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} | 6 | 2020-09-08T12:09:13Z | 2020-11-06T15:35:25Z | 2020-10-22T19:36:59Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Attempted to figure out what is wrong with the issue https://github.com/pandas-dev/pandas/issues/36211.
I noticed that the private methods ``_write`` are defined in each child of class ``Writer``, which simply re-define ``obj`` and call the parent ``_write`` method.
It turns out that the source of the recursion does not lie there, but I refactored the module slightly by removing the mediator ``_write`` method. Instead I put the differences between the writers into their ``__init__`` method.
It seems that source of the problem with the recursion mentioned in the bug report is in ``pandas/_libs/src/ujson/python/objToJSON.c``. | {
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} | 0 | 2020-09-08T14:06:41Z | 2020-09-08T14:55:08Z | 2020-09-08T14:55:07Z | NONE | null | Backport PR #36141: BUG: copying series into empty dataframe does not preserve dataframe index name | {
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} | 1 | 2020-09-08T14:10:59Z | 2020-09-08T15:28:25Z | 2020-09-08T15:28:22Z | CONTRIBUTOR | null | #35925
From this set of 10 files, only the first 3 needed changes to comply if the new version of black:
- [x] pandas/tests/io/pytables/test_timezones.py
- [x] pandas/tests/io/test_feather.py
- [x] pandas/tests/io/test_s3.py
- [ ] pandas/tests/io/test_sql.py
- [ ] pandas/tests/io/test_stata.py
- [ ] pandas/tests/plotting/test_frame.py
- [ ] pandas/tests/resample/test_datetime_index.py
- [ ] pandas/tests/reshape/merge/test_merge_index_as_string.py
- [ ] pandas/tests/reshape/test_crosstab.py
- [ ] pandas/tests/reshape/test_get_dummies.py | {
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} | 3 | 2020-09-08T16:14:42Z | 2020-09-09T05:11:04Z | 2020-09-08T23:56:47Z | CONTRIBUTOR | null | - [x] closes #35706
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Cleans a very minor formatting error to be consistent with w3schools specification.
`colspan=2 ---> colspan="2"`
Note this generally has no effect in browsers parsing HTML. | {
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} | 3 | 2020-09-08T17:07:18Z | 2020-09-13T13:15:12Z | 2020-09-13T13:15:08Z | CONTRIBUTOR | null | - [x] closes #36212
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry | {
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https://github.com/pandas-dev/pandas/releases/tag/v1.1.2
#### Documentation problem
The "what's new" link doesn't appear to function for update 1.1.2.
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... | open | false | null | [] | null | 9 | 2020-09-08T18:31:34Z | 2021-08-13T02:32:55Z | null | MEMBER | null | TL;DR: we should add `_validate_fill_value` to the EA interface and define `_can_hold_element` in terms of it.
ATM `ExtensionBlock._can_hold_element` incorrectly always returns True. This needs to be an EA method that returns True if and only if `self[0] = value` is allowed on a non-empty array.
This method can in turn to defined in terms of a more broadly useful `_validate_fill_value` that we use DTA/TDA/PA/PandasArray/Categorical:
```
def _can_hold_element(self, value):
try:
self._validate_fill_value(value)
return True
except (ValueError, TypeError):
return False
```
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} | 3 | 2020-09-08T19:25:57Z | 2021-04-20T22:50:55Z | 2021-04-20T22:50:55Z | NONE | 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.
- [ ] (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
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
s = pd.Series(np.random.randn(8), index=index)
s2 = s.drop(labels='one', level='second') #drop the label 'one' from the second level
print(s2.index.levels) #the levels still include the label 'one' in the second level
```
outputs:
`FrozenList([['bar', 'baz', 'foo', 'qux'], ['one', 'two']])`
despite dropping `one` from the second level
#### Problem description
When calling `df.drop(labels='one', level='second')`, one would expect that the dropped label would no longer appear in `df.index.levels` since all rows with that label are dropped. However, the label persists in the `df.index.levels` despite not showing up in `df.index.get_level_values('second')`. I would expect that `df.index.levels` only include labels of entries currently in `df`, not ones that were previously there but dropped.
#### Expected Output
`FrozenList([['bar', 'baz', 'foo', 'qux'], ['two']])`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.182-llgrid-10ms
Version : #1 SMP Fri May 29 11:57:47 EDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C
LANG : C.UTF-8
LOCALE : None.None
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.3.1.post20200810
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 : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | 2020-09-08T19:35:57Z | 2020-09-18T11:35:31Z | 2020-09-13T22:54:42Z | CONTRIBUTOR | null | - [x] closes #17154
- [x] tests added / passed
- modified `tests/io/parser/test_c_parser.py` to make sure all 4 options are tested
- added `tests/io/parser/test_c_parser.py:test_high_is_default`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- for version 1.2
See discussion at bottom of #36149 for the performance tests. Added `float_precision="legacy"` so people can pick up the old parser. Can't change default to "high" because of incompatibility with python parser
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- [ ] closes #29699
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Not sure where the best place to put the test is.
Also, this code generates a warning which doesn't seem to disappear when following the guidance of the warning i.e: set `sort=False`. As the default behaviour is meant to return a warning this is tested for.
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- [x] tests added / passed
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Tangential question: when I run flake8 it passes, but the pre-commit hook often gives me a bunch of messages like `pandas/core/indexes/base.py:956:51: F821 undefined name 'ignore'`. Is this something I can fix by pinning a version or something? | {
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"... | closed | false | null | [] | null | 9 | 2020-09-08T23:06:11Z | 2020-12-11T04:47:21Z | 2020-12-11T04:47:21Z | CONTRIBUTOR | null | - [x] closes #31771
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- [x] added test_empty_frame for test_to_markdown.py (#31771)
- [x] Required tabulate >= 0.8.7
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} | 2 | 2020-09-08T23:34:30Z | 2020-09-09T00:58:12Z | 2020-09-09T00:55:22Z | MEMBER | null | Also fixes a test that is failing on master because _np_version_under_118 is no longer defined. | {
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} | 4 | 2020-09-09T04:03:17Z | 2020-09-13T17:25:07Z | 2020-09-13T13:13:54Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
New performance comparison with 10k groups
```
In [1]: df_g = pd.DataFrame({'a': range(10**4), 'b': range(10**4), 'c': range(10**4)})
In [2]: df_g.groupby('a').transform(lambda x: x + 1)
Out[2]:
b c
0 1 1
1 2 2
2 3 3
3 4 4
4 5 5
... ... ...
9995 9996 9996
9996 9997 9997
9997 9998 9998
9998 9999 9999
9999 10000 10000
[10000 rows x 2 columns]
In [3]: %timeit df_g.groupby('a').transform(lambda x: x + 1)
9.48 s ± 885 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [4]: def f(values, index):
...: return values + 1
...:
In [5]: df_g.groupby('a').transform(f, engine='numba', engine_kwargs={'parallel': True})
Out[5]:
b c
0 1.0 1.0
1 2.0 2.0
2 3.0 3.0
3 4.0 4.0
4 5.0 5.0
... ... ...
