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CI/TST: Supply dtype to coo_matrix until scipy is fixed
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2021-02-24T15:48:17Z
2021-02-24T23:15:40Z
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them Fixes CI for Windows 3.7 np1.6; regression in scipy 1.6.1 (https://github.com/scipy/scipy/issues/13585) Alternative would be to pin build, wasn't sure which is prefered over the other. Will open tracking issue once the method is decided.
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TST: find solution for extension/tests_numpy.py (PandasArray base extension tests)
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2021-02-24T15:55:33Z
2021-02-24T17:14:06Z
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MEMBER
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We currently run the base extension tests also for the `PandasArray` (to have coverage of this array class), but because we actually don't allow to store this in a Series/DataFrame, the tests do some patching to workaround this: https://github.com/pandas-dev/pandas/blob/dec7d213fb27e9eafb61201f173c241d7105f772/pandas/tests/extension/test_numpy.py#L36-L54 However, this regularly gives problems, see eg https://github.com/pandas-dev/pandas/pull/39991#discussion_r581114674 or https://github.com/pandas-dev/pandas/pull/39722#discussion_r573896611, where for some reason those PandasArrays do / don't get preserved in a DataFrame/Series due to some other code changes that impact this, and the tests are not written to deal with it. (one example: `extract_array` will convert a pandas Series with numpy dtype into a PandasArray, when the patch is enabled. This can then cause the result to include the PandasArray EA, while the expected result is not created that way) So we should try to find a solution for this (as it gets quite annoying to deal with it just to get those tests passing). But I don't directly know what the best solution would be. Some ideas: - Simply remove `tests/extension/test_numpy.py` and thus don't run the base extension interface tests for PandasArray. We still have `tests/arrays/test_numpy.py` for some other tests. The easiest solution, but would also remove test coverage. - In some way only run the tests in `tests/extension/test_numpy.py` that only deal with the array, and not with the array stored in DataFrame/Series. cc @TomAugspurger @jbrockmendel
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2021-02-24T15:59:13Z
2021-04-08T01:15:19Z
2021-04-08T00:53:58Z
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 xerf #37277 cc @TomAugspurger this is failing one of the dask tests locally, looks like dask is calling `index.array`. Thoughts on how to handle this? Might be worth folding the RangeIndex check into a keyword in extract_array.
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Adding a down-sampling function based on an input list/array for new column/index resolution. Does not depend on uniform distribution.
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#### Is your feature request related to a problem? I recently posted an inquiry on Stack Overflow requesting assistance regarding Pandas and down-sampling. I had some forward binned data, particle counts logged at various diameters: 296.54,303.14,311.88,320.87,330.12,339.63,349.42,359.49,369.86,380.52,391.49,402.77 nm The counts were logged at between 296.54 and 303.14 nm, and so on. I wanted to re-bin those counts to a new set of diameters: 300,325,350,375,400 nm where the counts would be logged between 300 and 325 nm and so on. I had to, column by column, brute force add and interpolate matching diameters. For instance the 300-325 nm bin would contain part of the 296-303 bin, plus the bins up to 320 nm, and part of the 320-330nm bin. and so on. #### Describe the solution you'd like I would like to see a solution where one pandas dataframe can be interpolated to not a higher resolution, but a lower one, set by a second set of values, either on columns (my specific example) or indices. Currently the interpolate function only fills missing data, it does not, to my knowledge, completely rebuild the dataframe to an entirely new resolution, one that may not, in fact, be uniformly distributed. #### API breaking implications [this should provide a description of how this feature will affect the API] #### Describe alternatives you've considered The challenge is that the size spectrum cannot be linearly interpreted between the smallest and largest diameters. So a simple sum, re-index and interpolate would not conserve spectral data. Nor does the resample function do the trick since it applies to datetime indices only. #### Additional context This particular example if for forward-binned data. Some thought would have to be given to manage backward-binned or center-binned data. ### My code example I apologize for the crudity, but I am not a Pandas developer. Here is what my implementation ended up being: ```python # set up a 10x12 test array test_array=(np.arange(120)).reshape(10,12) # set a simple index index_list=list(string.ascii_lowercase)[:10] # initialize the high-resolution dataframe df=pd.DataFrame(test_array, index=index_list) # set the high-resolution column headers df.columns=[296.54,303.14,311.88,320.87,330.12,339.63,349.42,359.49,369.86,380.52,391.49,402.77] # set a column diameter array to search sort through df_column_array=np.array(df.columns) # set the new low-resolution diameters new_columns=np.array([300,325,350,375,400]) # intialize a low-res dataframe new_df=pd.DataFrame(columns=new_columns,index=index_list) # loop through the low-res columns up to the second last (since these bins are forward counted) for i,column in enumerate(new_columns[:-1]): df_columns_backward_index=np.searchsorted(df.columns,column)-1 # locate the closest high-res bin before the current low res bin df_columns_forward_index=np.searchsorted(df.columns,new_columns[i+1]) # locate the closest high-res bin after the current low res bin backward_bin_size=df.columns[df_columns_backward_index+1]-df.columns[df_columns_backward_index] # calculate the first high-res bin size # calculate the offset and fraction for interpolation backward_offset=column-df.columns[df_columns_backward_index] backward_fraction=1-backward_offset/backward_bin_size # repeat for the last high-res bins closest to the current low-res bin forward_bin_size=df.columns[df_columns_forward_index]-df.columns[df_columns_forward_index-1] forward_offset=df.columns[df_columns_forward_index]-new_columns[i+1] forward_fraction=1-forward_offset/forward_bin_size # sum the fraction of high-res bins partially in the low-res bin along with the high-res bins fully within the low-res bin new_df[column]=df.iloc[:,df_columns_backward_index]*backward_fraction+df.iloc[:,df_columns_backward_index+1:df_columns_forward_index-1].sum(axis=1)+df.iloc[:,df_columns_forward_index-1]*forward_fraction ```
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815,662,770
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40,024
BUG: Calling to_html with float_format strips all trailing zeros if an integer string is returned from the formatter
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2
2021-02-24T16:52:41Z
2021-04-11T12:39:10Z
2021-04-11T12:39:10Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd df = pd.DataFrame(data={'x': [1000.0, 'test']}) # Column dtype must be object df.to_html(float_format=lambda x: '{:,.0f}'.format(x)) ``` #### Output: ```html '<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>x</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1,</td>\n </tr>\n <tr>\n <th>1</th>\n <td>test</td>\n </tr>\n </tbody>\n</table>' ``` #### Problem description This caused by the rstrip in pandas.io.formats.format._trim_zeros_single_float() #### Expected Output ```html '<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>x</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1,000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>test</td>\n </tr>\n </tbody>\n</table>' ``` #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 20.3.3 setuptools : 51.3.3 ``` </details>
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Backport PR #40019 on branch 1.2.x (DOC: Edit regression fix release note)
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2021-02-24T17:03:28Z
2021-02-25T00:45:25Z
2021-02-25T00:45:25Z
NONE
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Backport PR #40019: DOC: Edit regression fix release note
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815,693,044
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40,026
REF: extract_array earlier in block construction
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1
2021-02-24T17:29:07Z
2021-02-25T07:35:55Z
2021-02-25T00:40:59Z
MEMBER
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The idea is to eventually get the extract_array out of maybe_coerce_values which will improve `Block.__init__` perf. The main remaining blocker to that is downstream usage. @jorisvandenbossche i know pyarrow accesses some of core.internals, do you know if it is just `make_block` or is anything else accessed?
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CLN/TST: Revert supplying dtype to scipy.sparse.coo_matrix
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2021-02-24T18:01:58Z
2021-02-24T18:01:58Z
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Ref: #40020
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40,028
REF: catch less in maybe_cast_to_datetime
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1
2021-02-24T18:07:52Z
2021-02-24T21:15:55Z
2021-02-24T21:15:55Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0NTc5NTA3NDI1
40,029
STYLE: Inconsistent namespace - apply (pandas-dev#39992)
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5
2021-02-24T18:13:39Z
2021-02-24T18:55:17Z
2021-02-24T18:55:16Z
CONTRIBUTOR
null
Fix inconsistent namespace in apply directory. - [x] xref #39992 - [ ] 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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815,746,847
MDExOlB1bGxSZXF1ZXN0NTc5NTI1NTk5
40,030
REF: share common code in ArrayManager.get_numeric_data/get_bool_data
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1
2021-02-24T18:45:16Z
2021-02-24T20:45:42Z
2021-02-24T20:45:38Z
MEMBER
null
See https://github.com/pandas-dev/pandas/pull/39719/files#r578859353 @jreback
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STYLE: Inconsistent namespace - base (pandas-dev#39992)
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4
2021-02-24T18:53:05Z
2021-02-25T04:45:43Z
2021-02-24T19:23:44Z
CONTRIBUTOR
null
- [x] xref #39992 - [ ] 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: Styler format `na_rep` arg overwrites existing when not given.
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0
2021-02-24T19:41:58Z
2021-03-08T21:08:24Z
2021-03-08T21:08:24Z
CONTRIBUTOR
null
`Styler` has its own `format()` method that sets a formatter for columns, and a function `set_na_rep()` for dealing with missing data. It seems the two do not play well together: ``` df = pd.DataFrame([[1,2],[3,4]]) df.iloc[0,0] = np.nan df.style.set_na_rep('MISS').set_precision(1) MISS | 2 3.0 | 4 df.style.set_na_rep('MISS').format('{:,.1f}') nan | 2.0 3.0 | 4.0 df.style.set_na_rep('MISS').format('{:,.1f}', na_rep='PASS') PASS | 2.0 3.0 | 4.0 ``` could probably do with a review how the attribute `na_rep` interacts with the formatter and try to make it more dynamic or use pointers instead of static value, if possible.
