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MDU6SXNzdWU1ODc4NzEwODM=
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Unspecific error message when setting singular index with np dtype
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| 1
| 2020-03-25T17:37:55Z
| 2020-03-26T13:24:10Z
| 2020-03-26T13:24:10Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
some_np_array = np.array([43, 56])
# unspecific error message 'TypeError: len() of unsized object':
pd.DataFrame([4324, 345], index=some_np_array[0])
pd.Series([4324, 345], index=some_np_array[0])
# specific error message 'TypeError: Index(...) must be called with a collection of some kind, 5 was passed':
pd.DataFrame([4324, 345], index=5)
pd.Series([4324, 345], index=5)
```
#### Problem description
When passing a non-collection type as an index, pandas typically raises a quite helpful error message of the following form:
`TypeError: Index(...) must be called with a collection of some kind, 5 was passed`
Whereas when passing a non-standard type non-collection item as an index, pandas seems to raise some unspecific and misleading error:
`TypeError: len() of unsized object`
#### Expected Output
Specific error message, even when using np.float64, np.int64 etc. as type of singular values.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1.post20200323
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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REF: CategoricalIndex indexing tests
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| 0
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MEMBER
| null |
Current goal is to get the tests.indexes.foo.test_indexing files into alignment (and to actually hold tests for all/only indexing methods)
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REF: collect .align tests
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read_csv ParserError
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| 2020-03-25T20:42:21Z
| 2020-05-02T09:31:39Z
| 2020-05-02T09:31:39Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
#import pandas as pd
import pandas as pd
#Indicate file path
url = 'http://localhost:8888/edit/Dropbox/COURSES/Data%20Analysis%20Using%20Python/BookNEW.csv'
#Read data
df= pd.read_csv(url)
df.header()
```
#### Problem description
I am reading CSV file but getting ParserError
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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MDExOlB1bGxSZXF1ZXN0MzkzODIxNzcy
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CLN: a batch of replacements of @Appender() with @doc()
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| 2020-04-10T11:45:15Z
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- [x] xref #31942
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] replaces a number of uses of @Appender() with @doc() in `core/generic.py`
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MDExOlB1bGxSZXF1ZXN0MzkzODM4MTgy
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Fiy Typo in datetimes (parced)
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MEMBER
| null |
Fixed a typo in pandas/core/tools/datetimes (parced -> parsed)
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Fix 25648 improve read csv stacktrace on bad argument
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- [x] closes #25648
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Using DateTimeIndex in rolling() to define time-based windows
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NONE
| null |
Hi,
I would like to be able to provide a DateTimeIndex directly to rolling() so that the window is defined as [timestamp in provided DateTimeIndex that is immediately previous to timestamp of current row ; timestamp of current row[
Please, is that possible?
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DOC: improve readability of signature description (type hints)
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NONE
| null |
## Problem description
I'm generally grateful for the efficient documentation of pandas, and I like the advancements in the new layout of recent versions, except for one thing:
The function/object signature at the very top is much harder to read now that the type annotations are printed as well. How about shading the type annotation in gray, or leaving it away altogether? It's enough if the type is described in the "Parameters" section IMHO. Also, the argument could be formatted differently than the default values.

I don't want to unleash a discussion about aesthetics, simply knock the issue away in case it doesn't comply with the guidelines. Thanks anyway!
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ERR: Raise a better error for numpy singletons in Index
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MEMBER
| null |
- [x] closes #33017
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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| 588,057,308
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MDU6SXNzdWU1ODgwNTczMDg=
| 33,027
|
concat with Misaligned Column Labels and Extension Types loses Dtype
|
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| 0
| 2020-03-25T23:42:54Z
| 2020-07-01T01:43:26Z
| 2020-07-01T01:43:26Z
|
MEMBER
| null |
Was a little surprised by this behavior when combining frames with the string dtype and misaligned labels
```python
In [2]: one = pd.DataFrame([["a"]], dtype="string", columns=["a"])
In [3]: two = pd.DataFrame([["b"]], dtype="string", columns=["b"])
In [4]: pd.concat([one, two]).dtypes
Out[4]:
a object
b object
dtype: object
```
A similar issue is exhibited with the integer types:
```python
In [13]: one = pd.DataFrame([[1]], dtype="uint16", columns=["a"])
In [14]: two = pd.DataFrame([[2]], dtype="uint16", columns=["b"])
In [15]: pd.concat([one, two]).dtypes
Out[15]:
a float64
b float64
dtype: object
```
Occurs on master
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| 588,080,643
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MDExOlB1bGxSZXF1ZXN0MzkzODk5NzEy
| 33,028
|
CLN: de-duplicate Block.should_store and related
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| 5
| 2020-03-26T00:56:50Z
| 2020-03-27T15:23:33Z
| 2020-03-27T07:01:17Z
|
MEMBER
| null |
FooBlock.should_store is equivalent to `is_dtype_equal(self.dtype, other.dtype)` for almost all of our Block subclasses. This makes that the implementation for the base class, so we define it in fewer places. xref #32878 on remaining cases.
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| 588,080,925
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MDExOlB1bGxSZXF1ZXN0MzkzODk5OTMx
| 33,029
|
DOC: Fixing links to the contributing page
|
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| 2
| 2020-03-26T00:57:50Z
| 2020-04-07T23:21:43Z
| 2020-04-07T23:12:14Z
|
MEMBER
| null |
Not 100% sure on this changes.
---
Feel free to close this at anytime.
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| 588,085,349
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MDU6SXNzdWU1ODgwODUzNDk=
| 33,030
|
Feature request: series.fillna('mean')
|
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| 2020-03-26T01:12:55Z
| 2020-03-27T15:31:35Z
| 2020-03-27T15:31:35Z
|
NONE
| null |
I would request pandas devs to implement various nans filling methods.
For example:
- mean
- median
- max
- random forest regressor
so on.
Output:
`df['A'].fillna('mean')`
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MDExOlB1bGxSZXF1ZXN0MzkzOTI3NjY1
| 33,031
|
fixed #32904
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| 2020-03-26T02:57:27Z
| 2020-03-26T02:59:57Z
| 2020-03-26T02:59:57Z
|
NONE
| null |
- [x] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzkzOTM1NDU0
| 33,032
|
PERF: remove large-array-creating path in fast_xs
|
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| 3
| 2020-03-26T03:27:56Z
| 2020-03-29T15:56:33Z
| 2020-03-29T15:40:25Z
|
MEMBER
| null |
When frame.columns is non-unique, `frame.iloc[n]` goes through an unnecessary path that effectively creates `frame.values` and looking up `[n]` on that. That's a lot of casting to access just one row.
Luckily, that case is obsolete, so this rips it right out.
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MDExOlB1bGxSZXF1ZXN0MzkzOTU2NjEz
| 33,033
|
fix #32904
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NONE
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- [x] closes #32904
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzkzOTY5MTA2
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|
Bug: TypeError: ufunc 'sqrt' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
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|
NONE
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MDU6SXNzdWU1ODgyMjE4NDk=
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|
Can't Seem to install pandas in python
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| 2020-03-26T07:50:34Z
| 2020-06-04T08:53:09Z
| 2020-03-26T08:10:28Z
|
NONE
| null |
I tried running
```
sudo pip install pandas
```
#### Problem description
Tried Installing pandas for python 2.7 . Can't seem to figure it out. Checked similar issues, but they didn't seem to help me.
Numpy version : 1.16.6 (tried other versions as well)
#### Expected Output
Pandas installed successfully.
####Got this output instead:
```
DEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support
Defaulting to user installation because normal site-packages is not writeable
Collecting pandas
Using cached pandas-1.0.3.tar.gz (5.0 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
ERROR: Command errored out with exit status 1:
command: /System/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python /Users/kd/Library/Python/2.7/lib/python/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/tmpUhhGpr
cwd: /private/var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/pip-install-6SkeqV/pandas
Complete output (19 lines):
Traceback (most recent call last):
File "/Users/kd/Library/Python/2.7/lib/python/site-packages/pip/_vendor/pep517/_in_process.py", line 257, in <module>
main()
File "/Users/kd/Library/Python/2.7/lib/python/site-packages/pip/_vendor/pep517/_in_process.py", line 240, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "/Users/kd/Library/Python/2.7/lib/python/site-packages/pip/_vendor/pep517/_in_process.py", line 91, in get_requires_for_build_wheel
return hook(config_settings)
File "/private/var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/pip-build-env-IK81LY/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 146, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
File "/private/var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/pip-build-env-IK81LY/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 127, in _get_build_requires
self.run_setup()
File "/private/var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/pip-build-env-IK81LY/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 243, in run_setup
self).run_setup(setup_script=setup_script)
File "/private/var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/pip-build-env-IK81LY/overlay/lib/python2.7/site-packages/setuptools/build_meta.py", line 142, in run_setup
exec(compile(code, __file__, 'exec'), locals())
File "setup.py", line 42
f"numpy >= {min_numpy_ver}",
^
SyntaxError: invalid syntax
----------------------------------------
ERROR: Command errored out with exit status 1: /System/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python /Users/kd/Library/Python/2.7/lib/python/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /var/folders/30/00757twx74l3jppj5c4nq7380000gn/T/tmpUhhGpr Check the logs for full command output
```
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MDU6SXNzdWU1ODgyNjQ2NTY=
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BUG: DataFrame.mad(axis=1) not working for nullable integer dtype
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MEMBER
| null |
```
In [32]: df = pd.DataFrame(np.random.randn(100000, 4).astype(int)).astype("Int64")
In [33]: df.mad()
Out[33]:
0 0.363361
1 0.365456
2 0.369014
3 0.368195
dtype: float64
In [34]: df.mad(axis=1)
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-34-ebf68ac71360> in <module>
----> 1 df.mad(axis=1)
~/scipy/pandas/pandas/core/generic.py in mad(self, axis, skipna, level)
10042 demeaned = data - data.mean(axis=0)
10043 else:
> 10044 demeaned = data.sub(data.mean(axis=1), axis=0)
10045 return np.abs(demeaned).mean(axis=axis, skipna=skipna)
10046
~/scipy/pandas/pandas/core/ops/__init__.py in f(self, other, axis, level, fill_value)
751 axis = self._get_axis_number(axis) if axis is not None else 1
752 return _combine_series_frame(
--> 753 self, other, pass_op, axis=axis, str_rep=str_rep
754 )
755 else:
~/scipy/pandas/pandas/core/ops/__init__.py in _combine_series_frame(left, right, func, axis, str_rep)
551
552 array_op = get_array_op(func, str_rep=str_rep)
--> 553 bm = left._data.apply(array_op, right=values.T)
554 return type(left)(bm)
555
~/scipy/pandas/pandas/core/internals/managers.py in apply(self, f, filter, align_keys, **kwargs)
431
432 if callable(f):
--> 433 applied = b.apply(f, **kwargs)
434 else:
435 applied = getattr(b, f)(**kwargs)
~/scipy/pandas/pandas/core/internals/blocks.py in apply(self, func, **kwargs)
365 """
366 with np.errstate(all="ignore"):
--> 367 result = func(self.values, **kwargs)
368
369 return self._split_op_result(result)
~/scipy/pandas/pandas/core/ops/array_ops.py in arithmetic_op(left, right, op, str_rep)
203 if should_extension_dispatch(lvalues, rvalues) or isinstance(rvalues, Timedelta):
204 # Timedelta is included because numexpr will fail on it, see GH#31457
--> 205 res_values = op(lvalues, rvalues)
206
207 else:
~/scipy/pandas/pandas/core/ops/common.py in new_method(self, other)
61 other = item_from_zerodim(other)
62
---> 63 return method(self, other)
64
65 return new_method
~/scipy/pandas/pandas/core/arrays/integer.py in integer_arithmetic_method(self, other)
618
619 if getattr(other, "ndim", 0) > 1:
--> 620 raise NotImplementedError("can only perform ops with 1-d structures")
621
622 if isinstance(other, IntegerArray):
NotImplementedError: can only perform ops with 1-d structures
```
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Inconsistent behavior of `read_csv` when given an additional value on the first row of CSV file
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| 2020-03-26T11:56:30Z
| 2021-01-01T21:53:32Z
| 2021-01-01T21:53:24Z
|
NONE
| null |
#### Short description
Using the following invalid CSV:
```csv
col1_name,col2_name,col3_name
0,1,2,X
4,5,6,
6,7,8
```
It has 3 columns but the *first row* has 4 values and loading it should raise a `ParsingError`.
