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https://api.github.com/repos/pandas-dev/pandas/issues/28713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28713/events | https://github.com/pandas-dev/pandas/pull/28713 | 500,857,734 | MDExOlB1bGxSZXF1ZXN0MzIzMjQwODM5 | 28,713 | Update contributing.rst | {
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} | 3 | 2019-10-01T12:18:47Z | 2019-11-02T15:57:41Z | 2019-11-02T15:57:31Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28714 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28714/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28714/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28714/events | https://github.com/pandas-dev/pandas/issues/28714 | 500,870,798 | MDU6SXNzdWU1MDA4NzA3OTg= | 28,714 | Error while installing dependencies | {
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} | [] | closed | false | null | [] | null | 9 | 2019-10-01T12:44:00Z | 2019-10-02T11:44:27Z | 2019-10-02T11:44:27Z | MEMBER | null | While running:
```
python3 -m venv pandas-venv
source pandas-venv/bin/activate
cd pandas-MyUserName
python -m pip install -r requirements-dev.txt
```
I get an error on line 20:
> ERROR: Could not find a version that satisfies the requirement dask-core (from -r requirements-dev.txt (line 20)) (from versions: none)
ERROR: No matching distribution found for dask-core (from -r requirements-dev.txt (line 20))
the output of ```uname -a``` is
> Linux null 5.3.1-arch1-1-ARCH #1 SMP PREEMPT Sat Sep 21 11:33:49 UTC 2019 x86_64 GNU/Linux
Error output:
```
(pandas-venv) [bummy@null pandas-MomIsBestFriend]$ python -m pip install -r requirements-dev.txt
Collecting numpy>=1.15 (from -r requirements-dev.txt (line 1))
Downloading https://files.pythonhosted.org/packages/ba/e0/46e2f0540370f2661b044647fa447fef2ecbcc8f7cdb4329ca2feb03fb23/numpy-1.17.2-cp37-cp37m-manylinux1_x86_64.whl (20.3MB)
|ββββββββββββββββββββββββββββββββ| 20.3MB 4.1MB/s
Collecting python-dateutil>=2.6.1 (from -r requirements-dev.txt (line 2))
Downloading https://files.pythonhosted.org/packages/41/17/c62faccbfbd163c7f57f3844689e3a78bae1f403648a6afb1d0866d87fbb/python_dateutil-2.8.0-py2.py3-none-any.whl (226kB)
|ββββββββββββββββββββββββββββββββ| 235kB 3.9MB/s
Collecting pytz (from -r requirements-dev.txt (line 3))
Downloading https://files.pythonhosted.org/packages/87/76/46d697698a143e05f77bec5a526bf4e56a0be61d63425b68f4ba553b51f2/pytz-2019.2-py2.py3-none-any.whl (508kB)
|ββββββββββββββββββββββββββββββββ| 512kB 4.1MB/s
Collecting asv (from -r requirements-dev.txt (line 4))
Downloading https://files.pythonhosted.org/packages/6e/94/4521cc0183a5656de9470452ddd2b6170a2d04ba9b18b84c597db09b8b0d/asv-0.4.1.tar.gz (470kB)
|ββββββββββββββββββββββββββββββββ| 471kB 2.0MB/s
Collecting cython>=0.29.13 (from -r requirements-dev.txt (line 5))
Downloading https://files.pythonhosted.org/packages/f1/d3/03a01bcf424eb86d3e9d818e2082ced2d512001af89183fca6f550c32bc2/Cython-0.29.13-cp37-cp37m-manylinux1_x86_64.whl (2.1MB)
|ββββββββββββββββββββββββββββββββ| 2.1MB 4.4MB/s
Collecting black (from -r requirements-dev.txt (line 6))
Downloading https://files.pythonhosted.org/packages/30/62/cf549544a5fe990bbaeca21e9c419501b2de7a701ab0afb377bc81676600/black-19.3b0-py36-none-any.whl (89kB)
|ββββββββββββββββββββββββββββββββ| 92kB 6.2MB/s
Collecting cpplint (from -r requirements-dev.txt (line 7))
Downloading https://files.pythonhosted.org/packages/30/9f/a44a503d457ebdb78298a1cb4dad99c1e506b901b300829fc28f3b3ddd6a/cpplint-1.4.4-py3-none-any.whl (73kB)
|ββββββββββββββββββββββββββββββββ| 81kB 5.2MB/s
Collecting flake8 (from -r requirements-dev.txt (line 8))
Downloading https://files.pythonhosted.org/packages/26/de/3f815a99d86eb10464ea7bd6059c0172c7ca97d4bdcfca41051b388a653b/flake8-3.7.8-py2.py3-none-any.whl (70kB)
|ββββββββββββββββββββββββββββββββ| 71kB 6.5MB/s
Collecting flake8-comprehensions (from -r requirements-dev.txt (line 9))
Downloading https://files.pythonhosted.org/packages/1e/5d/8e71c58199e70ee5e102212e4a6e8cd9ac6da004b03c1461c883cdbc3f83/flake8_comprehensions-2.2.0-py3-none-any.whl
Collecting flake8-rst<=0.7.0,>=0.6.0 (from -r requirements-dev.txt (line 10))
Downloading https://files.pythonhosted.org/packages/81/50/96c9207354feae7ff27dcbd5e27585bac55a1a7b539f6b15e1fced1904a8/flake8_rst-0.7.0-py3-none-any.whl
Collecting isort (from -r requirements-dev.txt (line 11))
Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)
|ββββββββββββββββββββββββββββββββ| 51kB 10.0MB/s
Collecting mypy (from -r requirements-dev.txt (line 12))
Downloading https://files.pythonhosted.org/packages/12/b0/b89484a61af650b731284aa20a3d0c268645ab28ffdeed41beab6a7ed640/mypy-0.730-cp37-cp37m-manylinux1_x86_64.whl (22.8MB)
|ββββββββββββββββββββββββββββββββ| 22.8MB 2.1MB/s
Collecting pycodestyle (from -r requirements-dev.txt (line 13))
Downloading https://files.pythonhosted.org/packages/0e/0c/04a353e104d2f324f8ee5f4b32012618c1c86dd79e52a433b64fceed511b/pycodestyle-2.5.0-py2.py3-none-any.whl (51kB)
|ββββββββββββββββββββββββββββββββ| 51kB 3.6MB/s
Collecting gitpython (from -r requirements-dev.txt (line 14))
Downloading https://files.pythonhosted.org/packages/8e/c7/70bd352e8a561a9b6d1cde9aa313b9d7c871b0c94c3821f44c01f3187e1d/GitPython-3.0.2-py3-none-any.whl (453kB)
|ββββββββββββββββββββββββββββββββ| 460kB 5.3MB/s
Collecting sphinx==1.8.5 (from -r requirements-dev.txt (line 15))
Downloading https://files.pythonhosted.org/packages/7d/66/a4af242b4348b729b9d46ce5db23943ce9bca7da9bbe2ece60dc27f26420/Sphinx-1.8.5-py2.py3-none-any.whl (3.1MB)
|ββββββββββββββββββββββββββββββββ| 3.1MB 2.7MB/s
Collecting numpydoc>=0.9.0 (from -r requirements-dev.txt (line 16))
Downloading https://files.pythonhosted.org/packages/6a/f3/7cfe4c616e4b9fe05540256cc9c6661c052c8a4cec2915732793b36e1843/numpydoc-0.9.1.tar.gz
Collecting nbconvert>=5.4.1 (from -r requirements-dev.txt (line 17))
Downloading https://files.pythonhosted.org/packages/f9/df/4505c0a7fea624cac461d0f41051f33456ae656753f65cee8c2f43121cb2/nbconvert-5.6.0-py2.py3-none-any.whl (453kB)
|ββββββββββββββββββββββββββββββββ| 460kB 5.5MB/s
Collecting nbsphinx (from -r requirements-dev.txt (line 18))
Downloading https://files.pythonhosted.org/packages/39/1f/c912f2f95d53d818dc76867f950883ae8a92849b7bef12a783106143cf08/nbsphinx-0.4.3-py2.py3-none-any.whl
Collecting pandoc (from -r requirements-dev.txt (line 19))
Downloading https://files.pythonhosted.org/packages/49/b1/d2d4b30ee81ea5cb7aee5ba3591752a637fdc49d0a42fa9683874b60b9fb/pandoc-1.0.2.tar.gz (488kB)
|ββββββββββββββββββββββββββββββββ| 491kB 4.4MB/s
Collecting dask-core (from -r requirements-dev.txt (line 20))
ERROR: Could not find a version that satisfies the requirement dask-core (from -r requirements-dev.txt (line 20)) (from versions: none)
ERROR: No matching distribution found for dask-core (from -r requirements-dev.txt (line 20))
(pandas-venv) [bummy@null pandas-MomIsBestFriend]$
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28715/events | https://github.com/pandas-dev/pandas/pull/28715 | 500,874,473 | MDExOlB1bGxSZXF1ZXN0MzIzMjU0NzQ1 | 28,715 | precursor to Split out test_pytables.py to sub-module of tests | {
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} | 3 | 2019-10-01T12:50:51Z | 2019-10-01T15:54:04Z | 2019-10-01T15:51:05Z | CONTRIBUTOR | null | - precursor to #18498
- All tests in test_pytables are passing
@simonjayhawkins as discussed this is the pre work to splitting this large test file into smaller files. So far, I have fixturised the class base class and updated methods using self.path from base to use the setup_path fixture instead.
Next I plan to create a test file for TestTimezones class and make the test functional so the class itself is no longer required.
Once this is running fine, I will then apply similar approach to the other bigger classes. | {
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list like object like Series
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} | 1 | 2019-10-01T13:57:22Z | 2019-10-01T16:53:23Z | 2019-10-01T16:51:32Z | MEMBER | null | Broken off from #28649 to focus in on what is breaking there. | {
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} | 3 | 2019-10-01T14:17:31Z | 2019-10-25T12:17:17Z | 2019-10-25T12:17:14Z | CONTRIBUTOR | null | This PR does two things
1. Enforces the deprecation of pandas auto registering our formatters.
2. Provides a new option, "auto", which will only apply our formatters to our plots. By default, the value will be "auto".
Background: pandas used to register formatters for Period, datetime, date, and time. This formatters affected plots created using matplotlib only, not pandas. For various reasons (including import time) we don't want to do this on an `import pandas`.
A minimal change would be to just not do the registration on `import pandas`. Users would either manually do it, or pandas does it automatically when we do DataFrame.plot. The downside of pandas doing it automatically is then in the following
```
fig1 = plt.plot(x, y)
DataFrame.plot(...) # registers formatters
fig2 = plt.plot(x, y)
```
fig1 and fig2 will be different because pandas has implicilty registered formatters.
The `auto` option will automatically register the formatters, make the plot, then deregister the formatters.
Closes https://github.com/pandas-dev/pandas/issues/18720 | {
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} | 1 | 2019-10-01T14:21:52Z | 2019-10-01T16:33:40Z | 2019-10-01T16:33:25Z | CONTRIBUTOR | null | - [x] xref #28724
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- [ ] whatsnew entry
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} | 11 | 2019-10-01T14:40:18Z | 2021-09-09T05:09:26Z | null | CONTRIBUTOR | null | Fix the docstrings where there is an error in a parameter format, eg string instead of str.
