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BUG: Reindex null timedelta series with `pd.Timedelta` fails
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"2021-08-12T23:25:01Z"
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here ts = pd.date_range("2020-01-01", "2020-01-03", freq="1d") s = pd.Series(pd.Timedelta(None), index=ts) s.reindex(pd.date_range("2020-01-01", "2020-01-04", freq="1d"), fill_value=pd.Timedelta(0)) ``` #### Problem description To begin with, `s` has the wrong type `datetime64[ns]` instead of `timedelta64[ns]`. This causes the `reindex` to fail. I've also tested the code above on version 1.2.4, it does not produce error and the type is casted as `object`: ![image](https://user-images.githubusercontent.com/35905338/128576282-0c30b6f8-1bff-4cac-9d87-6344b0272d10.png) #### Expected Output ![image](https://user-images.githubusercontent.com/35905338/128576379-935484b3-318d-4ae7-8308-f7cc9d8f7075.png) #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.7.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 52.0.0.post20210125 Cython : 0.29.24 pytest : 6.1.2 hypothesis : 6.14.1 sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.1 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.0 </details>
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"2021-08-06T22:37:34Z"
"2021-08-12T13:55:31Z"
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CONTRIBUTOR
null
- [ ] closes #4817 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Added an example in the documentation for DataFrame.pivot_table to show how to display a pivot table as having a multi-level index, simulating Excel's pivot table with MI and partial group aggregates.
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CONTRIBUTOR
null
- [x] closes #42123 - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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CONTRIBUTOR
null
- [x] closes #38813 - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them This is failing across multiple locations. Is there a specific location where get/set_option can be used? or across everything?
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"2021-09-05T10:21:13Z"
"2021-09-05T01:45:01Z"
CONTRIBUTOR
null
- [x] closes #42880 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry I made the test more thorough, as it only checked the output's type, but not that the output matrix was actually the expected one. Here is the output from the asv benchmark: ``` before after ratio [e045034e] [042dbbc3] <master> <sseries-to-coo-perf> - 12.6±0.6ms 2.35±0.1ms 0.19 sparse.ToCoo.time_sparse_series_to_coo_single_level(False) - 19.9±0.6ms 3.68±0.05ms 0.19 sparse.ToCoo.time_sparse_series_to_coo(True) - 19.6±0.4ms 3.51±0.09ms 0.18 sparse.ToCoo.time_sparse_series_to_coo(False) - 12.5±1ms 220±10μs 0.02 sparse.ToCoo.time_sparse_series_to_coo_single_level(True) ``` As you can see the performance improvement is the most dramatic when there is only one row and column level, and for an output with sorted labels. This is simply due to the fact that the labels are sorted in the `levels` attribute of an `Index`, so I covered this case, which should be the most common, with a simpler (and faster) implementation. For this reason I was thinking maybe `sort_labels` could default to `True`, but that would probably require prior deprecation warnings and all. In any case I can add this information as a performance tip to the docs, let me know what you think.
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CONTRIBUTOR
null
pyright says that a few methods that are marked with `typing.Final` are overridden: ``` pandas/core/frame.py:4552:9 - error: Method "_reindex_axes" cannot override final method defined in class "NDFrame" pandas/core/frame.py:10727:9 - error: Method "mask" cannot override final method defined in class "NDFrame" pandas/core/series.py:5446:9 - error: Method "mask" cannot override final method defined in class "NDFrame" pandas/core/groupby/generic.py:676:9 - error: Method "describe" cannot override final method defined in class "GroupBy" pandas/core/groupby/generic.py:856:9 - error: Method "pct_change" cannot override final method defined in class "GroupBy" ```
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ENH: Sliding window by value interval on non-time index?
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#### Is your feature request related to a problem? I would like to compute the number of values in a sliding window like this: ```python import pandas as pd df = pd.DataFrame({"group": ["A", "B"] * 5, "distance": [0.1, 0.3, 1.7, 3.2, 2.6, 3.7, 8.3, 0.6, 5.9, 6.3]}) df ``` ``` Out[5]: group distance 0 A 0.1 2 A 1.7 4 A 2.6 8 A 5.9 6 A 8.3 1 B 0.3 7 B 0.6 3 B 3.2 5 B 3.7 9 B 6.3 ``` ```python # possible API: df.groupby("group").sliding(on="distance", window_size=3.0, step_size=2.0, min=0, max=10).count() ``` ``` count group distance A [0, 3) 3 [2, 5) 1 [4, 7) 1 [6, 9) 1 [8, 11) 0 B [0, 3) 2 [2, 5) 2 [4, 7) 1 [6, 9) 0 [8, 11) 0 ``` #### Describe the solution you'd like There should be some sliding window function that slices by value instead of rows: ```python def sliding( self, on: str, # window_size: numeric, step_size: numeric=None, # equals to window size by default min: numeric=None, # minimum value in group by default max: numeric=None, # maximum value in group by default ): pass ``` #### API breaking implications No idea, maybe integrate to `rolling`? #### Describe alternatives you've considered 1) Create IntervalArray: ```python intervals = pd.arrays.IntervalArray.from_arrays(np.arange(10), np.arange(10) + 2) Out[53]: <IntervalArray> [(0, 2], (1, 3], (2, 4], (3, 5], (4, 6], (5, 7], (6, 8], (7, 9], (8, 10], (9, 11]] ``` 2) Join on `intervals`: No idea how to do this... #### Additional background This question also appeared on StackOverflow: https://stackoverflow.com/questions/43538064/how-to-do-sliding-window-by-value-interval-on-non-time-index-in-pandas
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Backport PR #42799: BUG: `Styler.set_sticky` fix the names rows 2/2
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Backport PR #42776: BUG: Incorrect variable window bounds for first row when offset cover all rows
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TST: reorganize misplaced year / business year tests (#27085)
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CONTRIBUTOR
null
- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909
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"2021-08-08T01:48:06Z"
"2021-08-11T13:26:10Z"
"2021-08-11T13:26:05Z"
CONTRIBUTOR
null
This patch closes #42768 and fixes a previously-reported issue #33043. When `ujson` falls back to encoding a Python object, it iterates over all of the non-callable attributes of the object which do not start with an underscore, using them as JSON keys. While iterating the `dirs(obj)` list, an index was incremented twice causing every second attribute to be skipped. I've fixed this increment, and moved some of the declarations inside the function so that control flow is more clear. I've also added a regression test for this behaviour. - [x] closes #42768 - [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 This patch also xref #41174, but I'm not sure that turning complex numbers into `{"real": ..., "imag": ...}` JSON objects was intended in the first place, so I haven't added a test for this.
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"2021-08-08T04:15:59Z"
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CONTRIBUTOR
null
- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909 * https://github.com/pandas-dev/pandas/pull/42930
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CLN: replace styler code for refactor `level` arg with codebase generic
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CONTRIBUTOR
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after #42914 styler has private code to refactor a `level` arg to a consistent format, either: - name of an index level: `my_level` - a specific integer index level: `1` - or a list of such: `['my_lev', 'other_lev']` produces a consistent list of integer levels: `[1,2]`. This code should be replaced by more generic code which does the same thing in the rest of the codebase. (if anyone has the location would be helpful to share..)
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BUG: should constructing Index from a Series make a copy?