9995 9996.0 9996.0
9996 9997.0 9997.0
9997 9998.0 9998.0
9998 9999.0 9999.0
9999 10000.0 10000.0
[10000 rows x 2 columns]
In [6]: %timeit df_g.groupby('a').transform(f, engine='numba', engine_kwargs={'parallel': True})
4.41 ms ± 34.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) | {
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} | 1 | 2020-09-09T07:11:56Z | 2020-09-12T21:17:49Z | 2020-09-12T21:17:49Z | NONE | null | #### Code Sample, a copy-pastable example
```pycon
>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series(["a", "b", np.nan], index=["a", "b", np.nan], dtype="category")
>>> df = pd.DataFrame().assign(
... cat_contains=s.str.contains("a", na=False),
... cat_startswith=s.str.startswith("a", na=False),
... cat_endswith=s.str.endswith("a", na=False),
... str_contains=s.astype("string").str.contains("a", na=False),
... str_startswith=s.astype("string").str.startswith("a", na=False),
... str_endswith=s.astype("string").str.endswith("a", na=False),
... )
>>> df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 3 entries, a to nan
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 cat_contains 3 non-null bool
1 cat_startswith 2 non-null object
2 cat_endswith 2 non-null object
3 str_contains 3 non-null boolean
4 str_startswith 3 non-null boolean
5 str_endswith 3 non-null boolean
dtypes: bool(1), boolean(3), object(2)
memory usage: 93.0+ bytes
>>> df
cat_contains cat_startswith cat_endswith str_contains str_startswith str_endswith
a True True True True True True
b False False False False False False
NaN False NaN NaN False False False
```
#### Problem description
`.str.startswith(..., na=False)` and `.str.endswith` should make missing values `False` when the calling series is of type categorical just like it does for string series.
Similar to #22158, but `.str.contains` works here.
#### Expected Output
```pycon
>>> df
cat_contains cat_startswith cat_endswith str_contains str_startswith str_endswith
a True True True True True True
b False False False False False False
NaN False False False False False False
```
#### Output of ``pd.show_versions()``
Using conda env with `conda create -n pandas112 -c conda-forge pandas=1.1.2`
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Korean_Korea.949
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
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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} | 0 | 2020-09-09T08:32:45Z | 2020-09-09T09:28:47Z | 2020-09-09T09:28:47Z | NONE | null | Backport PR #36183: DOC: Start 1.1.3 | {
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<img width="90" alt="Figure_1" src="https://user-images.githubusercontent.com/66080348/92575683-edf5bb80-f288-11ea-8d41-e6e58832e7db.PNG">
Good morning,
I have a Pandas dataframe with several missing values.
What I want to do is to use next valid observation to fill gap like the bfill method.
However, I do not need to simply fill missing value with the same value.
I need to fill them with values randomly sampled from a normal distribution centered in the next valid observation value and standard deviation of 1.
I try to explain myself with the attached figure (Figure 1).
In the dataframe in the figure I want to fill the rows from 3 to 8 with values randomly sampled from a normal distribution
centered in 2 a standard deviation of 1.
the same thing for cell from 10 to 14 I want to fill them with values randomly sampled from a normal distribution
centered in 5 a standard deviation of 1.
Is there a way to do that in pandas? maybe applying a custom function to bfill method?
Thank 'you very much for your help.
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https://api.github.com/repos/pandas-dev/pandas/issues/36244 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36244/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36244/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36244/events | https://github.com/pandas-dev/pandas/pull/36244 | 696,846,923 | MDExOlB1bGxSZXF1ZXN0NDgyODcxMDUz | 36,244 | DOC: Update numeric.py | {
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- [x] closes #36239
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/36245 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36245/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36245/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36245/events | https://github.com/pandas-dev/pandas/issues/36245 | 696,886,813 | MDU6SXNzdWU2OTY4ODY4MTM= | 36,245 | BUG: pd.json_normalize on a column loses rows that have an empty list for that column | {
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"description": "read_json, to_json, json... | open | false | null | [] | null | 5 | 2020-09-09T14:55:23Z | 2021-04-29T14:49:07Z | 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
We lose the first row when we normalize the "sections" field:
```python
import pandas as pd
import pandas as pd
df = pd.DataFrame({'id': [1, 2, 3],
'sections': [[], [{"id": 1, "answers": [{"id": 1, "value": 10, "question_id": 1}]}], [{"id": 2, "answers": []}]]
})
df2 = pd.json_normalize(df.to_dict(orient="records"), meta=["id"], record_path="sections", record_prefix="section_")
df2
```
The problem would then impact the section_answers field if that column gets normalized (we end up with only a single row for ID 2)
`pd.json_normalize(df2.to_dict(orient="records"), meta=["id", "section_id"], record_path="section_answers", record_prefix="answer_")`
#### Problem description
I am losing the first row when I normalize the "sections" field in my example. This is because the field contains an empty list.
The end result is that I have a dataframe with fewer unique IDs than I started out with which is not desirable since the distinct count of these IDs are used as a metric.
#### Expected Output
I would expect one at least one row per meta column that I passed to pd.json_normalize. I do not think that I should lose the row ID 1 without some type of warning at least:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-1028-azure
Version : #29~18.04.1-Ubuntu SMP Fri Jun 5 14:32:34 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : 1.0.6
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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} | 1 | 2020-09-09T14:57:05Z | 2020-09-09T15:12:12Z | 2020-09-09T15:12:06Z | CONTRIBUTOR | null | Fixed a link to JSON Table Schema specification hosted on https://specs.frictionlessdata.io/table-schema/
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/36247 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36247/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36247/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36247/events | https://github.com/pandas-dev/pandas/issues/36247 | 696,909,430 | MDU6SXNzdWU2OTY5MDk0MzA= | 36,247 | [BUG] loc does not preserve datatype with a single element | {
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"description": "Unexpected or buggy dtyp... | closed | false | null | [] | null | 7 | 2020-09-09T15:23:15Z | 2020-09-10T11:22:13Z | 2020-09-10T11:22:12Z | CONTRIBUTOR | null | Opening a new issue as per @Dr-Irv suggestion on #31763
Sorry issue formatting got lost
```py
import pandas as pd
import numpy as np
not_preserved = pd.DataFrame(np.arange(24).reshape((8, 3)), columns=list("ABC"))
not_preserved['F'] = np.random.random(8)
print("Mixed datatype DataFrame")
print(not_preserved.dtypes)
print("Single value loc: ")
print(not_preserved.loc[0, ['A', 'B', 'C']].dtypes)
print("Single value through slice: ")
print(not_preserved.loc[0:0, ['A', 'B', 'C']].dtypes)
print("The columns are cast to float:")
print(not_preserved.loc[0, ['A', 'B', 'C']])
# This prints:
# Mixed datatype DataFrame
# A int64
# B int64
# C int64
# F float64
# dtype: object
# Single value loc:
# float64
# Single value through slice:
# A int64
# B int64
# C int64
# dtype: object
# The columns are cast to float:
# A 0.0
# B 1.0
# C 2.0
```
As a test:
```py
# A single cell is fine
assert isinstance(not_preserved.loc[0, "A"], np.int64) # passes
assert not_preserved.loc[0:0, ["A", "B"]].dtypes["A"] is np.dtype('int64') # passes
assert not_preserved.loc[0, ["A", "B"]].dtypes is np.dtype('int64') # AssertionError
```
This is with pandas 1.0.3 but was already the case in 0.24.1, it seems that what changed was the behavior of a `replace` method downstream in my code.
