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MDU6SXNzdWU3NTI0NDU5NTA=
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REF: dtypes.cast
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| 2020-11-27T19:46:07Z
| 2020-12-29T22:59:27Z
| 2020-12-29T22:59:27Z
|
MEMBER
| null |
There are too many functions in `dtypes.cast`, making it difficult to know which one to use in a given situation. e.g. when working with `Index.insert` in #38102 I considered `maybe_promote`, `maybe_upcast`, `maybe_casted_values`, `find_common_type`.
Best case scenario we can de-duplicate some of these and just remove some of them, but failing that, I think we could find new homes for e.g. `construct_1d_arraylike_from_scalar`, `construct_1d_object_array_from_listlike`, `construct_1d_ndarray_preserving_na`.
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MDExOlB1bGxSZXF1ZXN0NTI4NzY4NDc5
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API: preserve freq in DTI/TDI.factorize
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| 8
| 2020-11-27T19:48:55Z
| 2021-11-20T23:21:28Z
| 2020-11-30T13:40:57Z
|
MEMBER
| null |
cc @simonjayhawkins
closes #35563
this is an old branch I resurrected and rebased, so has some excess cruft that would need to be removed if we want to move forward.
LMK if there are any tests you'd like to see added based on the other issue (or just go ahead and push them if you like)
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CLN remove duplicated entries in valid_resos in pandas/core/indexes/datetimes.py
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NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Before running, it is easy! just check the lines 560-570 of the pandas/code/indexes/datetime.py and you will understand the human error some developer did :)
```python
# Your code here
import pandas as pd
import datetime as dt
```
Inside Github.csv, we have:
```python
'''
,RxID,msgtype,message,confirmed,SNR,strength,format,icao24,correctedBits,LowConf,freqOffst
2020-08-25 11:00:08.187503455,1,10,4000,,0.0,-80.5,,,,,
2020-08-25 11:00:08.189013753,1,10,4000,,0.0,-78.5,,,,,
2020-08-25 11:00:08.189746310,1,8,,,0.0,-79.0,,,,,
2020-08-25 11:00:08.189986916,1,10,2700,,0.0,-78.0,,,,,
2020-08-25 11:00:08.190476779,1,6,20A93040502C94,1,0.0,-79.5,4.0,A1F014,,,
2020-08-25 11:00:08.190482661,1,9,0000,,0.0,-79.0,,,,,
2020-08-25 11:00:08.190963454,1,6,58EB0000171D17,1,0.0,-76.5,11.0,,,,
2020-08-25 11:00:08.191085134,1,9,7F00,,0.0,-78.5,,,,,
2020-08-25 11:00:08.191087092,1,9,0000,,0.0,-79.5,,,,,
2020-08-25 11:00:08.191123383,1,9,1000,,0.0,-72.0,,,,,
2020-08-25 11:00:08.191139020,1,7,,,0.0,-77.0,,,,,
2020-08-25 11:00:08.191150695,1,7,,,0.0,-76.5,,,,,
2020-08-25 11:00:08.191590978,1,10,0000,,0.0,-70.5,,,,,
2020-08-25 11:00:08.193015479,1,10,0000,,0.0,-83.0,,,,,
2020-08-25 11:00:08.193509041,1,9,2800,,0.0,-78.0,,,,,
2020-08-25 11:00:08.193664650,1,8,,,0.0,-80.5,,,,,
2020-08-25 11:00:08.193992571,1,10,0000,,0.0,-81.5,,,,,
2020-08-25 11:00:08.194459459,1,10,7D00,,0.0,-76.5,,,,,
2020-08-25 11:00:08.194461492,1,10,0001,,0.0,-76.0,,,,,
2020-08-25 11:00:08.195045194,1,10,0000,,0.0,-80.0,,,,,
2020-08-25 11:00:08.195061385,1,8,,,0.0,-80.0,,,,,
2020-08-25 11:00:08.195102628,1,10,0000,,0.0,-75.0,,,,,
'''
```
```python
dfPKT = pd.read_csv('Github.csv',dtype={'RxId': int, 'msgtype': int, 'message': str, 'confirmed': object,'SNR': float, 'strength': float, 'format': float, 'icao24': str, 'correctedBits': float,'LowConf': float, 'freqOffst': float},index_col=0)
i=pd.DatetimeIndex(dfPKT.index,dayfirst=True)
dfPKT.set_index(i,inplace=True)
#occupancy constant
occupancyMode3Ainterrogation = 0.00015675
#How many messages are inside valid Mode A interrogation.
validModeAint=[]
#We start with the first timestamp as a reference.
Next_timestamp = dfPKT.index[0]
#Then, we get all the minutes of our study/analysis.
allminutes = dfPKT.index.floor('60S').unique()
#and for every minute, we do a data analysis.
for minute in allminutes:
#Here, since the dataset is very big, I get small datasets of 1 minute each.
dfAnalysis = dfPKT.loc[(dfPKT.index > minute) & (dfPKT.index < minute + dt.timedelta(seconds=60))]
for message, index, format, code, msgtype in zip(dfAnalysis["message"], dfAnalysis.index, dfAnalysis["format"],dfAnalysis["icao24"], dfAnalysis["msgtype"]):
#Depending on the type of the message, we process it in one way or another
if msgtype == 10:
bits = bin(int(message, 16))[2:].zfill(16) #this is the 16 bits inside the messagetype 10.
if bits[:2] == '00': #if first two bits are zero
'''
when these two specific bits on the message are 0, we have to check on the same dataset if there is a message linked to it, from 8 to 13 microseconds after the actual message. HERE is were we get the error. It is trying to slice the datetime strings, and internally, it does not have a "milisecond" resolution. Altough my resolution in here is microseconds. But internally, it tries to do something with miliseconds and It breaks the code.