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2021-02-25T00:36:39Z
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Backport PR #40020: CI/TST: Supply dtype to coo_matrix until scipy is fixed
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2021-02-24T23:10:37Z
2021-03-05T00:39:16Z
2021-03-05T00:39:10Z
CONTRIBUTOR
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- [X] closes #15621 - [X] tests 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 Proposed speed up for very simple use cases using the `pd.json_normalize` function. E.g. `pd.json_normalize(data)` The speed up can be seen in this example: ``` import datetime import pandas as pd # example json data data = {"hello": ["thisisatest", 999898, datetime.date.today()], "nest1": {"nest2": {"nest3": "nest3_value", "nest3_int": 3445}}, "nest1_list": {"nest2": ["blah", 32423, 546456.876, 92030234]}, "hello2": "string"} hundred_thousand_rows = [data for i in range(100000)] s = time() pd.json_normalize(hundred_thousand_rows) pandas_json_normalize_time_taken = time() - s print(f"\npandas time taken for a 100,000 rows: {pandas_json_normalize_time_taken} seconds") ``` With output from Pandas 1.2.2: `pandas time taken for a 100,000 rows: 3.0518009662628174 seconds` From this branch: `pandas time taken for a 100,000 rows: 0.632451057434082 seconds` To show tests pass for the appropriate file, ran `pytest pandas/tests/io/json/test_normalize.py -v` [test_normalize.py_pytest.log](https://github.com/pandas-dev/pandas/files/6039361/test_normalize.py_pytest.log) To show pre-commit passed on file, ran `pre-commit run --files pandas/io/json/_normalize.py` [_normalize.py_pre-commit.log](https://github.com/pandas-dev/pandas/files/6039363/_normalize.py_pre-commit.log) There was one code check that was caught, running `./ci/code_checks.sh` [code_checks.log](https://github.com/pandas-dev/pandas/files/6039360/code_checks.log) ``` pandas/io/json/_normalize.py:208: error: Incompatible types in assignment (expression has type "List[Union[List[Dict[Any, Any]], Dict[Any, Any]]]", variable has type "Dict[Any, Any]") [assignment] ``` As it a type hint issue, decided to still make pull request. Can you advice on how to fix, I'm fairly new to type hints? Kind regards, Sam
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REF: catch less in maybe_cast_to_datetime
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2021-02-24T23:39:11Z
2021-02-25T01:49:16Z
2021-02-25T00:46:31Z
MEMBER
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815,939,507
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40,038
ENH: change behavior in reading malformat file
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2021-02-24T23:49:13Z
2021-08-19T01:15:41Z
2021-08-19T01:15:41Z
NONE
null
#### Is your feature request related to a problem? Currently, for large CSV files, pandas parse a column with mixed data types with only a warning (DtypeWarning) [https://github.com/pandas-dev/pandas/blob/v1.2.2/pandas/errors/__init__.py#L57-L112](url) [https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/parsers.pyx](url) This behavior of keeping mix data type in a column is totally unexpected and a warning for it is not enough. #### Describe the solution you'd like Just convert all the values of the column to one type and produce a warning. For example, if it is a mix of int and object, just convert all to object and then produce a warning. This way, there would be no unexpected behavior/error later in the manipulation of DataFrame. #### API breaking implications API will not change. The behavior of API becomes more predictable.
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40,039
BUG: Sampling over selected groupbys does not reflect the selection
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1
2021-02-25T03:01:58Z
2021-03-01T03:31:38Z
2021-02-27T19:10:19Z
CONTRIBUTOR
null
- [x] closes #39928 - [x] 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 - [x] whatsnew entry
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40,040
Backport PR #40010 on branch 1.2.x
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2
2021-02-25T04:29:55Z
2021-06-05T20:50:47Z
2021-02-26T00:11:36Z
CONTRIBUTOR
null
Backport PR #40010
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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 ROWS=[1000, 2000, 4000, 8000, 16000, 32000] for rows in ROWS: print("rows", rows) df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) %timeit df.groupby("a").apply(lambda x: x.head(1)) for rows in ROWS: print("rows", rows) df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) %timeit df.groupby("a").apply(lambda x: x.max()) ``` #### Problem description As a result of comparing 1.2.2 with 0.25.3, 1.2.2 is much slower. In particular, the head processing time is proportional to the number of lines in 0.25.3, but in 1.2.2, when the number of lines doubles, the processing time triples. ``` pandas 1.2.2 In [1]: for rows in ROWS: ...: print("rows", rows) ...: df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) ...: %timeit df.groupby("a").apply(lambda x: x.head(1)) ...: rows 1000 187 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) rows 2000 421 ms ± 2.86 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 4000 1.06 s ± 13 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 8000 3.01 s ± 59.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 16000 9.49 s ± 81.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 32000 32.3 s ± 214 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) In [2]: for rows in ROWS: ...: print("rows", rows) ...: df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) ...: %timeit df.groupby("a").apply(lambda x: x.max()) ...: rows 1000 314 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 2000 599 ms ± 2.48 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 4000 1.23 s ± 17.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 8000 2.49 s ± 18.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 16000 4.94 s ± 22.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 32000 9.81 s ± 50.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` ``` pandas 0.25.3 In [9]: for rows in ROWS: ...: print("rows", rows) ...: df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) ...: %timeit df.groupby("a").apply(lambda x: x.head(1)) ...: rows 1000 103 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) rows 2000 212 ms ± 1.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 4000 414 ms ± 1.88 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 8000 837 ms ± 2.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 16000 1.69 s ± 2.62 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 32000 3.41 s ± 36.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) In [10]: for rows in ROWS: ...: print("rows", rows) ...: df = pd.DataFrame({"a": np.random.randint(0, rows, size=rows).astype(str), "b": range(0, rows)}) ...: %timeit df.groupby("a").apply(lambda x: x.max()) ...: rows 1000 118 ms ± 827 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) rows 2000 235 ms ± 652 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 4000 473 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 8000 945 ms ± 9.25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 16000 1.87 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) rows 32000 3.74 s ± 19.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.8.8.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16-arch1-1 Version : #1 SMP PREEMPT Sat, 13 Feb 2021 20:50:18 +0000 machine : x86_64 processor : byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 53.0.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 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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40,042
TST: collect indexing tests by method
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BUG: Sum on large integers incorrect after rolling timewindow
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2021-02-25T08:15:31Z
2021-02-27T18:06:31Z
2021-02-27T18:06:25Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd from datetime import datetime, timedelta s1 = pd.Series([138, 2377900603251717750], index=pd.Index([datetime(year=1970, month=1, day=1), datetime(year=1970, month=1, day=1)+timedelta(microseconds=1)])) s2 = s1.rolling("1D").sum() print(s2.sum() >= s1.sum()) ``` #### Problem description The sum of the rolling window should be higher or equal to the original series because only positive values are in it. However, 'False' is printed. #### Expected Output True #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.7.10.final.0 python-bits : 64 OS : Darwin OS-release : 20.3.0 Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.2.2 numpy : 1.19.4 pytz : 2020.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : None pytest : 6.2.2 hypothesis : 6.3.1 sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : 4.5.2 html5lib : None pymysql : 1.0.2 psycopg2 : None jinja2 : 2.11.2 IPython : 7.20.0 pandas_datareader: None bs4 : 4.7.1 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.2.2 numexpr : None odfpy : None openpyxl : 3.0.6 pandas_gbq : None pyarrow : 3.0.0 pyxlsb : None s3fs : None scipy : 1.6.0 sqlalchemy : 1.3.23 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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BUG: plotting with `secondary_y` and then plotting another line results in wrong color for (right) line
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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 as pd df = pd.DataFrame({'a': [1, 1], 'b': [2, 2]}) df2 = pd.DataFrame({'d': [2.5, 2.5]}) ax = df.plot(legend=True, secondary_y='b') df2.plot(legend=False, ax=ax); ``` #### Problem description Here's what we get: ![image](https://user-images.githubusercontent.com/33491632/109143309-c96a9e80-7757-11eb-81e0-1fd586cd36e1.png) #### Expected Output The colours in the legend should be correct, i.e.: ![image](https://user-images.githubusercontent.com/33491632/109143461-f4ed8900-7757-11eb-9cdd-3c629f106bc4.png) #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 272435073c70b4421f72184663837f4864601f11 python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-66-generic Version : #74-Ubuntu SMP Wed Jan 27 22:54:38 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 1.3.0.dev0+841.g272435073c numpy : 1.19.5 pytz : 2021.1 dateutil : 2.8.1 pip : 20.3.3 setuptools : 49.6.0.post20201009 Cython : 0.29.21 pytest : 6.2.2 hypothesis : 6.2.0 sphinx : 3.5.1 blosc : None feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.2 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.19.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.8.5 fastparquet : 0.5.0 gcsfs : 0.7.2 matplotlib : 3.3.3 numexpr : 2.7.2 odfpy : None openpyxl : 3.0.6 pandas_gbq : None pyarrow : 2.0.0 pyxlsb : None s3fs : 0.5.2 scipy : 1.6.0 sqlalchemy : 1.3.23 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.2 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.52.0 </details>
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BUG: Series.groupby.sum fails because result has _values attribute but not ndim attribute
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2021-02-25T10:59:17Z
2021-02-26T11:08:31Z
2021-02-26T11:07:21Z
CONTRIBUTOR
