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541,511,177
MDExOlB1bGxSZXF1ZXN0MzU2MTI3OTc5
30,414
REF: method-specific test files for Series/DataFrame
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1
2019-12-22T22:55:13Z
2019-12-23T15:31:25Z
2019-12-23T14:02:36Z
MEMBER
null
xref #30385, #30381.
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541,511,199
MDU6SXNzdWU1NDE1MTExOTk=
30,415
DISCUSS: disambiguation of NA and "NA" in reprs
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17
2019-12-22T22:55:35Z
2020-01-09T16:18:58Z
2020-01-09T16:18:58Z
NONE
null
## Change to `str` `dtype` behaviour for missing elements Following comments the discussion about how to handle missing `NA` scalar values in https://github.com/pandas-dev/pandas/issues/28778 I was asked to raise my question as this seperate issue. My rather prosaic question is how if missing `str` elements are given the value `NA`, how would I distinguish between a missing `str` value and the two-character string 'NA'? I ask as NA is a common abbreviation for 'Not Applicable', 'North America' et al, in a way that in my experience that 'NaN' or 'Not a Number' isn't That is, if 'NA' were generated as the default missing `str` `dtype` value, especially if introduced as change rather than as a opt-in, it risks becoming a UX developer issue as I (for one) would no longer know if 'NA' is a valid or a missing data value. For what it's worth, current idiomatic behaviour is that in a missing values would be replaced by `None` `dtype`: ```python >>> array = [['No-one', 'Nadie'], ['Expects']] >>> df = pd.DataFrame(array, columns=['En', 'Es']) En Es 0 No-one Nadie 1 Expects *None* ``` The `dtypes` here are: ```python >>> [type(i) for i in df['Es']] [<class 'str'>, <class 'NoneType'>] ``` Given this, my thought is that `NA` is not a suitable *default* replacement for missing `str` `dtype` elements rather `None` of `NoneType` `dtype`
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541,511,899
MDExOlB1bGxSZXF1ZXN0MzU2MTI4NDc5
30,416
BUG: Series.any() and .all() don't return bool values if dtype=object
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16
2019-12-22T23:02:28Z
2020-10-08T13:32:15Z
2020-01-12T16:52:20Z
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- [x] closes #12863 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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541,517,109
MDExOlB1bGxSZXF1ZXN0MzU2MTMyNDAy
30,417
Replaced xrange with range
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2019-12-22T23:57:35Z
2019-12-23T08:43:49Z
2019-12-23T08:27:45Z
MEMBER
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- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzU2MTQ0NDUz
30,418
Fixing pandas/tests/indexes/multi/test_analytics.py to utilize python…
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2
2019-12-23T01:26:17Z
2019-12-23T15:25:27Z
2019-12-23T15:25:24Z
CONTRIBUTOR
null
Modified reflect python3 format strings on pandas/tests/indexes/multi/test_analytics.py. I ran the test and it passed like before. ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547).
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541,533,248
MDExOlB1bGxSZXF1ZXN0MzU2MTQ1Nzcz
30,419
BUG: strengthen typing in get_c_string, fix StringHashTable segfault
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5
2019-12-23T01:35:17Z
2019-12-24T16:51:15Z
2019-12-24T14:26:40Z
MEMBER
null
Start with util.pxd, we can tighten the arg from "object" to "str" and simplify it a bit. Tracking down the places where get_c_string is used, the main one is in StringHashTable, where in `set_item` it is currently used incorrectly. The test added by this PR segfaults in master. @TomAugspurger it looks like StringHashTable has relatively light testing. Should we be using it for StringArray?
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30,420
Fixing pandas/tests/indexes/datetimes/test_ops.py to utilize python3 …
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1
2019-12-23T01:41:24Z
2019-12-23T11:46:59Z
2019-12-23T08:03:55Z
CONTRIBUTOR
null
Updated pandas/tests/indexes/datetimes/test_ops.py to utilize python3 format strings. Ran the test and it seems good. I moved the msg into the for loop because it seems cleaner with the new format string logic. ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547).
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1
2019-12-23T02:23:35Z
2019-12-23T11:47:26Z
2019-12-23T08:05:22Z
CONTRIBUTOR
null
Updated pandas/io/formats/printing.py formatting. Replaced % and {}.format() examples to python3 formatting. Ran tests and they passed. ref [#29886](https://github.com/pandas-dev/pandas/issues/29886) and [#29547](https://github.com/pandas-dev/pandas/issues/29547).
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REF: standardize test_constructor filenames
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2019-12-23T02:34:38Z
2019-12-23T15:47:36Z
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ISORT: Add _typing to pre_core isort block
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3
2019-12-23T03:57:55Z
2019-12-23T18:09:50Z
2019-12-23T18:09:27Z
CONTRIBUTOR
null
- [x] closes #27534
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CLN: changed .format to fstring in pandas/io/formats/csvs.py
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2019-12-23T06:49:29Z
2019-12-23T07:08:46Z
2019-12-23T07:08:28Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry https://github.com/pandas-dev/pandas/issues/29547 This file is not involved in https://github.com/pandas-dev/pandas/issues/2988
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CLN: changed .format to fstring in pandas/io/formats/csvs.py
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4
2019-12-23T07:21:05Z
2019-12-24T15:04:43Z
2019-12-24T15:04:40Z
CONTRIBUTOR
null
- [ ] closes #xxxx - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry https://github.com/pandas-dev/pandas/issues/29547 My first contribution.
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30,426
CLN/TYP: Removed redundant 'int' from pandas/_typing.py
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2019-12-23T10:17:27Z
2019-12-24T15:20:01Z
2019-12-23T19:35:19Z
MEMBER
null
- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry As @simonjayhawkins says [here](https://github.com/pandas-dev/pandas/pull/30403#discussion_r360828949) (comment), having ```Union``` with both ```int``` and ```float``` is redundant.
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CLN: Use of Iterable from collections.abc instead of typing.Iterable
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3
2019-12-23T10:40:14Z
2019-12-24T16:16:53Z
2019-12-24T16:14:25Z
MEMBER
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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Error initialising DataFrame with array of shape (1, 1) and extensiondtype
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2019-12-23T11:10:54Z
2020-03-12T10:20:45Z
2020-03-12T10:20:45Z
MEMBER
null
Here's something I noticed while working on #30277 #### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np import pytz temp_1 = pd.Series([pd.Timestamp("2019-07-19 07:04:57")]) temp_2 = pd.Series([pd.Timestamp("2019-07-19 07:04:57+0100", tz=pytz.FixedOffset(60))]) data = np.array([["2019-06-19 07:04:57"]]) pd.DataFrame(data, dtype=temp_1.dtype) # works pd.DataFrame(data, dtype=temp_2.dtype) # fails ``` #### Problem description The example that fails throws the error: ``` ValueError: If using all scalar values, you must pass an index ``` If I pass an index: ``` pd.DataFrame(data, dtype=temp_2.dtype, index=[0]) # fails ``` then it fails with ``` ValueError: Buffer has wrong number of dimensions (expected 1, got 2) ``` although this doesn't: ``` pd.DataFrame(data, dtype=temp_1.dtype, index=[0]) # works ``` #### Expected Output I wouldn't expect this behaviour to change based on the `dtype` passed. #### Output of ``pd.show_versions()`` <details> /home/SERILOCAL/m.gorelli/pandas/pandas/core/index.py:29: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace. FutureWarning, INSTALLED VERSIONS ------------------ commit : 3577b5a34d7f9695bc2e99766cc6c49e7bdab6fe python : 3.7.3.final.0 python-bits : 64 OS : Linux OS-release : 4.15.0-72-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : en_GB.UTF-8 pandas : 0.25.0+1383.g3577b5a34 numpy : 1.17.3 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 42.0.2.post20191201 Cython : 0.29.14 pytest : 5.3.2 hypothesis : 4.55.4 sphinx : 2.3.0 blosc : None feather : None xlsxwriter : 1.2.6 lxml.etree : 4.4.2 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.9.0 pandas_datareader: None bs4 : 4.8.1 bottleneck : 1.3.1 fastparquet : 0.3.2 gcsfs : None lxml.etree : 4.4.2 matplotlib : 3.1.2 numexpr : 2.7.0 odfpy : None openpyxl : 3.0.1 pandas_gbq : None pyarrow : 0.15.1 pytables : None pytest : 5.3.2 s3fs : 0.4.0 scipy : 1.4.0 sqlalchemy : 1.3.11 tables : 3.6.1 xarray : 0.14.1 xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.2.6 </details>
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541,832,622
MDU6SXNzdWU1NDE4MzI2MjI=
30,429
Suggestion: add feature to show in detail changes in 1 df over time
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4
2019-12-23T17:01:06Z
2020-05-28T17:08:46Z
