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} | 1 | "2021-04-18T17:52:55Z" | "2021-04-20T13:04:51Z" | "2021-04-20T12:52:12Z" | MEMBER | null | - [x] closes #40935
- [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
Think further improvements like zero-copy access fall under #34873, so #40935 can be closed. | {
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} | 1 | "2021-04-18T18:02:16Z" | "2021-10-18T15:48:47Z" | "2021-10-18T15:48:47Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
When giving a string in the `subset` argument to dropna based on a single column, we get an error:
```
Index(...) must be called with a collection of some kind, 'C' was passed
```
#### Describe the solution you'd like
`DataFrame.dropna` should accept a single column subset as a string just like `DataFrame.drop_duplicates`
#### API breaking implications
None I could think of
```python
df = pd.DataFrame({"A": [1, 1, 3], "B": list("abc"), "C": [4, np.NaN, 5]})
print(df)
A B C
0 1 a 4.0
1 2 b NaN
2 3 c 5.0
df = df.dropna(subset="C")
print(df)
A B C
0 1 a 4.0
2 3 c 5.0
```
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} | 10 | "2021-04-18T18:04:22Z" | "2021-10-18T15:48:58Z" | "2021-10-18T15:48:48Z" | CONTRIBUTOR | null | - [x] closes #41021
- [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
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} | 4 | "2021-04-18T18:14:48Z" | "2021-05-07T08:49:55Z" | null | NONE | null | Code to reproduce:
```python
import pandas as pd
from scipy import sparse as sc
import numpy as np
np.random.seed(42)
vals = np.random.randint(0, 10, size=(1000, 1000))
keep = vals > 3
vals[keep] = 0
sparse_mtx = sc.coo_matrix(vals)
sparse_pd = pd.DataFrame.sparse.from_spmatrix(sparse_mtx)
num_tries = 30
t1 = timeit.timeit(lambda: sparse_pd.to_csv('sparse_pd.csv'), number=num_tries)
t2 = timeit.timeit(lambda: sparse_pd.sparse.to_dense().to_csv('sparse_pd.csv'), number=num_tries)
overhead = t1/t2
print(t1, t2, overhead)
```
Output:
```
56.591012510471046 3.7841985523700714 14.954556883657089
```
Versions:
* python == 3.9.2
* pandas == 1.2.4
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} | 0 | "2021-04-18T18:27:47Z" | "2021-04-19T14:55:09Z" | "2021-04-19T13:41:50Z" | MEMBER | null | ATM when we call self._inner_indexer in Index._union it raises on CategoricalIndex bc we are calling it incorrectly. Fixing that required moving the unwrapping/wrapping for Index._foo_indexer closer to just around the cython calls. | {
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} | 12 | "2021-04-18T19:41:59Z" | "2021-05-01T15:51:56Z" | "2021-04-26T12:15:39Z" | MEMBER | null | not yet dealt with `na`. no tests are failing so we need tests for this.
we can either use the fallback when `na` is specified or handle `na` in the array method, which may be more performant.
should we replicate StringArray:
```
>>> s = pd.Series(["Mouse", "dog", "house and parrot", "23", np.NaN], dtype="string")
>>>
>>> s.str.contains("og", na=3, regex=False)
0 False
1 True
2 False
3 False
4 True
dtype: boolean
>>>
>>> s.str.contains("og", na=np.nan, regex=False)
0 False
1 True
2 False
3 False
4 <NA>
dtype: boolean
>>>
```
or return an object array to preserve `na` if not pd.NA, True or False instead of coercing to bool/null? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41026 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41026/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41026/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41026/events | https://github.com/pandas-dev/pandas/pull/41026 | 860,754,837 | MDExOlB1bGxSZXF1ZXN0NjE3NTQ4NDkx | 41,026 | BUG: Changing return is_named_tuble to a tuple sublclass #40682 | {
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- [ ] tests added / passed
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Changed pandas/core/dtypes/inference.py so that the return of the is_named_tuple function to be a tuple subclass so that it is compatible with sqlalchemy.
Passed test_inference.py | {
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Handles the NA/NaN case before the dtype conversion | {
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- [x] closes #40933
- [x] tests added / passed
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} | 3 | "2021-04-19T03:52:38Z" | "2021-04-28T01:06:37Z" | "2021-04-28T00:46:56Z" | MEMBER | null | @mroeschke the type:ignores added in ewm.py look like potential bugs. any thoughts? | {
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} | 1 | "2021-04-19T04:01:55Z" | "2021-04-19T14:48:38Z" | "2021-04-19T13:38:44Z" | 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
@phofl parts of this may be worth doing in #40967 | {
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] | closed | false | null | [] | null | 8 | "2021-04-19T04:05:21Z" | "2021-04-21T04:34:20Z" | "2021-04-21T04:34:20Z" | NONE | null | I need to hide some excel tabs in an output from `to_excel`, and I'd like to do that with a parameter like `hidden=True`, but [no such parameter exists](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html). The only solution I can find is [hiding the sheet manually with openpyxl](https://stackoverflow.com/questions/23157643/openpyxl-and-hidden-unhidden-excel-worksheets/23170772#23170772), but this is prohibitively slow for large files.
[My StackOverflow question on this](https://stackoverflow.com/questions/67154554/pandas-to-excel-with-sheets-being-hidden-or-efficiently-hiding-excel-tabs) is unanswered, which suggests I'm out of luck.
Testing openpyxl showed that we can't save .xlsxs without at least one visible tab, so it looks like this would only work like so:
```
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
with writer:
df.to_excel(writer, sheet_name='df')
df.to_excel(writer, sheet_name='2nd tab')
```
Would adding `hidden=True` to `to_excel` be practical?
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https://api.github.com/repos/pandas-dev/pandas/issues/41032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41032/events | https://github.com/pandas-dev/pandas/issues/41032 | 860,938,168 | MDU6SXNzdWU4NjA5MzgxNjg= | 41,032 | Some issues about processing empty strings into NaT when using pandas to_datetime | {
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```python
import pandas as pd
data = [{'dt': '2020-01-11T11:00:00'},{'dt': ''}, {'dt': '2021-04-19T10:00:00+08'}]
df1 = pd.DataFrame(data)
df2 = pd.DataFrame(data)
assert df1['dt'].dtype.name == 'object'
assert df2['dt'].dtype.name == 'object'
df1['dt'] = pd.to_datetime(df1['dt'])
df2['dt'] = df2['dt'].apply(pd.to_datetime)
print(df1['dt'][1], type(df1['dt'][1])) # Result: str: 'NaT'
print(df2['dt'][1], type(df2['dt'][1])) # Result: pandas._libs.tslibs.nattype.NaTType: NaT
``` | {
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} | 2 | "2021-04-19T07:14:08Z" | "2021-05-13T22:26:29Z" | "2021-04-19T19:38:04Z" | 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-2.12.1-py2.py3-none-any.whl (189 kB)
Collecting nodeenv>=0.11.1
Downloading nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.3-py2.py3-none-any.whl (98 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.3-py2.py3-none-any.whl (7.2 MB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-2.2.3 nodeenv-1.6.0 pre-commit-2.12.1 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.3
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/absolufy-imports.
already up to date.
Updating 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 ... [INFO] Initializing environment for 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.
=====> /home/runner/.cache/pre-commit/repof0nncua3/.pre-commit-hooks.yaml does not exist
Updating https://gitlab.com/pycqa/flake8 ... [INFO] Initializing environment for https://gitlab.com/pycqa/flake8.
updating 3.9.0 -> 3.9.1.
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.
already up to date.
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 https://github.com/cpplint/cpplint.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8:flake8-bugbear==21.3.2,flake8-comprehensions==3.1.0,pandas-dev-flaker==0.2.0.
