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https://api.github.com/repos/pandas-dev/pandas/issues/42921 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42921/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42921/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42921/events | https://github.com/pandas-dev/pandas/issues/42921 | 963,075,709 | MDU6SXNzdWU5NjMwNzU3MDk= | 42,921 | BUG: Reindex null timedelta series with `pd.Timedelta` fails | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
ts = pd.date_range("2020-01-01", "2020-01-03", freq="1d")
s = pd.Series(pd.Timedelta(None), index=ts)
s.reindex(pd.date_range("2020-01-01", "2020-01-04", freq="1d"), fill_value=pd.Timedelta(0))
```
#### Problem description
To begin with, `s` has the wrong type `datetime64[ns]` instead of `timedelta64[ns]`. This causes the `reindex` to fail.
I've also tested the code above on version 1.2.4, it does not produce error and the type is casted as `object`:

#### Expected Output

#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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} | 1 | "2021-08-06T22:37:34Z" | "2021-08-12T13:55:31Z" | "2021-08-12T13:55:27Z" | CONTRIBUTOR | null | - [ ] closes #4817
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Added an example in the documentation for DataFrame.pivot_table to show how to display a pivot table as having a multi-level index, simulating Excel's pivot table with MI and partial group aggregates. | {
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} | 3 | "2021-08-07T11:39:12Z" | "2021-08-08T05:40:19Z" | "2021-08-07T21:58:39Z" | CONTRIBUTOR | null | - [x] closes #42123
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
This is failing across multiple locations. Is there a specific location where get/set_option can be used? or across everything?
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} | 4 | "2021-08-07T17:36:25Z" | "2021-09-05T10:21:13Z" | "2021-09-05T01:45:01Z" | CONTRIBUTOR | null | - [x] closes #42880
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I made the test more thorough, as it only checked the output's type, but not that the output matrix was actually the expected one.
Here is the output from the asv benchmark:
```
before after ratio
[e045034e] [042dbbc3]
<master> <sseries-to-coo-perf>
- 12.6±0.6ms 2.35±0.1ms 0.19 sparse.ToCoo.time_sparse_series_to_coo_single_level(False)
- 19.9±0.6ms 3.68±0.05ms 0.19 sparse.ToCoo.time_sparse_series_to_coo(True)
- 19.6±0.4ms 3.51±0.09ms 0.18 sparse.ToCoo.time_sparse_series_to_coo(False)
- 12.5±1ms 220±10μs 0.02 sparse.ToCoo.time_sparse_series_to_coo_single_level(True)
```
As you can see the performance improvement is the most dramatic when there is only one row and column level, and for an output with sorted labels. This is simply due to the fact that the labels are sorted in the `levels` attribute of an `Index`, so I covered this case, which should be the most common, with a simpler (and faster) implementation. For this reason I was thinking maybe `sort_labels` could default to `True`, but that would probably require prior deprecation warnings and all. In any case I can add this information as a performance tip to the docs, let me know what you think.
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} | 3 | "2021-08-07T18:44:46Z" | "2021-09-04T02:36:27Z" | "2021-09-04T02:36:26Z" | CONTRIBUTOR | null | pyright says that a few methods that are marked with `typing.Final` are overridden:
```
pandas/core/frame.py:4552:9 - error: Method "_reindex_axes" cannot override final method defined in class "NDFrame"
pandas/core/frame.py:10727:9 - error: Method "mask" cannot override final method defined in class "NDFrame"
pandas/core/series.py:5446:9 - error: Method "mask" cannot override final method defined in class "NDFrame"
pandas/core/groupby/generic.py:676:9 - error: Method "describe" cannot override final method defined in class "GroupBy"
pandas/core/groupby/generic.py:856:9 - error: Method "pct_change" cannot override final method defined in class "GroupBy"
``` | {
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] | open | false | null | [] | null | 0 | "2021-08-07T19:50:00Z" | "2021-08-21T20:41:08Z" | null | NONE | null | #### Is your feature request related to a problem?
I would like to compute the number of values in a sliding window like this:
```python
import pandas as pd
df = pd.DataFrame({"group": ["A", "B"] * 5, "distance": [0.1, 0.3, 1.7, 3.2, 2.6, 3.7, 8.3, 0.6, 5.9, 6.3]})
df
```
```
Out[5]:
group distance
0 A 0.1
2 A 1.7
4 A 2.6
8 A 5.9
6 A 8.3
1 B 0.3
7 B 0.6
3 B 3.2
5 B 3.7
9 B 6.3
```
```python
# possible API:
df.groupby("group").sliding(on="distance", window_size=3.0, step_size=2.0, min=0, max=10).count()
```
```
count
group distance
A [0, 3) 3
[2, 5) 1
[4, 7) 1
[6, 9) 1
[8, 11) 0
B [0, 3) 2
[2, 5) 2
[4, 7) 1
[6, 9) 0
[8, 11) 0
```
#### Describe the solution you'd like
There should be some sliding window function that slices by value instead of rows:
```python
def sliding(
self,
on: str, #
window_size: numeric,
step_size: numeric=None, # equals to window size by default
min: numeric=None, # minimum value in group by default
max: numeric=None, # maximum value in group by default
):
pass
```
#### API breaking implications
No idea, maybe integrate to `rolling`?
#### Describe alternatives you've considered
1) Create IntervalArray:
```python
intervals = pd.arrays.IntervalArray.from_arrays(np.arange(10), np.arange(10) + 2)
Out[53]:
<IntervalArray>
[(0, 2], (1, 3], (2, 4], (3, 5], (4, 6], (5, 7], (6, 8], (7, 9], (8, 10], (9, 11]]
```
2) Join on `intervals`: No idea how to do this...
#### Additional background
This question also appeared on StackOverflow:
https://stackoverflow.com/questions/43538064/how-to-do-sliding-window-by-value-interval-on-non-time-index-in-pandas | {
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} | 1 | "2021-08-08T01:03:25Z" | "2021-08-08T04:03:23Z" | "2021-08-08T02:20:18Z" | CONTRIBUTOR | null | - [ ] xref #27085 (chips away at, yes)
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} | 4 | "2021-08-08T01:48:06Z" | "2021-08-11T13:26:10Z" | "2021-08-11T13:26:05Z" | CONTRIBUTOR | null | This patch closes #42768 and fixes a previously-reported issue #33043.
When `ujson` falls back to encoding a Python object, it iterates over all of the non-callable attributes of the object which do not start with an underscore, using them as JSON keys. While iterating the `dirs(obj)` list, an index was incremented twice causing every second attribute to be skipped.
I've fixed this increment, and moved some of the declarations inside the function so that control flow is more clear. I've also added a regression test for this behaviour.
