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https://api.github.com/repos/pandas-dev/pandas/issues/26313 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26313/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26313/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26313/events | https://github.com/pandas-dev/pandas/pull/26313 | 441,517,993 | MDExOlB1bGxSZXF1ZXN0Mjc2ODI4ODY3 | 26,313 | DOC: Replace .values with .to_numpy() in enhancingperf | {
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Replace `.values` with `.to_numpy()` in the benchmark demonstration code. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26314 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26314/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26314/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26314/events | https://github.com/pandas-dev/pandas/issues/26314 | 441,643,259 | MDU6SXNzdWU0NDE2NDMyNTk= | 26,314 | Pivot / unstack on large data frame does not work int32 overflow | {
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```python
predictor_purchases_p = predictor_purchases.groupby(["ag", "artikelnr"])["som"].max().unstack().fillna(0)
or
predictor_purchases_p = predictor_purchases.pivot(index="ag", columns="artikelnr", value="som")
```
#### Problem description
I'm worki... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26315/events | https://github.com/pandas-dev/pandas/pull/26315 | 441,680,417 | MDExOlB1bGxSZXF1ZXN0Mjc2OTU0ODc5 | 26,315 | Remove other pandas.core.index files from MyPy Blacklist | {
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- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26316 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26316/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26316/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26316/events | https://github.com/pandas-dev/pandas/pull/26316 | 441,697,121 | MDExOlB1bGxSZXF1ZXN0Mjc2OTY3Nzgy | 26,316 | [ENH] Use default EA repr for IntervalArray | {
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- [x] 1 test deleted
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- [ ] closes #xxxx
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just a remark, as it's all alpha. using cgohlke first wheel on Python-3.8.0a4, I have a strange message
````
Python 3.8.0a4 (v3.8.0a4:c1004b8546, May 7 2019, 12:46:11) [MSC v.1916 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more in... | {
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Third part of the removal of ``compat.lrange`` from the code base. | {
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```python
In [1]: class Test:
...: def __ror__(self, other):
...: print('here')
...: return self
...:
In [2]: {} | Test()
here
Out[2]: <__main__.Test at 0x7f37a1601ba8>
In [3]: import pandas
In [4]: pandas.DataFram... | {
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```python
import pandas as pd
import numpy as np
pd.show_versions()
dates = ['2019-05-09', '2019-05-09', '2019-05-09']
date_series = pd.Series(dates)
# it appears to happen on object dtype aggregations
# where there is another object dtype in the frame with missi... | {
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Removal of ``compat.lmap`` from the code base. | {
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- [x] 2 tests added
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
When working on the original issue, I came across something similar :
- `RangeIndex` had its own implementation of `max`/`min` with the same behavior
- `Index` & `RangeIndex` could... | {
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```python
import pandas as pd
df = pd.DataFrame({'a': [], 'b':[], 'c': []})
df = df.set_index('c')
print(df.index.name) # prints 'c'
# using index to generate new column
df['d'] = df.index
# looking at the name of the index
print(df.index.name... | {
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```python
m_index = pd.MultiIndex.from_product([['Mona', 'Dennis'], ['good', 'great']], names=['name', 'desc'])
df = pd.DataFrame({'foo': [1, 2, 1, 2], 'bar': np.random.randn(4)}, index=m_index)
n1 = df.groupby([pd.Grouper(level='name'), 'foo']).ngroups
n2 = len(df.groupby([pd.Grouper(level='name'), ... | {
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- [x] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Improves robustness of json_normalize for inconsistently formatted json.