[INFO] Initializing environment for https://github.com/asottile/yesqa:flake8-bugbear==21.3.2,flake8-comprehensions==3.1.0,flake8==3.9.0,pandas-dev-flaker==0.2.0.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports...............................................................................Passed
black..........................................................................................Passed
codespell......................................................................................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
Generate pip dependency from conda.............................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml.......................Failed
- hook id: sync-flake8-versions
- exit code: 1
flake8 in 'environment.yml' does not match in 'flake8' from 'pre-commit'
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>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41034/events | https://github.com/pandas-dev/pandas/issues/41034 | 860,996,674 | MDU6SXNzdWU4NjA5OTY2NzQ= | 41,034 | DOC: there is no example for plotting asymmetrical errorbars | {
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} | 0 | "2021-04-19T07:29:55Z" | "2021-04-21T04:27:08Z" | "2021-04-21T04:27:08Z" | CONTRIBUTOR | null | #### Location of the documentation
[errorbar visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#plotting-with-error-bars)
#### Documentation problem
Currently there's only an example of the symmetrical case, i.e. where the lower/upper errors are the same. The asymmetrical case is supported but actually quite tricky to get to work. I could only find one [stackoverflow post](https://stackoverflow.com/questions/45798492/asymmetrical-errorbar-with-pandas) and it is not especially helpful
#### Suggested fix for documentation
I suggest adding an example that showcases how to plot asymmetrical errorbars
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https://api.github.com/repos/pandas-dev/pandas/issues/41035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41035/events | https://github.com/pandas-dev/pandas/pull/41035 | 860,998,746 | MDExOlB1bGxSZXF1ZXN0NjE3NzQwODg4 | 41,035 | DOC: add example for plotting asymmetrical error bars | {
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} | 2 | "2021-04-19T07:32:11Z" | "2021-04-21T04:27:21Z" | "2021-04-21T04:27:08Z" | CONTRIBUTOR | null | - [x] closes #41034
- [ ] 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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https://api.github.com/repos/pandas-dev/pandas/issues/41036 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41036/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41036/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41036/events | https://github.com/pandas-dev/pandas/issues/41036 | 861,168,850 | MDU6SXNzdWU4NjExNjg4NTA= | 41,036 | BUG: Conversion from parquet to csv problem | {
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] | closed | false | null | [] | null | 1 | "2021-04-19T10:39:39Z" | "2021-04-19T10:47:50Z" | "2021-04-19T10:44:02Z" | NONE | null | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41037/events | https://github.com/pandas-dev/pandas/issues/41037 | 861,193,444 | MDU6SXNzdWU4NjExOTM0NDQ= | 41,037 | QST: SettingWithCopyWarning -->self.obj[item_labels[indexer[info_axis]]] = value issue | {
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} | 2 | "2021-04-19T11:09:32Z" | "2021-04-19T19:00:27Z" | "2021-04-19T19:00:20Z" | NONE | null | Hello,
I am struggling in assigning new columns in my data frame, based on existing columns in the same data frame.
I keep getting this SettingWithCopyWarning that is driving me crazy:
_C:\ProgramData\Anaconda3\envs\PLAXIS_V20.3_python37\lib\site-packages\pandas\core\indexing.py:1596: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[key] = _infer_fill_value(value)
C:\ProgramData\Anaconda3\envs\PLAXIS_V20.3_python37\lib\site-packages\pandas\core\indexing.py:1783: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self.obj[item_labels[indexer[info_axis]]] = value_
The code that I am trying to run is the following:
__dfe = df_sampled.loc[mask]
af=dfe["Dist_{}_{}_UKPN_ mm".format(i,j)]
af.apply(lambda x: ((x*0.001)/ref)*100)
dfe.loc[:,"Dist_{}_{}_UKPN_ perc".format(i,j)]=af
My goal is to create a new column on the dfe dataframe ( which is a sub dataframe from another dataframe), which is based on the existing column on the dfe, but multiple by scalars values ( e.g to convert them into percentage)
I hope someone can help to understand how to overcome this
Thanks
F. | {
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] | closed | false | null | [] | null | 5 | "2021-04-19T11:20:13Z" | "2021-08-20T02:14:32Z" | "2021-08-20T02:14:31Z" | NONE | null | ## Problem
Sometimes, one would like to get a column regardless whether it was previously set as index.
Consider the following example:
```python
df = pd.DataFrame(np.arange(50).reshape(5,10).T, columns=list("ABCDE"))
df = df.set_index(["A","B"])
col = df.get("B")
```
In the above code `col` is set to `None`.
## Solution
I suggest to add an argument `include_index`, so that the following is possible:
```python
item = df.get("B", include_index=True)
```
It first checks the column names, and if not successful, continues searching the index names. It still returns None if no item was found.
This permits simpler queries that combine indices and columns:
```python
mask = (df.get("B", include_index=True) > 15) & (df.get("C", include_index=True) > 20)
```
## Implications on API
Backwards compatibility is guaranteed by setting the default value of `include_index=False`.
The return type of `get(key, include_index=True)` is now Optional[Union[pd.Series, pd.Index]]. If `key` belongs to the index, a `pd.Index` must be returned. A conversion to `pd.Series` is infeasible.
## Alternatives
Currently, the idiomatic way to return either column or index is:
```python
def get_item(df, key):
return df[key] if key in df else (df.index.get_level_values(key) if key in df.index.names else None)
```
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} | 3 | "2021-04-19T13:21:30Z" | "2021-04-26T19:34:02Z" | "2021-04-26T18:25:05Z" | MEMBER | null | - [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
Doing as was asked [here](https://github.com/pandas-dev/pandas/pull/40903#discussion_r612486254)
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} | 2 | "2021-04-19T13:23:40Z" | "2021-04-21T19:45:16Z" | "2021-04-21T19:44:50Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**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 pyarrow as pa
import pandas as pd
df = pd.DataFrame({"string": pd.array([], dtype="string")})
table = pa.Table.from_pandas(df)
print(table.column(0))
# <pyarrow.lib.ChunkedArray object at 0x12c5cec70>
# [
# []
# ]
# Construct a Table with zero chunks as returned by `pyarrow.dataset`
empty_table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema)
print(empty_table.column(0))
# <pyarrow.lib.ChunkedArray object at 0x12c5cee00>
# [
#
# ]
empty_table.to_pandas()
```
yields the following traceback:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-5047304e95a2> in <module>
----> 1 empty_table.to_pandas()
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/array.pxi in pyarrow.lib._PandasConvertible.to_pandas()
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/table.pxi in pyarrow.lib.Table._to_pandas()
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/pandas_compat.py in table_to_blockmanager(options, table, categories, ignore_metadata, types_mapper)
790 _check_data_column_metadata_consistency(all_columns)
791 columns = _deserialize_column_index(table, all_columns, column_indexes)
--> 792 blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
793
794 axes = [columns, index]
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/pandas_compat.py in _table_to_blocks(options, block_table, categories, extension_columns)
1131 result = pa.lib.table_to_blocks(options, block_table, categories,
1132 list(extension_columns.keys()))
-> 1133 return [_reconstruct_block(item, columns, extension_columns)
1134 for item in result]
1135
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/pandas_compat.py in <listcomp>(.0)
1131 result = pa.lib.table_to_blocks(options, block_table, categories,
1132 list(extension_columns.keys()))
-> 1133 return [_reconstruct_block(item, columns, extension_columns)
1134 for item in result]
1135
~/mambaforge/envs/fletcher/lib/python3.8/site-packages/pyarrow/pandas_compat.py in _reconstruct_block(item, columns, extension_columns)
749 raise ValueError("This column does not support to be converted "
750 "to a pandas ExtensionArray")
--> 751 pd_ext_arr = pandas_dtype.__from_arrow__(arr)
752 block = _int.make_block(pd_ext_arr, placement=placement,
753 klass=_int.ExtensionBlock)
~/Development/pandas/pandas/core/arrays/string_.py in __from_arrow__(self, array)
119 results.append(str_arr)
120
--> 121 return StringArray._concat_same_type(results)
122
123
~/Development/pandas/pandas/core/arrays/_mixins.py in _concat_same_type(cls, to_concat, axis)
240 dtypes = {str(x.dtype) for x in to_concat}
241 if len(dtypes) != 1:
--> 242 raise ValueError("to_concat must have the same dtype (tz)", dtypes)
243
244 new_values = [x._ndarray for x in to_concat]
ValueError: ('to_concat must have the same dtype (tz)', set())
```
#### Problem description
The problem here is that we are checking in `_concat_same_type` whether we concat arrays of the same type but as we don't concatenate anything, there are also no types to check. This should instead create an empty array. | {
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marked as draft since AFAICT there is performance issues with BooleanDtype().__from_arrow__ | {
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} | 23 | "2021-04-19T15:17:22Z" | "2021-09-13T14:00:22Z" | "2021-09-10T08:49:20Z" | CONTRIBUTOR | null | GH38454
- [x] closes #38454
- [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 | {
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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
Attempting an approach to fix this. Let's see if it works.
Not surprisingly, the bruteforce approach breaks a bunch of stuff, because I ended up with twice the utcoffset shift instead of once, which makes perfect sense.
What we want here instead is to get the first valid nanosecond of the day by default and normalize to that. And probably expose to the user the ability to shift back for normalization if midnight does not exist.
Will try to fix this tomorrow. | {
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} | 4 | "2021-04-19T16:44:47Z" | "2021-04-21T22:34:07Z" | "2021-04-21T22:34:07Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
dfo = pd.DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype="object")
dfs = pd.DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype="string")
dfo.loc[0, :] = {"A": "newA", "B": "newB"}
dfs.loc[0, :] = {"A": "newA", "B": "newB"}
print(dfo)
# Output:
# A B
# 0 newA newB
# 1 def jkl
print(dfs)
# Output:
# A B
# 0 A B
# 1 def jkl
```
#### Problem description
The interpretation of the RHS_dict depends on the dtype of the dataframe assigned to:
Assigning to an object-dataframe: Interpretation as ``pd.Series(RHS_dict)``
Assigning to a string-dataframe: Interpretation as ``list(RHS_dict)``
This is inconsistent and unexpected.