Current errors:
```
pandas.Series.to_json: Parameter "indent" type should use "int" instead of "integer"
pandas.Timestamp.ceil: Parameter "freq" type should use "str" instead of "string"
pandas.Timestamp.day_name: Parameter "locale" type should use "str" instead of "string"
pandas.Timestamp.floor: Parameter "freq" type should use "str" instead of "string"
pandas.Timestamp.month_name: Parameter "locale" type should use "str" instead of "string"
pandas.Timestamp.round: Parameter "freq" type should use "str" instead of "string"
pandas.Timedelta: Parameter "value" type should use "int" instead of "integer"
pandas.Timedelta: Parameter "value" type should use "str" instead of "string"
pandas.Timedelta.max: Parameter "value" type should use "int" instead of "integer"
pandas.Timedelta.max: Parameter "value" type should use "str" instead of "string"
pandas.Timedelta.min: Parameter "value" type should use "int" instead of "integer"
pandas.Timedelta.min: Parameter "value" type should use "str" instead of "string"
pandas.Timedelta.ceil: Parameter "freq" type should use "str" instead of "string"
pandas.Timedelta.floor: Parameter "freq" type should use "str" instead of "string"
pandas.Timedelta.round: Parameter "freq" type should use "str" instead of "string"
pandas.Period.asfreq: Parameter "freq" type should use "str" instead of "string"
pandas.Period.to_timestamp: Parameter "freq" type should use "str" instead of "string"
pandas.arrays.IntervalArray.from_arrays: Parameter "copy" type should use "bool" instead of "boolean"
pandas.arrays.IntervalArray.from_tuples: Parameter "copy" type should use "bool" instead of "boolean"
pandas.arrays.IntervalArray.from_breaks: Parameter "copy" type should use "bool" instead of "boolean"
pandas.arrays.IntervalArray.to_tuples: Parameter "na_tuple" type should use "bool" instead of "boolean"
pandas.Categorical.from_codes: Parameter "codes" type should use "int" instead of "integer"
pandas.Categorical.from_codes: Parameter "dtype" type should use "str" instead of "string"
pandas.SparseArray: Parameter "kind" type should use "int" instead of "integer"
pandas.read_table: Parameter "cache_dates" type should use "bool" instead of "boolean"
pandas.read_csv: Parameter "cache_dates" type should use "bool" instead of "boolean"
pandas.read_msgpack: Parameter "iterator" type should use "bool" instead of "boolean"
pandas.ExcelWriter: Parameter "path" type should use "str" instead of "string"
pandas.ExcelWriter: Parameter "engine" type should use "str" instead of "string"
pandas.ExcelWriter: Parameter "date_format" type should use "str" instead of "string"
pandas.ExcelWriter: Parameter "datetime_format" type should use "str" instead of "string"
pandas.read_json: Parameter "orient" type should use "str" instead of "string"
pandas.read_html: Parameter "flavor" type should use "str" instead of "string"
pandas.HDFStore.append: Parameter "append " type should use "bool" instead of "boolean"
pandas.HDFStore.append: Parameter "min_itemsize" type should use "str" instead of "string"
pandas.HDFStore.append: Parameter "nan_rep " type should use "str" instead of "string"
pandas.HDFStore.append: Parameter "encoding " type should use "str" instead of "string"
pandas.HDFStore.append: Parameter "dropna " type should use "bool" instead of "boolean"
pandas.read_spss: Parameter "path" type should use "str" instead of "string"
pandas.read_sql_query: Parameter "sql" type should use "str" instead of "string"
pandas.read_sql_query: Parameter "con" type should use "str" instead of "string"
pandas.read_sql_query: Parameter "index_col" type should use "str" instead of "string"
pandas.read_sql_query: Parameter "coerce_float" type should use "bool" instead of "boolean"
pandas.read_sql: Parameter "sql" type should use "str" instead of "string"
pandas.read_sql: Parameter "con" type should use "str" instead of "string"
pandas.read_sql: Parameter "index_col" type should use "str" instead of "string"
pandas.read_sql: Parameter "coerce_float" type should use "bool" instead of "boolean"
pandas.read_gbq: Parameter "reauth" type should use "bool" instead of "boolean"
pandas.read_gbq: Parameter "auth_local_webserver" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "convert_dates" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "convert_categoricals" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "encoding" type should use "str" instead of "string"
pandas.read_stata: Parameter "index_col" type should use "str" instead of "string"
pandas.read_stata: Parameter "convert_missing" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "preserve_dtypes" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "order_categoricals" type should use "bool" instead of "boolean"
pandas.read_stata: Parameter "iterator" type should use "bool" instead of "boolean"
pandas.io.stata.StataReader.data: Parameter "convert_dates" type should use "bool" instead of "boolean"
pandas.io.stata.StataReader.data: Parameter "convert_categoricals" type should use "bool" instead of "boolean"
pandas.io.stata.StataReader.data: Parameter "index_col" type should use "str" instead of "string"
pandas.io.stata.StataReader.data: Parameter "convert_missing" type should use "bool" instead of "boolean"
pandas.io.stata.StataReader.data: Parameter "preserve_dtypes" type should use "bool" instead of "boolean"
pandas.io.stata.StataReader.data: Parameter "order_categoricals" type should use "bool" instead of "boolean"
pandas.core.resample.Resampler.pipe: Parameter "func" type should use "str" instead of "string"
pandas.testing.assert_frame_equal: Parameter "check_index_type" type should use "str" instead of "string"
pandas.testing.assert_frame_equal: Parameter "check_column_type" type should use "str" instead of "string"
pandas.testing.assert_series_equal: Parameter "check_index_type" type should use "str" instead of "string"
pandas.testing.assert_index_equal: Parameter "exact" type should use "str" instead of "string"
pandas.api.types.union_categoricals: Parameter "sort_categories" type should use "bool" instead of "boolean"
pandas.api.types.union_categoricals: Parameter "ignore_order" type should use "bool" instead of "boolean"
pandas.api.types.is_list_like: Parameter "allow_sets" type should use "bool" instead of "boolean"
pandas.qcut: Parameter "q" type should use "int" instead of "integer"
pandas.qcut: Parameter "labels" type should use "bool" instead of "boolean"
pandas.merge_asof: Parameter "left_index" type should use "bool" instead of "boolean"
pandas.merge_asof: Parameter "right_index" type should use "bool" instead of "boolean"
pandas.merge_asof: Parameter "tolerance" type should use "int" instead of "integer"
pandas.merge_asof: Parameter "allow_exact_matches" type should use "bool" instead of "boolean"
pandas.to_numeric: Parameter "downcast" type should use "int" instead of "integer"
pandas.period_range: Parameter "start" type should use "str" instead of "string"
pandas.period_range: Parameter "end" type should use "str" instead of "string"
pandas.period_range: Parameter "periods" type should use "int" instead of "integer"
pandas.period_range: Parameter "freq" type should use "str" instead of "string"
pandas.period_range: Parameter "name" type should use "str" instead of "string"
pandas.timedelta_range: Parameter "start" type should use "str" instead of "string"
pandas.timedelta_range: Parameter "end" type should use "str" instead of "string"
pandas.timedelta_range: Parameter "periods" type should use "int" instead of "integer"
pandas.timedelta_range: Parameter "freq" type should use "str" instead of "string"
pandas.timedelta_range: Parameter "name" type should use "str" instead of "string"
pandas.timedelta_range: Parameter "closed" type should use "str" instead of "string"
pandas.infer_freq: Parameter "warn" type should use "bool" instead of "boolean"
pandas.interval_range: Parameter "periods" type should use "int" instead of "integer"
pandas.interval_range: Parameter "freq" type should use "str" instead of "string"
pandas.interval_range: Parameter "name" type should use "str" instead of "string"
pandas.eval: Parameter "parser" type should use "str" instead of "string"
pandas.eval: Parameter "engine" type should use "str" instead of "string"
pandas.util.hash_array: Parameter "encoding" type should use "str" instead of "string"
pandas.util.hash_array: Parameter "hash_key" type should use "str" instead of "string"
pandas.util.hash_pandas_object: Parameter "index" type should use "bool" instead of "boolean"
pandas.util.hash_pandas_object: Parameter "encoding" type should use "str" instead of "string"
pandas.util.hash_pandas_object: Parameter "hash_key" type should use "str" instead of "string"
pandas.Index.shift: Parameter "freq" type should use "str" instead of "string"
pandas.CategoricalIndex: Parameter "dtype" type should use "str" instead of "string"
pandas.IntervalIndex.from_arrays: Parameter "copy" type should use "bool" instead of "boolean"
pandas.IntervalIndex.from_tuples: Parameter "copy" type should use "bool" instead of "boolean"
pandas.IntervalIndex.from_breaks: Parameter "copy" type should use "bool" instead of "boolean"
pandas.IntervalIndex.to_tuples: Parameter "na_tuple" type should use "bool" instead of "boolean"
pandas.MultiIndex.to_frame: Parameter "index" type should use "bool" instead of "boolean"
pandas.MultiIndex.to_frame: Parameter "name" type should use "str" instead of "string"
pandas.MultiIndex.sortlevel: Parameter "ascending" type should use "bool" instead of "boolean"
pandas.DatetimeIndex.indexer_at_time: Parameter "time" type should use "str" instead of "string"
pandas.PeriodIndex: Parameter "data" type should use "int" instead of "integer"
pandas.PeriodIndex: Parameter "freq" type should use "str" instead of "string"
pandas.PeriodIndex.to_timestamp: Parameter "freq" type should use "str" instead of "string"
pandas.core.groupby.GroupBy.pipe: Parameter "func" type should use "str" instead of "string"
pandas.core.groupby.DataFrameGroupBy.idxmax: Parameter "skipna" type should use "bool" instead of "boolean"
pandas.core.groupby.DataFrameGroupBy.idxmin: Parameter "skipna" type should use "bool" instead of "boolean"
pandas.core.groupby.DataFrameGroupBy.nunique: Parameter "dropna" type should use "bool" instead of "boolean"
pandas.core.groupby.DataFrameGroupBy.shift: Parameter "freq" type should use "str" instead of "string"
pandas.io.formats.style.Styler.set_table_attributes: Parameter "attributes" type should use "str" instead of "string"
pandas.api.extensions.ExtensionArray._from_sequence: Parameter "copy" type should use "bool" instead of "boolean"
pandas.api.extensions.ExtensionArray._from_sequence_of_strings: Parameter "copy" type should use "bool" instead of "boolean"
pandas.api.extensions.ExtensionArray.take: Parameter "indices" type should use "int" instead of "integer"
pandas.DataFrame.idxmax: Parameter "skipna" type should use "bool" instead of "boolean"
pandas.DataFrame.idxmin: Parameter "skipna" type should use "bool" instead of "boolean"
pandas.DataFrame.unstack: Parameter "fill_value" type should use "str" instead of "string"
pandas.DataFrame.to_json: Parameter "indent" type should use "int" instead of "integer"
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28725 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28725/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28725/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28725/events | https://github.com/pandas-dev/pandas/pull/28725 | 500,950,712 | MDExOlB1bGxSZXF1ZXN0MzIzMzE2OTc3 | 28,725 | Change maybe_promote fill_value to dt64/td64 NaT instead of iNaT | {
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} | 1 | 2019-10-01T14:51:15Z | 2019-10-01T17:02:45Z | 2019-10-01T16:59:10Z | MEMBER | null | Unambiguous, preliminary for fixing more xfails. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28726/events | https://github.com/pandas-dev/pandas/issues/28726 | 500,962,054 | MDU6SXNzdWU1MDA5NjIwNTQ= | 28,726 | DOC: Add docstring to fixture "frame" in "tests/io/excel/conftest.py" | {
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] | closed | false | null | [] | null | 2 | 2019-10-01T15:08:48Z | 2019-10-01T15:40:40Z | 2019-10-01T15:39:37Z | CONTRIBUTOR | null | #### Problem description
Add docstring to fixture "frame" in "tests/io/excel/conftest.py" as it is not clear what the method does. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28727/events | https://github.com/pandas-dev/pandas/pull/28727 | 500,967,376 | MDExOlB1bGxSZXF1ZXN0MzIzMzMwNzQ5 | 28,727 | Added doctstring to fixture | {
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} | 0 | 2019-10-01T15:17:33Z | 2019-10-02T10:09:38Z | 2019-10-02T10:04:31Z | CONTRIBUTOR | null | - xref https://github.com/pandas-dev/pandas/pull/26579#pullrequestreview-245987920
- 0 tests added
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-01T15:22:44Z | 2019-10-01T18:46:38Z | 2019-10-01T18:46:32Z | CONTRIBUTOR | null | fixing methods defined in this file
this is part of a larger clean up of
the dataframe class
this fixes a subset of #28702
- [x] tests added / passed
- [x] passes `black pandas`
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Closes https://github.com/pandas-dev/pandas/issues/26626 | {
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} | 1 | 2019-10-01T16:10:14Z | 2019-10-02T09:02:45Z | 2019-10-02T08:50:19Z | CONTRIBUTOR | null | - [x] xref https://github.com/pandas-dev/pandas/pull/26735#issuecomment-500156276
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] I've replaced `safe_import` function with a corresponding test decorator. I've also investigated whether `safe_import` could be replaced with `_safe_import` and the answer is negative (see `pandas/tests/io/excel/test_writers.py`).