2020-05-28T17:08:46Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np from tabulate import tabulate def print_table(df, doc=False): """ Print out a nice looking table. Set doc to True if you are printing this out for a Microsoft Word (11 x 8) document. When doc is False: Input: A B 0 1 0 1 2 0 2 3 0 Output: +----+-----+-----+ | | A | B | |----+-----+-----| | 0 | 1 | 0 | | 1 | 2 | 0 | | 2 | 3 | 0 | +----+-----+-----+ When doc is True: Input: a b c d e f g h i j k l m n p 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 Output: Part 1/3 +----+-----+-----+-----+-----+-----+-----+ | | a | b | c | d | e | f | |----+-----+-----+-----+-----+-----+-----| | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 1 | 1 | 1 | 1 | 1 | 1 | 1 | | 2 | 2 | 2 | 2 | 2 | 2 | 2 | | 3 | 3 | 3 | 3 | 3 | 3 | 3 | | 4 | 4 | 4 | 4 | 4 | 4 | 4 | +----+-----+-----+-----+-----+-----+-----+ Part 2/3 +----+-----+-----+-----+-----+-----+-----+ | | g | h | i | j | k | l | |----+-----+-----+-----+-----+-----+-----| | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 1 | 1 | 1 | 1 | 1 | 1 | 1 | | 2 | 2 | 2 | 2 | 2 | 2 | 2 | | 3 | 3 | 3 | 3 | 3 | 3 | 3 | | 4 | 4 | 4 | 4 | 4 | 4 | 4 | +----+-----+-----+-----+-----+-----+-----+ Part 3/3 +----+-----+-----+-----+ | | m | n | p | |----+-----+-----+-----| | 0 | 0 | 0 | 0 | | 1 | 1 | 1 | 1 | | 2 | 2 | 2 | 2 | | 3 | 3 | 3 | 3 | | 4 | 4 | 4 | 4 | +----+-----+-----+-----+ :param df: DataFrame :param doc: bool :return: None """ def _simple_print(df): """ Single line helper function to print a dataframe. :param df: DataFrame :return: None """ print(tabulate(df, headers='keys', tablefmt='psql')) # Just print with no extra slicing. if not doc: _simple_print(df) # Slice the dataframe to show to Word. else: # Set the number of columns Word can handle. It typically can handle 6 columns. max_columns = 6 # Find how many tables we will need to print if we cut the columns by 6ths. max_tables = int(np.ceil(df.shape[1] / max_columns)) # For each 6th, print the table. for iteration in range(1, max_tables + 1): # Print which iteration we're on. print(f'Part {iteration}/{max_tables}') # Slice the larger dataframe to obtain the current dataframe. current_df = df.iloc[0:5, (iteration * max_columns) - max_columns:iteration * max_columns] # Print current dataframe. _simple_print(current_df) def test(old_df, new_df, desired_columns, index=None, verbose=False, print_full=True): """ Description ----------- Check for differences (across time, business rule changes, etc.) over the same dataframe. Recommended to start without verbose, then add it if needed. You can toggle whether to print out the sample dataframes (head and tail), or not. Sample Usage ------------ old_data = {'ID': ['CHA','COC','COF'], 'Name': ['Chai Tea', 'Cocoa', 'Coffee'], 'Description': ['Chai Kcup','Hot Chocolate Kcup','Coffee Kcup'], 'Cost': [2,2,3]} new_data = {'ID': ['CHA','COC','COF'], 'Name': ['Chai Tea', 'Cocoa', 'Coffee'], 'Description': ['Chai','Hot Chocolate Kcup','Coffee Kcup'], 'Cost': [2,3,3]} old_df = (pd.DataFrame(old_data) .set_index('ID')) new_df = (pd.DataFrame(new_data) .set_index('ID')) test(old_df=old_df, new_df=new_df, desired_columns=old_df.columns) Output In Console ----------------- Match? False Quick Head Test Old Records: +------+----------+--------------------+--------+ | ID | Name | Description | Cost | |------+----------+--------------------+--------| | CHA | Chai Tea | Chai Kcup | 2 | | COC | Cocoa | Hot Chocolate Kcup | 2 | | COF | Coffee | Coffee Kcup | 3 | +------+----------+--------------------+--------+ New Records: +------+----------+--------------------+--------+ | ID | Name | Description | Cost | |------+----------+--------------------+--------| | CHA | Chai Tea | Chai | 2 | | COC | Cocoa | Hot Chocolate Kcup | 3 | | COF | Coffee | Coffee Kcup | 3 | +------+----------+--------------------+--------+ Quick Tail Test Old Records: +------+----------+--------------------+--------+ | ID | Name | Description | Cost | |------+----------+--------------------+--------| | CHA | Chai Tea | Chai Kcup | 2 | | COC | Cocoa | Hot Chocolate Kcup | 2 | | COF | Coffee | Coffee Kcup | 3 | +------+----------+--------------------+--------+ New Records: +------+----------+--------------------+--------+ | ID | Name | Description | Cost | |------+----------+--------------------+--------| | CHA | Chai Tea | Chai | 2 | | COC | Cocoa | Hot Chocolate Kcup | 3 | | COF | Coffee | Coffee Kcup | 3 | +------+----------+--------------------+--------+ Quick Cell to Cell Differences All differences: (2, 2) Old New ID CHA Description Chai Kcup Chai COC Cost 2 3 Parameters ---------- :param old_df: DataFrame This could be cases you know to be correct, or cases from last week, etc. :param new_df: DataFrame This could be the current output created by code you wish to debug or double check, or cases from today, etc. :param desired_columns: list of strings <old_df.columns> :param index: string name of column to match cases against (currently not implemented) <Student ID> :return: None """ new_df = new_df[desired_columns] old_df = old_df[desired_columns] # overall test (returns true or false) print(f'Match? {old_df.equals(new_df)}') print('\n') if print_full: # quick head and tail test print('Quick Head Test') print('Old Records:') print_table(old_df.head(5)) print('New Records:') print_table(new_df.head(5)) print('\n') print('Quick Tail Test') print('Old Records:') print_table(old_df.tail(5)) print('New Records:') print_table(new_df.head(5)) print('\n') def _diff_table(old_df, new_df): """ Create the table that only shows differences (or "errors"). This is code that manesioz on stackoverflow wrote as a response to my question for how to achieve this. :param old_df: pandas DataFrame :param new_df: pandas DataFrame :return: pandas DataFrame """ # create frame of comparison bools bool_df = (old_df != new_df).stack() diff_table = pd.concat([old_df.stack()[bool_df], new_df.stack()[bool_df]], axis=1) diff_table.columns = ["Old", "New"] return diff_table diff_table = _diff_table(old_df,new_df) if verbose==False: # specific cell by cell test for differences print('Quick Cell to Cell Differences') print(f'All differences: {diff_table.shape}') print(diff_table.head()) print('\n') # Offer a verbose option. Because the following code can produce errors if the # dataframes are too different (eg. if the column names differ), we offer the # non-verbose option. else: print('Cell to Cell Differences: All') print(f'All differences: {diff_table.shape}') print(diff_table) print('\n') ``` #### Problem description Often I need to do a quick analysis of the same dataframe over some time. I need to look at the specific cells that changed, and how. Frequently, it's at a volume where it's impossible to just eyeball this. I'm surprised that under pandas' testing suite there's not currently an in-built method to do this. I ended up using manesioz's code from my stackoverflow question [here](https://stackoverflow.com/questions/57224294/more-specific-pandas-testing-for-equality-between-two-dataframes) in my own utility function which is included here. But, I think a feature like this (specifically the output under Quick Cell to Cell Differences) could be really useful for other data analysts too. More sample output of other times I've used this feature: - Ad hoc, timed request to find any differences in ordering exams over a time period of a month from schools ``` >>>last_week_df.columns Index(['BCO', 'Superintendent', 'DBN', 'OrderedExams', 'FLOrderedExams', 'Algebra I', 'ELA', 'Chemistry', 'Earth Science', 'Global Transition', 'Global II', 'Algebra II', 'Living Environment', 'US History', 'Physics', 'Geometry', 'All Exams'], dtype='object') >>>last_week_df.shape (616, 17) Cell to Cell Differences: All All differences: (66, 2) Old New 36 FLOrderedExams Yes No 40 FLOrderedExams Yes No 50 FLOrderedExams Yes No 66 FLOrderedExams Yes No ELA 60 10 All Exams 110 60 81 OrderedExams No Yes Algebra I 0 30 Living Environment 0 15 Geometry 0 25 All Exams 0 70 90 FLOrderedExams Yes No 165 OrderedExams Yes No Algebra I 50 0 ELA 170 0 Earth Science 25 0 Global II 25 0 Algebra II 25 0 Living Environment 25 0 Geometry 15 0 All Exams 335 0 200 Physics 35 34 All Exams 1010 1009 282 Living Environment 250 150 All Exams 750 650 286 ELA 615 440 All Exams 1640 1465 432 Algebra I 695 620 All Exams 1841 1766 540 OrderedExams No Yes Algebra I 0 130 ELA 0 50 Global Transition 0 30 Living Environment 0 140 US History 0 40 All Exams 0 390 552 OrderedExams No Yes Algebra I 0 15 ELA 0 25 Chemistry 0 5 Earth Science 0 15 Global Transition 0 20 Algebra II 0 20 Living Environment 0 10 US History 0 10 Geometry 0 10 All Exams 0 130 560 OrderedExams No Yes Algebra I 0 200 Global Transition 0 100 All Exams 0 300 561 OrderedExams No Yes Algebra I 0 12 Living Environment 0 25 All Exams 0 37 576 OrderedExams No Yes FLOrderedExams No Yes Algebra I 0 30 ELA 0 140 All Exams 0 170 606 OrderedExams No Yes FLOrderedExams No Yes Earth Science 0 15 Living Environment 0 15 Geometry 0 25 All Exams 0 55 ``` - Ad hoc, timed request to update a data file dependency for another file on locations of foreign language personnel at sites ``` MATHI Match? False Cell to Cell Differences: All All differences: (5, 2) Old New Exams go to? 14J558 MATHI_K 10 2 MATHI_R 1 2 20J445 MATHI_K 1 3 28W440 MATHI_K 1 4 29W283 MATHI_K 1 4 USH Match? False Cell to Cell Differences: All All differences: (3, 2) Old New Exams go to? 02N600 USH_K 1 0 USH_R 0 1 13J499 USH_K 2 1 PHYS Match? False Cell to Cell Differences: All All differences: (8, 2) Old New Exams go to? 02N475 PHYS_K 1 0 PHYS_R 0 1 15J519 PHYS_K 4 2 PHYS_R 1 2 28W690 PHYS_K 1 3 30W445 PHYS_K 1 2 31T450 PHYS_K 1 0 PHYS_R 0 1 GLOBALI Match? False Cell to Cell Differences: All All differences: (6, 2) Old New Exams go to? 19J660 GLOBALI_K 3 1 GLOBALI_R 1 2 21J540 GLOBALI_K 3 2 GLOBALI_R 1 2 29W272 GLOBALI_K 1 3 GLOBALI_R 2 1 LIVING Match? True Cell to Cell Differences: All All differences: (0, 2) Empty DataFrame Columns: [Old, New] Index: [] GLOBALII Match? False Cell to Cell Differences: All All differences: (7, 2) Old New Exams go to? 13J499 GLOBALII_K 6 2 GLOBALII_R 1 2 19J660 GLOBALII_K 4 2 21J540 GLOBALII_K 2 3 28W620 GLOBALII_K 2 4 GLOBALII_R 2 1 29W272 GLOBALII_K 2 4 ```
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541,832,770
MDExOlB1bGxSZXF1ZXN0MzU2MzkzNjMz
30,430
CLN: use f-string for JSON related files
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1
2019-12-23T17:01:30Z
2020-06-23T17:09:06Z
2019-12-24T17:06:19Z
CONTRIBUTOR
null
- [x] ref #29547 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzU2Mzk2MjYw
30,431
CI: Remove powershell scripts (Azure)
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4
2019-12-23T17:09:57Z
2019-12-25T20:35:59Z
2019-12-24T14:56:57Z
CONTRIBUTOR
null
