Datasets:

DELith

/

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PR_kwDOAA0YD84tLEfV

Bug multiple css selectors GH44011

closed

CONTRIBUTOR

- [x ] closes #44011 - [x ] tests added / passed - [x ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x ] whatsnew entry

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I_kwDOAA0YD849Je14

ENH: Why neg rolling is too much hard?!

closed

NONE

#### Is your feature request related to a problem? No #### Describe the solution you'd like When we have negative shift why we cant implement negative rolling same as that? #### API breaking implications Running `df.rolling(-2)` whithout error #### Describe alternatives you've considered There is no any alternative before #### Additional context [Stackoverflow question ❓](https://stackoverflow.com/questions/52681679/checking-condition-in-negative-rolling-window-within-groupby-in-pandas) ```python def roller(x): return x.rolling(window=5, min_periods=1)['Value'].max().shift().gt(10).astype(int) df['Result'] = df.iloc[::-1].apply(roller).iloc[::-1].values print(df) ```

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I_kwDOAA0YD849Jhv2

BUG: DatetimeIndex.intersection with non-anchored freq

open

MEMBER

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd off = pd.offsets.CDay(1, False) ts = pd.Timestamp("2021-10-13 09") ts2 = ts + pd.Timedelta("1 hour") dti = pd.date_range(start=ts, periods=10, freq=off) dti2 = pd.date_range(start=ts2, periods=10, freq=off) assert set(dti).intersection(set(dti2)) == set() # yep! res = dti.intersection(dti2) assert len(res) == 0 # nope! ``` ### Issue Description _can_fast_intersect is wrong for the non-anchored case, returning `self.freq.n == 1` which is not sufficient in this case. It may be that we just need to return False for non-anchored. ### Expected Behavior The intersection should be empty ### Installed Versions master

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I_kwDOAA0YD849Jt7y

BUG: Using pd.to_datetime function in groupby.apply function causes key value error

closed

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd def test_apply(data): print(data) data['date'] = pd.to_datetime(data['date']) dt1 = pd.DataFrame({ 'key': ['aa', 'bb', 'cc', 'dd'], 'date': ['2020-01-01', '2020-01-01', '2020-02-01', '2020-02-01'], 'qty': [1.0, 1.0, 3.0, 3.0]}) dt1.groupby("key").apply(lambda x: test_apply(x)) ``` ### Issue Description 在“test_apply”中对输入参数data中类型为object的字段'date'使用pd.to_datetime()函数之后，在test_apply函数中循环打印lambda函数中的x，期望结果为每次打印key值不同，分别为“aa”,"bb","cc","dd",但实际结果为"aa","aa","aa","aa"。 After using the pd.to_datetime() function in the "test_apply" field "date" with the type of object in the input parameter data, print the x in the lambda function in the test_apply function in a loop. The expected result is that the key value is different each time it is printed, respectively "aa","bb","cc","dd", but the actual result is "aa","aa","aa","aa". key date qty 0 aa 2020-01-01 1.0 key date qty 1 aa 2020-01-01 1.0 key date qty 2 aa 2020-01-01 3.0 key date qty 3 aa 2020-01-01 3.0 ### Expected Behavior key date qty 0 aa 2020-01-01 1.0 key date qty 1 bb 2020-01-01 1.0 key date qty 2 cc 2020-01-01 3.0 key date qty 3 dd 2020-01-01 3.0 ### Installed Versions <details> python : 3.6.5.final.0 pandas : 1.1.5 </details>

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I_kwDOAA0YD849KYpw

Consider cibuildwheel for building wheels?

open

MEMBER

Disclaimer: I haven't been involved recently in maintaining our wheel building infrastructure, so I don't know how well it is working or not. But, I recently had a [good experience](https://github.com/pygeos/pygeos/blob/64af221b02a8fba2dbe28de5702b01afcb045306/.github/workflows/release.yml#L39) with cibuildwheel with a relatively complex package, so therefore thought to bring it up. Cibuildwheel (https://cibuildwheel.readthedocs.io/en/stable/) is a package helping to build wheels for all different platforms and architectures / python versions on your CI of choice (they support most of the common CI services). And if using GitHub Actions, you can build all wheels with a single CI service. Scikit-learn started to use it last year (https://github.com/scikit-learn/scikit-learn/pull/17921). You can see their github workflow as an example: https://github.com/scikit-learn/scikit-learn/blob/main/.github/workflows/wheels.yml (this single workflow config includes building all the wheels, building the sdist, and uploading them to the anaconda wheelhouse for nightly builds). (cc @ogrisel @thomasjpfan in case you have feedback on how the switch worked out) And it seems numpy also started considering this: https://github.com/numpy/numpy/pull/20102 Another example mentioned in the dev meeting: https://github.com/googleapis/python-crc32c/blob/b1a8d14bf0a1b4f1faa0f9fdd4307e15699daeff/.github/workflows/python-publish.yml#L245 Some potential advantages: - Easier to maintain (no issues with Travis credits, be able to do everything with a single CI, easier to support all architectures and new python versions, no need to synchronize with a different repo, ..) - Having it in the repo here also allows triggering the builds on a PR on demand, if a PR would influence wheel building (I wouldn't enable the workflow by default for every PR). cc @simonjayhawkins @lithomas1 since you are the ones mostly working on https://github.com/MacPython/pandas-wheels, it's of course mostly up to you to decide (and even if the end result might be better, every migration also has a cost).

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PR_kwDOAA0YD84tMNnc

Temporarily add back Index._get_attributes_dict for dask compat

closed

MEMBER

See https://github.com/pandas-dev/pandas/pull/43895#issuecomment-938472299. There was of course no need to revert the full PR, just to add this method again.

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PR_kwDOAA0YD84tMbFh

ENH/CI: Add Cache for Azure pipelines - posix

open

CONTRIBUTOR

- [x] closes ##42580 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Trying to leverage the use of cache on Azure pipelines - currently WIP will make some investigations

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I_kwDOAA0YD849LPli

BUG: .fillna({}) doesn't work

open

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd df = pd.DataFrame([1,2,np.nan, pd.NA], columns=['a']) # case 1 df.fillna({}) # case 2 df.fillna({np.nan: {}, pd.NA: {}}) ``` ### Issue Description .fillna() doesn't work as expected for case then `value = {}` as written in ```pandas.DataFrame.fillna```: > DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None) > > Fill NA/NaN values using the specified method. > > Parameters > > value scalar, dict, Series, or DataFrame > > Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. > ### Expected Behavior all values (`np.nan` and `pd.NA`) should be changed to `{}` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.6.final.0 python-bits : 64 OS : Linux OS-release : 5.10.47-linuxkit Version : #1 SMP Sat Jul 3 21:51:47 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : 0.29.24 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.27.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.08.1 fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.4.23 tables : 3.6.1 tabulate : None xarray : None xlrd : 1.2.0 xlwt : None numba : 0.54.0 </details>