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] | closed | false | null | [] | null | 16 | 2019-10-01T17:08:33Z | 2021-01-18T15:21:15Z | 2020-11-21T12:37:21Z | CONTRIBUTOR | null | The tick position for BarPlot can be define using the convert tool from matplotlib.
The main advantage is that it will reuse the same position when you have text as axis values. Previously, the tick position was determined by the order of the given element, so that ['A', 'B'] where given label [0, 1], and if updating the plot with order ['B', 'A'], you will not draw at the right position.
###
The resulting plot for each issue would be:
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- [x] closes #11465
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- [x] passes `black pandas`
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} | 2 | 2019-10-01T17:52:29Z | 2019-10-02T16:59:08Z | 2019-10-02T11:44:27Z | CONTRIBUTOR | null | - [x] closes #28714
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} | 3 | 2019-10-01T17:53:11Z | 2019-10-06T09:06:14Z | 2019-10-05T23:09:55Z | CONTRIBUTOR | null | MultiIndex lexsort_depth now return sortorder if sortorder is not set to None
At first the error come from the intersection of the two indexes, that resulted in an unsorted index with an invalid lexsort_depth value. Then when looking at the lexsort_depth function I found the deeper error, where the returned lexsort_depth value were the number of level instead of the self.sortorder value.
After fixing that I implemented a check in the MultiIndex._verify_integrity function when a sortorder is given to raise if it no coherent with the lexsort_depth.
I added tests for the issue, for the lexsort_depth function, and fo raising error in the _verify_integrity function.
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} | 0 | 2019-10-01T18:02:39Z | 2019-10-02T06:43:10Z | 2019-10-02T06:43:10Z | CONTRIBUTOR | null | adds a test to check for regressions later. Replacement PR for #28732
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} | 1 | 2019-10-01T18:49:27Z | 2019-10-02T02:37:37Z | 2019-10-02T02:04:40Z | MEMBER | null | Manually backporting #28400 since the bot isn't responding; did a cherry-pick of 6d47fabaf94d7427b48b30a3808545738cd778c6. This should fix the failing doc builds on the 0.25.x branch.
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] | closed | false | null | [] | null | 1 | 2019-10-01T19:18:30Z | 2019-10-15T19:07:48Z | 2019-10-15T19:07:48Z | CONTRIBUTOR | null | This is a subset of:
- [#28602](https://github.com/pandas-dev/pandas/issues/28602) the PR09 issue overview,
- [#25232](https://github.com/pandas-dev/pandas/issues/25232) the PR08 issue overview,
- [#27977](https://github.com/pandas-dev/pandas/issues/27977) the overall docstring error overview
Fixing PR08 and PR09 errors in pandas.Series:
```
pandas.Series.to_numpy: Parameter "dtype" description should finish with "."
pandas.Series.corr: Parameter "method" description should start with a capital letter
pandas.Series.corr: Parameter "method" description should finish with "."
pandas.Series.quantile: Parameter "q" description should start with a capital letter
pandas.Series.rank: Parameter "method" description should finish with "."
pandas.Series.rank: Parameter "na_option" description should finish with "."
pandas.Series.sem: Parameter "skipna" description should finish with "."
pandas.Series.sem: Parameter "level" description should finish with "."
pandas.Series.std: Parameter "skipna" description should finish with "."
pandas.Series.std: Parameter "level" description should finish with "."
pandas.Series.var: Parameter "skipna" description should finish with "."
pandas.Series.var: Parameter "level" description should finish with "."
pandas.Series.align: Parameter "axis" description should finish with "."
pandas.Series.align: Parameter "level" description should finish with "."
pandas.Series.align: Parameter "fill_value" description should finish with "."
pandas.Series.align: Parameter "method" description should finish with "."
pandas.Series.align: Parameter "fill_axis" description should finish with "."
pandas.Series.align: Parameter "broadcast_axis" description should finish with "."
pandas.Series.drop_duplicates: Parameter "keep" description should start with a capital letter
pandas.Series.duplicated: Parameter "keep" description should start with a capital letter
pandas.Series.reindex: Parameter "index" description should finish with "."
pandas.Series.reindex: Parameter "method" description should finish with "."
pandas.Series.reindex_like: Parameter "method" description should finish with "."
pandas.Series.rename: Parameter "index" description should start with a capital letter
pandas.Series.argsort: Parameter "kind" description should finish with "."
pandas.Series.reorder_levels: Parameter "order" description should start with a capital letter
pandas.Series.reorder_levels: Parameter "order" description should finish with "."
pandas.Series.asfreq: Parameter "method" description should finish with "."
pandas.Series.asfreq: Parameter "how" description should finish with "."
pandas.Series.asfreq: Parameter "normalize" description should finish with "."
pandas.Series.tz_localize: Parameter "level" description should finish with "."
pandas.Series.tz_localize: Parameter "copy" description should finish with "."
pandas.Series.tz_localize: Parameter "ambiguous" description should finish with "."
pandas.Series.tz_localize: Parameter "nonexistent" description should finish with "."
pandas.Series.tshift: Parameter "periods" description should finish with "."
pandas.Series.tshift: Parameter "freq" description should finish with "."
pandas.Series.tshift: Parameter "axis" description should finish with "."
pandas.Series.slice_shift: Parameter "periods" description should finish with "."
pandas.Series.dt.tz_localize: Parameter "ambiguous" description should finish with "."
pandas.Series.dt.tz_localize: Parameter "nonexistent" description should finish with "."
pandas.Series.dt.tz_localize: Parameter "errors" description should start with a capital letter
pandas.Series.dt.round: Parameter "ambiguous" description should finish with "."
pandas.Series.dt.round: Parameter "nonexistent" description should finish with "."
pandas.Series.dt.floor: Parameter "ambiguous" description should finish with "."
pandas.Series.dt.floor: Parameter "nonexistent" description should finish with "."
pandas.Series.dt.ceil: Parameter "ambiguous" description should finish with "."
pandas.Series.dt.ceil: Parameter "nonexistent" description should finish with "."
pandas.Series.str.center: Parameter "width" description should finish with "."
pandas.Series.str.center: Parameter "fillchar" description should finish with "."
pandas.Series.str.find: Parameter "sub" description should finish with "."
pandas.Series.str.find: Parameter "start" description should finish with "."
pandas.Series.str.find: Parameter "end" description should finish with "."
pandas.Series.str.index: Parameter "sub" description should finish with "."
pandas.Series.str.index: Parameter "start" description should finish with "."
pandas.Series.str.index: Parameter "end" description should finish with "."
pandas.Series.str.ljust: Parameter "width" description should finish with "."
pandas.Series.str.ljust: Parameter "fillchar" description should finish with "."
pandas.Series.str.match: Parameter "flags" description should start with a capital letter
pandas.Series.str.normalize: Parameter "form" description should finish with "."
pandas.Series.str.replace: Parameter "case" description should start with a capital letter
pandas.Series.str.replace: Parameter "case" description should finish with "."
pandas.Series.str.replace: Parameter "flags" description should start with a capital letter
pandas.Series.str.replace: Parameter "flags" description should finish with "."
pandas.Series.str.replace: Parameter "regex" description should start with a capital letter
pandas.Series.str.rfind: Parameter "sub" description should finish with "."
pandas.Series.str.rfind: Parameter "start" description should finish with "."
pandas.Series.str.rfind: Parameter "end" description should finish with "."
pandas.Series.str.rindex: Parameter "sub" description should finish with "."
pandas.Series.str.rindex: Parameter "start" description should finish with "."
pandas.Series.str.rindex: Parameter "end" description should finish with "."
pandas.Series.str.rjust: Parameter "width" description should finish with "."
pandas.Series.str.rjust: Parameter "fillchar" description should finish with "."
pandas.Series.str.translate: Parameter "table" description should start with a capital letter
pandas.Series.cat.rename_categories: Parameter "new_categories" description should start with a capital letter
pandas.Series.cat.rename_categories: Parameter "new_categories" description should finish with "."
pandas.Series.plot: Parameter "data" description should finish with "."
pandas.Series.plot: Parameter "kind" description should finish with "."
pandas.Series.plot: Parameter "use_index" description should finish with "."
pandas.Series.plot: Parameter "grid" description should finish with "."
pandas.Series.plot: Parameter "legend" description should finish with "."
pandas.Series.plot: Parameter "style" description should finish with "."
pandas.Series.plot: Parameter "logx" description should finish with "."
pandas.Series.plot: Parameter "logy" description should finish with "."
pandas.Series.plot: Parameter "loglog" description should finish with "."
pandas.Series.plot: Parameter "rot" description should finish with "."
pandas.Series.plot: Parameter "colorbar" description should finish with "."
pandas.Series.plot: Parameter "position" description should finish with "."
pandas.Series.plot: Parameter "mark_right" description should finish with "."
pandas.Series.hist: Parameter "by" description should finish with "."
pandas.Series.hist: Parameter "ax" description should finish with "."
pandas.Series.hist: Parameter "grid" description should finish with "."
pandas.Series.hist: Parameter "xlabelsize" description should finish with "."
pandas.Series.hist: Parameter "xrot" description should start with a capital letter
pandas.Series.hist: Parameter "xrot" description should finish with "."
pandas.Series.hist: Parameter "ylabelsize" description should finish with "."
pandas.Series.hist: Parameter "yrot" description should start with a capital letter
pandas.Series.hist: Parameter "yrot" description should finish with "."
pandas.Series.hist: Parameter "figsize" description should start with a capital letter
pandas.Series.hist: Parameter "figsize" description should finish with "."
pandas.Series.hist: Parameter "**kwargs" description should finish with "."
pandas.Series.to_pickle: Parameter "protocol" description should finish with "."
pandas.Series.to_msgpack: Parameter "path" description should finish with "."
pandas.Series.to_json: Parameter "orient" description should finish with "."
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28739 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28739/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28739/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28739/events | https://github.com/pandas-dev/pandas/pull/28739 | 501,098,608 | MDExOlB1bGxSZXF1ZXN0MzIzNDM3Njgy | 28,739 | DOC: fix PR09,PR08 errors for pandas.Timestamp | {
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} | 5 | 2019-10-01T19:27:20Z | 2019-10-04T17:58:34Z | 2019-10-04T15:33:29Z | CONTRIBUTOR | null | Alright, trying this again.
Note: for `Timestamp.tz_localize` , the `deprecated:: 0.24.0` was in the wrong place. the indentation implied it was only deprecating the `coerce` argument of the `errors` parameter, but if you read both of the arguments for `errors`, it implies the entire parameter is deprecated in favor of `nonexistent` (added in 0.24.0)
Regarding the length of this PR, it also involved editing the NaT type class and Timedelta class. This is because there is a validation which enforces all of these classes to have the same docstrings.
- [x] closes #28673
- [x] tests added / passed | {
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} | 4 | 2019-10-01T20:05:18Z | 2021-07-21T05:12:06Z | 2021-07-21T05:12:06Z | NONE | null | This is a proposal for two new methods that makes filtering dataframe rows easier based in value of two or more column values.
In my use of pandas there is often need to filter (exclude or keep) all the rows that meet a certain condition on multiple column **simultaneously**. For example column A is equal to 1 and column B is equal to 2. I know that I can write a code like the following
```python
new_df = df[(df['A'] == 1) & (df['B'] == 2)]
```
but this seems too much of writing for a simple task, especially if the number of conditions gets larger or the names are not as concise as df, A, and B.
