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BUG: DataFrame.combine_first() works incorrect with with string indexes
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| 2
| 2020-10-30T12:42:12Z
| 2020-11-02T00:42:21Z
| 2020-11-02T00:42:21Z
|
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
def test_astype(self):
df_data = {'a': {4: '162', 7: '85'}, 'b': {4: NA, 7: NA}}
df2_data = {'a': {139: '85'}, 'b': {139: NA}}
df = DataFrame(df_data).astype({
"a": 'string',
"b": 'string',
})
df2 = DataFrame(df2_data).astype({
"a": 'string',
"b": 'string',
})
sum_df = df.set_index(['a', 'b']).combine_first(
df2.set_index(['a', 'b']),
).reset_index()
assert sum_df
```
#### Problem description
You can see that in resulting `sum_df` there are duplicate rows like ('85', nan). This behavior is incorrect. combine_first() method should join such rows
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DOC: Start v1.1.5 release notes
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MDExOlB1bGxSZXF1ZXN0NTEzMjQ2NzI1
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REF: _setitem_with_indexer_split_path
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| 0
| 2020-10-30T19:44:23Z
| 2020-10-31T15:18:38Z
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|
MEMBER
| null |
Splitting this off to get a cleaner diff in an upcoming PR(s) that make setitem_with_indexer go through split_path for _all_ DataFrame cases, giving us less special-casing to worry about.
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TST: suppress warnings we cant do anything about
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MEMBER
| null |
and remove redundant filterwarnings from excel.test_readers
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MDExOlB1bGxSZXF1ZXN0NTEzMjYzNTg4
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BUG: slice_canonize incorrectly raising
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| 1
| 2020-10-30T20:21:29Z
| 2020-10-31T19:32:58Z
| 2020-10-31T19:31:24Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Doesn't appear to affect anything in master, but stumbled on it when implementing the PR do always return views when indexing on columns.
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| 2020-10-30T20:31:52Z
| 2020-10-31T16:03:15Z
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|
MEMBER
| null |
I was surprised we didnt already have a test_copy
Also an easter egg:
```
def test_keys(self, datetime_series):
# HACK: By doing this in two stages, we avoid 2to3 wrapping the call
# to .keys() in a list()
getkeys = datetime_series.keys
assert getkeys() is datetime_series.index
```
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ENH: Smaller package size
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| 1
| 2020-10-30T21:44:17Z
| 2020-10-30T21:48:32Z
| 2020-10-30T21:48:16Z
|
NONE
| null |
Could the `pandas.tests` package be an optional package? It consumes a huge portion of the package size, which may not be required for many production purposes.
```
$ du -s /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/* | sort -n
4 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/testing.py
4 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/_typing.py
4 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/_version.py
12 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/__init__.py
16 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/arrays
24 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/errors
32 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/conftest.py
64 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/api
92 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/compat
92 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/_testing.py
96 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/_config
100 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/tseries
140 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/__pycache__
160 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/util
472 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/plotting
2252 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/io
7360 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/core
16364 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/_libs
20060 /opt/conda/envs/myproject/lib/python3.6/site-packages/pandas/tests
```
The feature-request is that a default `pandas` install would exclude `pandas.tests` and a `pandas[tests]` install would include that package. The general pattern is that production dependencies (requirements.txt) would use `pandas` and any dev/test/CI dependencies (requirements_dev.txt) would add the optional extra for `pandas[tests]`.
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MDExOlB1bGxSZXF1ZXN0NTEzMzE3NTc3
| 37,527
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TYP: use typing.final in indexes.base
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| 2020-11-01T14:47:59Z
| 2020-11-01T13:56:23Z
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MEMBER
| null |
cc @simonjayhawkins this imports final conditionally following how you did it with Literal
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MDExOlB1bGxSZXF1ZXN0NTEzMzI5MjM3
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BUG: isin incorrectly casting ints to datetimes
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- [x] closes #36621
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
De-duplication in follow-up.
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MDExOlB1bGxSZXF1ZXN0NTEzMzUwNzY4
| 37,529
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REF: share delete, putmask, insert between ndarray-backed EA indexes
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| 0
| 2020-10-31T01:27:09Z
| 2020-10-31T18:09:02Z
| 2020-10-31T17:14:09Z
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| 733,606,008
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MDExOlB1bGxSZXF1ZXN0NTEzMzU0ODMz
| 37,530
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CLN: Use ABCNDFrame instead of ABCDataFrame and ABCSeries
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| 2021-01-02T14:15:04Z
| 2020-12-12T13:29:44Z
|
MEMBER
| null |
- [ ] closes #xxxx
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CLN/TYP: Alias for aggregation dictionary argument
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[] | null | 2
| 2020-10-31T10:49:09Z
| 2021-02-11T07:48:15Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Function passed to `DataFrame.apply` with `axis=1` receives `Series` with the `name` corresponding to the row's index. Unfortunately, this doesn't work the other way around - returning a `Series` from the applied function does not change the row's index in dataframe to `Series`'s name.
#### Describe the solution you'd like
If returned `Series` has a name, set it as a row's or column's index, depending on `axis` (similarly as it works with 'index', but on the other axis).
#### API breaking implications
This changes established behavior, but I assume people don't specify name if they don't have to. And if someone would return the `Series` given in parameter, this wouldn't change indices, as they would be simply the same.
#### Describe alternatives you've considered
I haven't found any other mean of setting the index in `apply`, which an option would be very useful to me.
#### Additional context
Example:
```python
>>> df = pd.DataFrame([[1,2],[3,4],[5,6]], columns=['A', 'B'])
>>> df
A B
0 1 2
1 3 4
2 5 6
>>> df.apply(lambda row: pd.Series(row, name=row.A+row.B), axis=1)
A B
3 1 2
7 3 4
11 5 6
```
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CONTRIBUTOR
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- [x] closes #37157
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| 2020-10-31T14:21:07Z
| 2021-10-30T14:10:06Z
| 2021-10-30T14:10:06Z
|
CONTRIBUTOR
| null |
Here we disable collection of the windowing tests *only* on windows: https://github.com/pandas-dev/pandas/pull/37455
let's see if we an figure out where this is coming on.
cc @mroeschke @AlexKirko @jbrockmendel
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#37535 pandas-CI: revert disable of windows window tests
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| 2020-10-31T15:03:06Z
| 2020-10-31T18:54:39Z
| 2020-10-31T18:54:39Z
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CONTRIBUTOR
| null |
- [x] closes #37535
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
removed the restrictions on windows test
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Backport PR #37520 on branch 1.1.x (DOC: Start v1.1.5 release notes)
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NONE
| null |
Backport PR #37520: DOC: Start v1.1.5 release notes
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TST: split up tests/plotting/test_frame.py into subdir & modules #34769
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| 2020-11-05T21:18:37Z
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CONTRIBUTOR
| null |
- [ ] closes #34769
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I moved the file to a new directory.