'''
if (2 in dfAnalysis[str(index + dt.timedelta(seconds=0.000008)):str(index + dt.timedelta(seconds=0.000013))]["msgtype"].to_numpy()):
validModeAint.append(index)
Next_timestamp = index + dt.timedelta(seconds=occupancyMode3Ainterrogation)
```
#### Problem description
```
Traceback (most recent call last):
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 718, in slice_indexer
return Index.slice_indexer(self, start, end, step, kind=kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\base.py", line 4966, in slice_indexer
start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\base.py", line 5169, in slice_locs
start_slice = self.get_slice_bound(start, "left", kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\base.py", line 5079, in get_slice_bound
label = self._maybe_cast_slice_bound(label, side, kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 665, in _maybe_cast_slice_bound
lower, upper = self._parsed_string_to_bounds(reso, parsed)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 536, in _parsed_string_to_bounds
raise KeyError
KeyError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\OccupancyScripts\OccupancyToKML\Github.py", line 31, in <module>
if (2 in dfAnalysis[str(index + dt.timedelta(seconds=0.000008)):str(index + dt.timedelta(seconds=0.000013))]["msgtype"].to_numpy()):
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\frame.py", line 2881, in __getitem__
indexer = convert_to_index_sliceable(self, key)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexing.py", line 2132, in convert_to_index_sliceable
return idx._convert_slice_indexer(key, kind="getitem")
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\base.py", line 3190, in _convert_slice_indexer
indexer = self.slice_indexer(start, stop, step, kind=kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 728, in slice_indexer
start_casted = self._maybe_cast_slice_bound(start, "left", kind)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 665, in _maybe_cast_slice_bound
lower, upper = self._parsed_string_to_bounds(reso, parsed)
File "C:\Users\bkaguila\dev\anaconda\envs\Occupancies\lib\site-packages\pandas\core\indexes\datetimes.py", line 536, in _parsed_string_to_bounds
raise KeyError
KeyError
Process finished with exit code 1
```
The current behavior is that the indexes are partitioned, as they are strings, and the reso or Resolution class, which has nanosecond and minisecond resolution, then is passed to the datetime.py file on the pandas/core/indexes, which has the valid_resos (on line 520) wrong.
#### Expected Output
Process finished with exit code 0
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.9.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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BUG: Index.intersection casting to object instead of numeric
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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
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Backport PR #38094 on branch 1.1.x (REGR: fix regression in groupby aggregation with out-of-bounds datetimes)
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Backport PR #38094: REGR: fix regression in groupby aggregation with out-of-bounds datetimes
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NONE
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Backport PR #38087: BLD: Only enable -Werror in the CI jobs
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BUG: to/read_* do not use user-provided file handle if handle implements os.PathLike and also opened the file
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| 0
| 2020-11-27T23:55:08Z
| 2020-12-14T01:07:31Z
| 2020-12-14T01:07:31Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas (theoretically affected as well but my example doesn't work for <1.2 (need binary file handle) other examples should trigger this bug in <1.2).
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import os
import fsspec
import pandas as pd
# create a 'normal' file handle
with open("abc.test", mode="w") as open_obj:
assert not isinstance(open_obj, os.PathLike) # is not converted to a string
position = open_obj.tell()
# let to_csv write to the opened file
pd.DataFrame({"a": [1, 2, 3]}).to_csv(open_obj)
# the position of the file buffer should have changed if to_csv used it
assert open_obj.tell() != position
# create a file handle that also implements os.PathLike/has __fspath__
fsspec_obj = fsspec.open("file://abc.test", mode="wb").open()
with fsspec_obj:
assert isinstance(fsspec_obj, os.PathLike) # is converted to a string
position = fsspec_obj.tell()
# let to_csv write to the opened file
pd.DataFrame({"a": [1, 2, 3]}).to_csv(fsspec_obj)
# the position of the file buffer should have changed if to_csv used it
assert fsspec_obj.tell() != position # fails
```
#### Problem description
`get_filepath_or_buffer` (<1.2) or `get_handle` (1.2) call `stringify_path` to convert `pathlib.Path` and other `os.PathLike` to a string. This string is then later opened. It seems that there is at least one file object that implements `os.PathLike` but at the same time already opens the file. In this case case, all `to/read_*` that use `get_handle` (or `get_filepath_or_buffer` in <1.2) extract the string and then open the file (even though the user already opened it).
I'm not sure whether there are other examples. I will look into how to fix this.
|
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MDExOlB1bGxSZXF1ZXN0NTI4ODI3NTkz
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|
ENH: Implement dict-like support for rename and set_names in MultiIndex
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| null |
- [x] closes #20421
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I tried my hand in adding support for dict-like renamings for MultiIndexes. This seemed as the most straight forward way to me, because we do not have to add complex logic as long as we can cast the dict-like objects to regular configurations.
We probably must add a docstring to MultiIndex.rename now?
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CLN: simplify mask_missing
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REF: de-duplicate ndarray[datetimelike] wrapping
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REF: use np.where instead of maybe_upcast_putmask in nanops
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This way we make at most one copy. Also we get rid of the only use of maybe_upcast_putmask that has fill_value of anything other than np.nan, so in a follow-up it can be simplified.
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|
BUG: HDFStore mishandles pd.categorical when initialized with custom categories
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| 2021-03-24T15:11:40Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(columns=['col1'])
df['col1'] = pd.Categorical(["a","b","c","a"], categories=["c","b","a"])
store = pd.HDFStore('bug.h5')
store.append('df', df, data_columns=df.columns)
store.select('df', "col1='a'")
```
#### Problem description
yields
```
col1
2 c
```
#### Expected Output
should yield
```
col1
0 a
3 a
```
#### workaround
simply don't provide categories to the pd.Categorical(). everything works fine with the inferred categories – at least in my project from what I have seen.
#### reference & questions
issue in pytables (where I don't think it belongs at this point): https://github.com/PyTables/PyTables/issues/714
the bug is invariant with regard to the following HDFStore settings:
- choosing "fixed" or "table" format
- using compression
are categories even officially supported in HDFStore at this point? The IO guide still does not mention the type as supported. ([https://pandas.pydata.org/pandas-docs/dev/user_guide/io.html#storing-types](url)) Maybe create a warning if it really is not supported still.
#### Output of ``pd.show_versions()``
default Google Colaboratory as of 28.11.2020
```
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.112+
Version : #1 SMP Thu Jul 23 08:00:38 PDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.18.5
pytz : 2018.9
dateutil : 2.8.1
pip : 19.3.1
setuptools : 50.3.2
Cython : 0.29.21
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.13.3
pyarrow : 0.14.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : 3.4.4
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.48.0
```
</details>
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DOC: add contibutors to 1.2.0 release notes
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test backportability of #36927
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REF: de-duplicate Block.__init__
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- [ ] closes #xxxx
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CONTRIBUTOR
| null |
There are situations where we want to keep the same dtype as in the original after applying `unique` For example
```python
>>> dtype = pd.categoricalDtype(['very good', 'good', 'neutral', 'bad', 'very bad'], ordered=True)
>>> cat = pd.Categorical(['good','good', 'bad', 'bad'], dtype=dtype)
>>> cat
[good, good, bad, bad]
Categories (5, object): [very good < good < neutral < bad < very bad]
>>> cat.unique().dtype == cat.dtype
False # this is a bug IMO, but others may not agree
```
Even if it's not a bug, there are situations where we want the comparison above to return True. To alleviate the above, I've added a new parameter, so we can do
```
>>> cat.unique(remove_unused_categories=False).dtype == cat.dtype
True
```
Helps #18291, but does not close the issue.