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python import pandas as pd class TestClass: def __init__(self, num): self.num = num self._values = [] def __add__(self, other): return TestClass(self.num + other.num) s = pd.Series( [TestClass(1), TestClass(2), TestClass(3)], index=pd.Index([0,1,1], name="ind"), ) s.sum() s.groupby("ind").sum() ``` #### Problem description s.sum() works but, for pandas >= 1.2.0, s.groupby("ind").sum() results in the following error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\groupby.py", line 1654, in sum npfunc=np.sum, File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\groupby.py", line 1044, in _agg_general result = self.aggregate(lambda x: npfunc(x, axis=self.axis)) File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\generic.py", line 259, in aggregate return self._python_agg_general(func, *args, **kwargs) File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\groupby.py", line 1173, in _python_agg_general result, counts = self.grouper.agg_series(obj, f) File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\ops.py", line 706, in agg_series return self._aggregate_series_fast(obj, func) File "C:\Users\rclement\tempenv\lib\site-packages\pandas\core\groupby\ops.py", line 732, in _aggregate_series_fast result, counts = grouper.get_result() File "pandas\_libs\reduction.pyx", line 260, in pandas._libs.reduction.SeriesGrouper.get_result File "pandas\_libs\reduction.pyx", line 77, in pandas._libs.reduction._BaseGrouper._apply_to_group File "pandas\_libs\reduction.pyx", line 289, in pandas._libs.reduction.extract_result AttributeError: 'list' object has no attribute 'ndim' The error is due to an assumption in pandas\_libs\reduction.pyx in the following function, where if the result of the sum has an attribute _values, then it is also assumed to have an attribute ndim (and the __len__ method), which is not necessarily true for user defined objects like "TestClass" ```python cpdef inline extract_result(object res, bint squeeze=True): """ extract the result object, it might be a 0-dim ndarray or a len-1 0-dim, or a scalar """ if hasattr(res, "_values"): # Preserve EA res = res._values if squeeze and res.ndim == 1 and len(res) == 1: res = res[0] if hasattr(res, 'values') and is_array(res.values): res = res.values if is_array(res): if res.ndim == 0: res = res.item() elif squeeze and res.ndim == 1 and len(res) == 1: res = res[0] return res ``` #### Expected Output ```python >>> s.sum() <__main__.TestClass object at 0x000002359C7F2C08> >>> s.groupby("ind").sum() ind 0 <__main__.TestClass object at 0x000002359C7F2E48> 1 <__main__.TestClass object at 0x000002359C80B788> dtype: object ``` We could check hasattr(res, "ndim") and hasattr(res, "__len__") is True, before trying to access those attributes, but it seems like the check for _values attribute is being used as a proxy for a more concrete assumption, eg is the result a pandas object? Could the check for _values attribute be replaced with a more explicit check? #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.7.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.18362 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 19.2.3 setuptools : 41.2.0 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 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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40,046
VIS Wrong legend when combining multiple plots on the same axes , plotting with `legend=False` messes up legend
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2021-02-25T12:36:46Z
2021-02-25T13:11:42Z
2021-02-25T13:11:42Z
MEMBER
null
- [x] closes #39522 - [x] closes #40044 - [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 So, in #27808 , `leg.legendHandles` was replaced with `ax.get_legend_handles_labels()[0]` in order to preserve marker info. However, `ax.get_legend_handles_labels()[0]` may contain extra elements which aren't (yet) visible, which means we may end up at https://github.com/pandas-dev/pandas/blob/272435073c70b4421f72184663837f4864601f11/pandas/plotting/_matplotlib/core.py#L625 with ```python (Pdb) handles [<matplotlib.lines.Line2D object at 0x7f0938a37430>, <ErrorbarContainer object of 3 artists>, <matplotlib.lines.Line2D object at 0x7f0938a37430>] (Pdb) labels ['orange', 'blue'] ``` This causes the permuting noted in #39522 Ideally, there would be a way to get `leg.legendHandles` in a way that preserves the marker info. However, I can't find a way to do that - have asked on the matplotlib discourse. Also, the current test doesn't quite work (as it passes on `master` too). I'm working on finding a satisfactory solution --- On master: ![image](https://user-images.githubusercontent.com/33491632/109154213-e4440f80-7765-11eb-8c76-7d70ccddf447.png) On this branch: ![image](https://user-images.githubusercontent.com/33491632/109154268-f8880c80-7765-11eb-839f-e239a909b8fe.png) --- cc @charlesdong1991 in case you have comments / suggestions
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[ArrayManager] Groupby cython aggregation - python pyfallback
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2
2021-02-25T13:37:05Z
2021-02-25T17:05:43Z
2021-02-25T16:17:52Z
MEMBER
null
Follow-up on #39885
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CI: tests failing on 3.7 builds
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2
2021-02-25T13:41:47Z
2021-02-26T11:49:17Z
2021-02-25T23:56:20Z
CONTRIBUTOR
null
clearly something changed for dateutil ``` pytest -m "not slow and not network" -n auto --dist=loadfile -s --strict-markers --durations=30 --junitxml=test-data.xml pandas ImportError while loading conftest '/Users/runner/work/1/s/pandas/conftest.py'. pandas/__init__.py:51: in <module> from pandas.core.api import ( pandas/core/api.py:24: in <module> from pandas.core.algorithms import ( pandas/core/algorithms.py:39: in <module> from pandas.core.dtypes.cast import ( pandas/core/dtypes/cast.py:29: in <module> from dateutil.parser import ParserError E ImportError: cannot import name 'ParserError' from 'dateutil.parser' (/Users/runner/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/dateutil/parser/__init__.py) ```
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40,049
BUG: read_csv accepts bad lines when option usecols is used
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2021-02-25T14:25:06Z
2021-08-19T01:20:37Z
null
NONE
null
- [x] I have checked that this issue has not already been reported (couldn't find it) - [x] I have confirmed this bug exists on the latest version of pandas (current version is 1.2.2 on conda) - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example Bad CSV file: ```csv A,B,C foo,1,2 bar,bar,3,4 baz,5,6 ``` Python code: ```python import pandas as pd df_all = pd.read_csv('bad.csv', error_bad_lines = False, warn_bad_lines = False) print(df_all) # A B C # 0 foo 1 2 # 1 baz 5 6 df_sub = pd.read_csv('bad.csv', error_bad_lines = False, warn_bad_lines = False, usecols = ['A','B']) print(df_sub) # A B # 0 foo 1 # 1 bar bar # 2 baz 5 ``` #### Problem description As can be seen in the example above, when not using `usecols`, `read_csv` correctly ignores the line that is too long. However, this is not the case when `usecols` is used. #### Expected Output Using `usecols` should not affect which lines are discarded due to being too long. #### Output of ``pd.show_versions()`` <details> ``` INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.3.0-64-generic Version : #58-Ubuntu SMP Fri Jul 10 19:33:51 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.0 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None ``` </details>
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40,050
[ArrayManager] Remaining GroupBy tests (fix count, pass on libreduction for now)
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2021-02-25T16:41:01Z
2021-03-01T09:14:13Z
2021-02-27T19:02:03Z
MEMBER
null
Follow-up on #39885 and #40047 This passes / skips all groupby tests (in `/tests/groupby`). Actual code changes: - fix `groupby().count()` to not create a ArrayManager with 2D arrays - for now skip the usage of `fast_apply` / `libreduction.apply_frame_axis0` for DataFrames with ArrayManager. This can be tackled separately, while for now already getting more test coverage by using the python fallback (as we eg also do for EAs). - implement `ignore_failures` for `ArrayManager.grouped_reduction` (this keyword is actually always True at the moment, so we could also leave away the keyword itself) Most of the skips are related `quantile` (or `describe`) not yet being implemented. And it also uncovered a few other failures, that can be worked on as follow-ups.
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40,051
TST: fix moved test reference
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1
2021-02-25T16:51:24Z
2021-02-25T19:27:25Z
2021-02-25T19:27:25Z
MEMBER
null
Fixup for https://github.com/pandas-dev/pandas/pull/40042
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REF/BUG: cast back to datetimelike in pad/backfill
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0
2021-02-25T17:01:16Z
2021-02-27T19:02:03Z
2021-02-27T18:59:34Z
MEMBER
null
Goal: simplify/perf for simple_new by requiring correct-type (no i8) values Problem: a bunch of places pass i8 values Solution: track then down and pass/return the right thing in the first place.
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BUG: revert parts of #40037
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1
2021-02-25T18:15:41Z
2021-02-26T00:17:09Z
2021-02-25T23:56:20Z
MEMBER
null
- [x] closes #40048 - [ ] 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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40,054
PERF: NDArrayBacked in cython
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2021-02-25T18:36:34Z
2021-04-15T02:28:29Z
2021-03-04T02:36:09Z
MEMBER
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Context: looking at backing `DatetimeBlock`/`TimedeltaBlock` by `DatetimeArray`/`TimedeltaArray` directly (as opposed to the status quo where we back by ndarrays) and profiling, the constructors stand out as a major source of overhead. Idea: put the ndarray-wrapping methods in cython Upsides: performance! stats currently pasted into the NDArrayBacked docstring: ``` import pandas as pd from pandas._libs.lib import NDArrayBacked as cls dti = pd.date_range("2016-01-01", periods=3) dta = dti._data arr = dta._ndarray obj = cls._simpler_new(arr, arr.dtype) # for foo in [arr, dta, obj]: ... %timeit foo.copy() 299 ns ± 30 ns per loop # <-- arr underlying ndarray (for reference) 530 ns ± 9.24 ns per loop # <-- dta with cython NDArrayBacked 1.66 µs ± 46.3 ns per loop # <-- dta without cython NDArrayBacked <-- i.e. master 328 ns ± 5.29 ns per loop # <-- obj with NDArrayBacked.__cinit__ 371 ns ± 6.97 ns per loop # <-- obj with NDArrayBacked._simpler_new %timeit foo.T 125 ns ± 6.27 ns per loop # <-- arr underlying ndarray (for reference) 226 ns ± 7.66 ns per loop # <-- dta with cython NDArrayBacked 911 ns ± 16.6 ns per loop # <-- dta without cython NDArrayBacked <-- i.e. master 215 ns ± 4.54 ns per loop # <-- obj with NDArrayBacked._simpler_new ``` i.e. `dta.copy()` is 3x faster, and within 2x of numpy. With further tweaks we could get within 10% of numpy (the 328 ns number). `dta.T` is 4x faster and within 2x of numpy. (T is more-improved than copy bc we override copy in python to copy `.freq`) Downsides: 1) pickle is a PITA, 2) constructor gymnastics Future: - lib.pyx is likely not the best place for this - can squeeze out more perf if we use `__cinit__`; requires more constructor gymnatics - can use in PeriodArray, Categorical, PandasArray with some effort - more methods could be optimized
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How to stop sorting in pivot_table and pivot?
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2021-02-25T19:31:24Z
2021-02-27T02:11:46Z
2021-02-27T02:11:46Z
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<img width="646" alt="stack1" src="https://user-images.githubusercontent.com/41958569/109206385-cb6f4480-77cd-11eb-8cee-f1cc90af0a10.PNG"> <img width="669" alt="stack2" src="https://user-images.githubusercontent.com/41958569/109206396-d0cc8f00-77cd-11eb-8633-42bd91a15877.PNG"> as in the image 2 the transformation from dataframe has been done but it is auto sorted, i couldn't find any way to unsort it or stop sorting! Is there any way sorting it?