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I_kwDOAA0YD849Mahn

BUG: apply swallows exceptions, shows inconsistent behaviour

open

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd # The problem case is 2.3 # --- Dataframe with 1 row --- # 1.1 instantiate dataframe with 1 row. df_full = pd.DataFrame([{"a": 1, "b": 2, "c": 3}], columns=["a", "b", "c"]) print(f"df_full =\n{df_full}\n") # 1.2 returns Series with 1 element ("Series([6], dtype: float64)""). df_full_apply_succeeded = df_full.apply(lambda row: row.sum(), axis=1) print( f"df_full_apply_succeeded =\n{df_full_apply_succeeded}\n" f"type = {type(df_full_apply_succeeded)}\n" ) # 1.3 raises exception correctly. try: df_full_apply_raises = df_full.apply(lambda row: row["not_in_df"], axis=1) except Exception as e: print(f"df_full_apply_raises raises with msg: {e}\n") # --- Dataframe with no rows --- # 2.1 instantiate "Empty DataFrame". df_empty = pd.DataFrame([], columns=["a", "b", "c"]) print(f"df_empty =\n{df_empty}\n") # 2.2 returns Series with 0 elements ("Series([], dtype: float64)"). # behaviour mirrors example 1.2, consistent for both dataframes. df_empty_apply_succeeded = df_empty.apply(lambda row: row.sum(), axis=1) print( f"df_empty_apply_succeeded =\n{df_empty_apply_succeeded}\n" f"type = {type(df_empty_apply_succeeded)}\n" ) # 2.3 swallows exception and returns "Empty DataFrame". # ----> behaviour DIFFERENT from example 1.3, not consistent! df_empty_apply_does_not_raise = df_empty.apply(lambda row: row["not_in_df"], axis=1) print( f"df_empty_apply_does_not_raise =\n{df_empty_apply_does_not_raise}\n" f"type = {type(df_empty_apply_does_not_raise)}\n" ) ``` ### Issue Description __Inconsistency in semantics__ When calling `apply` on a `DataFrame` with >1 elements and the `func` provided to `apply` raises, the exception is propagated to the caller, which is what one would expect. When calling `apply` on a `DataFrame` with 0 elements and the `func` provided to `apply` raises, the exception is swallowed and `apply` returns an original empty dataframe. __Inconsistency in return types__ As the code snippet demonstrates, `apply` returns `Series` in all the cases except when it fails on an empty dataframe and swallows the exception - then it returns an empty `DataFrame`. The downstream code eventually breaks but with an error which is hard to track. This is where exceptions are swallowed: https://github.com/pandas-dev/pandas/blob/22de58e63f9271d4ddb2bf49d008c5a9550c5cc4/pandas/core/apply.py#L779-L782 Because the code doesn't fail right away but returns an unexpected empty `DataFrame` identical to the original, debugging this issue has been hard and time consuming. ### Expected Behavior It is reasonable to expect that both empty and non-empty dataframes should behave in the same way when it comes to errors raised inside `func` functions provided to `apply` - namely to let them propagate to the user, as it's done in case of non-empty dataframes. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.9.0.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Tue Aug 24 20:28:00 PDT 2021; root:xnu-6153.141.40~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.0.1 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 : 7.20.0 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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PR_kwDOAA0YD84tOENn

[PERF] fixing memory leak in aggregation.pyx

closed

CONTRIBUTOR

- [x] closes #43339 - [x] tests passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry The old memory needs to be released, before the pointer switches to the new list.

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PR_kwDOAA0YD84tOGZ_

REGR: Barplot broken on Index(dtype='object')

open

NONE

- [ ] closes #38947 - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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PR_kwDOAA0YD84tOH9s

CLN: no need for suffices anymore in test_hashtable.py

closed

CONTRIBUTOR

- [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Since we have version with fused types, there is no need to call precise version (i.e. with suffices like int64, int32 and so on) any longer.

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PR_kwDOAA0YD84tOMlK

TST: adds test for .loc on multiindex for series GH43908

closed

CONTRIBUTOR

- [x] closes #43908 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them

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PR_kwDOAA0YD84tOSl0

Backport PR #43729: DEPS: Upgrade Deps for Python 3.10

closed

MEMBER

- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry **THIS IS A MANUAL BACKPORT OF LARGE BUILD CHANGES. PLEASE REVIEW CAREFULLY BEFORE MERGING**

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PR_kwDOAA0YD84tOWLj

DOC: Document and annotate Index.reindex (#40328).

closed

CONTRIBUTOR

- [x] closes #40328 - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them

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PR_kwDOAA0YD84tOqGI

TST: added groupby apply test for nan coerce

closed

CONTRIBUTOR

- [x] closes #24903 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] Bug was reported in #24903 that showed empty strings being coerced into Nan when certain apply was used with groupby. Although bug has been fixed, this test identifies the issue if it were to happen again.

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PR_kwDOAA0YD84tOxG4

REF: dispatch DTI/TDI setops to RangeIndex

closed

MEMBER

- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry Includes #44019 (but not that whatsnew) I'm optimistic we can eventually dispatch all the relevant cases and then can get rid of the special-cased _fast_union/_fast_intersect.

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PR_kwDOAA0YD84tOxoV

DOC: Fix Docstring for DataFrame nlargest `keep` option (#44040)

closed

CONTRIBUTOR

- [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] Add double backticks for DataFrame nlargest `keep` option to keep the style consistent From https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.nlargest.html  To 

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PR_kwDOAA0YD84tO2Ht

Fixed metadata propagation in Dataframe.apply (issue #28283)

closed

CONTRIBUTOR

Co-authored-by: Mohamad Rkein <mohamad_rkein@usp.br> Co-authored-by: Rafael Rodrigues <rrvsrafael@gmail.com> In reference to #28283 - [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry

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I_kwDOAA0YD849PMu1

ENH: Aggregate pass args and kwargs to function list

open

NONE

#### Describe the solution you'd like When aggregating dataframe with different functions, it is currently not possible to pass kwargs to the functions. My use case is a dataframe I want to aggregate by values, and apply either "count" or "sum" on it: Ex than **works** (1 function): ```py df.agg( less_than_1j, aggregation_func="count" ) ``` Ex than **does not work** (2+ functions): ```py df.agg( [ less_than_1j, between_1j_2j, ], aggregation_func="count" ) ``` with `less_than_1j` and `between_1j_2j` signature being something like: `def less_than_1j(x, aggregation_func="sum")` #### Describe alternatives you've considered The issue is in [core/apply.py#124](https://github.com/pandas-dev/pandas/blob/73c68257545b5f8530b7044f56647bd2db92e2ba/pandas/core/apply.py#L124) ```py # curry if needed if ( (kwargs or args) and not isinstance(func, (np.ufunc, str)) and not is_list_like(func) ): def f(x): return func(x, *args, **kwargs) else: f = func ``` Fix proposed: add this check: ```py elif (kwargs or args) and is_list_like(func): def f(x): return [fun(x, *args, **kwargs) for fun in func] ```

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PR_kwDOAA0YD84tQmdv

Fix several typos under pandas/_libs

closed

CONTRIBUTOR

- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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I_kwDOAA0YD849QtXL

BUG: Error with the upgrade. pd.DataFrame(dict) not working. Exception: Data must be 1-dimensional

closed

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd dicto = {'Hello':['1','2'],'Hey':['4','5']} df = pd.DataFrame(dicto) ``` ### Issue Description Exception: Data must be 1-dimensional. Downgrading to 0.25.3 works ### Expected Behavior Return Dataframe of the dic ### Installed Versions <details> pandas : 1.3.3 </details>

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I_kwDOAA0YD849Q1ce

ENH:Possible expansion of codespell to more file types

open

CONTRIBUTOR

At the moment, [codespell](https://github.com/pandas-dev/pandas/blob/c30afb23dfa948e3b34b4d71abb16ce455012428/.pre-commit-config.yaml#L15-L20) is enabled only for the following file types: `python, rst, markdown` As a consequence, typos in various other file types can easily slip through (see #44043 as an example). Could the expansion of codespell to more file types be beneficial?

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PR_kwDOAA0YD84tRk8E

added test to indexing on groupby, #32464

open

CONTRIBUTOR

- [x] closes #32464 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them This makes sure an index of a groupby with more then one column is always a MultiIndex.