In my script I started with an object-dataframe and later changed to a string-dataframe. This change led to an incorrect result, due to the effect desribed above.
#### Expected Output
The interpretation of the RHS_dict does NOT depend on the dtype of the dataframe asigned to.
Personally I'd prefer ``pd.Series(RHS_dict)``, but if wiser men than me decide that ``list(RHS_dict)`` is correct, I won't object, as long as the behaviour is consistent across dtypes.
#### 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 : 10.0.18362
machine : AMD64
...
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
...
</details>
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} | 1 | "2021-04-19T17:38:19Z" | "2021-04-19T23:24:42Z" | "2021-04-19T23:21:23Z" | MEMBER | null | I'm seeing a 13-19% improvement in the motivating benchmark https://github.com/pandas-dev/pandas/pull/40171#issuecomment-790219422 | {
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} | 5 | "2021-04-19T18:14:00Z" | "2021-04-28T13:07:44Z" | "2021-04-28T13:07:33Z" | CONTRIBUTOR | null | Add Arrow buffer slicing before handing it over to numpy which is
needed in case the Arrow buffer contains padding or offset.
- [x] closes #40896
- [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
Points I'm not sure about:
- I created a new test file. Would it be better to place the test somewhere else?
- The test only tests int32. Should I add more dtypes?
I'm still new to the internals of pandas and always trying to improve so feedback of any kind is highly appreciated! | {
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} | 2 | "2021-04-20T00:32:45Z" | "2021-09-14T12:35:17Z" | "2021-09-14T12:35:17Z" | 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
dates = ['2020-05-01T18:04:03Z', '2020-04-28T17:54:06Z', '2020-04-26T18:04:09Z']
print(pd.to_datetime(dates, infer_datetime_format=True)) #drops UTC
print(pd.to_datetime(dates, infer_datetime_format=False)) #works!
dates = ['2020-05-01T18:04:03.0Z', '2020-04-28T17:54:06.0Z', '2020-04-26T18:04:09.0Z']
print(pd.to_datetime(dates, infer_datetime_format=True)) #works!
```
Output:
```
DatetimeIndex(['2020-05-01 18:04:03', '2020-04-28 17:54:06',
'2020-04-26 18:04:09'],
dtype='datetime64[ns]', freq=None)
DatetimeIndex(['2020-05-01 18:04:03+00:00', '2020-04-28 17:54:06+00:00',
'2020-04-26 18:04:09+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
DatetimeIndex(['2020-05-01 18:04:03+00:00', '2020-04-28 17:54:06+00:00',
'2020-04-26 18:04:09+00:00'],
dtype='datetime64[ns, UTC]', freq=None)
```
#### Problem description
Using infer_datetime_format=True drops the timezone info (UTC) if datestrings do not have decimal seconds as shown in the example above. '2020-05-01T18:04:03Z' is a valid ISO 8601 datetime (https://tools.ietf.org/html/rfc3339#section-5.6).
#### Expected Output
```
dates = ['2020-05-01T18:04:03Z', '2020-04-28T17:54:06Z', '2020-04-26T18:04:09Z']
print(pd.to_datetime(dates, infer_datetime_format=True))
#DatetimeIndex(['2020-05-01 18:04:03+00:00', '2020-04-28 17:54:06+00:00',
# '2020-04-26 18:04:09+00:00'],
# dtype='datetime64[ns, UTC]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.177-139.253.amzn2.x86_64
Version : #1 SMP Wed Apr 29 09:56:20 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.7.5
pip : 20.3.4
setuptools : 49.6.0.post20210108
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : 1.10.2
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.04.0
fastparquet : None
gcsfs : 0.7.2
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.6.0
scipy : 1.6.2
sqlalchemy : 1.4.9
tables : None
tabulate : 0.8.9
xarray : 0.17.0
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-04-20T02:11:16Z" | "2021-04-20T16:02:00Z" | "2021-04-20T13:12:04Z" | MEMBER | null | Simplify _get_cython_function
Raise TypeError instead of NotImplementedError in cases where we shouldn't bother falling back to py_fallback | {
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} | 4 | "2021-04-20T03:26:33Z" | "2021-04-21T16:24:23Z" | "2021-04-21T16:24:18Z" | CONTRIBUTOR | null | Returns Object Type Index when Mixed Timezones are used in list-like input to pandas.to_datetime()
- [x] closes #40808
- [ ] 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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} | 4 | "2021-04-20T07:52:10Z" | "2021-04-21T16:09:18Z" | "2021-04-21T16:09:14Z" | MEMBER | null | For arrays without missing values it doesn't change, but gives a decent speed-up when having missing values:
```
In [1]: arr1 = pa.array([True, False, True, True, False]*100_000)
In [2]: arr2 = pa.array([True, False, None, True, False]*100_000)
In [3]: %timeit pd.BooleanDtype().__from_arrow__(arr1)
9.34 ms ± 27.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- master
9.31 ms ± 23.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
In [4]: %timeit pd.BooleanDtype().__from_arrow__(arr2)
60.6 ms ± 678 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
18.8 ms ± 99.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
```
cc @simonjayhawkins | {
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} | 4 | "2021-04-20T08:06:20Z" | "2021-04-22T08:19:28Z" | "2021-04-21T19:44:50Z" | CONTRIBUTOR | null | - [x] closes #41040
- [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
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} | 4 | "2021-04-20T10:33:26Z" | "2021-04-26T12:57:27Z" | "2021-04-26T12:57:27Z" | 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
tmp = pd.DataFrame(
[
0.03,
0.03,
0.001,
np.NaN,
0.002,
0.008,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
np.NaN,
0.005,
0.2
]
)
tmp.iloc[1:].rolling(5, min_periods=0).mean() # Returns 0 at indexes 10 and 11
tmp.iloc[2:].rolling(5, min_periods=0).mean() # Returns np.NaN at indexes 10 and 11
```
#### Problem description
Pandas rolling mean computation inconsistently treats windows with NaN values only, depending on how far they are in the DataFrame. Here we can see how the values for the windows indexed as 10 and 11 are set to 0 or NaN depending on whether we start the computation starting from the 2nd observation or the 3rd.
Other aggregation functions (like sum) don't seem to suffer from this
This only happens when min_periods is set to 0
This doesn't happen in pandas version 1.0.3
This doesn't happen if non NaN values are set to 1 instead of floating values
This could be linked to another reported issue that I haven't identified after a quick search
#### Expected Output
The expected output would be for the mean computation to return np.NaN for the indexes 10 and 11 in both cases, consistent with pandas documentation and other aggregation functions behavior
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-142-generic
Version : #146-Ubuntu SMP Tue Apr 13 01:11:19 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 8 | "2021-04-20T13:56:07Z" | "2021-04-20T14:48:58Z" | "2021-04-20T14:48:11Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
```
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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
# Your code here
>>> import pyperclip
>>> pyperclip.copy('hello,world\n1,2\n3,4\n')
>>> pyperclip.paste()
'hello,world\r\n1,2\r\n3,4\r\n'
>>> import pandas as pd
>>> df = pd.read_clipboard(sep=',')
---------------------------------------------------------------------------
PyperclipException Traceback (most recent call last)
<ipython-input-6-7d5bcbe66479> in <module>
----> 1 df = pd.read_clipboard(sep=',')
~/envs/default/lib/python3.8/site-packages/pandas/io/clipboards.py in read_clipboard(sep, **kwargs)
36 from pandas.io.parsers import read_csv
37
---> 38 text = clipboard_get()
39
40 # Try to decode (if needed, as "text" might already be a string here).
~/envs/default/lib/python3.8/site-packages/pandas/io/clipboard/__init__.py in lazy_load_stub_paste()
647 global copy, paste
648 copy, paste = determine_clipboard()
--> 649 return paste()
650
651
~/envs/default/lib/python3.8/site-packages/pandas/io/clipboard/__init__.py in __call__(self, *args, **kwargs)
285 class ClipboardUnavailable:
286 def __call__(self, *args, **kwargs):
--> 287 raise PyperclipException(EXCEPT_MSG)
288
289 def __bool__(self) -> bool:
PyperclipException:
Pyperclip could not find a copy/paste mechanism for your system.
For more information, please visit
https://pyperclip.readthedocs.io/en/latest/#not-implemented-error
```
#### Problem description
pandas is unable to copy from the clipboard on windows using wsl2. This affects workflows where data is copied between spreadsheets, from the web and a dataframe is created, or vice-versa (i.e. dataframe is copied into a spreadsheet).