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STYLE: Inconsistent namespace - groupby (#39992)
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2021-02-25T19:47:08Z
2021-02-25T20:34:26Z
2021-02-25T19:54:00Z
CONTRIBUTOR
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- [ ] closes #xxxx - [ ] 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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ENH: Group names in a groupby should be `namedtuple` instead of `tuple`
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2021-02-25T20:00:47Z
2021-08-19T01:21:25Z
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#### Is your feature request related to a problem? The columns in a DataFrame have names. When you iterate over a groupby object and get the names of the groups (which correspond to values in the original DataFrame), the group names are returned as ordinary `tuple`s and so their correspondence to the columns of the DataFrame is lost. Fields in the `name` tuple can only be accessed by their index; the more natural option of using the corresponding column name in the original DataFrame is not possible. #### Describe the solution you'd like When iterating over a groupby -- `for name, group in df.groupby(...):` -- `name` should be a `namedtuple` instead of an ordinary `tuple` so as to allow accessing fields by column name. Any other function that returns the name of a group should return it as a `namedtuple` and not a tuple. The ordinary considerations about what happens when an invalid Python identifier is used as a `namedtuple` field should occur (I think they get replaced with `_1`, `_2`, etc, but not sure about this), especially when the columns have names that are not strings. #### API breaking implications As far as I know, `namedtuple`s expose a strict superset of the API of `tuple`s, so there shouldn't be any breaking changes. #### Describe alternatives you've considered This very ugly solution: grouping_cols = ["col1", "col2"] g = df.groupby(grouping_cols) name, _ = next(iter(g)) name_col1 = name[grouping_cols.index("col1")] # now name_col1 is the value in the `name` tuple corresponding to the `"col1"` column #### Additional context Ideally I could simply write `name.col1`.
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2021-02-25T20:02:42Z
2021-02-28T14:14:15Z
2021-02-28T13:41:21Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] 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: pandas read_json
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2021-02-25T20:08:36Z
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NONE
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- [ ] 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 ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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2021-02-25T20:11:26Z
2021-03-05T21:17:05Z
2021-03-03T13:21:04Z
CONTRIBUTOR
null
- [X] closes #40032 - [X] tests added: `test_display_format_subset_interaction`: why: see below. - [X] tests removed: `test_format_bad_na_rep`: why: `na_rep` works if not string in some cases - changed to duck-typing. - [X] tests lefts unchanged that pass: `test_display_format`, `test_display_set_precision`, `test_format_subset`, `test_display_dict` - [X] tests expanded: `test_display_format_raises` - [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 The issue is that there are currently two competing methods in `Styler` for missing value represenatation: - `.set_na_rep('x')` and - `.format(na_rep='y')`. The two do not interact well together dynamically. This PR brings the `format wrapping` into the class so that `format` can be updated and inherit values in `set_na_rep` after it has been called. The image below highlights the impact this PR has and the added test replicates this pattern: ![Screen Shot 2021-02-25 at 20 38 45](https://user-images.githubusercontent.com/24256554/109210160-bfbf5600-77ac-11eb-8be7-8938d80f4ce6.png) Also at the bottom here, we add a `KeyError` to remove the ambiguity between what happens when a dict is provided with a column Key that is not specifically included within the provided `subset` slice.
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TYP: incorrect annotations in internals.construction
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BUG: maybe_convert_objects with timedelta64 nat
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1
2021-02-25T20:21:38Z
2021-02-25T23:04:49Z
2021-02-25T23:04:49Z
NONE
null
- [x] I have confirmed this bug exists on the latest version of pandas. import pandas as pd print(pd.__version__) 1.2.2 Problem described in other places: 1) https://github.com/pandas-dev/pandas/issues/21892 2) https://github.com/pandas-dev/pandas/pull/25573 3) https://github.com/pandas-dev/pandas/pull/30728 --- **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 pandas as pd content = '[{"score": 1,"color":"red"},{"score":10,"color":"green"}]' dtypes_dict = {'score': 'uint8', 'colour': 'category'} data_frame = pd.read_json(content, dtype=dtypes_dict) print(data_frame.dtypes) ``` #### Problem description When I run the above, I get the following dtypes for the dataframe columns: score uint8 color object #### Expected Output score uint8 color category
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816,788,095
MDExOlB1bGxSZXF1ZXN0NTgwMzkxMDQ1
40,064
REF/BUG: ensure we get DTA/TDA/PA back from Index._intersection
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0
2021-02-25T20:37:51Z
2021-02-27T19:25:35Z
2021-02-27T19:08:46Z
MEMBER
null
motivation: stop allowing i8 in _simple_new
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40,065
ERR: re-raise OverflowError as OutOfBoundsTimedelta
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0
2021-02-25T21:10:58Z
2021-02-26T22:42:57Z
2021-02-26T22:24:49Z
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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40,066
Mystery: bi-modal benchmarks
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1
2021-02-25T21:29:51Z
2021-06-26T18:57:01Z
2021-06-26T18:57:01Z
MEMBER
null
Running some asvs and seeing changes in benchmarks that should be unaffected, yet those changes are consistent across runs. Hunting this down, I'm replicating the most-weird benchmark (asv reports 1.27±0ms vs 789±3μs) in ipython ``` arr = np.ones(10**6, dtype="int8") ser = pd.Series(arr) func = ser.sum %timeit func() ``` The first few runs matched the asv results really closely: ``` In [4]: %timeit func() 781 µs ± 552 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- branch 1.27 ms ± 1.82 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master ``` But running it again just for kicks the results flipped: ``` In [5]: %timeit func() 1.27 ms ± 5.05 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- branch 780 µs ± 4.81 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master In [5]: %timeit func() 1.26 ms ± 691 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- branch 1.26 ms ± 578 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master In [10]: %timeit func() 1.26 ms ± 818 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- branch 773 µs ± 200 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master ``` Eventually the ~780 number stopped showing up and got replaced a mode between 895-907, but the 1.26-1.27 mode hasn't moved a bit. No obvious pattern as to which mode will show up in any given run (through they are positively auto-correlated). But in the asv results there must be something causing the modes to be split between the two branches. I get the same behavior even if I use the same branch, just different terminal windows. Looking at this with `%prun -s cumtime for i in range(1000): func()` points at `{method 'reduce' of 'numpy.ufunc' objects}` ``` ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 0.796 0.796 {built-in method builtins.exec} 1 0.001 0.001 0.796 0.796 <string>:1(<module>) 1000 0.001 0.000 0.795 0.001 generic.py:10747(sum) 1000 0.001 0.000 0.794 0.001 generic.py:10489(sum) 1000 0.002 0.000 0.793 0.001 generic.py:10454(_min_count_stat_function) 1000 0.004 0.000 0.791 0.001 series.py:4133(_reduce) 1000 0.004 0.000 0.778 0.001 nanops.py:82(_f) 1000 0.001 0.000 0.763 0.001 nanops.py:392(new_func) 1000 0.003 0.000 0.761 0.001 nanops.py:528(nansum) 1000 0.001 0.000 0.738 0.001 {method 'sum' of 'numpy.ndarray' objects} 1000 0.000 0.000 0.738 0.001 _methods.py:45(_sum) 1000 0.737 0.001 0.737 0.001 {method 'reduce' of 'numpy.ufunc' objects} # <-- 1000 0.003 0.000 0.017 0.000 nanops.py:256(_get_values) 4000 0.005 0.000 0.010 0.000 _ufunc_config.py:32(seterr) ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 1.292 1.292 {built-in method builtins.exec} 1 0.001 0.001 1.292 1.292 <string>:1(<module>) 1000 0.001 0.000 1.292 0.001 generic.py:10747(sum) 1000 0.001 0.000 1.291 0.001 generic.py:10489(sum) 1000 0.002 0.000 1.290 0.001 generic.py:10454(_min_count_stat_function) 1000 0.004 0.000 1.288 0.001 series.py:4133(_reduce) 1000 0.004 0.000 1.275 0.001 nanops.py:82(_f) 1000 0.001 0.000 1.260 0.001 nanops.py:392(new_func) 1000 0.002 0.000 1.258 0.001 nanops.py:528(nansum) 1000 0.001 0.000 1.235 0.001 {method 'sum' of 'numpy.ndarray' objects} 1000 0.000 0.000 1.234 0.001 _methods.py:45(_sum) 1000 1.234 0.001 1.234 0.001 {method 'reduce' of 'numpy.ufunc' objects} # <-- 1000 0.003 0.000 0.018 0.000 nanops.py:256(_get_values) 4000 0.005 0.000 0.010 0.000 _ufunc_config.py:32(seterr) ``` The total difference is 1.292 - 0.796 = 0.496 and the difference in the `reduce` ufunc is 1.234 - 0.737 = 0.497, which is pretty suggestive.