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"2021-09-16T08:11:55Z"
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MEMBER
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From a comment of @jbrockmendel at https://github.com/pandas-dev/pandas/pull/41878#issuecomment-881557871: ``` ser = pd.Series(range(5)) idx = pd.Index(ser) ser[0] = 10 >>> idx[0] 10 ``` In the above, we create an Index from a Series, then mutate the Series, which also updated the Index, while an Index is assumed to be immutable. Changing the example a bit, you can obtain wrong values with indexing this way: ``` ser = pd.Series(range(5)) idx = pd.Index(ser) ser.index = idx >>> ser[0] 0 >>> ser.iloc[0] = 10 >>> ser[0] 10 >>> ser 10 10 1 1 2 2 3 3 4 4 dtype: int64 ``` So `ser[0]` is still giving a result, while that key doesn't actually exist in the Series' index at that point. I know that generally we consider this a user error if you would do this with a numpy array (`idx = pd.Index(arr)` and mutating the array), but here you get that by only using high-level pandas objects itself. In which case we should prevent this from happening?
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https://api.github.com/repos/pandas-dev/pandas/issues/42935
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963,471,572
MDExOlB1bGxSZXF1ZXN0NzA2MDczMjE2
42,935
DOC add example to reorder levels
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"2021-08-23T16:54:59Z"
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CONTRIBUTOR
null
Response to changes suggested on my previous pull request - [x] closes #42124 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NzA2MDgyODQw
42,936
BUG:Can't calculate quantiles from Int64Dtype Series when results are floats
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CONTRIBUTOR
null
- [x] closes #42626 - [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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CLN: clean warning assertions in sql tests
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CONTRIBUTOR
null
- [x] closes #42881 - [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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CONTRIBUTOR
null
- [x] closes #38543 - [ x] tests added / passed - [x ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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CONTRIBUTOR
null
- [x] closes #34037 - [ x] tests added / passed - [x ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: how do we perform pandas series comparison based on level?
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I have a multi-index dataframe `y_test`: blockNumber_base10@coarsegrained#1024 address_idx 9441280 2 -0.012421 3 0.001400 6 -0.012696 7 0.003558 15 0.003649 ... 9854976 935783 0.000000 935785 0.000000 935786 0.000000 935788 0.000000 935789 0.000000 Name: price@fwd#001024@div#price#m1, Length: 1146272, dtype: float64 We want to compare with groupby min. y_test_tmp = y_test.groupby(level = 0).quantile(0.8) blockNumber_base10@coarsegrained#1024 9441280 0.005130 9442304 0.000973 9443328 0.000461 9444352 0.001354 9445376 0.000921 ... 9850880 0.000552 9851904 0.000953 9852928 0.000591 9853952 0.000000 9854976 0.000000 Name: price@fwd#001024@div#price#m1, Length: 405, dtype: float64 I want to compare with them based on `blockNumber_base10@coarsegrained#1024`, so i wanted to compute `y_test > y_test_tmp`. But I got this error: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-318-ad2813982941> in <module> ----> 1 y_test > y_test_tmp ~/anaconda3/lib/python3.8/site-packages/pandas/core/ops/common.py in new_method(self, other) 63 other = item_from_zerodim(other) 64 ---> 65 return method(self, other) 66 67 return new_method ~/anaconda3/lib/python3.8/site-packages/pandas/core/arraylike.py in __gt__(self, other) 43 @unpack_zerodim_and_defer("__gt__") 44 def __gt__(self, other): ---> 45 return self._cmp_method(other, operator.gt) 46 47 @unpack_zerodim_and_defer("__ge__") ~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in _cmp_method(self, other, op) 4971 4972 if isinstance(other, Series) and not self._indexed_same(other): -> 4973 raise ValueError("Can only compare identically-labeled Series objects") 4974 4975 lvalues = extract_array(self, extract_numpy=True) ValueError: Can only compare identically-labeled Series objects Any suggestion? Thanks!
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ERR: clarify PerformanceWarning for fragmented frame
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MEMBER
null
xref some confusion in #42477. If this is thought an improvement, could target 1.3.2 since more users now see this error than in 1.2.x
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ENH: add new method `DataFrame.repeat`
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CONTRIBUTOR
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#### Is your feature request related to a problem? I sometimes would generate some faker but having rule data. Just like in a (2d) data, repeat some rows or columns serial times. The steps I do: 1. This is a DataFrame called `df`. 2. `value = df.values # change DataFrame to ndarray` 3. use `np.repeat` or `np.tile` method to repeat. #### Describe the solution you'd like I thought why not add a method `repeat` to `DataFrame`, since `Series` has already owned the `repeat` [method](https://pandas.pydata.org/docs/reference/api/pandas.Series.repeat.html). #### About the `DataFrame.repeat` parameters Similar to [`numpy.repeat(a, repeats, axis=None)`](https://numpy.org/doc/stable/reference/generated/numpy.repeat.html) ```python def repeat( self, repeats: int | list[int], axis: int | str = 0, ) -> pd.DataFrame | None: ... ``` DataFrame is a 2d data structure, so it is hard to repeat each element. And the aim of `DataFrame.repeat` is to repeat one row/column or some rows/columns. So the `axis` argument must be specific to `0` or `1`. And if the `repeats` is a single value `int`, that means repeat all row/column `repeats` times. #### Examples of this method ```python >>> df = pd.DataFrame({'a': [1, 2], 'b':[3, 4]}) >>> df a b 0 1 3 1 2 4 Each row repeat two times. >>> df.repeat(2) a b 0 1 3 0 1 3 1 2 4 1 2 4 Each column repeat two times. >>> df.repeat(2, 1) a a b b 0 1 1 3 3 1 2 2 4 4 ``a`` column repeat 1 times, ``b`` column repeat 2 times. >>> df.repeat([1, 2], 1) a b b 0 1 3 3 1 2 4 4 ```
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42,944
BUG: read_csv with mixed bools and NaNs sometimes reads NaNs as 1.0 (#42808)
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"2021-10-16T20:41:26Z"
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CONTRIBUTOR
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When reading a column of mixed booleans and missing values into a specific floating dtype, for example `read_csv(..., dtype='float')`, missing values were being incorrectly converted to 1.0 rather than NaN. This patch fixes this issue. - [x] closes #42808 - [x] closes #34120 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry I've never been in the CSV parsing code before, so I've tried to put this change in the spot that makes the most sense and causes the least impact, but corrections are very welcome. Why this issue was happening is that the [_try_bool_flex()](https://github.com/pandas-dev/pandas/blob/126a19d038b65493729e21ca969fbb58dab9a408/pandas/_libs/parsers.pyx#L1752) function parses a mixed column of bools and missing values into a uint8 array, storing False/True/missing as 0/1/255. It then returns a bool view of this array, "hiding" the missing values and making them just look like True. These missing values are meant to be later 'unhidden' by a call to [_maybe_upcast()](https://github.com/pandas-dev/pandas/blob/126a19d038b65493729e21ca969fbb58dab9a408/pandas/_libs/parsers.pyx#L1040), which views the array as uint8 again, and maps to a dtype=object array containing False/True/NaN. However, when the user specifies an explicit float dtype, this upcast is skipped since the bool array gets converted to a float array before hitting _maybe_upcast(). This patch just replicates the upcast logic in this case. Like I said above, this is the spot in the code that made the most sense to me to put the correction - I feel like a more robust fix would be to rewrite the `_convert_tokens()` function (and the methods it calls, and so on) to actually return the positions of the missing values, as well as the parsed values, which would be a large change.