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test backportability of #38120
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| 21
| 2020-11-28T19:49:18Z
| 2020-12-09T17:11:15Z
| 2020-12-09T17:09:15Z
|
MEMBER
| null |
The following files contain flake8 C408 Unnecessary dict call - rewrite as a literal. Good for first time contributors. See #38078 and #38116 for how to fix them. Use `flake8 [file_path]` to check for each file.
### C408
- [x] ./pandas/core/generic.py
- [x] ./pandas/core/series.py
- [x] ./pandas/core/frame.py
- [x] ./pandas/core/strings/accessor.py
- [x] ./pandas/core/dtypes/dtypes.py
- [x] ./pandas/core/computation/pytables.py
- [x] ./pandas/core/computation/expressions.py
- [x] ./pandas/core/arrays/floating.py
- [x] ./pandas/core/arrays/interval.py
- [x] ./pandas/core/arrays/numpy_.py
- [x] ./pandas/core/arrays/base.py
- [x] ./pandas/core/ops/methods.py
- [x] ./pandas/core/indexes/interval.py
- [x] ./pandas/core/indexes/multi.py
- [x] ./pandas/core/indexes/numeric.py
- [x] ./pandas/core/indexes/base.py
- [x] ./pandas/io/pytables.py
- [x] ./pandas/tests/reshape/concat/test_datetimes.py
- [x] ./pandas/tests/reshape/merge/test_merge.py
- [x] ./pandas/tests/reshape/merge/test_multi.py
- [x] ./pandas/tests/tools/test_to_numeric.py
- [x] ./pandas/tests/resample/test_time_grouper.py
- [x] ./pandas/tests/util/test_assert_series_equal.py
- [x] ./pandas/tests/util/test_assert_interval_array_equal.py
- [x] ./pandas/tests/util/test_assert_index_equal.py
- [x] ./pandas/tests/util/test_assert_extension_array_equal.py
- [x] ./pandas/tests/io/test_parquet.py
- [x] ./pandas/tests/io/generate_legacy_storage_files.py
- [x] ./pandas/tests/io/formats/test_format.py
- [x] ./pandas/tests/io/parser/test_comment.py
- [x] ./pandas/tests/io/parser/test_parse_dates.py
- [x] ./pandas/tests/io/parser/test_c_parser_only.py
- [x] ./pandas/tests/io/parser/test_dialect.py
- [x] ./pandas/tests/io/parser/test_skiprows.py
- [x] ./pandas/tests/io/parser/test_encoding.py
- [x] ./pandas/tests/io/parser/test_read_fwf.py
- [x] ./pandas/tests/io/parser/test_python_parser_only.py
- [x] ./pandas/tests/io/parser/test_na_values.py
- [x] ./pandas/tests/io/parser/test_usecols.py
- [x] ./pandas/tests/io/parser/test_quoting.py
- [x] ./pandas/tests/io/parser/test_header.py
- [x] ./pandas/tests/io/parser/test_index_col.py
- [x] ./pandas/tests/io/json/test_ujson.py
- [x] ./pandas/tests/io/json/test_normalize.py
- [x] ./pandas/tests/io/pytables/test_timezones.py
- [x] ./pandas/tests/io/pytables/test_store.py
- [x] ./pandas/tests/frame/test_reductions.py
- [x] ./pandas/tests/frame/test_arithmetic.py
- [x] ./pandas/tests/frame/methods/test_to_records.py
- [x] ./pandas/tests/frame/methods/test_sort_values.py
- [x] ./pandas/tests/frame/methods/test_fillna.py
- [x] ./pandas/tests/frame/methods/test_to_csv.py
- [x] ./pandas/tests/frame/indexing/test_indexing.py
- [x] ./pandas/tests/frame/indexing/test_where.py
- [x] ./pandas/tests/dtypes/test_inference.py
- [x] ./pandas/tests/groupby/test_function.py
- [x] ./pandas/tests/groupby/transform/test_transform.py
- [x] ./pandas/tests/plotting/test_boxplot_method.py
- [x] ./pandas/tests/plotting/frame/test_frame_subplots.py
- [x] ./pandas/tests/io/parser/test_common.py
### Remove ignore setting from flake8 section
- [x] setup.cfg
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TST: Add tests for observance rules in holiday.py
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| 2020-12-02T03:19:48Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
None of the observance rules (e.g. `next_workday`, `before_nearest_workday`) are tested
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BUG: Categorical.unique should keep dtype unchanged
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| 36
| 2020-11-28T19:59:54Z
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|
CONTRIBUTOR
| null |
- [x] closes #18291
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
We want to keep the same dtype as in the original after applying `unique`. For example:
```python
>>> dtype = pd.CategoricalDtype(['very bad', 'bad', 'neutral', 'good', 'very good'], ordered=True)
>>> cat = pd.Categorical(['good','good', 'bad', 'bad'], dtype=dtype)
>>> cat
['good', 'good', 'bad', 'bad']
Categories (5, object): ['very bad' < 'bad' < 'neutral' < 'good' < 'very good']
>>> cat.unique()
['good', 'bad']
Categories (5, object): ['bad' < 'good']
>>> cat.unique().dtype == cat.dtype
False # master
True # this PR
```
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|
CONTRIBUTOR
| null |
- [x] closes #38125
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I will finish this PR (fix the mypy issues) after #38018 is merged.
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DEPR: ExtensionOpsMixin -> OpsMixin
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MDExOlB1bGxSZXF1ZXN0NTI5MDM0NTA0
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REF: refactor out EA dispatch for _cython_operation
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Make our dispatching/casting explicit and self-contained.
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MDExOlB1bGxSZXF1ZXN0NTI5MDQ3NTAz
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CLN: remove unnecesary cast.maybe_convert_objects
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BUG: combine_first does not retain dtypes with Timestamp DataFrames
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- [x] tests added / passed
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- [x] whatsnew entry
Picking up #33278
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BUG: reset_index() Fails on a Series/DataFrame Object that has "False" (bool type) as the Index Name
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| 1
| 2020-11-29T07:12:40Z
| 2020-12-14T20:31:24Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [] (optional) I have confirmed this bug exists on the master branch of pandas.