Fixes all observations in #26284 | {
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df = df.assign(block=(df.index.to_series().diff() != pd.Timedelta(15,'m')).cumsum())
> FutureWarning: The default of the 'keep_tz' keyword will change to True in a future release. You can set 'keep_tz=True' to obtain the future behav... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26332/events | https://github.com/pandas-dev/pandas/pull/26332 | 442,530,740 | MDExOlB1bGxSZXF1ZXN0Mjc3NjI1MzAw | 26,332 | Remove Panel References from Tests | {
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I think after this and #25567 get merged we can starting finalizing the Panel removal and get out of docs as well | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26333 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26333/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26333/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26333/events | https://github.com/pandas-dev/pandas/issues/26333 | 442,625,206 | MDU6SXNzdWU0NDI2MjUyMDY= | 26,333 | REGR: assigning scalar with a length no longer works | {
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Consider the following example:
```
In [1]: df = pd.DataFrame({'a': [1, 2, 3], 'b': [(1, 2), (1, 2, 3), (3, 4)]})
In [2]: df
Out[2]:
a b
0 1 (1, 2)
1 2... | {
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- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
For detection details see: https://github.com/pandas-dev/pandas/pull/26220#issuecomment-491143978
The slowdown was due to the use of `convert_to_tsobject` function.
Asv result:
master| patch| ... | {
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```python
# Your code here
df1 = pd.DataFrame({
'date': pd.date_range(start='2018-01-01', periods=5, tz='America/Chicago'),
'vals': list('abcde')}
)
df2 = pd.DataFrame({
'da... | {
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We currently allow CategoricalDtype.ordered to be None. It should just be a bool (default False).
```
In [8]: CategoricalDtype(categories=None, ordered=None).ordered
# None
```
categories can be None, which means "infer later on". B... | {
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Minor cleanup: Cleanup some regexes that guard against python2 style longs in their error messages. | {
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```python
# Your code here
import pandas as pd
df = pd.DataFrame([["c", "a", 1, 2], ["c", "b", 3, 4]])
df.groupby(0).sum()
```
#### Problem description
I expect the sum aggregation after groupby to work
similarly to normal aggregation: df.sum().
Howeve... | {
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@WillAyd | {
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"followers_url": "https://api.github.com/users/jorisva... | 3 | 2019-05-11T01:06:38Z | 2019-05-12T06:51:25Z | 2019-05-11T02:46:53Z | NONE | null | According to these sources, `pyarrow.parquet` supports an `nthreads` argument for parallel reads, i.e.
- https://wesmckinney.com/blog/python-parquet-multithreading/ the pyarrow.parquet engine
- https://arrow.apache.org/docs/python/parquet.html#multithreaded-reads
```python
import pyarrow.parquet as pq
table = pq... | {
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- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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```python
>>> pd.DataFrame(list(range(3))) # ok
0
0 0
1 1
2 2
>>> pd.DataFrame(iter(range(3))) # ok
0
0 0
1 1
2 2
>>> pd.DataFrame((i for i in range(3)) # ok
0
0 0
1 1... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Fixes bug when constructing a ``DataFrame`` using a ``range``. | {
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```sh
CondaValueError: Malformed version string '~': invalid character(s).
```
Which I assume is the root cause, though the dependency file hasn't been updated in some time.
@TomAugspurger if you have any thoughts; maybe a conda-forge issue? | {
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```python
import resource
import pandas as pd
# some random data, some of them as array columns
path = 'data.parquet'
batches = 5000
df = pd.DataFrame({
't': [pd.np.array(range(0, 180 * 60, 5))] * batches,
})
# read the data above and convert... | {
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] | closed | false | null | [] | null | 5 | 2019-05-11T18:40:34Z | 2019-05-12T06:49:51Z | 2019-05-12T06:31:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import resource
import pandas as pd
# some random data, some of them as array columns
batches = 5000
df = pd.DataFrame({
't': [pd.np.array(range(0, 180 * 60, 5))] * batches,
})
# read the data above and convert it to json (e.g. the b... | {
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```python
>>> pd.Series(reversed(range(3))) # Iterator work for Series
0 0
1 1
2 2
dtype: int64
>>> pd.Series((i for i in range(3))) # Generators work for Series
0 0
1 1
2 2
dty... | {
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- [ - ] closes #xxxx (no issue)
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```python
Python 3.7.3 (default, May 8 2019, 05:28:42)
[GCC 6.3.0 20170516] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import datetime
>>> df = pd.DataFrame([[datetime.time(1, 1, 0), 1]], co... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry (Not needed)
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- tests added
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`_values_for_argsort` returns different values for missing value in different EA. For example, missing value in `PandasArray` gets `np.nan` and in `PeriodArray` gets `-9223372036854775808` (in my m... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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This fix for #26303 would avoid an indexing issue caused by using MultiIndexes which results to a segfault in the example provided in #26303. | {
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- [x] tests added / passed
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Allows iterators as values when instantiating DataFrames using dicts.