However, loading using:
```python
pd.read_csv("test.csv", index_col=False)
```
does not raise any exception and returns the following DataFrame:
```
col1_name col2_name col3_name
0 0 1 2
1 4 5 6
2 6 7 8
```
Thus, it silently drops the additional value `X`.
#### Problem description
This will happening if the first row and the following ones are invalid.
If the first invalid row is not the first row, it will throw a `ParsingError` exception.
I.e. the following CSV produces the same results (silently dropping additional value):
```csv
col1_name,col2_name,col3_name
0,1,2,X
4,5,6,X
6,7,8
```
```csv
col1_name,col2_name,col3_name
0,1,2,X
4,5,6,X
6,7,8,X
```
```csv
col1_name,col2_name,col3_name
0,1,2,X
4,5,6
6,7,8,X
```
But this, as expected, throws an exception:
```csv
col1_name,col2_name,col3_name
0,1,2
4,5,6,X
6,7,8,X
```
Finally, if there are two additional values instead of a single one, it throws the following exception:
```
IndexError: list index out of range
```
Having a consistent behavior by throwing an exception in every of the previous cases would be enjoyable.
The fact that it is silent make it harder to validate CSV files.
#### Expected Output
Throw a `ParsingError` in the previous cases.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1.post20200323
Cython : 0.29.15
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
#### Edits
- corrected copy-paste error in the returned DataFrame example (cf. [reply by @gfyoung](https://github.com/pandas-dev/pandas/issues/33037#issuecomment-604811382))
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MDU6SXNzdWU1ODg0MDI4MDE=
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|
Why pandas doesn't support python 3.6.1?
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| 2020-03-26T12:59:41Z
| 2020-03-26T13:20:34Z
| 2020-03-26T13:11:00Z
|
NONE
| null |
Hello,
the requirements for pandas 1 explicit says, that it needs python>=3.6.1. Why is 3.6.0 not supported?
fin swimmer
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MDU6SXNzdWU1ODg1MzQyOTE=
| 33,039
|
TST: arithmetic tests going through numexpr path
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| 2020-03-26T16:02:48Z
| 2021-10-29T02:31:42Z
| 2021-10-29T02:31:42Z
|
MEMBER
| null |
Many of the tests in tests.arithmetic operate on small arrays/index/series/frames, which means they will not go through the numexpr path. We should be running the relevant tests through both the numexpr and non-numexpr paths.
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REF: test_searchorted for PeriodIndex
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MDU6SXNzdWU1ODg1NTc1NTk=
| 33,041
|
BUG: DataFrame.at implicitly assumes unique axes
|
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| 1
| 2020-03-26T16:34:24Z
| 2020-04-15T18:58:47Z
| 2020-04-15T18:58:46Z
|
MEMBER
| null |
Update: `DataFrame.lookup` is also affected
```
arr = np.random.randn(6).reshape(3, 2)
df = pd.DataFrame(arr, columns=["A", "A"])
>>> df.at[0, "A"]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/indexing.py", line 2069, in __getitem__
return super().__getitem__(key)
File "pandas/core/indexing.py", line 2034, in __getitem__
return self.obj._get_value(*key, takeable=self._takeable)
File "pandas/core/frame.py", line 2706, in _get_value
series = self._get_item_cache(col)
File "pandas/core/generic.py", line 3550, in _get_item_cache
res = self._box_item_values(item, values)
File "pandas/core/frame.py", line 2880, in _box_item_values
return self._constructor(values.T, columns=items, index=self.index)
AttributeError: 'BlockManager' object has no attribute 'T'
```
Same problem for `df.at.__setitem__`. `Series.at` looks OK.
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MDU6SXNzdWU1ODg1NTg0NzQ=
| 33,042
|
Index value changes unexpectedly
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| 2020-03-26T16:35:35Z
| 2021-07-30T04:27:13Z
| 2021-07-30T04:27:13Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
First we create a raw dataset with MultiIndex-
```
In [166]: import numpy as np; import pandas as pd
In [167]: data_raw = pd.DataFrame([
...: {'frame': 1, 'face': np.NaN, 'lmark': np.NaN, 'x': np.NaN, 'y': np.NaN},
...: {'frame': 197, 'face': 0, 'lmark': 1, 'x': 969, 'y': 737},
...: {'frame': 197, 'face': 0, 'lmark': 2, 'x': 969, 'y': 740},
...: {'frame': 197, 'face': 0, 'lmark': 3, 'x': 970, 'y': 744},
...: {'frame': 197, 'face': 0, 'lmark': 4, 'x': 972, 'y': 748},
...: {'frame': 197, 'face': 0, 'lmark': 5, 'x': 973, 'y': 752},
...: {'frame': 300, 'face': 0, 'lmark': 1, 'x': 745, 'y': 367},
...: {'frame': 300, 'face': 0, 'lmark': 2, 'x': 753, 'y': 411},
...: {'frame': 300, 'face': 0, 'lmark': 3, 'x': 759, 'y': 455},
...: {'frame': 301, 'face': 0, 'lmark': 1, 'x': 741, 'y': 364},
...: {'frame': 301, 'face': 0, 'lmark': 2, 'x': 746, 'y': 408},
...: {'frame': 301, 'face': 0, 'lmark': 3, 'x': 750, 'y': 452}]).set_index(['frame', 'face', 'lmark'])
```
Next we calculate the z-scores for each lmark -
```
In [168]: ((data_raw - data_raw.mean(level='lmark')).abs()) / data_raw.std(level='lmark')
Out[168]:
x y
frame face lmark
1 NaN NaN NaN NaN
197 0.0 1.0 1.154565 1.154672
2.0 1.154260 1.154665
3.0 1.153946 1.154654
4.0 NaN NaN
5.0 NaN NaN
300 0.0 1.0 0.561956 0.570343
2.0 0.549523 0.569472
3.0 0.540829 0.568384
301 0.0 1.0 0.592609 0.584329
2.0 0.604738 0.585193
3.0 0.613117 0.586270
```
The index values don't change, as expected. Now we filter out records where lmark > 3 -
```
[170]: data_filtered = data_raw.loc[(slice(None), slice(None), [np.NaN, slice(3)]),:]
In [171]: data_filtered
Out[171]:
x y
frame face lmark
1 NaN NaN NaN NaN
197 0.0 1.0 969.0 737.0
2.0 969.0 740.0
3.0 970.0 744.0
300 0.0 1.0 745.0 367.0
2.0 753.0 411.0
3.0 759.0 455.0
301 0.0 1.0 741.0 364.0
2.0 746.0 408.0
3.0 750.0 452.0
```
and recalculate the z-scores -
```
In [172]: ((data_filtered - data_filtered.mean(level='lmark')).abs()) / data_filtered.std(level='lmark')
Out[172]:
x y
frame face lmark
1 NaN 1.0 NaN NaN
197 0.0 1.0 1.154565 1.154672
2.0 1.154260 1.154665
3.0 1.153946 1.154654
300 0.0 1.0 0.561956 0.570343
2.0 0.549523 0.569472
3.0 0.540829 0.568384
301 0.0 1.0 0.592609 0.584329
2.0 0.604738 0.585193
3.0 0.613117 0.586270
```
#### Problem description
The value of the first record's lmark index changed from NaN to 1.0
#### Expected Output
The index values should not change
#### Output of ``pd.show_versions()``
<details>
```
In [9]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Netherlands.1252
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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MDU6SXNzdWU1ODg2MDA2NjE=
| 33,043
|
to_json() not including all attributes
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| 2020-03-26T17:35:49Z
| 2021-08-11T13:26:43Z
| 2020-03-27T02:28:31Z
|
NONE
| null |
`edit: simplified example`
#### Code Sample
```python
class MyClass:
def __init__(self,value):
self.value=value
self.one=value+1
self.two=value+2
self.three=value+3
def __repr__(self):
return str(self.value)
# Sample
x = MyClass(1)
x.__dict__
# Output
# {'value': 1, 'one': 2, 'two': 3, 'three': 4}
# Sample frame
values = [MyClass(value=x) for x in range(3)]
df = pd.DataFrame(values,columns=['amount'])
df.to_json()
```
#### Problem description
I have a custom class i'm using in dataframes, a simplified version of which is shown above.
The output `to_json()` does not provide the full list of attributes, which I'd expected. Adding additional attributes to the class, *some* of them are included, but not all.
#### Current output
```json
{"amount": {
"0": {
"one": 1,
"two": 2
},
"1": {
"one": 2,
"two": 3
},
"2": {
"one": 3,
"two": 4
}
}}
```
#### Expected Output
```json
{"amount":{
"0": {
"value": 0,
"one": 1,
"two": 2,
"three": 3
},
"1": {
"value": 1,
"one": 2,
"two": 3,
"three": 4
},
"2": {
"value": 2,
"one": 3,
"two": 4,
"three": 5
}
}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
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Missing show_versions() in documentation
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| 5
| 2020-03-26T17:43:07Z
| 2020-03-31T19:05:10Z
| 2020-03-31T19:05:10Z
|
CONTRIBUTOR
| null |
```python
pandas.show_versions()
```
is not in pandas API Reference.
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BUG: frame.lookup with non-unique axes
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| 1
| 2020-03-26T17:47:50Z
| 2020-03-26T23:37:05Z
| 2020-03-26T22:35:34Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xref #33041, one more PR coming up to close that.
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MDU6SXNzdWU1ODg2MTU4Mjg=
| 33,046
|
PERF/DISC: Cache .loc, .at and reset whenever index/columns change
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| 2020-03-26T17:59:46Z
| 2020-04-28T03:30:40Z
| 2020-04-28T03:30:40Z
|
MEMBER
| null |
Series.index, DataFrame.index, and DataFrame.columns are `AxisProperty`s and whenever they are set, a callback updates the underlying BlockManager.
We could have that same callback also invalidate the cached `obj.loc` and `obj.at` objects.
If we did that, then `LocIndexer` and `AtIndexer` could assume that they would only ever see set of axes, so a bunch of checks that are now done at lookup-time could be cached or otherwise de-duplicated.
No idea what the perf impact would look like. Thoughts?
|
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BUG: DataFrame.at with non-unique axes
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| 6
| 2020-03-26T18:09:35Z
| 2020-04-15T19:04:17Z
| 2020-04-15T18:58:47Z
|
MEMBER
| null |
- [x] closes #33041
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Closes #33041 in conjunction with #33045.
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|
Convenient way to use offset aliases to create a DateOffset?
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| 2020-03-26T18:23:33Z
| 2020-03-26T18:45:46Z
| 2020-03-26T18:45:46Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
alias_1H = '1H'
alias_1M = '1M'
offset_dict = {alias_1H: ('hours',1), alias_1M: ('months',1)}
def freq_to_offset(freq):
custom_dict = {offset_dict[freq][0] : offset_dict[freq][1]}
return custom_dict
offset_1H = pd.tseries.offsets.DateOffset(**freq_to_offset(alias_1H))
offset_1M = pd.tseries.offsets.DateOffset(**freq_to_offset(alias_1M))
```
#### Problem description
Hello,
I would like to be able to use the same type of offset aliases as defined [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) for date_range() and period_range() function to create DateOffset.