- [x] closes #26344 Ref @jbrockmendel comment here https://github.com/pandas-dev/pandas/pull/23454#discussion_r360933924 CI: fails as expected example [here](https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=23715&view=logs&j=fd89438c-1fd3-5981-222e-115d06c89c2d)
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541,853,417
MDExOlB1bGxSZXF1ZXN0MzU2NDEwOTAz
30,432
REF: more method-specific test files
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1
2019-12-23T18:07:15Z
2019-12-23T20:17:53Z
2019-12-23T19:29:37Z
MEMBER
null
among other things, found a sort_index test that got misplaced
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541,856,091
MDU6SXNzdWU1NDE4NTYwOTE=
30,433
Pytest warnings JUnit XML Format
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0
2019-12-23T18:14:57Z
2020-01-04T22:57:37Z
2020-01-04T22:57:37Z
CONTRIBUTOR
null
See the following in CI: ``` pytest -m "not slow and not network" -n auto --dist=loadfile -s --strict --durations=10 --junitxml=test-data.xml pandas ##[warning]The 'junit_family' default value will change to 'xunit2' in pytest 6.0. Add 'junit_family=legacy' to your pytest.ini file to silence this warning and make your suite compatible. ``` Unsure if the xunit2.0 format will affect any of our coverage etc.. https://docs.pytest.org/en/latest/reference.html#confval-junit_family
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541,857,914
MDExOlB1bGxSZXF1ZXN0MzU2NDE0NzQ4
30,434
ENH: Allow scatter plot to plot objects and datetime type data
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1
2019-12-23T18:21:38Z
2020-01-01T03:39:12Z
2020-01-01T03:39:08Z
MEMBER
null
closes #18755 closes #30391 xref #8113 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry Because this is a plot PR, so I put the figures gotten by the two examples in those two PRs in this description to facilitate seeing output, and more cases are tested in test files. ![Screen Shot 2019-12-23 at 7 34 21 PM](https://user-images.githubusercontent.com/9269816/71374603-91982200-25bb-11ea-8ffd-4328fb1ea882.png)
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ENH/PERF: allow mask to be optional in our masked ExtensionArrays
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2
2019-12-23T18:33:53Z
2021-06-11T16:55:12Z
null
MEMBER
null
Our nullable, mask-based ExtensionArrays (currently integer and boolean, inheriting from MaskedArray) have a `_data` and `_mask` numpy arrays stored under the hood. SO we use a numpy boolean array as mask (8bit), also when there are no missing values. One, relatively easy, memory + performance improvement could be achieved by allowing the mask to be None when there are no missing data. Since the mask data is completely internal to the Array implementations, this should be possible to do. (to be checked how involved the ops code would become to handle this as optional)
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541,866,406
MDU6SXNzdWU1NDE4NjY0MDY=
30,436
PERF: avoid casting to float in IntegerArray reducing ops
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0
2019-12-23T18:52:37Z
2021-07-25T03:04:57Z
null
MEMBER
null
Currently, we cast to float in the IntegerArray reducing ops: https://github.com/pandas-dev/pandas/blob/db620397328567ff8153ae6031685b88c84dc8f8/pandas/core/arrays/integer.py#L673-L678 However, the `nanops` functions can already handle a mask. So with the appropriate fill_value, there should be no need to cast the integers to float (and check for missing values). At least this is the case for `skipna=True`, the case of `skipna=False` might need to be handled separately.
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30,437
REF: method-specific tests for cov, corr, corrwith, count, round
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1
2019-12-23T21:25:24Z
2019-12-24T17:02:30Z
2019-12-24T13:05:54Z
MEMBER
null
in the process of organizing these, some parametrization opportunities become clear. Didnt do them in this PR to keep this just a moving-around diff
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30,438
REF: de-duplicate/parametrize arithmetic tests
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1
2019-12-23T21:31:14Z
2019-12-24T16:52:01Z
2019-12-24T13:04:36Z
MEMBER
null
Use helper methods implemented in #30354. Some of this is things made possible since we enforced the integer add/sub deprecation.
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DataFrame.loc multiple columns replace
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15
2019-12-24T01:29:46Z
2021-07-25T04:17:02Z
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Python 3.6.8 pandas==0.25.3 ```python #! -*- coding:utf-8 -*- import pandas as pd df = pd.DataFrame([ {'a':'a1','b':'b1','c':'c1','d':'d1','e':'e1'}, {'a':'a2','b':'b2','c':'c2','d':'d2','e':'e2'} ]) import copy df2 = copy.deepcopy(df) print(df) # a b c d e # 0 a1 b1 c1 d1 e1 # 1 a2 b2 c2 d2 e2 df.loc[df['a']=='a2', ['c']] = df['e'] print(df) # a b c d e # 0 a1 b1 c1 d1 e1 # 1 a2 b2 e2 d2 e2 # loc muti columns replace has some problem df.loc[df['a']=='a2', ['b','c']] = df[['d','e']] print(df) # a b c d e # 0 a1 b1 c1 d1 e1 # 1 a2 NaN NaN d2 e2 ```
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Replaced .format{}/% with f-strings in core/tools/datetime.py
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2
2019-12-24T02:25:18Z
2019-12-24T13:39:46Z
2019-12-24T13:39:42Z
CONTRIBUTOR
null
Modified to python3 format strings ref: https://github.com/pandas-dev/pandas/issues/29547 - [x] contributes to fstring corrections in #29547 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` ~~whatsnew entry~~ (will not add now. I think the full set of changes in #29547 should get a single update entry rather than a piecemeal one)
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541,967,644
MDExOlB1bGxSZXF1ZXN0MzU2NTAzMTE2
30,441
TST: clean up skips and xfails
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2
2019-12-24T02:51:27Z
2019-12-24T20:41:51Z
2019-12-24T20:41:18Z
MEMBER
null
- [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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541,982,727
MDExOlB1bGxSZXF1ZXN0MzU2NTE0NTY4
30,442
CLN .format to f-strings for several files
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2
2019-12-24T04:14:13Z
2019-12-24T14:14:42Z
2019-12-24T13:01:26Z
CONTRIBUTOR
null
updated to f-strings for: pandas/tests/scalar/interval/test_ops.py pandas/tests/computation/test_eval.py pandas/tests/dtypes/* pandas/tests/dtypes/cast/test_infer_dtype.py pandas/tests/dtypes/test_dtypes.py pandas/tests/dtypes/test_inference.py held off on implementing for regex match strings pending PEP 536 - [x] contributes to fstring corrections in #29547 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0MzU2NTE2Nzk4
30,443
TST: dont skip test_replace_series
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0
2019-12-24T04:31:26Z
2019-12-24T16:47:33Z
2019-12-24T13:02:00Z
MEMBER
null
Passes locally, we'll see about the CI
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30,444
TST: Add tests for fixed issues
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5
2019-12-24T06:16:02Z
2019-12-26T05:58:28Z
2019-12-26T00:25:35Z
MEMBER
null
- [x] closes #5647 - [x] closes #5586 - [x] closes #5286 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NDIwODExMTY=
30,445
Check that dtype is preserved when appending categorical, nullable integer and other extension types
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2019-12-24T10:28:40Z
2019-12-30T17:49:41Z
2019-12-30T17:49:41Z
MEMBER
null
Similar to #30238 This issue may already be fixed by #30277. Opening this as requested in https://github.com/pandas-dev/pandas/pull/30277#pullrequestreview-335987704 .
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542,111,453
MDExOlB1bGxSZXF1ZXN0MzU2NjIwMzk3
30,446
BUG: Fix wrong error in df drop with non unique datetime index and invalid keys
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1
2019-12-24T12:16:53Z
2019-12-26T01:33:01Z
2019-12-26T00:32:30Z
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null
- [x] closes #30399 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzU2NjMxMTAw
30,447
TST: Add tests for if_exists keyword argument in df.to_gbq
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4
2019-12-24T13:08:07Z
2019-12-24T20:28:11Z
2019-12-24T17:30:40Z
MEMBER
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- [x] closes #29598 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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30,448
Fix EX03 errors in generic.py
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1
2019-12-24T13:18:00Z
2019-12-24T16:00:06Z
2019-12-24T15:59:41Z
CONTRIBUTOR
null
Fixes EX03 errors in the examples of some docstrings in the script `pandas/core/ generic.py`. Specifically, flake8 errors in the docstrings of the following functions were corrected: ``` pandas.DataFrame.add_prefix pandas.DataFrame.add_suffix pandas.DataFrame.head pandas.DataFrame.last pandas.DataFrame.pop pandas.DataFrame.reindex pandas.DataFrame.reindex_like pandas.DataFrame.tail pandas.DataFrame.take pandas.DataFrame.to_xarray pandas.DataFrame.truncate pandas.DataFrame.tz_localize ``` Related to #27977 cc @datapythonista - [ ] closes #xxxx - [ ] tests added / passed - [X] passes `black pandas` - [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,449
Type annotations are inconsistent for 'columns' in IO methods
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2019-12-24T16:46:46Z
2019-12-28T12:33:41Z
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* ref https://github.com/pandas-dev/pandas/pull/30398#discussion_r361198265 * ref https://github.com/pandas-dev/pandas/pull/30398#pullrequestreview-336243679
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MDExOlB1bGxSZXF1ZXN0MzU2Njg0MTI5
30,450
CLN: remove unnecessary scipy attr checks
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1
2019-12-24T18:13:34Z
2019-12-24T21:18:26Z
2019-12-24T21:15:52Z
MEMBER
null
- [x] closes #30383 akima and pchip have existed since at least scipy 0.14, and we require 1.1, so these checks are unnecessary.