Pandas ships a vendored version of pyperclip that is old. The recent version of pyperclip resolves this issue on windows wsl2 in this pull request. https://github.com/asweigart/pyperclip/pull/168
As can be seen in my pasted code sample, I have the most recent pyperclip installed on my system, and the clipboard functionality works as expected.
We can fix pandas' clipboard functionality by updating the vendored code (it seems that copy-pasting the `__init__.py` file from pyperclip ought to do the trick.
I'd be happy to contribute this change if the issue is approved. There seems to be a separate effort to move the backend from pyperclip to pyclip
#### Expected Output
I would expect to see
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.19.4
pytz : 2020.4
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 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.5.2
scipy : 1.5.4
sqlalchemy : 1.3.23
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 2.0.1
xlwt : None
numba : 0.52.0
```
</details>
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} | 8 | "2021-04-20T15:01:06Z" | "2021-07-03T15:10:13Z" | "2021-04-20T22:58:18Z" | MEMBER | null | ```
data = ["Mouse", "dog", "house and parrot", "23", np.NaN] * 100_000
s = pd.Series(data, dtype="string")
s1 = pd.Series(data, dtype="arrow_string")
%timeit s.str.upper()
# 63.8 ms ± 259 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit s1.str.upper()
# 92 ms ± 152 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
# 4.44 ms ± 35.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
%timeit s.str.lower()
# 49.1 ms ± 273 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
%timeit s1.str.lower()
# 78.2 ms ± 814 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
# 4.34 ms ± 32.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) <-- PR
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} | 1 | "2021-04-20T17:40:46Z" | "2021-04-20T22:59:46Z" | "2021-04-20T22:55:13Z" | MEMBER | null | This came up the other day in https://github.com/pandas-dev/pandas/pull/40651#issuecomment-820671958. As I [originally](https://github.com/pandas-dev/pandas/pull/35208#issuecomment-656879885) said in the PR that put this inaccurate wording in, this overstates the degree to which there is consensus. | {
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} | 0 | "2021-04-20T17:46:47Z" | "2021-04-20T23:01:14Z" | "2021-04-20T22:54:47Z" | 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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https://api.github.com/repos/pandas-dev/pandas/issues/41059 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41059/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41059/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41059/events | https://github.com/pandas-dev/pandas/pull/41059 | 863,084,468 | MDExOlB1bGxSZXF1ZXN0NjE5NTUwNzEy | 41,059 | TYP: libinterval | {
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} | 1 | "2021-04-20T19:20:35Z" | "2021-07-01T22:01:09Z" | 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
str_obj_ser = pd.Series(["1", "2", "3", None], dtype="object")
mix_obj_ser = pd.Series([1, "2", 3.0, None], dtype="object")
num_obj_ser = pd.Series([1, 2, 3.0, None], dtype="object")
str_obj_ser.astype("Int16") # Exception
mix_obj_ser.astype("Int16") # different Exception
num_obj_ser.astype("Int16") # works
str_obj_ser.astype("string").astype("Int16") # works
mix_obj_ser.astype("string").astype("Int16") # Exception
num_obj_ser.astype("string").astype("Int16") # Exception
str_obj_ser.astype("string").astype("Float64").astype("Int16") # works
mix_obj_ser.astype("string").astype("Float64").astype("Int16") # works
num_obj_ser.astype("string").astype("Float64").astype("Int16") # works
str_obj_ser.astype("Float64").astype("Int16") # Exception
mix_obj_ser.astype("Float64").astype("Int16") # works
num_obj_ser.astype("Float64").astype("Int16") # works
```
#### Problem description
The conversion of an object-series with some text in it to one of the nullable integer dtypes fails even though all elements of the series are convertable to integers (or to pd.NA).
This issue seems to be related to #40729, but the workaround described there for Floatxx doesn't work in all cases here:
The detour via dtype string is not evough if an element in the object-series is a float because int("3.0") doesn't work (but int(3.0) does). A detour via string and then Float64 is necessary for all examples given above to work (for some cases, but not all, the string step can be omitted).
But even the detour via string and Float64 to Int16 is not guaranteed to always work, e.g. if an element of the series is an object with an ``__int__()`` method (returning a number) and a ``__str__()`` method (returning a description, not an integer literal).
I think the topic of this issue has also been mentioned in the discussion of #39616.
#### Expected Output
The conversion of a series of dtype object to one of the nullable integer dtypes should always work if all elements of the series are convertable to the target dtype.
At least something along the lines of
* element is None, pd.NA, np.nan, ... -> pd.NA
* otherwise -> int(element)
I'd even prefer something like
* element is None, pd.NA, np.nan, ... -> pd.NA
* element is string, bytes or bytearray -> int(element, 0)
* otherwise -> int(element)
such that string literals like "0x7f" work.
As the latter doesn't work with the current string -> Int16 conversion though, that would be more like an enhancement than a bugfix.
#### 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 : 10.0.18362
machine : AMD64
...
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
...
</details>
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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: Not added since I'm not sure whether it's necessary (there's an unanswered question in the issue regarding that) but am happy to do so provided some guidelines. Would it be simply modifying the latest file in `pandas/doc/source/whatsnew/`, i.e., `v1.3.0.rst`? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41062 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41062/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41062/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41062/events | https://github.com/pandas-dev/pandas/issues/41062 | 863,273,569 | MDU6SXNzdWU4NjMyNzM1Njk= | 41,062 | BUG: df[frozenset] fails if (other) column names are not unique | {
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] | open | false | null | [] | null | 2 | "2021-04-20T21:53:11Z" | "2021-08-20T02:18:56Z" | 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
k = frozenset(["KEY"])
df1 = pd.DataFrame([[1,2,3,4], [5,6,7,8]], columns=[k, "B", "C", "D"])
df2 = pd.DataFrame([[1,2,3,4], [5,6,7,8]], columns=[k, "B", "C", "C"])
print(df1[k]) # works
print(df2[k]) # KeyError: "None of [Index(['KEY'], dtype='object')] are in the [columns]"
```
#### Problem description
Indexing a dataframe with a frozenset raises a KeyError if column names are not unique (the name of the indexed column is unique):
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\test\venv124\lib\site-packages\pandas\core\frame.py", line 3030, in __getitem__
indexer = self.loc._get_listlike_indexer(key, axis=1, raise_missing=True)[1]
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1266, in _get_listlike_indexer
self._validate_read_indexer(keyarr, indexer, axis, raise_missing=raise_missing)
File "C:\test\venv124\lib\site-packages\pandas\core\indexing.py", line 1308, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['KEY'], dtype='object')] are in the [columns]"
```
Indexing another dataframe that only differs by having unique column names works, so using a frozenset as column name seems to be ok.
The traceback seems to indicate that a "listlike_indexer" is involved - maybe with non-unique column names a different indexer is used that has problems with non-atomic column names, turning the column name ``frozenset(["KEY"])`` into ``"KEY"``. Just a guess based on the error message, I don't know anything about how pandas indexers work.
#### Expected Output
Indexing works with a column name of arbitrary type regardless of whether the other column names are unique or not.
#### 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 : 10.0.18362
machine : AMD64
...
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
...
</details>
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} | 1 | "2021-04-20T22:43:48Z" | "2021-04-21T12:50:29Z" | "2021-04-21T12:43:55Z" | CONTRIBUTOR | null | The use of a dict with `_typ` attributes as keys in `NumericIndex._validate_dtype` makes it complicated to subclass numeric indexes and change the subclass' `_typ` attribute.
This PR makes the dtype validation in `NumericIndex._validate_dtype` not dependent on the value of the `_typ` attribute, making subclassing more robust. | {
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} | 3 | "2021-04-20T22:56:48Z" | "2021-07-01T21:44:20Z" | null | NONE | null | - [ X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [X ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). Here: https://stackoverflow.com/questions/67183791/pandas-dataframe-with-time-index-and-freq-with-multiplier
---
#### Question about pandas
I have a DataFrame with a time series as index.
import pandas as pd
from numpy.random import rand
df = pd.DataFrame(rand(100000), index=None, columns=['a'])
df['time'] = pd.date_range('2020-01-01 12:30:15',
periods=len(df['a']), freq='ms')
df.set_index('time', inplace=True)
df.plot()
When I put a multiplier on the frequency, it becomes extremely slow to plot `df2`, even if it has less elements than `df`. It actually crashed my Python kernel, and my laptop almost ran out of RAM
df2 = pd.DataFrame(rand(50000), index=None, columns=['a'])
df2['time'] = pd.date_range('2020-01-01 12:30:15',
periods=len(df2['a']), freq='2.5ms')
df2.set_index('time', inplace=True)
df2.plot()
Trying with `freq=2500U` does not make any difference
I was wondering if this behavior is normal. Haven't tried to run this on a code profiler. Or with any other algorithms, to see if has any effect.