Also collected the iterable-to-list functionality from core/series.py into core/common.py, so it is shar... | {
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Also realized that the `gcsfs` install was placed in the wrong place (needs to be placed after the `activate` command). However, you will notice then that the install is the culprit for the earlier 3.7 build failures (something is not right on `conda-forge` and / with `gcsfs` and `3.7`). Note thou... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A way to avoid incorrect coercion to float64 in the case of uint64 input is to introduce an additional check to `ensure_int64_or_float64`
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- [x] tests added
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Gap in performance between use dayfirst or format is acceptable now.
`asv run -E existing -b ParseDateComparison -a warmup_time=2 -a sample_time=2`:
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I think this was just working
```
=================================== FAILURES ===================================
______________________ TestRangeIndex.test_numpy_argsort _______________________
[gw1] linux -- Python 3.7.3 /home/vsts/minicon... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26362 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26362/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26362/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26362/events | https://github.com/pandas-dev/pandas/pull/26362 | 443,217,726 | MDExOlB1bGxSZXF1ZXN0Mjc4MTMxOTMw | 26,362 | BUG: DataFrame.join on tz aware index and column | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26363 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26363/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26363/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26363/events | https://github.com/pandas-dev/pandas/issues/26363 | 443,270,319 | MDU6SXNzdWU0NDMyNzAzMTk= | 26,363 | Fix type annotations for pandas.core.indexes.base | {
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Current errors are:
pandas\core\indexes\base.py:9: error: Module 'pandas._libs' has no attribute 'join' | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26364 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26364/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26364/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26364/events | https://github.com/pandas-dev/pandas/issues/26364 | 443,285,935 | MDU6SXNzdWU0NDMyODU5MzU= | 26,364 | Fix type annotations for pandas.core.indexes.datetimelike | {
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Current errors are:
pandas\core\indexes\datetimelike.py:65: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:66: error: "Callable[[Any], Any]" has no attribute "fget"
pandas\core\indexes\datetimelike.py:67: error: "Callable[[Any], Any]" has no ... | {
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- [ ] tests passed
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https://api.github.com/repos/pandas-dev/pandas/issues/26366 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26366/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26366/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26366/events | https://github.com/pandas-dev/pandas/issues/26366 | 443,309,042 | MDU6SXNzdWU0NDMzMDkwNDI= | 26,366 | Fix type annotations for pandas.core.indexes.datetimes | {
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Current errors are:
pandas\core\indexes\datetimes.py:7: error: Module 'pandas._libs' has no attribute 'join'
pandas\core\indexes\datetimes.py:89: error: Definition of "_data" in base class "DatetimeIndexOpsMixin" is incompatible with definition in base class "Index"
pandas\core\indexe... | {
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```
>>> import warnings
>>> warnings.filterwarnings('error')
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/joris/miniconda3/lib/python3.7/site-packages/pandas/__init__.py", line 26, in <... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26368 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26368/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26368/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26368/events | https://github.com/pandas-dev/pandas/issues/26368 | 443,414,241 | MDU6SXNzdWU0NDM0MTQyNDE= | 26,368 | corrwith in 0.24 is much slower than 0.23 (especially if corr axis is smaller than other axis) | {
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Hi,
I've noticed that corrwith on pandas 0.24 is much slower than in 0.23, especially when trying to correlate dataframes where the length of the axis of correlation is much smaller than the length of the other axis.