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- [x] tests added / passed
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This patch also xref #41174, but I'm not sure that turning complex numbers into `{"real": ..., "imag": ...}` JSON objects was intended in the first place, so I haven't added a test for this. | {
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] | open | false | null | [] | null | 0 | "2021-08-08T12:34:14Z" | "2021-08-08T12:34:31Z" | null | CONTRIBUTOR | null | after #42914 styler has private code to refactor a `level` arg to a consistent format, either:
- name of an index level: `my_level`
- a specific integer index level: `1`
- or a list of such: `['my_lev', 'other_lev']`
produces a consistent list of integer levels: `[1,2]`.
This code should be replaced by more generic code which does the same thing in the rest of the codebase.
(if anyone has the location would be helpful to share..)
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] | open | false | null | [] | null | 7 | "2021-08-08T14:09:04Z" | "2021-09-16T08:11:55Z" | null | MEMBER | null | From a comment of @jbrockmendel at https://github.com/pandas-dev/pandas/pull/41878#issuecomment-881557871:
```
ser = pd.Series(range(5))
idx = pd.Index(ser)
ser[0] = 10
>>> idx[0]
10
```
In the above, we create an Index from a Series, then mutate the Series, which also updated the Index, while an Index is assumed to be immutable.
Changing the example a bit, you can obtain wrong values with indexing this way:
```
ser = pd.Series(range(5))
idx = pd.Index(ser)
ser.index = idx
>>> ser[0]
0
>>> ser.iloc[0] = 10
>>> ser[0]
10
>>> ser
10 10
1 1
2 2
3 3
4 4
dtype: int64
```
So `ser[0]` is still giving a result, while that key doesn't actually exist in the Series' index at that point.
I know that generally we consider this a user error if you would do this with a numpy array (`idx = pd.Index(arr)` and mutating the array), but here you get that by only using high-level pandas objects itself. In which case we should prevent this from happening? | {
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} | 4 | "2021-08-08T16:47:28Z" | "2021-08-23T16:54:59Z" | "2021-08-23T16:54:49Z" | CONTRIBUTOR | null | Response to changes suggested on my previous pull request
- [x] closes #42124
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] closes #42626
- [x] tests added / passed
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} | 1 | "2021-08-08T19:39:38Z" | "2021-08-10T20:06:49Z" | "2021-08-10T20:06:45Z" | CONTRIBUTOR | null | - [x] closes #42881
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 10 | "2021-08-08T21:00:00Z" | "2021-09-13T02:18:33Z" | "2021-09-13T02:18:26Z" | CONTRIBUTOR | null | - [x] closes #38543
- [ x] tests added / passed
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https://api.github.com/repos/pandas-dev/pandas/issues/42940 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42940/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42940/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42940/events | https://github.com/pandas-dev/pandas/pull/42940 | 963,531,011 | MDExOlB1bGxSZXF1ZXN0NzA2MTE5Mjg3 | 42,940 | TST: GH34037 add test for type error when using parameter 'min_period' | {
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} | 1 | "2021-08-08T22:49:58Z" | "2021-08-17T00:20:40Z" | "2021-08-17T00:20:32Z" | CONTRIBUTOR | null | - [x] closes #34037
- [ x] tests added / passed
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blockNumber_base10@coarsegrained#1024 address_idx
9441280 2 -0.012421
3 0.001400
6 -0.012696
7 0.003558
15 0.003649
...
9854976 935783 0.000000
935785 0.000000
935786 0.000000
935788 0.000000
935789 0.000000
Name: price@fwd#001024@div#price#m1, Length: 1146272, dtype: float64
We want to compare with groupby min.
y_test_tmp = y_test.groupby(level = 0).quantile(0.8)
blockNumber_base10@coarsegrained#1024
9441280 0.005130
9442304 0.000973
9443328 0.000461
9444352 0.001354
9445376 0.000921
...
9850880 0.000552
9851904 0.000953
9852928 0.000591
9853952 0.000000
9854976 0.000000
Name: price@fwd#001024@div#price#m1, Length: 405, dtype: float64
I want to compare with them based on `blockNumber_base10@coarsegrained#1024`, so i wanted to compute `y_test > y_test_tmp`. But I got this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-318-ad2813982941> in <module>
----> 1 y_test > y_test_tmp
~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/common.py in new_method(self, other)
63 other = item_from_zerodim(other)
64
---> 65 return method(self, other)
66
67 return new_method
~/anaconda3/lib/python3.8/site-packages/pandas/core/arraylike.py in __gt__(self, other)
43 @unpack_zerodim_and_defer("__gt__")
44 def __gt__(self, other):
---> 45 return self._cmp_method(other, operator.gt)
46
47 @unpack_zerodim_and_defer("__ge__")
~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in _cmp_method(self, other, op)
4971
4972 if isinstance(other, Series) and not self._indexed_same(other):
-> 4973 raise ValueError("Can only compare identically-labeled Series objects")
4974
4975 lvalues = extract_array(self, extract_numpy=True)
ValueError: Can only compare identically-labeled Series objects
Any suggestion? Thanks!
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} | 4 | "2021-08-09T01:27:30Z" | "2021-08-09T23:06:18Z" | "2021-08-09T22:52:17Z" | MEMBER | null | xref some confusion in #42477. If this is thought an improvement, could target 1.3.2 since more users now see this error than in 1.2.x | {
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] | open | false | null | [] | null | 5 | "2021-08-09T02:51:47Z" | "2021-08-21T20:41:53Z" | null | CONTRIBUTOR | null | #### Is your feature request related to a problem?
I sometimes would generate some faker but having rule data.
Just like in a (2d) data, repeat some rows or columns serial times.
The steps I do:
1. This is a DataFrame called `df`.
2. `value = df.values # change DataFrame to ndarray`
3. use `np.repeat` or `np.tile` method to repeat.
#### Describe the solution you'd like
I thought why not add a method `repeat` to `DataFrame`, since `Series` has already owned the `repeat` [method](https://pandas.pydata.org/docs/reference/api/pandas.Series.repeat.html).
#### About the `DataFrame.repeat` parameters
Similar to [`numpy.repeat(a, repeats, axis=None)`](https://numpy.org/doc/stable/reference/generated/numpy.repeat.html)
```python
def repeat(
self,
repeats: int | list[int],
axis: int | str = 0,
) -> pd.DataFrame | None:
...
```
DataFrame is a 2d data structure, so it is hard to repeat each element.
And the aim of `DataFrame.repeat` is to repeat one row/column or some rows/columns.
So the `axis` argument must be specific to `0` or `1`.
And if the `repeats` is a single value `int`, that means repeat all row/column `repeats` times.
#### Examples of this method
```python
>>> df = pd.DataFrame({'a': [1, 2], 'b':[3, 4]})
>>> df
a b
0 1 3
1 2 4
Each row repeat two times.
>>> df.repeat(2)
a b
0 1 3
0 1 3
1 2 4
1 2 4
Each column repeat two times.
>>> df.repeat(2, 1)
a a b b
0 1 1 3 3
1 2 2 4 4
``a`` column repeat 1 times, ``b`` column repeat 2 times.