I have written the following two methods to accomplish the task for arbitrary number of conditions and have registered it as native functions using pandas_flavor library. I was wondering if there is attractions/thoughts from others to incorporate this into the original pandas package
``` python
@pf.register_dataframe_method
def include(df, **kwargs):
'''
Include only the rows that match the input condition simultaneously.
Parameters:
-----------
df : pandas.DataFrame
input dataframe
**kwargs : keyword arguments
The name of column and the value that are being checked
Returns:
--------
pandas.DataFrame that the matching rows are kept
Example:
--------
Assume df is a dataframe with the following columns: column1, column2, column3
Then we can only keep the rows that have column1 = value1 and column2 = value2 simultaneously by following command
include(df, column1=value1, column2=value2)
'''
arg = np.ones(shape=(len(df),)).astype(bool)
for key, value in kwargs.items():
arg &= (df[key] == value)
return df[arg]
@pf.register_dataframe_method
def exclude(df, **kwargs):
'''
Exclude the rows that match the input condition simultaneously.
Parameters:
-----------
df : pandas.DataFrame
input dataframe
**kwargs : keyword arguments
The name of columns and their values that are being checked
Returns:
--------
pandas.DataFrame that the matching rows are excluded
Example:
--------
Assume df is a dataframe with the following columns: column1, column2, column3
Then we can exclude the rows that have column1 = value1 and column2 = value2 simultaneously by following command
exclude(df, column1=value1, column2=value2)
'''
arg = np.ones(shape=(len(df),)).astype(bool)
for key, value in kwargs.items():
arg &= (df[key] == value)
return df[~arg]
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} | 5 | 2019-10-02T01:18:56Z | 2019-10-06T13:43:32Z | 2019-10-05T23:03:30Z | MEMBER | null | closes #5284
closes #5035
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Will need to look and see if this closes anything else.
This will have a merge conflict with #28638, that is a slightly higher priority.
Not sure if this constitutes an API change as the behavior for these ops is all over the place.
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} | 1 | 2019-10-02T01:29:08Z | 2019-10-02T16:31:05Z | 2019-10-02T15:47:13Z | CONTRIBUTOR | null | - [x] addresses, but **does not fully close**, #27976
- [x] tests added / passed - I didn't run the full `pytest` suite on the actual code, but rather re-ran:
```
./scripts/validate_docstrings.py pandas.Series.drop --errors=PR02
```
to confirm that the docstring formatting passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] ~whatsnew entry~ - not needed
First PR to pandas, which has been my go-to tool for many years now! Thanks, maintainers! π
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} | 3 | 2019-10-02T02:52:30Z | 2019-10-02T22:11:03Z | 2019-10-02T06:29:51Z | CONTRIBUTOR | null | - closes #28656
- tests added/ passed - Checked with
```
./scripts/validate_docstrings.py pandas.io.formats.style.Styler --errors=PR08,PR09
```
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry - not needed
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} | 24 | 2019-10-02T04:34:18Z | 2019-11-08T16:41:18Z | 2019-11-08T16:41:09Z | CONTRIBUTOR | null | - [x] xref #27976
- [x] tests added / passed - Didn't run all tests, only those concerning the issue (`./scripts/validate_docstrings.py pandas.Series.drop --errors=PR02`)
- [x] passes `black pandas` - Only docstrings were edited, so there were no major changes
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] ~~whatsnew entry~~ - Not needed, only docs changed
General fixes to the documentation in different places. I discovered that the root of many of the conflicts lies in decorators. We might need to re-evaluate how we're getting the signature of functions in the validation script.
First time submitting a PR! Let me know if I need to do anything, or if there's anything I should do differently next time :) | {
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} | 0 | 2019-10-02T06:51:26Z | 2019-10-02T09:50:42Z | 2019-10-02T09:50:42Z | NONE | null | Backport PR #28680: BUG: Fix RangeIndex.get_indexer for decreasing RangeIndex | {
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} | 4 | 2019-10-02T10:05:57Z | 2019-10-05T22:35:25Z | 2019-10-05T22:35:08Z | CONTRIBUTOR | null | - precursor to #18498 and continuation of #28715
- No new tests added
- black pandas ran successfully
- This PR is the next step reference in #28715.
@simonjayhawkins and @jreback as promised, this PR contains the following new and EXCITING changes:
1. Created a contest.py in the pytables sub directory containing all the shared fixtures
2. Created common.py which contains all the functions and variables used by the test classes such as tables, safe_remove, safe_close, create_tempfile, ensure_clean_path, ensure_clean_store, _maybe_remove
3. Created separate modules for each of the test classes i.e. test_timezones, test_hdf_store, test_hdf_complex_values
4. As a result of creating the common.py, I have update the ensure_clean_path import to the test_compat module to ensure safe deletion of the test_pytables module.
There has been no amendments to the body of the code. The next PR will involve focusing on removing the class in test_timezone and making it strictly functional. Followed by me doing this to the other 2 new modules i.e. test_hdf_store and test_hdf_complex_values.
Once the code base is strictly functional, I can start working on more fun refactoring of the functions :) | {
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} | 2 | 2019-10-02T10:33:59Z | 2019-10-18T12:35:35Z | 2019-10-02T14:35:28Z | CONTRIBUTOR | null | Fixing PR08, PR09 errors in pandas.io:
```
pandas.io.formats.style.Styler.apply: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.apply: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "func" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.applymap: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "cond" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "value" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "other" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.where: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.set_properties: Parameter "**kwargs" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_max: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.highlight_min: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "cmap" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "low" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "high" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "subset" description should start with a capital letter
pandas.io.formats.style.Styler.background_gradient: Parameter "text_color_threshold" description should start with a capital letter
pandas.io.formats.style.Styler.bar: Parameter "axis" description should start with a capital letter
pandas.io.formats.style.Styler.use: Parameter "styles" description should start with a capital letter
``` | {
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} | 1 | 2019-10-02T10:54:34Z | 2019-10-02T14:35:45Z | 2019-10-02T14:35:28Z | CONTRIBUTOR | null | - closes #28747
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"d... | closed | false | null | [] | null | 1 | 2019-10-02T15:03:21Z | 2019-11-07T21:07:15Z | 2019-11-07T21:07:15Z | CONTRIBUTOR | null | - [x] xref https://github.com/pandas-dev/pandas/pull/26662#discussion_r291258182, https://github.com/pandas-dev/pandas/pull/26543#issuecomment-497126487, #26784
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} | 4 | 2019-10-02T15:53:02Z | 2019-10-13T22:34:57Z | 2019-10-13T22:34:36Z | CONTRIBUTOR | null | - xref #26784
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 6 | 2019-10-02T16:33:39Z | 2019-10-06T22:21:22Z | 2019-10-06T21:38:23Z | MEMBER | null | Fix a handful of these, clean up a bunch of others.
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} | 7 | 2019-10-02T17:09:44Z | 2021-03-31T00:48:05Z | 2021-03-31T00:48:04Z | CONTRIBUTOR | null | Many groupby aggregations like sum, min, max, mean, and so on take a `sort=` keyword. Count doesn't for some reason.
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"x": [3, 4, 1, 3, 4, 1], "y": [1, 2, 3, 4, 5, 6]})
In [3]: df.groupby("x").y.sum(sort=False)
Out[3]:
x
1 9
3 5
4 7
Name: y, dtype: int64
In [4]: df.groupby("x").y.count(sort=False)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-76e5507e57cb> in <module>
----> 1 df.groupby("x").y.count(sort=False)
TypeError: count() got an unexpected keyword argument 'sort'
```
Is this intentional? Is it a bug? I was trying to turn off sorting intermediate results in dask dataframe by default to speed things up a bit (it's also for cudf/RAPIDS work) and ran into this. The inconsistency makes things slightly inconvenient. | {
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} | 1 | 2019-10-02T17:58:37Z | 2019-10-02T22:50:22Z | 2019-10-02T22:50:17Z | CONTRIBUTOR | null | - [x] addresses #28724
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff
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I discover the following,
```
import pandas.util.testing as pdt
m1 = pd.MultiIndex.from_product([[1], list('ab')])
m2 = pd.MultiIndex.from_product([[1], list('abc')])
m3 = pd.MultiIndex.from_product([[1], list('cab')])
# 1. this raises error
pdt.assert_index_equal(m1, m2)
# 2. this should not raise error
pdt.assert_index_equal(m3, m2)
# 3. this does not raise error
pdt.assert_index_equal(m3.sort_values(), m2.sort_values())
```
* I am not sure if the `order` of the index should play any role in equality (**iff when they are unique**)
* If performance, was issue of not including (sorting by default), but for testing, frame's are relatively small.
Am I missing something ??. If Not, Please consider to change `default with sorting`.
```
pd.show_versions()
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 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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] | closed | false | null | [] | null | 2 | 2019-10-02T19:45:21Z | 2019-10-02T23:03:24Z | 2019-10-02T23:03:24Z | NONE | null | pip install cython -> pip install Cython
- [x] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"c... | closed | false | null | [] | null | 4 | 2019-10-02T19:50:49Z | 2020-10-03T16:19:53Z | 2020-10-03T16:19:53Z | MEMBER | null | In both Series and DataFrame ops, we have inconsistent behavior for when we call `self.align` vs when we raise. Everything discussed here is for non-flex ops.
Case 1: consider `op(ser1, ser2)` for two Series with non-matching indexes.
- arithmetic ops call `self.align(other)`
- comparison ops raise `ValueError("Can only compare identically-labeled Series objects")`
- logical ops call `self.align(other)`
Case 2: consider `op(df1, df2)` for two DataFrames with non-matching axes
- arithmetic ops call `self.align(other)`
- comparison ops raise `ValueError("Can only compare identically-labeled DataFrame objects")`
- logical ops call `self.align(other)`
Case 3) consider `op(df, ser)`. This always aligns, with comparison not being treated differently from the other two.
The policy (and code) would be simpler if we changed this so that either:
a) the comparison op in case 3 doesn't align, matching cases 1 and 2
b) comparison ops always align, matching arithmetic and logical ops | {
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} | 2 | 2019-10-02T21:11:48Z | 2019-10-07T00:33:25Z | 2019-10-07T00:32:06Z | CONTRIBUTOR | null | DOC: Fixed PR06 docstring errors in pandas.interval_range
- [ ] xref #28724
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} | 1 | 2019-10-02T22:37:35Z | 2019-10-02T23:57:13Z | 2019-10-02T23:55:32Z | MEMBER | null | Fixes a typo in the contributing guidelines. | {
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} | 5 | 2019-10-02T23:01:39Z | 2019-10-08T12:52:52Z | 2019-10-08T12:40:09Z | MEMBER | null | - [x] closes #28668
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
Modifies `categorical.astype` to allow for casting to extension dtypes. Also fixes the merge issue identified in #28668. | {
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Maybe at the end of this process we can add a code check to disallow `except Exception` altogether. | {
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} | 3 | 2019-10-03T02:19:02Z | 2019-10-03T18:42:32Z | 2019-10-03T17:50:42Z | MEMBER | null | Not all of them, but whittling them down.
The tests are copied almost verbatim. The only change is updating the datetime64/timedelta64 expected results to be datetime64/timedelta64 instead of iNaT. | {
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} | 23 | 2019-10-03T04:04:31Z | 2019-10-15T20:43:16Z | 2019-10-14T16:05:37Z | CONTRIBUTOR | null | This PR addresses #20298, does not close.
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
This changes the method used to find arguments from `inspect.getfullargspec` to `inspect.signature` and changes the handling accordingly. It also now checks for "*args, **kwargs" and other variations as a doc parameter, and splits them up so they can be checked against.