Example:
```python
import pandas as pd
import numpy as np
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] tests passed
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- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Cleanup of type annotations in files that only had a handful of straightforward errors (or no longer threw errors). These include:
# pandas.core.util.hasing
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adds radix sort to np.argsort. For versions of NumPy with this change
(>=1.17), we need to adjust our validator.
Closes https://github.com/pandas-dev/pandas/issues/26361 | {
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- [x] tests added / passed
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Dictionary should have one set of brackets (as seen below in documentation example `{'a': np.float64, 'b': np.int32}`).
Brackets don't make sense around `'w' or 'a'`.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --dif... | {
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... | 3 | 2019-05-13T18:23:18Z | 2021-09-12T17:47:53Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import urllib.request
import pandas as pd
url='ftp://ftp.ncdc.noaa.gov/pub/data/igra/igra2-station-list.txt'
urllib.request.urlretrieve(url,url.split('/')[-1])
igrainv=pd.read_fwf(url.split('/')[-1],widths=(2,1,3,5,9,10,7,... | {
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- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Corrects these errors in `pandas.core.ops`:
```
pandas/core/ops.py:632: error: Unsupported target for indexed assignment
pandas/core/ops.py:633: error: Value of type "object" is not indexable
pandas/core/ops.... | {
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```python
import pandas as pd # v0.24.2
import scipy.sparse # v1.1.0
df = pd.SparseDataFrame(scipy.sparse.random(1000, 1000),
columns=list(map(str, range(1000))),
default_fill_value=0.0)
df.to_parquet('rpd.pq', engine='pyarrow')
```
Give... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew: handle the case where adding of business hour to the timestamp results in an ending time landing in the next business time period. Example
Input:
```python
bh = BusinessHour(n=4, start='00:00', end='23... | {
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```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.DataFrame({'a': np.random.randint(0, 3, 10)}).groupby('a').apply(lambda x:print(x.name))
0
0
1
2
In [4]: pd.__version__
Out[4]: '0.24.2'
```
#### Problem description
A... | {
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"followers_url": "https://api.github.... | 1 | 2019-05-14T06:00:42Z | 2019-08-20T19:02:10Z | 2019-08-20T19:02:10Z | MEMBER | null | Was looking to consolidate nth, first and last in the GroupBy world but was hitting some issues with categorical tests and handling of missing data. Note that the following calls are not equivalent, though they should be:
```python
>>> import numpy as np
>>> import pandas as pd
>>> s1 = pd.Categorical([np.nan, ... | {
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```python
>>> pd.to_datetime(['30/04/19', '01/05/19'])
DatetimeIndex(['2019-04-30', '2019-01-05'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
The converter should infer the same format for both values - so it should infer that both val... | {
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"followers_url": "https://api.github.c... | 15 | 2019-05-14T10:36:57Z | 2021-06-15T12:34:00Z | 2020-04-26T21:41:36Z | NONE | null | When using spark to process data and save to s3, the files are like
```bash
s3://bucket/path/_SUCCESS
s3://bucket/path/part-00000-uuid.snappy.parquet
s3://bucket/path/part-00002-uuid.snappy.parquet
s3://bucket/path/part-00001-uuid.snappy.parquet
s3://bucket/path/part-00003-uuid.snappy.parquet
```
It would be n... | {
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```python
# Your code here
dirs = os.listdir(path)
frame = pandas.DataFrame()
#frame = frame.astype(str)
output_folder_time_stamp = str(datetime.datetime.now()).replace(":", " ").rsplit(".", 1)[0]
final_output_path = outputp... | {
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```python
In [1]: df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': ['a', 'b', 'c', 'd']})
In [2]: df['c'] = pd.array([1, 2, N... | {
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- benchmark added
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
This PR follows #25159 (in which I broke something when merging).
```
All benchmarks:
before after ratio
[304d8d4b] [4ec1fe97]
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- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26393 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26393/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26393/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26393/events | https://github.com/pandas-dev/pandas/issues/26393 | 443,990,506 | MDU6SXNzdWU0NDM5OTA1MDY= | 26,393 | Holiday based on the lunar calendar? | {
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I happened to notice that pandas.tseries.offsets.CustomBusinessDay can not handle holidays based
on the lunar calendar.
