Correct inference of datetime: 2 / 396 cols with name date, time or datetime (in 17 languages)
***Reading CSV files has been treated as an encoding problem. It evidently isn't. It's a pattern recognition problem.***
Existing csv-readers and file sniffers fail because they:
- don't detect encoding correctly or work on bad encoding.
- typically only read the first line after the headers.
- don't handle text escapes during column detection.
- `dateutils` parser only evaluates a single value and will always say that 1.2.2012 is mm.dd.yyyy despite that the next value might be 29.2.2012 (I've got plenty of tests with that problem).
Here's an ugly example:
`Birthdate, (Family\nnames), Father, Mother, Child, known for\n1879-4-14, Einstein, Hermann, Pauline, Albert,"\nGeneral relativity,\nSpecial relativity,\nPhotoelectric effect"`
That should be (including some text escape):
|Birthdate| (Family<br>names)| Father | Mother | Child | known for |
|---|---|---|---|---|---|
|1879-4-14| Einstein| Hermann| Pauline| Albert| General relativity,<br>Special relativity,<br>Photoelectric effect|
#### Describe the solution you'd like
I would like to add a non-breaking package to pandas, so that to pandas csv_reader can receive the keyword "analyse" and ignore the default keywords.
When `pandas.csv_reader(somepath, analyse=True)` is called, the keyword will trigger the usage of a function in the top of the file_reader that will:
- Detect the encoding using one of the ~300 python accepted [encodings](https://github.com/Ousret/charset_normalizer/blob/51af624b59a7f1a1aaa36f9ae71bee4364e39409/charset_normalizer/constant.py#L310)
- analyse the file,
- return the correct formats to pandas as **kwargs
If the function is external to pandas, it could be used as:
```
import pandas as pd
import csv_analyze
df = pandas.csv_reader(somepath, **csv_analyze(somepath))
```
I can imagine additional output that pandas could react to, such as more detailed information from the analysis:
```
d = csv_analyze(path)
d
{"encoding": {"depth": 10043, # characters checked.
'cp855': 10043, # meaning 10043/10043 = 100% characters match.
'utf_8_sig': 12, # meaning decode error after 12th character.
'utf-8': 5,
"....."},
"text_escape": [False, True, False, False, True],
"column_names": ["Birthdate", "(Family\nnames)", "Father", "Mother ", "Child", "known for"],
"datatypes": ["date", "str", "str", "str", "str"],
"metadata": [ # in order like columns, with list of probabilities for each type.
{"date": {'yyyy-mm-dd': (100, 100),
'mm-dd-yyyy': (0, 100),
"....": (0, 0)},
"time": 0,
"datetime": 0,
"str": "pass",
"int": 0,
"float": 0},
{"date": ValueError, "time": ValueError, "datetime": ValueError, "str": (100, 100), "int": ValueError,
"float": ValueError},
{"date": ValueError, "time": ValueError, "datetime": ValueError, "str": (100, 100), "int": ValueError,
"float": ValueError},
{"date": ValueError, "time": ValueError, "datetime": ValueError, "str": (100, 100), "int": ValueError,
"float": ValueError},
{"date": ValueError, "time": ValueError, "datetime": ValueError, "str": (100, 100), "int": ValueError,
"float": ValueError}
]
}
```
#### API breaking implications
An additional keyword will probably not break much. `csv_reader` already has 52 keywords.
#### Describe alternatives you've considered
Create a package and have the option for pandas:
```
import pandas as pd
import csv_analyze
d = csv_analyze(somepath)
pandas.csv_reader(somepath, **d['pandas'])
```
Keeping the package external will be the pain-point if there is be a disconnect between the pd.csv_reader `kwargs` and the dict that `csv_analyze` returns.
#### Additional context
The strings I've encountered in csv data are:
SEPARATORS
#,###.##### last non-digit character indicates decimal, preceding different characters are thousand separators.
#.###,#####
Examples:
```
4 294 967 295,000 Canadian (English and French), Danish, Finnish, French, German
4.294.967.295,000 Italian, Norwegian, Spanish,
4 294 967 295,000 Swedish
4,294,967,295.000 GB-English, US-English, Thai
```
(full treaty on: https://en.wikipedia.org/wiki/Decimal_separator)
However, notice that Hindi uses a 2-digit grouping, except for the 3-digit grouping for denoting hundreds: 12,34,56,789.00
SCIENTIFIC NOTATION
###E### integer before and after E
###e###
#.####E### floating point before, integer after E
#.####e###
###.###N <=3 digit float, followed by N belongs to (K,M,G/B, T,E,P) for kilo, mega, giga/bill, tera, exa,...