Thanks
Python 3.9.3-1,
Pandas 1.2.3-1,
Numpy 1.20.1-1,
Matplotlib 3.4.1-2
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Once non-direct move is _cython_agg_general, where the implementation is moved to SeriesGroupBy instead of GroupBy, bc DataFrameGroupBy overrides it. | {
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```
df = pd.DataFrame.from_dict({1:'Single line', 2:'Multiline \n with bkslash_N', 3:'Multiline \r with bkslash_R'}, orient="index", columns=['text'])
df.to_csv()
',0\n1,Single line\n2,"Multiline \n with bkslash_N"\n3,Multiline \r with bkslash_R\n'
```
No quotes on line 3
#### Problem description
When text has '\r' as a line separator, `pandas.to_csv()` does not consider such text as multiline and does not surround it with quotes. As a result such CSV files are incompatible other parsers.
#### Expected Output
The text around "Multiline \r with bkslash_R" should be quoted
```
',0\n1,Single line\n2,"Multiline \n with bkslash_N"\n3,"Multiline \r with bkslash_R"\n'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.12.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-21T05:19:46Z" | "2021-05-06T01:37:07Z" | "2021-05-06T01:37:07Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (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 pathlib
import sys
import pandas
text = """I am able.
One powerful way.
"Trouble concentrating, television?"
"Maybe, or. You and family, or your best work."
assessment!
more about them and help.
"The more you say, the better I'll be"
"""
file_name = "boo.csv"
pathlib.Path(file_name).write_text(text)
pandas.read_csv(file_name, names=("text",))
print("Works!")
sys.setprofile(lambda *a, **k: None)
print("And it's broken...")
pandas.read_csv(file_name, names=("text",))
```
#### Problem description
`Pandas.read_csv` breaks if I run `sys.setprofile(lambda *a, **k: None)`...
```bash
Works!
And it's broken...
Traceback (most recent call last):
File "bam.py", line 22, in <module>
pandas.read_csv(file_name, names=("text",))
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 610, in read_csv
return _read(filepath_or_buffer, kwds)
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 468, in _read
return parser.read(nrows)
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 1057, in read
index, columns, col_dict = self._engine.read(nrows)
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 2496, in read
alldata = self._rows_to_cols(content)
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 3189, in _rows_to_cols
self._alert_malformed(msg, row_num + 1)
File "/Users/michaelp/Code/Text-to-Speech/venv/lib/python3.8/site-packages/pandas/io/parsers.py", line 2948, in _alert_malformed
raise ParserError(msg)
pandas.errors.ParserError: Expected 3 fields in line 6, saw 5
```
#### Expected Output
`read_csv` shouldn't be affected by `setprofile`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.8.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
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 : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.9
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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] | closed | false | null | [] | null | 5 | "2021-04-21T06:11:16Z" | "2021-04-29T08:15:58Z" | "2021-04-22T16:01:18Z" | NONE | null | Pandas frequently errors about importing NumPy. When I try to import pandas, it tries to import NumPy and creates an error.
I don't know why but when I delete venv folder and create a new venv and install pandas again, it automatically resolves but after 3-4 days, it throws an error about importing NumPy. There isn't a problem with the NumPy version because I don't manually install NumPy or any package with NumPy dependency before installing pandas. Anyway, I don't use another package using Numpy.
If you know what is the problem, please reply. | {
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] | open | false | null | [] | null | 0 | "2021-04-21T06:15:28Z" | "2021-08-20T02:19:22Z" | null | CONTRIBUTOR | null | Currently `Styler.format` has its own formatting logic, for example determining precision or na_rep, and replacing thousands or decimal symbols.
Pandas DataFrame formatters have similar logic but noyt identical and are applied in different ways: For example:
- FloatArrayFormatter doesn't have the thousands argument which is quite useful for Europeans.
- the FloatArrayFormatter is vectorised but Styler does display_funcs element-wise (which it can't avoid for processing individual cell styles). This may make it difficult to refactor across module (unless the fomat_class was called element-wise)
- it is difficult to replicate other parameters such as the escape_html (escape_latex) arguments in the same processing order to preserve features and the subset argument makes vectorisation difficult, in addition to item 2.
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} | 8 | "2021-04-21T08:46:45Z" | "2021-05-26T02:11:06Z" | null | MEMBER | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
ts = pd.Timestamp(year=2001, month=10, day=14, hour=0, minute=30, tz='America/Santiago')
print(ts)
```
#### Problem description
There was a [DST transition](https://www.timeanddate.com/time/change/chile/santiago?year=2001) on the 14th of October 2001, and the time from midnight to 1 a.m. does not exist. Ran into this while working on #40517 where we ran into an error while normalizing a `Timestamp` when midnight does not exist in a particular timezone. Yet, we are able to create this timestamp directly, even though it does not make any sense.
#### Expected Output
I would expect `NonExistentTimeError: 2001-10-14 00:30:00`
#### Notes
I wasn't able to find a prior issue for this. I'm a bit swamped right now, so I would appreciate if someone else looked at this bug. If no one takes it up, I'll investigate next week.
#### Output of ``pd.show_versions()``
<details>
This is on my dev environment, but I'm getting the same behavior on the latest stable version.
INSTALLED VERSIONS
------------------
commit : 692bba578efb5e305c9b116568e5aad75b3fdbb3
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1251
pandas : 0.26.0.dev0+6766.g692bba578e
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.3.4
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/85"
} | 3 | "2021-04-21T10:48:10Z" | "2021-06-01T14:54:44Z" | "2021-06-01T14:54:44Z" | 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
import pandas
pdf = pandas.DataFrame([1, 'a', True])
# behavior for pandas 1.2.3
pdf.prod(axis=0, min_count=1, numeric_only=False)
0 a
dtype: object
# behavior for pandas 1.2.4
pdf.prod(axis=0, min_count=1, numeric_only=False)
AssertionError: assert numeric_only is None
# behavior for pandas 1.2.3
pdf.sum(axis=0, min_count=1, numeric_only=False)
TypeError: unsupported operand type(s) for +: 'int' and 'str'
# behavior for pandas 1.2.4
pdf.sum(axis=0, min_count=1, numeric_only=False)
AssertionError: assert numeric_only is None
```
#### Problem description
Some of the reduction operations (namely, `sum` and `prod`) has changed own behavior in 1.2.4 comparing to 1.2.3. `prod` has become failing and `sum` has thrown another exception. Other reduction operations need to be checked as well.
#### Output of ``pd.show_versions()``
<details>
pandas : 1.2.4
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : 0.14.1
pyarrow : 1.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-21T11:40:19Z" | "2021-04-21T17:20:53Z" | "2021-04-21T17:20:50Z" | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146
This already enables most of the JSON tests (it are only the table schema tests that are segfaulting). Those are mostly working because the JSON writer falls back to using `df.values`. This only gives some errors with datetime formatting, for which one test is skipped (and this will then be fixed by letting the JSON code work column-by-column) | {
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} | 5 | "2021-04-21T12:31:54Z" | "2021-04-21T22:52:59Z" | "2021-04-21T22:34:07Z" | CONTRIBUTOR | null | - [x] closes #41044
- [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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} | 2 | "2021-04-21T13:03:36Z" | "2021-04-27T12:45:39Z" | "2021-04-26T12:21:47Z" | CONTRIBUTOR | null | - [x] closes #35865
- [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 asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
Hi there, my understanding of the df.groupby.rolling for a multi index dataframe should preserve the level-1 index, but I can't seem to get that. I am running Pandas 1.2.4. Hope that somebody can provide some insight of what is happening
```python
x = range(0, 6)
id = ['a', 'a', 'a', 'b', 'b', 'b']
temp = pd.DataFrame(zip(id, x), columns = ['id', 'x'])
temp['sequence']=[0,1,2,0,1,2]
temp=temp.set_index(['id','sequence'])
print(temp)
x
id sequence
a 0 0
1 1
2 2
b 0 3
1 4
2 5
temp.groupby(level=0,group_keys=True).rolling(2, min_periods=1).min()
x
id
a 0.0
a 0.0
a 1.0
b 3.0
b 3.0
b 4.0
```
base don the online guides, I would expect the level 1 index to be preserved...but I can't understand what is currently happening at this case? or what is the correct way of doing it.
Hope that somebody can provide some insights with regards to this problem
EDIT: Edited as text for readability, apologies for posting images earlier, forgot about it while posting it just now
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] | open | false | null | [] | null | 4 | "2021-04-21T13:46:03Z" | "2021-04-22T15:01:03Z" | 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.