The way I have found is the one copied/pasted here above, but I find the code not that straight.
Please, is there any better way to achieve that?
I understand the reversed is already possible: from a DateOffset, getting its offset alias.
I ask if the round way is possible?
Thanks for your help!
Bests,
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Comparing monthly to dayly frequencies: DateOffset or Timedelta?
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| 2020-03-26T18:51:21Z
| 2020-03-28T05:53:21Z
| 2020-03-26T20:37:34Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
offset_1M = pd.tseries.frequencies.to_offset('1MS')
offset_1D = pd.tseries.frequencies.to_offset('1D')
>>> offset_1M > offset_1D
Traceback (most recent call last):
File "/.local/lib/python3.7/site-packages/pandas/tseries/offsets.py", line 2534, in f
return op(self.delta, other.delta)
AttributeError: 'MonthBegin' object has no attribute 'delta'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.local/lib/python3.7/site-packages/pandas/tseries/offsets.py", line 2538, in f
f"Invalid comparison between {type(self).__name__} "
TypeError: Invalid comparison between Day and MonthBegin
```
#### Problem description
Hi, I would like to compare 2 frequencies, among which monthly frequency.
Two tools appear relevant for that but have each an issue:
- Timedelta that supports comparison, but not Month frequency
- DateOffset that supports Month frequency, but not comparison
Please, how should be this managed?
Thanks for your help.
Have a good day!
Bests
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MDExOlB1bGxSZXF1ZXN0Mzk0MzYzMTc5
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REF: RangeIndex tests
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MDU6SXNzdWU1ODg3MTMyMDI=
| 33,051
|
A bug of CustomBusinessDay
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[] | null | 5
| 2020-03-26T20:39:05Z
| 2020-06-28T20:42:31Z
| 2020-06-28T20:42:30Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.offsets import CustomBusinessDay
US_BD = CustomBusinessDay(calendar=USFederalHolidayCalendar())
from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, nearest_workday, \
USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay, \
USLaborDay, USThanksgivingDay
class USTradingCalendar(AbstractHolidayCalendar):
rules = [
Holiday('NewYearsDay', month=1, day=1, observance=nearest_workday),
USMartinLutherKingJr,
USPresidentsDay,
GoodFriday,
USMemorialDay,
Holiday('USIndependenceDay', month=7, day=4, observance=nearest_workday),
USLaborDay,
USThanksgivingDay,
Holiday('Christmas', month=12, day=25, observance=nearest_workday)
]
US_TD = CustomBusinessDay(calendar=USTradingCalendar())
print(pd.to_datetime("1982-12-30") + US_BD)
print(pd.to_datetime("1982-12-30") + US_TD)
```
#### Problem description
Both give the result of 1983-01-03.
#### Expected Output
The correct answer should be 1982-12-31 as it is Friday and not a public holiday.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: 0.8.1
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk0NDM3ODY3
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CLN: remove BlockManager.get
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| 3
| 2020-03-26T21:39:34Z
| 2020-04-15T19:37:32Z
| 2020-04-15T19:21:58Z
|
MEMBER
| null |
This should not be merged until after #33047 and #33045, since the assertions made here will not always hold until then.
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REF: MultiIndex Indexing tests
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| 2020-03-27T01:17:19Z
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MEMBER
| null |
Trying to make the tests.indexes.foo.test_indexing files cover a uniform collection of methods
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MDExOlB1bGxSZXF1ZXN0Mzk0NTEwMDE2
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REF: rename _data->_mgr
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| 14
| 2020-03-27T01:59:24Z
| 2020-04-06T18:07:37Z
| 2020-04-06T18:05:23Z
|
MEMBER
| null |
We have a long-term goal of reducing the exposed surface of the internals. One difficulty is that it is not trivial to identify all the places that access the internal, in part because grepping for `._data` will turn up a ton of non-internals Index and EA attributes. By renaming _data -> _mgr, we'll make it easier to identify places that we can de-internalize.
If this isn't a route we want to go down (in the past when I've pitched this it hasn't caught on), I'll try to keep this up-to-date so we an use the diff as the relevant reference.
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| 588,852,694
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MDExOlB1bGxSZXF1ZXN0Mzk0NTIzNzY0
| 33,055
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[WIP] Make Index.values read-only
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| 2020-09-14T20:48:10Z
| 2020-09-14T20:48:10Z
|
CONTRIBUTOR
| null |
- [ ] closes #33001
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a PR for looking around enforcing immutability of `pd.Index` objects. It will be a draft, but the main idea is to show what tests fail and what would need to change for this to be implemented. Because of this, I'm going to add some commits incrementally, so there is a history in the build logs.
|
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MDExOlB1bGxSZXF1ZXN0Mzk0NTI5NTkx
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REF: avoid internals in tshift
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| 2020-03-27T16:36:55Z
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MDExOlB1bGxSZXF1ZXN0Mzk0NTMyNzA2
| 33,057
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ERR: Raise NotImplementedError with BaseIndexer and certain rolling operations
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| 2
| 2020-03-27T03:39:00Z
| 2020-03-29T16:42:09Z
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|
MEMBER
| null |
- [x] xref #32865
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As a temporary solution for returning incorrect results when using a BaseIndexer subclass with certain rolling operations, a `NotImplementedError` will now raise with instructions to use `apply` instead.
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MDU6SXNzdWU1ODg4Njg2NDk=
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Indexing broken inside groupby - apply
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| 2020-03-27T03:53:59Z
| 2020-04-17T11:16:49Z
| 2020-04-17T11:16:48Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pdb
df = pd.DataFrame(
{
'col1': ['A', 'A', 'A', 'B', 'B', 'B'],
'col2': [1, 2, 3, 4, 5, 6],
}
)
def fn(x):
pdb.set_trace()
x.col2[x.index[-1]] = 0
return x.col2
result = df.groupby(['col1'], as_index=False).apply(fn)
print(result)
```
#### Problem description
The expected output is:
```
0 0 1
1 2
2 0
1 3 4
4 5
5 0
```
Instead, I get a Series one row longer than expected:
```
0 0 1
1 2
2 0
1 3 4
4 5
5 6
5 0
```
The problem seems to come from processing the second group (col1 == 'B'), where indices do not match row numbers. If I stand at the breakpoint (pdb.set_trace()), I can run this with the following results:
```
-> x.col2[x.index[-1]] = 0
(Pdb) x.col2
3 4
4 5
5 6
Name: col2, dtype: int64
(Pdb) x.col2[5]
*** KeyError: 5
(Pdb) x.col2[5] = 0
(Pdb) x.col2
3 4
4 5
5 6
5 0
Name: col2, dtype: int64
(Pdb) x.col2[5]
5 6
5 0
Name: col2, dtype: int64
(Pdb) x.col2[5] = 0
(Pdb) x.col2
3 4
4 5
5 0
5 0
Name: col2, dtype: int64
```
#### Expected output
```
0 0 1
1 2
2 0
1 3 4
4 5
5 0
```
This was working before. Unfortunately, I do not know what Pandas version it was.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-050513-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.6.2
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
numba : 0.45.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/33059
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| 588,869,875
|
MDU6SXNzdWU1ODg4Njk4NzU=
| 33,059
|
Series Group-by Boxplot has Overlaid Plots
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[] | null | 0
| 2020-03-27T03:58:30Z
| 2021-07-30T04:27:32Z
| null |
NONE
| null |
#### Code Sample
```python
import numpy as np
import pandas as pd
sr = pd.Series(index=pd.MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C', 'D', 'E']]), data=np.random.rand(15))
sr.groupby(level=0).plot.box()
```
#### Problem description
Below is an image of what currently occurs:

All three boxplots are plotted on top of each other in the same figure.
#### Expected Output
Either plot each group next to each other in the same figure, as in
```python
sr.unstack(0).plot.box()
```

and similarly as in
```python
df = pd.DataFrame(sr)
df.groupby(level=0).boxplot(subplots=False)
```

or create three separate figures instead of one, which is what happens in the `DataFrame` case:
```python
df = pd.DataFrame(sr)
df.groupby(level=0).plot.box()
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_NZ.UTF-8
LOCALE : en_NZ.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
```
</details>
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|
xlsx cannot open on windows 10
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[] | null | 4
| 2020-03-27T04:07:15Z
| 2020-03-27T12:43:55Z
| 2020-03-27T11:22:50Z
|
NONE
| null |
#### pd.ExcelWriter xlsx cannot open on windows 10
# Code
xlsx_writer = pd.ExcelWriter(os.path.join(output_path, "report.xlsx"), engine='openpyxl')
df = pd.DataFrame(data=[{"val": val} for val in summary.values()], index=summary.keys()).sort_index()
df.to_excel(xlsx_writer, sheet_name="summary")
xlsx_writer.save()
```
#### Problem description
I used pd.ExcelWriter to create a report.xlsx file and on Mac system it can be open.
But when change to Windows it cannot be open and size of file is 0.
#### df data
val
STOCK 100000
alpha NaN
annualized_returns 0.205
beta NaN
cash 3845.03
downside_risk 0.257
end_date 2015-12-31
information_ratio 0.676
max_drawdown 0.444
run_type BACKTEST
sharpe 0.567
sortino 0.054
start_date 2014-01-02
strategy_file examples/buy_and_hold.py
strategy_name buy_and_hold
total_returns 0.437
total_value 143696
tracking_error 0.387
unit_net_value 1.437
units 100000
volatility 0.387
#### Expected Output

#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pandas: 0.24.2
pytest: 5.4.1
pip: 20.0.2
setuptools: 46.1.1.post20200323
Cython: 0.29.15
numpy: 1.18.2
scipy: 1.4.1
pyarrow: None
xarray: None
IPython: 7.13.0
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: 3.6.1
numexpr: 2.7.1
feather: None
matplotlib: 3.2.1
openpyxl: 3.0.3
xlrd: None
xlwt: None
xlsxwriter: 1.2.8
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.15
pymysql: 0.9.3
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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|
DateTimeIndex.tz_convert() does not apply DST from 2038 onward
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| 17
| 2020-03-27T06:37:37Z
| 2020-05-10T14:41:06Z
| 2020-05-10T14:41:06Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
assert np.all(pd.date_range('1/1/2037', periods=8760, freq='H', tz='EST').time == pd.date_range('1/1/2037', periods=8760, freq='H', tz='US/Eastern').time) == False
assert np.all(pd.date_range('1/1/2038', periods=8760, freq='H', tz='EST').time == pd.date_range('1/1/2038', periods=8760, freq='H', tz='US/Eastern').time) == False # fails
assert np.all(pd.date_range('1/1/2039', periods=8760, freq='H', tz='EST').time == pd.date_range('1/1/2039', periods=8760, freq='H', tz='US/Eastern').time) == False # fails
```
#### Problem description
Wow, this one hurt. US/Eastern timezone is DST-adjusted (blend of EST/EDT) whereas EST is just EST.
The second and third assert statements above should both return False.
Surprised this hasn't come up before.
This is apparently related to a UNIX issue: https://en.wikipedia.org/wiki/Year_2038_problem. With that said, it seems the dtype is datetime64 with some pandas customizations on timezone. Supposedly 64 bit should have solved this.