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PR_kwDOAA0YD84tRmHQ

Removed a manual file handler pitfall

closed

NONE

It is a simple PR. It only removes a manual file handler pitfall from versioneer.py As stated by [Pylint documentation](https://pylint.pycqa.org/en/latest/technical_reference/features.html) " consider-using-with (R1732) Consider using 'with' for resource-allocating operations Emitted if a resource-allocating assignment or call may be replaced by a 'with' block. By using 'with' the release of the allocated resources is ensured even in the case of an exception. "

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I_kwDOAA0YD849RVl4

BUG: Index.fillna ignores 'downcast'

open

MEMBER

### Issue Description ``` def fillna(self, value=None, downcast=None): value = self._require_scalar(value) if self.hasnans: result = self.putmask(self._isnan, value) if downcast is None: return Index._with_infer(result, name=self.name) return self._view() ``` In the case with `downcast` not None and `self.hasnans`, we fall through to return `self._view()` and ignore `result`. We don't have any such cases in the test suite. ### Expected Behavior Not ignoring `result` or `downcast`. I'd be OK with deprecating the keyword; i expect it is just there to match NDFrame.

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I_kwDOAA0YD849Retm

ENH:

closed

NONE

#### Is your feature request related to a problem? When using jupyter-book with the pdflatex i can create beautiful tables from data frames, but when using a styler object this does not work at all. I suspect this is due to differences in how to use the _repr_latex_ methods for styler and for dataframe objects. #### Describe the solution you'd like I would like to be able to use styler object like dataframe objects to show up in the pdflatex generated pdf. I was able to achieve that by using this function.: ````python styler = fancy_styled_pandas_dataframe def latex_save_styler(styler): def a(*args, **kwargs): return styler.data._repr_latex_(*args, **kwargs) styler._repr_latex_ = a return styler latex_save_styler(styler) ```` see also this discussion.: https://github.com/executablebooks/jupyter-book/issues/1512 #### API breaking implications I do believe the offending property are the mandatory keyword arguments for the _repr_latex methof of the styler object. parse_index, sparse_columns #### Describe alternatives you've considered I am unsure if this is a problem of pandas, or expected behaviour. (and the way pandas is called from jupyter-book should be changed) however, the given code solved my problem at hand, it may or may not be usefull for other people or in understanding/fixing the issue. #### Additional context I am using pandas ==1.3.3 and jupyter-book==0.11.3

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I_kwDOAA0YD849RuNg

BUG: Hexbin plots does not display x label and xtick labels in pandas 1.3.3

open

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np df = pd.DataFrame(np.random.rand(1000, 2), columns=['a', 'b']) df.plot.hexbin(x='a', y='b'); #thanks @rabernat ``` ### Issue Description Hexbin plots does not display x label and xtick labels. This has been a bug in the past, see closed issue #10678 , it must have been fixed with #20446 and must have been introduced again later. ### Expected Behavior Expect to see 'a' as the x label and numbers under the x axis ticks. This is only achievable with workaround: ```python import pandas as pd import numpy as np df = pd.DataFrame(np.random.rand(1000, 2), columns=['a', 'b']) df.plot.hexbin(x='a', y='b',sharex=False); ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.7.10.final.0 python-bits : 64 OS : Linux OS-release : 4.19.0-18-cloud-amd64 Version : #1 SMP Debian 4.19.208-1 (2021-09-29) machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.19.5 pytz : 2021.3 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.2.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.2 IPython : 7.28.0 pandas_datareader: None bs4 : None bottleneck : 1.3.2 fsspec : 2021.10.0 fastparquet : None gcsfs : 2021.10.0 matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.4.25 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.54.1 </details>

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I_kwDOAA0YD849R1K5

API: Index.__getitem__(True) returns 2D ndarray

open

MEMBER

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python Not clear if this is intentional: import numpy as np import pandas as pd arr = np.random.randn(4) idx = pd.Index(arr) idx[True] >>> idx[True] array([[ 1.39365849, -0.03655461, 0.17112114, -1.4612337 ]]) ``` ### Issue Description For other cases where indexing on index._data returns 2D (e.g. `idx[:, None]`) we issue a FutureWarning, but this goes through a different code path. ### Expected Behavior I don't have an opinion here, just think whatever we do should be intentional ### Installed Versions <details> Replace this line with the output of pd.show_versions() </details>

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I_kwDOAA0YD849R1Mq

BUG: Creating a MultiIndex using pd.Index and the name argument is now broken (worked in 1.2.5 with names)

open

CONTRIBUTOR

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python In [1]: import pandas as pd In [2]: pd.__version__ Out[2]: '1.4.0.dev0+905.gdace93d694' In [3]: pd.Index([(1,0), (1,1)], names=["foo", "bar"]) <ipython-input-3-99fe0b033485>:1: FutureWarning: Passing keywords other than 'data', 'dtype', 'copy', 'name', 'tupleize_cols' is deprecated and will raise TypeError in a future version. Use the specific Index subclass directly instead. pd.Index([(1,0), (1,1)], names=["foo", "bar"]) Out[3]: MultiIndex([(1, 0), (1, 1)], names=['foo', 'bar']) In [4]: pd.Index([(1,0), (1,1)], name=["foo", "bar"]) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-4-138c3fe61553> in <module> ----> 1 pd.Index([(1,0), (1,1)], name=["foo", "bar"]) c:\Code\pandas_dev\pandas\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs) 403 from pandas.core.indexes.range import RangeIndex 404 --> 405 name = maybe_extract_name(name, data, cls) 406 407 if dtype is not None: c:\Code\pandas_dev\pandas\pandas\core\indexes\base.py in maybe_extract_name(name, obj, cls) 6876 # GH#29069 6877 if not is_hashable(name): -> 6878 raise TypeError(f"{cls.__name__}.name must be a hashable type") 6879 6880 return name TypeError: Index.name must be a hashable type ``` ### Issue Description In pandas 1.2.5, you could create a `MultiIndex` using `pd.Index` with the `names` argument: ```python In [1]: import pandas as pd In [2]: pd.__version__ Out[2]: '1.2.5' In [3]: pd.Index([(1,0), (1,1)], names=["foo", "bar"]) Out[3]: MultiIndex([(1, 0), (1, 1)], names=['foo', 'bar']) ``` With `names` being removed as an argument in the future, the user then has to be explicit about using the `pd.MultiIndex()` constructor. See discussion with @jreback starting here: https://github.com/pandas-dev/pandas/pull/35292#discussion_r730277198 ### Expected Behavior Unclear. Should we accept `names` in `pd.Index()` ? Because you can create a `MultiIndex` without names via ```python In [5]: pd.Index([(1,0), (1,1)]) Out[5]: MultiIndex([(1, 0), (1, 1)], ) ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : dace93d694757069c69983d5ce5a2df3f4e1df3a python : 3.8.6.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 1.4.0.dev0+905.gdace93d694 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 49.6.0.post20210108 Cython : 0.29.24 pytest : 6.2.5 hypothesis : 6.19.0 sphinx : 4.1.2 blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.27.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.05.0 fastparquet : 0.7.1 gcsfs : 2021.05.0 matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 4.0.0 pyxlsb : 1.0.6 s3fs : 2021.05.0 scipy : 1.7.1 sqlalchemy : 1.4.23 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>