POSITIVE/NEGATIVE
Negative numbers can have tailing minus
```
527-
-527
(527)
[527]
```
ADDITIONAL SIGNS
The same applies for percentages: 98%, 98 %, 98 pct, %98
And for currencies: $100.00, kr100,00
NON-LATIN numbers
NUMBER FORMATTING
Script Digits Used
Latin 0 1 2 3 4 5 6 7 8 9
Arabic ٠ ١ ٢ ٣ ٤ ٥ ٦ ٧ ٨ ٩
Chinese / Japanese 〇 一 二 三 四 五 六 七 八 九 十…
Hebrew א ,ב ,ג, ד, ה, ו, ז, ח ,ט…
Korean 일 이 삼 사 오 육 칠 팔 구…
The Korean regularly uses both a Sino-Korean system and a native Korean system. Everything that can be counted will use one of the two systems, but seldom both. 하나 둘 셋 넷 다섯 여섯 일곱 여덟 아홉….
Bengla ০ ১ ২ ৩ ৪ ৫ ৬ ৭ ৮ ৯
Devanagari (script used to write Hindi,Marathi, and other languages) ० १ २ ३ ४ ५ ६ ७ ८ ९
Gujarati ૦ ૧ ૨ ૩ ૪ ૫ ૬ ૭ ૮ ૯
Gurmukhi (one of the scripts used to write Punjabi) ੦ ੧ ੨ ੩ ੪ ੫ ੬ ੭ ੮ ੯
Kannada ೦ ೧ ೨ ೩ ೪ ೫ ೬ ೭ ೮ ೯
Malayalam ൦ ൧ ൨ ൩ ൪ ൫ ൬ ൭ ൮ ൯
Odia ୦ ୧ ୨ ୩ ୪ ୫ ୬ ୭ ୮ ୯
Tamil ௦ ௧ ௨ ௩ ௪ ௫ ௬ ௭ ௮ ௯
Telugu ౦ ౧ ౨ ౩ ౪ ౫ ౬ ౭ ౮ ౯
Thai ๐ ๑ ๒ ๓ ๔ ๕ ๖ ๗ ๘ ๙
Tibetan ༠ ༡ ༢ ༣ ༤ ༥ ༦ ༧ ༨ ༩
FLOATING POINT
the floating point precision issue (which in particular haunts anyone who reads long barcodes that aren't imported as integers):
```
val = "0.3066101993807095471566981359501369297504425048828125"
print(float(val))
0.30661019938070955
```
NOT A NUMBER
```
"", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan", "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "n/a", "nan", "null". "-", "--", "###"
```
Similar variation appears in datetime locale:
```
yyyy-mm-dd Canadian (English and French), Danish, German, Swedish
dd.mm.yyyy Finnish
dd.mm.yy Italian, Norwegian
dd-mm-yy Spanish
dd/mm/yy GB-English
mm-dd-yy US-English
dd/mm/yyyy Thai
```
And so for time:
```
23:59 Canadian
23.59 Finnish
23.59 Uhr German
Kl 23.59 Norwegian
11:59 PM Thai
11.59 PM UK english
```
TEXT ESCAPE
Finally we also see newline characters in headers which the csv-reader cannot deal with. To detect the correct format of the example below multiple lines have to be read and the internal between newline and separators. These will also have to be text and bracket escaped.
`Birthdate, (Family\nnames), Father, Mother, Child, known for\n1879-4-14, Einstein, Hermann, Pauline, Albert,"\nGeneral relativity,\nSpecial relativity,\nPhotoelectric effect"`
See table in introduction as `ugly example`
LINEBREAK
very old files can have linebreaks \r \r\n or \n
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} | 0 | "2021-07-11T01:42:25Z" | "2021-07-12T14:39:08Z" | "2021-07-12T13:06:38Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | "2021-07-11T11:56:41Z" | "2021-10-29T02:13:05Z" | null | NONE | null | - [ X] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
ser = pd.Series([1,2,3], name='ser1')
f = ser.rename('ser2', inplace=True)
```
#### Problem description
f should be None like other inplace functionality.
instead f is Series object.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-59-generic
Version : #66~20.04.1-Ubuntu SMP Thu Jun 17 11:14:10 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.53.1
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https://api.github.com/repos/pandas-dev/pandas/issues/42482 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42482/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42482/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42482/events | https://github.com/pandas-dev/pandas/issues/42482 | 941,454,648 | MDU6SXNzdWU5NDE0NTQ2NDg= | 42,482 | BUG: groupby with sort=False not respected for categorical data | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
In [1]: import numpy as np
...: import pandas as pd
In [2]: months = ['Jan', 'Feb', 'Mar', 'Apr']
...: colors = ['Red', 'Green', 'Blue']
...: directions = ['Up', 'Down']
In [3]: df = pd.DataFrame(
...: np.random.random(24),
...: index=pd.MultiIndex.from_product([months, pd.CategoricalIndex(colors), directions], names=['month', 'color', 'direction']),
...: columns=['value']
...: )
In [4]: df.groupby(['month', 'color'], sort=False).sum()
Out[4]:
value
month color
Apr Blue 1.369042
Green 1.430864
Red 0.346581
Feb Blue 1.068234
Green 0.888100
Red 0.708154
Jan Blue 1.119714
Green 0.731628
Red 0.624529
Mar Blue 0.605137
Green 1.400212
Red 0.871417
```
#### Problem description
When grouping two columns with categorical data and using `sort=False`, the groups are sorted anyway. If the index was not Categorical, then the output is not sorted as expected.