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542,200,518
MDU6SXNzdWU1NDIyMDA1MTg=
30,451
TYPING: keywords in df.sort_values and sr.sort_values cannot be annotated
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2
2019-12-24T19:46:44Z
2021-07-25T04:17:55Z
null
MEMBER
null
Observed in the PR #30402 and PR #27237 Annotation seems not to be able to added for `sort_values`. If added, error message will pop up in CI: ``` pandas/core/series.py:2691: error: Signature of "sort_values" incompatible with supertype "NDFrame" pandas/core/frame.py:4701: error: Signature of "sort_values" incompatible with supertype "NDFrame" ```
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542,200,748
MDExOlB1bGxSZXF1ZXN0MzU2NjkzNTUx
30,452
Parallel build for CI
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7
2019-12-24T19:48:43Z
2019-12-27T02:36:02Z
2019-12-26T18:50:23Z
MEMBER
null
Letting this sit in draft state for a while to see if issues pop up Note that this doesn't affect Windows (not sure yet if it can). Also previous attempts had `-j4` but I believe we only get 2 virtual cores for the CI machines so that was probably overkill.
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MDExOlB1bGxSZXF1ZXN0MzU2NjkzNzY4
30,453
TST: more method-specific test files
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1
2019-12-24T19:51:08Z
2019-12-26T00:20:03Z
2019-12-26T00:17:51Z
MEMBER
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30,454
CI: code check for " " introduced by black
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1
2019-12-24T20:07:34Z
2020-01-02T01:10:32Z
2020-01-02T01:10:32Z
MEMBER
null
Sometimes when we run `black` on something like ``` foo = ( "bar " "baz" ) ``` we end up with `foo = "bar " "baz"` when we would want `foo = "bar baz"`. This is [not](https://github.com/psf/black/issues/1051) considered a bug in `black`, so is something we're currently checking for manually. We should add it to code_checks.
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30,455
CLN: Fix abc imports
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3
2019-12-24T21:10:04Z
2019-12-25T20:27:29Z
2019-12-24T22:23:52Z
CONTRIBUTOR
null
invgrep/Github actions exit status doesn't seem to be working correctly. Currently on master we get these errors in the code_checks stage: https://github.com/pandas-dev/pandas/pull/30452/checks#step:4:7 As per discussion here: https://github.com/pandas-dev/pandas/pull/30370#discussion_r361213525
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30,456
TST/REF: fixturize so as to avoid 500+ skips
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1
2019-12-24T21:17:55Z
2019-12-26T00:19:22Z
2019-12-26T00:14:36Z
MEMBER
null
Re-order the fixturizatin of engine and read_ext so as to yield only valid combinations, avoiding the need to pytest.skip the invalid combinations. This cuts out 500+ pytest.skips, which is about a third of the skips I see when i test with `--not-slow`. The idea is that by cutting down on skips we can keep a closer eye on them, and things like [this](https://github.com/pandas-dev/pandas/pull/30441#discussion_r361222254) wont fall through the cracks quite as easily.
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542,212,933
MDExOlB1bGxSZXF1ZXN0MzU2NzAzMTI3
30,457
CI: Code checks ensure return status is handled
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6
2019-12-24T21:47:12Z
2019-12-25T19:10:33Z
2019-12-25T19:08:14Z
CONTRIBUTOR
null
Follow up from discussion here. https://github.com/pandas-dev/pandas/pull/30370#discussion_r361213525 Seems like we are not return the return code of all these code checks - unless I am missing something? Expected this to fail on master until https://github.com/pandas-dev/pandas/pull/30455 is merged cc. @WillAyd @datapythonista
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MDExOlB1bGxSZXF1ZXN0MzU2NzA1MjYy
30,458
DEPR: remove ptp
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2
2019-12-24T22:20:35Z
2019-12-26T00:07:41Z
2019-12-25T23:51:17Z
MEMBER
null
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542,216,181
MDExOlB1bGxSZXF1ZXN0MzU2NzA1NDgw
30,459
Removed dead intervaltree code
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4
2019-12-24T22:24:28Z
2020-01-16T00:33:23Z
2019-12-26T07:06:04Z
MEMBER
null
While looking to reduce build warnings I came across these methods and couldn't find any public use to them. The only one that seems to get hit is `get_loc` and that is in tests only so I think dead code
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542,225,989
MDExOlB1bGxSZXF1ZXN0MzU2NzEyNjY1
30,460
BUG/Compat: dt64/td64 cummin, cummax on np 1.18
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1
2019-12-25T00:37:25Z
2019-12-25T23:30:27Z
2019-12-25T23:27:30Z
MEMBER
null
- [x] closes #29058 - [x] closes #15553 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry This also fixes+tests Series cummin/cummax with dt64tz, will have to look to see if there are any GH issues for that. Following this and #29872 I think we should refactor a bunch of this out into core.nanops.
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TYP: Annotations in pandas/core/nanops.py
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2019-12-25T01:58:33Z
2020-01-20T20:02:08Z
2020-01-20T17:17:45Z
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- [ ] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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BUG/ENH: groupby quantile arraylike fails with integer columns
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2019-12-25T05:41:33Z
2020-01-03T11:45:33Z
2019-12-26T12:34:16Z
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When columns are integers, `df.groupby(label).quantile(<arraylike>)` fails. This PR fixes this issue by telling `MultiIndex` to only look for positional levels during an reordering operation. Two new internal methods in `MultiIndex` class, and one new parameter for `DataFrame.reorder_levels` and `MultiIndex.reorder_levels` are added during the debugging process. - [ ] closes #30289 - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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pct_change can't work well with groupby, when fill_method =None
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5
2019-12-25T09:40:33Z
2020-01-02T18:09:58Z
2020-01-02T18:09:58Z
NONE
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#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np data = pd.DataFrame(np.random.random((10,2)), index=['a', 'b']*5) data.iloc[1:3,:] = np.nan ``` for pct_change function, when the fill_method = None, it works ``` data.pct_change(1, fill_method=None, limit=1) 0 1 a NaN NaN b NaN NaN a NaN NaN b NaN NaN a -0.498169 -0.568501 b -0.315982 1.340587 a 1.341901 -0.489576 b 0.088594 -0.691063 a -0.514451 0.054695 b 0.844514 -0.604511 ``` but when use it with gourpby , it raise a error ``` data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-18-60898304743e> in <module> ----> 1 data.reset_index().groupby('index')[0].pct_change(1, fill_method=None, limit=1) ~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in pct_change(self, periods, fill_method, limit, freq) 1344 ) 1345 ) -> 1346 filled = getattr(self, fill_method)(limit=limit) 1347 fill_grp = filled.groupby(self.grouper.labels) 1348 shifted = fill_grp.shift(periods=periods, freq=freq) TypeError: getattr(): attribute name must be string ``` else, `limit = 0` worked with gourpby, but can't run in pct_change ``` data.reset_index().groupby('index')[0].pct_change(1, fill_method='pad', limit=0) 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 -0.656739 6 0.601904 7 1.549380 8 -0.471434 9 -0.104398 Name: 0, dtype: float64 ``` ``` data[0].pct_change(1, fill_method='pad', limit=0) ~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/missing.py in pad_2d(values, limit, mask, dtype) 546 547 if np.all(values.shape): --> 548 algos.pad_2d_inplace(values, mask, limit=limit) 549 else: 550 # for test coverage pandas/_libs/algos.pyx in pandas._libs.algos.pad_2d_inplace() ValueError: Limit must be greater than 0 ``` pandas version is 0.25.1
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STY: Removed unconcatenated strings
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2019-12-25T10:31:39Z
2019-12-26T19:40:58Z
2019-12-25T23:12:01Z
MEMBER
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- [x] ref #30454 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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TYP: Add typing to DataFrameGroupBy._cython_agg_blocks
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1
2019-12-25T14:21:58Z
2019-12-25T22:27:41Z
2019-12-25T22:27:36Z
CONTRIBUTOR
null
This helps navigate groupby code.
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STYLE: Use f-string in io/parsers
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8
2019-12-25T18:40:28Z
2019-12-25T23:59:54Z
2019-12-25T23:59:50Z
CONTRIBUTOR
null
xref #29547 Fixed many of the strings in pandas/io/parsers.py except for the string at line 1501 as that will require more time to make it function properly. Please note that this is my first contribution ever so please double check my work!