What is the best structure for this folder?
|
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CI Move unwanted typing checks to pre-commit
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MEMBER
| null |
There's still some grep-based checks left, but running doctests / running mypy can stay in code_checks
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| 2020-10-31T18:38:26Z
| 2020-11-08T15:11:08Z
| 2020-11-06T22:17:55Z
|
MEMBER
| null |
PoC implementation for using honest ABCs for pandas objects. Currently only implements ABCNDFrame. Compare:
```
def foo(obj: ABCNDFrame):
reveal_type(obj.attrs)
def bar(obj: ABCDataFrame):
reveal_type(obj.attrs)
```
In this PR, mypy will output `Revealed type is 'builtins.dict[Union[typing.Hashable, None], Any]'` for the former whereas the latter is `Revealed type is 'Any'`.
A script can be written (and I plan to, if this idea gets buy-in) to generate the ABC class off of the concrete class. Likewise, tests can be written to ensure that ABCNDFrame and NDFrame don't diverge.
docstrings can be removed from the ABC class; I just wanted to highlight that we could just have the docs in the ABC class (don't know if that would be desirable).
cc @simonjayhawkins, @jreback, @jbrockmendel
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MDExOlB1bGxSZXF1ZXN0NTEzNDg2NjA5
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REF: make EA reductions, nanops funcs keyword-only
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MDExOlB1bGxSZXF1ZXN0NTEzNDg2OTk2
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DOC Add note about virtualenv and conda
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xref https://github.com/pandas-dev/pandas/pull/37023#issuecomment-716591356
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MDExOlB1bGxSZXF1ZXN0NTEzNDk0MzUw
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|
Added example to the aggregate function
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| 2020-10-31T20:36:47Z
| 2020-11-01T06:10:25Z
| 2020-11-01T05:02:52Z
|
NONE
| null |
Added example of k-hot encoding to the aggregate function
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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BUG: Agg and Apply with custom function improperly ignore column labels when function returns a dict-like
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| 3
| 2020-10-31T20:56:10Z
| 2020-11-04T23:47:59Z
| 2020-11-04T23:47:59Z
|
NONE
| null |
Hello folks! I've discovered an odd corner case regression that I hope can be fixed.
The problem is that if you are running an aggregation/apply on a dataframe using a custom function - if that function returns a dict-like object, it doesn't use the appropriate column labels in the original dataframe.
As a simple replication
```{python}
df = pd.DataFrame([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[np.nan, np.nan, np.nan]],
columns=['A', 'B', 'C'])
print(df.agg(lambda x : {"sum" :np.sum(x)}))
```
prints
```
0 {'sum': 12.0}
1 {'sum': 15.0}
2 {'sum': 18.0}
```
instead of the expected
```
A {'sum': 12.0}
B {'sum': 15.0}
C {'sum': 18.0}
```
This is a regression/breaking change since in prior versions of pandas (I believe 1.0.X and earlier) the expected results were produced.
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MDExOlB1bGxSZXF1ZXN0NTEzNDk5Mjg3
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REF: Categorical.is_dtype_equal -> categories_match_up_to_permutation
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| 0
| 2020-10-31T21:21:37Z
| 2020-11-02T22:48:23Z
| 2020-11-02T22:39:27Z
|
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
There are too many notions of CategoricalDtype equality. This gives a clearer name to one of them.
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MDExOlB1bGxSZXF1ZXN0NTEzNTAxNjkx
| 37,546
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ENH: Add dtype argument to read_sql_query (GH10285)
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| 11
| 2020-10-31T21:44:46Z
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|
CONTRIBUTOR
| null |
- [x] closes #10285
- [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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MDExOlB1bGxSZXF1ZXN0NTEzNTAzNzMz
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ENH: Improve error reporting for wrong merge cols
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| 2
| 2020-10-31T22:05:53Z
| 2020-11-08T12:06:18Z
| 2020-11-08T03:08:00Z
|
MEMBER
| null |
- [x] closes #16228
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixed error reporting when double conditions are given. Additionally I found, that something like
```
left = DataFrame({"a": [1, 2], "b": [3, 4]})
right = DataFrame({"a": [1, 1], "c": [5, 6]})
pd.merge(left, right, right_on="a")
```
raised an uncontrolled error:
```
Traceback (most recent call last):
File "/home/developer/.config/JetBrains/PyCharm2020.2/scratches/scratch_4.py", line 25, in <module>
print(pd.merge(left, right, right_on="a"))
File "/home/developer/PycharmProjects/pandas/pandas/core/reshape/merge.py", line 72, in merge
op = _MergeOperation(
File "/home/developer/PycharmProjects/pandas/pandas/core/reshape/merge.py", line 643, in __init__
self._validate_specification()
File "/home/developer/PycharmProjects/pandas/pandas/core/reshape/merge.py", line 1247, in _validate_specification
if len(self.right_on) != len(self.left_on):
TypeError: object of type 'NoneType' has no len()
```
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MDExOlB1bGxSZXF1ZXN0NTEzNTA2NDE3
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CLN: de-duplicate recode_for_categories
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| 2
| 2020-10-31T22:32:38Z
| 2020-11-02T00:26:48Z
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|
MEMBER
| null |
Step towards figuring out the small differences between Categorical's _validate_foo methods
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TST/REF: collect base tests by method
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BUG: Loc changes dtype when condition is completly False
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| 2021-08-14T04:32:05Z
| 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
df = pd.DataFrame({
"a": ["a"],
"b": [1],
"c": [1]
})
df.loc[[False], ["b"]] = 10 - df["c"]
print(df)
```
Thus converts ``b`` to float and returns
```
a b c
0 a 1.0 1
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
Would expect that this returns an integer.