#### Expected Output
I was hoping to get this output
```python
Jan Red 0.624529
Green 0.731628
Blue 1.119714
Feb Red 0.708154
Green 0.888100
Blue 1.068234
Mar Red 0.871417
Green 1.400212
Blue 0.605137
Apr Red 0.346581
Green 1.430864
Blue 1.369042
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-77-generic
Version : #86-Ubuntu SMP Thu Jun 17 02:35:03 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.0
numpy : 1.17.5
pytz : 2020.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 41.2.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.3.1
blosc : 1.8.3
feather : None
xlsxwriter : None
lxml.etree : 4.4.3
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.51.2
</details>
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} | 3 | "2021-07-11T15:29:21Z" | "2021-07-13T13:40:08Z" | "2021-07-13T12:02:03Z" | CONTRIBUTOR | null | - [x] closes #36419
- [x] tests added / passed
- [x] whatsnew entry
I originally thought the issue was too complicated to code for such a small feature, but turns out it was only one or two lines of coding, so I added it in. Quite a nice addition for visualisations I think.
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xref #27181, a couple more steps to get that all the way accomplished | {
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Regression was due to each scalar value hitting `is_datetime_or_timedelta_dtype` -> `_is_dtype_type` where it falls most of the way through.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
cc @simonjayhawkins | {
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} | 0 | "2021-07-12T06:29:12Z" | "2021-07-28T01:06:32Z" | "2021-07-28T01:06:32Z" | MEMBER | null | This is a follow-up of #28373
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} | 0 | "2021-07-12T07:12:35Z" | "2021-07-12T13:42:33Z" | "2021-07-12T13:04:45Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.13.0-py2.py3-none-any.whl (190 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.11-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.7-py2.py3-none-any.whl (7.2 MB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.11 nodeenv-1.6.0 pre-commit-2.13.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.4.7
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
updating 5.9.1 -> 5.9.2.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.20.0 -> v2.21.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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See also https://github.com/pandas-dev/pandas/issues/42229#issuecomment-870009579
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-07-12T09:04:06Z" | "2021-07-12T13:45:40Z" | "2021-07-12T13:04:10Z" | CONTRIBUTOR | null | renamed a file called `test_align` since it tests `Styler.bar()` method in its entirety and not only the `align` argument.
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] | open | false | null | [] | null | 0 | "2021-07-12T09:44:53Z" | "2021-08-21T04:20:12Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
from io import StringIO
import pandas as pd
data = """Date,Time,Value date,Product,ISIN,Description,FX,Change,,Balance,,Order Id
08-07-2021,04:33,07-07-2021,AAPL,,Dividend,,USD,0.97,USD,0.82,
09-07-2021,16:05,09-07-2021,Interest,LU0904784781,Buy 0.59 at 1 GBP,,,,GBP,109.53,"""
fields_en = (
'Date',
'Time',
'Value date',
'Product',
'ISIN',
'Description',
'FX',
'c_change',
'Change', # unknown
'c_balance',
'Balance', # unknown
'Order Id'
)
def format_datetime(x):
return pd.to_datetime(x, format='%d-%m-%Y %H:%M')
if __name__ == '__main__':
lines = data.splitlines(True)
header = lines[0]
del lines[0]
lines = [header] + list(reversed(lines))
df = pd.read_csv(
StringIO(''.join(lines)), encoding='utf-8', header=0, names=fields_en,
parse_dates={'datetime': ['Date', 'Time']},
date_parser=format_datetime,
)
```
#### Problem description
The above script produces following stack trace:
```
Traceback (most recent call last):
File "/home/fugkco/tmp/beancount-degiro/venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 488, in _read
return parser.read(nrows)
File "/home/fugkco/tmp/beancount-degiro/venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1047, in read
index, columns, col_dict = self._engine.read(nrows)
File "/home/fugkco/tmp/beancount-degiro/venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 260, in read
raise NotImplementedError("file structure not yet supported")
NotImplementedError: file structure not yet supported
```
What's really odd is that adding a single new line at the end of this data, seems to fix the issue, i.e.:
```
data = """Date,Time,Value date,Product,ISIN,Description,FX,Change,,Balance,,Order Id
08-07-2021,04:33,07-07-2021,AAPL,,Dividend,,USD,0.97,USD,0.82,
09-07-2021,16:05,09-07-2021,Interest,LU0904784781,Buy 0.59 at 1 GBP,,,,GBP,109.53,
"""
```
Does not produce the same exception. After investigating using a debugger, it seems that for reason I do not understand, not having the new line causes `_reader.leading_cols` to contain more than 0 columns, causing it to go into [this branch](https://github.com/pandas-dev/pandas/blob/d113a3ba559bccf285565aa934eefa2c2b5d0677/pandas/io/parsers/c_parser_wrapper.py#L258-L260). This seems odd behaviour, and it sounds to me like having a single newline or not at the end of the file shouldn't make a difference in the output.