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2019-12-25T19:29:47Z
2020-01-02T01:31:05Z
2020-01-02T01:10:32Z
MEMBER
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- [x] closes #30454 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry Can be merged, after merging #30464
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2019-12-25T19:50:28Z
2019-12-28T17:28:59Z
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3
2019-12-25T20:13:50Z
2020-09-29T22:48:23Z
2020-09-29T22:48:23Z
CONTRIBUTOR
null
- Follow up from #29212 Continue work done in https://github.com/pandas-dev/pandas/pull/29923 As of CPython 3.6 the implementation of dict maintains insertion order. This became a language feature in python 3.7. Note in python 3.8 support for reversed(dict) was added - but we don't seem to use that in the use cases i've eliminated. ( OrderedDict support this) Add code check as per comment here: https://github.com/pandas-dev/pandas/pull/29923#issuecomment-559889216
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30,470
CI: Travis failure in master because of GBQ table
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10
2019-12-25T20:57:02Z
2019-12-27T16:21:19Z
2019-12-27T16:21:19Z
MEMBER
null
Looks like there is a problem with GBQ. A test is finding that a table already exists. It's making Travis fail `master`. Didn't check in detail, the log is here: https://travis-ci.org/pandas-dev/pandas/jobs/629453901#L2297 @tswast mind having a look? CC: @jreback
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30,471
CLN: OrderedDict -> Dict
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3
2019-12-25T21:10:59Z
2019-12-26T03:06:22Z
2019-12-26T00:00:36Z
CONTRIBUTOR
null
- [x] ref #30469
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30,472
TST: Xfailing broken GBQ test
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4
2019-12-25T23:11:57Z
2019-12-26T01:17:45Z
2019-12-25T23:52:30Z
MEMBER
null
xref #30470 Test recently introduced in #30447 seems to be failing in master. Xfailing it for now. CC: @jreback
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4
2019-12-25T23:13:39Z
2019-12-26T15:31:54Z
2019-12-26T13:27:52Z
MEMBER
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Style: F-String Fixs in pandas/__init__.py
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2019-12-26T00:16:02Z
2019-12-26T00:35:20Z
2019-12-26T00:35:19Z
CONTRIBUTOR
null
Fixed a few f-strings in __init__.py
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STYLE: Using f-strings in pandas/__init__.py
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2
2019-12-26T00:41:52Z
2020-01-04T11:48:09Z
2019-12-26T13:13:15Z
CONTRIBUTOR
null
Replaced the old style strings with the newer f-strings. (Sorry for the confusion with the earlier draft) #29886
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30,476
REF/TST: directories for CategoricalIndex, RangeIndex tests
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2
2019-12-26T01:01:01Z
2019-12-26T15:33:30Z
2019-12-26T13:27:09Z
MEMBER
null
try to make the layout match the way it does for the other index classes, similar to what we're doing with Series/DataFrame tests
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CI: XFail GBQ Tests
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1
2019-12-26T01:27:54Z
2019-12-26T03:58:40Z
2019-12-26T03:58:32Z
CONTRIBUTOR
null
xref #30470 Non strict xfail flakey test @jreback Reference: https://github.com/pandas-dev/pandas/pull/30447
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CI: Fix GBQ Tests
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9
2019-12-26T03:03:31Z
2020-01-01T18:04:12Z
2019-12-27T16:21:19Z
CONTRIBUTOR
null
Believe these test only run on master on travis? So will keep xfail for now Fixturize as per @jreback comment [here](https://github.com/pandas-dev/pandas/issues/30470#issuecomment-568942914). closes #30470
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Pandas sql
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10
2019-12-26T03:35:32Z
2019-12-27T16:27:21Z
2019-12-26T13:02:27Z
CONTRIBUTOR
null
closes #30480 This merge will add in a functionality that works as follows: A table can be registered using register_temp_table(dataframe, table_name) Then using pandas.query("select * from table_name") you can query the data frame or any other ones registered using standard sql syntax.
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Adding sql functionality to pandas similar to spark sql
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25
2019-12-26T04:17:35Z
2020-09-15T22:40:55Z
2020-09-15T22:40:55Z
CONTRIBUTOR
null
I would like to add functionality to pandas so that data frames can be queried like database tables, similar to the way that they can be in spark-sql. I think it should work in a similar fashion. A table can be registered using register_temp_table(dataframe, table_name). Then using ```pandas.query("select * from table_name")``` you can query the data frame or any other ones registered using standard sql syntax. I've already implemented the entire thing, but I was told to open an issue for it. Also I'm aware that there is a package called pandassql but this package actually just puts a data frame into a sql lite database, as opposed to querying a data frame directly, and transforming the sql into pandas methods that are then applied to the data frame. Motivation: The motivation for this enhancement is to make pandas more accessible to a crowd of users that may not be as technical and also to provide ease of transition for legacy code in systems like sas that have SQL already embedded in their programs. I'll supply a context free grammar in my documentation to show exactly what this system can handle, but it can basically handle any traditional SQL select statement, including subqueries, joins, where clauses, group by clauses, any aggregate function already supported by pandas, limit, and order by clauses. It also has support for rank and dense_rank window functions. It can't do things that sql wouldn't normally do like cross tab and you can't use a user defined function in it although I think that could be a good add-on. Datatypes: The interface supports all pandas datatypes, so to cast something as an integer the syntax would currently be cast(some_number as int64) or cast(some_int as object). I've played around with the idea of varchar, char, bigint and smallint, but I think those would be misleading as those aren't datatypes that are supported by pandas currently. Errors: Currently the exceptions that it will throw that come this api are based solely around trying to select from an unregistered table, or from submitting an improperly written sql query, both of which you wouldn't want to silence so there's only one error mode. Api Choices: The reason I made the register_temp_table section of the api top level was to avoid attaching a method to DataFrame although if others think it might be better as a method, I would change it in that manner (DataFrame.register_temp_table(table_name)). The reason pandas.query is a top level method is that it's relational in structure. You can select from multiple tables and join them and such and so it wouldn't make sense for it to be on a DataFrame level. The only similarity to the .query DataFrame method though is the name. DataFrame.query is just an alternate way of expressing things like DataFrame[some_condition] whereas my .query encompasses a large amount of the pandas api. Built In: I have two reasons that I think this would be better built in. The first is that the target audience for this is less technical pandas users. Part of making this api easier to use is lessening the burden of researching code and learning how python works, so I think that for them to go looking for an external package may be hard to begin with and they would also need to know to look for one. My second reason is that, from using what I've built, I've found pandas a lot easier to use just as a developer. Suppose we have a DataFrame with one column called A, it goes from This code: ``` dataframe[name_1] = dataframe[a] - 1 dataframe[name_2] = dataframe[a] + 1 dataframe = dataframe[dataframe[name_1] == dataframe[name_2]] dataframe.drop(columns=['a'], inplace=True) ``` To this code: ```pd.query("select a - 1 as name_1, a + 1 as name_2 from some_table where name_1 = name_2")``` Also although I did implement register_temp_table as an api level function, it would serve best as a method on a DataFrame so that's another thing to consider. I can't really provide any support for the lark part, other than that it seemed like the best tool for what I was making. I apologize for the style and such, I'll be fixing all that before I'm done. I implemented this outside of pandas first, so that's why there are so many style and documentation discrepancies.
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30,481
Fix typos
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2019-12-26T04:46:35Z
2019-12-26T05:58:02Z
2019-12-26T05:57:42Z
CONTRIBUTOR
null
Should be non-semantic.
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MDExOlB1bGxSZXF1ZXN0MzU2ODg2MjI4
30,482
DEPR. Change RangeIndex._start/_stop/_step to FutureWarning
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4
2019-12-26T06:34:45Z
2019-12-26T18:50:48Z
2019-12-26T18:26:58Z
MEMBER
null
- [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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30,483
Add option `encoding` to `to_html`?
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3
2019-12-26T10:03:51Z
2019-12-30T09:32:26Z
2019-12-28T12:49:02Z
CONTRIBUTOR
null