-----
#### Code Sample, a copy-pastable example
```python
import pandas as pd
# Works
df1 = pd.Series(data=range(5, 10), index=range(0, 5))
df1.index.name = 'False'
df1.reset_index()
# Fails
df2 = pd.Series(data=range(5, 10), index=range(0, 5))
df2.index.name = False
df2.reset_index()
# Works
df1 = pd.DataFrame(data=range(5, 10), index=range(0, 5))
df1.index.name = 'False'
df1.reset_index()
# Fails
df2 = pd.DataFrame(data=range(5, 10), index=range(0, 5))
df2.index.name = False
df2.reset_index()
```
Stacktrace:
<details>
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-13-55bb91831e54> in <module>
9 df2 = pd.Series(data=range(5, 10), index=range(0, 5))
10 df2.index.name = False
---> 11 df2.reset_index()
~\Miniconda3\envs\vectorbt\lib\site-packages\pandas\core\series.py in reset_index(self, level, drop, name, inplace)
1340 else:
1341 df = self.to_frame(name)
-> 1342 return df.reset_index(level=level, drop=drop)
1343
1344 # ----------------------------------------------------------------------
~\Miniconda3\envs\vectorbt\lib\site-packages\pandas\core\frame.py in reset_index(self, level, drop, inplace, col_level, col_fill)
4602 # to ndarray and maybe infer different dtype
4603 level_values = _maybe_casted_values(lev, lab)
-> 4604 new_obj.insert(0, name, level_values)
4605
4606 new_obj.index = new_index
~\Miniconda3\envs\vectorbt\lib\site-packages\pandas\core\frame.py in insert(self, loc, column, value, allow_duplicates)
3494 self._ensure_valid_index(value)
3495 value = self._sanitize_column(column, value, broadcast=False)
-> 3496 self._data.insert(loc, column, value, allow_duplicates=allow_duplicates)
3497
3498 def assign(self, **kwargs) -> "DataFrame":
~\Miniconda3\envs\vectorbt\lib\site-packages\pandas\core\internals\managers.py in insert(self, loc, item, value, allow_duplicates)
1171 if not allow_duplicates and item in self.items:
1172 # Should this be a different kind of error??
-> 1173 raise ValueError(f"cannot insert {item}, already exists")
1174
1175 if not isinstance(loc, int):
ValueError: cannot insert False, already exists
</details>
#### Problem description
Pandas index names support multiple object types as opposed to just `str`. I presume that the intention was that an index name is never anything other than a string, but I can only speculate. Interestingly enough, I did not encounter this issue on a DataFrame. This leads me to believe that the issue has to do with
I originally encountered this bug when loading data from a csv that had a header of `False`. Perhaps a clean enough work-around is to convert headers to strings when loading from any file type?
#### Expected Output
The index reset without the ValueError. It should match `df1`'s output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.5.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 : 1.0.5
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200712
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.50.1
</details>
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ENH: Improve performance for df.__setitem__ with list-like indexers
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| 2020-11-29T14:07:15Z
| 2020-12-01T10:54:21Z
| 2020-11-29T21:52:39Z
|
MEMBER
| null |
- [x] closes #37954
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Reindexing the Block Manager improves the performance significantly. I hope I have not missed anything, concerning the reindexing of the blocks.
Time spent in ``_ensure_listlike_indexer`` is pretty low now.
timeit result for the ops methods:
```
In [20]: %timeit setitem(x, x_col, df)
1.09 ms ± 9.65 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [21]: %timeit concat(x, x_col, df)
293 µs ± 4.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Should we add tests here? I have added an asv to capture this case.
cc @jbrockmendel
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| 1
| 2020-11-29T16:00:49Z
| 2021-02-07T16:48:03Z
| 2021-02-07T16:48:03Z
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CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/pull/38098
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MDExOlB1bGxSZXF1ZXN0NTI5MTMxMDU0
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BUG: loc dropping levels when df has only one row
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| 12
| 2020-11-29T16:01:18Z
| 2021-07-04T16:24:03Z
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|
MEMBER
| null |
- [x] closes #10521
- [x] tests added / passed
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Originally this case was dispatched to xs, which dispatched to ``_get_loc_level``, which raised if a slice is not ``slice(None, None)`` and went back to our original function. If the ``DataFrame`` contains only one row, ``_get_loc_level`` does not raise and hence we drop levels.
I added len(self.obj) for performance reasons becasue I am not sure if the any check for slices is faster than supressing the IndexingError in general.
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CLN: remove unused coerce arg in NDFrame._convert
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- [x] closes #27180
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I looked into this and added an implementation for list-like indexers containing slices and DatetimeIndexes. If we want to support this we probably have to do something like this.
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| 752,971,622
|
MDExOlB1bGxSZXF1ZXN0NTI5MTQ3NTAx
| 38,154
|
CLN: improve is_unique check in Index.intersection
|
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| 7
| 2020-11-29T18:02:34Z
| 2020-12-02T12:19:35Z
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|
MEMBER
| null |
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
```
before after ratio
[e99e5ab3] [79d6a50f]
<cln_intersection~1> <cln_intersection>
- 918±20ns 819±2ns 0.89 index_object.Range.time_min_trivial
- 465±7ns 414±7ns 0.89 index_object.Range.time_get_loc_dec
- 464±6ns 411±7ns 0.89 index_object.Range.time_get_loc_inc
- 16.6±0.7ms 12.6±0.03ms 0.76 index_object.SetOperations.time_operation('date_string', 'union')
```
Seems to have no impact.
cc @jreback
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Backport PR #36927: BUG: Fix duplicates in intersection of multiindexes
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| 3
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MEMBER
| null |
Backport PR #36927
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CLN: remove _recast_datetimelike_result
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| 0
| 2020-11-29T19:14:28Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 2
| 2020-11-29T19:26:37Z
| 2020-12-02T23:31:34Z
| 2020-12-02T23:31:30Z
|
CONTRIBUTOR
| null |
closes #37609
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Working with @richardwong8 on this issue.
This addresses issue #37609 where a check `needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype)` in assert_series_equal caused an assert_extension_array on left._values and right._values when DatetimeArray or TimedeltaArray were used. This caused an exception to be raised when either the right or left was not an instance of ExtensionArray but was used with DatetimeArray or TimedeltaArray.
This would have been covered in the earlier check `is_extension_array_dtype(left.dtype) and is_extension_array_dtype(right.dtype)`, however, DatetimeArray and TimedeltaArray are still experimental and their dtypes are not instances of ExtensionDtype subclass.
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Revert "DEPR: ExtensionOpsMixin -> OpsMixin"
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Reverts pandas-dev/pandas#38142 (see discussion over there)
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BUG: Groupy By
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| 2020-11-29T20:41:46Z
| 2021-06-12T08:10:57Z
| 2020-11-30T22:14:00Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import pandas as pd
df = pd.DataFrame([[1,2,3],[1,4,5],[2,3,4]],columns=['a','b','c'])
grouped = df.groupby('a')
print("GROUPED:")
print(grouped) #prints pandas.core.groupby.generic.DataFrameGroupBy no frame...
grpA = grouped.apply(lambda grp: grp[:])
print('GROUPED A:')
print(grpA) #prints grouped df.
grpB = grouped.apply(lambda grp: grp)
print('GROUPED B:')
print(grpB) #doesn't print grouped df just prints the normal df.