#### Expected Output
No errors
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-7620-generic
Version : #21~1624379747~21.04~3abeff8-Ubuntu SMP Wed Jun 23 02:34:03 UTC
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.4
setuptools : 44.1.1
Cython : None
pytest : 6.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
None
</details>
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] | open | false | null | [] | null | 0 | "2021-07-12T10:31:35Z" | "2021-08-21T04:20:44Z" | null | NONE | null | **Problem & Proposed feature**
Pandas ewm function works similar to the pandas expanding function in that it rolls over the whole dataframe. In my case however, I need to specify a fixed window or offset over which the ewm function is applied. In other words, an ewm function with a cutoff that would work similar to the 'window' parameter in the pandas rolling function.
**Alternatives considered**
My workaround to the problem is as follows (however extremely inefficient):
```
df = pd.DataFrame({'a': np.random.randint(5, size=24),
'b': ["S", "A"] * 12,
'c': pd.date_range(start='1/1/2018', end='12/12/2018', freq='15D')})
df.groupby('b').rolling('60d', on='c')['a'].apply(lambda x: x.ewm(halflife='15d', times=x.index).mean().tail(1))
```
In the example above, I am trying to do an exponentially-weighted moving average, where decay is specified in terms of halflife on a datetime column. The rolling apply ensures that the maximum offset is '60d' which is the feature I propose to be added directly in the ewm function.
So in summary, I propose a window parameter to be introduced to the ewm function which limits the ewm calculation to a fixed window or offset. | {
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} | 12 | "2021-07-12T11:39:24Z" | "2021-08-11T12:55:41Z" | "2021-08-11T12:55:41Z" | NONE | null | #### Code Sample, a copy-pastable example
```python
import pandas as pd
from pandas.core.arrays.integer import coerce_to_array
class IntegerArrayNoCopy(pd.core.arrays.IntegerArray):
@classmethod
def _from_sequence(cls, scalars, *, dtype=None, copy=False):
values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
return IntegerArrayNoCopy(values, mask)
def copy(self):
raise NotImplementedError
class Int16DtypeNoCopy(pd.Int16Dtype):
@classmethod
def construct_array_type(cls):
return IntegerArrayNoCopy
if __name__ == '__main__':
df = pd.DataFrame({"col": [1, 4, None, 5]}, dtype=object)
print(df.dtypes)
df = df.astype({"col": Int16DtypeNoCopy()}, copy=False)
print(df.dtypes)
print(df)
```
#### Problem description
In 1.3.0, `astype` attempts to create an extension array copy even when explicitly passed `copy=False`:
```
Traceback (most recent call last):
File "test_astype.py", line 24, in <module>
df = df.astype({"col": Int16DtypeNoCopy()}, copy=False)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/generic.py", line 5814, in astype
result = concat(results, axis=1, copy=False)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/util/_decorators.py", line 311, in wrapper
return func(*args, **kwargs)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 307, in concat
return op.get_result()
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/reshape/concat.py", line 508, in get_result
df = cons(data, index=index)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/frame.py", line 614, in __init__
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 458, in dict_to_mgr
for x in arrays
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/internals/construction.py", line 458, in <listcomp>
for x in arrays
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/generic.py", line 5924, in copy
data = self._mgr.copy(deep=deep)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 595, in copy
res = self.apply("copy", deep=deep)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 327, in apply
applied = getattr(b, f)(**kwargs)
File "/home/gsk/miniconda/envs/pdenv/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 651, in copy
values = values.copy()
File "test_astype.py", line 13, in copy
raise NotImplementedError
NotImplementedError
```
#### Expected Output
1.2.5 works as expected (at least for this example):
```
col object
dtype: object
col Int16
dtype: object
col
0 1
1 4
2 <NA>
3 5
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-1029-oem
Version : #30-Ubuntu SMP Fri May 28 23:53:50 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.7.0
jinja2 : 2.11.3
IPython : 7.20.0
fsspec : 2021.04.0
fastparquet : 0.5.0
matplotlib : 3.4.1
pyarrow : 4.0.1
s3fs : 2021.04.0
scipy : 1.6.0
xarray : 0.17.0
numba : 0.53.1
</details>
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~~- [ ] tests added / passed~~
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [X] whatsnew entry
This PR improves the error message when trying to interpolate dtype integer with method="linear", as discussed in #41565. | {
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} | 0 | "2021-07-12T14:20:40Z" | "2021-07-12T16:55:09Z" | "2021-07-12T16:54:51Z" | CONTRIBUTOR | null | cleanup 2/3 of `style/test_bar.py`.
Parametrises the tests for `vmin/vmax` clipping and widening, to test with different combinations of `align`.
Also adds fixtures for existing tests in the file.