Many thanks for the vast range of I/O possibilities that pandas allows! I just met one edge (or maybe not so edge) case where an extra parameter could save some work, especially for newbie users. #### Code Sample, a copy-pastable example if possible ```python pd.DataFrame({"a": ['☿']}).to_html("a.html") # Would be nice to have: # pd.DataFrame({"a": ['☿']}).to_html("a.html", encoding="utf-8") ``` #### Problem description With the current signature of `DataFrame.to_html`, it is not possible to easily write non-ascii / non-latin1 characters to HTML directly or, more generally, to specify the output encoding. It is necessary to pass an open file: ``` with open("a.html", "w", encoding="utf-8") as out: pd.DataFrame({"a": ['☿']}).to_html(out) ``` It would be nice to have a parameter (admittedly, a 24th one) to allow this, consistent with the `to_csv` one. I see that there is some discussion on parameter consistency in #15008 and #28377 (hopefully, I did my searching well and this is not a duplicate issue), so it might be against the design principles. Do you think this would be a viable idea? If yes, I am ready to implement it. Note: It is then questionable, whether an explicit encoding should also result in a correct `<meta charset...>` tag being added to the file. My motivation: I am currently writing lesson materials for an EDA course and wanted to show how easy it is to export data frames (by chance containing planet symbols but can be any non-Western character) to any format ;-) Thanks, Jan #### Expected Output ``` None ``` #### Unexpected output ``` UnicodeEncodeError Traceback (most recent call last) <ipython-input-67-2972ac7a12d7> in <module> ----> 1 pd.DataFrame({"a": ['☿']}).to_html("a.html") ~\Miniconda3\lib\site-packages\pandas\core\frame.py in to_html(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, max_rows, max_cols, show_dimensions, decimal, bold_rows, classes, escape, notebook, border, table_id, render_links) 2315 ) 2316 # TODO: a generic formatter wld b in DataFrameFormatter -> 2317 formatter.to_html(classes=classes, notebook=notebook, border=border) 2318 2319 if buf is None: ~\Miniconda3\lib\site-packages\pandas\io\formats\format.py in to_html(self, classes, notebook, border) 843 elif isinstance(self.buf, str): 844 with open(self.buf, "w") as f: --> 845 buffer_put_lines(f, html) 846 else: 847 raise TypeError("buf is not a file name and it has no write " " method") ~\Miniconda3\lib\site-packages\pandas\io\formats\format.py in buffer_put_lines(buf, lines) 1808 if any(isinstance(x, str) for x in lines): 1809 lines = [str(x) for x in lines] -> 1810 buf.write("\n".join(lines)) ~\Miniconda3\lib\encodings\cp1252.py in encode(self, input, final) 17 class IncrementalEncoder(codecs.IncrementalEncoder): 18 def encode(self, input, final=False): ---> 19 return codecs.charmap_encode(input,self.errors,encoding_table)[0] 20 21 class IncrementalDecoder(codecs.IncrementalDecoder): UnicodeEncodeError: 'charmap' codec can't encode character '\u263f' in position 193: character maps to <undefined> ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel byteorder : little LC_ALL : None LANG : en_GB.UTF-8 LOCALE : None.None pandas : 0.25.1 numpy : 1.16.4 pytz : 2019.3 dateutil : 2.8.1 pip : 19.3.1 setuptools : 42.0.2.post20191203 Cython : 0.29.13 pytest : 5.2.1 hypothesis : None sphinx : None blosc : None feather : 0.4.0 xlsxwriter : None lxml.etree : 4.4.1 html5lib : None pymysql : None psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64) jinja2 : 2.10.3 IPython : 7.10.1 pandas_datareader: None bs4 : 4.8.1 bottleneck : None fastparquet : None gcsfs : None lxml.etree : 4.4.1 matplotlib : 3.1.1 numexpr : 2.7.0 odfpy : None openpyxl : 3.0.1 pandas_gbq : None pyarrow : 0.13.0 pytables : None s3fs : None scipy : 1.3.2 sqlalchemy : 1.3.7 tables : None xarray : 0.14.0 xlrd : None xlwt : 1.3.0 xlsxwriter : None </details>
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30,484
Strange behaviour of pd.DataFrame.drop() with inplace argument
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4
2019-12-26T10:38:06Z
2021-10-29T02:22:58Z
null
NONE
null
#### Code Sample, a copy-pastable example if possible ```python # input - 1 df = pd.DataFrame({}) df["x1"] = [1, 2, 3, 4, 5] df["x2"] = [0, 0, 0, 1, 1] df["target"] = [10, 20, 30, 40, 50] y = df["target"] df.drop("target", axis=1, inplace=True) y = y + np.min(y) # output df (without removed target column) x1 x2 0 1 0 1 2 0 2 3 0 3 4 1 4 5 1 # input - 2 df = pd.DataFrame({}) df["x1"] = [1, 2, 3, 4, 5] df["x2"] = [0, 0, 0, 1, 1] df["target"] = [10, 20, 30, 40, 50] y = df["target"] df.drop("target", axis=1, inplace=True) y += np.min(y) # output df (with the removed target column)   | x1 | x2 | target -- | -- | -- | -- 0 | 1 | 0 | 20 1 | 2 | 0 | 30 2 | 3 | 0 | 40 3 | 4 | 1 | 50 4 | 5 | 1 | 60 ``` #### Problem description Pandas return DataFrame with removed column after doing in-place operation with a column. Is it a bug or intentional behavior? #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None pandas : 0.25.1 numpy : 1.16.2 pytz : 2018.9 dateutil : 2.7.5 pip : 18.1 setuptools : 42.0.2 Cython : 0.29.2 pytest : 4.1.0 hypothesis : None sphinx : 1.8.3 blosc : None feather : None xlsxwriter : 1.1.2 lxml.etree : 4.3.0 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10 IPython : 7.2.0 pandas_datareader: None bs4 : 4.7.1 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.3.0 matplotlib : 3.0.2 numexpr : 2.6.9 odfpy : None openpyxl : 2.5.12 pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.2.1 sqlalchemy : 1.2.15 tables : 3.4.4 xarray : 0.14.0 xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.1.2 </details>
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542,546,727
MDExOlB1bGxSZXF1ZXN0MzU2OTYzNzkw
30,485
Bug groupby quantile listlike q and int columns
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2
2019-12-26T12:45:24Z
2019-12-27T16:42:24Z
2019-12-27T16:16:50Z
MEMBER
null
When columns are integers, `df.groupby(label).quantile(<arraylike>)` fails. - [x] closes #30289 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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MDU6SXNzdWU1NDI1NTUwMTg=
30,486
REF: window/test_moments.py
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3
2019-12-26T13:22:32Z
2020-05-15T12:53:25Z
2020-05-15T12:53:25Z
CONTRIBUTOR
null
https://github.com/pandas-dev/pandas/blob/master/pandas/tests/window/test_moments.py this includes testing for ewm/rolling and others. let's put into separate files to make this simpler.
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542,590,420
MDExOlB1bGxSZXF1ZXN0MzU2OTk5MzE4
30,487
PEP 3118 Buffer Protocol Support for Series
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7
2019-12-26T15:53:55Z
2020-01-13T23:17:18Z
2020-01-13T23:17:18Z
MEMBER
null
Just a POC and not sure we want this but was curious nonetheless... This extension module allows a Series to conform to the buffer protocol, which makes it readable / writeable through memoryviews. Right now in places like groupby we always in user code extract out the underlying array and pass that through Cython, which this in theory could centralize while helping third party code that may use this as well Getting this to work with a DataFrame would be the bigger win but also takes more effort, so leaving that for another day
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30,488
CLN/DOC: standardize wording on previous-removals
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2
2019-12-26T16:12:45Z
2019-12-26T18:17:51Z
2019-12-26T17:54:46Z
MEMBER
null
In the 1.0.0 whatsnew we currently have 3 variants of "Removed :foo:X" ``` - Removed the previously deprecated :meth:`X` - Removed previously deprecated :meth:`X` - Removed :meth:`X` ``` I think we should standardize on one of these (lest readers think the difference is meaningful). I don't particularly care which, but for this PR went with the last one as it is the least verbose
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30,489
DEPR: Deprecate pandas.datetime
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6
2019-12-26T18:48:06Z
2020-01-02T04:24:54Z
2020-01-02T04:01:45Z
CONTRIBUTOR
null
- [x] closes #30610 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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542,648,435
MDExOlB1bGxSZXF1ZXN0MzU3MDQ4OTg3
30,490
DOC: standardize wording on whatsnew keyword removal
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1
2019-12-26T19:15:03Z
2019-12-26T19:48:47Z
2019-12-26T19:44:14Z
MEMBER
null
Same idea as #30488. I don't particularly care which wording we use, but would like it to be consistent. After this is doing the same thing for "default value for keyword has changed" and last possibly re-ordering in some meaningful way.
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30,491
BUG: The setting xrot=0 in DataFrame.hist() doesn't work with by and subplots #30288
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2
2019-12-26T19:39:04Z
2019-12-27T16:34:28Z
2019-12-27T16:29:54Z
CONTRIBUTOR
null
- [x] closes #30288 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,492
BUG: sql different offsets (1/2 modify infer_dtype)
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21
2019-12-26T20:00:00Z
2020-06-14T15:34:25Z
2020-06-14T15:34:24Z
CONTRIBUTOR
null
- [x] closes #30207 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry # Explanations For to_sql no errors are raised anymore when we have Series containing datetimes with timezone and different offsets (unlike in GH30207) and the data is saved as expected (I tested with postgres which does not save offsets anyways). Concerning read_sql however there are several points to discuss: 1. if we want to have datetime64 instead of "object" (as described in [GH30207](https://github.com/pandas-dev/pandas/issues/30207)) we lose the offsets. So this would be a breaking change and would require first a warning in the next version then we implement it in the version after, right? 2. is this patch I made acceptable? The probem is that read_sql uses pd.DataFrame.from_records and it reads datetimes with different offsets as "object" instead of datetime64[ns,...] (e.g. datetime64[ns, psycopg2.tz.FixedOffsetTimezone(offset=60, name=None)]). If we wanted to modify this behavior we would have to modify the C extension _libs.lib. I don't know how to help with that and I am not sure we want to do this. Even if we could pack the datetimes with differents offsets into a Series with a datetime64 dtype the methods of the .dt accessor are not compatible anyways: currently a ValueError is thrown if one attempts to use the accessor in this case (see traceback in the example in "Before PR" below). # Before PR ```python import datetime import psycopg2 import sqlalchemy import pandas as pd from pandas.io.sql import read_sql, to_sql # CREATE ENGINE # engine = sqlalchemy.create_engine("CONNECTION_STRING") # create datetimes with timezone that have different offsets data = [[datetime.datetime(2019, 11, 14, 16, 12, 0, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=60, name=None))], [datetime.datetime(2019, 8, 7, 15, 37, 4, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=120, name=None))]] df = pd.DataFrame(data, columns = ['ts_tz']) # save data to_sql(frame = df, name = 'timezone_diff_offsets_test', con = engine, schema = 'public', if_exists = 'replace', index = False) ``` <details> <summary>Traceback</summary> ```python-traceback --------------------------------------------------------------------------- ValueError Traceback (most recent call last) ~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object) 1968 try: -> 1969 values, tz_parsed = conversion.datetime_to_datetime64(data) 1970 # If tzaware, these values represent unix timestamps, so we ~/GitHub/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64() ValueError: Array must be all same time zone