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I_kwDOAA0YD849R1QB

BUG: to_datetime with xarray DataArray and specifie unit errors

closed

MEMBER

When passing the values as a `xarray.DataArray` and specifying a unit, we get an error: ```python In [1]: import xarray as xr In [2]: arr = xr.DataArray([1, 2, 3]) In [3]: pd.to_datetime(arr, unit='ns') --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-3-547fdd08888f> in <module> ----> 1 pd.to_datetime(arr, unit='ns') ~/miniconda3/envs/DS-geospatial/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache) 910 result = _convert_and_box_cache(arg, cache_array) 911 else: --> 912 result = convert_listlike(arg, format) 913 else: 914 result = convert_listlike(np.array([arg]), format)[0] ~/miniconda3/envs/DS-geospatial/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact) 354 if format is not None: 355 raise ValueError("cannot specify both format and unit") --> 356 return _to_datetime_with_unit(arg, unit, name, tz, errors) 357 elif getattr(arg, "ndim", 1) > 1: 358 raise TypeError( ~/miniconda3/envs/DS-geospatial/lib/python3.8/site-packages/pandas/core/tools/datetimes.py in _to_datetime_with_unit(arg, unit, name, tz, errors) 526 tz_parsed = None 527 else: --> 528 arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors) 529 530 if errors == "ignore": TypeError: Argument 'values' has incorrect type (expected numpy.ndarray, got DataArray) ``` Since this is an array-like, I expect that we coerce that to a numpy array before passing to our cython routines (eg passing a list or Series does work this way). And actually when not specifying the unit, it does work: ``` In [4]: pd.to_datetime(arr) Out[4]: DatetimeIndex(['1970-01-01 00:00:00.000000001', '1970-01-01 00:00:00.000000002', '1970-01-01 00:00:00.000000003'], dtype='datetime64[ns]', freq=None) ``` ### Installed Versions <details> ``` In [5]: pd.show_versions() INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-generic Version : #99-Ubuntu SMP Thu Sep 23 17:29:00 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.28.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 2021.09.0 fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : 2021.09.0 scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : 0.19.0 xlrd : None xlwt : None numba : 0.53.1 ``` </details>

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I_kwDOAA0YD849R2Vo

ENH: also check type of right DataFrame to determine (sub)class of result DataFrame

open

MEMBER

Currently, we use the type of the left object to construct the result of `pd.merge`: https://github.com/pandas-dev/pandas/blob/445bb9f57c3d59141a059a5afd2762aef2e96cad/pandas/core/reshape/merge.py#L731-L732 For GeoPandas users, this means that the order of the arguments passed matters: `pd.merge(df, gdf, ..)` (or `df.merge(gdf, ..)`) returns a DataFrame while `pd.merge(gdf, df, ...)` (or `gdf.merge(df, ..)`) returns a GeoDataFrame. This can be surprising for users. I think it should be rather easy / safe to also check the type of the right object and use that class (if `left` is a non-subclassed pandas.DataFrame, otherwise `left` still has "precedence") #### API breaking implications This can result in a different class type as return value. But for subclasses that are mostly compatible with pandas.DataFrame (and only add functionality), this should not have a big impact.

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PR_kwDOAA0YD84tSA8_

Backport PR #44032 on branch 1.3.x ([PERF] fixing memory leak in aggregation.pyx)

closed

NONE

Backport PR #44032: [PERF] fixing memory leak in aggregation.pyx

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I_kwDOAA0YD849R7XV

BUG: com.maybe_make_list does not resolve correctly for np.array

open

CONTRIBUTOR

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python from pandas.core.common import maybe_make_list import numpy as np arr = np.array(["A", "C"]) result = maybe_make_list(arr) print(result) [array(['A', 'C'], dtype='<U1')] ``` ### Issue Description This discussion started in [this](https://github.com/pandas-dev/pandas/pull/41022/files#r730277602) thread. Should `com.maybe_make_list` take `np.array` into account and maybe some other list like objects as well. For example Index objects. ### Expected Behavior ```python from pandas.core.common import maybe_make_list import numpy as np arr = np.array(["A", "C"]) result = maybe_make_list(arr) print(result) array(['A', 'C'], dtype='<U1') ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 445bb9f57c3d59141a059a5afd2762aef2e96cad python : 3.8.12.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.4.0.dev0+904.g445bb9f57c.dirty numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : 0.29.24 pytest : 6.2.5 hypothesis : 6.23.2 sphinx : 4.2.0 blosc : 1.10.6 feather : None xlsxwriter : 3.0.1 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.2 IPython : 7.28.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : 1.3.2 fsspec : 2021.10.0 fastparquet : 0.7.1 gcsfs : 2021.10.0 matplotlib : 3.4.3 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.9 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : 2021.10.0 scipy : 1.7.1 sqlalchemy : 1.4.25 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : 1.3.0 numba : None </details>

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PR_kwDOAA0YD84tSBqw

DOC: 1.3.4 release date

closed

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PR_kwDOAA0YD84tSB9j

DOC: Start v1.3.5 release notes

closed

MEMBER

this fails until 1.3.4 tag exists.

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PR_kwDOAA0YD84tSCOQ

REF: share ExtensionIndex astype, __getitem__ with Index

closed

MEMBER

- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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PR_kwDOAA0YD84tSFRc

Backport PR #44057 on branch 1.3.x (DOC: 1.3.4 release date)

closed

NONE

Backport PR #44057: DOC: 1.3.4 release date

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PR_kwDOAA0YD84tSGek

Backport PR #43291: BUG: Fixes to FixedForwardWindowIndexer and GroupbyIndexer (#43267)

closed

MEMBER

Backport PR #43291

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PR_kwDOAA0YD84tSJiq

BUG: 43909 - check monoticity of rolling groupby

closed

NONE

- [y ] closes #43909 - [y ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ test and bugfix ] whatsnew entry

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PR_kwDOAA0YD84tSJ9N

CLN: collected cleanups

closed

MEMBER

- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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PR_kwDOAA0YD84tSOFK

DEPS: bump pyarrow min to 1.0 #41329

closed

MEMBER

- [x] closes #41329 - [ ] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry

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PR_kwDOAA0YD84tSPrS

BUG: sort_index did not respect ignore_index when not sorting

closed

CONTRIBUTOR

- [X] closes #43591 - [X] tests added / passed - [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [X] whatsnew entry

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PR_kwDOAA0YD84tSdh5

Backport PR #43199 on branch 1.3.x (BUG: convert_dtypes incorrectly converts byte strings to strings in 1.3+)

closed

NONE

Backport PR #43199: BUG: convert_dtypes incorrectly converts byte strings to strings in 1.3+

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I_kwDOAA0YD849S2Uv

BUG: CI cannot create benchmark environments with Cython < 0.29.24

closed

CONTRIBUTOR

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python asv continuous -f 1.1 -E virtualenv upstream/master HEAD -b style.Render RuntimeError: Cannot cythonize with old Cython version (0.29.21 installed, needs 0.29.24) ``` ### Issue Description Needed to update `asv.conf.json` to get this to work. ### Expected Behavior probably need to update dependency verisons. ### Installed Versions master at 17 Oct.