```
a b c
0 a 1 1
```
Interestingly, this is returned if we drop the column ``a``.
```
df = pd.DataFrame({
"b": [1],
"c": [1]
})
df.loc[[False], ["b"]] = 10 - df["c"]
print(df)
```
or if we are not using df["c"] and an integer instead.
```
df = pd.DataFrame({
"a": ["a"],
"b": [1],
"c": [1]
})
df.loc[[False], ["b"]] = 10 - 1
print(df)
```
Both return ``1`` instead of ``1.0`` for column ``b``
Edit: The condition [False] is just a simplification. This also happens if we use a condition, which evaluates sometimes to completly False based on the input data.
#### Output of ``pd.show_versions()``
<details>
master
</details>
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REF: simplify Index.take, MultiIndex.take
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implemented additionally functionality of formatters_col in to_latex
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NONE
| null |
- [ x] closes #26278
- [ x] tests added / passed
- [ x ] passes `black pandas`
- [ x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ x] whatsnew entry
Was having some issues rebasing on https://github.com/pandas-dev/pandas/pull/32666 so making a new PR with same changes. Closing other PR as well.
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BUG: Improve error message when casting ExtensionDtype to datetime
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| 3
| 2020-11-01T04:55:49Z
| 2021-08-31T23:32:33Z
| 2021-08-31T23:32:33Z
|
MEMBER
| null |
We support casting `int` and `float` dtypes to `DatetimeTZDtype`:
``` python
In [11]: s = pd.Series(np.random.randint(10, size=3), dtype="int64")
...: s.astype(pd.DatetimeTZDtype(tz="US/Pacific"))
Out[11]:
0 1969-12-31 16:00:00.000000004-08:00
1 1969-12-31 16:00:00.000000007-08:00
2 1969-12-31 16:00:00.000000001-08:00
dtype: datetime64[ns, US/Pacific]
```
We should be able to do this with the corresponding extension dtypes (`Int`/`Float`). Currently this errors:
``` python
In [12]: s = pd.Series(np.random.randint(10, size=5), dtype="Int64")
...: s.astype(pd.DatetimeTZDtype(tz="US/Pacific"))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-12-469a4b536444> in <module>
1 s = pd.Series(np.random.randint(10, size=5), dtype="Int64")
----> 2 s.astype(pd.DatetimeTZDtype(tz="US/Pacific"))
/workspaces/pandas-arw2019/pandas/core/generic.py in astype(self, dtype, copy, errors)
5797 else:
5798 # else, only a single dtype is given
-> 5799 new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
5800 return self._constructor(new_data).__finalize__(self, method="astype")
5801
/workspaces/pandas-arw2019/pandas/core/internals/managers.py in astype(self, dtype, copy, errors)
620 self, dtype, copy: bool = False, errors: str = "raise"
621 ) -> "BlockManager":
--> 622 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
623
624 def convert(
/workspaces/pandas-arw2019/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
422 applied = b.apply(f, **kwargs)
423 else:
--> 424 applied = getattr(b, f)(**kwargs)
425 except (TypeError, NotImplementedError):
426 if not ignore_failures:
/workspaces/pandas-arw2019/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors)
608 if self.is_extension:
609 try:
--> 610 values = self.values.astype(dtype)
611 except (ValueError, TypeError):
612 if errors == "ignore":
/workspaces/pandas-arw2019/pandas/core/arrays/integer.py in astype(self, dtype, copy)
471 na_value = lib.no_default
472
--> 473 return self.to_numpy(dtype=dtype, na_value=na_value, copy=False)
474
475 def _values_for_argsort(self) -> np.ndarray:
/workspaces/pandas-arw2019/pandas/core/arrays/masked.py in to_numpy(self, dtype, copy, na_value)
222 data[self._mask] = na_value
223 else:
--> 224 data = self._data.astype(dtype, copy=copy)
225 return data
226
TypeError: data type not understood
```
We should get this to work for both `ExtensionArray` and the masked versions.
|
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MDU6SXNzdWU3MzM4NzMwMjE=
| 37,554
|
ENH: implement concat for EAs
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[] | null | 2
| 2020-11-01T05:19:19Z
| 2020-11-09T22:29:11Z
| null |
MEMBER
| null |
Currently `pd.concat` doesn't accept `ExtensionArrays`:
``` python
In [3]: a = pd.array([1, 2], dtype='Int64')
...: b = pd.array([3, 4], dtype='Int64')
In [4]: pd.concat([a, b])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-714da278d09e> in <module>
----> 1 pd.concat([a, b])
/workspaces/pandas-arw2019/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
273 ValueError: Indexes have overlapping values: ['a']
274 """
--> 275 op = _Concatenator(
276 objs,
277 axis=axis,
/workspaces/pandas-arw2019/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
358 "only Series and DataFrame objs are valid"
359 )
--> 360 raise TypeError(msg)
361
362 # consolidate
TypeError: cannot concatenate object of type '<class 'pandas.core.arrays.integer.IntegerArray'>'; only Series and DataFrame objs are valid
```
We should make this work for both base EAs and the masked versions. For example:
``` python
In [5]: a = pd.array([pd.NA, 1], dtype='Int64')
...: b = pd.array([1, pd.NA], dtype='Int64')
...: pd.concat([a, b])
```
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| 733,895,056
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MDExOlB1bGxSZXF1ZXN0NTEzNTY3NzY1
| 37,555
|
Fixed the documentation for pandas.DataFrame.set_index inplace parameter
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| 2020-11-01T06:33:52Z
| 2020-11-17T17:27:47Z
| 2020-11-17T17:27:37Z
|
CONTRIBUTOR
| null |
Fixed the documentation for `pandas.DataFrame.set_index` 's `inplace` parameter.
Added proper explanation for True case.
|
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TYP: Check untyped defs (except vendored)
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| 12
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| 2020-11-09T13:21:17Z
| 2020-11-09T12:51:09Z
|
MEMBER
| null |
@jbrockmendel has recently addressed many of the modules that were not previously checked. There are now many less modules outstanding.