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} | 14 | "2021-07-12T14:50:03Z" | "2021-11-20T21:12:08Z" | "2021-11-20T21:12:08Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Pandas 1.25:
```python
import pandas as pd
dt = pd.to_datetime('2019-11-03 01:00:00-0700').tz_convert('America/Los_Angeles')
pd.DataFrame({'dt': dt, 'value': [1]})
# dt value
# 0 2019-11-03 01:00:00-07:00 1
```
Pandas 1.3:
```python
import pandas as pd
dt = pd.to_datetime('2019-11-03 01:00:00-0700').tz_convert('America/Los_Angeles')
pd.DataFrame({'dt': dt, 'value': [1]})
# File "pandas/_libs/tslibs/tzconversion.pyx", line 284, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
# pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from 2019-11-03 01:00:00, try using the 'ambiguous' argument
```
#### Problem description
The `dt` object already has a timezone attribute, so it shouldn't be converting/inferring at all.
#### Expected Output
1.25 and 1.3.0 should have the same output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f00ed8f47020034e752baf0250483053340971b0
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.5.0
Version : Darwin Kernel Version 20.5.0: Sat May 8 05:10:33 PDT 2021; root:xnu-7195.121.3~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0
numpy : 1.21.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 27 | "2021-07-12T14:53:27Z" | "2021-09-07T16:28:56Z" | "2021-08-12T12:41:12Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
```
#### Problem description
[I am just trying to import pandas to run my notebook and it is throwing this error
```--------------------------------------
ImportErrorTraceback (most recent call last)
<ipython-input-1-f20def6a2be1> in <module>
1 import numpy as np
----> 2 import pandas as pd
~/anaconda3/lib/python3.8/site-packages/pandas/__init__.py in <module>
142 from pandas.util._print_versions import show_versions
143
--> 144 from pandas.io.api import (
145 # excel
146 ExcelFile,
~/anaconda3/lib/python3.8/site-packages/pandas/io/api.py in <module>
6
7 from pandas.io.clipboards import read_clipboard
----> 8 from pandas.io.excel import ExcelFile, ExcelWriter, read_excel
9 from pandas.io.feather_format import read_feather
10 from pandas.io.gbq import read_gbq
~/anaconda3/lib/python3.8/site-packages/pandas/io/excel/__init__.py in <module>
----> 1 from pandas.io.excel._base import ExcelFile, ExcelWriter, read_excel
2 from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
3 from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
4 from pandas.io.excel._util import register_writer
5 from pandas.io.excel._xlsxwriter import XlsxWriter as _XlsxWriter
~/anaconda3/lib/python3.8/site-packages/pandas/io/excel/_base.py in <module>
31 pop_header_name,
32 )
---> 33 from pandas.io.parsers import TextParser
34
35 _read_excel_doc = (
~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers/__init__.py in <module>
----> 1 from pandas.io.parsers.readers import (
2 TextFileReader,
3 TextParser,
4 read_csv,
5 read_fwf,
~/anaconda3/lib/python3.8/site-packages/pandas/io/parsers/readers.py in <module>
15 import pandas._libs.lib as lib
16 from pandas._libs.parsers import STR_NA_VALUES
---> 17 from pandas._typing import (
18 ArrayLike,
19 DtypeArg,
ImportError: cannot import name 'DtypeArg' from 'pandas._typing' (/home/tope/anaconda3/lib/python3.8/site-packages/pandas/_typing.py)```]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``--------------------------------------
NameErrorTraceback (most recent call last)
<ipython-input-4-3d232a07e144> in <module>
----> 1 pd.show_versions()
NameError: name 'pd' is not defined`` here leaving a blank line after the details tag]
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-07-12T16:52:17Z" | "2021-08-24T15:46:54Z" | null | MEMBER | null | Tracker for getting PyPy support
- [ ] Add PyPy wheels on MacPython/pandas-wheels and get them on PyPI(we have them already on conda-forge)
- ~~We can probably add this for 1.3.1.~~
- Actually, it will probably be easier to add after 1.3.1, since 1.3.1 includes a fix for the PyPy build.
- [ ] Add whatsnew, putting PyPy as experimentally supported.
- [ ] Testing/CI on master(requires PyPy to release a python 3.8 version), allow failures on this build
- [ ] Xfail failing tests, and remove allowing failures on this build
- [ ] Move out of experimental support. | {
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This removes the NaN test cases and adds it as parameter to an existing test. | {
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] | open | false | null | [] | null | 0 | "2021-07-12T21:04:43Z" | "2021-07-13T16:23:37Z" | null | MEMBER | null | ```
ser = pd.Series(range(5))
df = ser.to_frame()
ser._mgr.arrays[0][0] = 10
>>> df.iloc[0, 0]
0 # <-- ArrayManager
10 # <-- BlockManager
```
cc @jorisvandenbossche | {
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- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com)
- [x] Asked on GIS StackExchange: https://gis.stackexchange.com/q/403762/155601
---
#### Question about pandas
**Note**: If you'd still like to submit a question, please read [this guide](
https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your question.