```
#### Current Output
GROUPED:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1205a1a90>
GROUPED A:
a b c
a
1 0 1 2 3
1 1 4 5
2 2 2 3 4
GROUPED B:
a b c
0 1 2 3
1 1 4 5
2 2 3 4
#### Problem description
Why is they print(grouped) not working ?
Also why is there different output for grpA, grpB : grpA get's nicely grouped while grbB just prints the orginal df.
shouldn't df[:] and df be the same as I have used in apply function , why are they producing different output?
#### Expected Output
GROUPED:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1205a1a90>
GROUPED A:
a b c
a
1 0 1 2 3
1 1 4 5
2 2 2 3 4
GROUPED B:
a b c
a
1 0 1 2 3
1 1 4 5
2 2 2 3 4
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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REF: implement disallow_invalid_ops
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MEMBER
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I've identified some bugs in _cython_operation, trying to address them in small pieces to make sure we get appropriate coverage. this is a preliminary refactor.
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REF: more distinctive names for BaseGrouper.aggregate, transform
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MEMBER
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ATM it is way too difficult to reason about where BaseGrouper.transform/aggregate is being called from. This removes two one-liners to make it this easy.
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REF: implement _ea_wrap_cython_operation
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MEMBER
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I've identified some bugs in _cython_operation. As a preliminary to fixing them, this splits out the EA-handling from the ndarray-handling. This will also make it easier to keep track of what we need to generalize if we want this to support arbitrary EAs.
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NONE
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CONTRIBUTOR
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
>>>l = pd.DataFrame([['g', 'h', 1], ['g', 'h', 3]], columns=list('GHT'))
>>>l
G H T
0 g h 1
1 g h 3
>>>r = pd.DataFrame([[2, 1]], columns=list('TE'))
>>>r
T E
0 2 1
```
results
```python
>>>pd.merge_ordered(l, r, on='T', left_by=['G'])
```
| | G | H | T | E |
|---:|:----|:----|----:|----:|
| 0 | g | h | 1 | nan |
| 1 | g | h | 3 | nan |
expected
```python
>>>pd.merge_ordered(l, r, on='T', left_by=['G'])
```
| | G | H | T | E |
|---:|:----|:----|----:|----:|
| 0 | g | h | 1 | nan |
| 1 | g | h | 2 | 1 |
| 2 | g | h | 3 | nan |
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MDU6SXNzdWU3NTMwNjgzMTA=
| 38,167
|
BUG: merge_ordered should raise when elements in by not exist in df
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| 0
| 2020-11-30T01:10:58Z
| 2020-12-02T02:31:02Z
| 2020-12-02T02:31:02Z
|
CONTRIBUTOR
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
>>>l = pd.DataFrame([['g', 'h', 1], ['g', 'h', 3]], columns=list('GHT'))
>>>l
G H T
0 g h 1
1 g h 3
>>>r = pd.DataFrame([[2, 1]], columns=list('TE'))
>>>r
T E
0 2 1
```
Result
```python
>>>pd.merge_ordered(l, r, on='T', left_by=['G', 'h'])
```
| | G | H | T | E | h |
|---:|:----|:----|----:|----:|:----|
| 0 | G | h | 1 | nan | G |
| 1 | G | nan | 2 | 1 | G |
| 2 | h | nan | 2 | 1 | h |
| 3 | h | h | 3 | nan | h |
expected
```python
>>>pd.merge_ordered(l, r, on='T', left_by=['G', 'h'])
KeyError: "['h'] not found in left columns"
```
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|
BUG/CLN: Minimize number of ResourceWarnings
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| 1
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| 2020-12-01T00:34:33Z
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|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I think I got all `ResourceWarning`s when running:
`PYTHONWARNINGS=default pytest pandas/tests/io`
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fix frame, generic, series.py
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| 2
| 2020-11-30T01:23:30Z
| 2020-11-30T16:43:11Z
| 2020-11-30T14:30:16Z
|
CONTRIBUTOR
| null |
- [x] xref #38138
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Identify unnecessary dict call - rewrite as a literal
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BUG: unexpected merge_ordered results caused by wrongly groupby
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| 2
| 2020-11-30T01:28:22Z
| 2020-12-02T02:45:26Z
| 2020-12-02T02:31:03Z
|
CONTRIBUTOR
| null |
- [x] closes #38166
- [x] closes #38167
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TST: add comment that plotting tests are slow
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| 2020-11-30T03:20:25Z
| 2020-12-03T07:56:14Z
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MEMBER
| null |
- [ ] xref https://github.com/pandas-dev/pandas/pull/38079#issuecomment-735262800
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Add comment (module docstring) in ``pandas/tests/plotting/common.py``,
mentioning that all plotting tests are marked as slow.
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CI: update tests for numpy 1.20 change to floordiv
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Looks like https://github.com/numpy/numpy/pull/16161 changed numpy's floordiv behavior
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BUG: array-like quantile fails on column groupby
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| 2
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CONTRIBUTOR
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- [x] closes #33795
- [x] tests added / passed
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REF/CLN: DataFrame.combine_first
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| 1
| 2020-11-30T05:31:21Z
| 2020-12-14T20:29:11Z
| null |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/38145#issuecomment-735346546
`DataFrame.combine_first` should be refactored to use `BlockManager.where` instead of `pandas.core.computation.expressions.where`
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MDExOlB1bGxSZXF1ZXN0NTI5Mjk3NjYx
| 38,175
|
ENH: support nanoseconds in Period constructor
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| 1
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| 2020-12-05T13:13:44Z
| 2020-12-02T12:12:18Z
|
MEMBER
| null |
- [x] closes #34621
- [x] closes #17053
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Picking up #34720
ASVs:
```
asv continuous -f 1.1 upstream/master HEAD -b ^period
before after ratio
[3f510600] [2b2f1d04]
<GH34621-nanos-for-Period^2> <GH34621-nanos-for-Period>
+ 7.46±0.2ms 11.6±0.1ms 1.55 period.PeriodIndexConstructor.time_from_ints('D', False)
+ 7.57±0.3ms 11.4±0.03ms 1.51 period.PeriodIndexConstructor.time_from_ints('D', True)
+ 52.8±0.6ms 59.2±0.4ms 1.12 period.PeriodIndexConstructor.time_from_ints_daily('D', True)
+ 52.9±0.9ms 59.0±0.1ms 1.11 period.PeriodIndexConstructor.time_from_ints_daily('D', False)
- 3.21±0.2μs 2.90±0.02μs 0.90 period.Indexing.time_get_loc
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
cc @jreback https://github.com/pandas-dev/pandas/pull/34720#discussion_r491536763 in case we want this for 1.2
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MDExOlB1bGxSZXF1ZXN0NTI5MzMwNTA3
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The error message for unsupported index on shift() could be more explicit
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| 4
| 2020-11-30T06:54:41Z
| 2020-12-04T02:00:17Z
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|
CONTRIBUTOR
| null |
When the type of data index is not datetime-like, the following error message will raise:
`NotImplementedError: Not supported for type Index`
The error message could be more useful by explicitly stating which types of data that `shift` supports.