>>> df.repeat([1, 2], 1)
a b b
0 1 3 3
1 2 4 4
``` | {
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} | 6 | "2021-08-09T06:38:22Z" | "2021-10-16T20:41:26Z" | "2021-10-16T20:41:26Z" | CONTRIBUTOR | null | When reading a column of mixed booleans and missing values into a specific floating dtype, for example `read_csv(..., dtype='float')`, missing values were being incorrectly converted to 1.0 rather than NaN. This patch fixes this issue.
- [x] closes #42808
- [x] closes #34120
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I've never been in the CSV parsing code before, so I've tried to put this change in the spot that makes the most sense and causes the least impact, but corrections are very welcome.
Why this issue was happening is that the [_try_bool_flex()](https://github.com/pandas-dev/pandas/blob/126a19d038b65493729e21ca969fbb58dab9a408/pandas/_libs/parsers.pyx#L1752) function parses a mixed column of bools and missing values into a uint8 array, storing False/True/missing as 0/1/255. It then returns a bool view of this array, "hiding" the missing values and making them just look like True. These missing values are meant to be later 'unhidden' by a call to [_maybe_upcast()](https://github.com/pandas-dev/pandas/blob/126a19d038b65493729e21ca969fbb58dab9a408/pandas/_libs/parsers.pyx#L1040), which views the array as uint8 again, and maps to a dtype=object array containing False/True/NaN.
However, when the user specifies an explicit float dtype, this upcast is skipped since the bool array gets converted to a float array before hitting _maybe_upcast(). This patch just replicates the upcast logic in this case.
Like I said above, this is the spot in the code that made the most sense to me to put the correction - I feel like a more robust fix would be to rewrite the `_convert_tokens()` function (and the methods it calls, and so on) to actually return the positions of the missing values, as well as the parsed values, which would be a large change. | {
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} | 0 | "2021-08-09T07:10:32Z" | "2021-08-10T11:03:32Z" | "2021-08-09T09:38:37Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.14.0-py2.py3-none-any.whl (191 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.7.0-py2.py3-none-any.whl (5.3 MB)
Collecting identify>=1.0.0
Downloading identify-2.2.13-py2.py3-none-any.whl (98 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.0-py2.py3-none-any.whl (7.3 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting backports.entry-points-selectable>=1.0.4
Downloading backports.entry_points_selectable-1.1.0-py2.py3-none-any.whl (6.2 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Collecting platformdirs<3,>=2
Downloading platformdirs-2.2.0-py3-none-any.whl (13 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.0 distlib-0.3.2 filelock-3.0.12 identify-2.2.13 nodeenv-1.6.0 platformdirs-2.2.0 pre-commit-2.14.0 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.7.0
```
### stderr:
```Shell
WARNING: You are using pip version 21.2.2; however, version 21.2.3 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.9.6/x64/bin/python -m pip install --upgrade pip' command.
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.23.1 -> v2.23.3.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for local:pyyaml,toml.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
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absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................Passed
Debug Statements (Python)......................................................................Passed
Fix End of Files...............................................................................Passed
Trim Trailing Whitespace.......................................................................Passed
cpplint........................................................................................Passed
flake8.........................................................................................Passed
flake8 (cython)................................................................................Passed
flake8 (cython template).......................................................................Passed
isort..........................................................................................Passed
pyupgrade......................................................................................Passed
rst ``code`` is two backticks..................................................................Passed
rst directives end with two colons.............................................................Passed
rst ``inline code`` next to normal text........................................................Passed
Strip unnecessary `# noqa`s....................................................................Passed
flake8-rst.....................................................................................Passed
Unwanted patterns..............................................................................Passed
Check for backticks incorrectly rendering because of missing spaces............................Passed
Check for unnecessary random seeds in asv benchmarks...........................................Passed
Check for invalid EA testing...................................................................Passed
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Passed
Validate correct capitalization among titles in documentation..................................Passed
Import pandas.array as pd_array in core........................................................Passed
Use bool_t instead of bool in pandas/core/generic.py...........................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
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} | 7 | "2021-08-09T08:24:53Z" | "2021-09-13T08:30:10Z" | "2021-09-12T14:03:27Z" | NONE | null | Hello.
Could you please release CPython 3.10 manylinux wheels on PyPI?
Python 3.10.0 release candidate 1 was released this week: https://www.python.org/downloads/release/python-3100rc1/
It should be quite safe to release wheels for release candidate. ABI for Python 3.10 should be frozen from now on, even for bugfixes. From release notes:
> There will be no ABI changes from this point forward in the 3.10 series and the goal is that there will be as few code changes as possible.
Thank You. | {
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} | 28 | "2021-08-09T12:01:35Z" | "2021-10-18T19:32:53Z" | "2021-10-15T13:28:43Z" | CONTRIBUTOR | null | Closes #42864
New indexable attribute GroupBy.iloc to perform most grouped slices analagous to DataFrame.iloc
Row indexes can be integers or slices with +ve step
Row slices preserve the DataFrame order and grouping and extend the existing GroupBy head() and tail() methods
All column indexes are handled as for DataFrame.iloc
-ve slice step is not currently handled, and would have to reverse the order.
There is no plan to implement Integer lists since these do not preserve the order at all and cannot be easily vectorized.
Note
----
Neither GroupBy.nth() nor GroupBy.take() take a slice argument and neither of them preserve the original DataFrame order and index. They are both slow for large integer lists and take() is very slow for large group counts.
Example use case
--------------------
Supose that we have a multi-indexed DataFrame with a large primary index and a secondary sorted
in a different order for each primary index.
To reduce the DataFrame to a middle slice of each secondary, we can groupby the primary index and then use iloc.
This preserves the original DataFrame's order and indexing.
(See tests/groupby/test_groupby_iloc) | {
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} | 1 | "2021-08-09T12:43:17Z" | "2021-08-09T13:56:52Z" | "2021-08-09T13:56:52Z" | NONE | null | This is reference to other question i found on Stackoverflow, but i am not intersted add the reference here without their permission. My question is what will be the reason for i cannot add more than 240 years to pandas dataframe with pd.offsets.DateOffset?.
Here is my code
```
df=pd.date_range(start="2020-10-29",end="2020-10-30").to_pydatetime().tolist()
df=pd.DataFrame(df,columns=['dte'])
df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=241)
```
Output is
```
dte dte_
0 2020-10-29 2261-10-29
1 2020-10-30 2261-10-30
df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=242)
```
The output of above code is
```
dte dte_
0 2020-10-29 1678-04-09 00:25:26.290448384
1 2020-10-30 1678-04-10 00:25:26.290448384
```
If i change start and end date to 2021, I am able to add only 240 years as follows ,
`df=pd.date_range(start="2021-10-29",end="2021-10-30").to_pydatetime().tolist()`
Other code with year 2200
```
df=pd.date_range(start="2200-10-29",end="2200-10-30").to_pydatetime().tolist()
df=pd.DataFrame(df,columns=['dte'])
df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=62) # i.e I can add only 61 years
```
Output is
```
dte dte_
0 2200-10-29 1678-04-09 00:25:26.290448384
1 2200-10-30 1678-04-10 00:25:26.290448384
```
- Is this due to limitation of availability calender date after 240 Years from now?