#### Expected Output
Both of the following should pass:
assert np.all(pd.date_range('1/1/2038', periods=8760, freq='H', tz='EST').time == pd.date_range('1/1/2038', periods=8760, freq='H', tz='US/Eastern').time) == False # fails
assert np.all(pd.date_range('1/1/2039', periods=8760, freq='H', tz='EST').time == pd.date_range('1/1/2039', periods=8760, freq='H', tz='US/Eastern').time) == False # fails
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
NSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk0NjE3ODM1
| 33,062
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BUG: implement astype from string dtype to nullable int dtype
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| 1
| 2020-03-27T08:27:37Z
| 2020-03-27T15:06:57Z
| 2020-03-27T14:59:58Z
|
MEMBER
| null |
Closes #32450
This implements a conversion from nullable string dtype to nullable int dtype.
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MDExOlB1bGxSZXF1ZXN0Mzk0NjIxMTI5
| 33,063
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CLN: remove CategoricalBlock.to_native_types
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| 4
| 2020-03-27T08:35:25Z
| 2020-03-27T15:07:20Z
| 2020-03-27T14:58:21Z
|
MEMBER
| null |
The implementation of `to_native_types` on CategoricalBlock seems duplicated with the one of ExtensionBlock (the default for `na_rep` is different, but internally we always pass a value for that and never use the default).
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ENH/PERF: use mask in factorize for nullable dtypes
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/30037
This adds the option to use a mask in the `HashTable.factorize` (implementation itself is in `HashTable._unique`).
That allows eg IntegerArray to use its mask and avoid the need to convert to object dtype or float dtype (which also avoids a copy of the data), and gives a nice speed-up (so checking for na_value or nan, only checking the mask):
Small example (for easier testing purposes I just added `factorize2` that uses the mask, vs existing `factorize`, but that is not meant to stay of course):
```python
In [1]: a = np.random.randint(0, 10000, 100000)
...: mask = np.zeros(len(a), dtype=bool)
...: mask[np.random.randint(0, len(a), 1000)] = True
...: arr = pd.arrays.IntegerArray(a, mask)
In [2]: arr.factorize()
Out[2]:
(array([ 0, 1, 2, ..., 3726, 2673, 5358]), <IntegerArray>
[2532, 8355, 1885, 2253, 8517, 5615, 3146, 386, 9183, 6497,
...
794, 8600, 823, 1541, 4373, 1205, 9605, 4576, 443, 2070]
Length: 10000, dtype: Int64)
# using mask gives the same result
In [3]: arr.factorize2()
Out[3]:
(array([ 0, 1, 2, ..., 3726, 2673, 5358]), <IntegerArray>
[2532, 8355, 1885, 2253, 8517, 5615, 3146, 386, 9183, 6497,
...
794, 8600, 823, 1541, 4373, 1205, 9605, 4576, 443, 2070]
Length: 10000, dtype: Int64)
# on master we actually convert to object dtype with NaNs, which is very slow
In [4]: %timeit arr.factorize()
12.3 ms ± 849 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# on this PR, for comparision, I changed this to convert to float dtype with NaNs
# (however this is not robust for large ints)
In [4]: %timeit arr.factorize()
2.83 ms ± 51.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# on this PR using the mask is faster than both approaches above
In [5]: %timeit arr.factorize2()
771 µs ± 13.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Since this is adding an extra if branch to `factorize`, this can impact existing cases that don't use the mask, but I didn't see any noticeable effect (with few times switching between branches and rebuilding):
```python
In [1]: a = np.random.randint(0, 10000, 100000)
...: mask = np.zeros(len(a), dtype=bool)
...: mask[np.random.randint(0, len(a), 1000)] = True
...:
...: a2 = a.copy().astype(float)
...: a2[mask] = np.nan
# factorize on an integer array
In [4]: %timeit pd.factorize(a)
769 µs ± 31.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <- PR
745 µs ± 39.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <- master
779 µs ± 60.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <- PR
759 µs ± 50 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <- master
# factorize on a float array with nans
In [5]: %timeit pd.factorize(a2)
2.12 ms ± 18.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- PR
2.2 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- master
2.2 ms ± 102 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- PR
2.13 ms ± 47.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <- master
```
So for the int array there seems to be a small slowdown (as expected), although it is still within the +/- bounds. And I would say it is also small enough (around 2-3%) to be OK with this slowdown given the benefits of being able to use the mask for other cases.
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MDU6SXNzdWU1ODkwNTEwNTA=
| 33,065
|
BUG: truncated repr with pd.NA in object dtype column shows "NaN"
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[] | null | 1
| 2020-03-27T10:57:29Z
| 2020-03-27T13:37:40Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# this return '<NA>', cool
str(pd.DataFrame(np.full((60, 1), pd.NA)))
# this return 'NaN', not cool
str(pd.DataFrame(np.full((61, 1), pd.NA)))
# It's just a problem of str, the entry is actually pd.NA
# this return '<NA>'
str(pd.DataFrame(np.full((61, 1), pd.NA)).iloc[0,0])
```
#### Problem description
`str(pd.NA)` should always be `<NA>` or it will be confusing.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1ODkwNjEzNDY=
| 33,066
|
BUG: pd.NA acts differently when inside/outside a series/dataframe with object dtype
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| 2020-03-27T11:16:13Z
| 2020-04-28T06:09:32Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# return pd.NA, cool
pd.NA | pd.NA
# return False, not cool
pd.Series([pd.NA]) | pd.Series([pd.NA])
```
#### Problem description
pd.NA acts differently when inside/outside a series/dataframe may be confusing. It force me to handle each entry of a series/dataframe.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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BUG: Fix unwanted type casting while replacing values in a DataFrame
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[] | null | 4
| 2020-03-27T11:54:17Z
| 2020-06-14T21:37:18Z
| 2020-05-11T09:17:24Z
|
MEMBER
| null |
- [x] closes #32988
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As mentioned in #32988 [here](https://github.com/pandas-dev/pandas/issues/32988#issuecomment-604727057) I think, that I have found a way to fix this, but I don't know, if this results in a desired behavior.
I would add tests after ensuring, that this does not break anything else.
Any thoughts about this?
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df.to_feather pandas: Failed to open local file, error: The requested operation cannot be performed on a file with a user-mapped section open.
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| 2020-03-27T12:31:25Z
| 2020-04-01T11:56:45Z
| 2020-04-01T11:56:44Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
df = pd.DataFrame({'Loan':["L1","L2"], 'Gender':["male","Female"], "income":[25000.324, 43422.2754]})
df
df['income'] = df['income'].round(decimals=2)
df = df.astype(dict(zip(['Gender'], ['category'])), copy = True)
df
df.to_feather("temp")
df = pd.read_feather("temp")
df.to_feather("temp")
```
#### Problem description
I applied round function on a column and then applied object to category conversion.
I saved the final dataframe into feather file.
When i tried to read the feather file and save the feather file again, I'm getting file is open as per the error. Now able to understand, why the file is getting locked when i read the feather file. Any help on this is appreciated
Using pandas 1.0.0 and python 3.6.5v
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read_sas fails when passed a file object from GCSFS
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| 1
| 2020-03-27T14:56:17Z
| 2020-04-07T01:23:29Z
| 2020-04-07T01:23:29Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
From https://stackoverflow.com/q/60848250/101923
```bash
export BUCKET_NAME=swast-scratch-us
curl -L https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/DEMO_J.XPT | gsutil cp - gs://${BUCKET_NAME}/sas_sample/Nchs/Nhanes/2017-2018/DEMO_J.XPT
```
```python
import pandas as pd
import gcsfs
bucket_name = "swast-scratch-us"
project_id = "swast-scratch"
fs = gcsfs.GCSFileSystem(project=project_id)
with fs.open(
"{}/sas_sample/Nchs/Nhanes/2017-2018/DEMO_J.XPT".format(bucket_name),
"rb"
) as f:
df = pd.read_sas(f, format="xport")
print(df)
```
#### Problem description
This throws the following exception:
```
Traceback (most recent call last):
File "after.py", line 15, in <module>
df = pd.read_sas(f, format="xport")
File "/Users/swast/miniconda3/envs/scratch/lib/python3.7/site-packages/pandas/io/sas/sasreader.py", line 70, in read_sas
filepath_or_buffer, index=index, encoding=encoding, chunksize=chunksize
File "/Users/swast/miniconda3/envs/scratch/lib/python3.7/site-packages/pandas/io/sas/sas_xport.py", line 280, in __init__
contents = contents.encode(self._encoding)
AttributeError: 'bytes' object has no attribute 'encode'
(scratch)
```
#### Expected Output
```
SEQN SDDSRVYR RIDSTATR RIAGENDR ... SDMVSTRA INDHHIN2 INDFMIN2 INDFMPIR
0 93703.0 10.0 2.0 2.0 ... 145.0 15.0 15.0 5.00
1 93704.0 10.0 2.0 1.0 ... 143.0 15.0 15.0 5.00
2 93705.0 10.0 2.0 2.0 ... 145.0 3.0 3.0 0.82
3 93706.0 10.0 2.0 1.0 ... 134.0 NaN NaN NaN
4 93707.0 10.0 2.0 1.0 ... 138.0 10.0 10.0 1.88
... ... ... ... ... ... ... ... ... ...
9249 102952.0 10.0 2.0 2.0 ... 138.0 4.0 4.0 0.95
9250 102953.0 10.0 2.0 1.0 ... 137.0 12.0 12.0 NaN
9251 102954.0 10.0 2.0 2.0 ... 144.0 10.0 10.0 1.18
9252 102955.0 10.0 2.0 2.0 ... 136.0 9.0 9.0 2.24
9253 102956.0 10.0 2.0 1.0 ... 142.0 7.0 7.0 1.56
[9254 rows x 46 columns]
```
Note: the expected output **is** printed when a local file is read.
#### Output of ``pd.show_versions()``
<details>
Python 3.7.3 | packaged by conda-forge | (default, Jul 1 2019, 14:38:56)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.18.1
pytz : 2019.2
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200311
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : 0.6.0
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : 0.12.3
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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|
BUG: AttributeError when read_sas used with GCS
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| 5
| 2020-03-27T15:14:19Z
| 2020-04-07T14:09:48Z
| 2020-04-07T01:23:30Z
|
CONTRIBUTOR
| null |
With GCSFS (and when a binary file buffer is passed in), the output from
the file read is `bytes`, not a Unicode `str`. The `encode` call is unnecessary
in this case.
- [x] closes #33069
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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Why pd.BooleanDtype() is casted to Float64 by groupby/last?
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| 9
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| 2020-05-26T09:32:20Z
| 2020-04-07T10:04:15Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
>>> import pandas as pd
>>>
>>> df = pd.DataFrame({'a': ['x', 'x', 'y', 'y'], 'b': ['x', 'x', 'y', 'y'], 'c': [False, False, True, False]})
>>> df['d'] = df.c.astype(pd.BooleanDtype())
>>>
>>> df.dtypes
a object
b object
c bool
d boolean
dtype: object
>>>
>>> df.groupby(['a', 'b']).c.last()
a b
x x False
y y False
Name: c, dtype: bool
>>>
>>> df.groupby(['a', 'b']).d.last()
a b
x x 0.0
y y 0.0
Name: d, dtype: float64
>
```
#### Problem description
df.groupby(['a', 'b']).c.last() returns False, but df.groupby(['a', 'b']).d.last() returns Float64.
Why the difference?
#### Expected Output
I expect that both values should be False
#### Output of ``pd.show_versions()``
python : 3.7.4.final.0
pandas : 1.0.3
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BUG: Add test to ensure, that bug will not occur again. #33058
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| 12
| 2020-03-27T17:04:59Z
| 2020-06-14T21:38:00Z
| 2020-04-10T20:47:58Z
|
MEMBER
| null |
- [x] xref #33058
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Should I add a whatsnew entry? It's not really new, if it is backported to 1.0.1.