---
I'm familiar with several issues being reported relating to skipna=False, but I have not seen this particular problem elsewhere. In short, the 'skipna=False' setting causes errors in rows not having any missing values.
#### Code Sample, a copy-pastable example
```python
# Minimal code to reproduce in pandas 1.2.4:
import pandas as pd
df1 = pd.DataFrame({"A": [False,pd.NA,pd.NA], "B": [True,True,False]})
df1
A B
0 False True
1 <NA> True
2 <NA> False
df1.all(axis=1, skipna=False)
0 True
1 True
2 False
dtype: bool
# And again with experimental BooleanDtype
df2 = pd.DataFrame({"A": [False,pd.NA,pd.NA], "B": [True,True,False]}, dtype=pd.BooleanDtype())
df2
A B
0 False True
1 <NA> True
2 <NA> False
df2.all(axis=1, skipna=False)
0 True
1 True
2 True
dtype: bool
```
#### Problem description
With df1 row '0' is clearly wrong; with df2 '0' and '2' are undeniably wrong. The rest are imho also suspicious, but they at least follow the documented behavior of:
> If skipna is False, then NA are treated as True, because these are not equal to zero.
...even if I do believe the mathematically correct answers are as given e.g. here:
https://www.ibm.com/docs/en/spss-statistics/SaaS?topic=command-missing-values-logical-operators-if
In other words, row '1' should, imho, evaluate to 'NA'.
#### Expected Output
Rows '0' and '2' should clearly evaluate to 'False'.
Row '1' should maybe evaluate to 'NA'.
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.12.2.el7.x86_64
Version : #1 SMP Fri Apr 19 21:09:07 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C
LANG : C
LOCALE : None.None
pandas : 1.2.4
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : 4.9.3
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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I'm a huge fan on pandas, but I primarily use vtkf files (.vti mostly) and analyse them in Paraview. I hate paraview, and would much rather use pandas, so was wondering if there's any way to include the capabilities of something like vtkf explorer within pandas to allow people to read in vtk files as dataframes
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} | 6 | "2021-04-21T14:33:46Z" | "2021-04-26T07:51:32Z" | "2021-04-26T07:51:29Z" | MEMBER | null | Closes #27164
This is a small refactor of the JSON writing code (`objToJSON.c`) to remove the access of the Blocks. Serializing to JSON was already done column-by-column (also when accessing the blocks), but currently we were iterating over the different "columns" of the 2D array per block in the C code. While with this PR, the C code directly iterates over all the columns (instead of per block).
This gives a less invasive change as the previous attempt of https://github.com/pandas-dev/pandas/pull/27166
To be able to iterate over the column arrays in C, I added the `column_arrays` attribute on the manager, for this.
I ran the JSON ASV benchmarks with this branch:
`$ asv continuous -b json -f 1.01 upstream/master HEAD`
```diff
before after ratio
[692bba57] [4142a532]
<master> <refactor-json-blocks>
+ 162±2ms 192±2ms 1.19 io.json.ToJSON.time_to_json_wide('values', 'df_td_int_ts')
+ 164±2ms 191±2ms 1.16 io.json.ToJSON.time_to_json_wide('split', 'df_td_int_ts')
+ 96.2±0.3ms 111±0.8ms 1.15 io.json.ToJSON.time_to_json('values', 'df')
+ 192±3ms 221±3ms 1.15 io.json.ToJSON.time_to_json_wide('records', 'df_td_int_ts')
+ 106±0.3ms 121±1ms 1.15 io.json.ToJSON.time_to_json('split', 'df')
+ 96.4±2ms 110±2ms 1.15 io.json.ToJSON.time_to_json('values', 'df_date_idx')
+ 192±4ms 219±4ms 1.14 io.json.ToJSON.time_to_json_wide('index', 'df_td_int_ts')
+ 177±4ms 198±3ms 1.12 io.json.ToJSON.time_to_json_wide('columns', 'df_td_int_ts')
+ 112±1ms 124±2ms 1.11 io.json.ToJSON.time_to_json('records', 'df')
+ 129±0.6ms 143±2ms 1.11 io.json.ToJSON.time_to_json('index', 'df')
+ 138±1ms 152±1ms 1.10 io.json.ToJSON.time_to_json('index', 'df_date_idx')
+ 168±0.9ms 182±1ms 1.09 io.json.ToJSONLines.time_floats_with_int_idex_lines
+ 168±2ms 182±1ms 1.08 io.json.ToJSONLines.time_floats_with_dt_index_lines
+ 117±0.9ms 127±1ms 1.08 io.json.ToJSON.time_to_json_wide('split', 'df_date_idx')
+ 157±2ms 165±2ms 1.05 io.json.ToJSON.time_to_json_wide('split', 'df_int_floats')
- 177M 175M 0.99 io.json.ToJSON.peakmem_to_json_wide('split', 'df_int_float_str')
- 185±5ms 172±3ms 0.93 io.json.ToJSON.time_to_json('index', 'df_int_floats')
- 127±8ms 110±0.6ms 0.87 io.json.ToJSON.time_to_json('split', 'df_td_int_ts')
```
</details>
There is a slight slowdown in some of the benchmarks for wide dataframes, but only 20%.
So we can decide to keep the original block-iteration code for BlockManager for now, instead of fully replacing it as I did now, if we don't want this slight slowdown.
This also fixes some remaining cases for ArrayManager (xref https://github.com/pandas-dev/pandas/issues/39146) | {
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`str.rsplit` can probably also be added to this PR (the tests are parameterised, one failing for StringArray.. maybe precursor needed)
also issue with empty string
failing tests are xfailed.. maybe fix later. | {
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] | closed | false | null | [] | null | 2 | "2021-04-21T21:06:22Z" | "2021-04-22T05:46:36Z" | "2021-04-22T05:46:36Z" | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
```
Hello all,
I'm new to Pandas and I can't solve these errors I'm getting. Any assistance would be appreciated.
Traceback (most recent call last):
File "C:\Users\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 515, in
main()
File "C:\Users\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 471, in main
[all_data,suite_list] = openSpreadsheet()
File "C:\Users\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 91, in openSpreadsheet
all_data = pd.read_excel(workbook_name, 1 ,usecols=['Name','Test Stand','Status',
File "C:\Users\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\util_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
TypeError: read_excel() got an unexpected keyword argument 'index'
C:\Users\HA_Report_Generation\Report Generator Legacy>python3 report_generator_1_5_3.py
Opening spreadsheet from .\Regression\CVB Logix Firmware-Test Run-20210411.xls...
Traceback (most recent call last):
File "C:\Users\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 515, in
main()
File "C:\Users\HA_Report_Generation\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 471, in main
[all_data,suite_list] = openSpreadsheet()
File "C:\Users\HA_Report_Generation\Report Generator Legacy\report_generator_1_5_3.py", line 91, in openSpreadsheet
all_data = pd.read_excel(workbook_name, 1 ,usecols=['Name','Test Stand','Status',
File "C:\Users\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\pandas\util_decorators.py", line 299, in wrapper
return func(*args, **kwargs)
TypeError: read_excel() got an unexpected keyword argument 'index' | {
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} | 0 | "2021-04-21T21:35:48Z" | "2021-04-22T22:36:57Z" | "2021-04-22T22:27:32Z" | MEMBER | null | Remove a no-longer-needed Categorical kludge in wrap_setop_result
isinstance(obj, ABCMultiIndex) -> isinstance(obj, MultiIndex) in core.indexing | {
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AFAICT this is not documented, supports only one test (parametrized over 4 dtypes):
```
@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"])
def test_basic(dtype):
data = pd.Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
[...]
group_constants = {0: 10, 1: 20, 2: 30}
agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
assert agged[1] == 21
```
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} | 2 | "2021-04-22T00:59:22Z" | "2021-04-23T01:54:53Z" | "2021-04-23T01:47:10Z" | MEMBER | null | Or more accurately, simplify BaseGroupBy._cython_agg_general by not having ohlc go through it. This will in turn allow us to simplify SeriesGroupBy._wrap_aggregated_output.
This will have a merge conflict with #41066. Doesn't matter which order they are merged in. | {
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} | 4 | "2021-04-22T07:07:53Z" | "2021-04-22T11:22:14Z" | "2021-04-22T11:00:17Z" | MEMBER | null | The doc build is failing on master (`/home/runner/work/pandas/pandas/doc/source/reference/api/pandas.Series.dt.rst: WARNING: document isn't included in any toctree`), and the build that starts failing seems to have switched from python 3.8 to 3.9 (I don't see any other relevant change in the environment). I suppose the default python changed at the conda(-forge) side. | {
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] | null | 0 | "2021-04-22T08:58:09Z" | "2021-05-27T02:16:49Z" | "2021-05-27T02:16:49Z" | MEMBER | null | - [Thursday, April 22nd 9-10am US Eastern Time](https://everytimezone.com/s/585bce01)
- [Zoom link](https://nyu.zoom.us/j/98201283174?pwd=d0VoUy9GTDRlVVZ0cCtTK0s4NnlBZz09)
- [Agenda/notes](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#heading=h.snd8dmkwtkdi)
---
A time to talk about pandas documentation. [Agenda/notes doc](https://docs.google.com/document/d/1JoiqTdK6gi5W1QiTWrqtTV-rNmIqU3xROgHY5iT4J5g/edit#heading=h.snd8dmkwtkdi) — feel free to add things in advance. All are welcome. Sorry for the late reminder. Hope to see you there!