Expected error message:
` f"This method is only implemented for DatetimeIndex, PeriodIndex and TimedeltaIndex; Got type {type(self).__name__}."`
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MDExOlB1bGxSZXF1ZXN0NTI5MzYxODY4
| 38,177
|
Revert "REF: define nanargminmax without values_for_argsort"
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| 3
| 2020-11-30T07:53:14Z
| 2020-11-30T13:21:28Z
| 2020-11-30T13:21:22Z
|
MEMBER
| null |
Reverts pandas-dev/pandas#37815
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| 38,178
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BUG: preserve nullable dtype for float result in IntegerArray/BooleanArray arithmetic ops
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| 3
| 2020-11-30T08:46:51Z
| 2020-12-01T09:32:52Z
| 2020-12-01T09:32:48Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/38110
This ensures that an operation on IntegerArray that results in floats (eg division) returns a FloatingArray instead of numpy float array.
This is only a small change in the actual code, the bulk of the changes is updating the tests.
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PERF: Introducing hash tables for complex64 and complex128
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| 2020-11-30T09:39:23Z
| 2020-12-31T08:01:48Z
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CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Next step for #33287
The complex case is somewhat less straight forward, because Cython [defines](https://github.com/cython/cython/blob/master/Cython/Includes/numpy/__init__.pxd#L755) `complex128`/`complex64` as:
```
ctypedef float complex complex64_t
ctypedef double complex complex128_t
```
where `double complex` actually means a Cython-datatype defined here https://github.com/cython/cython/blob/master/Cython/Utility/Complex.c#L56
Thus a conversion between numpy's complex and Cython's complex must happens somewhere.
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Rewrite dict literal for files in tests/reshape/
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| 1
| 2020-11-30T09:50:15Z
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|
CONTRIBUTOR
| null |
- [ ] xref #38138
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Rewrites as dictionary literals for the following files:
- ./pandas/tests/reshape/concat/test_datetimes.py
- ./pandas/tests/reshape/merge/test_merge.py
- ./pandas/tests/reshape/merge/test_multi.py
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Backport PR #38148: ENH: Improve performance for df.__setitem__ with list-like indexers
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| 5
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MEMBER
| null |
Backport PR #38148
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BUG/DEPR: Fix unwanted warnings from the Series(Index[datetime objects]) deprecation
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MEMBER
| null |
See https://github.com/pandas-dev/pandas/pull/36697#discussion_r497786546 and https://github.com/pandas-dev/pandas/pull/37193#issuecomment-715228272
Due to the `Index.is_all_dates` + `Series(Index[datetime object])` deprecation warnings, there are several code examples that are not using this attribute or constructor method directly, that still cause this warning (from internal pandas code, without direct control over this by the user).
Users should not see unrelated deprecation warnings from the internals of pandas that they can't do anything about themselves, so IMO we should either fix those for 1.2, or delay the deprecationg for 1.3
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BUG: assert_frame_equal can be very slow
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| 11
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| 2020-12-24T20:40:41Z
| 2020-12-24T20:40:41Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Comparisons which can take less than a second, take 50 seconds when they are combined:
```python
import pandas as pd
import numpy as np
from string import ascii_letters
idx = np.random.choice(np.array(list(ascii_letters)), size=10000).astype(object)
a = np.random.random_integers(0, 100, (10000, 1000))
# This is fine
%time pd.testing.assert_frame_equal(pd.DataFrame(a), pd.DataFrame(a.copy()))
# CPU times: user 409 ms, sys: 20.8 ms, total: 429 ms
# Wall time: 429 ms
# As is this
%time pd.testing.assert_index_equal(pd.Index(idx), pd.Index(idx.copy()))
# CPU times: user 1.06 ms, sys: 26 µs, total: 1.08 ms
# Wall time: 1.08 ms
# This is weird:
%time pd.testing.assert_frame_equal(pd.DataFrame(a, index=idx), pd.DataFrame(b, index=idx))
# CPU times: user 50.5 s, sys: 62.6 ms, total: 50.6 s
# Wall time: 50.6 s
```
#### Problem description
It seems weird that the whole process slows down when the index has strings in it. It's especially weird since comparing the index itself is fairly fast. This may be a regression, since I've never noticed this before, and this seem very noticeable.
This might have something to do with #38091
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.4
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
```
</details>
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MDExOlB1bGxSZXF1ZXN0NTI5NDM5NTAw
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DOC: trim error traceback in allows_duplicate_labels whatsnew entry
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| 0
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|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/37784#issuecomment-733183201
Ensure a more readable section (shorter error traceback) by using a plain code-block instead of the dynamic ipython directive
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Backport PR #38120: API: preserve freq in DTI/TDI.factorize
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MEMBER
| null |
Backport PR #38120
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| 1
| 2020-11-30T15:36:54Z
| 2020-11-30T18:52:31Z
| 2020-11-30T18:52:03Z
|
CONTRIBUTOR
| null |
- [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Rewrites as dictionary literals for the following files:
- pandas/tests/plotting/test_boxplot_method.py
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MDExOlB1bGxSZXF1ZXN0NTI5NjQ2ODc1
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BUG: groupby.rank for dt64tz, period dtypes
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| 2
| 2020-11-30T16:01:42Z
| 2020-12-05T20:35:34Z
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|
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| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This sits on top of #38162
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|
Rewrite dict literal for files in tests/plotting/frame/test_frame_subplots.py
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| 1
| 2020-11-30T16:19:54Z
| 2020-11-30T18:53:56Z
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|
CONTRIBUTOR
| null |
- [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
identify unnecessary dict call - rewrite as a literal
Rewrites as dictionary literals for the following files:
pandas/tests/plotting/frame/test_frame_subplots.py
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MDExOlB1bGxSZXF1ZXN0NTI5NjkzNjY0
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DOC: fix redirects to user guide in README
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| 1
| 2020-11-30T17:18:53Z
| 2020-12-02T01:44:25Z
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CONTRIBUTOR
| null |
Documentation pages were moved so this commit updates those links to
open more directly rather than getting a "This page has been moved
to..." message.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTI5NzA2Mjkx
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REF: IntervalIndex.intersection match pattern in other intersection methods
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| 4
| 2020-11-30T17:42:05Z
| 2020-12-02T01:51:30Z
| 2020-12-02T01:45:02Z
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MEMBER
| null |
Looking to share the boilerplate and have subclasses just implement _intersection.