_Originally posted by @glyg in https://github.com/pandas-dev/pandas/issues/31763#issuecomment-586213659_ | {
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"description": "Related to indexing o... | open | false | null | [] | null | 2 | 2020-09-09T16:25:44Z | 2020-09-10T02:57:02Z | null | NONE | 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.
---
**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
animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama'],index=[-1,0,1,2,3])
animals.get([1,-1,2]) # ok
animals = pd.Series(['lama', 'cow', 'lama', 'beetle', 'lama'])
animals.get([1,2]) # ok
BUT：
animals.get([1,-1,2]) return None!!!
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
If you mix existing labels with non-existent labels, at least need to return the corresponding results of the existing labels?
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.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 : Chinese (Simplified)_China.936
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
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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} | 2 | 2020-09-09T16:31:45Z | 2020-09-13T12:25:48Z | 2020-09-12T21:17:49Z | CONTRIBUTOR | null | - [x] closes #36241
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I parametrized dtype in existing tests. Should I create a separate test instead? | {
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} | 1 | 2020-09-09T17:45:09Z | 2020-09-12T21:18:29Z | 2020-09-12T21:14:36Z | MEMBER | null | We have way too many ways of comparing Categorical dtypes.
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} | 3 | 2020-09-09T17:59:08Z | 2020-11-05T01:22:24Z | 2020-11-05T01:22:24Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
Documentation doesn't specify what happens when you slice an index that has duplicates. Does it denote the range from the first occurrence of the start element, to the last occurrence of the end element?
#### Suggested fix for documentation
Clarify how slicing interacts with non-unique index elements.
I would also emphasize, at
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-with-labels
that the actual values of the index labels, and their natural comparison order and the elements between them in that natural order, do _not_ matter for slicing; i.e. that a slice of 'Z':'A' is not empty but depends on the positions that the labels denote in the array. In all current documentation examples, the label denoting the start of the slice end also happens to be lexicographically smaller than the label denoting the end of the slice, which can mislead readers into thinking the natural order of the labels has some meaning, when all they do is denote integer positions.
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... | open | false | null | [] | null | 0 | 2020-09-09T18:12:27Z | 2021-08-13T02:35:39Z | 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
```python
import pandas as pd
import numpy as np
pd.DataFrame(dict(
a=[np.nan, np.nan],
b=[1, 2],
ts = [pd.Timestamp('2020-01-01'), pd.Timestamp('2020-02-02')]
)).set_index('ts').plot()
```
#### Problem description
The x-axis (time axis) start in before 2000 where it really should start at 2020-01-01
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
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 : 3.3.1
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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] | closed | false | null | [] | null | 3 | 2020-09-09T19:06:11Z | 2020-09-10T00:07:46Z | 2020-09-10T00:07:46Z | NONE | null | #### Problem:
I am using Pandas for reading in data to do machine learning tasks. And one problem I encounter is that the data I have is usually very very big. For example one data set I have is about 16 GB big, and my RAM is only 12 GB. So instead of reading all the lines from this data, I know I can read in the first million rows from my data, and do machine learning on that. But what I really want to do is select 100 random lines from the entire file for it to be a valid sample from the 16 GB data. Pandas currently doesn't allow that.
#### Solution I would like
[pd.read_csv() should take in an additional parameter, called random. Random should be a tuple of 3 ints. (start, end, number of random lines). For example: pd.read_csv('my_data.csv', random = (100, 1000, 100)) should read in 100 random lines into python datafraem from 100 lines to 1000 lines within the data.csv file.]
#### API breaking implications
[existing API about nrows should be modified so that when random = (100, 1000, 100) is present, nrows doesn't do anything, since we specify the number of rows we need to read in parameter random already.]
#### alternatives I considered
[what we could do also is to have another parameter specifying the starting line and end line, for example start line = 10, end line = 100 will allow the reader to read from 10 lines and up to 100 lines in the csv file, nrows specifies how many lines starting from 10 we read. If Random = True then we read 10 random lines from 10 to 100 lines instead of reading from 10 - 20 lines.]
#### Additional context
[I haven't looked too deep into pandas to see if my idea is already being developed, but if there is any development, I would appreciate the link to that discussion post. I would very much like to help and code this thing if no one has done this before.]
[FYI: this is my first time to contribute to open source, so please let me know how to help code this feature.]
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} | 0 | 2020-09-09T19:44:52Z | 2020-09-12T21:17:52Z | 2020-09-12T21:15:13Z | 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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] | closed | false | null | [] | null | 3 | 2020-09-09T20:32:37Z | 2020-09-23T21:03:35Z | 2020-09-23T21:03:29Z | MEMBER | null | - [x] closes #36227
Improved the docs a bit here. Closes the issue | {
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] | closed | false | null | [] | null | 4 | 2020-09-09T20:39:49Z | 2020-09-10T12:24:27Z | 2020-09-10T11:25:50Z | NONE | null | Pandas is a requirement for MLflow, which we are trying to deploy in production. However, our security scan is showing us that pandas has a security vulnerability (CVE-2020-13091) and this vulnerability is preventing us from using MLflow in production, due to compliance issues.
It would be great if you could launch an alternative version of pandas, without the read_pickle method. This would be really useful and would solve the current security vulnerability. | {
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... | open | false | null | [] | null | 0 | 2020-09-09T21:02:38Z | 2020-09-09T21:03:13Z | null | MEMBER | 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
ds = pd.Series(['a', 'b', 'c'])
print(ds.get([1,-1]))
print(ds.get(slice(-1,2)))
```
#### Problem description
The first get call returns the default value None as expected. The second returns an empty series. This seems inconsistent. I propose that we return here None too.
#### Expected Output
None and None
#### Output of ``pd.show_versions()``
<details>
master
</details>
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} | 1 | 2020-09-09T21:18:43Z | 2020-09-11T21:33:03Z | 2020-09-11T21:33:03Z | MEMBER | null | #### Location of the documentation
https://pandas.pydata.org/docs/user_guide/indexing.html#indexing-deprecate-loc-reindex-listlike
#### Documentation problem
We have a lot of warnings in the documentation ``Starting with 0.x the following is deprecated.`` Most of these things stopped working with 1.0. Should we update the docs and say, that this stopped working with 1.0?
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} | 1 | 2020-09-09T23:49:42Z | 2020-09-11T21:51:23Z | 2020-09-11T20:51:55Z | MEMBER | null | After this we're down to ~5 i think | {
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2020-09-10T00:59:20Z | 2020-09-12T23:41:15Z | 2020-09-12T23:24:34Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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ATM we cast strings for listlike inputs but not for scalars.
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https://api.github.com/repos/pandas-dev/pandas/issues/36262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/36262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/36262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/36262/events | https://github.com/pandas-dev/pandas/issues/36262 | 697,384,755 | MDU6SXNzdWU2OTczODQ3NTU= | 36,262 | Support resample('D').agg(new_col_name=('col_name', func) ) | {
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"description": "resample... | open | false | null | [] | null | 3 | 2020-09-10T03:41:47Z | 2021-03-15T22:45:50Z | null | NONE | null | Normal agg support:
```
df.agg(
sale_hour=('sale_count', 'count')
)
```
But resample().agg doesn't :
```
df.resample('D').agg(
sale_hour=('sale_count', 'count')
)
```
got
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-159-8afb38e3b96b> in <module>
1 df.resample('D').agg(
----> 2 sale_hour=('sale_count', 'count')
3 )
TypeError: aggregate() missing 1 required positional argument: 'func'
```
-----
Now I have to write
```
df.resample('D').agg(
dict(
sale_count="sum",
home_rec="mean",
discount_money_rate="mean",
is_flashsale="mean",
is_plan_market="mean",
is_plan_store="mean",
discount_rate="mean",
in_stock_rate="mean",
),
).join(
df.resample('D').agg(
dict(
sale_count="count",
),
).rename(columns={'sale_count':'sale_hours'})
)
```
This because rename multiple level is much more difficult than join .