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PR_kwDOAA0YD84tSqPS

BUG: 43909 - check monoticity of rolling groupby

open

NONE

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I_kwDOAA0YD849S7wA

BUILD: upgrade asv to cython 0.29.24

closed

MEMBER

### - [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas). ### Platform macOS-10.16-x86_64-i386-64bit ### Installation Method conda install ### pandas Version 1.3.3 (upstream/master) ### Python Version 3.8.12 ### Installation Logs <details> asv continuous -f 1.1 upstream/master HEAD · Creating environments....... · Discovering benchmarks. ·· Uninstalling from conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt.. ·· Building 3eeef2ab <asv-upgrade-cython> for conda-py3.8-Cython0.29.21-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pyarrow-pytables-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt. ·· Error running /Users/xanderhlim/Documents/dev/pandas-alexhlim/asv_bench/env/46dec818773cf9720d7354158b888d0d/bin/python setup.py build -j4 (exit status 1) STDOUT --------> STDERR --------> Traceback (most recent call last): File "setup.py", line 650, in <module> ext_modules=maybe_cythonize(extensions, compiler_directives=directives), File "setup.py", line 410, in maybe_cythonize raise RuntimeError( RuntimeError: Cannot cythonize with old Cython version (0.29.21 installed, needs 0.29.24) ·· Failed to build the project and import the benchmark suite. </details>

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PR_kwDOAA0YD84tSrfP

REF: share Index subclass formatting code

closed

MEMBER

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I_kwDOAA0YD849S9GV

CLN: Change implementation of maybe_make_list

closed

MEMBER

Ref: https://github.com/pandas-dev/pandas/pull/41022#discussion_r616658973 Currently, the implementation is https://github.com/pandas-dev/pandas/blob/40274ac92b62105ad86d61a8d1d7aa499d40ab41/pandas/core/common.py#L278-L281 This means that any list-like that is not a tuple nor list will be packed as a single element list, which is likely undesirable. For example, `pd.crosstab` works with ndarrays/Series but not lists/tuples for this reason (issue to come). I propose to change this method to: ``` def maybe_make_list_like(obj): if obj is None or is_list_like(obj): return obj else: return [obj] ``` I plan update this issue, listing out each part of the code that calls this function and what actions to take.

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PR_kwDOAA0YD84tSuVY

Backport PR #44058 on branch 1.3.x (DOC: Start v1.3.5 release notes)

closed

NONE

Backport PR #44058: DOC: Start v1.3.5 release notes

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PR_kwDOAA0YD84tSyWT

BUG: to_datetime with xarray DataArray and specifie unit errors

closed

CONTRIBUTOR

- [x] closes #44053 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry Converted values of xarray.DataArray to numpy.ndarray to solve the problem.

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PR_kwDOAA0YD84tS9Kh

BUG: to_datetime with xarray DataArray and specifie unit errors

closed

CONTRIBUTOR

- [x] closes #44053 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] coerce to numpy array before calling ```tslib.array_with_unit_to_datetime```function Let me know if any change need to be done. Let me know if you need to make any changes. I love the package hope I can start helping more :)

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I_kwDOAA0YD849TtXe

BUG: pd.cut() sometimes puts `NaNs` into bins.

open

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np bins = pd.IntervalIndex.from_breaks( range(0, 102, 1), closed='left', dtype='interval[int64]' ) print( pd.DataFrame(dict(x=[1.2, np.nan, 10.2])) .pipe(lambda _: _.assign( bin=pd.cut(_.x, bins) )) ) # x bin # 0 1.2 [1, 2) # 1 NaN [50, 51) # 2 10.2 [10, 11) ``` ### Issue Description With certain bin setups, pd.cut puts `NaN`s into the central bin instead of setting as NaN. It seems to only be certain bin setups which is weird: ```py bins = pd.IntervalIndex.from_breaks( range(0, 102, 1), # as per repro - breaks # range(0, 100, 1), # works # range(0, 101, 1), # works # range(0, 202, 1), # breaks closed='left', dtype='interval[int64]' ) ``` It also seems to go away if I use `pd.NA` instead of `np.nan` or `math.nan`. Seems different to https://github.com/pandas-dev/pandas/issues/31586 as searchsorted seems to work correctly (tested with ```py def mybin(val: pd.Series, bins: pd.IntervalIndex): bins_np = bins.values.left return bins_np.searchsorted(val.to_numpy()) ... .assign(mybin=mybin(_.x, bins) ``` ) ### Expected Behavior Would expect the output of repro to be something like ```py # x bin # 0 1.2 [1, 2) # 1 NaN pd.NA # 2 10.2 [10, 11) ``` ### Installed Versions <details> ``` INSTALLED VERSIONS ------------------ commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5 python : 3.9.5.final.0 python-bits : 64 OS : Linux OS-release : 5.11.0-37-generic Version : #41-Ubuntu SMP Mon Sep 20 16:39:20 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_AU.UTF-8 LOCALE : en_AU.UTF-8 pandas : 1.3.4 numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.0.1 setuptools : 56.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.2 IPython : 7.28.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.3 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None ``` </details>

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I_kwDOAA0YD849Tzg-

BUG: crosstab fails with lists/tuples

closed

MEMBER

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python df = pd.DataFrame({'a': [1, 1, 2], 'b': [3, 4, 5]}) print(pd.crosstab(df["a"].to_list(), df["b"].to_list())) ``` ### Issue Description The example raises a ValueError, whereas using `to_numpy` instead of `to_list` successfully produces the correct output. This is due to the use of `maybe_make_list` treating lists/tuples differently than other list-likes, and is related to #44056. ### Expected Behavior ``` col_0 3 4 5 row_0 1 1 1 0 2 0 0 1 ``` ### Installed Versions <details> Replace this line with the output of pd.show_versions() </details>

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I_kwDOAA0YD849T7Oc

DOC: Merge/join treats lists/tuples differently than other list-likes

open

MEMBER

### - [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/) ### Location of the documentation https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html?highlight=merge#pandas.DataFrame.merge ### Documentation problem The code for merge, and hence join (and other methods?) uses maybe_make_list which treats lists/tuples differently than other list-likes. https://github.com/pandas-dev/pandas/blob/c4cce9b75b34179edf000314edf708768486fcbb/pandas/core/reshape/merge.py#L638-L640 The current documentation only mentions a list of strings (join) or a list (merge). The special treatment of lists/tuples should be made more explicit. ### Suggested fix for documentation merge, left_on argument (right_on is similar): > label, list/tuple of labels, or array-like, optional > > A single label or a list or tuple of labels will be treated as column or index level name(s) to join on in the left DataFrame. Any other array-like or list of array-likes of the length of the left DataFrame are treated as if they are columns. join, on argument: > label, list/tuple of labels, or array-like, optional > > A single label or a list or tuple of labels will be treated as column or index level name(s) in the caller to join on the index in other. If multiple values given, the other DataFrame must have a MultiIndex. Passing array-likes other than lists and tuples will be used as the join key if it is not already contained in the calling DataFrame. When not specified, joins index-on-index. Like an Excel VLOOKUP operation.

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I_kwDOAA0YD849UFKu

BUG: groupby.rolling.corr incorrect/fails when grouping columns contain tuples

open

MEMBER

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python df = pd.DataFrame({'a': [(1,), (1,), (1,)], 'b': [4, 5, 6]}) gb = df.groupby(['a']) print(gb.rolling(2).corr(other=df)) df = pd.DataFrame({'a': [(1,2,), (1,2,), (1,2,)], 'b': [4, 5, 6]}) gb = df.groupby(['a']) print(gb.rolling(2).corr(other=df)) ``` ### Issue Description The first block produces ``` a b a 1 0 NaN NaN 1 NaN 1.0 2 NaN 1.0 ``` where the first index level should be `(1, )` instead. The second block raises a ValueError. It is due to usage of maybe_make_list in _apply_pairwise (related: #44056). Instead of using this function, we can pack the index keys in lists when `self._grouper.nkeys` is 1 and otherwise leave them as-is. I.e. ``` if self._grouper.nkeys == 1: gb_pairs = ( [pair] for pair in self._grouper.indices.keys() ) else: gb_pairs = ( pair for pair in self._grouper.indices.keys() ) ``` ### Expected Behavior ``` a b a (1, ) 0 NaN NaN 1 NaN 1.0 2 NaN 1.0 a b a (1, 2) 0 NaN NaN 1 NaN 1.0 2 NaN 1.0 ``` ### Installed Versions <details> Replace this line with the output of pd.show_versions() </details>