- Or pandas Limitation?
- Or is this my misunderstanding about DateOffset?
- I undestand 2261 Supposed to be the last year. Is there any speciality for 2261?
- From one of answer i understand `timestamp(9200000000000000000) was Monday, July 15, 2261 2:33:20 PM approximately Timestamp('2262-04-11 23:47:16.854775807'). So what is the solution? | {
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} | 0 | "2021-08-09T14:24:42Z" | "2021-08-10T19:53:59Z" | "2021-08-10T19:53:59Z" | CONTRIBUTOR | null | Fix Sphinx cross-references in docs.
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] | open | false | null | [] | null | 2 | "2021-08-09T14:28:25Z" | "2021-11-15T19:06:51Z" | null | MEMBER | null | This came up while discussing the CoW proof of concept (and need for item_cache / speed of creating a Series of a column). Those small changes gives a decent reduction of some overhead (~30% reduction):
```python
arr = np.array([1, 2, 3])
idx = pd.Index(["a", "b", "c"])
pd.Series(arr, index=idx, name="name", fastpath=True)
```
```
In [5]: %timeit pd.Series(arr, index=idx, name="name", fastpath=True)
10.9 µs ± 202 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master
7.48 µs ± 187 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR
``` | {
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Seems like a good way to improve issue quality, while also making writing issues easier for users (through improvements like built-in code formatting). | {
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} | 1 | "2021-08-09T16:09:42Z" | "2021-08-10T21:25:37Z" | "2021-08-10T19:00:37Z" | CONTRIBUTOR | null | - [ ] xref #27085 (chips away at, yes)
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
* https://github.com/pandas-dev/pandas/pull/42284
* https://github.com/pandas-dev/pandas/pull/42896
* https://github.com/pandas-dev/pandas/pull/42909
* https://github.com/pandas-dev/pandas/pull/42930
* https://github.com/pandas-dev/pandas/pull/42932 | {
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} | 13 | "2021-08-09T17:16:11Z" | "2021-08-12T18:12:02Z" | "2021-08-10T20:03:57Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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(cc @jbrockmendel do you have any idea what the reason is/was for this?) | {
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} | 2 | "2021-08-09T21:51:52Z" | "2021-09-25T15:35:31Z" | "2021-09-25T15:35:31Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
import numpy as np
import pandas as pd
a = pd.DataFrame({'a':[pd.NA]})
b = pd.DataFrame({'b': [np.nan]})
pd.to_datetime(b['b']) # -> Work
pd.to_datetime(b['b'], format="%Y%m%d%H%M%S") # -> Work
pd.to_datetime(a['a']) # -> Work
pd.to_datetime(a['a'], format="%Y%m%d%H%M%S") # -> Error
```
#### Problem description
I try to use `pd.NA` to define NaN values in field what contains datetime strings.
I try convert them to datetime, but it doesn't work.
I get error `ValueError: time data '<NA>' does not match format '%Y%m%d%H%M%S' (match)`
Actually the same code with `np.nan` works correctly
#### Expected Output
pd.NaT with no errors
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-305.7.1.el8_4.x86_64
Version : #1 SMP Mon Jun 14 17:25:42 EDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.utf8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.25.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-08-09T23:08:24Z" | "2021-09-08T09:08:51Z" | "2021-09-08T09:08:37Z" | CONTRIBUTOR | null | - [ ] closes #42818
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
issue url : https://github.com/pandas-dev/pandas/issues/42818
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] | open | false | null | [] | null | 1 | "2021-08-09T23:57:30Z" | "2021-08-23T12:13:25Z" | null | MEMBER | null | Failing CI is actions 3.9 numpy dev.
It fails with a FileNotFound error I think and I believe that its probably an upstream issue.
Any reason why we're installing dateutil from source here on the CI? I know that its a dependency but we aren't installing pytz from source on the same env. | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 12 | "2021-08-10T06:58:11Z" | "2021-11-04T10:49:38Z" | null | CONTRIBUTOR | null | - [ ] closes #8962
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
This is an initial implementation of a conditional join proposed here: https://github.com/pandas-dev/pandas/issues/8962#issuecomment-871958622
This is by no means meant to be a final solution, but just meant to get some momentum behind the feature.
The following issue provides the proposed initial implementation and examples for the usefulness an optimized version of this feature could provide: https://github.com/pandas-dev/pandas/issues/41150
Example Usage:
```
Merge dataframes df1 and df2, where column df1.timestep falls within the range
of df2.timestart to df2.timeend, using `condition`.
>>> df1 = pd.DataFrame({'timestep': range(5)})
>>> df2 = pd.DataFrame({'mood': ['happy', 'jolly', 'joy', 'cloud9'],
'timestart': [0, 2, 2, 3],
'timeend': [1, 3, 4, 4]})
>>> df1
timestep
0 0
1 1
2 2
3 3
4 4
>>> df2
mood timestart timeend
0 happy 0 1
1 jolly 2 3
2 joy 2 4
3 cloud9 3 4
>>> df1.merge(
... df2,
... condition=lambda dfx: (dfx.timestart <= dfx.timestep)
... & (dfx.timestep <= dfx.timeend)
... ).pivot_table(index='timestep', values='mood', aggfunc=', '.join)
mood
timestep
0 happy
1 happy
2 jolly, joy
3 jolly, joy, cloud9
4 joy, cloud9
``` | {
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I write a function use apply and lambda for one column in a DataFrame. But because every time excute once fucntion use lot of time, so I turn the column into a list and use coroutines and for loop. I create a new list then insert into my DataFrame. I want to know if I can use both apply funtion and coroutines at the same time? But I think apply has been a parallel operation. It seems not work in real.
```python
# Your code here, if applicable
list1 = ["a", "b", "c", "d",....]
list2 = [1, 2, 3, 4,...]
df = pd.DataFrame({"col1":list1, "col2":list2})
def add_suffix(x):
return x+"_suffixsuffix"
df["col_with_suffix"] = df["col1"].apply(lambda x: add_suffix(x))
# another method use coroutines
col1_list = df["col1"].values.tolist()
import genvent
from gevent import monkey; monkey.patch_all()
g_list = []
i = 0
for x in col1_list:
i =i + 1
g = gevent.spawn(add_suffix, x)
g_list.append(g)
if i > 200:
gevent.joinall(g_list)
i = 0
g_list = []
```
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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
print(f"pandas: {pd.__version__}")
!python --version
qf = pd.Series(index = pd.to_datetime(['12Dec2020', '13Dec2020','14Dec2020', '15Dec2020','16Dec2020']),
data= [1,1,-1,-1,-1],
name=('coz1', 'this_bar'),
)
qf.index.name="Date"
qf = qf.groupby(
(qf != qf.shift())
.cumsum()
).cumsum().apply(int)
qf.head(10)
```
#### Problem description
I pass the `groupby` function a `by` parameter which is series whose name is a Tuple. In older pandas (0.25.3), this worked. In modern (1.2.4) pandas this raises an `InvalidIndexError`.