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BUG: groupby.rolling drops MultiIndex level
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1
2021-02-25T22:10:08Z
2021-02-26T00:20:25Z
2021-02-26T00:20:14Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. #### Code Sample, a copy-pastable example ```python # prepare a dataframe where problem becomes obvious (details not important) df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8, 'B': np.arange(40)}) df = df.reset_index().set_index(['index','A']).reorder_levels([1,0]) index_mapping = {a:(a-20)/10 for a in range(20,32)} df = df.rename(index=index_mapping) index_mapping = {a:(a-32)/10 for a in range(32,40)} df = df.rename(index=index_mapping) df = df.rename_axis(index={'index':'time'}) # groupby - rolling operation (here is the bug) df.groupby('A').rolling(4).mean() ``` #### Problem description Applying groupby.rolling on a multiindex dataframe should preserve the level that the rolling operation is applied to. Instead that index level is removed. This loses information and causes non-unique index values. ### **df** <img width="97" alt="Screen Shot 2021-02-25 at 4 58 03 PM" src="https://user-images.githubusercontent.com/35338267/109225102-d2c12e80-778a-11eb-92b5-27e094b60c78.png"> ### **df after applying groupby('A').rolling misses index level 'time'** <img width="73" alt="Screen Shot 2021-02-25 at 4 58 09 PM" src="https://user-images.githubusercontent.com/35338267/109225082-cb9a2080-778a-11eb-9ee8-c76351852e71.png"> #### Expected Output All index levels should be preserved. In older versions of pandas (1.1.2) the index levels were preserved, but there used to be a different bug: The whole multilevel index structure was duplicated, leading to twice the number of original levels. I guess that in an attempt to fix this, this new bug got introduced. #### Output of ``pd.show_versions()`` INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.8.1.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.UTF-8 pandas : 1.2.2 numpy : 1.20.1 pytz : 2020.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 41.2.0 Cython : 0.29.20 pytest : 5.4.2 hypothesis : None sphinx : 3.0.4 blosc : None feather : None xlsxwriter : None lxml.etree : 4.5.2 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.14.0 pandas_datareader: None bs4 : 4.9.1 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.1 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 0.16.0 pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.0 xlrd : 1.2.0 xlwt : None numba : 0.48.0 </details>
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MDExOlB1bGxSZXF1ZXN0NTgwNDU2NTk0
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BUG: construct_1d_ndarray_preserving_na with dt64/td64 and object dtype
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2
2021-02-25T22:39:29Z
2021-02-26T22:41:05Z
2021-02-26T22:25:42Z
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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2021-02-25T23:51:04Z
2021-02-26T22:36:05Z
2021-02-26T19:16:35Z
CONTRIBUTOR
null
- [x] xref #39992 - [ ] 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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1
2021-02-26T03:17:20Z
2021-02-26T15:17:05Z
2021-02-26T14:44:23Z
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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817,054,188
MDExOlB1bGxSZXF1ZXN0NTgwNjExNTA4
40,072
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3
2021-02-26T05:31:16Z
2021-07-01T14:16:55Z
2021-02-26T14:46:40Z
MEMBER
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- [x] closes #39784 - [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 ``` In [1]: idx=pd.date_range('20000101','20201231',periods=50000) ...: ...: df=pd.DataFrame(data=range(50000),index=idx) In [2]: %timeit df.ewm(halflife=pd.Timedelta('100d'),times=df.index).mean() 1.2 ms ± 13.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) <-- PR 10.2 s ± 32.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <-- master ```
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817,250,408
MDU6SXNzdWU4MTcyNTA0MDg=
40,073
BUG: Outer merge casts EA dtype to object
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3
2021-02-26T10:49:21Z
2021-09-03T21:52:08Z
2021-09-03T21:52:08Z
NONE
null
- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [ ] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python d1 = pd.DataFrame([("a",)], columns=["id"], dtype="string") d2 = pd.DataFrame([("b",)], columns=["id"], dtype="string") result = d1.merge(d2, on="id", how="outer") dtype = result.dtypes["id"] print(dtype) # expected 'string', but is 'object' ``` #### Problem description When doing an outer merge on a column of type `string`, the result contains this column with the type `object`. This makes it more difficult to use the new `string` type. The problem doesn't occur with the types `Int32`, `Float32`, `boolean`. It seems to be specific to the `string` type. The problem doesn't occur with a left, right or inner-merge. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.7.3.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 Version : Darwin Kernel Version 18.7.0: Tue Nov 10 00:07:31 PST 2020; root:xnu-4903.278.51~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : en_US.UTF-8 LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 20.2.4 setuptools : 52.0.0.post20210125 Cython : None pytest : 6.0.1 hypothesis : 5.30.1 sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 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 : 1.3.23 tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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817,255,127
MDExOlB1bGxSZXF1ZXN0NTgwNzc3Njc2
40,074
CLN: rename init_dict/ndarray to dict/ndarray_to_mgr for consistency
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2021-02-26T10:55:53Z
2021-02-26T14:29:00Z
2021-02-26T14:22:53Z
MEMBER
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See https://github.com/pandas-dev/pandas/pull/39991#discussion_r582632186, in case this is preferred for consistency with `arrays_to_mgr` (I don't have a strong preference myself, since they are only used in `__init__` the current name is also nice)
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40,075
REF: move logic of 'block manager axis' into the BlockManager
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5
2021-02-26T11:32:46Z
2021-03-05T16:35:45Z
2021-03-05T16:35:36Z
MEMBER
null
@jreback @jbrockmendel what are your thoughts about doing this? Currently the 0/1 -> 1/0 axis switch for the BlockManager happens on the DataFrame side, while I think it would be a bit cleaner to let this be the responsibility of the BlockManager method being called. For illustration, the diff of this PR is such a change for the `diff()` method: instead of using `_get_block_manager_axis` in frame.py, it's the `BlockManager.diff()` method that takes care of this (so just moving it down a level).
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2021-02-26T11:45:54Z
2021-02-26T11:46:11Z
2021-02-26T11:46:11Z
NONE
null
- [ ] I have checked that this issue has not already been reported. - [ ] 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 ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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817,323,247
MDU6SXNzdWU4MTczMjMyNDc=
40,077
BUG: Empty dataframe created with Index, not RangeIndex
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2
2021-02-26T12:37:44Z
2021-07-01T23:57:58Z
null
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 # Your code here >>> import pandas as pd >>> df = pd.DataFrame(columns=['a', 'b']) >>> print(df.index) Index([], dtype='object') >>> df2 = df.append(df, ignore_index=True) >>> print(df2.index) RangeIndex(start=0, stop=0, step=1) >>> pd.testing.assert_frame_equal(df, df2) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/stefan/miniconda3/envs/pandas/lib/python3.9/site-packages/pandas/_testing.py", line 1657, in assert_frame_equal assert_index_equal( File "/home/stefan/miniconda3/envs/pandas/lib/python3.9/site-packages/pandas/_testing.py", line 773, in assert_index_equal _check_types(left, right, obj=obj) File "/home/stefan/miniconda3/envs/pandas/lib/python3.9/site-packages/pandas/_testing.py", line 740, in _check_types assert_class_equal(left, right, exact=exact, obj=obj) File "/home/stefan/miniconda3/envs/pandas/lib/python3.9/site-packages/pandas/_testing.py", line 868, in assert_class_equal raise_assert_detail(obj, msg, repr_class(left), repr_class(right)) File "/home/stefan/miniconda3/envs/pandas/lib/python3.9/site-packages/pandas/_testing.py", line 1073, in raise_assert_detail raise AssertionError(msg) AssertionError: DataFrame.index are different DataFrame.index classes are not equivalent [left]: Index([], dtype='object') [right]: RangeIndex(start=0, stop=0, step=1) ``` #### Problem description When creating an empty dataframe it is created with `Index`, and not with a `RangeIndex` as specified in the [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html). This is a problem, e.g. in the example shown above, where I would not expect the index to change when appending the empty dataframe to itself. However, `pd.testing.assert_frame_equals` raises `AssertionError` since the indeces are not equivalent. #### Expected Output ```python >>> df = pd.DataFrame(columns=['a', 'b']) >>> print(df.index) RangeIndex(start=0, stop=0, step=1) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.9.2.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-59-generic Version : #65-Ubuntu SMP Thu Dec 10 12:01:51 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.2 numpy : 1.20.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 49.6.0.post20210108 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : None pandas_datareader: None bs4 : None bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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817,327,874
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40,078
REF, TYP: factor out _mark_right from _add_legend_handle
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5
2021-02-26T12:44:46Z
2021-03-27T11:11:01Z
2021-03-27T07:42:45Z
MEMBER
null
- [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 PR factors out adding ` (right)` to labels which are meant to go on the right y-axis. This does not change anything user-facing, and is simply a precursor to fixing #40044 and #39522 . The change is that on `master`, only `leg.get_texts()` shows `b (right)` while `ax.right_ax.get_legend_handles_labels()` shows `b`. The former is used for constructing the user-facing legend. The reason I think it needs doing is that I believe that to address #40044 and #39522, we need to use `.get_legend_handles_labels()` for both handles and labels, while currently `.get_legend_handles_labels` is used for handles and `leg.get_texts()` is used for labels. Putting a breakpoint after https://github.com/pandas-dev/pandas/blob/ab687aec38668cdb0139ca02d6e37e8c8386ae7d/pandas/plotting/_matplotlib/core.py#L591 results in , on this branch: ```python (Pdb) ax.right_ax.get_legend_handles_labels() ([<matplotlib.lines.Line2D object at 0x7fc94adf1dc0>], ['b (right)']) ``` and on master: ```python (Pdb) ax.right_ax.get_legend_handles_labels() ([<matplotlib.lines.Line2D object at 0x7ff2892abbe0>], ['b']) ```
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817,386,113
MDExOlB1bGxSZXF1ZXN0NTgwODg3NjYy
40,079
[ArrayManager] Remove apply_with_block usage for diff()
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0
2021-02-26T14:08:21Z
2021-03-01T10:01:11Z
2021-02-27T19:04:30Z
MEMBER
null
xref https://github.com/pandas-dev/pandas/issues/39146/ This turned out to be an easy one
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BUG: Categorical dtype in MultiIndex levels is lost during set operations (difference, union, intersection,...)