I'm trying to process some data, with geopandas0.9 which uses geopandas. And I've `pandas==1.2.5`
Here is my code:
```
locations = select_polygons.groupby(class_col).apply(lambda x: \
sample_locations_from_polygon(x, sample_on_boundary='road'
in x[class_col].iloc[0].lower() or 'railway'
in x[class_col].iloc[0].lower()))
return locations
```
Here `class_col="ITEM"`
It gives me error saying: **`ValueError: 'ITEM' is both an index level and a column label, which is ambiguous.`**
Couldn't paste it here, due to it's extensive size.
How can I resolve this? I'm fresh in GIS.
I've tried this:
```
class_col='_ITEM'
locations = select_polygons.groupby(class_col).apply(lambda x: \
sample_locations_from_polygon(x, sample_on_boundary='road'
in x[class_col].iloc[0].lower() or 'railway'
in x[class_col].iloc[0].lower()))
return locations
```
Then it says: **`KeyError: '_ITEM'`**
I've also tried setting the index, manually
```
select_polygons.set_index('_ITEM')
```
But that raises another error, saying:
**`"None of ['_ITEM'] are in the columns"`**
And the data looks like this:
```
CITIES LUZ_OR_CIT CODE ITEM PROD_DATE SHAPE_LEN SHAPE_AREA geometry samples
ITEM
Agricultural + Semi-natural areas + Wetlands 45607 Aalborg DK004L 20000 Agricultural + Semi-natural areas + Wetlands 2011 5.983639 0.633446 POLYGON ((9.52711 57.15122, 9.52715 57.15108, 9.52716 57.15102, 9.52727 57.15060, 9.52727 57.15060, 9.52742 57.15011, 9.52758 57.14955, 9.52758 57.14955, 9.52771 57.14918, 9.52771 57.14918, 9.52771 57.14918, 9.52779 57.14897, 9.52780 57.14897, 9.52780 57.14896, 9.52780 57.14896, 9.52784 57.14889, 9.52785 57.14888, 9.52785 57.14888, 9.52785 57.14888, 9.52785 57.14888, 9.52808 57.14866, 9.52808 57.14866, 9.52809 57.14866, 9.52809 57.14866, 9.52870 57.14825, 9.52967 57.14760, 9.52972 57.14757, 9.52974 57.14754, 9.52982 57.14744, 9.53068 57.14491, 9.52965 57.14476, 9.52960 57.14476, 9.52895 57.14648, 9.52805 57.14646, 9.52816 57.14607, 9.52702 57.14596, 9.52726 57.14486, 9.52748 57.14471, 9.52862 57.14473, 9.52866 57.14448, 9.52860 57.14445, 9.52859 57.14445, 9.52859 57.14445, 9.52859 57.14445, 9.52859 57.14445, 9.52858 57.14445, 9.52858 57.14445, 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44563 Aalborg DK004L 20000 Agricultural + Semi-natural areas + Wetlands 2011 2.983306 0.287320 POLYGON ((9.35536 57.06600, 9.35698 57.06564, 9.35655 57.06460, 9.35552 57.06487, 9.35515 57.06430, 9.35629 57.06399, 9.35581 57.06288, 9.35577 57.06285, 9.35574 57.06283, 9.35568 57.06282, 9.35562 57.06281, 9.35552 57.06281, 9.35480 57.06291, 9.35480 57.06291, 9.35480 57.06291, 9.35480 57.06291, 9.35479 57.06291, 9.35479 57.06291, 9.35479 57.06291, 9.35466 57.06290, 9.35466 57.06290, 9.35465 57.06290, 9.35465 57.06290, 9.35465 57.06290, 9.35456 57.06289, 9.35455 57.06289, 9.35455 57.06289, 9.35455 57.06289, 9.35455 57.06289, 9.35446 57.06286, 9.35446 57.06286, 9.35446 57.06286, 9.35445 57.06286, 9.35445 57.06286, 9.35445 57.06286, 9.35437 57.06282, 9.35437 57.06282, 9.35436 57.06281, 9.35436 57.06281, 9.35436 57.06281, 9.35430 57.06276, 9.35429 57.06276, 9.35429 57.06276, 9.35429 57.06276, 9.35422 57.06269, 9.35422 57.06269, 9.35422 57.06269, 9.35422 57.06269, 9.35422 57.06269, 9.35422 57.06268, 9.35419 57.06262, 9.35386 57.06193, 9.35369 57.06170, 9.35358 57.06172, 9.35358 57.06172, 9.35358 57.06172, 9.35060 57.06226, 9.35060 57.06226, 9.35060 57.06226, 9.34992 57.06236, 9.34991 57.06236, 9.34958 57.06240, 9.34958 57.06240, 9.34958 57.06240, 9.34893 57.06244, 9.34893 57.06244, 9.34893 57.06244, 9.34810 57.06248, 9.34810 57.06248, 9.34809 57.06248, 9.34809 57.06248, 9.34744 57.06247, 9.34744 57.06247, 9.34744 57.06247, 9.34683 57.06243, 9.34689 57.06264, 9.34709 57.06336, 9.34710 57.06336, 