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7
"2021-08-09T08:24:53Z"
"2021-09-13T08:30:10Z"
"2021-09-12T14:03:27Z"
NONE
null
Hello. Could you please release CPython 3.10 manylinux wheels on PyPI? Python 3.10.0 release candidate 1 was released this week: https://www.python.org/downloads/release/python-3100rc1/ It should be quite safe to release wheels for release candidate. ABI for Python 3.10 should be frozen from now on, even for bugfixes. From release notes: > There will be no ABI changes from this point forward in the 3.10 series and the goal is that there will be as few code changes as possible. Thank You.
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ENH: A new GroupBy method to slice rows preserving index and order
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28
"2021-08-09T12:01:35Z"
"2021-10-18T19:32:53Z"
"2021-10-15T13:28:43Z"
CONTRIBUTOR
null
Closes #42864 New indexable attribute GroupBy.iloc to perform most grouped slices analagous to DataFrame.iloc Row indexes can be integers or slices with +ve step Row slices preserve the DataFrame order and grouping and extend the existing GroupBy head() and tail() methods All column indexes are handled as for DataFrame.iloc -ve slice step is not currently handled, and would have to reverse the order. There is no plan to implement Integer lists since these do not preserve the order at all and cannot be easily vectorized. Note ---- Neither GroupBy.nth() nor GroupBy.take() take a slice argument and neither of them preserve the original DataFrame order and index. They are both slow for large integer lists and take() is very slow for large group counts. Example use case -------------------- Supose that we have a multi-indexed DataFrame with a large primary index and a secondary sorted in a different order for each primary index. To reduce the DataFrame to a middle slice of each secondary, we can groupby the primary index and then use iloc. This preserves the original DataFrame's order and indexing. (See tests/groupby/test_groupby_iloc)
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BUG: Limitation of adding years in dateoffset pandas
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1
"2021-08-09T12:43:17Z"
"2021-08-09T13:56:52Z"
"2021-08-09T13:56:52Z"
NONE
null
This is reference to other question i found on Stackoverflow, but i am not intersted add the reference here without their permission. My question is what will be the reason for i cannot add more than 240 years to pandas dataframe with pd.offsets.DateOffset?. Here is my code ``` df=pd.date_range(start="2020-10-29",end="2020-10-30").to_pydatetime().tolist() df=pd.DataFrame(df,columns=['dte']) df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=241) ``` Output is ``` dte dte_ 0 2020-10-29 2261-10-29 1 2020-10-30 2261-10-30 df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=242) ``` The output of above code is ``` dte dte_ 0 2020-10-29 1678-04-09 00:25:26.290448384 1 2020-10-30 1678-04-10 00:25:26.290448384 ``` If i change start and end date to 2021, I am able to add only 240 years as follows , `df=pd.date_range(start="2021-10-29",end="2021-10-30").to_pydatetime().tolist()` Other code with year 2200 ``` df=pd.date_range(start="2200-10-29",end="2200-10-30").to_pydatetime().tolist() df=pd.DataFrame(df,columns=['dte']) df["dte_"]=df["dte"]+pd.offsets.DateOffset(years=62) # i.e I can add only 61 years ``` Output is ``` dte dte_ 0 2200-10-29 1678-04-09 00:25:26.290448384 1 2200-10-30 1678-04-10 00:25:26.290448384 ``` - Is this due to limitation of availability calender date after 240 Years from now? - Or pandas Limitation? - Or is this my misunderstanding about DateOffset? - I undestand 2261 Supposed to be the last year. Is there any speciality for 2261? - From one of answer i understand `timestamp(9200000000000000000) was Monday, July 15, 2261 2:33:20 PM approximately Timestamp('2262-04-11 23:47:16.854775807'). So what is the solution?
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Fix cross-references in docs
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0
"2021-08-09T14:24:42Z"
"2021-08-10T19:53:59Z"
"2021-08-10T19:53:59Z"
CONTRIBUTOR
null
Fix Sphinx cross-references in docs.
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PERF: improve perf of Series fastpath constructor
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"2021-08-09T14:28:25Z"
"2021-11-15T19:06:51Z"
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MEMBER
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This came up while discussing the CoW proof of concept (and need for item_cache / speed of creating a Series of a column). Those small changes gives a decent reduction of some overhead (~30% reduction): ```python arr = np.array([1, 2, 3]) idx = pd.Index(["a", "b", "c"]) pd.Series(arr, index=idx, name="name", fastpath=True) ``` ``` In [5]: %timeit pd.Series(arr, index=idx, name="name", fastpath=True) 10.9 µs ± 202 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- master 7.48 µs ± 187 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) # <-- PR ```
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Use new GitHub issue forms?
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MEMBER
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Github recently released a beta for issue forms (https://github.blog/changelog/2021-06-23-issues-forms-beta-for-public-repositories/). Scipy already has an open pr to use these to replace existing issue templates (https://github.com/scipy/scipy/pull/14493) Seems like a good way to improve issue quality, while also making writing issues easier for users (through improvements like built-in code formatting).
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TST: move index tests to correct files (#27045)
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CONTRIBUTOR
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- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909 * https://github.com/pandas-dev/pandas/pull/42930 * https://github.com/pandas-dev/pandas/pull/42932
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REF: format_object_attrs -> Index._format_attrs
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"2021-08-09T16:49:45Z"
"2021-08-10T22:26:51Z"
"2021-08-10T21:56:25Z"
MEMBER
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Preliminary to trying to implement generic EA-backed Index (and in particular IntegerArray/BooleanArray), trying to identify all the Index methods that implicitly assume we have an ndarray. This refactors a used-only-once function into the method to make it more obvious that it does _not_ assume an ndarray.
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Revert fastparquet nullable dtype support
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MEMBER
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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.html#code-standards) for how to run them - [ ] whatsnew entry
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CLN: remove unused Series._index attribute
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"2021-08-09T17:27:18Z"
"2021-08-11T06:44:20Z"
"2021-08-10T22:30:03Z"
MEMBER
null
Noticed this in https://github.com/pandas-dev/pandas/pull/42950, the attribute seems completely unused. (cc @jbrockmendel do you have any idea what the reason is/was for this?)
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42,956
BENCH: Fix misleading Interpolate asv
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MEMBER
null
The ArrayManager benchmark is misleading ATM.
