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} | 1 | 2021-02-28T00:35:44Z | 2021-03-01T16:55:58Z | 2021-03-01T13:56:52Z | MEMBER | null | * Use more `const`
* Change some indexing variables to `Py_ssize_t`
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} | 2 | 2021-02-28T03:26:10Z | 2021-03-01T20:50:40Z | 2021-03-01T19:28:37Z | MEMBER | null | 1) the argument annotation for columns in to_arrays isnt quite right. this is fixed by adding an ensure_index inside `DataFrame.__init__`
2) call ensure_index in the empty case in to_arrays so we can tighten from `Tuple[Any, Any]` to Tuple[Any, Index]`
3) in the structured array case in to_arrays, return a list of empty ndarrays instead of list of empty lists, so we can further tighten to `Tuple[List[ArrayLike], Index]`
4) This breaks test_from_records_empty_with_nonempty_fields_gh3682, at which point we decide that the new behavior is better, so calling it a bugfix. | {
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} | 2 | 2021-02-28T09:06:49Z | 2021-03-02T17:26:50Z | 2021-03-02T17:26:45Z | CONTRIBUTOR | null | - [X] closes #40091
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [X] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/40123 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40123/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40123/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40123/events | https://github.com/pandas-dev/pandas/issues/40123 | 818,203,908 | MDU6SXNzdWU4MTgyMDM5MDg= | 40,123 | Performance regression in reindex.Fillna.time_float_32, etc. | {
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] | open | false | null | [] | null | 0 | 2021-02-28T13:15:14Z | 2021-06-06T20:53:17Z | null | CONTRIBUTOR | null | Possible regression in https://pandas.pydata.org/speed/pandas/#reindex.Fillna.time_float_32?python=3.6&Cython=&p-method='backfill'&commits=213822a2 and a few others
points to https://github.com/pandas-dev/pandas/pull/33271
cc @ShaharNaveh , @WillAyd | {
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} | 0 | 2021-02-28T13:37:05Z | 2021-04-26T12:16:07Z | 2021-04-26T12:16:07Z | CONTRIBUTOR | null | `Styler` by definition is a conditional renderer. The functions it provides, `apply` and `applymap`, serve as conditional functions to render a style.
The function `Styler.where` therefore, in the first case, seems unnecessary and indirectly confusing since it is a conditional rendering function implying perhaps the other methods are not. Not only is it just a 'convenience' wrapper of `applymap`:
```
def where(cond, value, other, subset, **kwargs):
return self.applymap(lambda v: value if cond(v) else other, subset=subset, **kwargs)
```
But I also think, in the second case, it is quite inconvenient for a couple reasons:
1) The docs are ambiguous:
```
cond: callable: cond should take a scalar and return a boolean.
**kwargs: dict: pass along to cond.
```
and in fact `kwargs` appear in the code but do nothing.
2) It feels that there is more benefit to be had by a user learning how you use `apply` and `applymap` to the same effect through good examples.
For example:
`styler.applymap(lambda v: props if cond(v) else None, subset=subset)` or
`styler.apply(lambda d: np.where(d > 10, props, ""), axis=None, subset=subset)`
seem pretty straightforward replacements???
Perhaps documenting in the first instance that `apply` and `applymap` are preferred over `where` is non-controversial. | {
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} | 2 | 2021-02-28T14:15:50Z | 2021-03-01T14:38:30Z | 2021-03-01T14:37:35Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Am doing this manually (instead of the automated job tomorrow) in order to fixup some minor things | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40126 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40126/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40126/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40126/events | https://github.com/pandas-dev/pandas/issues/40126 | 818,223,844 | MDU6SXNzdWU4MTgyMjM4NDQ= | 40,126 | ENH: Renaming and changing dtypes using single dictionary | {
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] | closed | false | null | [] | null | 3 | 2021-02-28T14:47:06Z | 2021-02-28T18:41:34Z | 2021-02-28T18:41:34Z | CONTRIBUTOR | null | #### Is your feature request related to a problem?
When cleaning and preparing different datasets in pandas, I often find myself needing to rename and specify the dtypes of columns. When I have a large number of columns to rename and specify data types for, I like to use the current dictionary syntax that works with `DataFrame.rename` and `DataFrame.astype` and is often supported for `dtype` kwargs, for example in `pandas.read_csv`:
```python
renames = {
"originalColumnA": "column_a",
"ORIGinal Column B": "column_b",
" Original COLUMN C": "column_c",
}
dtypes = {
"originalColumnA": "int32",
"ORIGinal Column B": "string",
" Original COLUMN C": "category",
}
df = pandas.read_csv("data.csv", dtype=dtypes)
df = df.rename(columns=renames)
# Or without read_csv ...
df = pandas.read_csv("data.csv")
df = df.astype(dtype=dtypes)
df = df.rename(columns=renames)
```
The current syntax means you have to define two different dictionaries with very similar or often identical keys. When dealing with a lot of columns this can be non-ideal to maintain (e.g. there are now two place to update if the original column names change). Additionally, you have to change type and rename in two separate steps.
#### Describe the solution you'd like
Could we support a dictionary syntax to both rename and specify the dtype of a column: `Dict[str, Tuple[str, str]]`? This would be particularly useful for data loading functions like `read_csv` but could also be useful independently for renaming and changing dtypes in one step.