9.34710 57.06336, 9.34710 57.06336, 9.34710 57.06336, 9.34710 57.06337, 9.34709 57.06337, 9.34708 57.06341, 9.34708 57.06341, 9.34708 57.06341, 9.34708 57.06341, 9.34708 57.06341, 9.34708 57.06341, 9.34708 57.06342, 9.34708 57.06342, 9.34707 57.06342, 9.34704 57.06344, 9.34704 57.06344, 9.34704 57.06344, 9.34704 57.06344, 9.34704 57.06344, 9.34704 57.06344, 9.34703 57.06345, 9.34703 57.06345, 9.34703 57.06345, 9.34703 57.06345, 9.34697 57.06346, 9.34697 57.06346, 9.34697 57.06346, 9.34696 57.06346, 9.34696 57.06346, 9.34696 57.06346, 9.34622 57.06354, 9.34622 57.06354, 9.34575 57.06358, 9.34592 57.06440, 9.34498 57.06452, 9.34514 57.06508, 9.34416 57.06514, 9.34378 57.06449, 9.34349 57.06452, 9.34349 57.06455, 9.34349 57.06455, 9.34349 57.06455, 9.34351 57.06476, 9.34351 57.06476, 9.34351 57.06476, 9.34351 57.06494, 9.34351 57.06494, 9.34334 57.06652, 9.34362 57.06650, 9.34362 57.06650, 9.34362 57.06650, 9.34454 57.06647, 9.34508 57.06645, 9.34508 57.06645, 9.35154 57.06627, 9.35154 57.06627, 9.35295 57.06624, 9.35370 57.06622, 9.35455 57.06613, 9.35536 57.06600), (9.35293 57.06539, 9.35376 57.06521, 9.35413 57.06566, 9.35361 57.06599, 9.35324 57.06592, 9.35293 57.06539)) 1
47873 Aalborg DK004L 20000 Agricultural + Semi-natural areas + Wetlands 2011 4.254965 0.586887 POLYGON ((10.22571 57.39005, 10.22584 57.38836, 10.22584 57.38836, 10.22584 57.38836, 10.22611 57.38573, 10.22611 57.38573, 10.22416 57.38563, 10.22415 57.38521, 10.22414 57.38505, 10.22378 57.38504, 10.22321 57.38504, 10.22321 57.38504, 10.22138 57.38503, 10.22125 57.38504, 10.22116 57.38506, 10.22094 57.38514, 10.22077 57.38521, 10.22077 57.38521, 10.22077 57.38521, 10.22076 57.38521, 10.22076 57.38521, 10.22067 57.38524, 10.22067 57.38524, 10.22067 57.38524, 10.22067 57.38524, 10.22055 57.38526, 10.22055 57.38526, 10.22054 57.38526, 10.22054 57.38526, 10.22054 57.38526, 10.22044 57.38526, 10.22044 57.38526, 10.22043 57.38526, 10.22043 57.38526, 10.22043 57.38526, 10.22043 57.38526, 10.22042 57.38526, 10.22042 57.38526, 10.22042 57.38526, 10.22041 57.38526, 10.22041 57.38526, 10.22032 57.38524, 10.22032 57.38524, 10.22032 57.38524, 10.22032 57.38524, 10.22031 57.38524, 10.22031 57.38523, 10.22031 57.38523, 10.22024 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22023 57.38519, 10.22011 57.38504, 10.22007 57.38501, 10.22001 57.38499, 10.21964 57.38494, 10.21939 57.38491, 10.21938 57.38491, 10.21938 57.38491, 10.21921 57.38488, 10.21724 57.38456, 10.21724 57.38456, 10.21724 57.38456, 10.21680 57.38447, 10.21638 57.38440, 10.21638 57.38440, 10.21638 57.38440, 10.21494 57.38410, 10.21494 57.38410, 10.21494 57.38410, 10.21494 57.38410, 10.21494 57.38410, 10.21485 57.38407, 10.21485 57.38407, 10.21485 57.38407, 10.21484 57.38407, 10.21484 57.38407, 10.21484 57.38407, 10.21484 57.38406, 10.21483 57.38406, 10.21483 57.38406, 10.21483 57.38406, 10.21483 57.38406, 10.21483 57.38406, 10.21479 57.38400, 10.21479 57.38400, 10.21479 57.38400, 10.21478 57.38400, 10.21460 57.38359, 10.21460 57.38359, 10.21443 57.38318, 10.21443 57.38318, 10.21443 57.38318, 10.21407 57.38227, 10.21347 57.38245, 10.21375 57.38273, 10.21418 57.38299, 10.21259 57.38375, 10.21206 57.38385, 10.21144 57.38311, 10.21134 57.38299, 10.21072 57.38313, 10.21028 57.38325, 10.20731 57.38411, 10.20731 57.38411, 10.20730 57.38411, 10.20431 57.38488, 10.20439 57.38495, 10.20476 57.38561, 10.20503 57.38597, 10.20554 57.38585, 10.20600 57.38590, 10.20636 57.38601, 10.20668 57.38629, 10.20697 57.38639, 10.20704 57.38658, 10.20652 57.38666, 10.20610 57.38668, 10.20582 57.38668, 10.20562 57.38701, 10.20570 57.38743, 10.20617 57.38769, 10.20709 57.38794, 10.20737 57.38781, 10.20736 57.38760, 10.20718 57.38747, 10.20742 57.38726, 10.20830 