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} | 2 | 2020-09-10T04:47:03Z | 2021-09-22T17:24:19Z | null | NONE | null | I just found out that when we concatenate two dataframes horizontally, if one dataframe has duplicate indices, pd.concat() will crash, but df.join() will not crash. Instead, df.join() will spread the values into all rows with the same index value. Is this behavior by design? Thanks!
```
df1 = pd.DataFrame(np.random.randn(5), index=[0,1,2,3,3], columns=['a'])
df2 = pd.DataFrame(np.random.randn(5), index=[0,1,2,2,4], columns=['b'])
dfj = df1.join(df2, how='outer')
display(df1, df2, dfj)
dfc = pd.concat([df1, df2], axis=1)
display(dfc)
```
<img width="165" alt="image" src="https://user-images.githubusercontent.com/10172392/92682234-6e3c1f80-f362-11ea-8b07-21ffe291ad5c.png">
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-40-150ce3e802ea> in <module>
3 dfj = df1.join(df2, how='outer')
4 display(df1, df2, dfj)
----> 5 dfc = pd.concat([df1, df2], axis=1)
6 display(dfc)
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
282 )
283
--> 284 return op.get_result()
285
286
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in get_result(self)
495
496 new_data = concatenate_block_managers(
--> 497 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy
498 )
499 if not self.copy:
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
2025 blocks.append(b)
2026
-> 2027 return BlockManager(blocks, axes)
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in __init__(self, blocks, axes, do_integrity_check)
137
138 if do_integrity_check:
--> 139 self._verify_integrity()
140
141 self._consolidate_check()
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in _verify_integrity(self)
332 for block in self.blocks:
333 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
--> 334 construction_error(tot_items, block.shape[1:], self.axes)
335 if len(self.items) != tot_items:
336 raise AssertionError(
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
1692 if block_shape[0] == 0:
1693 raise ValueError("Empty data passed with indices specified.")
-> 1694 raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")
1695
1696
ValueError: Shape of passed values is (9, 2), indices imply (7, 2)
```
By right, if the dataframes have duplicate indices, it can behave like df.join() and at least it should NOT crash.
I suggest we introduce additional arguments to handle duplicate indices, e.g., if the same index has X(>0) rows in df1, Y(>0) rows in df2, then if `dup_index=`:
1. `combinatorial`: after merging, it will have `X*Y` rows for every combination possibility.
2. `outer-top-align`: after merging, it will have `max(X, Y)` rows, in which the rows align from top
3. `outer-bottom-align`: after merging, it will have `max(X, Y)` rows, in which the rows align from bottom
4. `inner-top-align`: after merging, it will have `min(X, Y)` rows, in which the rows align from top
5. `inner-bottom-align`: after merging, it will have `min(X, Y)` rows, in which the rows align from bottom
6. `raise`: raise an exception with the warning message | {
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} | 1 | 2020-09-10T07:48:04Z | 2020-09-12T21:08:11Z | 2020-09-12T21:07:53Z | CONTRIBUTOR | null | Fix bug when resampling with DST transition.
- [x] closes #35219
- [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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] | closed | false | null | [] | null | 0 | 2020-09-10T09:55:29Z | 2020-10-03T20:00:01Z | 2020-10-03T20:00:01Z | CONTRIBUTOR | null | I'm fed up dealing with the build issues from the ORC C++ conda package and consider just disabling the support of it in the `pyarrow` conda package.
Main discussion should/can happen in the dask issue tracker as they seem to have the largest user base of this feature: https://github.com/dask/dask/issues/6615 There I also posted what needs to be done.
This issue is just a FYI, feel free to close anytime. | {
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} | 12 | 2020-09-10T11:40:06Z | 2020-09-24T20:20:07Z | 2020-09-19T02:14:26Z | CONTRIBUTOR | null | Does a np.isnan if nan is given to isin and we have a large enough array to trigger the `np.in1d` path
- [x] closes #22205
- [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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} | 0 | 2020-09-10T14:43:09Z | 2020-10-17T16:23:32Z | 2020-10-17T16:23:32Z | MEMBER | null | #### Is your feature request related to a problem?
``DataFrame.to_latex()`` does not allow one to specify short caption.
Short caption is useful for the creation of neat-looking list of tables, where the caption is short, fitting into one line.
#### Describe the solution you'd like
Either add kwarg ``short_caption`` to ``to_latex`` method.
Alternatively expand the meaning of kwarg ``caption``, so that it can optionally be unpacked into ``full_caption, short_caption``.
#### API breaking implications
No.
#### Describe alternatives you've considered
No.
#### Additional context
Example of LaTeX table with short caption.
```
\begin{table}
\centering
\caption[Short caption]{Long-long full caption}
\label{tab:label}
\begin{tabular}{lr}
\toprule
Col1 & Col2 \\
\midrule
1 & 3 \\
2 & 4 \\
\bottomrule
\end{tabular}
\end{table}
```
Possible code snippet:
```
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df.to_latex(caption=('long caption', 'short caption'))
``` | {
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} | 4 | 2020-09-10T15:44:15Z | 2020-09-10T18:47:20Z | 2020-09-10T18:13:39Z | NONE | null | This behavior looks like a bug to me.
Inconsistent behaviors of the argument for **DataFrame.sort_values**( )
The first argument takes list, but fail to take tuple .