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} | 6 | 2019-10-03T04:24:05Z | 2019-10-23T17:48:08Z | 2019-10-23T17:48:08Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(data = {'Japanese' : 'γγγ«γ‘γ―', 'English' : 'Hello'}, index = [0])
df.to_string()
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.7
dateutil : 2.7.5
pip : 18.1
setuptools : 40.6.3
Cython : 0.29.2
pytest : 4.0.2
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28767 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28767/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28767/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28767/events | https://github.com/pandas-dev/pandas/issues/28767 | 501,953,824 | MDU6SXNzdWU1MDE5NTM4MjQ= | 28,767 | DataFrame.replace inconsistent behaviour with regex and nan | {
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When `to_replace` is a simple string, only the target values are replaced with `nan`
```python
import numpy as np
import pandas as pd
import re
df = pd.DataFrame([['1', '2'], ['**', '5']])
print(df.replace('**', np.nan)) # works as expected
# 0 1
# 0 1 2
# 1 NaN 5
print(df.replace(re.compile(r'\**'), np.nan))
# 0 1
# 0 NaN NaN
# 1 NaN NaN
print(df.replace(r'\**', np.nan, regex=True))
# 0 1
# 0 NaN NaN
# 1 NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.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: 0.24.2
pytest: 4.3.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.6
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 1 | 2019-10-03T09:32:30Z | 2019-10-03T15:40:46Z | 2019-10-03T15:40:40Z | NONE | null | #### Code Sample
```python
import pandas as pd
import datetime
# setup a dataframe with some dates in
df_dates = pd.DataFrame({
'a': [datetime.datetime(2010, 1, i) for i in range(2, 7)],
'b': [datetime.datetime(2010, 1, i) for i in range(1, 6)]
})
# set first date in first column to NaT
df_dates.a[0] = pd.np.nan
In [13]: df_dates.dtypes
Out[13]:
a datetime64[ns]
b datetime64[ns]
dtype: object
# pandas is happy to take the minimum as I'd like, even dealing with NaT
In [14]: df_dates.min(axis=1)
Out[14]:
0 2010-01-01
1 2010-01-02
2 2010-01-03
3 2010-01-04
4 2010-01-05
dtype: datetime64[ns]
In [15]: df_dates.min(axis=0)
Out[15]:
a 2010-01-03
b 2010-01-01
dtype: datetime64[ns]
# now set the datetimes to UTC
for col in df_dates.columns:
df_dates[col] = df_dates[col].dt.tz_localize('utc')
# now pandas doesn't seem to be able to deal with NaT
# No minimum for first axis
In [19]: df_dates.min(axis=0)
Out[19]: Series([], dtype: float64)
# NaNs everywhere for second axis
In [20]: df_dates.min(axis=1)
Out[20]:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
# skipna kwarg doesn't seem to do anything
In [21]: df_dates.min(axis=1, skipna=True)
Out[21]:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
```
#### Problem description
I'd like to be able to take the minimum on timezone aware datetimes dataframes. Found this issue when a NaT was hidden in there. It works fine when there are no NaTs but the behaviour seems to change as above for timezone aware datetimes. The minimum method should output the same for both timezone aware and naive data? This seems to be an issue to me. I haven't tried .max()/.mean()/... etc.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.2.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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} | 3 | 2019-10-03T14:42:58Z | 2019-11-16T21:35:56Z | 2019-11-16T21:35:56Z | NONE | null | #### Code Sample
```python
>>> import pandas
>>> pandas.DataFrame().append([])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".env\lib\site-packages\pandas\core\frame.py", line 7108, in append
elif isinstance(other, list) and not isinstance(other[0], DataFrame):
IndexError: list index out of range
>>> pandas.__version__
'0.25.1'
```
#### Problem description
Crash when passing empty sequence to `DataFrame.append`
#### Expected Output
No crash.
The source DataFrame is returned intact.
#### Version
Version 0.25.1. Happens in master.
Problem line
https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L7014
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} | 11 | 2019-10-03T15:34:13Z | 2021-07-21T05:12:55Z | null | MEMBER | null | ref https://github.com/pandas-dev/pandas/pull/28762#issuecomment-537760065
Right now the ExtensionArray is missing a few methods that would allow it to align with the `abc.Sequence` interface, namely `count`, `index`, `__reversed__` and `__contains__`
I think we might want to add those items to the interface to aid our type checking and make for more intuitive use cases of the ExtensionArray | {
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} | 6 | 2019-10-03T17:18:48Z | 2019-10-06T06:58:29Z | 2019-10-05T23:00:35Z | CONTRIBUTOR | null | In #28621 I hid the deprecated method ``Index.set_value`` in the docs (in ``indexing.rst``) and in the REPL/Ipython (by adding it to ``Index._deprecations``). I like that as it hides not-recommended parts of the pandas API and guides users away from deprecated methods.
I've given the already deprecated method ``Indexc.get_values`` the same treatment in this PR. If there's consensus about it, I could go through various deprecations (at least the more obscure ones, like ``get_ftype_counts`` and ``get_ftype_counts`` etc.). | {
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} | 7 | 2019-10-03T17:32:00Z | 2021-07-21T05:14:16Z | null | NONE | null | #### Problem description
`pandas.DataFrame.apply()`seems to be converting series from int64 to object in some circumstances, and I'm not sure why. An example of the strange behavior I'm seeing is shown below, along with comments on what I'm expecting to see versus what I actually see. Note, this issue was originally reported on SO here: https://stackoverflow.com/questions/58222263/unexpected-behavior-when-applying-function-to-all-columns-in-pandas-data-frame.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({
"col_1": [1, 2, 3],
"col_2": ["hi", "there", "friend"]
})
print(df)
#> col_1 col_2
#> 0 1 hi
#> 1 2 there
#> 2 3 friend
print(df.dtypes)
#> col_1 int64
#> col_2 object
#> dtype: object
# looks like np.issubdtype returns the expected result when calling the function
# on each series:
np.issubdtype(df.col_1, np.number)
#> True
np.issubdtype(df.col_2, np.number)
#> False
# but it doesn't return the expected result when using the apply function:
print(df.apply(lambda x: np.issubdtype(x, np.number)))
#> col_1 False
#> col_2 False
#> dtype: bool
# we can see that apply seems to be coercing the series to objects here:
print(df.apply(lambda x: x.dtype))
#> col_1 object
#> col_2 object
#> dtype: object
# what's also pretty weird is that i get the expected result when applying the
# replace_nulls() function below (e.g., median imputation is used if the series
# is a number, otherwise nulls are replaced with "MISSING"):
df = pd.DataFrame({
"col_1": [1, 2, np.nan],
"col_2": ["hi", "there", np.nan]
})
def replace_nulls(s):
is_numeric = np.issubdtype(s, np.number)
missing_value = s.median() if is_numeric else "MISSING"
return np.where(s.isnull(), missing_value, s)
print(df.apply(replace_nulls))
#> col_1 col_2
#> 0 1.0 hi
#> 1 2.0 there
#> 2 1.5 MISSING
```
<sup>Created on 2019-10-03 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
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 : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 2 | 2019-10-03T19:34:56Z | 2020-04-05T17:45:04Z | 2019-10-30T22:46:53Z | MEMBER | null | This extends #28583 to handle `op(frame, series)` (whereas 28583 handles only `op(frame, scalar)`).
I got this passing locally, haven't gone back through to do e.g. lint fixups. There are a few places where this surfaced bugs that can be fixed independently.
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} | 1 | 2019-10-03T19:56:15Z | 2019-10-06T13:40:44Z | 2019-10-05T22:30:42Z | MEMBER | null | These are left over from a time when maybe_promote treated iNaT as an NA value, which we stopped doing because it is ambiguous.
Removes 22 xfails (leaving 317 in this file) | {
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} | 1 | 2019-10-03T20:20:01Z | 2019-10-06T13:37:14Z | 2019-10-05T22:31:37Z | MEMBER | null | fixes 9 xfails | {
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} | 1 | 2019-10-03T20:45:25Z | 2019-10-06T13:36:41Z | 2019-10-05T22:32:52Z | MEMBER | null | Currently working on a branch that fixes (some of) these new xfails. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28778 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28778/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28778/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28778/events | https://github.com/pandas-dev/pandas/issues/28778 | 502,297,955 | MDU6SXNzdWU1MDIyOTc5NTU= | 28,778 | DISCUSS: boolean dtype with missing value support | {
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} | 23 | 2019-10-03T21:05:39Z | 2020-03-30T06:56:31Z | 2020-01-06T20:26:19Z | MEMBER | null | Part of the discussion on missing value handling in https://github.com/pandas-dev/pandas/issues/28095, detailed proposal at https://hackmd.io/@jorisvandenbossche/Sk0wMeAmB.
*if* we go for a new NA value, we also need to decide the behaviour of this value in comparison operations. And consequently, we also need to decide on the behaviour of boolean values with missing data in logical operations and indexing operations.
So let's use this issue for that part of the discussion.
Some aspects of this:
- Behaviour in **comparison operations**: currently np.nan compares unequal (`value == np.nan -> False`, `values > np.nan -> False`, but we can also propagate missing values (`value == NA -> NA`, ...)
- Behaviour in **logical operations**: currently we always return False for `|` or `&` with missing data. But we could also use a "three-valued logic" like [Julia](https://docs.julialang.org/en/v1/manual/missing/index.html#Logical-operators-1) and SQL (this has, eg, `NA | True = True` or `NA & True = NA`).
- Behaviour in **indexing**: currently you cannot do boolean indexing with a boolean series with missing values (which is object dtype right now). Do we want to change this? For example, interpret it as False (not select it)
(TODO: should check how other languages do this)
Julia has a nice documentation page explain how they support [missing values](https://docs.julialang.org/en/v1/manual/missing/index.html), the above ideas largely match with that.
Besides those behavioural API discussions, we also need to decide on how to approach this technically (boolean ExtensionArray with boolean numpy array + mask for missing values?) Shall we discuss that here as well, or keep that separate?
cc @pandas-dev/pandas-core | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28779/events | https://github.com/pandas-dev/pandas/issues/28779 | 502,312,403 | MDU6SXNzdWU1MDIzMTI0MDM= | 28,779 | `RollingGroupBy.quantile` ignores `interpolation` argument | {
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} | 0 | 2019-10-03T21:38:59Z | 2019-11-18T00:33:35Z | 2019-11-18T00:33:34Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame([
['a', 1], ['a', 2], ['a', 3],
['b', 1], ['b', 3], ['b', 5]
], columns=['key', 'val'])
print(df.groupby('key').rolling(2).quantile(.4, interpolation='lower'))
```
#### Problem description
The keyword argument `interpolation` is ignored - no matter what value it is set to, the default (linear) interpolation is used. This problem only applies to `RollingGroupBy.quantile` - `GroupBy.quantile` and `Rolling.quantile` both appear to work as expected.
#### Expected Output
```
val
key
a 0 NaN
1 1.0
2 2.0
b 3 NaN
4 1.0
5 3.0
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
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 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
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
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https://api.github.com/repos/pandas-dev/pandas/issues/28780 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28780/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28780/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28780/events | https://github.com/pandas-dev/pandas/issues/28780 | 502,348,458 | MDU6SXNzdWU1MDIzNDg0NTg= | 28,780 | DOC: wrong formatting of the set_option docs | {
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} | 2 | 2019-10-03T23:31:58Z | 2020-02-03T10:39:49Z | 2020-02-03T10:39:33Z | CONTRIBUTOR | null | https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.set_option.html
Notes section of the set_option docs seem to have lost some formatting/spacing on master.
PR coming to fix!
Ali | {
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} | 4 | 2019-10-04T00:35:15Z | 2019-11-18T21:56:23Z | 2019-11-18T21:56:23Z | CONTRIBUTOR | null | this is related to #28702
also fix a few formatting issues
related to default arguments
and argument ordering
- [x] tests added / passed
- [x] passes `black pandas` | {
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"id": ... | closed | false | null | [] | null | 8 | 2019-10-04T02:40:04Z | 2020-01-16T00:33:51Z | 2019-10-08T15:22:41Z | MEMBER | null | Just trying to simplify things...