You might say, "well then don't name your series with Tuple, name it with a string." How my situation happens in real-life is that I'm taking a `.loc` slice of a `DataFrame` with multilevel columns. This returns a Series, and its name is automatically a tuple of the column levels. I can do a gross temporary-rename of the series, but feels like a hack.
#### Expected Output
```
pandas: 0.25.3
Python 3.7.11
Date
2020-12-12 1
2020-12-13 2
2020-12-14 -1
2020-12-15 -2
2020-12-16 -3
Name: (coz1, this_bar), dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Singapore.1252
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 3 | "2021-08-10T09:23:36Z" | "2021-08-10T14:37:04Z" | "2021-08-10T14:31:07Z" | CONTRIBUTOR | null | - [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```import numpy as np
import pandas as pd
print(f"pandas: {pd.__version__}")
!python --version
cols = pd.MultiIndex.from_product([['coz',], ['alpha', 'beta', 'gamma']], names=['health', 'protocol'])
index=pd.date_range(start="1jan2020", end=None, periods=5, freq="d", name="Date")
data = np.random.rand(5,3)
df = pd.DataFrame(data=data, index=index, columns=cols)
def foo(row):
row.index = row.index.droplevel(0)
return row['beta'] > row['alpha']
df.apply(foo, axis="columns")
```
#### Problem description
In `Pandas 0.25.3` this function works. In `Pandas 1.2.4`` it fails with an `ValueError: Cannot remove 1 levels from an index with 1 levels`.
What seems to be happening is when I apply `.droplevel(0)` on a row inside the `.apply` function, it removes a level for that instance of the row.Index passed in as a parameter, but also for all the subsequent rows passed to the `.apply`. So if I have a ten level column, apply would process ten rows and then this this `ValueError` trips.
I can get around this by applying the `.droplevel()` on a copy of the row `row.index = row.index.droplevel(0)` but seems like a hack, and I'm confused because this used to work. Perhaps is a bug.
#### Expected Output
pandas: 0.25.3
```
Python 3.7.11
Date
2020-01-01 False
2020-01-02 True
2020-01-03 False
2020-01-04 True
2020-01-05 False
Freq: D, dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Singapore.1252
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.23
pytest : 6.2.3
hypothesis : None
sphinx : 4.0.1
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.7
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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] | open | false | null | [] | null | 1 | "2021-08-10T10:54:11Z" | "2021-11-10T14:51:29Z" | null | MEMBER | null | From https://github.com/dask/fastparquet/pull/623#discussion_r685904630
The guidelines on how to store column metadata in the pandas metadata in Parquet (https://pandas.pydata.org/docs/development/developer.html#column-metadata) was written before nullable dtypes existed, so based on that it's not unambiguous how those should be handled.
(and in general, it's also not clear how to handle general extension types, except for the few hardcoded ones (categorical, datetimetz) that already existed at that point)
cc @martindurant | {
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} | 7 | "2021-08-10T16:48:47Z" | "2021-08-30T12:09:54Z" | 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.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(
[["bar", "one", 'test'], ["bar", "one1", 'test']],
columns=["first", "second", "third"]
)
# Single-index dataframe. Regex works as expected
df.set_index(['second']).filter( regex='e$', axis=0 )
first third
second
one bar test
# Multi-index dataframe. Regex does not work
df.set_index(['first','second']).filter( regex='e$', axis=0 )
Empty DataFrame
Columns: [third]
Index: []
```
#### Problem description
The regex expression does not work if the dataframe is multi-index.
#### Expected Output
```
third
first second
bar one test
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.72-microsoft-standard-WSL2
Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.24.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 8 | "2021-08-10T16:56:40Z" | "2021-08-19T13:38:03Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df1 = pd.DataFrame(columns=["a", "b", "c"])
print(df1.groupby("a").sum().columns)
# => Index([], dtype='object')
df2 = pd.DataFrame({"a": [], "b": [], "c": []})
print(df2.groupby("a").sum().columns)
# => Index(['b', 'c'], dtype='object')
```
#### Problem description
`groupby`-ing and `sum`ming an empty dataframe led to dropped columns (`df1` above); this doesn't occur with non-empty dataframes. This changing the columns of a dataframe based on its content is counter-intuitive and leads to key errors. The expected behaviour is shown above with `df2`, and the fact that two empty dataframes show different behaviours when grouped and summed suggests that this isn't intended behaviour.
#### Expected Output
#### Output of the above snippet:
<details>
Index([], dtype='object')
Index(['b', 'c'], dtype='object')
</details>
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} | 1 | "2021-08-10T17:45:47Z" | "2021-09-09T06:05:00Z" | "2021-09-09T06:04:57Z" | CONTRIBUTOR | null | one more item towards deprecating `DataFrame.to_html` for #41693
- [x] adds options to config
- [x] update `options.rst`
- [x] add tests
- [x] update to_html docstring
- [x] whats new
- [ ] update user guide (will be follow on after all options in master tracker added)
and an item towards consistent `pandas.options` for #41395

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] | open | false | null | [] | null | 1 | "2021-08-10T19:40:24Z" | "2021-08-12T10:32:08Z" | null | MEMBER | null | Currently the keyword ``use_nullable_dtypes``(only implemented for read_parquet now) will use nullable dtypes for columns even when that column does not have nulls.
Should this behavior change?
xref discussion https://github.com/pandas-dev/pandas/pull/40687#issuecomment-833447019 https://github.com/pandas-dev/pandas/issues/42588#issuecomment-894981214
https://github.com/pandas-dev/pandas/issues/42588#issuecomment-882773411
(Note that always using nullable dtypes always(the current behavior) is a choice made on our end https://github.com/pandas-dev/pandas/blob/master/pandas/io/parquet.py#L215-L227 not by the pyarrow engine)
cc @pandas-dev/pandas-core | {
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This PR:
1. Create fixtures for all supported SQL connections, including sqlalchemy engine/connection and sqlite3 connection. Setup and cleanup are handled within fixtures.
2. Migrate several common tests to use connection fixtures. They serve as examples for future migrations.
We probably should put all the fixtures in a separate conftest.py file. But since there are unmerged PRs related to this test file, I kept everything in one place for now to avoid conflict. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/42977 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/42977/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/42977/comments | https://api.github.com/repos/pandas-dev/pandas/issues/42977/events | https://github.com/pandas-dev/pandas/issues/42977 | 966,022,217 | MDU6SXNzdWU5NjYwMjIyMTc= | 42,977 | Undefined variable | {
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```
class somecall:
def __call__(self):
return x # noqa
assert getname(fn) == "fn"
assert getname(lambda_)
assert getname(part1) == "fn"
assert getname(part2) == "fn"
assert getname(somecall()) == "somecall"
assert getname(1) is None
```
It looks like the statement inside __call__ function does not have x defined first.