These worked:
``` python
df.sort_values( ['A', 'B'], ascending= [1, 0] ) # list, list
df.sort_values( ['A', 'B'], ascending= (1, 0) ) # list, tuple
```
These failed:
``` python
df.sort_values( ('A', 'B'), ascending= [1, 0] ) # tuple, list
df.sort_values( ('A', 'B'), ascending= (1, 0) ) # tuple, tuple
```
Error:
``` phthon
raise ValueError(
ValueError: Length of ascending (2) != length of by (1)
``` | {
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} | 1 | 2020-09-10T16:26:00Z | 2020-09-12T20:54:28Z | 2020-09-12T20:54:23Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Slight clean-up of ``pandas/io/parsers.py``. | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "read_json, to_json, json... | open | false | null | [] | null | 9 | 2020-09-10T18:07:31Z | 2021-09-21T06:29:41Z | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
input_json = '[{"0":"this is a string ://"}]'
print('Input json string: {}'.format(input_json))
print('URL? {}'.format(str(pd.io.common.is_url(input_json))))
print('fsspec? {}'.format(str(pd.io.common.is_fsspec_url(input_json))))
print(pd.read_json(input_json))
```
output:
```
Input json string: [{"0":"this is a string ://"}]
URL? False
fsspec? True
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-f3bc94ba133f> in <module>
7 print('fsspec? {}'.format(str(pd.io.common.is_fsspec_url(input_json))))
8
----> 9 pd.read_json(input_json)
~/miniconda3/envs/eltest/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
197 else:
198 kwargs[new_arg_name] = new_arg_value
--> 199 return func(*args, **kwargs)
200
201 return cast(F, wrapper)
~/miniconda3/envs/eltest/lib/python3.7/site-packages/pandas/util/_decorators.py in wrapper(*args, **kwargs)
294 )
295 warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
--> 296 return func(*args, **kwargs)
297
298 return wrapper
~/miniconda3/envs/eltest/lib/python3.7/site-packages/pandas/io/json/_json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression, nrows)
592 compression = infer_compression(path_or_buf, compression)
593 filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(
--> 594 path_or_buf, encoding=encoding, compression=compression
595 )
596
~/miniconda3/envs/eltest/lib/python3.7/site-packages/pandas/io/common.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
201 if filepath_or_buffer.startswith("s3n://"):
202 filepath_or_buffer = filepath_or_buffer.replace("s3n://", "s3://")
--> 203 fsspec = import_optional_dependency("fsspec")
204
205 # If botocore is installed we fallback to reading with anon=True
~/miniconda3/envs/eltest/lib/python3.7/site-packages/pandas/compat/_optional.py in import_optional_dependency(name, extra, raise_on_missing, on_version)
108 except ImportError:
109 if raise_on_missing:
--> 110 raise ImportError(msg) from None
111 else:
112 return None
ImportError: Missing optional dependency 'fsspec'. Use pip or conda to install fsspec.
```
#### Problem description
The method `pd.read_json()` is widely used and accepts either a path or a json string. Since pandas==1.1.0 passing a string containing a json input is often interpreted as a fssspec_url and results in an error.
#### Expected Output
```
Input json string: [{"0":"this is a string ://"}]
URL? False
fsspec? False
0
0 this is a string ://
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-112-generic
Version : #113-Ubuntu SMP Thu Jul 9 23:41:39 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 3.0.0a1
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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"default": fa... | closed | false | null | [] | null | 10 | 2020-09-10T18:19:56Z | 2020-12-29T20:44:17Z | 2020-12-29T20:44:16Z | NONE | null | pd.read_json() fails currently for strings that look like fsspec_url and contain "://". adding another condition to fix this at least in most cases
- [X] closes #36271
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2020-09-10T18:21:12Z | 2020-09-12T21:19:38Z | 2020-09-12T21:13:32Z | MEMBER | null | I found it really confusing why we used slightly different dtype-comparisons in a bunch of different places (e.g CategoricalIndex.get_indexer vs CategoricalIndex.get_indexer_non_unique). AFAICT It's just a matter of optimizations accruing over time without a helper method to keep them in sync. | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
* Adds tests to cover sql cases combining parameters and `%`.
* Notably, these tests highlight two regressions in v1.1
- `pd.read_sql("SELECT 1 %% 2", engine)` fails. See #35871.
- `pd.read_sql(sqlalchemy.sql.text("SELECT 1 % 2"), engine)` fails. See #35484.
* Fixes second issue by modifying SqlDatabase to not use `no_parameter`, and wrapping string queries in `sql.text()` when no parameters are passed.
I was going nuts trying to keep track of all the cases, so tossed them into a table! I didn't have time to explicitly check v1.05, so would be helpful if someone could verify that column (or I may be able to loop back to it)!
| query | v1.05 | v.1.1 | PR | issue |
| --- | --- | --- | --- | --- |
| no params, `%` | ❌ | ✅ | ✅ | #34211 (merged) |
| no params, `%%` | ✅ | ❌ | ❌ | #35871 |
| params, `%` | ❌ | ❌ | ❌ | https://github.com/psycopg/psycopg2/issues/827 |
| params, `%%` | ✅ | ✅ | ✅ | |
| sqla declarative, `%` | ✅ | ❌ | ✅ | #35484 | | {
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] | closed | false | null | [] | null | 6 | 2020-09-10T21:25:00Z | 2020-09-19T03:34:41Z | 2020-09-12T20:46:36Z | NONE | null | - [x] closes #18691
- [x] tests added / passed
- [ ] passes black pandas
- [ ] passes git diff upstream/master -u -- "*.py" | flake8 --diff
- [x] whatsnew entry
added peek, which is essentially a concatenation of head and tail functions. The reason I add, is because often for sorted data, you are interested in looking at both the head and tail simultaneously to see the min/max range. | {
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} | 3 | 2020-09-10T21:46:06Z | 2020-09-13T23:54:12Z | 2020-09-13T23:25:30Z | MEMBER | null | - [x] closes #24768
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Issue was fixed in the past.
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} | 11 | 2020-09-10T22:23:16Z | 2020-09-16T15:28:46Z | 2020-09-16T15:28:46Z | NONE | null | I use Travis CI to test, and I've tested all of my projects against the upcoming python 3.9 release. The only problem I'm seeing is dependent packages is pandas.
Pandas + 3.9 takes a long time to build and then dies with a compiler error. It also does this when I install cython first, which is the usual workaround when a new version of Python is close to release. Is some not-yet-mainstream cython version needed? I suppose there will be a wheel by the time 3.9.0 comes out in October?
Example CI log: https://travis-ci.com/github/wumpus/paramsurvey/jobs/383337409
```
pandas/_libs/writers.c:4942:5: error: ‘_PyUnicode_get_wstr_length’ is deprecated [-Werror=deprecated-declarations]
__pyx_v_l = PyUnicode_GET_SIZE(__pyx_v_val);
^
In file included from /opt/python/3.9-dev/include/python3.9/unicodeobject.h:1026:0,
from /opt/python/3.9-dev/include/python3.9/Python.h:97,
from pandas/_libs/writers.c:33:
/opt/python/3.9-dev/include/python3.9/cpython/unicodeobject.h:446:26: note: declared here
static inline Py_ssize_t _PyUnicode_get_wstr_length(PyObject *op) {
^
pandas/_libs/writers.c:4942:5: error: ‘PyUnicode_AsUnicode’ is deprecated [-Werror=deprecated-declarations]
__pyx_v_l = PyUnicode_GET_SIZE(__pyx_v_val);
^
In file included from /opt/python/3.9-dev/include/python3.9/unicodeobject.h:1026:0,
from /opt/python/3.9-dev/include/python3.9/Python.h:97,
from pandas/_libs/writers.c:33:
/opt/python/3.9-dev/include/python3.9/cpython/unicodeobject.h:580:45: note: declared here
Py_DEPRECATED(3.3) PyAPI_FUNC(Py_UNICODE *) PyUnicode_AsUnicode(
^
pandas/_libs/writers.c:4942:5: error: ‘_PyUnicode_get_wstr_length’ is deprecated [-Werror=deprecated-declarations]
__pyx_v_l = PyUnicode_GET_SIZE(__pyx_v_val);
^
In file included from /opt/python/3.9-dev/include/python3.9/unicodeobject.h:1026:0,
from /opt/python/3.9-dev/include/python3.9/Python.h:97,
from pandas/_libs/writers.c:33:
/opt/python/3.9-dev/include/python3.9/cpython/unicodeobject.h:446:26: note: declared here
static inline Py_ssize_t _PyUnicode_get_wstr_length(PyObject *op) {
^
cc1: all warnings being treated as errors
error: command '/usr/bin/gcc' failed with exit code 1
```
The logfile contains complete version information about the travisci environment. | {
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} | 0 | 2020-09-11T01:52:01Z | 2020-09-13T15:03:54Z | 2020-09-13T12:29:32Z | MEMBER | null | Three special cases where we can get better performance
1) categories have different lengths --> never equal
2) categories have different dtypes --> never equal
3) categories are not object-dtype --> can use use get_indexer instead of hashing
```
import pandas as pd
import numpy as np
dti = pd.date_range("2016-01-01", periods=10000, tz="US/Pacific")
dti2 = type(dti)(dti._data.copy())
np.random.shuffle(dti._data._data)
assert not dti.equals(dti2)
cd1 = pd.CategoricalDtype(dti)
cd2 = pd.CategoricalDtype(dti2)
cd3 = pd.CategoricalDtype(dti2[:-1])
cd4 = pd.CategoricalDtype(pd.Index(range(len(dti))))
In [5]: %timeit cd1 == cd2
548 µs ± 3.94 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
239 µs ± 1.04 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
In [6]: %timeit cd1 == cd3
543 µs ± 4.05 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
5.05 µs ± 257 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
In [7]: %timeit cd1 == cd4
389 µs ± 13.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
2.17 µs ± 63.1 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
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] | closed | false | null | [] | null | 1 | 2020-09-11T02:44:08Z | 2021-08-13T02:45:19Z | 2021-08-13T02:45:19Z | NONE | null | Like a lot of people, I wrote some code that attempted to call `DataFrame.append()` a few million times, and it was slow. So I read the pandas docs ("don't do that, use `pd.concat()`") and StackOverflow (blah blah blah Ginger) and decided that if I was going to write a function that made chunks of dataframe and then concatenated them, the least I could do would be to make it general purpose, and a package others could use.