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1
2021-02-26T14:27:36Z
2021-08-19T01:23:13Z
null
NONE
null
- [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the latest version of pandas. - [X] (optional) I have confirmed this bug exists on the master branch of pandas. --- Coming from [here](https://github.com/pandas-dev/pandas/issues/13432#issuecomment-786604695), I tried my issue on current HEAD@master and the erroneous behaviour still persists. #### Code Sample, a copy-pastable example ```python import pandas as pd cat = pd.CategoricalDtype(categories=[1, 2, 3, 4]) df = pd.DataFrame({'a': [1, 2, 3, 4, 3, 2, 1], 'b': [4, 3, 2, 3, 1, 2, 3]}, dtype=cat) # flat categorical index, no MultiIndex df_cat = df.set_index('a', drop=False) diff_ind = df_cat.index.difference(df_cat.head(3).index) print(diff_ind.dtype) # -> category union_ind = df_cat.index.union(df_cat.head(3).index) print(union_ind.dtype) # -> category # same set operations on MultiIndex, categorical gets lost df_cat_multi = df.set_index(['a', 'b'], drop=False) # just make sure, we have a categorical in the first place print(df_cat_multi.index.get_level_values(0).dtype) # -> category print(df_cat_multi.index.get_level_values(1).dtype) # -> category diff_multi_ind = df_cat_multi.index.difference(df_cat_multi.head(3).index) print(diff_multi_ind.get_level_values(0).dtype) # -> int64 print(diff_multi_ind.get_level_values(1).dtype) # -> int64 union_multi_ind = df_cat_multi.index.union(df_cat_multi.head(3).index) print(union_multi_ind.get_level_values(0).dtype) # -> int64 print(union_multi_ind.get_level_values(1).dtype) # -> int64 ``` #### Problem description In contrast to set operations involving flat categorical indices, the same operations on multi-indices with categorical level values revert the result back to the underlying datatype. #### Expected Output The set-operations on multi-indices should preserve the categorical dtype of level values. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : ab687aec38668cdb0139ca02d6e37e8c8386ae7d 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 94 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : de_DE.cp1252 pandas : 1.3.0.dev0+849.gab687aec38 numpy : 1.19.5 pytz : 2021.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 53.1.0 Cython : 0.29.22 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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QST: Invalid representation of data
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2021-02-26T14:58:53Z
2021-02-27T08:56:58Z
2021-02-27T08:56:58Z
CONTRIBUTOR
null
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions. - [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com). --- #### Question about pandas Hello, I'm the maintainer of a Python library to store and manipulate (disjunctions of) intervals of arbitrary values (as long as they are comparable). It's called `portion` and it's available on PyPI and also on GitHub (https://github.com/AlexandreDecan/portion). This data type defines a `__repr__` method (e.g. the interval from 0 to 1 included is represented as "[0,1]"). When I use this data type in `pandas`, I got a representation that does not correct to what can be expected. Let me provide a very simple example: ```python >>> import portion as P >>> P.closed(0, 1) [0,1] >>> import pandas as pd >>> df = pd.DataFrame([P.closed(0,1)]) >>> df   | 0 -- | -- ((([0,1]))) ``` As you can see, the expected output has three extra "(" and three extra ")", this is: EXPECTED OUTPUT= "[0,1]" CURRENT_OUTPUT = "((([0,1])))" The same occurs when I display the corresponding index: ```python >>> df.loc[0] 0 ((([0,1]))) Name: 0, dtype: object ``` But not when I display the corresponding "cell": ```python >>> df.loc[0][0] [0,1] ``` Actually, if I get the object from `df.loc[0][0]`, I got the correct instance and its expected behaviour. I looked at how `__repr__` is handled by `pandas` but couldn't find the reason why this occurs. For what is worth, here's the implementation of `__repr__` in `portion`(see https://github.com/AlexandreDecan/portion/blob/master/portion/interval.py#L584): ```python def __repr__(self): intervals = [] for interval in self: if interval.empty: intervals.append('()') elif interval.lower == interval.upper: intervals.append('[{}]'.format(repr(interval.lower))) else: intervals.append( '{}{},{}{}'.format( '[' if interval.left == Bound.CLOSED else '(', repr(interval.lower), repr(interval.upper), ']' if interval.right == Bound.CLOSED else ')', ) ) return ' | '.join(intervals) ``` In the above snippet, `self` is an `Interval` instance that can be iterated over. Each `interval in self` is an `Interval` instance as well (they correspond to all the atomic intervals you can have in a disjunctive one, e.g., "[0,1] | [3,4]" has two atomic intervals: [0,1] and [3,4]). Each `interval` has several attributes, but the ones of interest here are `left` and `right` (either `Bound.CLOSED` or `Bound.OPEN`, two constants from an enum based on Booleans), and `upper` and `lower` that respectively correspond to the upper bound of the interval and the lower bound of the interval (e.g., for [0,1], upper = 1, and lower = 0). FYI, I'm using pandas 1.2.2 but I already got this "issue" with a previous version (unfortunately, I do not remember which one). Thank you!
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REF: avoid object-dtype casting in Block.replace
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2
2021-02-26T15:36:06Z
2021-02-26T23:10:46Z
2021-02-26T23:09:25Z
MEMBER
null
ATM we do `self.astype(object)`, where the idiomatic thing to do is `self.coerce_to_target_dtype(value)`, which in some cases means we dont need to cast on the back-end
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Backport PR #39451: BUG: raise when sort_index with ascending=None
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2021-02-26T16:37:32Z
2021-02-26T18:26:34Z
2021-02-26T18:26:30Z
MEMBER
null
Backport PR #39451
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MDExOlB1bGxSZXF1ZXN0NTgwOTkxNTM5
40,084
API: dont-special-case datetimelike in setting new column
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MDExOlB1bGxSZXF1ZXN0NTgwOTkzODc0
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[ArrayManager] TST: resample tests
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2021-02-26T16:56:21Z
2021-03-01T13:51:26Z
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xref https://github.com/pandas-dev/pandas/issues/39146/ This will only pass once https://github.com/pandas-dev/pandas/pull/40050 is merged
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CI: typo fixup for ResourceWarning
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2021-02-26T19:05:06Z
2021-02-27T03:59:43Z
2021-02-27T03:58:42Z
MEMBER
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MDExOlB1bGxSZXF1ZXN0NTgxMDgxOTgy
40,089
REF: remove sanitize_index
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2021-02-26T19:50:24Z
2021-02-27T18:35:22Z
2021-02-27T18:23:08Z
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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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MDExOlB1bGxSZXF1ZXN0NTgxMTEzNDUx
40,090
REG: DataFrame/Series.transform with list and non-list dict values
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2021-02-26T20:56:54Z
2021-02-28T14:08:34Z
2021-02-27T18:18:33Z
MEMBER
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- [x] closes #40018 - [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 Currently on master, `agg` does some preprocessing on dict-like arguments that `transform` does not, leading to incorrect behavior in `transform`. This PR moves `agg`'s preprocessing into `validate_dictlike_arg` which is used by both `agg` and `transform`, fixing the issue. As identified [in #40018](https://github.com/pandas-dev/pandas/issues/40018#issuecomment-785678614), this is a regression from 1.1 -> 1.2 (almost certainly from one of my PRs, but I haven't checked which). Backporting this to 1.2 will probably require a bit of a different fix.
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BUG: IO unit test creates file at home directory
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6
2021-02-26T21:27:49Z
2021-03-02T17:26:44Z
2021-03-02T17:26:44Z
CONTRIBUTOR
null
- [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the latest version of pandas. - [X] (optional) I have confirmed this bug exists on the master branch of pandas. --- The unit test at `pandas/tests/io/test_common.py` produces a file using a hardcoded name as shown below: ```python filename = path_type("~/sometest") with icom.get_handle(filename, "w") as handles: assert os.path.isabs(handles.handle.name) assert os.path.expanduser(filename) == handles.handle.name ``` #### Problem description A developer may encounter an empty file "sometest" appearing in their home directory (after running `pytest`). If the developer is unaware of that, then it may raise some wrong security concerns on their side. Also, in the extreme scenario that the developer has a file at the exact same path, then its content may be replaced. #### Expected Output I would expect the file produced by the unit test to be temporary and get deleted afterwards. Also, that the filename is generated rather than hardcoded.
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TST/REF: Consolidate tests in apply.test_invalid_arg
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MEMBER
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them Moves relevant tests into test_invalid_arg. Had to rename two tests because they were of the same name, but no other changes. I organized the file by the reason for the exception, and indicated this at the top of the file. Certainly open to any other ways of organizing.
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817,711,364
MDU6SXNzdWU4MTc3MTEzNjQ=
40,093
BUG: GroupBy.apply reruns the full loop if an exception is raised
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2021-02-26T22:04:24Z
2021-08-19T01:28:04Z
2021-08-19T01:28:03Z
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. #### Code Sample ```python import pandas as pd d = pd.DataFrame(dict(a = range(10))) def f(x): print(x) if x.iloc[0,0] == 2: raise Exception('problem') print(d.groupby('a').apply(f)) ``` The result is ``` a 0 0 a 1 1 a 2 2 a 0 0 a 1 1 a 2 2 Traceback (most recent call last): File "/tmp/ex.py", line 10, in <module> print(d.groupby('a').apply(f)) File "/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/groupby.py", line 894, in apply result = self._python_apply_general(f, self._selected_obj) File "/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/groupby.py", line 928, in _python_apply_general keys, values, mutated = self.grouper.apply(f, data, self.axis) File "/usr/local/lib/python3.8/dist-packages/pandas/core/groupby/ops.py", line 238, in apply res = f(group) File "/tmp/ex.py", line 8, in f raise Exception('problem') Exception: problem ``` #### Problem description If the `func` given to `GroupBy.apply(func)` raises an exception, the exception is suppressed and the application loop restarts from the beginning. My guess is that this is related to the behavior of one of pandas's `apply` functions that's supposed to ignore exceptions only for the first iteration, or only when an empty argument is provided, to guess the appropriate shape for the output. #17326 may be related. #### Expected Output The loop shouldn't catch the exception, terminating as soon as it hits the `raise`. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : 7d32926db8f7541c356066dcadabf854487738de python : 3.8.6.final.0 python-bits : 64 OS : Linux OS-release : 5.8.0-44-generic Version : #50-Ubuntu SMP Tue Feb 9 06:29:41 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.2.2 numpy : 1.18.4 pytz : 2020.1 dateutil : 2.8.1 pip : 21.0.1 setuptools : 51.3.3 Cython : None pytest : 6.2.1 hypothesis : None sphinx : 3.4.3 blosc : None feather : None xlsxwriter : None lxml.etree : 4.5.1 html5lib : 1.1 pymysql : None psycopg2 : 2.8.5 (dt dec pq3 ext lo64) jinja2 : 2.11.2 IPython : None pandas_datareader: None bs4 : 4.9.1 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.3.0 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.4.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : None </details>
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6
2021-02-26T22:33:39Z
2021-02-27T09:22:30Z
2021-02-26T23:53:33Z
CONTRIBUTOR
null
- [x] Fixes test failures introduced in #40069 - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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CONTRIBUTOR
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- [x] closes #39808 - [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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2021-03-02T22:16:21Z
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MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them - [x] whatsnew entry also CategoricalIndex.insert
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2021-02-27T02:32:48Z
2021-11-20T23:23:32Z
2021-02-27T18:24:20Z
MEMBER
null
Big picture, trying to call maybe_infer_to_datetimelike in fewer places so we can simplify scattered datetimelike casting.