Currently fails (locally at least) with 5 tests, which interestingly enough seem mostly related to `mean`. Need to debug further but submitting now for feedback | {
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"color": "d7... | closed | false | null | [] | null | 2 | 2019-10-04T03:06:22Z | 2019-10-10T10:47:36Z | 2019-10-10T09:35:55Z | CONTRIBUTOR | null | MultiIndex.get_loc can find nan when input is NA value(e.g. nan, None)
- [ ] closes #19132
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | open | false | null | [] | null | 1 | 2019-10-04T04:24:38Z | 2019-10-16T18:51:34Z | null | CONTRIBUTOR | null | There are a few edge cases where there is not sufficient guidance in the docstrings on how to proceed. For example, if a parameter argument takes a list of values but the list is sufficiently long, it is not clear how to properly format (see #28739).
Opening this as a placeholder to document edge cases encountered while cleaning up docstrings and agree on what the convention should be. | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 8 | 2019-10-04T09:11:40Z | 2019-11-02T02:26:56Z | null | NONE | null | #### Code example using pandas 0.25.1
```python
import pandas as pd
df1 = pd.DataFrame([], index=[], columns = ["foo"])
df2 = pd.DataFrame(data=list(range(20)),index=pd.date_range(start='2000', end='2020', freq='A-DEC'),columns=["bar"])
print(df2.index.dtype.name)
# Output: `datetime64[ns]`
df = pd.concat([df1, df2], axis=1)
print(df.index.dtype.name)
# Output: `object`
```
#### Problem description
The expected dtype of the concatenated DataFrame `df` should be `datetime64[ns]`. Previously, with Pandas 0.24.2, this was the case. After upgrading to pandas 0.25.1 it changed to `object`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-64-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_ZA.UTF-8
LOCALE : en_ZA.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.4.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : None
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.2.1
</details>
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} | 5 | 2019-10-04T13:32:37Z | 2019-10-07T13:50:38Z | 2019-10-07T13:50:38Z | NONE | null | #### Problem description
Consider the following data frame:
```
df = pd.DataFrame(data=(('Bob', 2), ('Greg', None), ('Greg', 6)), columns=['Name', 'Items'])
```
```
Name Items
0 Bob 2.0
1 Greg NaN
2 Greg 6.0
```
Now I want to group by `Name` and sum the `Items`, but I want the sum to be NaN if there are NaN elements. Due to a bug in pandas (https://github.com/pandas-dev/pandas/issues/20824) I cannot simply do
```
df.groupby('Name', observed=True).sum(skipna=False).reset_index()
```
because that results in:
```
Name Items
0 Bob 2.0
1 Greg 6.0
```
which is wrong because it's skipping the `NaN` for Greg even though it shouldn't (hence the bug). Thus I'm using the following workaround to get the correct result:
```
df.groupby('Name', observed=True).agg(pd.DataFrame.sum, skipna=False).reset_index()
```
which results in the expected:
```
Name Items
0 Bob 2.0
1 Greg NaN
```
However, if we change the `Name` column to categorical then the resulting column names are wrong:
```
df_cat = df.copy()
df_cat['Name'] = df_cat['Name'].astype('category')
df_cat.groupby('Name', observed=True).agg(pd.DataFrame.sum, skipna=False).reset_index()
```
which prints:
```
index Items
0 Bob 2.0
1 Greg NaN
```
As you can see, **the column that should be labelled `Name` is now called `index`.**
#### Expected Output
The same as the non-categorical version, i.e.:
```
Name Items
0 Bob 2.0
1 Greg NaN
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.5
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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https://api.github.com/repos/pandas-dev/pandas/issues/28788 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28788/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28788/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28788/events | https://github.com/pandas-dev/pandas/issues/28788 | 502,688,439 | MDU6SXNzdWU1MDI2ODg0Mzk= | 28,788 | Groupby.transform returns data with incorrect indexing when grouping on categoricals with missing values | {
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... | open | false | null | [] | null | 0 | 2019-10-04T15:03:07Z | 2020-06-28T06:35:33Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from pandas.api.types import CategoricalDtype
cat1_type = CategoricalDtype(categories=[101, 202, 303], ordered=True)
cat2_type = CategoricalDtype(categories=["a", "b", "c", "d"], ordered=True)
df = pd.DataFrame({'cat1': [101, 101, 101, 101, 101, 101],
'vals':[1, 2, 3, 4, 5, 6],
'cat2': ["a", "b", "d", "d", "a", "c"]
})
# Original DataFrame
print(df)
# Everything works as expected when "cat1" and "cat2" are not categories
df['cat_max'] = df.groupby(["cat1", "cat2"]).transform(max)
print(df)
# Convert to cat1 and cat2 to categories
df = df.astype({'cat1': cat1_type, 'cat2': cat2_type, 'vals': 'float64'})
# Everything works when there are no missing intermediate categories
df.cat_max = df.groupby(["cat1", "cat2"]).transform(max)
print(df)
# With missing "c" category everything works if observed=True is specified
df_miss_c = df[df.cat2 != "c"].copy()
df_miss_c.loc[:, 'cat_max'] = df_miss_c.groupby(["cat1", "cat2"], observed=True).transform(max)
print(df_miss_c)
# With missing "c" category the next group picks up its NaNs
df_miss_c = df[df.cat2 != "c"].copy()
df_miss_c.loc[:, 'cat_max'] = df_miss_c.groupby(["cat1", "cat2"]).transform(max)
print(df_miss_c)
# With with missing "202" category you get two different NaNs
# If you group by cat1 then cat2 you get missing values like above
df_miss_202 = df.copy()
df_miss_202.loc[5, 'cat1'] = 303
df_miss_202.cat_max = df_miss_202.groupby(["cat2", "cat1"]).transform(max)
print(df_miss_202)
# With with missing "101" everything works as expected as long as only one value for cat1 is used
df_miss_101 = df.copy()
df_miss_101.cat1 = 202
df_miss_101.cat_max = df_miss_101.groupby(["cat2", "cat1"]).transform(max)
print(df_miss_101)
print(f'pandas version {pd.show_versions()}')
```
#### Problem description
From the documentation:
**Transformation:** perform some group-specific computations and return a like-indexed object.
Given that transformation's purpose is to return a like-indexed object is seems that it should do this be default, even without specifying observed=True when there are missing categories. Also the default behavior with missing categories changes depending on which category is missing.
#### Expected Output
The expected output and output provided if the grouper values are not categories or if observed = True is specified follow.
```
>>> # Original DataFrame
>>> print(df)
cat1 vals cat2
0 101 1 a
1 101 2 b
2 101 3 d
3 101 4 d
4 101 5 a
5 101 6 c
>>>
>>> # Everything works when "cat1" and "cat2" are not categories
>>> df['cat_max'] = df.groupby(["cat1", "cat2"]).transform(max)
>>> print(df)
cat1 vals cat2 cat_max
0 101 1 a 5
1 101 2 b 2
2 101 3 d 4
3 101 4 d 4
4 101 5 a 5
5 101 6 c 6
>>> # Convert to cat1 and cat2 to categories
>>> df = df.astype({'cat1': cat1_type, 'cat2': cat2_type, 'vals': 'float64'})
>>> # With missing "c" category everything works if observed=True is specified
>>> df_miss_c = df[df.cat2 != "c"].copy()
>>> df_miss_c.loc[:, 'cat_max'] = df_miss_c.groupby(["cat1", "cat2"], observed=True).transform(max)
>>> print(df_miss_c)
cat1 vals cat2 cat_max
0 101 1.0 a 5.0
1 101 2.0 b 2.0
2 101 3.0 d 4.0
3 101 4.0 d 4.0
4 101 5.0 a 5.0
>>> # With with missing "101" everything works as expected as long as only one value for cat1 is used
>>> df_miss_101 = df.copy()
>>> df_miss_101.cat1 = 202
>>> df_miss_101.cat_max = df_miss_101.groupby(["cat2", "cat1"]).transform(max)
>>> print(df_miss_101)
cat1 vals cat2 cat_max
0 202 1.0 a 5.0
1 202 2.0 b 2.0
2 202 3.0 d 4.0
3 202 4.0 d 4.0
4 202 5.0 a 5.0
5 202 6.0 c 6.0
```
When an intermediate categorical is missing, "202", or "c" the indexing is thrown off.
```
>>> # With missing "c" category the next group picks up its NaNs
>>> df_miss_c = df[df.cat2 != "c"].copy()
>>> df_miss_c.loc[:, 'cat_max'] = df_miss_c.groupby(["cat1", "cat2"]).transform(max)
>>> print(df_miss_c)
cat1 vals cat2 cat_max
0 101 1.0 a 5.0
1 101 2.0 b 2.0
2 101 3.0 d NaN
3 101 4.0 d NaN
4 101 5.0 a 5.0
>>>
>>> # With with missing "202" category you get two different NaNs
>>> # If you group by cat1 then cat2 you get missing values like above
>>> df_miss_202 = df.copy()
>>> df_miss_202.loc[5, 'cat1'] = 303
>>> df_miss_202.cat_max = df_miss_202.groupby(["cat2", "cat1"]).transform(max)
>>> print(df_miss_202)
cat1 vals cat2 cat_max
0 101 1.0 a 5.0
1 101 2.0 b NaN
2 101 3.0 d 2.0
3 101 4.0 d 2.0
4 101 5.0 a 5.0
5 303 6.0 c NaN
```
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.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 : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
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 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.2.1
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.0
pytables : None
s3fs : None
scipy : 1.2.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | 7 | 2019-10-04T15:03:40Z | 2019-10-06T23:50:35Z | 2019-10-06T22:14:44Z | CONTRIBUTOR | null | These error types no longer show errors on master, so they're ready to be turned on for builds.