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I found the variable values_multi is not defined when values_passed is False, but the variable is used in line 243
```
if values_passed:
if is_list_like(values):
values_multi = True
values = list(values)
else:
values_multi = False
values = [values]
```
``` if values_passed and not values_multi and table.columns.nlevels > 1:``` | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] I have confirmed this bug exists on the latest version 1.3.1 of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
frame = pd.DataFrame([[1, 2], [3, 4]])
d = dict(frame.groupby(level=0))
```
Raised error:
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: attribute of type 'NoneType' is not callable
```
#### Problem description
Based on the documentation of [Python `dict`](https://docs.python.org/3/library/stdtypes.html#dict) & [`GroupBy.__iter__`](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.__iter__.html#pandas.core.groupby.GroupBy.__iter__), the two should work together. The above code should behave identically to the working alternative
```python
d = {}
for k, v in frame.groupby(level=0):
d[k] = v
```
#### Expected Output
The code sample shouldn't raise an error, and the result should be
```python
>>> d[0]
0 1
0 1 2
>>> d[1]
0 1
1 3 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.11.final.0
python-bits : 64
OS : Linux
OS-release : 5.13.8-1-MANJARO
Version : #1 SMP PREEMPT Thu Aug 5 09:47:52 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.1
pip : 21.2.3
setuptools : 57.0.0
Cython : 0.29.23
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.3
blosc : None
feather : None
xlsxwriter : 1.4.3
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : 2.9.1 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.23.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 4.0.1
pyxlsb : None
s3fs : None
scipy : 1.7.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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**This is a non-trivial backport. Please first review changed files before merging. Thanks**
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} | 3 | "2021-08-11T23:59:55Z" | "2021-08-12T16:48:35Z" | "2021-08-12T16:21:47Z" | MEMBER | null | - [x] closes #40446
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Discussed on call today. Just started an asv run.
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- [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
from pandas.api.types import is_datetime64_dtype
import pandas
def convert_to_datetime(df):
if is_datetime64_dtype(df['Date']) == False:
df['Date'] = pandas.to_datetime(df['Date'],errors='coerce')
df['Date'] = df['Date'].astype('object').where(pandas.notnull(df['Date']), None)
return df
df1 = pandas.DataFrame({'ID':[1,2],'Date':['2014-10-10 10:44:31', None]})
df2 = pandas.DataFrame({'ID':[1,2],'Date':[None, None]})
df1 = convert_to_datetime(df1)
df2 = convert_to_datetime(df2)
merge = df1.merge(df2, how = 'outer' ,indicator=True, validate="one_to_one", copy=False)
```
#### Problem description
expected behavior is to proceed with merge with no error but getting:
```
Traceback (most recent call last):
File "c:/Users/g/AppData/Local/Programs/Python/Python38/Lib/hcpcs_si_audit/test.py", line 16, in <module>
merge = df1.merge(df2, how = 'outer' ,indicator=True, validate="one_to_one", copy=False)
File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\frame.py", line 7336, in merge
return merge(
File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 83, in merge
return op.get_result()
File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 667, in get_result
self._maybe_add_join_keys(result, left_indexer, right_indexer)
File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 815, in _maybe_add_join_keys
key_col = Index(lvals).where(~mask, rvals)
File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\indexes\datetimelike.py", line 627, in where
result = np.where(cond, values, other).astype("i8")
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'
```
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} | 2 | "2021-08-12T01:02:54Z" | "2021-08-14T18:50:08Z" | "2021-08-14T18:50:02Z" | NONE | null | I have created a new workbook and a worksheet named `raw`:
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
dest_fn = '/tmp/mytest.xlsx'
ws.title = "raw"
Then to create a dataframe :
import pandas as pd
data = [{"code":"dow","value":35100}]
df = pd.DataFrame.from_dict(data)
To write and save it:
df.to_excel (dest_fn, index = False, header=False, startcol=1,startrow=2,sheet_name="raw")
wb.save(dest_fn)
wb.close()
Why no data in the dest_fn `/tmp/mytest.xlsx` when to open it?
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
x = pd.Series([np.nan, np.nan, 1, 2, 3])
print(x.nlargest(n=4))
# 4 3.0
# 3 2.0
# 2 1.0
# dtype: float64
print(x.sort_values(ascending=False).head(4))
# 4 3.0
# 3 2.0
# 2 1.0
# 0 NaN
# dtype: float64
```
#### Problem description
The [docs](https://pandas.pydata.org/docs/reference/api/pandas.Series.nlargest.html) state:
> Faster than `.sort_values(ascending=False).head(n)` for small `n` relative to the size of the Series object.
which sort of suggests they are the same when they are not.
#### Expected Output
I think the sort/head approach gives the correct answer here, so we should have:
``` python
print(x.nlargest(n=4))
# 4 3.0
# 3 2.0
# 2 1.0
# 0 NaN
# dtype: float64
```
or the difference should be documented.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-80-generic
Version : #90~18.04.1-Ubuntu SMP Tue Jul 13 19:40:02 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.3
setuptools : 57.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-08-12T18:03:26Z" | "2021-08-19T21:00:50Z" | "2021-08-19T20:58:50Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
```
from asv_bench.benchmarks.indexing import *
self = InsertColumns()
self.setup()
%timeit self.setup(); self.time_insert()
32.5 ms ± 382 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
20.7 ms ± 744 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
%timeit self.setup(); self.time_insert_middle()
37.2 ms ± 407 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
25.1 ms ± 513 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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} | 3 | "2021-08-12T18:10:11Z" | "2021-08-13T23:22:24Z" | "2021-08-13T21:38:24Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/42987#discussion_r687209605 | {
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Is it possible to add natively this method to pandas.DataFrame ?
```python
import random, string, typing
from pandas.io.sql import to_sql, read_sql
schars = """!"#$%&\'()*+,-./:;<=>?@[\\]^`{|}~ éèùà$£µç"""
def sql(
self, sql: str, con: "Engine" = None, dispose: bool = False,
schema: typing.Any = None, **kwargs: typing.Any
):
from sqlalchemy import create_engine, inspect
if con is None:
con, dispose = create_engine('sqlite:///:memory:'), True
elif isinstance(con, str):
con, dispose = create_engine(con), True
elif isinstance(con, dict):
con, dispose = create_engine(**con), True
# tmp name
name = ''.join(random.choice(string.ascii_uppercase) for i in range(24))
sql = sql.replace('{df}', name)
if isinstance(self.columns, pandas.MultiIndex):
new = self.copy()
new.columns = [" ".join(col).strip() for col in new.columns.values]
to_sql(
new, name=name, con=con, schema=schema, if_exists='replace', method='multi'
)
else:
to_sql(self, name=name, con=con, if_exists='replace', schema=schema, method='multi')
parse_dates = [c for c in self.columns.values if self[c].dtype.kind == 'M']
try:
return read_sql(
sql, con=con, parse_dates=parse_dates, **kwargs
)
except Exception as e:
raise e
finally:
# Clean
## Check sepical chars
if any(x in name for x in schars):
name = '"%s"' % name
if schema is not None:
if any(x in schema for x in schars):
schema = '"%s"' % schema
name = "%s.%s" % (schema, name)
con.execute('DROP TABLE IF EXISTS %s' % name)
if dispose:
con.dispose()
```
Could be cool feature
Br
Nicolas | {
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] | open | false | null | [] | null | 0 | "2021-08-12T18:51:26Z" | "2021-08-21T20:52:08Z" | null | NONE | null | #### Location of the documentation
[Ecosystem](https://pandas.pydata.org/community/ecosystem.html) (wepage), [ecosystem.rst](https://github.com/pandas-dev/pandas/blob/318bed9f7c16c3352178cbf68e8dddfaba32ed6d/doc/source/ecosystem.rst) (file)
#### Documentation problem
- There is some deprecated library:
[qgrid](https://github.com/quantopian/qgrid) is [one of them](https://github.com/quantopian/qgrid/issues/348).