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I_kwDOAA0YD849UKjf

BUG: AttributeError: 'BooleanArray' object has no attribute 'sum' while infer types

closed

CONTRIBUTOR

Line 694 in the pandas/io/parsers/base_parser.py file, ``` if issubclass(values.dtype.type, (np.number, np.bool_)): # error: Argument 2 to "isin" has incompatible type "List[Any]"; expected # "Union[Union[ExtensionArray, ndarray], Index, Series]" mask = algorithms.isin(values, list(na_values)) # type: ignore[arg-type] na_count =mask.sum() ^^^^^^^^^^^^^THIS if na_count > 0: if is_integer_dtype(values): values = values.astype(np.float64) np.putmask(values, mask, np.nan) return values, na_count ``` Caused an "AttributeError: 'BooleanArray' object has no attribute 'sum'" following some global ananconda update. Changed the line to ` na_count =mask.astype('uint8').sum()` fixed the error in my case. Edit by rhshadrach: Minimal example ``` from io import StringIO import pandas as pd df = pd.read_csv( StringIO("0,1"), names=['a', 'b'], index_col=['a'], dtype={'a': 'UInt8'}, ) ```

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I_kwDOAA0YD849VNe4

RLS: 1.3.5

open

MEMBER

Tracking issue for the 1.3.5 release. Currently scheduled for November 28, 2021. (This is a couple of weeks before 1.4.0rc0) The patch releases are on a calendar schedule of 3-3-4-5-6 weeks, however if 1.3.5 is the last in the 1.3.x series, we would delay to resolve any outstanding issues if necessary. But ideally get it done before the 1.4 release candidate) https://github.com/pandas-dev/pandas/milestone/91 List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression cc @pandas-dev/pandas-core @pandas-dev/pandas-triage

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PR_kwDOAA0YD84tU1Ze

BUG: Change of behavior in casting of datetime-like types in MultiIndex

closed

CONTRIBUTOR

- [x] closes #43091 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry Added try-except block in multi.py to reset the behaviour back to 1.2.5

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I_kwDOAA0YD849WNWl

ENH: cast unrecognised value as na in read_csv

closed

NONE

#### Is your feature request related to a problem? I have a csv file which can contain some mission part due to IO problem. Say if I have a csv file ``` 0 -1 -10 1 -2 -20 2 -3 -30 ``` However, due to the buffer problem, it can become ``` 0 -1 -10 1 -2 -20 2 -3 - ``` If I use pandas.read_csv to read it, I will get: ``` File "/Users/XXX/opt/miniconda3/envs/mda_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 610, in read_csv return _read(filepath_or_buffer, kwds) File "/Users/XXX/opt/miniconda3/envs/mda_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 468, in _read return parser.read(nrows) File "/Users/XXX/opt/miniconda3/envs/mda_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 1057, in read index, columns, col_dict = self._engine.read(nrows) File "/Users/XXX/opt/miniconda3/envs/mda_dev/lib/python3.7/site-packages/pandas/io/parsers.py", line 2061, in read data = self._reader.read(nrows) File "pandas/_libs/parsers.pyx", line 756, in pandas._libs.parsers.TextReader.read File "pandas/_libs/parsers.pyx", line 771, in pandas._libs.parsers.TextReader._read_low_memory File "pandas/_libs/parsers.pyx", line 850, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 982, in pandas._libs.parsers.TextReader._convert_column_data File "pandas/_libs/parsers.pyx", line 1056, in pandas._libs.parsers.TextReader._convert_tokens ValueError: could not convert string to float: '-' ``` #### Describe the solution you'd like I wonder if it is possible to cast any unrecognised value as NA rather than raise a Value error #### API breaking implications N/A #### Describe alternatives you've considered perhaps just skip this line?

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PR_kwDOAA0YD84tVehT

DOC: Fix cheatsheet automatic uploading

closed

MEMBER

Follow up of #44018. I didn't realize before merging that we were first uploading the web directory, and then copying the cheatsheets to it, so they won't be uploaded. The cheatsheets will be removed from the website in the CI of hte previous PR, so would be good to merge this asap, so the cheatsheets are restored.

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I_kwDOAA0YD849WiDu

BUG: get_indexer_for() does not always return list of indeces

closed

CONTRIBUTOR

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np interval_index = pd.IntervalIndex.from_tuples([(1.0, np.inf), (-np.inf, 0.0), (0.0, 1.)], name='interval') foo_index = pd.Index([1, 2, 3], name='foo') multi_index = pd.MultiIndex.from_product([foo_index, interval_index]) print(multi_index.get_level_values('foo').get_indexer_for([2])) print(multi_index.get_level_values('interval').get_indexer_for([pd.Interval(-np.inf, 0.0)])) multi_index = pd.MultiIndex.from_product([interval_index, foo_index]) print(multi_index.get_level_values('foo').get_indexer_for([2])) print(multi_index.get_level_values('interval').get_indexer_for([pd.Interval(-np.inf, 0.0)])) ``` ### Issue Description `get_indexer_for()` does not return a list of indeces as described in the docs but a mask like list under some circumstances. As far as I have investigated, the behavior occurs if all of the following conditions are met: * the index is a `MultiIndex` * the level in question is a `IntervalIndex` * the level in question is not the first level of the `MultiIndex` The script above outputs ``` [3 4 5] [0 1 0 0 1 0 0 1 0] [1 4 7] [3 4 5] ``` ### Expected Behavior Expected output ``` [3 4 5] [1 4 7] [1 4 7] [3 4 5] ``` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 3a6d4cd01d076762ad60c5fd6551012cb72a808d python : 3.9.7.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-lowlatency Version : #99-Ubuntu SMP PREEMPT Thu Sep 23 18:30:52 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : de_DE.UTF-8 LOCALE : de_DE.UTF-8 pandas : 1.4.0.dev0+933.g3a6d4cd01d numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : None pytest : 6.2.5 hypothesis : 6.23.3 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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PR_kwDOAA0YD84tVmCH

BUG: RangeIndex.difference with sort=None and step<0

closed

MEMBER

- [ ] closes #xxxx - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry

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PR_kwDOAA0YD84tVr3A

ENH: preserve RangeIndex in insert, delete

closed

MEMBER

- [ ] closes #xxxx - [x] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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I_kwDOAA0YD849W5wS

Cheatsheets not being served via nginx

closed

MEMBER

We started synchronizing the cheat sheets from this repo, to the pandas.pydata.org server. The cheatsheets have been correctly placed into our website directory, but https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf is still serving the old version. I restarted nginx, to make sure it's not a caching problem, but still the same. I deleted the file, and still being served. I checked the nginx logs, and all requests are being logged, except to the cheat sheet files. I checked the nginx configuration, to make sure there is nothing specific to that file, and it's not. I uploaded a different pdf to the same directory, and worked correctly. I stopped nginx for couple of seconds, and the website was down, but the cheat sheet was still being served. The only thing I guess it may be causing this, is a cache in a service like cloudflare or similar. Do you know if we're using that @TomAugspurger @aterrel ? And do you know if the cache will be purged anytime soon? Happy to wait until the cache is automatically purged if it'll happen, but would be good to understand if that's the case.