There has been a concern in the past that if we add ignores, the issues may not be addressed and sit in the codebase. (from previous experience with other linting tools)
There are however pros to including the ignores. The issues are visible to people working on the relevant code and when reviewing the code. **Also as mypy will be checking the relevant modules, PRs changing these modules will have additional static checking on ci and the chance of regression/errors is likely reduced.**
IMO the pros always outweighed the cons, but now that there are less ignores involved (admittedly the number is still not trivial), I think it is appropriate to reconsider this approach.
One caveat, NumPy types currently resolve to `Any` and we have not yet discussed how we will 'switch' to using NumPy types #36092. (reverting to using `check_untyped_defs=False` on some modules could make the switch more managable)
Another possible upside, is in the review of typing PRs to fix these issues. Reviewers of typing PRs cannot see explicitly see why changes are being made and a typical reviewer is normally suspicious of any changes to the codebase. This can lead to long review cycles of typing PRs. I'm not sure if having the resolved mypy errors in the diff will help, but if the hypothesis is correct could be an argument for following a similar approach for resolving the numerous errors that will be reported once NumPy types no longer resolve to `Any`
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ENH: Improve numerical stability for window functions skew and kurt
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| 8
| 2020-11-01T13:51:37Z
| 2020-11-09T21:44:11Z
| 2020-11-09T21:20:06Z
|
MEMBER
| null |
- [x] closes #6929
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Used the same procedure as in ``DataFrame.skew()``
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ENH: extend DataFrame.insert to also insert rows
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[] | null | 8
| 2020-11-01T14:35:20Z
| 2021-05-31T13:12:52Z
| 2021-05-31T13:12:51Z
|
NONE
| null |
#### Is your feature request related to a problem?
I wish I could use pandas to directly insert a row at a specific position in a data frame, just like I can insert a column via the `DataFrame.insert` method. This would be similar to the [add_row](https://tibble.tidyverse.org/reference/add_row.html) function that exist for R data frames.
#### Describe the solution you'd like
expand the functionality of `DataFrame.insert` to take an axis argument and allow for the insertion of rows when `axis` is set to `0`.
#### API breaking implications
as `insert` currently inserts a column by default, the default argument to `axis` should be set to `1`. When this is done, I don't foresee any breaks.
#### Describe alternatives you've considered
An alternative is to use concatenate. This is tedious though, because it requires several concat operations (upper block + row to insert + lower block) and error prone. Another alternative (not in-place) is to use `np.insert` like
```python
import seaborn as sns
import pandas as pd
import numpy as np
def insert_row(df, row, position):
out = pd.DataFrame(np.insert(arr=df.values,
obj=position,
values=row,
axis=0),
columns=df.columns)
return out
iris = sns.load_dataset("iris")
row_to_insert = iris.iloc[1, :]
insert_row(iris, row_to_insert, 1)
```
#### Additional context
Code example to insert column:
```python
import seaborn as sns
iris = sns.load_dataset("iris")
col_to_insert = iris.iloc[:, 1]
position_to_insert = 1
iris.insert(position_to_insert, "new_column", col)
```
Desired behaviour:
```python
row_to_insert = iris.iloc[1, :]
iris.insert(position_to_insert, "new_row", row, axis=0)
```
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Add ss02
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| 2
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CONTRIBUTOR
| null |
- [x] closes #25113, closes https://github.com/pandas-dev/pandas/issues/36745
- [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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REF: move _wrap_joined_index up to NDarrayBackedExtensionIndex
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MEMBER
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Small steps towards
a) supporting arbitrary ExtensionIndex
b) using `@final` more in index code
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REF: move IntervalIndex.equals up to ExtensionIndex.equals
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TST: fix xfailing indexing test
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| 2020-11-01T17:54:23Z
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MEMBER
| null |
When moving to the always-go-split_path, a bunch of tests fail without this fix.
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CLN: remove redundant variable in setitem_with_indexer
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| 2020-11-01T19:19:55Z
| 2020-11-04T08:07:23Z
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MEMBER
| null |
- [x] closes #35719
- [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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ENH: add DataFrame.is_unique method
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|
CONTRIBUTOR
| null |
Currently, to see columns with unique values, we have to do `df.apply(lambda x: x.is_unique)`, which is a bit awkward.
IMO checking for unique columns in a DataFrame is a common enough need to justify a method for that specific purpose.
Xref: #11948
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| 1
| 2020-11-01T21:47:38Z
| 2020-11-10T23:41:47Z
| 2020-11-10T23:41:47Z
|
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
import pandas as pd
import numpy as np
pd.Series(['yes','yes', None, np.nan]).nunique(dropna = True)
```
#### Problem description
The output was 2.
It seems that nunique can only ignore None or np.nan but not both together. This seems odd to me and I think it
should either be changed or the docs should reflect this.
#### Expected Output
I expected to get 1 as the result but I got 2.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.49+
Version : #1 SMP Sun Oct 18 19:43:35 PDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.18.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.4
setuptools : 46.1.3.post20200325
Cython : 0.29.21
pytest : 5.4.1
hypothesis : 5.10.0
sphinx : 3.0.2
blosc : None
feather : 0.4.1
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: 0.9.0
bs4 : 4.9.0
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : None
gcsfs : 0.6.1
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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CLN: de-duplicate asof_locs
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| 3
| 2020-11-01T21:48:15Z
| 2020-11-02T14:30:26Z
| 2020-11-02T13:38:13Z
|
MEMBER
| null |
We might want to deprecate allowing DatetimeIndex `where` in `PeriodIndex.asof_locs`. We only have one test for it, and allowing it is inconsistent with how we treat other comparisons (unless we want to start allowing dt64 in PeriodArray.searchsorted...)
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BUG: Fix bug in combine_first with string dtype and only NA
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| 1
| 2020-11-01T22:11:31Z
| 2020-12-13T19:29:30Z
| 2020-11-02T00:42:22Z
|
MEMBER
| null |
- [x] closes #37519
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This caused bugs in ``Index.union()`` and ``Index.join()`` with ``how=outer`` too, but they are called from combine_first. Could add test, if this would be preferable. Hope Adding ExtensionArray to the if condition does not crash anything else. Run most categorical tests already locally.