Thus I created [pandas-appender](https://github.com/wumpus/pandas-appender).
It has some features that I care about -- scalability to 100s of millions of rows, and features that help specify or identify categorical columns. But of course, it's not quite compatible with `DataFrame.append()`, and I'm new enough to pandas to be unsure if I've made it follow pandas conventions as far as possible.
So my question is, can someone who is better versed in pandas conventions than I am, preferably someone who would love to use a package that let them easily build up a dataframe with `.append()`, take a look at this code and give me some advice?
Thanks in advance!
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} | 3 | 2020-09-11T06:03:39Z | 2020-11-19T00:34:52Z | 2020-09-19T22:07:25Z | CONTRIBUTOR | null | - [ ] closes #31922
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] new test cases
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} | 8 | 2020-09-11T07:08:35Z | 2020-10-09T01:50:00Z | 2020-10-09T01:47:41Z | CONTRIBUTOR | null | The current documentation says the return value of GroupBy.all and GroupBy.any is `bool`.
```
def any(self, skipna: bool = True):
"""
Return True if any value in the group is truthful, else False.
Parameters
----------
skipna : bool, default True
Flag to ignore nan values during truth testing.
Returns
-------
bool
"""
```
However, the actual returned type is DataFrame or Series (with the same shape as `GroupBy.sum()`).
```
In [25]: df
Out[25]:
0 1 2 3
0 1.0 0.0 0.0 0.0
1 0.0 1.0 0.0 0.0
2 0.0 0.0 1.0 0.0
3 0.0 0.0 0.0 1.0
In [26]: df.groupby(0).any()
Out[26]:
1 2 3
0
0.0 True True True
1.0 False False False
In [27]: type(df.groupby(0).any())
Out[27]: pandas.core.frame.DataFrame
```
This PR fixes the wrong type hints for `GroupBy.{any, all}`.
- [ ] 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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} | 2 | 2020-09-11T08:47:01Z | 2020-09-11T12:15:01Z | 2020-09-11T12:06:13Z | CONTRIBUTOR | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
In pandas.core.indexes.base ``hash`` is called [here](https://github.com/pandas-dev/pandas/blob/a09259b5699b0f2667761356cb3b22ef43ef37f0/pandas/core/indexes/base.py#L4118) and the value is not used.
Why is this done?
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} | 6 | 2020-09-11T09:35:22Z | 2020-09-15T22:32:26Z | 2020-09-15T22:32:26Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
The [natural sort order](https://en.wikipedia.org/wiki/Natural_sort_order#:~:text=Natural%20sort%20order%20is%20an,machine%2Doriented%20pure%20alphabetical%20order) is a common use case when working with real-world data. For example, consider the following DataFrame of clinical data where the body temperature of patients was measured:
```
data = {'Patient_ID': {0: 'ID-1',
1: 'ID-11',
2: 'ID-2'},
'temperature': {0: 37.2, 1: 37.5, 2: 37.2}}
df = pd.DataFrame(data).sort_values(by=['Patient_ID'])
df.head(5)
```
will yield:
| | Patient_ID | temperature |
|---:|:-------------|--------------:|
| 0 | ID-1 | 37.2 |
| 1 | ID-11 | 37.5 |
| 2 | ID-2 | 37.2 |
whereas we would want
| | Patient_ID | temperature |
|---:|:-------------|--------------:|
| 0 | ID-1 | 37.2 |
| 2 | ID-2 | 37.2 |
| 1 | ID-11 | 37.5 |
#### Describe the solution you'd like
- sort_values could get a new parameter `sort_order` that is by default alphabetical and could be switched to natural.
- the implementation could be similar to the [natsort](https://github.com/SethMMorton/natsort) package without any of the extra options `natsort` brings.: modify all values and pass them to np.argsort() s.t. then transform them back.
#### API breaking implications
Since we are only adding a parameter this would not break any existing API.
#### Describe alternatives you've considered
Currently, one could use the `natsort` package. However, this seems cumbersome for such a common operation and makes it necessary to reindex the DataFrame. [Stackoverflow example](https://stackoverflow.com/questions/29580978/naturally-sorting-pandas-dataframe).
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} | 11 | 2020-09-11T10:04:07Z | 2020-10-10T23:07:49Z | 2020-10-10T23:07:42Z | CONTRIBUTOR | null | - [x] closes #32590
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
# Update
```
import numpy as np
from pandas import DataFrame, to_datetime
from datetime import timedelta
df = DataFrame(
{
"a": np.random.rand(10),
"b": [timedelta(np.random.randn()) for _ in range(10)],
"c": to_datetime(
np.random.randint(
1582800000000000000, 1583500000000000000, 10, dtype=np.int64
)
),
"d": to_datetime(
np.random.randint(
1582800000000000000, 1583500000000000000, 10, dtype=np.int64
),
utc=True
),
}
)
df.dtypes
a float64
b timedelta64[ns]
c datetime64[ns]
d datetime64[ns, UTC]
dtype: object
df.hist(xrot=90, figsize=(9, 6))
array([[<AxesSubplot:title={'center':'a'}>,
<AxesSubplot:title={'center':'c'}>],
[<AxesSubplot:title={'center':'d'}>, <AxesSubplot:>]], dtype=object)
```
# ~~WIP~~
Since there's no response for several days in https://github.com/pandas-dev/pandas/issues/32590, I decide to open this PR for further discussion.
There's two feasible options:
Plan A:
As is shown in the files changed, `data._get_numeric_data()` is modified to `data._get_numeric_or_datetime_data()`, and I will work on related tests as the next step.