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PR_kwDOAA0YD84tV5w1

BUG: crosstab fails with lists/tuples

closed

MEMBER

- [x] closes #44076 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [x] whatsnew entry

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I_kwDOAA0YD849XDRN

BUILD: Building wheels failing with one `requirements.txt` but passes with another

closed

NONE

### - [X] I have read the [installation guide](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-pandas). ### Platform Linux-3.10.0-1160.45.1.el7.x86_64-x86_64-with-glibc2.28; Linux-4.18.0-305.7.1.el8_4.x86_64-x86_64-with-redhat-8.4-Ootpa ### Installation Method pip install ### pandas Version 1.3.5 ### Python Version 3.9.2 ### Installation Logs I am trying to `pip install` (`-U -r requirements.txt`) `pandas` in the following set of requirements: ``` pandas numpy scipy python-dateutil wheel scikit-learn scikit-optimize tqdm networkx joblib twine mlflow requests Cython tables luigi boto3 schedule hyperopt statsmodels ``` It fails on `Building wheel for pandas (setup.py): started` with the following error (more details in the attached log): ``` ERROR: Command errored out with exit status 1: /home/venvs/model_env_py39/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-t9tmjsl2/scikit-learn_ec85c74cdc314b5dae36b86ff0618a56/setup.py'"'"'; __file__='"'"'/tmp/pip-install-t9tmjsl2/scikit-learn_ec85c74cdc314b5dae36b86ff0618a56/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record /tmp/pip-record-mjw2a43x/install-record.txt --single-version-externally-managed --compile --install-headers /home/venvs/model_env_py39/include/site/python3.9/scikit-learn Check the logs for full command output. ``` [log.txt](https://github.com/pandas-dev/pandas/files/7367688/log.txt) The interesting part is that installing the following set of requirements: ``` pandas numpy scipy python-dateutil wheel scikit-learn scikit-optimize tqdm networkx joblib twine mlflow requests ``` runs without issue. I tested this in two different Linux environments (see above) with the same result.

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I_kwDOAA0YD849XFW7

BUG: Calling apply on empty DataFrame on axis="columns" loops on an empty default Series

open

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python >>> import pandas as pd >>> pd.DataFrame([], columns=["a", "b", "c"]).apply(lambda row: print(row), axis=1) Series([], dtype: float64) ``` ### Issue Description Is it normal that DataFrame.apply(axis=1) loops once when the DataFrame is empty? Sounds like a weird, unexpected behaviour to me (and introduced breakage in our codebase). I guess this is to return an empty Series for assignment? ### Expected Behavior `DataFrame.apply(axis=1)` on an empty DataFrame should not loop (and still return an empty Series) ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 73c68257545b5f8530b7044f56647bd2db92e2ba python : 3.8.7.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.3 numpy : 1.21.2 pytz : 2021.1 dateutil : 2.8.2 pip : 21.0.1 setuptools : 54.1.2 Cython : None pytest : 6.2.5 hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 3.0.1 lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.9.1 (dt dec pq3 ext lo64) jinja2 : 2.11.3 IPython : 7.27.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 2021.08.1 fastparquet : None gcsfs : 2021.08.1 matplotlib : None numexpr : None odfpy : None openpyxl : 3.0.7 pandas_gbq : 0.14.1 pyarrow : 1.0.1 pyxlsb : None s3fs : None scipy : None sqlalchemy : 1.3.24 tables : None tabulate : 0.8.9 xarray : None xlrd : None xlwt : None numba : None </details>

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I_kwDOAA0YD849XTo3

BUG: DataFrame(dict_of_series) raises depending on order of dict

open

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### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python dti = pd.date_range("2016-01-01", periods=3) ser1 = pd.Series(range(3), index=dti) ser2 = pd.Series(range(3), index=dti.tz_localize("UTC")) ser3 = pd.Series(range(3)) >>> pd.DataFrame({"A": ser3, "B": ser2, "C": ser1}) # works fine >>> pd.DataFrame({"A": ser1, "B": ser2, "C": ser3}) TypeError: Cannot join tz-naive with tz-aware DatetimeIndex ``` ### Issue Description When we pass a dict of Series, we end up going through indexes.api.union_indexes, which checks if our _first_ index is a DatetimeIndex and if so dispatches to DatetimeIndex.union_many, which in this case raises. If the DatetimeIndex is not first, then it goes through Index.union and casts to object. Then there's the issue that we don't pass `sort` to union_many so it gets ignored. ### Expected Behavior Cast to object. ### Installed Versions <details> Replace this line with the output of pd.show_versions() </details>

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PR_kwDOAA0YD84tWcOD

TST: fix groupby-empty xfails

closed

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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry

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PR_kwDOAA0YD84tW9As

ENH: retain RangeIndex in RangeIndex.difference

closed

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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them - [ ] whatsnew entry This should handle _all_ cases where it is possible to retain RangeIndex, a claim which the hypothesis-baed tests should check.

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PR_kwDOAA0YD84tW_5y

testing broadcast on multiindex

open

CONTRIBUTOR

- [x] closes #34388 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit) for how to run them

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I_kwDOAA0YD849Zsbe

BUG: Accessing pd.Series Multiindex with get() does not find element

closed

NONE

### - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd a = pd.DataFrame({"breed": ["stallion", "stallion", "stallion", "stallion", "pony", "pony", "pony"], "stable": ["hogwarts", "hogwarts", "london", None, "hogwarts", "london", "london"], "weight": [800, 900, 982, 400, 230, 300, 500]}) grouped = a.groupby(["breed", "stable"], dropna=False)["weight"].mean() grouped = grouped.append(a.groupby("breed", dropna=False)["weight"].mean()) grouped = grouped.append(pd.Series(a["weight"].mean(), index=["all_breeds"])) print(grouped) print() print(grouped["stallion", "london"]) print(grouped.get(["stallion", "london"])) print(grouped.get(["stallion", "london"], -1)) print(f'The same? {grouped["stallion", "london"] == grouped.get(["stallion", "london"]) == grouped.get(["stallion", "london"], -1)}') ``` ### Issue Description I have a grouped pd.Series with Multiindices and can access single elements using `grouped["stallion", "london"]`, but when using `grouped.get(["stallion", "london"])` the result is `None` (or -1 when a default value is given). ### Expected Behavior I am expecting that all three lines give me the same result: `grouped["stallion", "london"] == grouped.get(["stallion", "london"]) == grouped.get(["stallion", "london"], -1)` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5 python : 3.8.10.final.0 python-bits : 64 OS : Darwin OS-release : 20.6.0 Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:20 PDT 2021; root:xnu-7195.141.6~3/RELEASE_ARM64_T8101 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.3.4 numpy : 1.20.2 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.1 setuptools : 52.0.0.post20210125 Cython : None pytest : 6.2.4 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 : 3.0.0 IPython : 7.24.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 2021.07.0 fastparquet : 0.7.0 gcsfs : None matplotlib : 3.4.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.25 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.53.1 </details>