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42,957
BUG: pd.to_datetime with format doesn't work with pd.NA
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"2021-09-25T15:35:31Z"
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NONE
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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 of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # Your code here import numpy as np import pandas as pd a = pd.DataFrame({'a':[pd.NA]}) b = pd.DataFrame({'b': [np.nan]}) pd.to_datetime(b['b']) # -> Work pd.to_datetime(b['b'], format="%Y%m%d%H%M%S") # -> Work pd.to_datetime(a['a']) # -> Work pd.to_datetime(a['a'], format="%Y%m%d%H%M%S") # -> Error ``` #### Problem description I try to use `pd.NA` to define NaN values in field what contains datetime strings. I try convert them to datetime, but it doesn't work. I get error `ValueError: time data '<NA>' does not match format '%Y%m%d%H%M%S' (match)` Actually the same code with `np.nan` works correctly #### Expected Output pd.NaT with no errors #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.9.6.final.0 python-bits : 64 OS : Linux OS-release : 4.18.0-305.7.1.el8_4.x86_64 Version : #1 SMP Mon Jun 14 17:25:42 EDT 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.utf8 LOCALE : en_US.UTF-8 pandas : 1.3.1 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 49.6.0.post20210108 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.7.0 sqlalchemy : None tables : 3.6.1 tabulate : 0.8.9 xarray : None xlrd : None xlwt : None numba : 0.53.1 </details>
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Backport PR #42942 on branch 1.3.x (ERR: clarify PerformanceWarning for fragmented frame)
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Backport PR #42942: ERR: clarify PerformanceWarning for fragmented frame
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MDExOlB1bGxSZXF1ZXN0NzA2OTA4NTI4
42,959
TST: use fixtures for sql test data
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MDExOlB1bGxSZXF1ZXN0NzA2OTEzMzEx
42,960
DOC: erroneous initialization of a DataFrame with Series objects #42818
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"2021-09-08T09:08:51Z"
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CONTRIBUTOR
null
- [ ] closes #42818 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry issue url : https://github.com/pandas-dev/pandas/issues/42818
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CI: Dateutil broke CI
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"2021-08-23T12:13:25Z"
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MEMBER
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Failing CI is actions 3.9 numpy dev. It fails with a FileNotFound error I think and I believe that its probably an upstream issue. Any reason why we're installing dateutil from source here on the CI? I know that its a dependency but we aren't installing pytz from source on the same env.
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CI: Pin dateutil
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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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MDExOlB1bGxSZXF1ZXN0NzA3MDA3OTMy
42,963
PERF: avoid repeating checks in interpolation
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"2021-08-10T03:59:06Z"
"2021-08-10T22:27:07Z"
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MEMBER
null
Not a particularly big difference.
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ENH: conditional merge - initial concept
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"2021-08-10T06:58:11Z"
"2021-11-04T10:49:38Z"
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CONTRIBUTOR
null
- [ ] closes #8962 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry This is an initial implementation of a conditional join proposed here: https://github.com/pandas-dev/pandas/issues/8962#issuecomment-871958622 This is by no means meant to be a final solution, but just meant to get some momentum behind the feature. The following issue provides the proposed initial implementation and examples for the usefulness an optimized version of this feature could provide: https://github.com/pandas-dev/pandas/issues/41150 Example Usage: ``` Merge dataframes df1 and df2, where column df1.timestep falls within the range of df2.timestart to df2.timeend, using `condition`. >>> df1 = pd.DataFrame({'timestep': range(5)}) >>> df2 = pd.DataFrame({'mood': ['happy', 'jolly', 'joy', 'cloud9'], 'timestart': [0, 2, 2, 3], 'timeend': [1, 3, 4, 4]}) >>> df1 timestep 0 0 1 1 2 2 3 3 4 4 >>> df2 mood timestart timeend 0 happy 0 1 1 jolly 2 3 2 joy 2 4 3 cloud9 3 4 >>> df1.merge( ... df2, ... condition=lambda dfx: (dfx.timestart <= dfx.timestep) ... & (dfx.timestep <= dfx.timeend) ... ).pivot_table(index='timestep', values='mood', aggfunc=', '.join) mood timestep 0 happy 1 happy 2 jolly, joy 3 jolly, joy, cloud9 4 joy, cloud9 ```
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How to add coroutines in apply lambda function
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"2021-08-10T09:12:41Z"
"2021-08-21T20:47:26Z"
"2021-08-21T20:47:26Z"
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#### Question about pandas I write a function use apply and lambda for one column in a DataFrame. But because every time excute once fucntion use lot of time, so I turn the column into a list and use coroutines and for loop. I create a new list then insert into my DataFrame. I want to know if I can use both apply funtion and coroutines at the same time? But I think apply has been a parallel operation. It seems not work in real. ```python # Your code here, if applicable list1 = ["a", "b", "c", "d",....] list2 = [1, 2, 3, 4,...] df = pd.DataFrame({"col1":list1, "col2":list2}) def add_suffix(x): return x+"_suffixsuffix" df["col_with_suffix"] = df["col1"].apply(lambda x: add_suffix(x)) # another method use coroutines col1_list = df["col1"].values.tolist() import genvent from gevent import monkey; monkey.patch_all() g_list = [] i = 0 for x in col1_list: i =i + 1 g = gevent.spawn(add_suffix, x) g_list.append(g) if i > 200: gevent.joinall(g_list) i = 0 g_list = [] ```
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BUG:Tuple Series name causes InvalidIndexError in Pandas 1.2.4 but not in 0.25.3
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"2021-08-12T01:17:30Z"
"2021-08-12T01:16:11Z"
CONTRIBUTOR
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd print(f"pandas: {pd.__version__}") !python --version qf = pd.Series(index = pd.to_datetime(['12Dec2020', '13Dec2020','14Dec2020', '15Dec2020','16Dec2020']), data= [1,1,-1,-1,-1], name=('coz1', 'this_bar'), ) qf.index.name="Date" qf = qf.groupby( (qf != qf.shift()) .cumsum() ).cumsum().apply(int) qf.head(10) ``` #### Problem description I pass the `groupby` function a `by` parameter which is series whose name is a Tuple. In older pandas (0.25.3), this worked. In modern (1.2.4) pandas this raises an `InvalidIndexError`. You might say, "well then don't name your series with Tuple, name it with a string." How my situation happens in real-life is that I'm taking a `.loc` slice of a `DataFrame` with multilevel columns. This returns a Series, and its name is automatically a tuple of the column levels. I can do a gross temporary-rename of the series, but feels like a hack. #### Expected Output ``` pandas: 0.25.3 Python 3.7.11 Date 2020-12-12 1 2020-12-13 2 2020-12-14 -1 2020-12-15 -2 2020-12-16 -3 Name: (coz1, this_bar), dtype: int64 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD byteorder : little LC_ALL : None LANG : None LOCALE : English_Singapore.1252 pandas : 1.2.4 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.9.0 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.7 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>
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BUG: Index.droplevel() used in an .apply() function affects subsequent rows in an accumulating fashion
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"2021-08-10T14:37:04Z"
"2021-08-10T14:31:07Z"
CONTRIBUTOR
null
- [x ] I have checked that this issue has not already been reported. - [x ] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```import numpy as np import pandas as pd print(f"pandas: {pd.__version__}") !python --version cols = pd.MultiIndex.from_product([['coz',], ['alpha', 'beta', 'gamma']], names=['health', 'protocol']) index=pd.date_range(start="1jan2020", end=None, periods=5, freq="d", name="Date") data = np.random.rand(5,3) df = pd.DataFrame(data=data, index=index, columns=cols) def foo(row): row.index = row.index.droplevel(0) return row['beta'] > row['alpha'] df.apply(foo, axis="columns") ``` #### Problem description In `Pandas 0.25.3` this function works. In `Pandas 1.2.4`` it fails with an `ValueError: Cannot remove 1 levels from an index with 1 levels`. What seems to be happening is when I apply `.droplevel(0)` on a row inside the `.apply` function, it removes a level for that instance of the row.Index passed in as a parameter, but also for all the subsequent rows passed to the `.apply`. So if I have a ten level column, apply would process ten rows and then this this `ValueError` trips. I can get around this by applying the `.droplevel()` on a copy of the row `row.index = row.index.droplevel(0)` but seems like a hack, and I'm confused because this used to work. Perhaps is a bug. #### Expected Output pandas: 0.25.3 ``` Python 3.7.11 Date 2020-01-01 False 2020-01-02 True 2020-01-03 False 2020-01-04 True 2020-01-05 False Freq: D, dtype: bool ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 2cb96529396d93b46abab7bbc73a208e708c642e python : 3.9.4.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD byteorder : little LC_ALL : None LANG : None LOCALE : English_Singapore.1252 pandas : 1.2.4 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.9.0 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.7 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1 </details>