- There is some cutting edge/popular library that are missing
[Great expectation](https://github.com/great-expectations/great_expectations) is probably one of them.
#### Suggested fix for documentation
##### Deprecated library
- [ ] List deprecated libraries
- [ ] Remove deprecated libraries (or put them in a deprecated section)
- [ ] Replace deprecated package for the best (or bests) available alternative
- [ ] [Governance] Define criteria for "what is a deprecated package" or a governance system (e.g. is @TomAugspurger Engarde library deprecated?)
##### library to include
- [ ] <details> <summary>List potential library to include</summary>
[Great expectation](https://github.com/great-expectations/great_expectations) ,
[Pandas-profiling](https://pandas-profiling.github.io/pandas-profiling/docs/master/index.html),
[ipydatagrid](https://github.com/bloomberg/ipydatagrid),
[VS-Code](https://github.com/microsoft/vscode),
[missingno](https://github.com/ResidentMario/missingno)
[streamlit](https://streamlit.io/),
[dataprep](https://dataprep.ai/),
[mito](https://trymito.io/),
⚠️This list is clearly no exhaustive.⚠️
</details>
- [ ] Define which to include or not
- [ ] [Governance] Define how to define packages to include
I am willing to try to do the job if I am helped ;-)
Ps:
This issue is related to https://github.com/pandas-dev/pandas/issues/24927 | {
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] | open | false | null | [] | null | 4 | "2021-08-12T19:51:56Z" | "2021-10-22T10:23:15Z" | null | MEMBER | null | There has been discussion in eg #39133 about having EA-backed Indexes held in base-class Index objects, similar discussion regarding the new NumericIndex.
I do not like the idea of having _some_ non-object dtypes return the base class Index, but would be on board for making _all_ dtypes use the base class. i.e. only have Index and deprecate/remove all the subclasses. I'm envisioning roughly
1) Implement relevant methods on the underlying EAs, eg DatetimeIndex.get_loc logic might live in DatetimeArray.get_loc.
2) ??? for RangeIndex and MultiIndex, maybe make corresponding EAs?
3) Methods currently available on subclasses but not Index would be accessed via accessor, e.g. `dti.is_year_start` becomes `idx.foo.is_year_start`
Related but independent, we could do the same with ExtensionArray subclasses. Instead of having authors implement methods on their EA subclass, they implement it on the ExtensionDtype subclass, which the EA object then dispatches to. | {
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} | 4 | "2021-08-12T21:26:18Z" | "2021-08-19T01:58:24Z" | "2021-08-19T01:58:19Z" | CONTRIBUTOR | null | - Closes #42809
- Adds check for GL02 in CI
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} | 2 | "2021-08-12T22:06:26Z" | "2021-08-17T16:20:41Z" | "2021-08-17T16:16:42Z" | CONTRIBUTOR | null | Users are misled by current `compression` param doc. Actually more compressions like "ZSTD" are supported by pyarrow engine. This patch updates the param doc to reflect the supported compressions. | {
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} | 4 | "2021-08-12T22:44:17Z" | "2021-08-13T23:22:34Z" | "2021-08-13T00:32:55Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Just testing the numpy wheels in preparation for making our own.
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- [x] closes #35888
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry (not sure if applicable here, can someone advise?)
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] | open | false | null | [] | null | 3 | "2021-08-13T00:34:09Z" | "2021-08-21T20:53:17Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
inner_foo = pd.Series(dtype=float)
inner_bar = inner_foo.copy()
foo = pd.Series({0: inner_foo})
bar = pd.Series({0: inner_bar})
foo.equals(bar)
```
#### Problem description
Get an exception: `ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().`
This issue applies to both `Series` and `DataFrame`, that `equals` method failed if there is a nested `Series` or `DataFrame`.
Note that `pd.testing.assert_series_equal(foo, bar)` still works.
#### Expected Output
`True`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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Wasn't sure about the best way to test this out, so I just have a repo https://github.com/mzeitlin11/issue-forms which I'll try to keep up to date with the state of the PR. Anyone should feel free to open issues there and play around with it. | {
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} | 1 | "2021-08-13T02:06:57Z" | "2021-08-13T15:24:14Z" | "2021-08-13T14:34:58Z" | MEMBER | null | ```
from asv_bench.benchmarks.stat_ops import *
self = SeriesOps()
self.setup("mad", "int")
%timeit self.time_op("mad", "int")
1.45 ms ± 22.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
474 µs ± 2.77 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
``` | {
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] | open | false | null | [] | null | 0 | "2021-08-13T02:58:18Z" | "2021-08-21T20:53:39Z" | null | NONE | null | #### Feature description
I wish I could use Pandas to efficiently group a large data frame by a sorted column. Since many sorting algorithms have time complexities of `O(nlog(n))` and splitting a column according to its consecutive same value requires time complexity of just `O(n)`, given that the current `groupby` of Pandas has the time complexity of O(n^2), it would be better if we can perform `dataframe.groupby` by the sorted column, by means of splitting the dataframe according to consecutive same values in the sorted column. In this way, the groupby operation has the time complexity of `O(n)`. Together with the time complexity of sorting, the total time complexity is `O(nlog(n))`.
#### Here is my solution
`DataFrame.groupby` should get a new parameter `sorted` or something intuitive to perform `groupby` with `O(n)` time complexity (see the code below, though the returned object should be a DataFrameGroupBy object, not a dict.)
#### Existing similar implementation
see [here](https://github.com/AdeBC/easyGroup/blob/be17515b3280a44d536cbe36f621656fa3e5d4d1/easyGroup/src/groupper.py#L5).
```python
def groupby(dataframe, by):
"""
Groupby the dataframe (:param dataframe) according to the values of the column by (:param by).
low_memory (:param low_memory) mode can only work when the input dataframe are sorted by the column by.
:return: extracted Groups.