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MDExOlB1bGxSZXF1ZXN0Mzk0ODk0MzEw
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DOC: Added docstring for show_versions()
|
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| 2020-03-31T19:05:17Z
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CONTRIBUTOR
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- [x] closes #33044
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk0ODk0NzIw
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TYP: require _update_inplace gets Frame/Series, never BlockManager
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MEMBER
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| 589,347,158
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MDExOlB1bGxSZXF1ZXN0Mzk0OTIyNDk3
| 33,075
|
CLN: avoid using internals methods for DataFrame.drop_duplicates
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MDU6SXNzdWU1ODkzNzI5MTA=
| 33,076
|
Pandas styling is not rendered in Github
|
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| 2020-03-27T19:37:40Z
| 2020-03-28T07:05:50Z
| 2020-03-27T19:45:22Z
|
NONE
| null |
**My pandas style for background in the local jupyter notebook looks like this** I am using version 1.0

**But once i push the notebook in Github the rendering does not work it looks like this in github**

Can u please solve this problem
|
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MDU6SXNzdWU1ODkzNzgzMTc=
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CI: Test failing on master
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"description": "on-merge: backport to 1.0.x",
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"title": "1.0.4",
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| 2
| 2020-03-27T19:49:03Z
| 2020-05-26T09:40:21Z
| 2020-03-28T17:01:42Z
|
CONTRIBUTOR
| null |
An s3 / pyarrow test is failing on master. Does anyone have time to debug it?
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=31733&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=add65f64-6c25-5783-8fd6-d9aa1b63d9d4&l=115.
There was an s3fs release earlier today, if that's related.
```pytb
2020-03-27T18:16:37.6354466Z =================================== FAILURES ===================================
2020-03-27T18:16:37.6354971Z _____________________ TestParquetPyArrow.test_s3_roundtrip _____________________
2020-03-27T18:16:37.6356687Z [gw1] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
2020-03-27T18:16:37.6357366Z
2020-03-27T18:16:37.6358186Z value = '五, 27 3月 2020 18:11:53 GMT', tzinfo = <class 'dateutil.tz.tz.tzlocal'>
2020-03-27T18:16:37.6358700Z
2020-03-27T18:16:37.6359050Z def _parse_timestamp_with_tzinfo(value, tzinfo):
2020-03-27T18:16:37.6359491Z """Parse timestamp with pluggable tzinfo options."""
2020-03-27T18:16:37.6359936Z if isinstance(value, (int, float)):
2020-03-27T18:16:37.6360574Z # Possibly an epoch time.
2020-03-27T18:16:37.6361048Z return datetime.datetime.fromtimestamp(value, tzinfo())
2020-03-27T18:16:37.6361681Z else:
2020-03-27T18:16:37.6362073Z try:
2020-03-27T18:16:37.6362431Z return datetime.datetime.fromtimestamp(float(value), tzinfo())
2020-03-27T18:16:37.6362828Z except (TypeError, ValueError):
2020-03-27T18:16:37.6363153Z pass
2020-03-27T18:16:37.6363414Z try:
2020-03-27T18:16:37.6363776Z # In certain cases, a timestamp marked with GMT can be parsed into a
2020-03-27T18:16:37.6364222Z # different time zone, so here we provide a context which will
2020-03-27T18:16:37.6364618Z # enforce that GMT == UTC.
2020-03-27T18:16:37.6365341Z > return dateutil.parser.parse(value, tzinfos={'GMT': tzutc()})
2020-03-27T18:16:37.6365750Z
2020-03-27T18:16:37.6366507Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/utils.py:607:
2020-03-27T18:16:37.6367110Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-03-27T18:16:37.6367487Z
2020-03-27T18:16:37.6368063Z timestr = '五, 27 3月 2020 18:11:53 GMT', parserinfo = None
2020-03-27T18:16:37.6368765Z kwargs = {'tzinfos': {'GMT': tzutc()}}
2020-03-27T18:16:37.6369118Z
2020-03-27T18:16:37.6369430Z def parse(timestr, parserinfo=None, **kwargs):
2020-03-27T18:16:37.6369874Z """
2020-03-27T18:16:37.6370113Z
2020-03-27T18:16:37.6370427Z Parse a string in one of the supported formats, using the
2020-03-27T18:16:37.6370810Z ``parserinfo`` parameters.
2020-03-27T18:16:37.6371081Z
2020-03-27T18:16:37.6371354Z :param timestr:
2020-03-27T18:16:37.6371687Z A string containing a date/time stamp.
2020-03-27T18:16:37.6371991Z
2020-03-27T18:16:37.6372246Z :param parserinfo:
2020-03-27T18:16:37.6372622Z A :class:`parserinfo` object containing parameters for the parser.
2020-03-27T18:16:37.6373134Z If ``None``, the default arguments to the :class:`parserinfo`
2020-03-27T18:16:37.6373533Z constructor are used.
2020-03-27T18:16:37.6373811Z
2020-03-27T18:16:37.6374133Z The ``**kwargs`` parameter takes the following keyword arguments:
2020-03-27T18:16:37.6374471Z
2020-03-27T18:16:37.6374723Z :param default:
2020-03-27T18:16:37.6375085Z The default datetime object, if this is a datetime object and not
2020-03-27T18:16:37.6375578Z ``None``, elements specified in ``timestr`` replace elements in the
2020-03-27T18:16:37.6375955Z default object.
2020-03-27T18:16:37.6376228Z
2020-03-27T18:16:37.6376479Z :param ignoretz:
2020-03-27T18:16:37.6376863Z If set ``True``, time zones in parsed strings are ignored and a naive
2020-03-27T18:16:37.6377278Z :class:`datetime` object is returned.
2020-03-27T18:16:37.6377589Z
2020-03-27T18:16:37.6378571Z :param tzinfos:
2020-03-27T18:16:37.6379161Z Additional time zone names / aliases which may be present in the
2020-03-27T18:16:37.6379643Z string. This argument maps time zone names (and optionally offsets
2020-03-27T18:16:37.6380091Z from those time zones) to time zones. This parameter can be a
2020-03-27T18:16:37.6380546Z dictionary with timezone aliases mapping time zone names to time
2020-03-27T18:16:37.6380981Z zones or a function taking two parameters (``tzname`` and
2020-03-27T18:16:37.6381394Z ``tzoffset``) and returning a time zone.
2020-03-27T18:16:37.6381699Z
2020-03-27T18:16:37.6382025Z The timezones to which the names are mapped can be an integer
2020-03-27T18:16:37.6382494Z offset from UTC in seconds or a :class:`tzinfo` object.
2020-03-27T18:16:37.6382838Z
2020-03-27T18:16:37.6383237Z .. doctest::
2020-03-27T18:16:37.6383606Z :options: +NORMALIZE_WHITESPACE
2020-03-27T18:16:37.6384047Z
2020-03-27T18:16:37.6384378Z >>> from dateutil.parser import parse
2020-03-27T18:16:37.6384771Z >>> from dateutil.tz import gettz
2020-03-27T18:16:37.6385594Z >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
2020-03-27T18:16:37.6386479Z >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
2020-03-27T18:16:37.6387401Z datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
2020-03-27T18:16:37.6388159Z >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
2020-03-27T18:16:37.6388663Z datetime.datetime(2012, 1, 19, 17, 21,
2020-03-27T18:16:37.6389349Z tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
2020-03-27T18:16:37.6390349Z
2020-03-27T18:16:37.6390823Z This parameter is ignored if ``ignoretz`` is set.
2020-03-27T18:16:37.6391283Z
2020-03-27T18:16:37.6391595Z :param dayfirst:
2020-03-27T18:16:37.6392353Z Whether to interpret the first value in an ambiguous 3-integer date
2020-03-27T18:16:37.6393105Z (e.g. 01/05/09) as the day (``True``) or month (``False``). If
2020-03-27T18:16:37.6393685Z ``yearfirst`` is set to ``True``, this distinguishes between YDM and
2020-03-27T18:16:37.6394156Z YMD. If set to ``None``, this value is retrieved from the current
2020-03-27T18:16:37.6394610Z :class:`parserinfo` object (which itself defaults to ``False``).
2020-03-27T18:16:37.6394968Z
2020-03-27T18:16:37.6395226Z :param yearfirst:
2020-03-27T18:16:37.6395841Z Whether to interpret the first value in an ambiguous 3-integer date
2020-03-27T18:16:37.6396511Z (e.g. 01/05/09) as the year. If ``True``, the first number is taken to
2020-03-27T18:16:37.6397349Z be the year, otherwise the last number is taken to be the year. If
2020-03-27T18:16:37.6398069Z this is set to ``None``, the value is retrieved from the current
2020-03-27T18:16:37.6398617Z :class:`parserinfo` object (which itself defaults to ``False``).
2020-03-27T18:16:37.6399037Z
2020-03-27T18:16:37.6399333Z :param fuzzy:
2020-03-27T18:16:37.6399809Z Whether to allow fuzzy parsing, allowing for string like "Today is
2020-03-27T18:16:37.6400291Z January 1, 2047 at 8:21:00AM".
2020-03-27T18:16:37.6400628Z
2020-03-27T18:16:37.6400954Z :param fuzzy_with_tokens:
2020-03-27T18:16:37.6401391Z If ``True``, ``fuzzy`` is automatically set to True, and the parser
2020-03-27T18:16:37.6401903Z will return a tuple where the first element is the parsed
2020-03-27T18:16:37.6402536Z :class:`datetime.datetime` datetimestamp and the second element is
2020-03-27T18:16:37.6403633Z a tuple containing the portions of the string which were ignored:
2020-03-27T18:16:37.6404036Z
2020-03-27T18:16:37.6404341Z .. doctest::
2020-03-27T18:16:37.6404638Z
2020-03-27T18:16:37.6405107Z >>> from dateutil.parser import parse
2020-03-27T18:16:37.6405634Z >>> parse("Today is January 1, 2047 at 8:21:00AM", fuzzy_with_tokens=True)
2020-03-27T18:16:37.6406513Z (datetime.datetime(2047, 1, 1, 8, 21), (u'Today is ', u' ', u'at '))
2020-03-27T18:16:37.6407199Z
2020-03-27T18:16:37.6407464Z :return:
2020-03-27T18:16:37.6407834Z Returns a :class:`datetime.datetime` object or, if the
2020-03-27T18:16:37.6408278Z ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
2020-03-27T18:16:37.6408981Z first element being a :class:`datetime.datetime` object, the second
2020-03-27T18:16:37.6409472Z a tuple containing the fuzzy tokens.
2020-03-27T18:16:37.6409793Z
2020-03-27T18:16:37.6416049Z :raises ValueError:
2020-03-27T18:16:37.6416719Z Raised for invalid or unknown string format, if the provided
2020-03-27T18:16:37.6417444Z :class:`tzinfo` is not in a valid format, or if an invalid date
2020-03-27T18:16:37.6417891Z would be created.
2020-03-27T18:16:37.6418311Z
2020-03-27T18:16:37.6418726Z :raises OverflowError:
2020-03-27T18:16:37.6419273Z Raised if the parsed date exceeds the largest valid C integer on
2020-03-27T18:16:37.6419711Z your system.