@pandas-dev/pandas-core @pandas-dev/pandas-triage @pandas-dev/pydata-sphinx-theme @pandas-dev/pandas-docs | {
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I cannot create DataFrames based on SQL view tables:
```
sqlalchemy.exc.InvalidRequestError: Could not reflect: requested table(s) not available in Engine(...) schema 'ml_dataset': (dataset)
```
#### Describe the solution you'd like
It would be great to be able to read views. `SELECT * FROM table;` does not behave differently if `table` is a table, a view, a materialized view or a foreign table.
#### API breaking implications
No backward compatible changes.
#### Describe alternatives you've considered
Instead of extending `read_sql_table` a new function could be created, like `read_sql_view`
#### Additional context
Workaround:
```python
df = pandas.read_sql_query("select * from sampletable", viltkamera_postgres)
```
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] | open | false | null | [] | null | 7 | "2021-04-22T11:22:01Z" | "2021-08-20T02:28:31Z" | null | MEMBER | null | The doc build was failing on master (`/home/runner/work/pandas/pandas/doc/source/reference/api/pandas.Series.dt.rst: WARNING: document isn't included in any toctree`), which started when the environment switched to Python 3.9.
It is now pinned to Python 3.8 (https://github.com/pandas-dev/pandas/pull/41092), but we should fix the warning for Python 3.9, and unpin the environment.
cc @pandas-dev/pandas-docs | {
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} | 7 | "2021-04-22T12:23:49Z" | "2021-04-26T11:40:01Z" | "2021-04-26T10:39:44Z" | CONTRIBUTOR | null | This is a fix for type hints of `ExtensionDtype`. `register_extension_dtype` was confusing pyright autocomplete. It was fixed by parameterizing its return value on its input value.
Example:
```python
import pandas as pd
pd.CategoricalDtype(categories=['a', 'b'])
```
Before this change:
```console
$ pyright --version
pyright 1.1.133
$ pyright --lib pd.py
No configuration file found.
No pyproject.toml file found.
stubPath /home/omer/src/ext/typings is not a valid directory.
Assuming Python platform Linux
Searching for source files
Found 1 source file
/home/omer/src/ext/pd.py
/home/omer/src/ext/pd.py:3:1 - error: Expected no arguments to "ExtensionDtype" constructor (reportGeneralTypeIssues)
1 error, 0 warnings, 0 infos
Completed in 0.72sec
```
After:
```console
$ pyright --lib ../pd.py
No configuration file found.
pyproject.toml file found at /home/omer/src/ext/pandas.
Loading pyproject.toml file at /home/omer/src/ext/pandas/pyproject.toml
Pyproject file "/home/omer/src/ext/pandas/pyproject.toml" is missing "[tool.pyright] section.
stubPath /home/omer/src/ext/pandas/typings is not a valid directory.
Assuming Python platform Linux
Searching for source files
Found 1 source file
0 errors, 0 warnings, 0 infos
Completed in 0.777sec
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} | 3 | "2021-04-22T12:29:41Z" | "2021-05-25T09:36:23Z" | "2021-05-25T09:04:19Z" | CONTRIBUTOR | null | This is minor code alteration to the `_background_gradient` function that permits a text-only coloring version, i.e. `Styler.text_gradient`.
This could alternatively be performed with a `text_only=True` keyword addition to `Styler.background_gradient` existing method, but it seemed dissimilar enough to merit its own method (and avoid adding more kwargs).


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] | closed | false | null | [] | null | 8 | "2021-04-22T12:36:30Z" | "2021-04-25T13:42:48Z" | "2021-04-22T20:16:53Z" | MEMBER | null | I didn't see any tests failing locally, so checking here with full CI. | {
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] | open | false | null | [] | null | 0 | "2021-04-22T14:19:23Z" | "2021-08-20T02:29:22Z" | null | NONE | null | I am trying to use arithmetic operations inside a `where` clause to select rows from a table stored via the `pandas.HDFStore` class. My concrete example would be something like `(A % 3) == 0`. Unfortunately, this kind of conditions does not seem to be possible in the current implementation, although https://www.pytables.org/usersguide/condition_syntax.html seems to suggest otherwise.
#### Code Sample
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': np.arange(0, 10)})
with pd.HDFStore('test.h5') as s:
s.put("test", df, format='t', data_columns=['A'])
print(s.select("test", where=["(A % 3) == 0"])
```
#### Problem Description
The condition fails to parse. It seems that the `A % 3` is interpreted as a boolean expression which obviously fails. Especially when loading the whole table into memory is not an option, filtering via such arithmetic expressions should be possible. PyTables seems to support this already, but it doesn't work in pandas.
```
Traceback (most recent call last):
File "C:\Python38\lib\code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 1, in <module>
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 845, in select
return it.get_result()
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 1913, in get_result
results = self.func(self.start, self.stop, where)
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 829, in func
return s.read(start=_start, stop=_stop, where=_where, columns=columns)
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 4458, in read
result = self._read_axes(where=where, start=start, stop=stop)
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 3662, in _read_axes
values = selection.select()
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\pandas\io\pytables.py", line 5179, in select
return self.table.table.read_where(
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\tables\table.py", line 1542, in read_where
self._where(condition, condvars, start, stop, step)]
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\tables\table.py", line 1504, in _where
compiled = self._compile_condition(condition, condvars)
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\tables\table.py", line 1359, in _compile_condition
compiled = compile_condition(condition, typemap, indexedcols)
File "D:\Studies\14 Master Thesis\Project\.venv\lib\site-packages\tables\conditions.py", line 411, in compile_condition
raise TypeError("condition ``%s`` does not have a boolean type"
TypeError: condition ``(A % 3)`` does not have a boolean type
```
#### Expected Output
A dataframe containing only rows 0, 3, 6, 9 is printed
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.8.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
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 : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-04-22T14:34:14Z" | "2021-06-12T11:43:13Z" | "2021-06-12T09:43:18Z" | MEMBER | null | For a doc-only change (so inside the `doc/` directory, not for docstrings), it should be possible to skip most of the tests builds in CI, and only running the linters and web/docs build should be sufficient.
This gives 1) faster CI feedback for doc PRs, 2) less chance on unrelated (flaky) failures on such PRs, and 3) in general reduces the CI load.
For the github actions, there is a `on.paths` option to do this, and it actually supports the "exclude paths" feature that we need: https://docs.github.com/en/actions/reference/workflow-syntax-for-github-actions#example-ignoring-paths
For the Azure builds, it seems this might also be possible: https://docs.microsoft.com/en-us/azure/devops/pipelines/repos/azure-repos-git?view=azure-devops&tabs=yaml#paths
cc @afeld | {
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} | 1 | "2021-04-22T16:32:40Z" | "2021-05-06T04:33:06Z" | "2021-05-06T01:35:18Z" | MEMBER | null | - [x] closes #12863, closes #27709, closes #35450
- [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
Also allows removing some of the any/all specific boolean conversion logic in frame reduction code, which (hopefully) also fixes part of #41079. | {
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} | 1 | "2021-04-22T19:16:46Z" | "2021-04-26T14:48:43Z" | "2021-04-26T14:46:10Z" | MEMBER | null | - [ ] closes #xxxx
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I was looking into getting rid of the Block fallback (`apply_with_block`) for `fillna` in ArrayManager. For that, I started with putting the bits and pieces from the several BlockManager methods (the different `fillna` implementations, maybe_downcast) together in a function that would do the same for plain arrays as input, to see what this would give.