"""
df = dataframe[~dataframe[by].isna()]
col = df[by]
group_sizes = pd.Series(perGroupSize(col)) # get size of each group according to the consecutive same values, `O(nlog(n))`
ends = group_sizes.cumsum()
begins = ends.shift(1).fillna(0).astype(int)
group_names = tqdm(group_sizes.index,
desc='Generating groups from original dataframe, progress: ',
total=group_sizes.shape[0])
groups = {name: df.iloc[begins[name]:ends[name], :].reset_index(drop=True) for name in group_names}
return groups
```
Does this make sense? If so, I can implement this in Pandas and create a PR. | {
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} | 3 | "2021-08-13T03:03:04Z" | "2021-08-13T09:21:10Z" | "2021-08-13T09:21:05Z" | NONE | null | pandas==1.0.3
In [16]: df.where(df.notnull(), None)
Out[16]:
A B
0 2013-01-01 1
1 NaT 2
2 2013-01-03 None
but pandas==1.3.1
In [16]: df.where(df.notnull(), None)
Out[16]:
A B
0 2013-01-01 1
1 NaT 2
2 2013-01-03 NaN
why?
@MarcoGorelli | {
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] | open | false | null | [] | null | 4 | "2021-08-13T11:09:31Z" | "2021-08-22T04:33:39Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [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
series = pandas.Series([1])
series.index = index=pandas.date_range("20180101", periods=1)
inOperatorOutput = 1 in series
listOperatorOutput = 1 in list(series)
assert inOperatorOutput == listOperatorOutput
```
#### Problem description
This code yields an assertion error. Because the `in` operator looks into the index of a series while the list function will take the values.
This is not the same way how python lists and other python objects work and therefore it confuses people. I think the best solution would be to let the `in` operator take the values instead of the index.
FYI, here is an SO thread about this topic: https://stackoverflow.com/questions/51962778/python-in-operator-not-working-as-expected-when-comparing-string-and-strftime-va/51962950?noredirect=1#comment121535570_51962950 | {
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I would like to concatenate two incomplete data frame with the same data (in theory) regarding a similar index. I tried with pd.concat but I don't managed to get what I need.
Here is a simple example of what I would like to do :
```python
df1 = pd.DataFrame(
{
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B4"],
"C": ["C0", "C1", "C2", "B5"],
"D": [np.nan,np.nan,np.nan,np.nan,]
},
index=[0, 1, 2, 3],)
df2 = pd.DataFrame(
{
"A": ["A0", "A1", "A5", "A6"],
"B": ["B0", "B1", "B5", "B6"],
"C": [np.nan,np.nan,np.nan,np.nan,],
"D": ["D0", "D1", "D5", "D6"],
},
index=[0, 1, 5, 6]
)
res_expected = pd.DataFrame(
{
"A": ["A0", "A1", "A2", "A3", "A5", "A6"],
"B": ["B0", "B1", "B2", "B3", "B5", "B6"],
"C": ["C0", "C1", "C2", "B5",np.nan,np.nan,],
"D": ["D0", "D1", np.nan,np.nan,"D5", "D6"],
},
index=[0, 1, 2, 3, 5, 6]
)
```
Does someone have an idea ?
Thanks ! | {
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} | 0 | "2021-08-13T12:44:58Z" | "2021-08-31T19:54:23Z" | "2021-08-31T19:54:23Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
s = pd.Series([0, 1, pd.NA], dtype="Int64")
s.rolling(2).mean()
```
#### Problem description
Get an exception `ValueError: cannot convert to 'float64'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.`
`Int` type is a good implementation, as it seems to be the only way to preserve integer and missing value (otherwise the type is cast as `float64` to accommodate `np.nan`). However, `pd.NA` as the missing value indicator seems not treated similarly to `np.nan` and cause the operation above to fail.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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} | 0 | "2021-08-13T13:18:38Z" | "2021-08-19T02:01:55Z" | "2021-08-19T02:01:55Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.Series(index=[0], dtype=int)
```
#### Problem description
For other types, the default missing value indicator is set properly:
```Python
pd.Series(index=[0], dtype=float)
```
0 NaN
dtype: float64
```Python
pd.Series(index=[0], dtype="datetime64[ns]")
```
0 NaT
dtype: datetime64[ns]
```Python
pd.Series(index=[0], dtype=bool)
```
0 False
dtype: bool
(This might be tricky, but understandable)
However, for integer types, the default missing value is set to 0 instead of `pd.NA`, which is dangerous for interpretation.
#### Expected Output
Either
```
0 <NA>
dtype: Int64
```
or
```
0 NaN
dtype: float64
```
makes sense, given how `int64` is converted when having missing values, the latter case may be better.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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] | open | false | null | [] | null | 3 | "2021-08-13T14:05:59Z" | "2021-08-30T18:04:38Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
print(
pd.Series(dtype="bool").reindex([0]),
"\n\n",
pd.Series(index=[0], dtype="bool"),
"\n\n",
pd.Series(dtype="bool").reindex([0]).astype(bool),
)
```
#### Problem description
This is the output:
```
0 NaN
dtype: object
0 False
dtype: bool
0 True
dtype: bool
```
Semantically we may expect the same behavior for the code above. The second and third cases are definitely contradictory and dangerous.
I use the following code to test some common types, and it fails for type `bool` (another case that would fail is `int`, which I reported in https://github.com/pandas-dev/pandas/issues/43017)
```Python
for dtype in [bool, "Int64", "float", "datetime64[ns]", "timedelta64[ns]"]:
print("***", dtype, "***")
s1 = pd.Series(dtype=dtype).reindex([0])
s2 = pd.Series(index=[0], dtype=dtype)
s3 = pd.Series(dtype=dtype).reindex([0]).astype(dtype)
print(s1.equals(s2), s1.equals(s3))
```
#### Expected Output
Either
```
0 NaN
dtype: object
```
or
```
0 False
dtype: bool
```
makes sense. However, we get `True` when testing `bool(np.nan)`, which explains some of the inconsistent behavior above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.1.2
hypothesis : 6.14.1
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 1.4.4
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.19.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43019/events | https://github.com/pandas-dev/pandas/pull/43019 | 970,447,188 | MDExOlB1bGxSZXF1ZXN0NzEyMzk5MDI3 | 43,019 | BUG: seperate df dtypes from different initialization | {
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] | closed | false | null | [] | null | 0 | "2021-08-13T14:23:19Z" | "2021-08-19T13:41:50Z" | "2021-08-19T13:41:50Z" | CONTRIBUTOR | null | - [x] closes #42971
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | "2021-08-13T15:17:29Z" | "2021-11-15T15:26:35Z" | "2021-11-15T15:26:21Z" | NONE | null | When printing a DataFrame/Series that is backed by an ExtensionArray the values are first converted to a numpy array:
https://github.com/pandas-dev/pandas/blob/c03ee857f880f94461d3344c11bfb703885fde00/pandas/io/formats/format.py#L1561-L1574
This is problematic for ExtensionArrays that are very expensive to convert to numpy arrays. i.e. converting an extremely large ExtensionArray to a numpy array to ultimately print ~10 values.
Can this be overcome or worked around? There is a already a special case for categorical here. | {
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