I think it would be great if the class CustomBusinessDay support the lunar holidays in Korea, China, etc.
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---
*Original report*:
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'Strings': ['fire', 'hose'], 'Numbers': [... | {
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```python
# Python 3.5.6 |Anaconda custom (64-bit)| (default, Aug 26 2018, 21:41:56)
# assignment behavior with DataFrame.loc and DataFrame.at
# df.loc[] = x will promote dataframe column type to x's type
# df.at[] = x will cast x value to column's type
# ap... | {
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- [x] xref #26281, #26289 & #26319
Fourth and final part of the removal of ``compat.lrange`` from the code base. | {
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"followers_url": "https://api.github.com/users/j... | 4 | 2019-05-14T20:58:13Z | 2019-06-01T14:52:36Z | 2019-06-01T14:52:36Z | CONTRIBUTOR | null | Right now, the output shape and dtype of DataFrame.describe for object columns depends on whether the DataFrame is empty.
#### Code Sample, a copy-pastable example if possible
```python
In [75]: x = pd.DataFrame({"A": ['a', np.nan, np.nan]})
In [76]: x.describe()
Out[76]:
A
count 1
unique 1
to... | {
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- [X] tests passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This PR addresses the following mypy errors in **pandas.core.resample**:
```
pandas/core/resample.py:34: error: Need type annotation for '_shared_docs_kwargs'
pandas/core/resample.py:876: error: All conditiona... | {
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"followers_url": "https://api.github.com/users/j... | 39 | 2019-05-15T03:39:08Z | 2019-05-30T01:29:59Z | 2019-05-30T01:29:41Z | CONTRIBUTOR | null | This adds special support for controlling the output column names when performing column-specific groupby aggregations. We use kwargs, using the keywords as the output names, and expecting tuples of `(selection, aggfunc)`.
```python
In [2]: df = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
...: ... | {
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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Doctests in `to_datetime()`.
#### Problem description
It is being failed as below:
```
Failed example:
df = pd.DataFrame({'year': [2015, 2016],
Exception raised:
Traceback (most recent call last):
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https://api.github.com/repos/pandas-dev/pandas/issues/26403 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26403/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26403/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26403/events | https://github.com/pandas-dev/pandas/pull/26403 | 444,258,378 | MDExOlB1bGxSZXF1ZXN0Mjc4OTUzODU5 | 26,403 | DEPR: Deprecate ordered=None for CategoricalDtype | {
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- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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All of the logic related to breaking changes looks to be consolidated in `CategoricalDtype.update_dtype`, so I'm raising the warning there when a breaking change would occur and... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26404 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26404/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26404/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26404/events | https://github.com/pandas-dev/pandas/pull/26404 | 444,266,673 | MDExOlB1bGxSZXF1ZXN0Mjc4OTYwMzg5 | 26,404 | Fix type annotations in pandas.core.indexes.datetimes | {
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- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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`pandas/core/indexes/timedeltas.py` taken care of as well since the errors were the same. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26405 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26405/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26405/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26405/events | https://github.com/pandas-dev/pandas/pull/26405 | 444,267,382 | MDExOlB1bGxSZXF1ZXN0Mjc4OTYwOTI0 | 26,405 | DEPR: deprecate Series/DataFrame.compound | {
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Not sure if we need to point to an alternative in the deprecation warning. I can put the exact "formula" for what we are currently calculating, but I don't know enough from finance to know if that is actually good to point to (eg I don't directly see that formul... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26406 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26406/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26406/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26406/events | https://github.com/pandas-dev/pandas/issues/26406 | 444,300,380 | MDU6SXNzdWU0NDQzMDAzODA= | 26,406 | BUG: to_numpy on categorical data with tz aware datetime categories returns datetime64 | {
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In [1]: s = pd.Series(pd.Categorical(pd.date_range("2012", periods=3, tz='Europe/Brussels')))
In [2]: s.to_numpy()
Out[2]:
array(['2011-12-31T23:00:00.000000000', '2012-01-01T23:00:00.000000000',
'2012-01-02T23:00:00.000000000'], dtype='datetime64[ns]')
In [3]: s = pd.Series(pd.date_range("2012",... | {
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```
In [14]: spdt = pd.SparseDtype(pd.DatetimeTZDtype(tz='Europe/Brussels'))
In [15]: spdt
Out[15]: Sparse[datetime64[ns, Europe/Brussels], NaT]
```
but I don't think that you can actually create a SparseArray with that?