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PERF: faster numeric indexes comparisons when self is identical to other
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| 1
| 2020-11-01T22:11:40Z
| 2021-01-01T22:13:54Z
| 2020-11-02T00:32:18Z
|
CONTRIBUTOR
| null |
Further performance improvement over #37130, this time improving all numeric indexes. The improvement is larger for `Int64Index`, because that doesn't need to check for `na` values.
Examples:
```python
>>> n = 100_000
>>> idx1 = pd.Int64Index(range(n))
>>> idx2 = idx1.view()
>>> %timeit idx1 == idx2
145 µs ± 2.43 µs per loop # master
11.4 µs ± 290 ns per loop # this PR
>>> idx3 = pd.Float64Index(range(n))
>>> idx4 = idx3.view()
>>> %timeit idx3 == idx4
104 µs ± 2.12 µs per loop # master
49.8 µs ± 886 ns per loop # this PR
```
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TST: avoid/suppress DeprecationWarnings
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MEMBER
| null |
After this, all thats left for me locally is plotting warnings about multiple subplots
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BUG: to_dict should return a native datetime object for NumPy backed dataframes
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| 2020-11-01T23:27:18Z
| 2020-11-04T23:50:47Z
| 2020-11-04T23:50:34Z
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MEMBER
| null |
- [x] closes #21256
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added a fix for datetime. Added tests for datetime and for bool
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ENH: df.assign() should accept a quoted name
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| 1
| 2020-11-01T23:42:08Z
| 2020-11-02T00:20:25Z
| 2020-11-02T00:20:03Z
|
NONE
| null |
#### Is your feature request related to a problem?
I believe that `df.assign()` should permit creation of columns using quoted names.
Note, this feature request is not because I am a masochist and prefer having non-syntactic (spaces, funny characters, etc..) column names, but to maintain consistency across the pandas ecosystem (see additional context).
The code in question:
```
import pandas as pd
df = pd.DataFrame({'temp_c': [17.0, 25.0]},
index=['Portland', 'Berkeley'])
df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
```
#### Describe the solution you'd like
It should (also?) work like this:
```
df.assign('temp_f'=lambda x: x.temp_c * 9 / 5 + 32)
```
#### API breaking implications
I am not sure what limitations this might have...
#### Describe alternatives you've considered
Suck it up, [take the blue pill](https://en.wikipedia.org/wiki/Red_pill_and_blue_pill) and start using `df.assign(temp_f=...`
#### Additional context
**This mini-discussion can likely be ignored** as I am sure the response to it will be that it's a "personal preference". I certainly do not have the clout to making such statements but I am including them here "for the record" 🙂.
<details>
Data frame columns can be accessed using:
`df['HP']` or `df.HP`
I have always taught my students to do `df['HP']` because it:
- makes you think about what you're doing more,
- permits sensible method chaining,
- improves readability and clearly differentiates column names from other actions
- is overall less error prone
More so, I think it just feels like the "right way to do things". I am dreaming of the day when one day, `df.HP` is deprecated but it sounds like that is not on the horizon...
My favourite (contrived) example of this is if you had a data frame with a column called "mean", `df.mean` gives a somewhat surprising result.
</details>
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TST/REF: collect tests by method
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| 0
| 2020-11-02T00:07:52Z
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|
MEMBER
| null |
This is something of an outlier in that it collects iter/iteritems/iterrows/itertuples into a test_iteration file. I think this is a sufficiently clear grouping, akin to test_arithmetic/test_reductions/test_unary, LMK if you disagree.
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BUG: series max aggregate function throws a TypeError.
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[] | null | 12
| 2020-11-02T03:38:08Z
| 2021-08-14T04:43:28Z
| 2021-08-14T04:43:28Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
series = pd.Series(['2018-01-01', None])
print(series.max())
```
#### Problem description
The max function skipna parameter defaults to true, which means that the current series only has ["2018-01-01"] when the comparison is actually done, so the max should be 2018-01-01, but an exception is thrown here.
The main error messages: `TypeError: '>=' not supported between instances of 'str' and 'float'`
I debugged the source code and found that the line `nanmax = _nanminmax("max", fill_value_typ="-inf")` converts None to inf by default, which ends up comparing the type types
#### Expected Output
2018-01-01
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 821f90c23e88561d6741714428b24c8cd3a21b25
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+1025.g821f90c23
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.4
sphinx : 3.2.1
blosc : 1.9.2
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.4
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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DOC: Add windows.rst
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| 1
| 2020-11-02T04:30:53Z
| 2020-11-14T21:16:14Z
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|
MEMBER
| null |
Adding a dedicated `user_guide/windows.rst` for `rolling/expanding/ewm` operations.
Mainly ported over from `computation.rst`.
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QST: Seems inconsistent behavior in MultiIndex.is_monotonic ??
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[] | null | 2
| 2020-11-02T07:45:03Z
| 2020-11-03T13:30:38Z
| 2020-11-03T13:30:38Z
|
NONE
| null |
#### Question about pandas
In the example below, I have no idea why their results are different in pandas 1.1.4.
```python
>>> pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b'), ('y', 'b'), ('z', 'a')]).is_monotonic
True
>>> pd.MultiIndex.from_tuples([('x', 'a'), ('y', 'b'), ('y', 'a'), ('z', 'a')]).is_monotonic
False
```
Is it intended behavior in pandas 1.1.4. or seems like bug ??
If this is a bug, which one is correct behavior ??
Thanks :)
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|
ENH: DataFrame Constructions from Data Classes
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| 2020-11-02T08:19:05Z
| 2020-11-08T08:45:17Z
| 2020-11-05T15:40:49Z
|
NONE
| null |
#### Is your feature request related to a problem?
I wish to construct `pandas.DataFrame` from iterable of `dataclasses.dataclass` as from iterable of tuples `DataFrame.from_records`. The rationale behind is that data classes is more typed object than general tuple or dictionary. Also, data classes more memory efficient than `tuple`'s. It makes data classes attractive to use them instead of `dict`'s or `tuple`'s whenever schema is known.
#### Describe the solution you'd like
I would like class method `.from_dataclasses` which allows `DataFrame` construction and type inference from uniform (for simplicity) sequence of data classes. See example below.