- [x] closes #23630
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] closes #6942
- [x] test added
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/28791 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28791/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28791/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28791/events | https://github.com/pandas-dev/pandas/issues/28791 | 502,716,584 | MDU6SXNzdWU1MDI3MTY1ODQ= | 28,791 | DOC: HDFStore.select documentation miss information about selecting MultiIndex | {
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} | 0 | 2019-10-04T15:56:40Z | 2019-10-16T12:11:17Z | 2019-10-16T12:11:17Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
index = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])
df = pd.DataFrame(range(4), index=index)
store = pd.HDFStore('store.h5')
store.append('dataframe', df)
store.select('dataframe', where="level_0 in 'b' and level_1 in 2")
Out[3]:
0
b 2 3
```
#### Problem description
When using a MultiIndex in a HDFStore, you can select from the MultiIndex using the keyword level_0, level_1, β¦
This behaviour is not yet documented in the user guide.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 02675e3376fea7d3cd484ace874d676c142cf003
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.1-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.26.0.dev0+446.g02675e337.dirty
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.13
pytest : 4.4.1
hypothesis : 4.18.2
sphinx : 1.8.5
blosc : 1.8.1
feather : None
xlsxwriter : 1.1.7
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.1
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.3
tables : 3.5.1
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.7
</details>
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} | 19 | 2019-10-04T15:56:47Z | 2021-11-20T05:48:53Z | 2021-11-20T05:48:53Z | CONTRIBUTOR | null | There are issues in some docstrings where the See Also section is not complete. All of the following errors exist because there is no description for a referenced function or method. If these can get fixed then CI will be able to check all of the See Also section errors! The command to check for the errors is `./scripts/validate_docstrings.py --errors=SA04`
```
pandas.melt: Missing description for See Also "DataFrame.melt" reference
pandas.melt: Missing description for See Also "pivot_table" reference
pandas.melt: Missing description for See Also "DataFrame.pivot" reference
pandas.melt: Missing description for See Also "Series.explode" reference
pandas.merge_ordered: Missing description for See Also "merge" reference
pandas.merge_ordered: Missing description for See Also "merge_asof" reference
pandas.merge_asof: Missing description for See Also "merge" reference
pandas.merge_asof: Missing description for See Also "merge_ordered" reference
pandas.unique: Missing description for See Also "Index.unique" reference
pandas.unique: Missing description for See Also "Series.unique" reference
pandas.eval: Missing description for See Also "DataFrame.query" reference
pandas.eval: Missing description for See Also "DataFrame.eval" reference
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pandas.api.extensions.register_dataframe_accessor: Missing description for See Also "register_index_accessor" reference
pandas.api.extensions.register_series_accessor: Missing description for See Also "register_dataframe_accessor" reference
pandas.api.extensions.register_series_accessor: Missing description for See Also "register_index_accessor" reference
pandas.api.extensions.register_index_accessor: Missing description for See Also "register_dataframe_accessor" reference
pandas.api.extensions.register_index_accessor: Missing description for See Also "register_series_accessor" reference
pandas.api.extensions.ExtensionDtype: Missing description for See Also "extensions.register_extension_dtype" reference
pandas.api.extensions.ExtensionDtype: Missing description for See Also "extensions.ExtensionArray" reference
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pandas.DataFrame.empty: Missing description for See Also "Series.dropna" reference
pandas.DataFrame.empty: Missing description for See Also "DataFrame.dropna" reference
pandas.DataFrame.pipe: Missing description for See Also "DataFrame.apply" reference
pandas.DataFrame.pipe: Missing description for See Also "DataFrame.applymap" reference
pandas.DataFrame.pipe: Missing description for See Also "Series.map" reference
pandas.DataFrame.corr: Missing description for See Also "DataFrame.corrwith" reference
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pandas.DataFrame.melt: Missing description for See Also "melt" reference
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pandas.DataFrame.melt: Missing description for See Also "DataFrame.pivot" reference
pandas.DataFrame.melt: Missing description for See Also "Series.explode" reference
pandas.DataFrame.asfreq: Missing description for See Also "reindex" reference
pandas.DataFrame.to_json: Missing description for See Also "read_json" reference
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pandas.Series.put: Missing description for See Also "numpy.ndarray.put" reference
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pandas.Series.cat.remove_categories: Missing description for See Also "set_categories" reference
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pandas.Series.to_json: Missing description for See Also "read_json" reference
pandas.Index: Missing description for See Also "DatetimeIndex" reference
pandas.Index: Missing description for See Also "TimedeltaIndex" reference
pandas.Index: Missing description for See Also "PeriodIndex" reference
pandas.Index: Missing description for See Also "Int64Index" reference
pandas.Index: Missing description for See Also "UInt64Index" reference
pandas.Index: Missing description for See Also "Float64Index" reference
pandas.Index.memory_usage: Missing description for See Also "numpy.ndarray.nbytes" reference
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pandas.CategoricalIndex.reorder_categories: Missing description for See Also "add_categories" reference
pandas.CategoricalIndex.reorder_categories: Missing description for See Also "remove_categories" reference
pandas.CategoricalIndex.reorder_categories: Missing description for See Also "remove_unused_categories" reference
pandas.CategoricalIndex.reorder_categories: Missing description for See Also "set_categories" reference
pandas.CategoricalIndex.add_categories: Missing description for See Also "rename_categories" reference
pandas.CategoricalIndex.add_categories: Missing description for See Also "reorder_categories" reference
pandas.CategoricalIndex.add_categories: Missing description for See Also "remove_categories" reference
pandas.CategoricalIndex.add_categories: Missing description for See Also "remove_unused_categories" reference
pandas.CategoricalIndex.add_categories: Missing description for See Also "set_categories" reference
pandas.CategoricalIndex.remove_categories: Missing description for See Also "rename_categories" reference
pandas.CategoricalIndex.remove_categories: Missing description for See Also "reorder_categories" reference
pandas.CategoricalIndex.remove_categories: Missing description for See Also "add_categories" reference
pandas.CategoricalIndex.remove_categories: Missing description for See Also "remove_unused_categories" reference
pandas.CategoricalIndex.remove_categories: Missing description for See Also "set_categories" reference
pandas.CategoricalIndex.remove_unused_categories: Missing description for See Also "rename_categories" reference
pandas.CategoricalIndex.remove_unused_categories: Missing description for See Also "reorder_categories" reference
pandas.CategoricalIndex.remove_unused_categories: Missing description for See Also "add_categories" reference
pandas.CategoricalIndex.remove_unused_categories: Missing description for See Also "remove_categories" reference
pandas.CategoricalIndex.remove_unused_categories: Missing description for See Also "set_categories" reference
pandas.CategoricalIndex.set_categories: Missing description for See Also "rename_categories" reference
pandas.CategoricalIndex.set_categories: Missing description for See Also "reorder_categories" reference
pandas.CategoricalIndex.set_categories: Missing description for See Also "add_categories" reference
pandas.CategoricalIndex.set_categories: Missing description for See Also "remove_categories" reference
pandas.CategoricalIndex.set_categories: Missing description for See Also "remove_unused_categories" reference
pandas.MultiIndex.to_frame: Missing description for See Also "DataFrame" reference
pandas.DatetimeIndex.indexer_at_time: Missing description for See Also "indexer_between_time" reference
pandas.DatetimeIndex.indexer_at_time: Missing description for See Also "DataFrame.at_time" reference
pandas.DatetimeIndex.indexer_between_time: Missing description for See Also "indexer_at_time" reference
pandas.DatetimeIndex.indexer_between_time: Missing description for See Also "DataFrame.between_time" reference
pandas.DatetimeIndex.mean: Missing description for See Also "numpy.ndarray.mean" reference
pandas.TimedeltaIndex.mean: Missing description for See Also "numpy.ndarray.mean" reference
pandas.core.window.rolling.Rolling.aggregate: Missing description for See Also "Series.rolling" reference
pandas.core.window.rolling.Rolling.aggregate: Missing description for See Also "DataFrame.rolling" reference
pandas.core.window.expanding.Expanding.aggregate: Missing description for See Also "DataFrame.expanding.aggregate" reference
pandas.core.window.expanding.Expanding.aggregate: Missing description for See Also "DataFrame.rolling.aggregate" reference
pandas.core.window.expanding.Expanding.aggregate: Missing description for See Also "DataFrame.aggregate" reference
pandas.plotting.deregister_matplotlib_converters: Missing description for See Also "register_matplotlib_converters" reference
pandas.plotting.register_matplotlib_converters: Missing description for See Also "deregister_matplotlib_converters" reference
pandas.read_html: Missing description for See Also "read_csv" reference
pandas.read_sql_query: Missing description for See Also "read_sql" reference
pandas.tseries.frequencies.to_offset: Missing description for See Also "DateOffset" reference
pandas.core.groupby.GroupBy.all: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.all: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.any: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.any: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.bfill: Missing description for See Also "Series.backfill" reference
pandas.core.groupby.GroupBy.bfill: Missing description for See Also "DataFrame.backfill" reference
pandas.core.groupby.GroupBy.bfill: Missing description for See Also "Series.fillna" reference
pandas.core.groupby.GroupBy.bfill: Missing description for See Also "DataFrame.fillna" reference
pandas.core.groupby.GroupBy.count: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.count: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.cummax: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.cummax: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.cummin: Missing description for See Also "Series.groupby" reference
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pandas.core.groupby.GroupBy.cumprod: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.cumprod: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.cumsum: Missing description for See Also "Series.groupby" reference
pandas.core.groupby.GroupBy.cumsum: Missing description for See Also "DataFrame.groupby" reference
pandas.core.groupby.GroupBy.ffill: Missing description for See Also "Series.pad" reference
pandas.core.groupby.GroupBy.ffill: Missing description for See Also "DataFrame.pad" reference
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pandas.core.groupby.DataFrameGroupBy.size: Missing description for See Also "DataFrame.groupby" reference
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pandas.core.resample.Resampler.aggregate: Missing description for See Also "DataFrame.resample.transform" reference
pandas.core.resample.Resampler.aggregate: Missing description for See Also "DataFrame.aggregate" reference
pandas.core.resample.Resampler.ffill: Missing description for See Also "Series.fillna" reference
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pandas.core.resample.Resampler.pad: Missing description for See Also "DataFrame.fillna" reference
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pandas.core.resample.Resampler.asfreq: Missing description for See Also "DataFrame.asfreq" reference
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pandas.core.resample.Resampler.count: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.mean: Missing description for See Also "Series.groupby" reference
pandas.core.resample.Resampler.mean: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.median: Missing description for See Also "Series.groupby" reference
pandas.core.resample.Resampler.median: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.ohlc: Missing description for See Also "Series.groupby" reference
pandas.core.resample.Resampler.ohlc: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.size: Missing description for See Also "Series.groupby" reference
pandas.core.resample.Resampler.size: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.sem: Missing description for See Also "Series.groupby" reference
pandas.core.resample.Resampler.sem: Missing description for See Also "DataFrame.groupby" reference
pandas.core.resample.Resampler.quantile: Missing description for See Also "Series.quantile" reference
pandas.core.resample.Resampler.quantile: Missing description for See Also "DataFrame.quantile" reference
pandas.core.resample.Resampler.quantile: Missing description for See Also "DataFrameGroupBy.quantile" reference
pandas.Timedelta.isoformat: Missing description for See Also "Timestamp.isoformat" reference
pandas.CategoricalDtype: Missing description for See Also "Categorical" reference
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pandas.Series.as_matrix: Missing description for See Also "DataFrame.values" reference
pandas.Series.compress: Missing description for See Also "numpy.ndarray.compress" reference
pandas.Series.divide: Missing description for See Also "Series.rtruediv" reference
pandas.Series.divmod: Missing description for See Also "Series.rdivmod" reference
pandas.Series.multiply: Missing description for See Also "Series.rmul" reference
pandas.Series.nonzero: Missing description for See Also "numpy.nonzero" reference
pandas.Series.rdivmod: Missing description for See Also "Series.divmod" reference
pandas.Series.subtract: Missing description for See Also "Series.rsub" reference
pandas.Series.tolist: Missing description for See Also "numpy.ndarray.tolist" reference
pandas.DataFrame.as_matrix: Missing description for See Also "DataFrame.values" reference
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https://api.github.com/repos/pandas-dev/pandas/issues/28793 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28793/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28793/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28793/events | https://github.com/pandas-dev/pandas/pull/28793 | 502,724,780 | MDExOlB1bGxSZXF1ZXN0MzI0NzUyNzQ2 | 28,793 | DOC: Reference level name as Term of HDFStore.select query (#28791) | {
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} | 3 | 2019-10-04T16:14:24Z | 2020-03-16T19:31:31Z | 2019-10-16T12:11:17Z | CONTRIBUTOR | null | Add documentation for default level name with HDFStore MultiIndex.
- [x] closes #28791
- [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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} | 1 | 2019-10-04T16:17:33Z | 2019-10-04T20:09:33Z | 2019-10-04T20:09:25Z | CONTRIBUTOR | null | - [x] xref #28724
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-10-04T21:28:34Z | 2019-10-06T22:33:20Z | 2019-10-06T22:13:29Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Improves performance of ``Categorical.searchsorted`` by avoiding expensive data convertions.
```python
>>> n = 100_000
>>> c = pd.Categorical(['a'] * n + ['b'] * n + ['c'] * n)
>>> %timeit c.searchsorted('b')
259 Β΅s Β± 2.95 Β΅s per loop # master
5.5 Β΅s Β± 165 ns per loop # this PR
>>> %timeit c.searchsorted(['b', 'c'])
240 Β΅s Β± 4.24 Β΅s per loop # master
9.9 Β΅s Β± 166 ns per loop # this PR
```
Also, ``CategoricalIndex.searchsorted`` now calls ``self.values.searchsorted`` directly instead of going through ``algorithms.searchsorted``, which always ends up calling ``self.values.searchsorted`` anyway. This ends up getting performance to 5.5 Β΅s instead of 12 Β΅s.
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} | 5 | 2019-10-05T02:08:30Z | 2019-12-23T13:42:48Z | 2019-12-23T13:42:39Z | MEMBER | null | Looks like something is wrong while rendering the documentation, regarding syntax highlighting of code blocks.
See for example: https://dev.pandas.io/docs/user_guide/reshaping.html#multiple-levels
The first block code has no colors, just black. The next block is coloring the `In[27]`, the literals.
Checking the markdown in `doc/source/user_guide/reshaping.rst` I can't see any obvious difference among them. But for some reason Sphinx just highlights one.