During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) <ipython-input-2-a66775d04fe3> in <module> 20 schema = 'public', 21 if_exists = 'replace', ---> 22 index = False) ~/GitHub/pandas/pandas/io/sql.py in to_sql(frame, name, con, schema, if_exists, index, index_label, chunksize, dtype, method) 519 chunksize=chunksize, 520 dtype=dtype, --> 521 method=method, 522 ) 523 ~/GitHub/pandas/pandas/io/sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype, method) 1318 index_label=index_label, 1319 schema=schema, -> 1320 dtype=dtype, 1321 ) 1322 table.create() ~/GitHub/pandas/pandas/io/sql.py in __init__(self, name, pandas_sql_engine, frame, index, if_exists, prefix, index_label, schema, keys, dtype) 620 if frame is not None: 621 # We want to initialize based on a dataframe --> 622 self.table = self._create_table_setup() 623 else: 624 # no data provided, read-only mode ~/GitHub/pandas/pandas/io/sql.py in _create_table_setup(self) 869 from sqlalchemy import Table, Column, PrimaryKeyConstraint 870 --> 871 column_names_and_types = self._get_column_names_and_types(self._sqlalchemy_type) 872 873 columns = [ ~/GitHub/pandas/pandas/io/sql.py in _get_column_names_and_types(self, dtype_mapper) 861 column_names_and_types += [ 862 (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False) --> 863 for i in range(len(self.frame.columns)) 864 ] 865 ~/GitHub/pandas/pandas/io/sql.py in <listcomp>(.0) 861 column_names_and_types += [ 862 (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False) --> 863 for i in range(len(self.frame.columns)) 864 ] 865 ~/GitHub/pandas/pandas/io/sql.py in _sqlalchemy_type(self, col) 971 # timezone information 972 try: --> 973 if col.dt.tz is not None: 974 return TIMESTAMP(timezone=True) 975 except AttributeError: ~/GitHub/pandas/pandas/core/accessor.py in _getter(self) 83 def _create_delegator_property(name): 84 def _getter(self): ---> 85 return self._delegate_property_get(name) 86 87 def _setter(self, new_values): ~/GitHub/pandas/pandas/core/indexes/accessors.py in _delegate_property_get(self, name) 60 from pandas import Series 61 ---> 62 values = self._get_values() 63 64 result = getattr(values, name) ~/GitHub/pandas/pandas/core/indexes/accessors.py in _get_values(self) 51 return PeriodArray(data, copy=False) 52 if is_datetime_arraylike(data): ---> 53 return DatetimeIndex(data, copy=False, name=self.name) 54 55 raise TypeError( ~/GitHub/pandas/pandas/core/indexes/datetimes.py in __new__(cls, data, freq, tz, normalize, closed, ambiguous, dayfirst, yearfirst, dtype, copy, name) 266 dayfirst=dayfirst, 267 yearfirst=yearfirst, --> 268 ambiguous=ambiguous, 269 ) 270 ~/GitHub/pandas/pandas/core/arrays/datetimes.py in _from_sequence(cls, data, dtype, copy, tz, freq, dayfirst, yearfirst, ambiguous) 418 dayfirst=dayfirst, 419 yearfirst=yearfirst, --> 420 ambiguous=ambiguous, 421 ) 422 ~/GitHub/pandas/pandas/core/arrays/datetimes.py in sequence_to_dt64ns(data, dtype, copy, tz, dayfirst, yearfirst, ambiguous) 1864 # or M8[ns] to denote wall times 1865 data, inferred_tz = objects_to_datetime64ns( -> 1866 data, dayfirst=dayfirst, yearfirst=yearfirst 1867 ) 1868 tz = maybe_infer_tz(tz, inferred_tz) ~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object) 1972 return values.view("i8"), tz_parsed 1973 except (ValueError, TypeError): -> 1974 raise e 1975 1976 if tz_parsed is not None: ~/GitHub/pandas/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object) 1963 dayfirst=dayfirst, 1964 yearfirst=yearfirst, -> 1965 require_iso8601=require_iso8601, 1966 ) 1967 except ValueError as e: ~/GitHub/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime() ~/GitHub/pandas/pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime() ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True ``` </details> ```python # save datetimes with UTC timezone in different periods of the year ## create test data data = [[datetime.datetime(2019, 11, 14, 15, 12, 0, tzinfo = datetime.timezone.utc)], [datetime.datetime(2019, 8, 7, 13, 37, 4, tzinfo = datetime.timezone.utc)]] df = pd.DataFrame(data, columns = ['ts_tz']) ## save test data to_sql(frame = df, name = 'timezone_diff_offsets_test', con = engine, schema = 'public', if_exists = 'replace', index = False) # upon reading if you have for instance "Europe/Paris" # configured as timezone on your local machine then you get datetimes with timezone # that have different offsets (because they are in different periods of the year) # due to this you have "object" dtype and not datetime64[...] df = read_sql('SELECT * FROM timezone_diff_offsets_test', engine) print(df,'\n') print(df.dtypes) ``` ts_tz 0 2019-11-14 16:12:00+01:00 1 2019-08-07 15:37:04+02:00 ts_tz object dtype: object # After PR ```python import datetime import psycopg2 import sqlalchemy import pandas as pd from pandas.io.sql import read_sql, to_sql # CREATE ENGINE # engine = sqlalchemy.create_engine("CONNECTION_STRING") # create datetimes with timezone that have different offsets data = [[datetime.datetime(2019, 11, 14, 16, 12, 0, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=60, name=None))], [datetime.datetime(2019, 8, 7, 15, 37, 4, tzinfo=psycopg2.tz.FixedOffsetTimezone(offset=120, name=None))]] df = pd.DataFrame(data, columns = ['ts_tz']) # save data - no exceptions occur to_sql(frame = df, name = 'timezone_diff_offsets_test', con = engine, schema = 'public', if_exists = 'replace', index = False) # read data - offsets are dropped and dtype of column ts_tz is datetime64[ns, UTC] df = read_sql('SELECT * FROM timezone_diff_offsets_test', engine) print(df,'\n') print(df.dtypes) ``` ts_tz 0 2019-11-14 15:12:00+00:00 1 2019-08-07 13:37:04+00:00 ts_tz datetime64[ns, UTC] dtype: object
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542,657,918
MDExOlB1bGxSZXF1ZXN0MzU3MDU2ODQ0
30,493
DOC: standardize wording for changed default args
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1
2019-12-26T20:01:30Z
2019-12-27T00:32:47Z
2019-12-26T21:25:22Z
MEMBER
null
last of the standardizations, a few typo fixups, made sure these all specify both the old and new keywords
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542,658,403
MDExOlB1bGxSZXF1ZXN0MzU3MDU3MjMx
30,494
BUG: compound dtype ndarray passed to Series
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2
2019-12-26T20:03:49Z
2019-12-27T00:32:21Z
2019-12-26T21:19:17Z
MEMBER
null
- [x] closes #13296 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,495
INT: Implement _set_freq for TDI/DTI
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3
2019-12-26T20:48:34Z
2019-12-27T16:17:38Z
2019-12-27T09:06:27Z
MEMBER
null
- [x] closes #29843 - [ ] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,496
Improve ISO Date Performance for JSON
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5
2019-12-26T21:53:14Z
2020-01-02T01:09:59Z
2020-01-02T01:08:40Z
MEMBER
null
benchmarks below ```sh before after ratio [9c6771c5] [5c0f5682] <master> <json-index-dates> - 231±60ms 188±3ms 0.82 io.json.ToJSON.time_iso_format('split', 'df_td_int_ts') - 496±20ms 218±40ms 0.44 io.json.ToJSON.time_iso_format('index', 'df_int_float_str') - 486±20ms 207±2ms 0.42 io.json.ToJSON.time_iso_format('columns', 'df_int_float_str') - 499±9ms 210±1ms 0.42 io.json.ToJSON.time_iso_format('index', 'df_date_idx') - 503±20ms 206±2ms 0.41 io.json.ToJSON.time_iso_format('columns', 'df_int_floats') - 515±20ms 210±1ms 0.41 io.json.ToJSON.time_iso_format('index', 'df') - 524±80ms 209±0.5ms 0.40 io.json.ToJSON.time_iso_format('index', 'df_int_floats') - 528±10ms 206±2ms 0.39 io.json.ToJSON.time_iso_format('columns', 'df_td_int_ts') diff --git a/asv_bench/asv.conf.json b/asv_bench/asv.conf.json index c04bbf53a..897de2f85 100644 --- a/asv_bench/asv.conf.json +++ b/asv_bench/asv.conf.json - 546±80ms 209±0.7ms 0.38 io.json.ToJSON.time_iso_format('index', 'df_td_int_ts') - 568±60ms 208±3ms 0.37 io.json.ToJSON.time_iso_format('columns', 'df_date_idx') - 598±100ms 206±2ms 0.35 io.json.ToJSON.time_iso_format('columns', 'df') SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. PERFORMANCE INCREASED. ``` Note that this mostly improves on DTI which on 0.25.3 can't even be written as ISO format, so I didn't add a whatsnew. Timedelta is the big bottleneck remaining
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CLN: OrderedDict -> Dict
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4
2019-12-27T00:59:18Z
2020-01-02T07:45:43Z
2020-01-02T00:33:07Z
CONTRIBUTOR
null
- [x] ref #30469
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542,705,922
MDExOlB1bGxSZXF1ZXN0MzU3MDk1NjE4
30,498
BLD: catch missing cython instead of tempita NameError
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3
2019-12-27T01:04:42Z
2019-12-27T16:16:00Z
2019-12-27T14:41:05Z
MEMBER
null
- [x] closes #28836 I named this branch "bld-misc" in the optimistic hope I could close a bunch of BLD issues, but nope, just this one.
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30,499
DOC: Make pyplot import explicit in the 10 minutes to pandas page
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6
2019-12-27T01:19:33Z
2020-01-01T03:18:34Z
2020-01-01T03:18:30Z
CONTRIBUTOR
null
Beginners don't know what is `plt`, thus it is better to show how to import it explicitly. This is a continuation of https://github.com/pandas-dev/pandas/pull/30274 - [ ] closes #xxxx - [ ] tests added / passed - [ ] passes `black pandas` - [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,500
DEPR: Tick comparison with str
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3
2019-12-27T01:25:05Z
2020-02-25T20:58:16Z
null
MEMBER
null
ATM Tick offset comparisons try casting strings to offsets with to_offset before doing == or !=, but not for inequalities: ``` >>> m = pd.offsets.Minute(4) >>> m == "4min" True >>> m < "5min" TypeError: Invalid comparison between Minute and str ``` In order to make this behavior more consistent, we should deprecate allowing str casting for == and !=. This would allow us to dispatch to the Timedelta inequalities, which have better handling for corner cases anyway (e.g. `m < np.ndarray([m, m]` which Timedelta gets right but Tick messes up)
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BUG: Fixed strange behaviour of pd.DataFrame.drop() with inplace argu…
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18
2019-12-27T01:27:01Z
2020-03-26T12:53:19Z
2020-03-26T12:53:14Z
CONTRIBUTOR
null
closes #30484 - [x] closes #xxxx - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry This is my first PR, so pardon me if I did something wrong. To fix this issue I started by creating a failing test based on the example given by the reporter. After, I fixed the test by removing the "inplace" special methods from add_special_arithmetic_methods, following what was suggested as a comment in the code.