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Backport PR #38172 on branch 1.1.x (CI: update tests for numpy 1.20 change to floordiv)
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NONE
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Backport PR #38172: CI: update tests for numpy 1.20 change to floordiv
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DOC: Fixed to_pickle compression argument list 'gzip' > 'gz'
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| 6
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| 2020-12-07T22:04:43Z
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|
CONTRIBUTOR
| null |
Removed reference to gzip and replaced with correct extension of gz.
- [x] closes #35364
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DOC: Clarify development environment creation for documentation changes
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| 2
| 2020-11-30T21:39:39Z
| 2020-12-02T01:52:29Z
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|
CONTRIBUTOR
| null |
#### Location of the documentation
[https://pandas.pydata.org/docs/dev/development/contributing.html#creating-a-development-environment](contributing.html)
#### Documentation problem
The documentation for creating a development environment is not really clear regarding the benefits/limitations of creating a development environment when changing documentation.
#### Suggested fix for documentation
To test out code changes, you’ll need to build pandas from source, which requires a C/C++ compiler and Python environment. If you’re making documentation changes, you can skip to Contributing to the documentation but if you skip creating the development environment you won’t be able to build the documentation locally before pushing your changes.
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| 1
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| 2020-12-07T22:05:28Z
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CONTRIBUTOR
| null |
Changed wording on paragraph to make clear advantages/disadvantages of creating development environment.
- [x] closes #38193
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MDExOlB1bGxSZXF1ZXN0NTI5ODY5MDg1
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BUG: Avoid duplicates in DatetimeIndex.intersection
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MEMBER
| null |
- [x] tests added / passed
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@jbrockmendel I think we should call ``_convert_can_do_setop`` here too, but https://github.com/pandas-dev/pandas/blob/56b9a80e1e47e123eb7a7c01448d1506400f8a5b/pandas/tests/indexes/datetimes/test_setops.py#L472 tests the opposite
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BUG: name attr in RangeIndex.intersection
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MEMBER
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MDU6SXNzdWU3NTM4ODY1Nzk=
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DOC: Duplicate Entry of Series.replace
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| 0
| 2020-11-30T23:48:42Z
| 2020-12-02T03:02:26Z
| 2020-12-02T03:02:26Z
|
CONTRIBUTOR
| null |
#### Location of the
https://pandas.pydata.org/pandas-docs/stable/reference/series.html
#### Documentation problem
There are two entries of `Series.replace`. One in the [Missing Data Handling](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#missing-data-handling) and another one in [Combining-Comparing-Joining-Merging](https://pandas.pydata.org/pandas-docs/stable/reference/series.html#combining-comparing-joining-merging)
#### Suggested fix for documentation
Remove the entry from Combining, comparing, joining, merging
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Remove duplicate entry on series.rst
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CONTRIBUTOR
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- [x] closes #38198
- [ ] tests passed. Nothing new fails. Few tails already were failing on `master`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry (Intentionally ignored as insignificant)
Removed the duplicate entry of `Series.replace` from doc
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MDExOlB1bGxSZXF1ZXN0NTI5OTUyNDUw
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|
Revert "REF: define nanargminmax without values_for_argsort"
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| 2020-12-01T03:41:19Z
| 2020-12-01T07:31:50Z
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MEMBER
| null |
Reverts pandas-dev/pandas#37815
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PERF: fix assert_frame_equal can be very slow
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| 15
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| 2020-12-24T20:40:45Z
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MEMBER
| null |
- [ ] closes #38183
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add kwarg ``check_index`` into ``assert_series_equal`` to allow elimination of multiple index checking (for each column) in ``assert_frame_equal``.
Performance wise.
```
import pandas as pd
import numpy as np
from string import ascii_letters
idx = np.random.choice(np.array(list(ascii_letters)), size=10000).astype(object)
a = np.random.random_integers(0, 100, (10000, 1000))
%time pd.testing.assert_frame_equal(pd.DataFrame(a), pd.DataFrame(a.copy()))
Wall time: 319 ms
%time pd.testing.assert_index_equal(pd.Index(idx), pd.Index(idx.copy()))
Wall time: 993 µs
%time pd.testing.assert_frame_equal(pd.DataFrame(a, index=idx), pd.DataFrame(a, index=idx))
Wall time: 281 ms
```
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| 1
| 2020-12-01T04:53:03Z
| 2020-12-02T03:15:00Z
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CONTRIBUTOR
| null |
- [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Rewrote dict literal for the following files:
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./pandas/tests/util/test_assert_series_equal.py
./pandas/tests/util/test_assert_interval_array_equal.py
./pandas/tests/util/test_assert_index_equal.py
./pandas/tests/util/test_assert_extension_array_equal.py
./pandas/tests/io/test_parquet.py
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MDExOlB1bGxSZXF1ZXN0NTI5OTc4NDg3
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Retain views with listlike indexers setitem
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| 8
| 2020-12-01T05:03:22Z
| 2020-12-02T14:47:06Z
| 2020-12-02T12:10:26Z
|
MEMBER
| null |
- [x] closes #37954
xref #38148
|
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| 3
| 2020-12-01T06:30:45Z
| 2020-12-02T03:14:16Z
| 2020-12-02T03:14:16Z
|
CONTRIBUTOR
| null |
- [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
identify unnecessary dict call - rewrite as a literal
Rewrites as dictionary literals for the following files:
./pandas/tests/dtypes/test_inference.py
./pandas/tests/groupby/test_function.py
./pandas/tests/groupby/transform/test_transform.py
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MDExOlB1bGxSZXF1ZXN0NTMwMDYwOTY5
| 38,206
|
TST: Added dataframe constructor tests confirming order is preserved with standard Python dicts
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| 1
| 2020-12-01T07:21:28Z
| 2020-12-02T03:04:46Z
| 2020-12-02T03:04:37Z
|
CONTRIBUTOR
| null |
- [ ] closes #38033
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Adds additional test scenarios that confirm dataframes created with standard Python dicts preserve order as expected.