2020-03-27T18:16:37.6420018Z """
2020-03-27T18:16:37.6420330Z if parserinfo:
2020-03-27T18:16:37.6420877Z return parser(parserinfo).parse(timestr, **kwargs)
2020-03-27T18:16:37.6421255Z else:
2020-03-27T18:16:37.6421644Z > return DEFAULTPARSER.parse(timestr, **kwargs)
2020-03-27T18:16:37.6422091Z
2020-03-27T18:16:37.6423228Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/dateutil/parser/_parser.py:1374:
2020-03-27T18:16:37.6423959Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-03-27T18:16:37.6424342Z
2020-03-27T18:16:37.6424658Z self = <dateutil.parser._parser.parser object at 0x7f1de46b9350>
2020-03-27T18:16:37.6425310Z timestr = '五, 27 3月 2020 18:11:53 GMT'
2020-03-27T18:16:37.6425800Z default = datetime.datetime(2020, 3, 27, 0, 0), ignoretz = False
2020-03-27T18:16:37.6426475Z tzinfos = {'GMT': tzutc()}, kwargs = {}, res = None, skipped_tokens = None
2020-03-27T18:16:37.6426876Z
2020-03-27T18:16:37.6427164Z def parse(self, timestr, default=None,
2020-03-27T18:16:37.6427538Z ignoretz=False, tzinfos=None, **kwargs):
2020-03-27T18:16:37.6427849Z """
2020-03-27T18:16:37.6428204Z Parse the date/time string into a :class:`datetime.datetime` object.
2020-03-27T18:16:37.6428565Z
2020-03-27T18:16:37.6428819Z :param timestr:
2020-03-27T18:16:37.6429210Z Any date/time string using the supported formats.
2020-03-27T18:16:37.6429526Z
2020-03-27T18:16:37.6429779Z :param default:
2020-03-27T18:16:37.6430167Z The default datetime object, if this is a datetime object and not
2020-03-27T18:16:37.6430776Z ``None``, elements specified in ``timestr`` replace elements in the
2020-03-27T18:16:37.6431441Z default object.
2020-03-27T18:16:37.6431742Z
2020-03-27T18:16:37.6432049Z :param ignoretz:
2020-03-27T18:16:37.6432467Z If set ``True``, time zones in parsed strings are ignored and a
2020-03-27T18:16:37.6432975Z naive :class:`datetime.datetime` object is returned.
2020-03-27T18:16:37.6433343Z
2020-03-27T18:16:37.6433628Z :param tzinfos:
2020-03-27T18:16:37.6434055Z Additional time zone names / aliases which may be present in the
2020-03-27T18:16:37.6434682Z string. This argument maps time zone names (and optionally offsets
2020-03-27T18:16:37.6435260Z from those time zones) to time zones. This parameter can be a
2020-03-27T18:16:37.6435702Z dictionary with timezone aliases mapping time zone names to time
2020-03-27T18:16:37.6436155Z zones or a function taking two parameters (``tzname`` and
2020-03-27T18:16:37.6436696Z ``tzoffset``) and returning a time zone.
2020-03-27T18:16:37.6437208Z
2020-03-27T18:16:37.6437620Z The timezones to which the names are mapped can be an integer
2020-03-27T18:16:37.6438077Z offset from UTC in seconds or a :class:`tzinfo` object.
2020-03-27T18:16:37.6438437Z
2020-03-27T18:16:37.6438699Z .. doctest::
2020-03-27T18:16:37.6439054Z :options: +NORMALIZE_WHITESPACE
2020-03-27T18:16:37.6439354Z
2020-03-27T18:16:37.6439678Z >>> from dateutil.parser import parse
2020-03-27T18:16:37.6440070Z >>> from dateutil.tz import gettz
2020-03-27T18:16:37.6440793Z >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
2020-03-27T18:16:37.6441604Z >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
2020-03-27T18:16:37.6442377Z datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
2020-03-27T18:16:37.6443465Z >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
2020-03-27T18:16:37.6443989Z datetime.datetime(2012, 1, 19, 17, 21,
2020-03-27T18:16:37.6444715Z tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
2020-03-27T18:16:37.6445160Z
2020-03-27T18:16:37.6445519Z This parameter is ignored if ``ignoretz`` is set.
2020-03-27T18:16:37.6445856Z
2020-03-27T18:16:37.6446238Z :param \\*\\*kwargs:
2020-03-27T18:16:37.6447528Z Keyword arguments as passed to ``_parse()``.
2020-03-27T18:16:37.6448032Z
2020-03-27T18:16:37.6448353Z :return:
2020-03-27T18:16:37.6448768Z Returns a :class:`datetime.datetime` object or, if the
2020-03-27T18:16:37.6449286Z ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
2020-03-27T18:16:37.6449817Z first element being a :class:`datetime.datetime` object, the second
2020-03-27T18:16:37.6450310Z a tuple containing the fuzzy tokens.
2020-03-27T18:16:37.6450667Z
2020-03-27T18:16:37.6450965Z :raises ParserError:
2020-03-27T18:16:37.6451396Z Raised for invalid or unknown string format, if the provided
2020-03-27T18:16:37.6451904Z :class:`tzinfo` is not in a valid format, or if an invalid date
2020-03-27T18:16:37.6452350Z would be created.
2020-03-27T18:16:37.6452800Z
2020-03-27T18:16:37.6453091Z :raises TypeError:
2020-03-27T18:16:37.6453885Z Raised for non-string or character stream input.
2020-03-27T18:16:37.6454326Z
2020-03-27T18:16:37.6454656Z :raises OverflowError:
2020-03-27T18:16:37.6455085Z Raised if the parsed date exceeds the largest valid C integer on
2020-03-27T18:16:37.6455507Z your system.
2020-03-27T18:16:37.6455922Z """
2020-03-27T18:16:37.6456431Z
2020-03-27T18:16:37.6456744Z if default is None:
2020-03-27T18:16:37.6457160Z default = datetime.datetime.now().replace(hour=0, minute=0,
2020-03-27T18:16:37.6457686Z second=0, microsecond=0)
2020-03-27T18:16:37.6458066Z
2020-03-27T18:16:37.6458428Z res, skipped_tokens = self._parse(timestr, **kwargs)
2020-03-27T18:16:37.6458778Z
2020-03-27T18:16:37.6459191Z if res is None:
2020-03-27T18:16:37.6459743Z > raise ParserError("Unknown string format: %s", timestr)
2020-03-27T18:16:37.6460612Z E dateutil.parser._parser.ParserError: Unknown string format: 五, 27 3月 2020 18:11:53 GMT
2020-03-27T18:16:37.6461135Z
2020-03-27T18:16:37.6461831Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/dateutil/parser/_parser.py:649: ParserError
2020-03-27T18:16:37.6462294Z
2020-03-27T18:16:37.6462654Z During handling of the above exception, another exception occurred:
2020-03-27T18:16:37.6463101Z
2020-03-27T18:16:37.6463488Z self = <pandas.tests.io.test_parquet.TestParquetPyArrow object at 0x7f1d76d75210>
2020-03-27T18:16:37.6464218Z df_compat = A B
2020-03-27T18:16:37.6464554Z 0 1 foo
2020-03-27T18:16:37.6464841Z 1 2 foo
2020-03-27T18:16:37.6465108Z 2 3 foo
2020-03-27T18:16:37.6465977Z s3_resource = s3.ServiceResource(), pa = 'pyarrow'
2020-03-27T18:16:37.6466419Z
2020-03-27T18:16:37.6466772Z def test_s3_roundtrip(self, df_compat, s3_resource, pa):
2020-03-27T18:16:37.6467176Z # GH #19134
2020-03-27T18:16:37.6467888Z > check_round_trip(df_compat, pa, path="s3://pandas-test/pyarrow.parquet")
2020-03-27T18:16:37.6468598Z
2020-03-27T18:16:37.6468923Z pandas/tests/io/test_parquet.py:488:
2020-03-27T18:16:37.6469382Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-03-27T18:16:37.6469883Z pandas/tests/io/test_parquet.py:176: in check_round_trip
2020-03-27T18:16:37.6470262Z compare(repeat)
2020-03-27T18:16:37.6470636Z pandas/tests/io/test_parquet.py:168: in compare
2020-03-27T18:16:37.6471190Z actual = read_parquet(path, **read_kwargs)
2020-03-27T18:16:37.6471616Z pandas/io/parquet.py:300: in read_parquet
2020-03-27T18:16:37.6472030Z return impl.read(path, columns=columns, **kwargs)
2020-03-27T18:16:37.6472668Z pandas/io/parquet.py:113: in read
2020-03-27T18:16:37.6473217Z path, _, _, should_close = get_filepath_or_buffer(path)
2020-03-27T18:16:37.6473645Z pandas/io/common.py:201: in get_filepath_or_buffer
2020-03-27T18:16:37.6474112Z filepath_or_buffer, encoding=encoding, compression=compression, mode=mode
2020-03-27T18:16:37.6474560Z pandas/io/s3.py:48: in get_filepath_or_buffer
2020-03-27T18:16:37.6475000Z file, _fs = get_file_and_filesystem(filepath_or_buffer, mode=mode)
2020-03-27T18:16:37.6475436Z pandas/io/s3.py:29: in get_file_and_filesystem
2020-03-27T18:16:37.6475860Z file = fs.open(_strip_schema(filepath_or_buffer), mode)
2020-03-27T18:16:37.6476799Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/fsspec/spec.py:774: in open
2020-03-27T18:16:37.6477280Z **kwargs
2020-03-27T18:16:37.6478236Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/s3fs/core.py:377: in _open
2020-03-27T18:16:37.6478798Z autocommit=autocommit, requester_pays=requester_pays)
2020-03-27T18:16:37.6479601Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/s3fs/core.py:1095: in __init__
2020-03-27T18:16:37.6480120Z cache_type=cache_type)
2020-03-27T18:16:37.6480867Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/fsspec/spec.py:1056: in __init__
2020-03-27T18:16:37.6481404Z self.details = fs.info(path)
2020-03-27T18:16:37.6482347Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/s3fs/core.py:528: in info
2020-03-27T18:16:37.6483012Z Key=key, **version_id_kw(version_id), **self.req_kw)
2020-03-27T18:16:37.6484814Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/s3fs/core.py:199: in _call_s3
2020-03-27T18:16:37.6485427Z return method(**additional_kwargs)
2020-03-27T18:16:37.6486423Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/client.py:316: in _api_call
2020-03-27T18:16:37.6487194Z return self._make_api_call(operation_name, kwargs)
2020-03-27T18:16:37.6488049Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/client.py:613: in _make_api_call
2020-03-27T18:16:37.6488645Z operation_model, request_dict, request_context)
2020-03-27T18:16:37.6489404Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/client.py:632: in _make_request
2020-03-27T18:16:37.6489999Z return self._endpoint.make_request(operation_model, request_dict)
2020-03-27T18:16:37.6491059Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/endpoint.py:102: in make_request
2020-03-27T18:16:37.6491692Z return self._send_request(request_dict, operation_model)
2020-03-27T18:16:37.6492529Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/endpoint.py:135: in _send_request
2020-03-27T18:16:37.6493171Z request, operation_model, context)
2020-03-27T18:16:37.6494139Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/endpoint.py:167: in _get_response
2020-03-27T18:16:37.6494730Z request, operation_model)
2020-03-27T18:16:37.6495541Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/endpoint.py:218: in _do_get_response
2020-03-27T18:16:37.6496167Z response_dict, operation_model.output_shape)
2020-03-27T18:16:37.6497203Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:242: in parse
2020-03-27T18:16:37.6497800Z parsed = self._do_parse(response, shape)
2020-03-27T18:16:37.6498590Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:774: in _do_parse
2020-03-27T18:16:37.6499160Z member_shapes, final_parsed)
2020-03-27T18:16:37.6499969Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:833: in _parse_non_payload_attrs
2020-03-27T18:16:37.6500723Z member_shape, headers[header_name])
2020-03-27T18:16:37.6501547Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:302: in _parse_shape
2020-03-27T18:16:37.6502128Z return handler(shape, node)
2020-03-27T18:16:37.6502916Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:175: in _get_text_content
2020-03-27T18:16:37.6503488Z return func(self, shape, text)
2020-03-27T18:16:37.6504298Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/parsers.py:462: in _handle_timestamp
2020-03-27T18:16:37.6504870Z return self._timestamp_parser(text)
2020-03-27T18:16:37.6505668Z ../../../miniconda3/envs/pandas-dev/lib/python3.7/site-packages/botocore/utils.py:626: in parse_timestamp
2020-03-27T18:16:37.6506266Z return _parse_timestamp_with_tzinfo(value, tzinfo)
2020-03-27T18:16:37.6506771Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2020-03-27T18:16:37.6507148Z
2020-03-27T18:16:37.6507825Z value = '五, 27 3月 2020 18:11:53 GMT', tzinfo = <class 'dateutil.tz.tz.tzlocal'>
2020-03-27T18:16:37.6508344Z
2020-03-27T18:16:37.6508684Z def _parse_timestamp_with_tzinfo(value, tzinfo):
2020-03-27T18:16:37.6509139Z """Parse timestamp with pluggable tzinfo options."""