Plan B, this may be a fotfix according to https://github.com/pandas-dev/pandas/issues/32590#issuecomment-687477764:
```
column_dt = data.select_dtypes(include='datetime64[ns]')
if len(column_dt) > 0:
data[column_dt] = data[column_dt].astype(np.int64)
data = data._get_numeric_data()
if len(column_dt) > 0:
data[column_dt] = data[column_dt].astype('datetime64[ns]')
```
Also, related tests will be modified.
Any comment / thought is welcomed. | {
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"description": "Incorrec... | open | false | null | [] | null | 1 | 2020-09-11T10:11:16Z | 2021-08-13T02:45:51Z | null | NONE | null | I would suggest to that indexing errors also state what they received, e.g. change [this](https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexing.py#L705) to:
except ValueError as err:
raise ValueError(
"Location based indexing can only have "
f"[{self._valid_types}] types, but got: {key}"
) from err
Or possibly a truncation for long types: `f"{(str(key)[:20] + '...') if len(str(key)) > 23 else str(key)}"`
It would particularly help sloppy data scientists like me :)
I'm happy to make a pull request, but I'd like to hear thoughts first. | {
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} | 2 | 2020-09-11T15:15:51Z | 2021-03-04T02:01:10Z | 2021-03-04T02:01:10Z | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
idx1 = pd.Index([0, 0, 1])
idx2 = pd.Index([0, 1])
print(idx1.union(idx2))
# Int64Index([0, 0, 1], dtype='int64')
print(idx2.union(idx1))
# Int64Index([0, 0, 1], dtype='int64')
idx1 = pd.Index([1, 0, 0])
print(idx1.union(idx2))
# Int64Index([0, 0, 1], dtype='int64')
print(idx2.union(idx1))
# Int64Index([0, 1], dtype='int64')
```
#### Problem description
In the fourth call ``idx2.union(idx1)`` one of the ``0`` is dropped. Meaning union() depends on the call order and on the index order, which seems buggy.
#### Expected Output
``Int64Index([0, 0, 1], dtype='int64')`` for every print Statement
#### Output of ``pd.show_versions()``
<details>
master
</details>
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} | 11 | 2020-09-11T15:27:36Z | 2020-12-24T15:09:44Z | 2020-11-19T19:01:09Z | MEMBER | null | - [x] closes #36263
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
If obj_labes Index has duplicates and they are not removed from new_labels before redindexing, they are multiplied. So we would get a way too big index. | {
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} | 1 | 2020-09-11T15:33:37Z | 2020-09-13T12:34:52Z | 2020-09-13T12:34:52Z | 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
>>> pd.Series(1000000000000000000000)
0 1000000000000000000000
dtype: object
>>> pd.Series(1000000000000000000000, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py", line 327, in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/construction.py", line 475, in sanitize_array
subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)
File "/Users/matt/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 1555, in construct_1d_arraylike_from_scalar
subarr.fill(value)
OverflowError: int too big to convert
>>> pd.Series(1000000000000000000000.0, index = pd.date_range(pd.Timestamp.now().floor("1D"), pd.Timestamp.now(), freq='T'))
2020-09-11 00:00:00 1.000000e+21
2020-09-11 00:01:00 1.000000e+21
2020-09-11 00:02:00 1.000000e+21
2020-09-11 00:03:00 1.000000e+21
2020-09-11 00:04:00 1.000000e+21
...
2020-09-11 11:24:00 1.000000e+21
2020-09-11 11:25:00 1.000000e+21
2020-09-11 11:26:00 1.000000e+21
2020-09-11 11:27:00 1.000000e+21
2020-09-11 11:28:00 1.000000e+21
Freq: T, Length: 689, dtype: float64
```
#### Problem description
Hi pandas, when creating a new series with very large integers, series creation fails. This is not true if you pass in a float, or if you just pass in one value of a series and no index. the traceback points to pandas code so I'm submitting a bug here.
#### Expected Output
I would expect this to fail more gracefully or either output a series of object type, or float type. when initializing an array in numpy, it transparently converts it to object type
```python
>>> np.array([1000000000000000000000]*1000).dtype
dtype('O')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : 1.7.0
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 1 | 2020-09-11T15:59:32Z | 2020-09-11T22:29:09Z | 2020-09-11T21:33:03Z | MEMBER | null | - [x] closes #36258
Updated the deprecation warnings.
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> x = pd.DataFrame({"a": range(10), "b": range(10, 20), "d": ["a", "v"] * 5}, index=pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2, 1],
... [0, 1, 2, 0, 1, 2, 0, 1, 2, 1]]))
>>> x
a b d
lama speed 0 10 a
weight 1 11 v
length 2 12 a
cow speed 3 13 v
weight 4 14 a
length 5 15 v
falcon speed 6 16 a
weight 7 17 v
length 8 18 a
cow weight 9 19 v
>>> x.drop(index='cow')
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/frame.py", line 4169, in drop
errors=errors,
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/generic.py", line 3884, in drop
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/generic.py", line 3933, in _drop_axis
indexer = ~axis.isin(labels)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 3600, in isin
values = MultiIndex.from_tuples(values, names=self.names)._values
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/indexes/multi.py", line 501, in from_tuples
arrays = list(lib.tuples_to_object_array(tuples).T)
File "pandas/_libs/lib.pyx", line 2471, in pandas._libs.lib.tuples_to_object_array
TypeError: Expected tuple, got str
```
#### Problem description
I'd expected `drop` to drop all the rows with `cow` as an index.
#### Expected Output
```python
>>> x.drop(index='cow')
a b d
lama speed 0 10 a
weight 1 11 v
length 2 12 a
falcon speed 6 16 a
weight 7 17 v
length 8 18 a
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.17.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
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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} | 2 | 2020-09-11T18:26:34Z | 2020-09-26T01:22:28Z | 2020-09-26T01:22:28Z | 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
print(pd.__version__)
1.1.1
df = pd.DataFrame({'year': [1969, 2016],'month': [1, 1],'day': [1, 1],'hour': [9, 9]})
df=pd.to_datetime(df)
df
Out[10]:
0 1969-01-01 09:00:00
1 2016-01-01 09:00:00
dtype: datetime64[ns]
df.dt.normalize()
Out[11]:
0 1969-01-02
1 2016-01-01
dtype: datetime64[ns]
```
#### Problem description
I would expect pre-epoch behavior to be the same as post-epoch
#### Expected Output
```
Out[10]:
0 1969-01-01 09:00:00
1 2016-01-01 09:00:00
dtype: datetime64[ns]
df.dt.normalize()
Out[11]:
0 1969-01-01
1 2016-01-01
dtype: datetime64[ns]
```
#### Output of ``pd.show_versions()``
<details>
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.6.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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Updated the following files:
pandas/tests/series/methods/test_interpolate.py
pandas/tests/series/methods/test_unstack.py
pandas/tests/series/test_cumulative.py
pandas/tests/test_algos.py
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 14 | 2020-09-11T21:37:31Z | 2020-09-16T15:06:57Z | 2020-09-16T15:06:57Z | CONTRIBUTOR | null | - [x] CI for the 3.9 build is from source, we should update this to use numpy wheels (just became available IIUC) #36323
- [ ] pass tests & update whatsnew for 3.9 build so we can include in 1.1.3
cc @pandas-dev/pandas-core | {
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The else-block was pretty buggy in case of non unique indexes (was dependent on order of indexes and if one was subset of the other). The function in the try block worked perfectly, so I figured it would be way safer to let that function compute the new index and sort it based on the input indexes. If there is a faster way to achieve the resorting, I would be happy to change the code block.