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| 1
| 2020-12-01T08:19:29Z
| 2020-12-02T03:13:02Z
| 2020-12-02T03:12:42Z
|
CONTRIBUTOR
| null |
- [ ] xref #38138
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
identify unnecessary dict call - rewrite as a literal
Rewrites as dictionary literals for the following files:
modified: pandas/tests/frame/indexing/test_indexing.py
modified: pandas/tests/frame/indexing/test_where.py
modified: pandas/tests/frame/methods/test_fillna.py
modified: pandas/tests/frame/methods/test_sort_values.py
modified: pandas/tests/frame/methods/test_to_csv.py
modified: pandas/tests/frame/methods/test_to_records.py
modified: pandas/tests/frame/test_arithmetic.py
modified: pandas/tests/frame/test_reductions.py
However, I did not change it to literal in the following two places, as they seem like valid dict call:
1. pandas/tests/frame/indexing/test_indexing.py
```
lambda l: dict(zip(l, range(len(l)))), # line 76
lambda l: dict(zip(l, range(len(l)))).keys(), # line 77
```
2. pandas/tests/frame/indexing/test_where.py
```
return DataFrame(dict((c, s + 1) if is_ok(s) else (c, s) for c, s in df.items())) # line 32
```
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Revert "ENH: Improve performance for df.__setitem__ with list-like indexers"
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Reverts pandas-dev/pandas#38148 xref https://github.com/pandas-dev/pandas/issues/37784#issuecomment-736200698
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CI/TST: fix CI with numpy dev for changed error message / dev version
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Backport PR #38099 on branch 1.1.x
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Backport PR #38057: PERF: fix regression in creation of resulting index in RollingGroupby
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MDExOlB1bGxSZXF1ZXN0NTMwNDgyNjUw
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dict rewritten as literal for files in directory /pandas/core/arrays/
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NONE
| null |
- [x] xref #38138
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry: Identify unnecessary dict call - rewrite as a literal for directory /pandas/core/arrays/floating.py, interval.py, numpy_.py & base.py
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TST: test_is_list_like_recursion segfaults sometimes?
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|
CONTRIBUTOR
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```sh
pytest pandas/tests/dtypes/test_inference.py # succeeds
pytest pandas/tests # fails during test_inference
```
```
pandas/tests/dtypes/test_generic.py .. [ 25%]
pandas/tests/dtypes/test_inference.py .................................. [ 25%]
..............................................Fatal Python error: Segmentation fault
Thread 0x00007f56c7fff700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 576 in _handle_results
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007f56e4ff9700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 528 in _handle_tasks
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007f56e57fa700 (most recent call first):
File "~/local/lib/python3.8/selectors.py", line 415 in select
File "~/local/lib/python3.8/multiprocessing/connection.py", line 931 in wait
File "~/local/lib/python3.8/multiprocessing/pool.py", line 499 in _wait_for_updates
File "~/local/lib/python3.8/multiprocessing/pool.py", line 519 in _handle_workers
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007f56e5ffb700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 114 in worker
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
[...]
Thread 0x00007f57d95f5700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 114 in worker
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Current thread 0x00007f5883dd3740 (most recent call first):
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 131 in foo
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 132 in foo
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 132 in foo
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 132 in foo
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 132 in foo
File "~/git/pandas/pandas/tests/dtypes/test_inference.py", line 132 in foo
[....]
```
That is probably not caused by something recent. I didn't run the entire test-suite in the past.
Maybe I just have a broken setup, ignoring test_inference leads to the next segfault (running test_json.py on its own works again):
```sh
pytest pandas/tests/extension/json/test_json.py # succeeds
pytest --ignore=pandas/tests/dtypes/test_inference.py pandas/tests # fails during test_json
```
```
pandas/tests/extension/json/test_json.py ............................... [ 32%]
...x..........sFatal Python error: Segmentation fault
Thread 0x00007fc0197fa700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 576 in _handle_results
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007fc019ffb700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 528 in _handle_tasks
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007fc01a7fc700 (most recent call first):
File "~/local/lib/python3.8/selectors.py", line 415 in select
File "~/local/lib/python3.8/multiprocessing/connection.py", line 931 in wait
File "~/local/lib/python3.8/multiprocessing/pool.py", line 499 in _wait_for_updates
File "~/local/lib/python3.8/multiprocessing/pool.py", line 519 in _handle_workers
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
Thread 0x00007fc01affd700 (most recent call first):
File "~/local/lib/python3.8/multiprocessing/pool.py", line 114 in worker
File "~/local/lib/python3.8/threading.py", line 870 in run
File "~/local/lib/python3.8/threading.py", line 932 in _bootstrap_inner
File "~/local/lib/python3.8/threading.py", line 890 in _bootstrap
[...]
Current thread 0x00007fc1c0977740 (most recent call first):
File "~/git/pandas/pandas/core/indexes/base.py", line 5888 in maybe_extract_name
File "~/git/pandas/pandas/core/series.py", line 237 in __init__
File "~/git/pandas/pandas/core/construction.py", line 666 in create_series_with_explicit_dtype
File "~/git/pandas/pandas/core/series.py", line 382 in _init_dict
File "~/git/pandas/pandas/core/series.py", line 289 in __init__
File "~/git/pandas/pandas/core/construction.py", line 666 in create_series_with_explicit_dtype
File "~/git/pandas/pandas/core/series.py", line 382 in _init_dict
File "~/git/pandas/pandas/core/series.py", line 289 in __init__
File "~/git/pandas/pandas/core/construction.py", line 666 in create_series_with_explicit_dtype
File "~/git/pandas/pandas/core/series.py", line 382 in _init_dict
File "~/git/pandas/pandas/core/series.py", line 289 in __init__
[...]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c2018c120021b6032e0bc52beb02b63bf7134c78
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-693.5.2.el7.x86_64
Version : #1 SMP Fri Oct 20 20:32:50 UTC 2017
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+1400.gc2018c1
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.3
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.4
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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TST: add cases for nearest_workday and before_nearest_workday
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MEMBER
| null |
- [ ] xref #38139
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add cases for testing observance rules ``nearest_workday`` and ``before_nearest_workday`` for their full coverage.
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Corrected the grammar in Exception
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|
CONTRIBUTOR
| null |
Exception on different arrays' length corrected
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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API: Groupby.idxmax drops nuisance columns, DataFrame.idxmax does not
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|
BUG: pd's std function inconsistency between series and values within a series
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[] | null | 2
| 2020-12-01T21:19:12Z
| 2020-12-01T22:33:30Z
| 2020-12-01T22:33:30Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
temp = pd.DataFrame({'col':[1,2]})
display(temp)
temp['col'].std(), temp['col'].values.std(), np.std(temp['col']), np.std(temp['col'].values)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
Results of std is inconsistent between a series and values within a series
#### Expected Output
(0.7071067811865476, 0.7071067811865476, 0.5, 0.5)
or
(0.5, 0.5, 0.5, 0.5)
#### Output of ``pd.show_versions()``
(0.7071067811865476, 0.5, 0.5, 0.5)
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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