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30,502
Replace "foo!r" to "repr(foo)" syntax #29886
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3
2019-12-27T01:35:59Z
2020-01-04T11:48:03Z
2019-12-27T15:29:37Z
CONTRIBUTOR
null
xref #29886 Fixed the remaining files in: Replace "foo!r" to "repr(foo)" syntax #29886
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542,711,820
MDExOlB1bGxSZXF1ZXN0MzU3MTAwMTE2
30,503
REF/TST: method-specific files for test_append
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1
2019-12-27T01:44:27Z
2019-12-27T16:17:01Z
2019-12-27T14:15:04Z
MEMBER
null
A couple of cleanups and parametrizations.
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542,713,978
MDExOlB1bGxSZXF1ZXN0MzU3MTAxNzkx
30,504
Mark unused parameters in objToJSON
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4
2019-12-27T01:57:44Z
2020-01-16T00:33:25Z
2019-12-27T14:12:00Z
MEMBER
null
If compiled with `-Wextra` you get 58 warnings; this brings things down to 2 and I think helps the reader
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542,717,927
MDExOlB1bGxSZXF1ZXN0MzU3MTA0ODE1
30,505
BUG: DTA/TDA/PA iadd/isub should actually be inplace
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4
2019-12-27T02:20:50Z
2019-12-27T16:19:07Z
2019-12-27T14:09:57Z
MEMBER
null
- [x] closes #24115 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [x] whatsnew entry
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542,725,871
MDExOlB1bGxSZXF1ZXN0MzU3MTEwODE0
30,506
TST: fix maybe_promote dt64tz case, 323 xfails
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1
2019-12-27T03:07:41Z
2019-12-30T16:42:36Z
2019-12-30T13:21:21Z
MEMBER
null
about a third of our remaining xfailed tests
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30,507
BUG: pass 2D ndarray and EA-dtype to DataFrame, closes #12513
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2
2019-12-27T03:28:12Z
2020-01-01T16:31:44Z
2020-01-01T16:28:29Z
MEMBER
null
- [x] closes #12513 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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30,508
BUG: hash_pandas_object fails on array containing tuple #28969
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5
2019-12-27T04:08:57Z
2019-12-31T16:06:42Z
2019-12-31T13:02:56Z
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null
- [x] closes #28969 - [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry ATM this implements a smoke-test that the cases don't raise, but I don't have a canonical answer to test them against. Could hard-code the result I get now. Thoughts?
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30,509
DEPR+BUG: Dispatch Tick comparisons to Timedelta
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- [x] tests added / passed - [x] passes `black pandas` - [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` - [ ] whatsnew entry
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2019-12-27T08:49:37Z
2019-12-27T08:49:52Z
2019-12-27T08:49:52Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```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.] **Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates! **Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary. For documentation-related issues, you can check the latest versions of the docs on `master` here: https://pandas-docs.github.io/pandas-docs-travis/ If the issue has not been resolved there, go ahead and file it in the issue tracker. #### Expected Output #### Output of ``pd.show_versions()`` <details> [paste the output of ``pd.show_versions()`` here below this line] </details>
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542,844,810
MDU6SXNzdWU1NDI4NDQ4MTA=
30,511
tz_localize creates freq inconsistency on time offset change
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2
2019-12-27T11:29:50Z
2020-04-16T21:27:52Z
2020-04-16T21:27:52Z
NONE
null
#### Code Sample, a copy-pastable example if possible ```python import pandas as pd index = pd.date_range("2019-3-31", freq="30T", periods=10, tz="Europe/London") print(index) # this has a freq print(index.freq) print(index.tz_localize(None)) # this shouldn't have it anymore, but it does print(index.tz_localize(None).freq) ``` #### Problem description `tz_localize(None)` doesn't check if the frequency is still consistent with the new localized index. In the case of Daylight Saving Time switch, the frequency of the index should become None (since duplicated/missing timestamps are created). #### Expected Output Achievable with: ```python new_index = index.tz_localize(None) new_index.freq = new_index.inferred_freq # drop frequency if not inferrable print(new_index) # this is correct print(new_index.freq) ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.5.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 0.25.3 numpy : 1.17.4 pytz : 2019.3 dateutil : 2.8.0 pip : 19.3.1 setuptools : 42.0.2.post20191203 Cython : 0.29.14 pytest : 5.3.2 hypothesis : 4.54.2 sphinx : 2.3.0 blosc : None feather : None xlsxwriter : 1.2.6 lxml.etree : 4.4.2 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.10.2 pandas_datareader: None bs4 : 4.8.1 bottleneck : 1.3.1 fastparquet : None gcsfs : None lxml.etree : 4.4.2 matplotlib : 3.1.1 numexpr : 2.7.0 odfpy : None openpyxl : 3.0.2 pandas_gbq : None pyarrow : None pytables : None s3fs : 0.4.0 scipy : 1.3.2 sqlalchemy : 1.3.11 tables : 3.6.1 xarray : None xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.2.6 </details>
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MDU6SXNzdWU1NDI4NjcyNjY=
30,512
DataFrame.replace changes dtype of columns not containing any value to be replaced.
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2019-12-27T12:56:22Z
2021-07-25T04:23:39Z
null
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#### Code Sample, a copy-pastable example if possible ```python import pandas as pd import numpy as np data = pd.DataFrame({'A':np.nan,'B':1.096, 'C':1}, index=[0]) data.replace(to_replace={np.nan:None}, inplace=True) ``` #### Problem description Checking data.dtypes before and after the call to replace shows that the datatype of column B changed from float to object whereas that of C stayed at int. If I remove column A from the original data that does not happen. When using replace I expected columns without any matching value not to be touched / changed. #### Expected Output Dtype of column 'B' should remain 'float64'. #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.3.final.0 python-bits : 64 OS : Linux OS-release : 5.0.0-37-generic machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 0.25.3 numpy : 1.16.4 pytz : 2019.1 dateutil : 2.8.0 pip : 19.1.1 setuptools : 41.0.1 Cython : 0.29.12 pytest : 5.0.1 hypothesis : None sphinx : 2.1.2 blosc : None feather : None xlsxwriter : 1.1.8 lxml.etree : 4.3.4 html5lib : 1.0.1 pymysql : None psycopg2 : None jinja2 : 2.10.1 IPython : 7.6.1 pandas_datareader: None bs4 : 4.7.1 bottleneck : 1.2.1 fastparquet : None gcsfs : None lxml.etree : 4.3.4 matplotlib : 3.1.0 numexpr : 2.6.9 odfpy : None openpyxl : 2.6.2 pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : 1.3.0 sqlalchemy : 1.3.5 tables : 3.5.2 xarray : None xlrd : 1.2.0 xlwt : 1.3.0 xlsxwriter : 1.1.8 </details>
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30,513
Performance of merge for categorical index vs category column
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2019-12-27T17:11:06Z
2021-07-25T19:11:42Z
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CONTRIBUTOR
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#### Code Sample ```python import pandas import numpy import time numpy.random.seed(11) N=1_000_000 ### 1. Merging on category index (3.85 seconds -slower) # Create two (random order) dataframes with a column A with unique category dtype set as index array_1 = numpy.arange(N) numpy.random.shuffle(array_1) df_1 = pandas.DataFrame(array_1, columns=["A"]) df_1["B"] = df_1["A"] * 2 df_1["A"] = df_1["A"].astype("category") df_1 = df_1.set_index("A") array_2 = array_1.copy() numpy.random.shuffle(array_2) df_2 = pandas.DataFrame(array_2, columns=["A"]) df_2["B"] = df_2["A"] * 2 df_2["A"] = df_2["A"].astype("category") df_2 = df_2.set_index("A") # merge two dataframes and time start = time.time() df_1.merge(df_2, how="left", left_index=True, right_index=True) print(time.time()-start) ### 2. Merging on category column (0.85 seconds - faster) # Create two (random order) dataframes with a column A with unique category dtype array_1 = numpy.arange(N) numpy.random.shuffle(array_1) df_1 = pandas.DataFrame(array_1, columns=["A"]) df_1["B"] = df_1["A"] * 2 df_1["A"] = df_1["A"].astype("category") array_2 = array_1.copy() numpy.random.shuffle(array_2) df_2 = pandas.DataFrame(array_2, columns=["A"]) df_2["B"] = df_2["A"] * 2 df_2["A"] = df_2["A"].astype("category") # merge two dataframes and time start = time.time() df_1.merge(df_2, how="left", left_on="A", right_on="A") print(time.time()-start) ``` #### Problem description I noticed that when I perform a merge on a unique category dtype column, the merge gets significantly (x4) slower if I set this column as the index. This was unexpected for me as for all other dtypes I have tried (str, int, datetime etc.) the merge is significantly faster when using the indexed column. I investigated this a bit further. Even though the category column "A" in the above example is unique (no repeated categories), the merge method still calls the `pandas.core.indexes.category.CategoricalIndex.get_indexer_non_unique` method. This is because the check `self.is_unique and self.equals(target)` evaluates to False on https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L662. This is because `self.equals(target)` is False https://github.com/pandas-dev/pandas/blob/67ee16a283b826b4adb07e5ca64a5206d242acaf/pandas/core/indexes/category.py#L309. I am not sure if this should be evaluating as True or False? Should two categorical indexes be equal if they have the exact same categories in different orders? Additionally, I am not sure why get_index_non_unique for a categorical index should be significantly slower than a merge on a categorical column. Let me know if you have any other insights into why the merge would be slower for a category type when using the index, or if I can provide any additional information / assistance. Thanks for your help! #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : None python : 3.7.5.final.0 python-bits : 64 OS : Windows OS-release : 10 machine : AMD64 processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : None.None pandas : 0.25.3 numpy : 1.17.3 pytz : 2019.3 dateutil : 2.8.1 pip : 19.3.1 setuptools : 41.6.0.post20191030 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.10.3 IPython : 7.9.0 pandas_datareader: None bs4 : None bottleneck : None fastparquet : None gcsfs : None lxml.etree : None matplotlib : 3.1.1 numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pytables : None s3fs : None scipy : None sqlalchemy : None tables : None xarray : None xlrd : None xlwt : None xlsxwriter : None </details>
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