CC: @jorisvandenbossche not sure if this is related to the new theme, or was happening before | {
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"id": 712683... | closed | false | null | [] | null | 6 | 2019-10-05T02:49:43Z | 2019-11-07T23:50:46Z | 2019-11-07T23:50:46Z | CONTRIBUTOR | null | Added documentation on MultiIndex.set_levels to clarify that MultiIndex.set_levels() interprets passed values as new components of the .levels attribute
- [x] closes #28294 - or at least attempts to
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Here's my first PR to pandas - looking forward to feedback on how I can further improve this. | {
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} | 3 | 2019-10-05T15:32:59Z | 2019-10-07T13:51:14Z | 2019-10-07T13:50:39Z | MEMBER | null | - [x] closes #28787
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
Fixes an issue where column name information was getting dropped when grouping by a categorical column. I had to change a couple existing tests which I think were incorrect since they were implicitly assuming this behavior was expected. Also confirmed that this fixes the problem from #28787:
```python
[ins] In [1]: import pandas as pd
...:
...: df = pd.DataFrame(data=(('Bob', 2), ('Greg', None), ('Greg', 6)), columns=['Name', 'Items'])
...:
...: df_cat = df.copy()
...: df_cat['Name'] = df_cat['Name'].astype('category')
...: df_cat.groupby('Name', observed=True).agg(pd.DataFrame.sum, skipna=True).reset_index()
...:
Out[1]:
Name Items
0 Bob 2.0
1 Greg 6.0
``` | {
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} | 3 | 2019-10-05T17:55:00Z | 2019-10-08T12:53:57Z | 2019-10-08T12:53:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
I ran into this on a more complicated `groupby().apply(lambda: ...)` call, but reproduced a simple version below of what I think seems like a bug (or at least undesirable behavior).
```python
import pandas as pd
# simple dataframe
test = pd.DataFrame({'var': ['a', 'a', 'b', 'b'], 'val': range(4)})
# simulated mistake of asking for a 'vau' column, not 'val'
test.groupby('var').apply(lambda rows: pd.DataFrame({'var': [rows.iloc[-1]['var']],
'val': [rows.iloc[-1]['vau']]}))
```
Doing this yields:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
4735 try:
-> 4736 return libindex.get_value_box(s, key)
4737 except IndexError:
pandas/_libs/index.pyx in pandas._libs.index.get_value_box()
pandas/_libs/index.pyx in pandas._libs.index.get_value_at()
pandas/_libs/util.pxd in pandas._libs.util.get_value_at()
pandas/_libs/util.pxd in pandas._libs.util.validate_indexer()
TypeError: 'str' object cannot be interpreted as an integer
During handling of the above exception, another exception occurred:
---------- 8< [snip] >8 ----------
KeyError: 'var'
```
#### Problem description
When attempting to troubleshoot this, `KeyError: 'foo'` is misleading, as it doesn't point to the key that failed, but one that succeeded. In my actual code, I was doing this for more variables, so while this case is trivial to troubleshoot, real world cases might be more confusing and challenging to figure out for the user.
#### Expected Output
A `KeyError: 'foo'` where `foo` is the incorrect key, not a successful one.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.1-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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https://api.github.com/repos/pandas-dev/pandas/issues/28800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28800/events | https://github.com/pandas-dev/pandas/pull/28800 | 503,010,492 | MDExOlB1bGxSZXF1ZXN0MzI0OTcyNTA5 | 28,800 | TST: Reorganize IntervalIndex tests | {
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} | 1 | 2019-10-05T20:13:37Z | 2019-10-08T18:21:16Z | 2019-10-07T00:17:53Z | MEMBER | null | There shouldn't be any behavioral changes here: mostly just moved things around and created new files for better organization. I tried to do separate granular commits, so looking at each commit individually might make things a little more obvious if there are concerns.
Summary:
- Removed `test_interval_new.py`
- Created `test_indexing.py` for tests specific to indexing methods
- Nearly all of `test_interval_new.py` is here
- Also moved relevant tests from `test_interval.py` here
- Created `test_base.py` for tests inherited from the common `Base` class
- Only tests related to common tests should be placed here (i.e. overriding, skipping)
- Created `test_format.py` for formatting related methods
All of the above is generally consistent with how the datetime/period indexing tests are organized. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28801 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28801/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28801/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28801/events | https://github.com/pandas-dev/pandas/pull/28801 | 503,016,749 | MDExOlB1bGxSZXF1ZXN0MzI0OTc2ODQ2 | 28,801 | TST: Move datetime specific test from common tests | {
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} | 1 | 2019-10-05T21:13:11Z | 2019-10-08T18:21:09Z | 2019-10-05T22:25:32Z | MEMBER | null | This test gets rerun multiple times unnecessarily since it is included in the common tests but is actually datetime specific. Moved it to the more appropriate `datetimes/test_datetime.py` file. Did a quick audit of the other common tests and the rest look fine. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28802 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28802/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28802/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28802/events | https://github.com/pandas-dev/pandas/pull/28802 | 503,017,106 | MDExOlB1bGxSZXF1ZXN0MzI0OTc3MDkw | 28,802 | BUG: Coercing bool types to int in qcut | {
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} | 1 | 2019-10-05T21:16:51Z | 2019-10-08T12:17:18Z | 2019-10-08T12:17:15Z | CONTRIBUTOR | null | - [x] closes #20303
- [x] tests added / passed: `pytest pandas/tests/reshape/test_qcut.py pandas/tests/reshape/test_cut.py -v`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master --name-only -- "*.py" | xargs flake8`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28803 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28803/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28803/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28803/events | https://github.com/pandas-dev/pandas/pull/28803 | 503,046,888 | MDExOlB1bGxSZXF1ZXN0MzI0OTk2ODky | 28,803 | TST: Test pivot_table() with categorical data | {
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} | 3 | 2019-10-06T03:32:19Z | 2019-10-12T17:38:25Z | 2019-10-12T17:38:20Z | CONTRIBUTOR | null | - [x] closes #21370
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-06T07:36:04Z | 2019-10-07T13:50:16Z | 2019-10-07T13:50:13Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/26058 and https://github.com/pandas-dev/pandas-sphinx-theme/issues/25
We need a newer sphinx version for the new theme. There were some styling issues with the parameter listings on the docstring API pages, but those probably need to redesigned anyway with the new theme. | {
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} | 14 | 2019-10-06T08:07:59Z | 2019-10-14T20:12:05Z | 2019-10-12T17:30:03Z | CONTRIBUTOR | null | - [x] xref #28772
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This add various deprecated attributes to their respective types' ``_deprecated``. The effect is that these are now also hidden when tab-completing, making navigating the pandas attributes easier.
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dt_mrc[dt_mrc.flag.eq('UnKnown')&dt_mrc.duplicated(subset=['id','cln_nm'],
keep=False)&dt_mrc.cln_nm.notna()]
```
#### Problem description
It's a simple contact table, slicing using 3 boolean expressions including duplicated. When try to slice those with duplicated id(contact id)-cln_nm(cleaned name) pair using 'duplicated' method **with the 'keep=False' parameter**, the return seems wrong with the last 2 rows clearly not duplicated on this pair. Could you pls check this out? Thx
**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>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
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 : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
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 : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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"updated_at": "2020-01-30T12:18:05Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/16"
} | 2 | 2019-10-06T10:44:49Z | 2019-10-11T20:09:16Z | 2019-10-11T20:08:24Z | CONTRIBUTOR | null | - This PR contains doctrine PR06 fixes mostly doing the conversion below:
- boolean to bool
- string to str
- integer to int
- Tests unchanged
- black pandas ran successfully
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https://api.github.com/repos/pandas-dev/pandas/issues/28808 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28808/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28808/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28808/events | https://github.com/pandas-dev/pandas/issues/28808 | 503,115,938 | MDU6SXNzdWU1MDMxMTU5Mzg= | 28,808 | QST: Backstick is not working in Python 3 | {
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```python
# Your code here
import pandas as pd
df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2), 'C C': range(10, 5, -1)})
df.query('B == `C C`')
```
#### Problem description
Hi,
In the link below, it is mentioned to use _backstick_ with _.query()_ method when the column name contains spaces.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html
Unfortunately, the sample code does not work anymore with Python 3.0 becayse if backstick deprecation.
I was wondering if you could please tell me how to reproduce the example below (taken from the link above) in Python 3.
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https://api.github.com/repos/pandas-dev/pandas/issues/28809 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28809/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28809/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28809/events | https://github.com/pandas-dev/pandas/pull/28809 | 503,167,985 | MDExOlB1bGxSZXF1ZXN0MzI1MDc5Njk3 | 28,809 | TST: Dataframe KeyError when getting nonexistent category (#28799) | {
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} | 1 | 2019-10-06T22:10:13Z | 2019-10-08T12:54:01Z | 2019-10-08T12:53:57Z | CONTRIBUTOR | null | - [x] closes #28799
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added test to check for correct exception when accessing a nonexistent category in a DataFrame. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28810/events | https://github.com/pandas-dev/pandas/issues/28810 | 503,186,784 | MDU6SXNzdWU1MDMxODY3ODQ= | 28,810 | Improve PandasSeriesGroupby operations performance | {
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"description": "Memory or execu... | closed | false | null | [] | null | 4 | 2019-10-07T00:50:57Z | 2019-10-07T01:28:30Z | 2019-10-07T01:01:24Z | NONE | null | #### Code Sample
```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,10000,size=(10000, 4)), columns=list('ABCD'))
df2 = df.groupby('B')
df2.apply(lambda x: x.A.values.mean() ) # Operation 1
df2.apply(lambda x: x.A.mean()) # Operation 2
```
#### Problem description
First operation takes 344 ms, second takes 800 ms.
For smaller arrays (10 elements), first operation takes 1.86 ms and second takes 2.26 ms.
The slowdown occurs also with the max function it's not exclusive of mean.
I had my theory in previous editions in this post. But now I don't have a clue.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.11
machine : x86_64
processor : Intel(R) Core(TM) i5-7200U CPU @ 2.50GHz
byteorder : little
LC_ALL : None
LANG : pt_BR.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 0.25.1
numpy : 1.14.5
pytz : 2019.2
dateutil : 2.7.2
pip : 19.1
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 : 2.10.1
IPython : 7.8.0
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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} | 4 | 2019-10-07T03:34:20Z | 2019-10-12T00:05:59Z | 2019-10-11T21:45:14Z | CONTRIBUTOR | null | Allow for multiple variables to share the same type and description in documentation. For example, `i, j, k : int` Would be three variables, i, j, and k, which are all ints.
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry - I'm not sure how to add this, if it needs done could I get a pointer?
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"description": "read_html, to_html, Styl... | closed | false | null | [] | null | 1 | 2019-10-07T05:12:24Z | 2021-02-20T19:27:23Z | 2021-02-20T19:27:23Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
axis=1)
df.iloc[0, 2] = np.nan
def color_negative_red(val):
"""
Takes a scalar and returns a string with
the css property `'color: red'` for negative
strings, black otherwise.
"""
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
print(df.style.applymap(color_negative_red).highlight_null(null_color='red').render())
```
Code is taken from the [user_guide](https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html)
#### Problem description
If you combine a style.applymap() with style.highlight_null() cells which are not NULL receive an extra colon in the ID in the CSS. This can cause render issues (especially Outlook) when trying to display the HTML.
ie:
```
<style type="text/css" >
#T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col0 {
color: black;
: ;
} #T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col1 {
color: black;
: ;
} #T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col2 {
color: black;
background-color: red;
...
```
#### Expected Output
```
<style type="text/css" >
#T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col0 {
color: black;
} #T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col1 {
color: black;
} #T_7854aabc_e8c0_11e9_b09d_f2f0444bd885row0_col2 {
color: black;
background-color: red;
...
```
Or in words, the expected output is that if a value is non Null that the CSS ID block does not have a colon added.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.71-21.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.15.4
pytz : 2018.9
dateutil : 2.7.5
pip : 19.2.3
setuptools : 40.7.3
Cython : None
pytest : None
hypothesis : None
sphinx : 1.8.4
blosc : None
feather : None
xlsxwriter : 1.1.4
lxml.etree : 4.3.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.1
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : 0.2.0
scipy : 1.2.1
sqlalchemy : 1.2.17
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.4
</details>
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