2020-03-27T18:16:37.6509563Z if isinstance(value, (int, float)):
2020-03-27T18:16:37.6509962Z # Possibly an epoch time.
2020-03-27T18:16:37.6510383Z return datetime.datetime.fromtimestamp(value, tzinfo())
2020-03-27T18:16:37.6510764Z else:
2020-03-27T18:16:37.6511070Z try:
2020-03-27T18:16:37.6511478Z return datetime.datetime.fromtimestamp(float(value), tzinfo())
2020-03-27T18:16:37.6511946Z except (TypeError, ValueError):
2020-03-27T18:16:37.6512297Z pass
2020-03-27T18:16:37.6512606Z try:
2020-03-27T18:16:37.6513006Z # In certain cases, a timestamp marked with GMT can be parsed into a
2020-03-27T18:16:37.6513528Z # different time zone, so here we provide a context which will
2020-03-27T18:16:37.6513992Z # enforce that GMT == UTC.
2020-03-27T18:16:37.6514846Z return dateutil.parser.parse(value, tzinfos={'GMT': tzutc()})
2020-03-27T18:16:37.6515508Z except (TypeError, ValueError) as e:
2020-03-27T18:16:37.6516237Z > raise ValueError('Invalid timestamp "%s": %s' % (value, e))
2020-03-27T18:16:37.6517538Z E ValueError: Invalid timestamp "五, 27 3月 2020 18:11:53 GMT": Unknown string format: 五, 27 3月 2020 18:11:53 GMT
```
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MDExOlB1bGxSZXF1ZXN0Mzk0OTU5MTgy
| 33,078
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Revert "CI: Fix jedi upgrades causes deprecation warning"
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Reverts pandas-dev/pandas#31323
---
Closes #31407
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DOC: whatsnew for #32831
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- [ ] closes #33077
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1ODk0NTA3MTE=
| 33,082
|
Accessing DataFrame with DatetimeIndex behaves unexpectedly with reverse chronological ordered data
|
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| 2020-03-27T22:52:08Z
| 2020-04-29T23:31:18Z
| 2020-04-29T23:31:18Z
|
NONE
| null |
#### Problem description
When a Panda's DataFrame with a DatetimeIndex is in reverse chronological order accessing the data using the .loc method does not work unless the range is _also_ in reverse order.
#### Code Sample
```python
import pandas as pd
data = [
("2020-03-25 12:00:00", "A"),
("2020-03-24 18:00:00", "B"),
("2020-03-24 12:00:00", "C"),
("2020-03-24 06:00:00", "D"),
("2020-03-23 12:00:00", "E"),
]
df = pd.DataFrame(data, columns=["timestamp", "x"])
df["timestamp"] = pd.to_datetime(df["timestamp"])
df.index = df.pop("timestamp")
```
Accessing the date range 2020-03-24 through 2020-03-25 for this DataFrame results in the following:
```python
df.loc["2020-03-24":"2020-03-25"]
>>>
Empty DataFrame
Columns: [x]
Index: []
```
However, reversing the date range returns the expected output.
```python
df.loc["2020-03-25":"2020-03-24"]
>>>
x
timestamp
2020-03-25 12:00:00 A
2020-03-24 18:00:00 B
2020-03-24 12:00:00 C
2020-03-24 06:00:00 D
```
Is this really how the DatetimeIndex should function? Either should work when working with a DatetimeIndex.
This can be fixed by executing the .sort_index() method of the DataFrame. However, when working with a DatetimeIndex, I believe chronological order should be implied.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 5.4.1
pip: 20.0.2
setuptools: 41.2.0
Cython: None
numpy: 1.18.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: 2.4.4
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.2.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.8.2
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.11.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk1MDA4NzU1
| 33,083
|
CLN: avoid internals, some misc cleanups
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| 1
| 2020-03-27T23:27:48Z
| 2020-03-29T15:22:59Z
| 2020-03-29T15:17:50Z
|
MEMBER
| null |
The only non-trivial thing here is in io.formats.info replacing `frame._data.get_dtype_counts` with an alternative that uses public methods
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CLN: de-kludge NDFrame.interpolate
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MEMBER
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i think i was responsible for these particular kludges a while back.
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BUG: Groupby lost index, when one of the agg keys had no function all…
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| 2020-03-28T00:07:06Z
| 2020-06-14T21:35:59Z
| 2020-06-14T18:04:54Z
|
MEMBER
| null |
…ocated
- [x] closes #32580
- [x] closes #33545 (is a duplicate of 32580)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The issue was the concatenation with an empty DataFrame and the result of the min function. This resulted in the lost index.
I changed the input for the concatenation, so that only non empty DataFrames would be concatenated. We have to catch the case, that all DataFrames are empty, because this would result in an error.
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Cannot specify pickle protocol used when writing HDF5.
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[] | null | 11
| 2020-03-28T00:33:01Z
| 2021-08-01T16:32:16Z
| null |
NONE
| null |
#### Problem description
It appears currently impossible (unless I'm mistaken) to specify what pickle protocol will be used by `DataFrame.to_hdf()` (and thus by `PyTables`) if pickling is necessary. That makes it impossible to share HDF5 data written and read by a mix of clients running py37 and py38.
In Python 3.8, pickle protocol 5 was introduced ([PEP-574](https://www.python.org/dev/peps/pep-0574/)). This prevents my team from supporting py38 in a system meant to support clients running a range of Python versions (from 3.6) and sharing a common distributed filesystem. We plan to eventually deprecate support for py36 and py37, but the problem is that there doesn't seem to be a way to manage the transition when such a new protocol is introduced.
**xref**: [this StackOverflow question](https://stackoverflow.com/q/60067953/758174).
#### Example
```bash
(base) $ conda activate py38
(py38) $ python
Python 3.8.1 (default, Jan 8 2020, 22:29:32)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.DataFrame(['hello', 'world']))
>>> df.to_hdf('foo', 'x')
>>> exit()
(py38) $ conda deactivate
(base) $ python
Python 3.7.4 (default, Aug 13 2019, 20:35:49)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> df = pd.read_hdf('foo', 'x')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 407, in read_hdf
return store.select(key, auto_close=auto_close, **kwargs)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 782, in select
return it.get_result()
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 1639, in get_result
results = self.func(self.start, self.stop, where)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 766, in func
return s.read(start=_start, stop=_stop, where=_where, columns=columns)
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 3206, in read
"block{idx}_values".format(idx=i), start=_start, stop=_stop
File "/opt/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py", line 2737, in read_array
ret = node[0][start:stop]
File "/opt/anaconda3/lib/python3.7/site-packages/tables/vlarray.py", line 681, in __getitem__
return self.read(start, stop, step)[0]
File "/opt/anaconda3/lib/python3.7/site-packages/tables/vlarray.py", line 825, in read
outlistarr = [atom.fromarray(arr) for arr in listarr]
File "/opt/anaconda3/lib/python3.7/site-packages/tables/vlarray.py", line 825, in <listcomp>
outlistarr = [atom.fromarray(arr) for arr in listarr]
File "/opt/anaconda3/lib/python3.7/site-packages/tables/atom.py", line 1227, in fromarray
return six.moves.cPickle.loads(array.tostring())
ValueError: unsupported pickle protocol: 5
>>>
```
#### Expected Behavior
```python
df.to_hdf('foo', 'x', pickle_protocol=4)
```
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CLN: remove ABCGeneric
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BUG: Don't cast nullable Boolean to float in groupby
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| 8
| 2020-03-28T02:34:30Z
| 2020-05-06T14:52:16Z
| 2020-04-07T10:04:16Z
|
MEMBER
| null |
- [x] closes #33071
- [x] closes #32194
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU1ODk0OTYwMTk=
| 33,090
|
SeriesGroupBy.first / last loses categorical dtype
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| 5
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| 2021-08-31T23:58:06Z
| 2021-08-31T23:58:06Z
|
MEMBER
| null |
On 1.0.3 and master:
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3]})
df["b"] = df["a"].astype("category")
print(df.groupby("a")["b"].first())
print(df.groupby("a")["b"].last())
```
gives
```python
a
1 1
2 2
3 3
Name: b, dtype: int64
a
1 1
2 2
3 3
Name: b, dtype: int64
```
but the dtype should still be categorical and not int64. This seemingly wrong output is explicitly tested for here: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/groupby/aggregate/test_aggregate.py#L461
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MDExOlB1bGxSZXF1ZXN0Mzk1MDQwMzAw
| 33,091
|
ENH: Add index option to_markdown()
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| 8
| 2020-03-28T03:29:13Z
| 2020-07-15T12:52:29Z
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|
CONTRIBUTOR
| null |
- [ ] closes #32667
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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| 589,500,397
|
MDExOlB1bGxSZXF1ZXN0Mzk1MDQwNzYy
| 33,092
|
TYP: enforce tighter inputs on SingleBlockManager
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| 5
| 2020-03-28T03:34:35Z
| 2020-03-30T18:03:28Z
| 2020-03-30T17:40:42Z
|
MEMBER
| null |
there is currently only one place where we pass a list of indexes instead of a single index, so just fixed that and were good to go.
|
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MDExOlB1bGxSZXF1ZXN0Mzk1MDQ2MDYw
| 33,093
|
DOC: Fix PR06
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
|
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MDU6SXNzdWU1ODk1MTY4ODk=
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Discrepancy in DateOffset & Timedelta string representation
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[] | null | 0
| 2020-03-28T06:06:40Z
| 2021-07-30T04:30:01Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# DateOffset creation and string representation
off = pd.tseries.frequencies.to_offset('2MS')
off.freqstr
>>> Out[20]: '2MS'
# Timedelta creation from DateOffset string representation
td = pd.to_timedelta(off.freqstr)
td
>>> Out[21] Timedelta('0 days 00:00:00.002000')
# Same for month end 'M'
```
#### Problem description
Hello
I am aware Timedelta & DateOffset objects are not supposed to have same representation, but could their string aliases be different when they cannot be mapped to each other?
For Timedelta / minute vs DateOffset / month start ('M'):
- I understand that representations should be 'min' or 'T' so I am surprised 'M' is accepted by to_timedelta(). To be removed?
For DateOffset / month start vs Timedelta / millisecond ('MS'):
- maybe month start can be replaced by MB 'Month Begin' ?
Would this make sense?
Bests,
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MDU6SXNzdWU1ODk1MjQ4MzE=
| 33,095
|
Question/Suggestion : Window or Rolling does not have a `get_window` public method
|
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| 2020-03-28T07:16:43Z
| 2020-09-02T05:19:14Z
| 2020-09-02T05:19:14Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# somewhere
rolling = df.rolling(7, win_type="bartlett")
#somewhere else
plot( rolling._get_window( win_type=rolling.win_type) )
```
#### Problem description
Sometime you receive a Rolling or Window from somewhere and want, e.g. plot its shape.
Would be convenient to have a public method or property to get the array window weight instead of private `._get_window` method.
could not found one.