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817,803,078
MDU6SXNzdWU4MTc4MDMwNzg=
40,098
BUG: ewma with time gives strange results with adjust = False vs. True, difference is too large to make sense
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2021-02-27T02:49:35Z
2021-03-09T04:59:48Z
null
NONE
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- [ yes ] I have checked that this issue has not already been reported. - [ yes ] 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 np.random.seed(0) idx=pd.date_range('20000101','20201231',periods=50000) df=pd.DataFrame(data=np.random.normal(0, 1, 50000),index=idx) # exclude first 1000 to avoid un-primed periods df.ewm(halflife=pd.Timedelta('10d'),times=df.index,adjust=True).mean().iloc[1000:].plot() df.ewm(halflife=pd.Timedelta('10d'),times=df.index,adjust=False).mean().iloc[1000:].plot() # Your code here ``` #### Problem description [this should explain **why** the current behaviour is a problem and why the expected output is a better solution] #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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40,099
BUG: ewma with time should not update value when timedelta between two steps is 0
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2021-02-27T03:09:06Z
2021-08-19T01:29:17Z
2021-08-19T01:29:17Z
NONE
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- [yes ] I have checked that this issue has not already been reported. - [ yes] 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 ``` idx=pd.date_range('20000101','20201231') idx2=[idx[0]]*5 + [idx[1]]*5 df=pd.DataFrame(data=range(10),index=idx2) df.ewm(halflife=pd.Timedelta('1D'), times=df.index).mean() ``` ```python # Your code here ```   | 0 -- | -- 0.000000 0.500000 1.000000 1.500000 2.000000 2.857143 3.555556 4.181818 4.769231 5.333333 #### Problem description Why is the value being updated when time hasn't elapsed yet? #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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817,817,383
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40,100
BUG: Series([Timestamp, int], dtype=m8ns) dropping nanoseconds
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2021-02-27T04:06:36Z
2021-02-27T19:31:26Z
2021-02-27T19:15:57Z
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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DEPS: replace pyperclip with pyclip #39834
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2021-02-27T04:14:08Z
2021-03-30T13:14:14Z
2021-03-30T13:14:14Z
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- [x] closes #39834
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40,102
BUG: groupby().tranforms return ValueError
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2021-02-27T08:25:49Z
2021-02-27T10:24:54Z
2021-02-27T10:24:23Z
NONE
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- [ ] 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. --- Here's the code: ```python In [9]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) df = pd.DataFrame({'A': s,'F': 'foo'}) df.loc[1,'F']=np.nan df Out[9]: A F 0 1.0 foo 1 3.0 NaN 2 5.0 foo 3 NaN foo 4 6.0 foo 5 8.0 foo In [10]: df['A']=df.groupby(['F'])['A'].transform(lambda x: x.fillna(x.mean())) Out[10]: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-111-5c30febb854f> in <module> ----> 1 df['A']=df.groupby(['F'])['A'].transform(lambda x: x.fillna(x.mean())) d:\python36\lib\site-packages\pandas\core\groupby\generic.py in transform(self, func, engine, engine_kwargs, *args, **kwargs) 492 if not isinstance(func, str): 493 return self._transform_general( --> 494 func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs 495 ) 496 d:\python36\lib\site-packages\pandas\core\groupby\generic.py in _transform_general(self, func, engine, engine_kwargs, *args, **kwargs) 560 561 result.name = self._selected_obj.name --> 562 result.index = self._selected_obj.index 563 return result 564 d:\python36\lib\site-packages\pandas\core\generic.py in __setattr__(self, name, value) 5152 try: 5153 object.__getattribute__(self, name) -> 5154 return object.__setattr__(self, name, value) 5155 except AttributeError: 5156 pass pandas\_libs\properties.pyx in pandas._libs.properties.AxisProperty.__set__() d:\python36\lib\site-packages\pandas\core\series.py in _set_axis(self, axis, labels, fastpath) 422 if not fastpath: 423 # The ensure_index call above ensures we have an Index object --> 424 self._mgr.set_axis(axis, labels) 425 426 # ndarray compatibility d:\python36\lib\site-packages\pandas\core\internals\managers.py in set_axis(self, axis, new_labels) 225 if new_len != old_len: 226 raise ValueError( --> 227 f"Length mismatch: Expected axis has {old_len} elements, new " 228 f"values have {new_len} elements" 229 ) ValueError: Length mismatch: Expected axis has 5 elements, new values have 6 elements ``` #### Problem description If I change column F's type to 'str' , everything goes well. ```python In [11]: df['F'] = df['F'].astype('str') df['A']=df.groupby(['F'])['A'].transform(lambda x: x.fillna(x.mean())) df Out[11]: A F 0 1.0 foo 1 3.0 nan 2 5.0 foo 3 5.0 foo 4 6.0 foo 5 8.0 foo ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : b5958ee1999e9aead1938c0bba2b674378807b3d python : 3.6.8.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 1.1.5 numpy : 1.17.2 pytz : 2018.9 dateutil : 2.7.5 pip : 19.3.1 setuptools : 41.4.0 Cython : 0.29.3 pytest : None hypothesis : None sphinx : 2.4.4 blosc : None feather : None xlsxwriter : None lxml.etree : 4.3.4 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.2.0 pandas_datareader: None bs4 : 4.9.0 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.0.2 numexpr : 2.6.9 odfpy : None openpyxl : 2.6.2 pandas_gbq : None pyarrow : None pytables : None pyxlsb : None s3fs : None scipy : 1.5.4 sqlalchemy : None tables : None tabulate : None xarray : 0.11.2 xlrd : None xlwt : None numba : 0.42.0 [paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag] </details>
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MDU6SXNzdWU4MTc4Njk1Mzc=
40,103
API: make CachedAccessor public in addition to register_dataframe_accessor
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2021-02-27T08:43:27Z
2021-08-19T01:29:31Z
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MEMBER
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In GeoPandas we added a `plot` accessor similar to the pandas one (so you can still access all the pandas plotting kinds, also from a GeoDataFrame where the default plot is a spatial map), see https://github.com/geopandas/geopandas/pull/1465. But, in that PR, we made use of `pandas.core.accessor.CachedAccessor`, which is how we add accessors in pandas under the hood: https://github.com/pandas-dev/pandas/blob/ffa6e20d7dadd262d9035a647dffed9903fc5929/pandas/core/frame.py#L9730-L9731 So in GeoPandas we basically copied the above code, but with our adapted GeoPlotAccessor. The reason that we can't use the public way to add accessors with `pd.api.extensions.register_dataframe_accessor` is that we are overriding an existing accessor of pandas (intentionally). So can we consider `CachedAccessor` to be "public"-like so it is fine for GeoPandas to use it? Or do we want to explicitly expose the class in the `pandas.api.extensions` namespace? Or can we change `register_dataframe_accessor` to allow it to override existing methods? (not sure we want to make that too easy)
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MDExOlB1bGxSZXF1ZXN0NTgxMjk1NDIw
40,104
STYLE exclude io from inconsistent namespace check
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2021-02-27T09:25:32Z
2021-02-27T18:37:30Z
2021-02-27T18:37:24Z
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xref https://github.com/pandas-dev/pandas/pull/40069#issuecomment-786931356 - [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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40,105
REF: don't call libalgos.groupsort_indexer directly for simple argsort usage
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2021-02-28T08:41:31Z
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40,106
CI update mypy pin
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2021-02-27T12:02:05Z
2021-02-27T18:31:25Z
2021-02-27T18:31:25Z
MEMBER
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There's a new `mypy` version out https://mypy-lang.blogspot.com/2021/02/mypy-0812-released.html - it doesn't look like the changes should affect type checking in pandas, but it's probably a good idea to stay up-to-date Task is to update the mypy pin in environment.yml and requirements-dev.txt, run `mypy pandas`, and then open a PR if it all passes (if it doesn't, please report back)
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40,107
BLD: updated mypy version to 0.812
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2021-02-27T13:54:11Z
2021-02-27T18:31:47Z
2021-02-27T18:31:25Z
CONTRIBUTOR
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- [x] closes #40106 - [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 After making necessary changes ```bash mypy pandas ``` O/P ``` Success: no issues found in 1237 source files ``` ![Screenshot from 2021-02-27 19-13-10](https://user-images.githubusercontent.com/31338369/109389161-5157d080-7931-11eb-9ead-82a0c8ca0d91.png)
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40,108
REF: Remove series_apply
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2021-02-27T14:35:28Z
2021-02-27T20:08:28Z
2021-02-27T18:14:21Z
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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MDExOlB1bGxSZXF1ZXN0NTgxMzU4MjYy
40,109
CLN: remove never-hit branch from maybe_infer_to_datetimelike
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0
2021-02-27T16:00:55Z
2021-11-20T23:23:32Z
2021-02-27T18:33:53Z
MEMBER
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Also some annotations and "pragma: no cover" for cases that are also checked by mypy
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2021-02-27T16:37:31Z
2021-08-19T01:31:46Z
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In most contexts, Series is strict about dtype, so will always either return the given dtype or raise. DataFrame is the opposite, often silently ignoring dtype (xref #24435) (i think on the theory that dtype may be intended to apply to some columns but not others). With floating data and integer dtypes, its the opposite: ``` arr = np.random.randn(5) >>> pd.Series(arr, dtype="int16") 0 1.002695 1 0.259332 2 -1.111468 3 -0.680714 4 -0.008943 >>> pd.DataFrame(arr, dtype="int16") 0 0 1 1 0 2 -1 3 0 4 0 ``` We have exactly one test that is broken if we change the latter behavior, and that is mostly by coincidence. There are other bugs (e.g. `Series(bigints, dtype="int8")` silently overflowing) that would be easier to fix if maintaining this behavior weren't a consideration. Is this intentional? cc @jreback @jorisvandenbossche @TomAugspurger
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2021-02-27T16:52:12Z
2021-11-06T01:56:43Z
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from `test_mixed_offsets_with_native_datetime_raises` ``` vals = [ "nan", pd.Timestamp("1990-01-01"), "2015-03-14T16:15:14.123-08:00", "2019-03-04T21:56:32.620-07:00", None, ] ser = pd.Series(vals) ``` As expected, this comes back as object-dtype, but it also does some casting: ``` >>> ser 0 NaT 1 1990-01-01 00:00:00 2 2015-03-14 16:15:14.123000-08:00 3 2019-03-04 21:56:32.620000-07:00 4 None dtype: object ``` `ser[0]` is a _str_ "NaT" and `ser[[2, 3]]` are stdlib datetime objects. ser[1] and ser[4] are unchanged. This casting is done inside a call to `to_datetime`, but has the surprising effect of not being idempotent: ``` >>> pd.to_datetime(ser) ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True ``` I'd expect `ser` to be constructed without doing any casting.