The draft result of that can be seen in this PR. This seems to pass all tests for the ArrayManager, so should have the same behaviour as BlockManager.fillna. I only hoped that we could then also use this in the BlockManager to have a shared implementation (as we did for eg `Block.astype` and others). However, that doesn't seem so easy in this case .. So not yet sure how to best proceed here. | {
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} | 2 | "2021-04-22T21:33:09Z" | "2021-10-08T04:18:20Z" | "2021-05-06T01:37:07Z" | MEMBER | null | - [x] closes #41069
- [x] tests added / passed
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According to https://docs.python.org/3/library/functions.html#locals, `locals()` should not be modified. AFAICT these are the only places `locals()` is used in this way without copying. | {
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} | 2 | "2021-04-22T22:34:17Z" | "2021-04-26T21:58:58Z" | "2021-04-26T12:57:27Z" | MEMBER | null | - [x] closes #41053
- [x] tests added / passed
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The problem is not numerical stable, so we are dividing -3e-18 / 0, which returns -inf running into line 177 setting to 0. Checking for nobs > 0 would solve these cases
cc @mroeschke | {
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- [x] tests added / passed
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} | 2 | "2021-04-22T23:20:36Z" | "2021-05-26T01:52:46Z" | "2021-05-26T01:52:46Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
copy this text, or data from excel/libreoffice with the same shape
```
col1 col2
1 red
blue
2 green
```
```python
In [3]: pandas.read_clipboard()
Out[3]:
col1 col2
0 1 red
1 blue None
2 2 green
```
#### Problem description
Using read_clipboard() on excel data when there are null values on the first few rows of the first columns causes pandas to use the default separator instead of the "\t" used for excel.
The result is the values shift into the wrong column
#### Expected Output
```python
In [4]: pandas.read_clipboard()
Out[4]:
col1 col2
0 1.0 red
1 NaN blue
2 2.0 green
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.11-zen1-1-zen
Version : #1 ZEN SMP PREEMPT Tue, 30 Mar 2021 14:10:21 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.2.1
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 : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 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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} | 4 | "2021-04-22T23:26:21Z" | "2021-05-26T01:52:51Z" | "2021-05-26T01:52:46Z" | CONTRIBUTOR | null | GH41108
Stripping whitespace on read_clipboard causes data copied from
excel to lose the tab separator between the first column and
the nexts, shifting the data into the wrong column
This just removes the .lstrip() except for the first line
containing headers, to avoid breaking header-less index in csv
files
- [x] closes #41108
- [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
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https://api.github.com/repos/pandas-dev/pandas/issues/41110 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41110/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41110/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41110/events | https://github.com/pandas-dev/pandas/pull/41110 | 865,653,211 | MDExOlB1bGxSZXF1ZXN0NjIxNzAxMjA5 | 41,110 | Revert "REF: Back DatetimeTZBlock with sometimes-2D DTA" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41111 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41111/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41111/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41111/events | https://github.com/pandas-dev/pandas/pull/41111 | 865,658,478 | MDExOlB1bGxSZXF1ZXN0NjIxNzA1ODM5 | 41,111 | BUG: DataFrameGroupBy agg with multi-column object block | {
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} | 3 | "2021-04-23T01:35:43Z" | "2021-04-30T03:02:05Z" | "2021-04-29T23:35:53Z" | MEMBER | null | This is analogous to what we did recently-ish with DataFrame._reduce. When we see an object block, split it and operate column-wise. Side-bonus: this makes the BM behavior match the AM behavior. | {
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] | open | false | null | [] | null | 5 | "2021-04-23T02:58:51Z" | "2021-08-20T02:32:15Z" | null | MEMBER | null | For methods that accept UDFs, I've been looking at a number of issues that would involve long-standing behavior changes, some I feel are quite significant:
- .agg should always reduce #35725
- Series.transform and DataFrame.transform should broadcast reduces (like groupby does) #18103
- Series.apply and DataFrame.apply use agg for list/dict arguments
- Series.agg and DataFrame.agg use apply if argument is a UDF
- UDF method arguments are inconsistent #40112
- Make groupby.apply dumb #39209
- groupby.apply includes grouping column(s) in computation unlike groupby.agg/groupby.transform
- Series.agg and DataFrame.agg don't allow partial failure when TypeError is raised #40539
- Ban mutation from UDF methods #12653
For some of these, it is difficult to emit a FutureWarning. For example, I'm not sure how we would emit a warning changing the behavior of agg for UDFs. I've been working toward consolidating the methods for apply/agg/transform et al in `pandas.core.apply`, resolving consistencies as they are encountered. This is very much a work in progress, and I expect to encounter more.
In order to make better progress here, I'd like to work on a new, experimental, implementation behind an option similar to ArrayManager. Progress can then be made without worrying about deprecation warnings and changing behaviors. When it's ready, we can then progress as:
- Add docs on the option and users can opt-in.
- Change the default of the option to no-default, emitting a FutureWarning when not set that the behavior of many methods will change and that users should use the new implementation.
- Change the default of the option to the new implementation, keeping the option to allow users to change back if necessary.
- Remove the option and old implementation. | {
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} | 6 | "2021-04-23T03:20:51Z" | "2021-05-26T19:12:30Z" | "2021-04-28T15:21:42Z" | MEMBER | null | This might not work now. Let's see.
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} | 2 | "2021-04-23T04:13:08Z" | "2021-04-26T18:16:07Z" | "2021-04-26T18:13:33Z" | MEMBER | null | This gets us most of the way towards having everything in the correct class.
The sticking point that makes this not-a-slam-dunk is that a [check](https://github.com/pandas-dev/pandas/compare/master...jbrockmendel:ref-gbop-wrapped?expand=1#diff-17420aa54e4b74826eaaeedc6ef6b320c16691aa74c09420e6098854284b4874L833-L835) is removed:
```
if self._filter_empty_groups and not counts.all():
assert result.ndim != 2
result = result[counts > 0]
```
This condition never holds in the tests, and the assertion would fail if it ever did. I don't have a compelling reason to remove this check other than it being inconvenient to move it to WrappedCythonOp. | {
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} | 1 | "2021-04-23T06:55:18Z" | "2021-04-23T10:54:59Z" | "2021-04-23T10:36:52Z" | MEMBER | null | This build is currently failing because it started to install numpy 1.20.1 yesterday, which gives an incompatibility with pyarrow. | {
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] | closed | false | null | [] | null | 2 | "2021-04-23T07:35:13Z" | "2021-05-03T18:34:34Z" | "2021-05-03T18:34:26Z" | NONE | null | https://github.com/pandas-dev/pandas/blob/v1.2.4/pandas/io/json/_normalize.py#L137
Hi,
I'm trying to use json_normalize to flatten a complex dictionary where there are nestes dict and nested list. While the record_path accepts a list of strings, the record_prefix only accepts a single string, this does not allow me to set a different prefix in corresponding to each element in record_path. Can we allow record_prefix to be a list?
Thanks
Hai | {
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] | open | false | null | [] | null | 3 | "2021-04-23T08:06:19Z" | "2021-05-15T14:59:45Z" | 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
*data.csv*
> key,value
> a,1.234
> b,"1,234.00"
> c,":"
Reading the data works ok without parsing the numbers
```python
>>> pd.read_csv('data.csv')
key value
0 a 1.234
1 b 1,234.00
2 c :
```
When parsing numbers it fails even when the correct thousand separator is set:
```python
>>> pd.read_csv('data.csv', dtype={'value': float}, thousands=',')
ValueError: could not convert string to float: '1,234.00'
```
However, with the right N/A value setting it works
```python
pd.read_csv('data.csv', dtype={'value': float}, thousands=',', na_values=':')
key value
0 a 1.234
1 b 1234.000
2 c NaN
```
#### Problem description
I think the error message saying that pandas could not convert value "1,234.00" to a number is uninformative as the error is rather in the missing value ":".
Might be related to #2570.
#### Expected Output
I’d expect the error message should be about the non-parsable value ":".
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.7.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
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 : 7.22.0
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 : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-23T09:36:07Z" | "2021-04-23T14:50:25Z" | "2021-04-23T14:50:21Z" | CONTRIBUTOR | null | - [x] xref #37643
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry | {
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https://api.github.com/repos/pandas-dev/pandas/issues/41119 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/41119/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/41119/comments | https://api.github.com/repos/pandas-dev/pandas/issues/41119/events | https://github.com/pandas-dev/pandas/issues/41119 | 865,941,944 | MDU6SXNzdWU4NjU5NDE5NDQ= | 41,119 | BUG: Unexpected behavior of groupby when a column has None values | {
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] | closed | false | null | [] | null | 4 | "2021-04-23T09:42:37Z" | "2021-04-26T06:14:56Z" | "2021-04-24T18:47:36Z" | 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
# Your code here
import pandas as pd
df = pd.DataFrame(data=[
{"a": 1, "b": None},
{"a": 2, "b": None},
])
df.groupby(["a", "b"]).size() # returns Series([], dtype: int64)
df.drop_duplicates() # returns the dataframe itself
```
#### Problem description
The 2 rows in my dataframe are different and grouping on the 2 columns should thus not return an empty `Series`.
Update: I observe the same behavior when I use np.nan` instead of `None`
#### Expected Output
```
a b
1 None 1
2 None 1
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.225-121.357.amzn1.x86_64
Version : #1 SMP Mon Mar 15 23:52:05 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.4
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
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 : 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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