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I_kwDOAA0YD849ZzPC

BUG: Joining MultiIndex with IntervalIndex level fails when IntervalIndex level is overlapping

open

CONTRIBUTOR

### - [X] I have checked that this issue has not already been reported. (might be related to #27456) - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [X] I have confirmed this bug exists on the master branch of pandas. ### Reproducible Example ```python import pandas as pd idx_1_working = pd.MultiIndex.from_tuples([ (1, pd.Interval(0.0, 1.0)), (1, pd.Interval(1.0, 2.0)), (1, pd.Interval(2.0, 5.0)), (2, pd.Interval(0.0, 1.0)), (2, pd.Interval(1.0, 2.0)), (2, pd.Interval(2.0, 5.0)), ], names=['num', 'interval']) # Same index but sequence slightly changed idx_2_working = pd.MultiIndex.from_tuples([ (1, pd.Interval(2.0, 5.0)), # in idx_1_working this is in 3rd row (1, pd.Interval(0.0, 1.0)), (1, pd.Interval(1.0, 2.0)), (2, pd.Interval(2.0, 5.0)), # in idx_1_working this is in 6th row (2, pd.Interval(0.0, 1.0)), (2, pd.Interval(1.0, 2.0)), ], names=['num', 'interval']) print(idx_1_working.join(idx_2_working, how='outer')) idx_1_broken = pd.MultiIndex.from_tuples([ (1, pd.Interval(0.0, 1.0)), (1, pd.Interval(1.0, 2.0)), (1, pd.Interval(2.0, 5.0)), (2, pd.Interval(0.0, 1.0)), (2, pd.Interval(1.0, 3.0)), # interval limit is here at 3.0, not at 2.0 (2, pd.Interval(3.0, 5.0)), ], names=['num', 'interval']) idx_2_broken = pd.MultiIndex.from_tuples([ (1, pd.Interval(2.0, 5.0)), (1, pd.Interval(0.0, 1.0)), (1, pd.Interval(1.0, 2.0)), (2, pd.Interval(3.0, 5.0)), (2, pd.Interval(0.0, 1.0)), (2, pd.Interval(1.0, 3.0)), ], names=['num', 'interval']) print(idx_1_broken.join(idx_2_broken, how='outer')) ``` ### Issue Description Joining `idx_1_broken` with `idx_2_broken` for no obvious reason. The difference between the `_working` and the `_broken` indeces is, that in `_working` the `interval` level is the same for the two `num` level entries, whereas in `_broken` they one limit differs (see comment in the code) Output of the example script: ``` MultiIndex([(1, (0.0, 1.0]), (1, (1.0, 2.0]), (1, (2.0, 5.0]), (2, (0.0, 1.0]), (2, (1.0, 2.0]), (2, (2.0, 5.0])], names=['num', 'interval']) Traceback (most recent call last): File "/home/jmu3si/tmp/intervalindex_test.py", line 47, in <module> print(idx_1_broken.join(idx_2_broken, how='outer')) File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 214, in join join_index, lidx, ridx = meth(self, other, how=how, level=level, sort=sort) File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 4302, in join return self._join_via_get_indexer(other, how, sort) File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 4336, in _join_via_get_indexer rindexer = other.get_indexer(join_index) File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 3665, in get_indexer return self._get_indexer(target, method, limit, tolerance) File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 3684, in _get_indexer tgt_values = self._engine._extract_level_codes(target) File "pandas/_libs/index.pyx", line 652, in pandas._libs.index.BaseMultiIndexCodesEngine._extract_level_codes level_codes = [lev.get_indexer(codes) + 1 for lev, codes File "/home/jmu3si/Devel/pandas/pandas/core/indexes/base.py", line 3602, in get_indexer raise InvalidIndexError(self._requires_unique_msg) pandas.errors.InvalidIndexError: cannot handle overlapping indices; use IntervalIndex.get_indexer_non_unique ``` ### Expected Behavior I would expect that the join operation works the same way as for `idx_1_working` and `idx_2_working`. Expected output of the scipt: ``` MultiIndex([(1, (0.0, 1.0]), (1, (1.0, 2.0]), (1, (2.0, 5.0]), (2, (0.0, 1.0]), (2, (1.0, 2.0]), (2, (2.0, 5.0])], names=['num', 'interval']) MultiIndex([(1, (0.0, 1.0]), (1, (1.0, 2.0]), (1, (2.0, 5.0]), (2, (0.0, 1.0]), (2, (1.0, 3.0]), (2, (3.0, 5.0])], names=['num', 'interval']) ``` ### Installed versions <details> INSTALLED VERSIONS ------------------ commit : 3a6d4cd01d076762ad60c5fd6551012cb72a808d python : 3.9.7.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-88-lowlatency Version : #99-Ubuntu SMP PREEMPT Thu Sep 23 18:30:52 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : de_DE.UTF-8 LOCALE : de_DE.UTF-8 pandas : 1.4.0.dev0+933.g3a6d4cd01d numpy : 1.21.2 pytz : 2021.3 dateutil : 2.8.2 pip : 21.2.4 setuptools : 58.0.4 Cython : None pytest : 6.2.5 hypothesis : 6.23.3 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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I_kwDOAA0YD849aGrj

Decrypting file in read_csv

open

NONE

## Basic idea of the feature request I was trying to read an encrypted file in `pandas`. As far as I know, there is no way to provide something to `read_csv` (or any other `read_*` function) to decrypt a file when reading (and not with *ex-post* `applymap` functions as in this [stack overflow thread](https://stackoverflow.com/questions/57194710/decrypting-a-fernet-csv-encoding-issue-pandas)) The solution proposed in the [aforementioned post](https://stackoverflow.com/questions/57194710/decrypting-a-fernet-csv-encoding-issue-pandas) seems quite slow with data > several Mb. My solution has been to decrypt the file using `cryptography` package and write that in a temporary location (there's room for improvement in the functions I will propose below, I am aware of that). This works but I was hoping this would be better to have an option in `pandas` to decrypt when reading the stream input. This would probably lead to: * speed improvements since you reduce the I/O * improved security since you don't write decrypted (and thus potentially sensible) data in the disk, even for a temporary purpose Here an example that makes possible to reproduce the feature: 1. The `encrypt_data` is just here to reproduce the setting of having a crypted file 2. It would be great to avoid the `decrypt_data` step to directly use `read_csv` with an extra argument. ```python import pandas as pd from cryptography.fernet import Fernet df = pd.DataFrame({'user': ['Bob', 'Jane', 'Alice'], 'income': [40000, 50000, 42000]}) df.to_csv("toto.csv") def encrypt_data(path, key, outpath = None): if outpath is None: outpath = '{}_encrypted'.format(path) f = Fernet(key) # opening the original file to encrypt with open(path, 'rb') as file: original = file.read() # encrypting the file encrypted = f.encrypt(original) # opening the file in write mode and # writing the encrypted data with open(outpath, 'wb') as encrypted_file: encrypted_file.write(encrypted) print("file {} encrypted ; written at {} location".format(path, outpath)) def decrypt_data(path, key, outpath = None): if outpath is None: outpath = '{}_encrypted'.format(path) f = Fernet(key) # opening the original file to encrypt with open(path, 'rb') as file: original = file.read() decrypted = f.decrypt(original) # opening the file in write mode and # writing the encrypted data with open(outpath, 'wb') as dfile: dfile.write(decrypted) print("file {} decrypted ; written at {} location".format(path, outpath)) dummykey = Fernet.generate_key() encrypt_data("toto.csv", dummykey, outpath = "toto_crypt.csv") decrypt_data("toto_crypt.csv", dummykey, outpath = "toto_decrypt.csv") pd.read_csv("toto_crypt.csv") pd.read_csv("toto_decrypt.csv") ``` ## A possible approach Let's say we call this argument `encryption`. We could provide an object from `cryptography` to decode datastream directly in `pd.read_csv` call. For instance: ```python pd.read_csv("toto_decrypt.csv", encryption = Fernet(dummykey)) ``` The same approach could be used to `to_csv` (or other writing functions) to directly write encrypted data in the disk. However, maybe this solution would imply to use the `python` engine. Directly providing the key and the encryption method (e.g. Fernet) is maybe better to work with the C engine (I am not familiar with C but there's probably equivalent method than the one I applied in python) ## API breaking implications As far as I understand how I/O works, I think this extra argument would not break any existing code with a default value to `None`.

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I_kwDOAA0YD849aTZP

BUG: Nullable integer dtype leads to a fragmentation PerformanceWarning

closed