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Update Parquet pandas metadata description on how to handle nullable / extension dtypes
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From https://github.com/dask/fastparquet/pull/623#discussion_r685904630 The guidelines on how to store column metadata in the pandas metadata in Parquet (https://pandas.pydata.org/docs/development/developer.html#column-metadata) was written before nullable dtypes existed, so based on that it's not unambiguous how those should be handled. (and in general, it's also not clear how to handle general extension types, except for the few hardcoded ones (categorical, datetimetz) that already existed at that point) cc @martindurant
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Backport PR #42962 on branch 1.3.x (CI: Pin dateutil)
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Backport PR #42962: CI: Pin dateutil
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pandas.DataFrame.filter does not work with regex on a multi-index dataframe
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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 of pandas. #### Code Sample, a copy-pastable example ```python df = pd.DataFrame( [["bar", "one", 'test'], ["bar", "one1", 'test']], columns=["first", "second", "third"] ) # Single-index dataframe. Regex works as expected df.set_index(['second']).filter( regex='e$', axis=0 ) first third second one bar test # Multi-index dataframe. Regex does not work df.set_index(['first','second']).filter( regex='e$', axis=0 ) Empty DataFrame Columns: [third] Index: [] ``` #### Problem description The regex expression does not work if the dataframe is multi-index. #### Expected Output ``` third first second bar one test ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.4.72-microsoft-standard-WSL2 Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 44.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.24.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: Different initialization methods lead to different dtypes (DataFrame)
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd df1 = pd.DataFrame(columns=["a", "b", "c"]) print(df1.groupby("a").sum().columns) # => Index([], dtype='object') df2 = pd.DataFrame({"a": [], "b": [], "c": []}) print(df2.groupby("a").sum().columns) # => Index(['b', 'c'], dtype='object') ``` #### Problem description `groupby`-ing and `sum`ming an empty dataframe led to dropped columns (`df1` above); this doesn't occur with non-empty dataframes. This changing the columns of a dataframe based on its content is counter-intuitive and leads to key errors. The expected behaviour is shown above with `df2`, and the fact that two empty dataframes show different behaviours when grouped and summed suggests that this isn't intended behaviour. #### Expected Output #### Output of the above snippet: <details> Index([], dtype='object') Index(['b', 'c'], dtype='object') </details>
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ENH: `max_rows` `max_cols` kw for `Styler.to_html` + `styler.options`
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"2021-09-09T06:05:00Z"
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CONTRIBUTOR
null
one more item towards deprecating `DataFrame.to_html` for #41693 - [x] adds options to config - [x] update `options.rst` - [x] add tests - [x] update to_html docstring - [x] whats new - [ ] update user guide (will be follow on after all options in master tracker added) and an item towards consistent `pandas.options` for #41395 ![Screen Shot 2021-08-29 at 09 26 45](https://user-images.githubusercontent.com/24256554/131242297-0ba1a7c6-c058-43f0-af94-c3acf14eca4a.png)
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DIS: Should the keyword use_nullable_dtypes use nullable dtypes in the absence of nulls
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MEMBER
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Currently the keyword ``use_nullable_dtypes``(only implemented for read_parquet now) will use nullable dtypes for columns even when that column does not have nulls. Should this behavior change? xref discussion https://github.com/pandas-dev/pandas/pull/40687#issuecomment-833447019 https://github.com/pandas-dev/pandas/issues/42588#issuecomment-894981214 https://github.com/pandas-dev/pandas/issues/42588#issuecomment-882773411 (Note that always using nullable dtypes always(the current behavior) is a choice made on our end https://github.com/pandas-dev/pandas/blob/master/pandas/io/parquet.py#L215-L227 not by the pyarrow engine) cc @pandas-dev/pandas-core
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Backport PR #42936 on branch 1.3.x (BUG:Can't calculate quantiles from Int64Dtype Series when results are floats)
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Backport PR #42936: BUG:Can't calculate quantiles from Int64Dtype Series when results are floats
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Backport PR #42838 on branch 1.3.x (REGR: Series.nlargest with masked arrays)
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Backport PR #42838: REGR: Series.nlargest with masked arrays
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TST: use fixtures for some sql database connections
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xref #40686 This PR: 1. Create fixtures for all supported SQL connections, including sqlalchemy engine/connection and sqlite3 connection. Setup and cleanup are handled within fixtures. 2. Migrate several common tests to use connection fixtures. They serve as examples for future migrations. We probably should put all the fixtures in a separate conftest.py file. But since there are unmerged PRs related to this test file, I kept everything in one place for now to avoid conflict.
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https://github.com/pandas-dev/pandas/blob/99cf794ae2f63f3137290e7df59b1d31b22ae3d3/pandas/tests/test_common.py#L28 ``` class somecall: def __call__(self): return x # noqa assert getname(fn) == "fn" assert getname(lambda_) assert getname(part1) == "fn" assert getname(part2) == "fn" assert getname(somecall()) == "somecall" assert getname(1) is None ``` It looks like the statement inside __call__ function does not have x defined first.
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https://github.com/pandas-dev/pandas/blob/99cf794ae2f63f3137290e7df59b1d31b22ae3d3/pandas/core/reshape/pivot.py#L243 I found the variable values_multi is not defined when values_passed is False, but the variable is used in line 243 ``` if values_passed: if is_list_like(values): values_multi = True values = list(values) else: values_multi = False values = [values] ``` ``` if values_passed and not values_multi and table.columns.nlevels > 1:```
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CONTRIBUTOR
null
- [x] xref #21749 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG: GroupBy.__iter__ doesn't work for dict initialization
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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 1.3.1 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd frame = pd.DataFrame([[1, 2], [3, 4]]) d = dict(frame.groupby(level=0)) ``` Raised error: ```python Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: attribute of type 'NoneType' is not callable ``` #### Problem description Based on the documentation of [Python `dict`](https://docs.python.org/3/library/stdtypes.html#dict) & [`GroupBy.__iter__`](https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.__iter__.html#pandas.core.groupby.GroupBy.__iter__), the two should work together. The above code should behave identically to the working alternative ```python d = {} for k, v in frame.groupby(level=0): d[k] = v ``` #### Expected Output The code sample shouldn't raise an error, and the result should be ```python >>> d[0] 0 1 0 1 2 >>> d[1] 0 1 1 3 4 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.8.11.final.0 python-bits : 64 OS : Linux OS-release : 5.13.8-1-MANJARO Version : #1 SMP PREEMPT Thu Aug 5 09:47:52 UTC 2021 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.1 pip : 21.2.3 setuptools : 57.0.0 Cython : 0.29.23 pytest : 6.2.4 hypothesis : None sphinx : 4.0.3 blosc : None feather : None xlsxwriter : 1.4.3 lxml.etree : None html5lib : 1.1 pymysql : None psycopg2 : 2.9.1 (dt dec pq3 ext lo64) jinja2 : 3.0.1 IPython : 7.23.1 pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : None scipy : 1.7.0 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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CONTRIBUTOR
null
- [ ] closes #40498 - [ ] 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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BUG: pd.to_datetime with format doesn't work with pd.NA
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CONTRIBUTOR
null
- [x] closes #42957 - [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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Backport PR #42823: BUG: Pass copy argument to expanddim constructor in concat.