57.38729, 10.20858 57.38725, 10.20854 57.38698, 10.20875 57.38696, 10.20957 57.38724, 10.21287 57.38814, 10.21415 57.38835, 10.21549 57.38842, 10.21567 57.38860, 10.21543 57.38876, 10.21547 57.38893, 10.21614 57.38907, 10.21695 57.38905, 10.21694 57.38936, 10.21988 57.38943, 10.21988 57.38943, 10.21988 57.38943, 10.22112 57.38947, 10.22162 57.38947, 10.22162 57.38947, 10.22162 57.38947, 10.22162 57.38947, 10.22195 57.38950, 10.22195 57.38950, 10.22257 57.38955, 10.22305 57.38960, 10.22306 57.38960, 10.22306 57.38960, 10.22338 57.38964, 10.22338 57.38964, 10.22394 57.38971, 10.22394 57.38971, 10.22394 57.38971, 10.22456 57.38981, 10.22456 57.38981, 10.22456 57.38981, 10.22519 57.38993, 10.22519 57.38993, 10.22519 57.38993, 10.22570 57.39004, 10.22571 57.39005)) 1
43727 Aalborg DK004L 20000 Agricultural + Semi-natural areas + Wetlands 2011 2.318143 0.291429 POLYGON ((9.73713 56.96362, 9.73713 56.96362, 9.73713 56.96362, 9.73780 56.96362, 9.73829 56.96360, 9.73874 56.96358, 9.74029 56.96351, 9.74042 56.96350, 9.74036 56.96263, 9.74036 56.96263, 9.74028 56.96143, 9.74008 56.95815, 9.73994 56.95812, 9.73994 56.95812, 9.73947 56.95799, 9.73920 56.95793, 9.73877 56.95785, 9.73846 56.95780, 9.73818 56.95776, 9.73656 56.95751, 9.73492 56.95727, 9.73451 56.95770, 9.73450 56.95799, 9.73467 56.95806, 9.73472 56.95821, 9.73468 56.95837, 9.73446 56.95848, 9.73401 56.95848, 9.73391 56.95894, 9.73325 56.95890, 9.73317 56.95889, 9.73313 56.95910, 9.73311 56.95929, 9.73312 56.95943, 9.73315 56.95959, 9.73335 56.96018, 9.73335 56.96018, 9.73335 56.96018, 9.73338 56.96032, 9.73338 56.96032, 9.73338 56.96033, 9.73338 56.96043, 9.73338 56.96044, 9.73338 56.96044, 9.73338 56.96044, 9.73335 56.96056, 9.73335 56.96056, 9.73335 56.96057, 9.73335 56.96057, 9.73329 56.96069, 9.73329 56.96069, 9.73329 56.96069, 9.73329 56.96069, 9.73311 56.96093, 9.73311 56.96093, 9.73311 56.96093, 9.73275 56.96143, 9.73275 56.96143, 9.73275 56.96143, 9.73233 56.96196, 9.73169 56.96276, 9.73109 56.96351, 9.73130 56.96351, 9.73223 56.96346, 9.73258 56.96344, 9.73258 56.96344, 9.73258 56.96344, 9.73258 56.96344, 9.73285 56.96344, 9.73286 56.96344, 9.73286 56.96344, 9.73286 56.96344, 9.73304 56.96346, 9.73304 56.96346, 9.73304 56.96346, 9.73305 56.96346, 9.73319 56.96348, 9.73319 56.96348, 9.73319 56.96348, 9.73381 56.96361, 9.73381 56.96361, 9.73381 56.96361, 9.73407 56.96367, 9.73425 56.96369, 9.73440 56.96369, 9.73596 56.96365, 9.73596 56.96365, 9.73713 56.96362)) 1
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xref https://github.com/pandas-dev/pandas/pull/42451#issuecomment-878918384
> I think for the backport, will only backport changes in ci/deps/actions-39.yaml and leave ci/deps/actions-39-slow.yaml which maps to ci/deps/actions-37-slow.yaml unchanged. | {
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} | 3 | "2021-07-13T17:38:56Z" | "2021-07-15T04:20:33Z" | "2021-07-15T04:20:24Z" | CONTRIBUTOR | null | This fixes an issue with the read_csv c engine when the input has more than one header row and arguments to dtype, na_values, or converters reference multi-index column names as tuples.
- [x] closes #42446
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I added a whatsnew entry for v1.4.0 since that's what other recent bug fixes have done, but I'm not yet sure of the criteria for that. Is it appropriate to add it for whichever release "owns" master? | {
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} | 1 | "2021-07-13T18:34:13Z" | "2021-07-28T01:06:36Z" | "2021-07-28T01:06:32Z" | MEMBER | null | - [x] closes #42492
- [ ] tests added / passed
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- [ ] whatsnew entry
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