We could delete the second sort block with the RunTimeWarning too, but then we would no longer get a Warning when incomparable objects should be sorted. | {
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Improved the docstring a bit in case of multi-index and no level given. | {
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... | open | false | null | [] | null | 2 | 2020-09-12T03:29:39Z | 2020-12-12T17:49:38Z | null | MEMBER | null | Currently warnings are only emitted when the length of a tuple is greater than 1. From [this comment](https://github.com/pandas-dev/pandas/pull/30546#discussion_r362300459) in PR where the deprecation was implemented, there was some confusion as to the behavior of `df.groupby('a')['b']` vs `df.groupby('a')[('b', )]`. The former is the SeriesGroupBy that the comment refers to where `key` is a string, the latter should still be deprecated. | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2020-09-12T12:17:42Z | 2020-09-12T21:33:08Z | 2020-09-12T21:21:16Z | CONTRIBUTOR | null | Improves performance of `pandas._libs.lib.ensure_string_array` is cases with many string elements.
Examples:
```python
>>> x = np.array([str(u) for u in range(1_000_000)], dtype=object)
>>> %timeit pd.Series(x, dtype=str)
344 ms ± 59.7 ms per loop # v1.1.0
157 ms ± 7.04 ms per loop # v1.1.1 and master
22.6 ms ± 191 µs per loop # this PR
>>> %timeit pd.Series(x, dtype="string")
357 ms ± 40.2 ms per loop # v1.1.0
148 ms ± 713 µs per loop # v1.1.1 and master
26.3 ms ± 291 µs per loop # this PR
```
#35519 is the cause of the improvement from 1.1.0 to 1.1.1.
Together with #35519 this PR means that the overhead of working with strings in pandas has gotten considerably smaller for cases when we many times instantiate new `Series`/`PandasArrays` with dtype `str`/`StringDtype`, i.e. probably quite often. | {
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} | 10 | 2020-09-12T12:46:15Z | 2021-07-23T21:26:09Z | 2020-11-19T02:09:10Z | CONTRIBUTOR | null | - [x] closes #34777
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Support MultiIndex for columns in parquet format by updating value column names check to handle MultiIndexes. | {
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"... | null | 1 | 2020-09-12T13:37:14Z | 2020-09-14T03:24:50Z | null | MEMBER | null | Example:
```
df = DataFrame({"A": [1, 2], "B": [3, 4]})
g = df.groupby("A", as_index=False)
result = g.apply(len)
print(result)
```
results in
```
A NaN
0 1 1
1 2 1
```
If we were to use `pandas.core.common.get_callable_name`, then we would be able to have the column name be "len" in this case, and presumably others. | {
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When adding check on inferred type for each level in MultiIndex for columns, error message pops up in CI (Typing validation with mypy):
`pandas/io/parquet.py:59: error: "Index" has no attribute "levels"; maybe "nlevels"? [attr-defined]`
I understand that single-level Indexes do not have the "levels" attribute while MultiIndexes have the "levels" attribute. It seems that mypy static type checking may have inferred "df.columns" as Index type by default, hence flagging the error when using "df.columns.levels" in the code change.
Would it make sense to add a "levels" attribute to "Index" for consistency with MultiIndex, or are there any possible workarounds for this mypy error? | {
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} | 8 | 2020-09-12T15:00:54Z | 2020-11-14T16:50:19Z | 2020-11-14T16:50:19Z | MEMBER | null | The following seems to happen on all transform groupby kernels:
```
df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df.groupby([1, 1], axis=1).transform("shift")
```
results in `ValueError: Length mismatch: Expected axis has 2 elements, new values have 3 elements`.
I think the issue is in `pandas.core.groupby.generic._wrap_transformed_output`; this method does not take into account `self.axis` when wrapping the output. All that needs to happen is `result = result.T` and to use the index labels rather than the column labels for `columns` there. | {
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} | 1 | 2020-09-12T18:49:45Z | 2020-09-12T20:55:51Z | 2020-09-12T20:40:21Z | MEMBER | null | Upshot: avoid doing lots of non-performant is_foo_dtype checks.
This fixes a little more than half of the performance hit in the benchmark discussed in #34683. | {
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} | 10 | 2020-09-12T19:04:34Z | 2020-10-03T07:21:22Z | 2020-10-02T23:34:11Z | MEMBER | null | The benefit I have in mind here is that we could back it by a single 2xN array and a) avoid the kludge needed to make `__setitem__` atomic, b) do a view to get native types for e.g uniqueness checks, c) possibly share some methods with NDarrayBackedExtensionArray.
Also just in principle having EAs not depend on Index is preferable dependency-structure-wise.
cc @jschendel | {
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} | 1 | 2020-09-12T19:46:28Z | 2020-09-13T01:14:44Z | 2020-09-12T21:31:59Z | CONTRIBUTOR | null |
- [ ] closes #35989
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} | 0 | 2020-09-12T19:48:43Z | 2020-09-12T21:29:35Z | 2020-09-12T21:20:30Z | MEMBER | null | This along with #36309 gets #34683 to performance parity with master. | {
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} | 0 | 2020-09-12T21:02:03Z | 2020-09-13T15:03:15Z | 2020-09-13T12:31:18Z | MEMBER | null | Follow-up to #36282
We'll also be able to clean up MultiIndex special-casing in _reindex_non_unique. I expect others but cant say for sure yet. | {
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} | 1 | 2020-09-12T21:07:01Z | 2020-09-12T22:02:20Z | 2020-09-12T22:02:20Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
Consider the following dataframe
```
Exams Labs
I II I II
Student Course
Ada Comp 70 71 72 73
Math 71 73 75 74
Sci 72 75 75 75
Quinn Comp 73 74 75 76
Math 74 76 78 77
Sci 75 78 78 78
Violet Comp 76 77 78 79
Math 77 79 81 80
Sci 78 81 81 81
```
to get the rows in for `Math`, there are currently 2 ways to do it as described in here https://pandas.pydata.org/pandas-docs/stable/user_guide/cookbook.html#slicing
1. Using`xs` like so `df.xs('Math', level=1, axis=0)`
2. Using `loc` like so `df.loc[(slice(None), 'Math'), slice(None)]`
Method 2 seems to be very verbose. Furthermore, merely using `df.loc[(slice(None), 'Math')]` does not work here and the columns `slice(None)` has to be specified. I believe this is inconsistent with how we slice single-indexed dataframes (e.g.. `df.loc[<INDEX>]` works just fine for single-indexed `df` without having to specify the columns like `df.loc[<INDEX>, slice(None)]`
#### Describe the solution you'd like
Improve method 2 (using .loc) describe by perhaps making it similar to NumPy`s `ndarray` here https://numpy.org/doc/stable/reference/arrays.ndarray.html
So then I guess the improvement would be to make the following:
`df.loc[( ,'Math')]` equivalent to `df.loc[(slice(None), 'Math'), slice(None)]`
#### API breaking implications
None if we still allow the old API to work
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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