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Backport PR #42983
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`types` and `methodtype` are swapped inside the `ValueError` raised in `AbstractMethodError`. - [ ] 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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- [ ] xref #42954 and #42919 - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry **This is a non-trivial backport. Please first review changed files before merging. Thanks**
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967,266,242
MDExOlB1bGxSZXF1ZXN0NzA5NDQ5OTg5
42,988
TST: refactor iris table creation in SQL test
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1
"2021-08-11T19:01:05Z"
"2021-08-17T04:19:56Z"
"2021-08-13T14:33:50Z"
MEMBER
null
split #42976 xref #40686
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42,989
TST: move orphaned business hour tests biz hour file (#27085)
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"2021-08-11T19:30:32Z"
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CONTRIBUTOR
null
- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909 * https://github.com/pandas-dev/pandas/pull/42930 * https://github.com/pandas-dev/pandas/pull/42932 * https://github.com/pandas-dev/pandas/pull/42952
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TST: move easter to own file and parameterize (#27085)
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"2021-08-11T20:06:23Z"
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CONTRIBUTOR
null
- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909 * https://github.com/pandas-dev/pandas/pull/42930 * https://github.com/pandas-dev/pandas/pull/42932 * https://github.com/pandas-dev/pandas/pull/42952 * https://github.com/pandas-dev/pandas/pull/42989
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42,991
TST: test_offsets files to better homes & parameterize (#27085)
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0
"2021-08-11T20:57:51Z"
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CONTRIBUTOR
null
- [ ] xref #27085 (chips away at, yes) - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry * https://github.com/pandas-dev/pandas/pull/42284 * https://github.com/pandas-dev/pandas/pull/42896 * https://github.com/pandas-dev/pandas/pull/42909 * https://github.com/pandas-dev/pandas/pull/42930 * https://github.com/pandas-dev/pandas/pull/42932 * https://github.com/pandas-dev/pandas/pull/42952 * https://github.com/pandas-dev/pandas/pull/42989 * https://github.com/pandas-dev/pandas/pull/42990
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42,992
REF: remove libreduction.apply_frame_axis0
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3
"2021-08-11T23:59:55Z"
"2021-08-12T16:48:35Z"
"2021-08-12T16:21:47Z"
MEMBER
null
- [x] closes #40446 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry Discussed on call today. Just started an asv run.
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42,993
BUG: df.merge not working after converting datetime64 object back to object when all fields are None
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"2021-08-12T15:24:01Z"
"2021-08-12T15:24:01Z"
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python from pandas.api.types import is_datetime64_dtype import pandas def convert_to_datetime(df): if is_datetime64_dtype(df['Date']) == False: df['Date'] = pandas.to_datetime(df['Date'],errors='coerce') df['Date'] = df['Date'].astype('object').where(pandas.notnull(df['Date']), None) return df df1 = pandas.DataFrame({'ID':[1,2],'Date':['2014-10-10 10:44:31', None]}) df2 = pandas.DataFrame({'ID':[1,2],'Date':[None, None]}) df1 = convert_to_datetime(df1) df2 = convert_to_datetime(df2) merge = df1.merge(df2, how = 'outer' ,indicator=True, validate="one_to_one", copy=False) ``` #### Problem description expected behavior is to proceed with merge with no error but getting: ``` Traceback (most recent call last): File "c:/Users/g/AppData/Local/Programs/Python/Python38/Lib/hcpcs_si_audit/test.py", line 16, in <module> merge = df1.merge(df2, how = 'outer' ,indicator=True, validate="one_to_one", copy=False) File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\frame.py", line 7336, in merge return merge( File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 83, in merge return op.get_result() File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 667, in get_result self._maybe_add_join_keys(result, left_indexer, right_indexer) File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\reshape\merge.py", line 815, in _maybe_add_join_keys key_col = Index(lvals).where(~mask, rvals) File "C:\Users\g\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\indexes\datetimelike.py", line 627, in where result = np.where(cond, values, other).astype("i8") TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType' ```
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Why can't write the dataframe into excel with sheetname?
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2
"2021-08-12T01:02:54Z"
"2021-08-14T18:50:08Z"
"2021-08-14T18:50:02Z"
NONE
null
I have created a new workbook and a worksheet named `raw`: from openpyxl import Workbook wb = Workbook() ws = wb.active dest_fn = '/tmp/mytest.xlsx' ws.title = "raw" Then to create a dataframe : import pandas as pd data = [{"code":"dow","value":35100}] df = pd.DataFrame.from_dict(data) To write and save it: df.to_excel (dest_fn, index = False, header=False, startcol=1,startrow=2,sheet_name="raw") wb.save(dest_fn) wb.close() Why no data in the dest_fn `/tmp/mytest.xlsx` when to open it?
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ENH: .to_csv open a pop-up window option
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"2021-08-12T04:47:11Z"
"2021-08-13T04:36:51Z"
"2021-08-13T04:36:51Z"
NONE
null
Can we get a pop-up window option to save csv files from the dataframe, instead of saving directly to a path?