```python
import pandas as pd
from dataclasses import dataclass
@dataclass
class Record:
id: int
name: str
constant: float
df = pd.DataFrame.from_dataclasses([
Record(0, 'Landau', 3.1415926),
Record(1, 'Kapitsa', 2.718281828459045),
Record(2, 'Bogolyubov', 6.62607015),
])
print(df.dtypes)
# id int64
# name object
# constant float64
# dtype: object
````
In the example above schema of `DataFrame` is infered with `Record.__annotations__` dictionary which contains type user provided type information. API could also provide ways to validate schema in runtime by comparying type of actual type and specified type for a column.
#### API breaking implications
There is no API breaking in general but there is requirements to minimum Python version (which is 3.7).
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sourcery refactoring
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|
MEMBER
| null |
I tried applying https://sourcery.ai/github/ , seems like there's some nice suggestions here. I removed the ones where they change things like
```diff
- if param:
- var = a
- else:
- var = b
+ var = a if param else b
```
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refactor core
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- if param:
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REF: prelims for single-path setitem_with_indexer
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| 2020-11-02T21:46:43Z
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MEMBER
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Removes a pytest.skip that was supposed to have been removed along with an xfail in a previous step.
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BUG: Index.where casting ints to str
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MEMBER
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Two things going on here (can separate if asked, but they got mixed together in the process).
1) don't re-raise in `DatetimeLikeIndexMixin.where`, so we end up giving the more standard exception message.
2) Use validate_fill_value in Index.where, and have that disallow strs for numeric. This fixes the following in master:
```
>>> idx = pd.Index(range(3))
>>> mask = np.array([True, False, True])
>>>
>>> idx.where(mask, "2")
Index(['0', '2', '2'], dtype='object')
```
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MDU6SXNzdWU3MzQ2MjgzNjI=
| 37,592
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ENH: pandas.Timestamp.round infer correct timezone around DST
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| 2020-11-02T16:05:49Z
| 2020-11-05T00:14:43Z
| 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.
---
**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
from datetime import timedelta as td
test_ts = pd.Timestamp('2020-11-02 05:00:01.167', tz='UTC')
lt_ts = test_ts.tz_convert('America/New_York')
lt_ts
# Timestamp('2020-11-02 00:00:01.167000-0500', tz='America/New_York')
lt_ts -= td(days=1)
lt_ts
# Timestamp('2020-11-01 01:00:01.167000-0400', tz='America/New_York')
lt_ts.round(freq='H')
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "pandas/_libs/tslibs/timestamps.pyx", line 1145, in pandas._libs.tslibs.timestamps.Timestamp.round
File "pandas/_libs/tslibs/timestamps.pyx", line 1099, in pandas._libs.tslibs.timestamps.Timestamp._round
File "pandas/_libs/tslibs/timestamps.pyx", line 1317, in pandas._libs.tslibs.timestamps.Timestamp.tz_localize
File "pandas/_libs/tslibs/tzconversion.pyx", line 63, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc_single
File "pandas/_libs/tslibs/tzconversion.pyx", line 275, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from 2020-11-01 01:00:00, try using the 'ambiguous' argument
```
#### Problem description
pandas.Timestamp.round method crashes.
#### Expected Output
```python
Timestamp('2020-11-01 01:00:00.0000-0400', tz='America/New_York')
```
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-52-generic
Version : #57-Ubuntu SMP Thu Oct 15 10:57:00 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.4
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
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 : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MzQ2NTQ3ODU=
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BUG: DataFrame iloc does not accept Series assignment
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| 10
| 2020-11-02T16:40:39Z
| 2021-10-17T13:44:02Z
| 2020-11-19T00:22:07Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(columns=[0], index=[0])
df.iloc[0,0] = pd.Series([1,2,3]) # this raises a ValueError
df.iloc[0] = [pd.Series([1,2,3])] # this works, but is ugly
```
#### Problem description
When trying to assign a pandas.Series to one cell of a pandas.DataFrame, the following error is raised:
`ValueError: Incompatible indexer with Series`
Series in DataFrames are a common thing e.g. in sktime, when working with multivariate time series data and (as the working example above shows) it is unproblematically supported by pandas. Just the assignment to one cell via .iloc seems to be erroneous.
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
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 61 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
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 : 7.18.1
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 : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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API: Should Index.where/insert raise or cast on invalid other?
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|
MEMBER
| null |
Index.where
ATM `index.where(mask, something_incompatible)` raises for EA-backed indexes and MultiIndex and casts (sometimes buggily xref #37591) for everything else.
These should all do the same thing. I lean towards raising rather than casting. Doing that breaks a handful of tests, but AFAICT they are all directly testing Index.where, so not used by other parts of the code.
----
Index.insert
CategoricalIndex and IntervalIndex raise on invalid, PeriodIndex casts to object, DatetimeIndex and TimedeltaIndex cast to object _only_ for strings and otherwise raise. The base class casts but uses _coerce_scalar_to_index instead of any of our more standard patterns.
If changed to always raise, 25 tests fail. Most of these are directly testing Index.insert so could be changed if the API changed. The main troubling one is test_pivot_periods_with_margins, which I haven't tracked down all the way.
---
Index.putmask I'm still getting a handle on
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CLN: de-duplicate _validate_where_value with _validate_setitem_value
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TST/REF: share method tests between DataFrame and Series
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By default, a test using the frame_or_series fixture goes in tests.frame
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REF: re-use _validate_setitem_value in Categorical.fillna
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WIP: Debug high memory failure window tests
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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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|
CI: Use conda deactivate instead of source deactivate
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MEMBER
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Seen in the logs when running `setup_env.sh`
```
DeprecationWarning: 'source deactivate' is deprecated. Use 'conda deactivate'.
```
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BUG: DataFrame.to_json ignores index if it is repeated in the DataFrame.
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| 2020-11-03T09:55:29Z
| 2021-08-14T04:45:31Z
| null |
NONE
| null |
- [x ] I have checked that this issue has not already been reported.