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There are many signatures of pandas methods that take a UDF. If I've missed methods, please let me know and will update. Below, `other_arg` is a placeholder for 1 or more keyword arguments, and `value` is a placeholder for the default value. Signature: `func, other_arg=value, args=value, **kwargs` - [Series.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.apply.html) - [DataFrame.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.apply.html) - [Expanding.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.window.expanding.Expanding.apply.html) - [Rolling.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.window.rolling.Rolling.apply.html) Signature: `func=None, other_arg=value, *args, **kwargs` - [Series.agg](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.agg.html#pandas.Series.agg) - [DataFrame.agg](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate) Signature: `func, other_arg=value, *args, **kwargs` - [Series.transform](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.transform.html#pandas.Series.transform) - [DataFrame.transform](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.transform.html#pandas.DataFrame.transform) - [DataFrameGroupBy.filter](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.groupby.DataFrameGroupBy.filter.html) Signature: `func, *args, other_arg=Value, **kwargs` - [DataFrameGroupBy.transform](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.transform.html) - [SeriesGroupBy.transform](https://pandas.pydata.org/docs/dev/reference/api/pandas.core.groupby.SeriesGroupBy.transform.html) Signature: `func=None, *args, other_arg=Value, **kwargs` - [SeriesGroupBy.agg](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.groupby.SeriesGroupBy.aggregate.html) - [DataFrameGroupBy.agg](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.groupby.DataFrameGroupBy.aggregate.html) Signature: `func, *args, **kwargs` - [GroupBy.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.groupby.GroupBy.apply.html) - [Resampler.aggregate](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.resample.Resampler.aggregate.html) - [Expanding.aggregate](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.window.expanding.Expanding.aggregate.html) - [Resampler.apply](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.resample.Resampler.apply.html) Signature: `arg, *args, **kwargs` - [Resampler.transform](https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.resample.Resampler.transform.html)
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817,980,592
MDExOlB1bGxSZXF1ZXN0NTgxMzc0NzU1
40,113
CLN/TST: normalize test_frame_apply
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3
2021-02-27T17:49:11Z
2021-03-03T03:11:13Z
2021-03-03T03:06:36Z
MEMBER
null
- [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 Some minor normalizations before breaking up or parametrizing these tests
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API: silent overflow in Series(bigints, dtype="int8")
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2021-02-27T19:54:30Z
2021-05-30T16:37:59Z
2021-03-23T15:07:56Z
MEMBER
null
- [ ] closes #xxxx - [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 This is made complicated by a) #40110 and b) MaskedArray behavior, which i need to check but may be changeable.
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BUG: index map leading to ValueError: Reindexing only valid with uniquely valued Index objects
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2021-02-27T19:55:33Z
2021-08-19T01:33:39Z
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I am using Pandas 1.1.5 and get to the error `ValueError: Reindexing only valid with uniquely valued Index objects`. Here is a self contained MRE: ``` import pandas as pd from pandas import Timestamp my_dict = my_dict = {('a',): 'x1', ('b',): 'x1', ('c', 'c1'): 'x1', ('c', 'c2'): 'x1', ('c', 'c3'): 'x1', ('c', 'c4'): 'x1', ('c', 'c5'): 'x1', ('c', 'c6'): 'x1', ('c', 'c7'): 'x1', ('c', 'c8'): 'x1', ('c', 'c9'): 'x1', ('c', 'c10'): 'x1', ('c', 'c11'): 'x1', ('c', 'c12'): 'x1', ('c', 'c13'): 'x1', ('c', 'c14'): 'x1', ('c', 'c15'): 'x1', ('c', 'c16'): 'x1', ('c', 'c17'): 'x1', ('c', 'c18'): 'x1', ('c', 'c19'): 'x1', ('c', 'c20'): 'x1', ('c', 'c21'): 'x1', ('c', 'c22'): 'x2', ('c', 'c23'): 'x2', ('c', 'c24'): 'x2', ('c', 'c25'): 'x2', ('c', 'c26'): 'x2', ('c', 'c27'): 'x2', ('c', 'c28'): 'x2', ('c', 'c29'): 'x2', ('c', 'c30'): 'x2', ('c', 'c31'): 'x2', ('c', 'c32'): 'x2', ('c', 'c33'): 'x2', ('c', 'c34'): 'x2', ('c', 'c35'): 'x2', ('c', 'c36'): 'x2', ('c', 'c37'): 'x2', ('c', 'c38'): 'x2', ('c', 'c39'): 'x2', ('c', 'c40'): 'x2', ('c', 'c41'): 'x2', ('c', 'c42'): 'x2', ('c', 'c43'): 'x2', ('c', 'c44'): 'x2', ('c', 'c45'): 'x3', ('c', 'c46'): 'x3', ('c', 'c47'): 'x3', ('c', 'c48'): 'x3', ('c', 'c49'): 'x3', ('c', 'c50'): 'x3', ('c', 'c51'): 'x3', ('c', 'c52'): 'x3', ('c', 'c53'): 'x3', ('c', 'c54'): 'x3', ('c', 'c55'): 'x3', ('c', 'c56'): 'x3', ('c', 'c57'): 'x3', ('c', 'c58'): 'x4', ('c', 'c59'): 'x4', ('c', 'c60'): 'x4', ('c', 'c61'): 'x4', ('c', 'c62'): 'x4', ('c', 'c63'): 'x4', ('c', 'c64'): 'x4', ('c', 'c65'): 'x4', ('c', 'c66'): 'x4', ('c', 'c67'): 'x4', ('c', 'c68'): 'x3', ('c', 'c69'): 'x3', ('c', 'c70'): 'x3', ('d', 'd1'): 'x1', ('d', 'd2'): 'x1', ('d', 'd3'): 'x2', ('d', 'd4'): 'x2', ('d', 'd5'): 'x2', ('d', 'd6'): 'x2', ('d', 'd7'): 'x4', ('d', 'd8'): 'x4', ('d', 'd9'): 'x4', ('d', 'd10'): 'x4', ('f',): 'x1', ('g',): 'x3'} print(my_dict) df = pd.DataFrame.from_dict({'date': {('a', 'a1'): Timestamp('2021-02-28'), ('a', 'a2'): Timestamp('2021-02-28'), ('a', 'a3'): Timestamp('2021-02-28'), ('a', 'a4'): Timestamp('2021-02-28'), ('a', 'a5'): Timestamp('2021-02-28'), ('a', 'a6'): Timestamp('2021-02-28'), ('a', 'a7'): Timestamp('2021-02-28'), ('a', 'a8'): Timestamp('2021-02-28'), ('a', 'a9'): Timestamp('2021-02-28'), ('a', 'a10'): Timestamp('2021-02-28'), ('a', 'a11'): Timestamp('2021-02-28'), ('a', 'a12'): Timestamp('2021-02-28'), ('a', 'a13'): Timestamp('2021-02-28'), ('a', 'a14'): Timestamp('2021-02-28'), ('a', 'a15'): Timestamp('2021-02-28'), ('a', 'a16'): Timestamp('2021-02-28'), ('a', 'a17'): Timestamp('2021-02-28'), ('a', 'a18'): Timestamp('2021-02-28'), ('a', 'a19'): Timestamp('2021-02-28'), ('a', 'a20'): Timestamp('2021-02-28'), ('a', 'a21'): Timestamp('2021-02-28'), ('a', 'a22'): Timestamp('2021-02-28'), ('a', 'a23'): Timestamp('2021-02-28'), ('a', 'a24'): Timestamp('2021-02-28'), ('a', 'a25'): Timestamp('2021-02-28'), ('a', 'a26'): Timestamp('2021-02-28'), ('a', 'a27'): Timestamp('2021-02-28'), ('a', 'a28'): Timestamp('2021-02-28'), ('a', 'a29'): Timestamp('2021-02-28'), ('a', 'a30'): Timestamp('2021-02-28'), ('b', 'b1'): Timestamp('2021-02-14'), ('b', 'b2'): Timestamp('2021-02-14'), ('b', 'b3'): Timestamp('2021-02-14'), ('b', 'b4'): Timestamp('2021-02-14'), ('b', 'b5'): Timestamp('2021-02-14'), ('b', 'b6'): Timestamp('2021-02-14'), ('b', 'b7'): Timestamp('2021-02-14'), ('b', 'b8'): Timestamp('2021-02-14'), ('b', 'b9'): Timestamp('2021-02-14'), ('b', 'b10'): Timestamp('2021-02-14'), ('c', 'c1'): Timestamp('2021-02-17'), ('c', 'c2'): Timestamp('2021-02-17'), ('c', 'c3'): Timestamp('2021-02-17'), ('c', 'c4'): Timestamp('2021-02-17'), ('c', 'c5'): Timestamp('2021-02-17'), ('c', 'c6'): Timestamp('2021-02-17'), ('c', 'c7'): Timestamp('2021-02-17'), ('c', 'c8'): Timestamp('2021-02-17'), ('c', 'c9'): Timestamp('2021-02-17'), ('c', 'c10'): Timestamp('2021-02-17'), ('c', 'c11'): Timestamp('2021-02-17'), ('c', 'c12'): Timestamp('2021-02-17'), ('c', 'c13'): Timestamp('2021-02-17'), ('c', 'c14'): Timestamp('2021-02-17'), ('c', 'c15'): Timestamp('2021-02-17'), ('c', 'c16'): Timestamp('2021-02-17'), ('c', 'c17'): Timestamp('2021-02-17'), 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print(df.index.map(my_dict)) ``` traceback: ```python-traceback Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/marco/pandas-marco/pandas/core/indexes/base.py", line 5366, in map new_values = super()._map_values(mapper, na_action=na_action) File "/home/marco/pandas-marco/pandas/core/base.py", line 930, in _map_values indexer = mapper.index.get_indexer(values) File "/home/marco/pandas-marco/pandas/core/indexes/base.py", line 3369, in get_indexer raise InvalidIndexError(self._requires_unique_msg) pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued Index objects ```
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MDExOlB1bGxSZXF1ZXN0NTgxMzkzMzM5
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PERF: DTA/TDA _simple_new disallow i8 values
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2021-03-11T13:02:16Z
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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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Backport PR #40090 on branch 1.2.x
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Backport PR #40090
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MDExOlB1bGxSZXF1ZXN0NTgxNDI2NjQ4
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CI: skip array-manager test
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2021-02-28T00:23:48Z
2021-03-01T09:28:37Z
2021-02-28T02:02:56Z
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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 should get the CI back to green
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