At least, there seem... | {
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Please take note that another bug is also fixed in this PR:
When the MultiIndex is constructed with code value < -1, a segmentation fault would be resulted in subsequent oper... | {
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Reviving an old branch I had lying around. So this was more a quick experiment to see what it involves. The main "problem" is that we ourselves use `get_values` in a bunch of places (eg `values_from_object` which is massively used).
The current situation:
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https://api.github.com/repos/pandas-dev/pandas/issues/26410 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26410/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26410/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26410/events | https://github.com/pandas-dev/pandas/issues/26410 | 444,366,635 | MDU6SXNzdWU0NDQzNjY2MzU= | 26,410 | API: getting the "densified" values of a Categorical (preserving categories dtype) ? | {
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"description": "Categori... | open | false | null | [] | null | 3 | 2019-05-15T10:43:54Z | 2021-07-02T06:01:25Z | null | MEMBER | null | Assume we have a Categorical, and want to convert to a dense array (not encoded). We have `np.asarray(..)` and the `to_dense()` method (which uses asarray under the hood):
```
In [1]: cat = pd.Categorical(['a', 'b', 'a'])
In [2]: np.asarray(cat)
Out[2]: array(['a', 'b', 'a'], dtype=object)
In [3]: cat.to_de... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26411 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26411/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26411/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26411/events | https://github.com/pandas-dev/pandas/issues/26411 | 444,378,587 | MDU6SXNzdWU0NDQzNzg1ODc= | 26,411 | Grouping, then resampling empty DataFrame leads to it losing column names and index | {
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... | closed | false | null | [] | {
"closed_at": "2021-07-02T07:59:17Z",
"closed_issues": 2396,
"created_at": "2020-11-11T19:05:43Z",
"creator": {
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"events_url": "https://api.github.com/users/jreback/events{/privacy}",
"followers_url": "https://api.github.com/users/j... | 3 | 2019-05-15T11:13:04Z | 2021-07-22T20:50:16Z | 2021-02-24T16:39:00Z | NONE | null | #### Code Sample
```python
import pandas as pd
empty_df = pd.DataFrame([], columns=["a", "b"], index=pd.TimedeltaIndex([]))
resampled_df = empty_df.groupby("a").resample(rule=pd.to_timedelta("00:00:01")).mean()
print resampled_df
print resampled_df["b"]
```
#### Problem description
After grouping and... | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26412 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26412/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26412/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26412/events | https://github.com/pandas-dev/pandas/issues/26412 | 444,451,632 | MDU6SXNzdWU0NDQ0NTE2MzI= | 26,412 | Using .loc.__setitem__ with integer slices does not raise | {
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"description": "Related to indexing on s... | closed | false | null | [] | {
"closed_at": "2020-07-28T18:13:47Z",
"closed_issues": 2378,
"created_at": "2019-12-02T12:52:48Z",
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"followers_url": "https://api.github.com/users/j... | 2 | 2019-05-15T13:51:49Z | 2020-03-08T15:45:26Z | 2020-03-08T15:45:26Z | NONE | null | #### Code Sample
```python
>>> d = pd.DataFrame({'a': range(5), 'b': range(2,7), 'c': range(3,8)})
>>> d
a b c
0 0 2 3
1 1 3 4
2 2 4 5
3 3 5 6
4 4 6 7
>>> d.loc[0:2, 0:2] = 42 # note that loc is used with an integer slice! should this raise?
>>> d # this is somehow the expected resul... | {
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