Similar to https://github.com/pandas-dev/pandas/issues/25513
- [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
import pandas as pd
df=pd.DataFrame({'column':[0,1,2,3,4], 'probe':[0,1,2,2,3]})
df.set_index('probe',inplace=True)
out = df.to_json(orient='table', index=True)
assert 'primaryKey' in out
```
#### Problem description
DataFrame.to_json ignores index if it is repeated in the DataFrame.
Since pandas allows dataframes with repeated indexes,
the function `to_json` should be able to write them.
If you want to limit this functionality, at least one warning should be implemented.
Note that 'to_pickle' works fine with repeated indexes, as expected.
#### Expected Output
Not Error in assert
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.6.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : ES
LOCALE : es_ES.cp1252
pandas : 1.1.4
numpy : 1.16.3
pytz : 2018.5
dateutil : 2.7.3
pip : 20.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 2.2.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.18
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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DEPR: DataFrame/Series.slice_shift
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| 2
| 2020-11-03T12:28:07Z
| 2020-11-04T09:23:51Z
| 2020-11-04T02:21:01Z
|
CONTRIBUTOR
| null |
- [x] ref #18262
- [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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|
ERR: fix error message in Period for invalid frequency
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| 0
| 2020-11-03T14:59:02Z
| 2020-11-03T19:18:30Z
| 2020-11-03T19:18:26Z
|
MEMBER
| null |
Small fix of the error message, it was missing the actual freq to fill into the message.
On master, you get:
```
In [1]: pd.Period("2012-01-02", freq="WOM-1MON")
...
ValueError: Invalid frequency: {0}
```
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TST: Remove stacklevel checks for deprecated resample kwargs
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| 2020-11-03T15:27:15Z
| 2020-12-18T09:09:39Z
| 2020-12-18T09:09:39Z
|
CONTRIBUTOR
| null |
- [N/A] closes #xxxx (see https://github.com/pandas-dev/pandas/pull/36629#issuecomment-720757082)
- [x] tests added / passed (tests are failing locally but on unrelated things, think it's a problem somewhere in master)
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [N/A] whatsnew entry
This removes `stacklevel` requirements from the deprecation warnings for `resample` and `Grouper` kwargs in an attempt to simplify the warning-handling code (see #36629 for "why"). This only changes `tests.resample.test_deprecated`.
The second commit is optional (it just extracts the deprecated argument checking code out from `Grouper.__new__` to declutter the latter); I can revert it if needed.
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| 0
| 2020-11-03T15:32:42Z
| 2020-11-08T02:11:13Z
| 2020-11-08T02:11:13Z
|
MEMBER
| null |
#### Is your feature request related to a problem?
Not necessarily an issue, but one can get some code improvement and consistency in ``pandas.plotting._matplotlib.style.get_standard_colors``, when it is related to cycling colors.
During refactoring of the module (#37203) it turned out that cycling the colors works in a strange (although tolerable way).
```python
>>> from pandas.plotting._matplotlib.style import get_standard_colors
>>> get_standard_colors(num_colors=2, color='rgb')
'rgb'
>>> get_standard_colors(num_colors=3, color='rgb')
'rgb'
>>> get_standard_colors(num_colors=4, color='rgb')
'rgbr'
>>>
```
From the snippets above one can see that in the second and third examples work reasonable.
Meanwhile, the first one returns three colors, rather than two.
Extra color(s) will be ignored anyway, but in regards to the discussion in the referenced PR, the way of cycling colors can be improved https://github.com/pandas-dev/pandas/pull/37203#discussion_r516294252
#### Describe the solution you'd like
I suggest using matplotlib's cycler (or other generator) to cycle through the colors obtained and get the exact number of colors required, without any extra.
This will simplify the code involving colors cycling in ``get_standard_colors``.
#### Expected Output
```python
>>> from pandas.plotting._matplotlib.style import get_standard_colors
>>> get_standard_colors(num_colors=2, color='rgb')
'rg'
```
#### API breaking implications
Will break some tests related to ``get_standard_colors`` only as they expect extra colors to be returned on some occasions.
#### Describe alternatives you've considered
See https://github.com/pandas-dev/pandas/pull/37203 without involving modifications to color cycling.
|
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MDU6SXNzdWU3MzU0NjAxMDU=
| 37,605
|
API: require tz equality or just tzawareness-compat in setitem-like methods?
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| 9
| 2020-11-03T16:22:21Z
| 2021-10-31T14:53:56Z
| 2021-10-31T14:53:56Z
|
MEMBER
| null |
There are a handful of methods that do casting/validation in DatetimeArray (and indirectly, Series and DatetimeIndex) methods:
`__setitem__`
shift
insert
fillna
take
searchsorted
`__cmp__`
In all cases, we upcast datetime64/datetime to Timestamp, try to parse strings, and check for tzawareness compat (i.e. raise if `(self.tz is None) ^ (other.tz is None)`)
In the cases of searchsorted and cmp, we stop there, and don't require the actual timezones to match. In the other cases, we are stricter and raise if `not tz_compare(self.tz, other.tz)`
In #37299 we discussed the idea of making these uniform. I am generally positive on this, as it would make things simpler both for users and in the code. cc @jorisvandenbossche @jreback
Side-notes
1) `__sub__` doesn't do the same casting, but also requires tz match but _could_ get by with only tzawareness compat, xref #37329. BTW the stdlib datetime only requires tzawareness compat.
2) In `Series/DataFrame.__setitem__` we cast to object instead of raising on tz mismatch. For brevity I lump this in with "we raise on tz mismatch".
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MDExOlB1bGxSZXF1ZXN0NTE0ODY2NTc3
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|
BUG: set index of DataFrame.apply(f) when f returns dict (#37544)
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| 2
| 2020-11-03T16:51:28Z
| 2020-11-12T16:12:14Z
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|
CONTRIBUTOR
| null |
- [x] closes #37544
- [x] tests (added) updated / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
BUG: nunique not ignoring both None and np.nan
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CONTRIBUTOR
| null |
- [x] closes #37566
- [x] tests added / passed
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: remove rebox_native
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BUG: pandas.testing.assert_frame_equal with check_dtype=False unexpectedly raises an exception
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| 3
| 2020-11-03T17:54:06Z
| 2021-02-24T15:10:18Z
| 2020-12-02T23:31:30Z
|
NONE
| null |
This code
```
import pandas
from datetime import datetime
df1 = pandas.DataFrame({'a': []})
df2 = pandas.DataFrame({'a': []})
df1['a'] = pandas.to_datetime(df1['a'])
pandas.testing.assert_frame_equal(df1, df2, check_dtype=False)
```
raises:
```
AssertionError: right is not an ExtensionArray
```
But should it raise instead no exception as df1 and df2 are identical, except for their dtypes ?