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MDExOlB1bGxSZXF1ZXN0Mzk1MDkxNTk5
| 33,096
|
TEST: Add test for #22541 to ensure that error does not occur again
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| 1
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MEMBER
| null |
- [x] closes #22541
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
While searching for related issue for my other pull request, I found that this issue must have been fixed with another commit in the past (don't know when this issue was fixed).
I added a test to ensure that this error won't know occur in the future.
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MDU6SXNzdWU1ODk1NzU3ODU=
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i m not able to use this .append function even after the syntax is correct
|
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| 2020-03-28T13:17:07Z
| 2020-03-28T16:39:58Z
| 2020-03-28T16:39:51Z
|
NONE
| null |
import pandas as pd
import xlsxwriter
cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A8'],
'Price': [22000,25000,27000,35000]}
df = pd.DataFrame(cars, columns = ['Brand', 'Price'])
def add():
ans = input ("Want to add more data.?(y/n):")
if (ans=='y'):
car_name = input ("Enter name:")
price = input ("Price:")
df2 = pd.DataFrame({'Brand' : [car_name],
'Price' : [price]})
df.append(df2, ignore_index = True)
add()
add()
df = pd.DataFrame(cars, columns = ['Brand', 'Price'])
#### Expected Output
Want to add more data.?(y/n):y
Enter name:Bugatti
Price:789456
Want to add more data.?(y/n):n
Brand Price
0 Honda Civic 22000
1 Toyota Corolla 25000
2 Ford Focus 27000
3 Audi A8 35000
4 Bugatti 789456
#### Output of ``pd.show_versions()``
Want to add more data.?(y/n):y
Enter name:Bugatti
Price:789456
Want to add more data.?(y/n):n
Brand Price
0 Honda Civic 22000
1 Toyota Corolla 25000
2 Ford Focus 27000
3 Audi A8 35000
<details>
here i m not able to use this .append function even after the syntax is correct can anyone plz help me out here i have sucessfully installed pandas also and i m running it in spyder
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk1MDk1MDg1
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TST: GroupBy(..., as_index=True).agg() drops index
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| 4
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MEMBER
| null |
- [x] closes #28641
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
While searching for related issue for my other pull request, I found that this issue must have been fixed with another commit in the past (don't know when this issue was fixed).
I added a test to ensure that this error won't know occur in the future.
|
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- [ ] closes #xxxx
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TYP: require Index objects earlier in internals
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Add test for named period index, doing group by index
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MDExOlB1bGxSZXF1ZXN0Mzk1MTE1NDEz
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PERF: fix performance regression in memory_usage(deep=True) for object dtype
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CONTRIBUTOR
| null |
- [x] closes #33012
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The pull request is to update lib.memory_usage_of_objects from taking self.arrays to self._values. An ASV included to benchmark with and without object-dtype columns.
Before:

After:

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Check error message for raised exception
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CONTRIBUTOR
| null |
- [x] xref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: ensure consistent behavior of Index.is_all_dates (#19204)
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NONE
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0Mzk1MTQ5NjQ2
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Add test for #32108 (error with groupby on series with period index)
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| 13
| 2020-03-28T20:22:26Z
| 2020-06-24T22:36:04Z
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CONTRIBUTOR
| null |
- [ ] closes #32108
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MDExOlB1bGxSZXF1ZXN0Mzk1MTYwNTEz
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Fix mixup between mean and median
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CONTRIBUTOR
| null |
Text said "median", but code uses "mean". Make text match the code
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH/VIZ: Allowing `s` parameter of scatter plots to be a column name
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| 2
| 2020-03-28T22:10:52Z
| 2020-03-31T21:28:17Z
| 2020-03-31T21:26:48Z
|
CONTRIBUTOR
| null |
- [x] closes #32904
- [x] tests added
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR is a continuation of #32937 (made an error when pulling changes from the master).
I had a more elaborate decision tree for what to do with a passed size variable `s`, here https://github.com/pandas-dev/pandas/pull/32937, but in the end decided to go with the simpler version since the other checks are redundant (they would contain `s=s` or `pass`).
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Docstring for show_versions in master
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REF: DataFrame delitem, take, pop, filter tests
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REF: push concat logic out of internals and into concat_compat
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| 2020-03-29T00:38:07Z
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MEMBER
| null |
This changes some empty-array behavior for concat_compat to match the behavior of pd.concat(list_of_series), see new test.
The follow-up to this basically gets concat out of internals altogether.
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Bug 29764 groupby loses index name sometimes
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MEMBER
| null |
- [x] closes #29764
- [x] tests added / passed
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Some group by functions lost the column index name. For the functions running into ```_get_cythonized_result``` in groupby.py, the index name was just ignored when defining the column names. So the following functions ```_wrap_aggregated_output``` and ```_wrap_transformed_output``` in the class DataFrameGroupBy had not acces to this information, because it was already lost there. I collected the information beforehand and defined the Index Name accordingly.
We could refactor both methods a bit at a later stage, because at it is right now (and was before) the first few lines are duplicates.
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CLN: update Appender to doc decorator with case __doc__
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| 1
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| 2020-03-30T15:39:07Z
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|
CONTRIBUTOR
| null |
- [x] working for #31942
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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.astype(SparseDtype(float)) on empty dataframe leads to "ValueError: No objects to concatenate"
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| 2020-06-25T23:13:00Z
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|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.DataFrame().astype(pd.SparseDtype(float))
```
#### Problem description
Converting an empty dataframe to a sparse representation leads to a `ValueError`.
```
ValueError: No objects to concatenate
```
#### Expected Output
I expected this to work even though the dataframe is empty.
#### Output of ``pd.show_versions()``
```
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0
Cython : None
pytest : 5.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.0
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.46.0
```
</details>
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|
concatenating frame and series with identical keys returns " int() argument must be a string"
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| 2020-03-29T09:53:51Z
| 2020-05-28T15:39:59Z
| 2020-05-28T15:39:59Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> s = pd.Series([1, 2])
>>> df = pd.DataFrame([[1, 2], [3, 4]])
>>> pd.concat([df, s], axis=1, keys=['a', 'a'])
```
#### Problem description
This returns
<details>
<summary>Traceback</summary>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-262b5243c6cf> in <module>
----> 1 pd.concat([df, s], axis=1, keys=['a', 'a'])
~/pandas/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
278 verify_integrity=verify_integrity,
279 copy=copy,
--> 280 sort=sort,
281 )
282
~/pandas/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
447 self.copy = copy
448
--> 449 self.new_axes = self._get_new_axes()
450
451 def get_result(self):
~/pandas/pandas/core/reshape/concat.py in _get_new_axes(self)
510 return [
511 self._get_concat_axis() if i == self.axis else self._get_comb_axis(i)
--> 512 for i in range(ndim)
513 ]
514
~/pandas/pandas/core/reshape/concat.py in <listcomp>(.0)
510 return [
511 self._get_concat_axis() if i == self.axis else self._get_comb_axis(i)
--> 512 for i in range(ndim)
513 ]
514
~/pandas/pandas/core/reshape/concat.py in _get_concat_axis(self)
566 else:
567 concat_axis = _make_concat_multiindex(
--> 568 indexes, self.keys, self.levels, self.names
569 )
570
~/pandas/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
648
649 return MultiIndex(
--> 650 levels=levels, codes=codes_list, names=names, verify_integrity=False
651 )
652
~/pandas/pandas/core/indexes/multi.py in __new__(cls, levels, codes, sortorder, names, dtype, copy, name, verify_integrity, _set_identity)
281 # we've already validated levels and codes, so shortcut here
282 result._set_levels(levels, copy=copy, validate=False)
--> 283 result._set_codes(codes, copy=copy, validate=False)
284
285 result._names = [None] * len(levels)
~/pandas/pandas/core/indexes/multi.py in _set_codes(self, codes, level, copy, validate, verify_integrity)
864 new_codes = FrozenList(
865 _coerce_indexer_frozen(level_codes, lev, copy=copy).view()
--> 866 for lev, level_codes in zip(self._levels, codes)
867 )
868 else:
~/pandas/pandas/core/indexes/multi.py in <genexpr>(.0)
864 new_codes = FrozenList(
865 _coerce_indexer_frozen(level_codes, lev, copy=copy).view()
--> 866 for lev, level_codes in zip(self._levels, codes)
867 )
868 else:
~/pandas/pandas/core/indexes/multi.py in _coerce_indexer_frozen(array_like, categories, copy)
3597 Non-writeable.
3598 """
-> 3599 array_like = coerce_indexer_dtype(array_like, categories)
3600 if copy:
3601 array_like = array_like.copy()
~/pandas/pandas/core/dtypes/cast.py in coerce_indexer_dtype(indexer, categories)
856 length = len(categories)
857 if length < _int8_max:
--> 858 return ensure_int8(indexer)
859 elif length < _int16_max:
860 return ensure_int16(indexer)
~/pandas/pandas/_libs/algos_common_helper.pxi in pandas._libs.algos.ensure_int8()
59 return arr
60 else:
---> 61 return arr.astype(np.int8, copy=copy)
62 else:
63 return np.array(arr, dtype=np.int8)
```
</details>
```
TypeError: int() argument must be a string, a bytes-like object or a number, not 'slice'
```
#### Expected Output
I would either expect this to work, or for it to return
```
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
```
, which is what happens when you do
```
pd.concat([df, df], axis=1, keys=['a', 'a'])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 36d6583cf51ff6de5b6d82a63391561056a696e6
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
Version : #92-Ubuntu SMP Fri Feb 28 11:09:48 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.26.0.dev0+2746.g36d6583cf
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
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| 2
| 2020-03-29T11:53:30Z
| 2020-05-19T08:41:32Z
| 2020-05-19T08:41:32Z
|
MEMBER
| null |
xref #28949
on master
```
>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'1.1.0.dev0+1008.g60b0e9fbc'
>>>
>>> cat = pd.Categorical(["a", "b", "c", "b"], ordered=False)
>>> cat
[a, b, c, b]
Categories (3, object): [a, b, c]
>>>
>>> np.min(cat)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<__array_function__ internals>", line 6, in amin
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\numpy\core\fromnumeric.py", line 2793, in amin
keepdims=keepdims, initial=initial, where=where)
File "C:\Users\simon\Anaconda3\envs\pandas-dev\lib\site-packages\numpy\core\fromnumeric.py", line 88, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
File "C:\Users\simon\pandas\pandas\util\_decorators.py", line 212, in wrapper
return func(*args, **kwargs)
TypeError: min() got an unexpected keyword argument 'axis'
```
on 0.25.3
```
>>> import numpy as np
>>> import pandas as pd
>>>
>>> pd.__version__
'0.25.3'
>>>
>>> cat = pd.Categorical(["a", "b", "c", "b"], ordered=False)
>>> cat
[a, b, c, b]
Categories (3, object): [a, b, c]
>>>
>>> np.min(cat)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 2618, in amin
initial=initial)
File "C:\Users\simon\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py", line 84, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
File "C:\Users\simon\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py", line 2278, in min
self.check_for_ordered("min")
File "C:\Users\simon\Anaconda3\lib\site-packages\pandas\core\arrays\categorical.py", line 1586, in check_for_ordered
"Categorical to an ordered one\n".format(op=op)
TypeError: Categorical is not ordered for operation min
you can use .as_ordered() to change the Categorical to an ordered one
```
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https://api.github.com/repos/pandas-dev/pandas/issues/33116
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| 589,776,490
|
MDExOlB1bGxSZXF1ZXN0Mzk1MjQwMDMz
| 33,116
|
Fix grammar
|
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| 1
| 2020-03-29T12:03:13Z
| 2020-03-29T18:20:18Z
| 2020-03-29T15:29:31Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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