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Add return_inverse to .unique in EA interface closes
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CONTRIBUTOR
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- [x] closes #24245 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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BUG:nlargest/nsmallest does not act according to documentation with nan values
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"2021-08-16T09:21:23Z"
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np x = pd.Series([np.nan, np.nan, 1, 2, 3]) print(x.nlargest(n=4)) # 4 3.0 # 3 2.0 # 2 1.0 # dtype: float64 print(x.sort_values(ascending=False).head(4)) # 4 3.0 # 3 2.0 # 2 1.0 # 0 NaN # dtype: float64 ``` #### Problem description The [docs](https://pandas.pydata.org/docs/reference/api/pandas.Series.nlargest.html) state: > Faster than `.sort_values(ascending=False).head(n)` for small `n` relative to the size of the Series object. which sort of suggests they are the same when they are not. #### Expected Output I think the sort/head approach gives the correct answer here, so we should have: ``` python print(x.nlargest(n=4)) # 4 3.0 # 3 2.0 # 2 1.0 # 0 NaN # dtype: float64 ``` or the difference should be documented. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.9.6.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-80-generic Version : #90~18.04.1-Ubuntu SMP Tue Jul 13 19:40:02 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.3.1 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.3 setuptools : 57.4.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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ENH: SQL apply
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"2021-08-22T16:31:09Z"
"2021-08-22T16:31:09Z"
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Hello, Is it possible to add natively this method to pandas.DataFrame ? ```python import random, string, typing from pandas.io.sql import to_sql, read_sql schars = """!"#$%&\'()*+,-./:;<=>?@[\\]^`{|}~ éèùà$£µç""" def sql( self, sql: str, con: "Engine" = None, dispose: bool = False, schema: typing.Any = None, **kwargs: typing.Any ): from sqlalchemy import create_engine, inspect if con is None: con, dispose = create_engine('sqlite:///:memory:'), True elif isinstance(con, str): con, dispose = create_engine(con), True elif isinstance(con, dict): con, dispose = create_engine(**con), True # tmp name name = ''.join(random.choice(string.ascii_uppercase) for i in range(24)) sql = sql.replace('{df}', name) if isinstance(self.columns, pandas.MultiIndex): new = self.copy() new.columns = [" ".join(col).strip() for col in new.columns.values] to_sql( new, name=name, con=con, schema=schema, if_exists='replace', method='multi' ) else: to_sql(self, name=name, con=con, if_exists='replace', schema=schema, method='multi') parse_dates = [c for c in self.columns.values if self[c].dtype.kind == 'M'] try: return read_sql( sql, con=con, parse_dates=parse_dates, **kwargs ) except Exception as e: raise e finally: # Clean ## Check sepical chars if any(x in name for x in schars): name = '"%s"' % name if schema is not None: if any(x in schema for x in schars): schema = '"%s"' % schema name = "%s.%s" % (schema, name) con.execute('DROP TABLE IF EXISTS %s' % name) if dispose: con.dispose() ``` Could be cool feature Br Nicolas
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DOC: update Ecosystem page, and define governance
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"2021-08-12T18:51:26Z"
"2021-08-21T20:52:08Z"
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#### Location of the documentation [Ecosystem](https://pandas.pydata.org/community/ecosystem.html) (wepage), [ecosystem.rst](https://github.com/pandas-dev/pandas/blob/318bed9f7c16c3352178cbf68e8dddfaba32ed6d/doc/source/ecosystem.rst) (file) #### Documentation problem - There is some deprecated library: [qgrid](https://github.com/quantopian/qgrid) is [one of them](https://github.com/quantopian/qgrid/issues/348). - There is some cutting edge/popular library that are missing [Great expectation](https://github.com/great-expectations/great_expectations) is probably one of them. #### Suggested fix for documentation ##### Deprecated library - [ ] List deprecated libraries - [ ] Remove deprecated libraries (or put them in a deprecated section) - [ ] Replace deprecated package for the best (or bests) available alternative - [ ] [Governance] Define criteria for "what is a deprecated package" or a governance system (e.g. is @TomAugspurger Engarde library deprecated?) ##### library to include - [ ] <details> <summary>List potential library to include</summary> [Great expectation](https://github.com/great-expectations/great_expectations) , [Pandas-profiling](https://pandas-profiling.github.io/pandas-profiling/docs/master/index.html), [ipydatagrid](https://github.com/bloomberg/ipydatagrid), [VS-Code](https://github.com/microsoft/vscode), [missingno](https://github.com/ResidentMario/missingno) [streamlit](https://streamlit.io/), [dataprep](https://dataprep.ai/), [mito](https://trymito.io/), ⚠️This list is clearly no exhaustive.⚠️ </details> - [ ] Define which to include or not - [ ] [Governance] Define how to define packages to include I am willing to try to do the job if I am helped ;-) Ps: This issue is related to https://github.com/pandas-dev/pandas/issues/24927
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DISC: remove Index and EA subclasses
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"2021-08-12T19:51:56Z"
"2021-10-22T10:23:15Z"
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There has been discussion in eg #39133 about having EA-backed Indexes held in base-class Index objects, similar discussion regarding the new NumericIndex. I do not like the idea of having _some_ non-object dtypes return the base class Index, but would be on board for making _all_ dtypes use the base class. i.e. only have Index and deprecate/remove all the subclasses. I'm envisioning roughly 1) Implement relevant methods on the underlying EAs, eg DatetimeIndex.get_loc logic might live in DatetimeArray.get_loc. 2) ??? for RangeIndex and MultiIndex, maybe make corresponding EAs? 3) Methods currently available on subclasses but not Index would be accessed via accessor, e.g. `dti.is_year_start` becomes `idx.foo.is_year_start` Related but independent, we could do the same with ExtensionArray subclasses. Instead of having authors implement methods on their EA subclass, they implement it on the ExtensionDtype subclass, which the EA object then dispatches to.
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DOC: Fixed all remaining GL02 documentation errors and added appropriate check in CI
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"2021-08-12T21:26:18Z"
"2021-08-19T01:58:24Z"
"2021-08-19T01:58:19Z"
CONTRIBUTOR
null
- Closes #42809 - Adds check for GL02 in CI
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DOC: Update param doc of to_parquet about compression
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"2021-08-17T16:20:41Z"
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CONTRIBUTOR
null
Users are misled by current `compression` param doc. Actually more compressions like "ZSTD" are supported by pyarrow engine. This patch updates the param doc to reflect the supported compressions.
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- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry Just testing the numpy wheels in preparation for making our own.
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Testing to_datetime, converting none to NaT
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"2021-10-16T20:34:01Z"
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Addition of a test. Bug was fixed for #35888 somewhere in the main branch, this test just adds a unit test. - [x] closes #35888 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry (not sure if applicable here, can someone advise?)
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Backport PR #43005 on branch 1.3.x (CI: Run python dev with numpy python 3.10 wheels)
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Backport PR #43005: CI: Run python dev with numpy python 3.10 wheels
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BUG: `equals` method failed for nested `Series`/`DataFrame`
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"2021-08-21T20:53:17Z"
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python inner_foo = pd.Series(dtype=float) inner_bar = inner_foo.copy() foo = pd.Series({0: inner_foo}) bar = pd.Series({0: inner_bar}) foo.equals(bar) ``` #### Problem description Get an exception: `ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().` This issue applies to both `Series` and `DataFrame`, that `equals` method failed if there is a nested `Series` or `DataFrame`. Note that `pd.testing.assert_series_equal(foo, bar)` still works. #### Expected Output `True` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.7.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 52.0.0.post20210125 Cython : 0.29.24 pytest : 6.1.2 hypothesis : 6.14.1 sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.1 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.0 </details>
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POC: use github issue form for usage questions
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MEMBER
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#42951 has the context - figured the usage question form would be a good starting point as the least crucial issue template, but a common source of issues not being completely filled in. Wasn't sure about the best way to test this out, so I just have a repo https://github.com/mzeitlin11/issue-forms which I'll try to keep up to date with the state of the PR. Anyone should feel free to open issues there and play around with it.
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``` from asv_bench.benchmarks.stat_ops import * self = SeriesOps() self.setup("mad", "int") %timeit self.time_op("mad", "int") 1.45 ms ± 22.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master 474 µs ± 2.77 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR ```
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43,011
ENH: add efficient `groupby` for sorted dataframe
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"2021-08-13T02:58:18Z"
"2021-08-21T20:53:39Z"
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#### Feature description I wish I could use Pandas to efficiently group a large data frame by a sorted column. Since many sorting algorithms have time complexities of `O(nlog(n))` and splitting a column according to its consecutive same value requires time complexity of just `O(n)`, given that the current `groupby` of Pandas has the time complexity of O(n^2), it would be better if we can perform `dataframe.groupby` by the sorted column, by means of splitting the dataframe according to consecutive same values in the sorted column. In this way, the groupby operation has the time complexity of `O(n)`. Together with the time complexity of sorting, the total time complexity is `O(nlog(n))`. #### Here is my solution `DataFrame.groupby` should get a new parameter `sorted` or something intuitive to perform `groupby` with `O(n)` time complexity (see the code below, though the returned object should be a DataFrameGroupBy object, not a dict.) #### Existing similar implementation see [here](https://github.com/AdeBC/easyGroup/blob/be17515b3280a44d536cbe36f621656fa3e5d4d1/easyGroup/src/groupper.py#L5). ```python def groupby(dataframe, by): """ Groupby the dataframe (:param dataframe) according to the values of the column by (:param by). low_memory (:param low_memory) mode can only work when the input dataframe are sorted by the column by. :return: extracted Groups. """ df = dataframe[~dataframe[by].isna()] col = df[by] group_sizes = pd.Series(perGroupSize(col)) # get size of each group according to the consecutive same values, `O(nlog(n))` ends = group_sizes.cumsum() begins = ends.shift(1).fillna(0).astype(int) group_names = tqdm(group_sizes.index, desc='Generating groups from original dataframe, progress: ', total=group_sizes.shape[0]) groups = {name: df.iloc[begins[name]:ends[name], :].reset_index(drop=True) for name in group_names} return groups ``` Does this make sense? If so, I can implement this in Pandas and create a PR.