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BUG: pd.Timestamp loses fold when removing timezone which is inconsistent with datetime.datetime
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| 2
| 2020-11-03T18:35:51Z
| 2020-11-08T02:22:48Z
| 2020-11-08T02:22:48Z
|
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.
I have tested on {'dirty': False, 'error': None, 'full-revisionid': '67a3d4241ab84419856b84fc3ebc9abcbe66c6b3', 'version': '1.1.4'}
- [-] (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.
#### Problem description
datetime.datetime saves the fold when replacing tzinfo, but pandas.Timestamp doesn't
#### Code Sample, a copy-pastable example
```python
import dateutil.tz
import pandas
from datetime import datetime as dt
print(pandas.Timestamp(1256427000000000000, tz=dateutil.tz.gettz('Europe/Moscow'), unit='ns'))
# Timestamp('2009-10-25 02:30:00+0300', tz='dateutil//usr/share/zoneinfo/Europe/Moscow')
print(pandas.Timestamp(1256427000000000000, tz=dateutil.tz.gettz('Europe/Moscow'), unit='ns').fold)
# 1
print(pandas.Timestamp(1256427000000000000, tz=dateutil.tz.gettz('Europe/Moscow'), unit='ns').replace(tzinfo=None).fold)
# 0
print(dt(2009, 10, 25, 2,30, fold=1, tzinfo=dateutil.tz.gettz('Europe/Moscow')).replace(tzinfo=None).fold)
# 1
```
#### Expected Output
```
Timestamp('2009-10-25 02:30:00+0300', tz='dateutil//usr/share/zoneinfo/Europe/Moscow')
1
1
1
```
#### Output of ``pd.show_versions()``
<details>
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/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 : 1.16.4
pytz : 2020.4
dateutil : 2.8.0
pip : 20.1.1
setuptools : 39.0.1
Cython : 3.0a6
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTE0OTU2Nzcx
| 37,611
|
Add tests for categorical with null ea as input
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| 1
| 2020-11-03T19:49:26Z
| 2020-11-13T13:14:46Z
| 2020-11-13T13:08:25Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
cc @jreback Is this what you had in mind for ExtensionArrays and Categoricals?
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| 735,625,825
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MDExOlB1bGxSZXF1ZXN0NTE0OTgzODk3
| 37,612
|
DOC: Fix punctuation
|
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| 1
| 2020-11-03T20:49:38Z
| 2020-11-04T01:58:17Z
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|
CONTRIBUTOR
| null |
Fixed punctuation to match others.
|
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MDExOlB1bGxSZXF1ZXN0NTE0OTg4MTg3
| 37,613
|
BUG: read-only values in cython funcs
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| 3
| 2020-11-03T20:59:30Z
| 2021-09-12T05:30:38Z
| 2020-11-04T13:43:47Z
|
MEMBER
| null |
- [x] closes #34857
- [x] closes #37264
- [x] closes #37312
- [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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| 37,614
|
ENH: should a sequence of integers be a valid input to BooleanArray?
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[] | null | 3
| 2020-11-03T21:09:15Z
| 2021-08-14T04:47:11Z
| null |
MEMBER
| null |
Came across this while working on #37377
xref #28778 for potentially relevant prior discussions
This works on NumPy:
``` python
In [10]: np.array([0, 1, 2], dtype=bool)
Out[10]: array([False, True, True])
```
but the following fails with `BooleanArray`:
``` python
In [11]: arr = pd.array([0, 1, 2], dtype="boolean")
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-44c03a9227eb> in <module>
----> 1 arr = pd.array([0, 1, 2], dtype="boolean")
/workspaces/pandas/pandas/core/construction.py in array(data, dtype, copy)
296 if is_extension_array_dtype(dtype):
297 cls = cast(ExtensionDtype, dtype).construct_array_type()
--> 298 return cls._from_sequence(data, dtype=dtype, copy=copy)
299
300 if dtype is None:
/workspaces/pandas/pandas/core/arrays/boolean.py in _from_sequence(cls, scalars, dtype, copy)
276 if dtype:
277 assert dtype == "boolean"
--> 278 values, mask = coerce_to_array(scalars, copy=copy)
279 return BooleanArray(values, mask)
280
/workspaces/pandas/pandas/core/arrays/boolean.py in coerce_to_array(values, mask, copy)
176 == values_object[~mask_values].astype(float)
177 ):
--> 178 raise TypeError("Need to pass bool-like values")
179
180 if mask is None and mask_values is None:
TypeError: Need to pass bool-like values
```
I'm +0 on supporting NumPy-like behavior here (or we should make sure to document the current behavior)
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MDU6SXNzdWU3MzU2NDk0MjY=
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|
BUG: Hash-function doesn't take upper bits into consideration for PyObjects
|
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| 1
| 2020-11-03T21:33:39Z
| 2021-02-07T16:42:08Z
| 2021-02-07T16:42:08Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
def create(M, shift=0):
lst=[x<<shift for x in range(M)]
return np.array(lst, dtype=np.object)
#linear running time:
%timeit pd.unique(create(10**3, 0))
%timeit pd.unique(create(10**4, 0))
# quadratic running time:
# for shift=32, the lower 32bits returned
# by Py_Hash are 0 for 64bit builds
%timeit pd.unique(create(10**3, 32))
%timeit pd.unique(create(10**4, 32))
```
#### Problem description
The times for small integers are: 222 µs vs 1.6 ms, i.e. about factor 10
The times for big integers are: 16.5 ms vs 1.68 s, i.e. about factor 100
#### Expected Output
Also for big integers, the factor should be about 10 (i.e. linear) and not quadratic.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 824869f5ee9f0fcc5b082d4a375395b620cb4667
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-53-generic
Version : #74-Ubuntu SMP Fri Dec 2 15:59:10 UTC 2016
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+1033.g824869f
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.36.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.50.1
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