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pandas==1.0.3 In [16]: df.where(df.notnull(), None) Out[16]: A B 0 2013-01-01 1 1 NaT 2 2 2013-01-03 None but pandas==1.3.1 In [16]: df.where(df.notnull(), None) Out[16]: A B 0 2013-01-01 1 1 NaT 2 2 2013-01-03 NaN why? @MarcoGorelli
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DEPR: in behavior for Series comparing index membership
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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 of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas series = pandas.Series([1]) series.index = index=pandas.date_range("20180101", periods=1) inOperatorOutput = 1 in series listOperatorOutput = 1 in list(series) assert inOperatorOutput == listOperatorOutput ``` #### Problem description This code yields an assertion error. Because the `in` operator looks into the index of a series while the list function will take the values. This is not the same way how python lists and other python objects work and therefore it confuses people. I think the best solution would be to let the `in` operator take the values instead of the index. FYI, here is an SO thread about this topic: https://stackoverflow.com/questions/51962778/python-in-operator-not-working-as-expected-when-comparing-string-and-strftime-va/51962950?noredirect=1#comment121535570_51962950
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--- I would like to concatenate two incomplete data frame with the same data (in theory) regarding a similar index. I tried with pd.concat but I don't managed to get what I need. Here is a simple example of what I would like to do : ```python df1 = pd.DataFrame( { "A": ["A0", "A1", "A2", "A3"], "B": ["B0", "B1", "B2", "B4"], "C": ["C0", "C1", "C2", "B5"], "D": [np.nan,np.nan,np.nan,np.nan,] }, index=[0, 1, 2, 3],) df2 = pd.DataFrame( { "A": ["A0", "A1", "A5", "A6"], "B": ["B0", "B1", "B5", "B6"], "C": [np.nan,np.nan,np.nan,np.nan,], "D": ["D0", "D1", "D5", "D6"], }, index=[0, 1, 5, 6] ) res_expected = pd.DataFrame( { "A": ["A0", "A1", "A2", "A3", "A5", "A6"], "B": ["B0", "B1", "B2", "B3", "B5", "B6"], "C": ["C0", "C1", "C2", "B5",np.nan,np.nan,], "D": ["D0", "D1", np.nan,np.nan,"D5", "D6"], }, index=[0, 1, 2, 3, 5, 6] ) ``` Does someone have an idea ? Thanks !
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BUG: `rolling` fails when `Series` contains `pd.NA`
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python s = pd.Series([0, 1, pd.NA], dtype="Int64") s.rolling(2).mean() ``` #### Problem description Get an exception `ValueError: cannot convert to 'float64'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.` `Int` type is a good implementation, as it seems to be the only way to preserve integer and missing value (otherwise the type is cast as `float64` to accommodate `np.nan`). However, `pd.NA` as the missing value indicator seems not treated similarly to `np.nan` and cause the operation above to fail. #### Expected Output #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.7.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 52.0.0.post20210125 Cython : 0.29.24 pytest : 6.1.2 hypothesis : 6.14.1 sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.1 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.0 </details>
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BUG: Default value for `Series(dtype=int)` is not `pd.NA`
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python pd.Series(index=[0], dtype=int) ``` #### Problem description For other types, the default missing value indicator is set properly: ```Python pd.Series(index=[0], dtype=float) ``` 0 NaN dtype: float64 ```Python pd.Series(index=[0], dtype="datetime64[ns]") ``` 0 NaT dtype: datetime64[ns] ```Python pd.Series(index=[0], dtype=bool) ``` 0 False dtype: bool (This might be tricky, but understandable) However, for integer types, the default missing value is set to 0 instead of `pd.NA`, which is dangerous for interpretation. #### Expected Output Either ``` 0 <NA> dtype: Int64 ``` or ``` 0 NaN dtype: float64 ``` makes sense, given how `int64` is converted when having missing values, the latter case may be better. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.7.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 52.0.0.post20210125 Cython : 0.29.24 pytest : 6.1.2 hypothesis : 6.14.1 sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.1 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.0 </details>
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BUG: Inconsistent behavior of setting default value for type `bool`
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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 of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python print( pd.Series(dtype="bool").reindex([0]), "\n\n", pd.Series(index=[0], dtype="bool"), "\n\n", pd.Series(dtype="bool").reindex([0]).astype(bool), ) ``` #### Problem description This is the output: ``` 0 NaN dtype: object 0 False dtype: bool 0 True dtype: bool ``` Semantically we may expect the same behavior for the code above. The second and third cases are definitely contradictory and dangerous. I use the following code to test some common types, and it fails for type `bool` (another case that would fail is `int`, which I reported in https://github.com/pandas-dev/pandas/issues/43017) ```Python for dtype in [bool, "Int64", "float", "datetime64[ns]", "timedelta64[ns]"]: print("***", dtype, "***") s1 = pd.Series(dtype=dtype).reindex([0]) s2 = pd.Series(index=[0], dtype=dtype) s3 = pd.Series(dtype=dtype).reindex([0]).astype(dtype) print(s1.equals(s2), s1.equals(s3)) ``` #### Expected Output Either ``` 0 NaN dtype: object ``` or ``` 0 False dtype: bool ``` makes sense. However, we get `True` when testing `bool(np.nan)`, which explains some of the inconsistent behavior above. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.7.11.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.None pandas : 1.3.1 numpy : 1.20.3 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.2 setuptools : 52.0.0.post20210125 Cython : 0.29.24 pytest : 6.1.2 hypothesis : 6.14.1 sphinx : 4.0.2 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : None jinja2 : 3.0.1 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.07.0 fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : 0.4.2 scipy : 1.6.2 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.0 </details>
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BUG: seperate df dtypes from different initialization
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CONTRIBUTOR
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- [x] closes #42971 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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43,020
Performance of ExtensionArray display in DataFrame/Series
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"2021-11-15T15:26:35Z"
"2021-11-15T15:26:21Z"
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When printing a DataFrame/Series that is backed by an ExtensionArray the values are first converted to a numpy array: https://github.com/pandas-dev/pandas/blob/c03ee857f880f94461d3344c11bfb703885fde00/pandas/io/formats/format.py#L1561-L1574 This is problematic for ExtensionArrays that are very expensive to convert to numpy arrays. i.e. converting an extremely large ExtensionArray to a numpy array to ultimately print ~10 values. Can this be overcome or worked around? There is a already a special case for categorical here.
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