```python
dtypes_and_names = {
"originalColumnA": ("column_a", "int32"),
"ORIGinal Column B": ("column_b", "string"),
" Original COLUMN C": ("column_c", "category"),
}
# can work with dtype kwarg in data loading functions or perhaps a new kwarg?
df = pandas.read_csv("data.csv", dtype=dtypes_and_names)
# possibly also for the astype method?
df = df.astype(dtype=dtypes_and_names)
```
The above `dtypes_and_names` dictionary is easier to maintain and clearer to see the new names and datatypes. There could also be conventions for when a subset of columns only need to be renamed (or change datatype) such as `"column_d":(None, "int32")`. This is a bit similar to the named aggregation syntax that lets you aggregate and define the output name in one step.
#### API breaking implications
This should only be an enhancement and doesn't need to affect any current functionality / syntax. If it uses the `dtype` kwarg we could check the type of the value in the dictionary to see what syntax is being used and handle appropriately. Alternatively, this could use a new kwarg.
#### Describe alternatives you've considered
Currently, to do this I define the dictionary as per my above solution and then use dictionary comprehension to split it into two different dictionaries (renames and dtypes). I then use the currently supported syntax/functionality:
```python
dtypes_and_names = {
"originalColumnA": ("column_a", "int32"),
"ORIGinal Column B": ("column_b", "string"),
" Original COLUMN C": ("column_c", "category"),
}
renames = {key: value[0] for key,value in dtypes_and_names.items()}
dtypes = {key: value[1] for key,value in dtypes_and_names.items()}
df = pandas.read_csv("data.csv", dtype=dtypes)
df = df.rename(columns=renames)
```
#### Additional context
Does anyone else think this would be useful or is there a better way to do it already that I am not aware of? If this could be useful I would be happy to work on a PR.
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 1 | 2021-02-28T21:30:16Z | 2021-03-01T13:56:01Z | 2021-03-01T13:55:58Z | MEMBER | null | xref #38852, those files are not maintained.
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} | 1 | 2021-03-01T00:06:13Z | 2021-03-01T20:50:03Z | 2021-03-01T19:27:52Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 34 | 2021-03-01T00:18:29Z | 2021-04-04T17:07:46Z | 2021-04-04T17:07:42Z | CONTRIBUTOR | null | - [x] closes #39367
I created a new PR due in an attempt to close #39367. Previously I had issues regarding environment setup but all seems to be working now thanks to Docker. I ran the linting tests prior to submitting this PR.
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**To-Do**
- [x] BUG: Fix declaration not showing for `xml_declaration=True` when `pretty_print=False` for etree parser.
- [x] CLN: Centralize docstrings to avoid repetition in `formats.py` and `frame.py`.
- [ ] CLN: When `etree` supports `pretty_print`, remove `xml.dom.minidom` reliance.
- [x] TYP: Refactor code for type hints on `parse_doc` methods for optional dependency, `lxml`.
- [ ] TST: Add tests for edge cases (`ParserError`, `OSError`, `URLError`, etc. ). See checklist in tests code.
- [ ] TST: Add more tests for `storage_options` (i.e., read/write to `pandas-test` S3 bucket).
**Enhancements**
- [ ] ENH: Add `parse_dates` and `dtype` converters similar to other IO methods.
See #43567
- [ ] ENH: Add support for nullable dtyes in reading and exporting XML.
See #43903
- [ ] ENH: Add `xpath_vars` to pass `$` variables in `xpath` expression. See [lxml xpath() method](https://lxml.de/xpathxslt.html#the-xpath-method).
- [ ] ENH: Add `xsl_params` to pass values into XSLT script. See [lxml stylesheet parameters](https://lxml.de/xpathxslt.html#stylesheet-parameters).
- [ ] ENH: Add `iterparse` for memory efficient parsing of large XML. See [etree iterparse](https://docs.python.org/3/library/xml.etree.elementtree.html#xml.etree.ElementTree.iterparse) and [lxml iterparse](https://lxml.de/3.2/parsing.html#iterparse-and-iterwalk).
- [ ] ENH: Add `prefix_cols` to specify which columns should have namespace prefixes.
- [ ] ENH: Add `nested` (bool) to write out nested node-sets for data frames with hierarchical columns or multindex.
- [ ] ENH: Add `engine` for external processors for XPath and XSLT 2.0 and 3.0, XQuery, streaming, and others.
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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- [ ] tests added / passed
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- [ ] whatsnew entry
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} | 3 | 2021-03-01T06:30:47Z | 2021-03-05T23:04:36Z | 2021-03-05T22:07:58Z | CONTRIBUTOR | null | - [x] closes issue #40032 and PR #40060
- [x] deprecates the methods `Styler.set_na_rep()` and `Styler.set_precision()` in favour of, and incorporating `precision` into `Styler.format()`. Maintains some backwards compatibility with a deprecation warning, the copy mechanics remain broken.
- [x] `Styler.set_na_rep('MISS')` is replaced and equivalent to `Styler.format(na_rep='MISS')`
- [x] `Styler.set_precision(5)` is replaced and equivalent to `Styler.format(precision=5)`
- [x] `Styler.set_na_rep('MISS').set_precision(5)` is equivalent to `Styler.format(na_rep='MISS', precision=5)`
- [x] `Styler.set_precision(5).set_na_rep('MISS')` is equivalent to `Styler.format(na_rep='MISS', precision=5)`
- [x] moves the `default_formatter` and `maybe_wrap_formatter` methods to `module` level.
- [x] documentation is added to show new functionality
- [x] most existing tests still pass with, some needed minor modification and some were no longer necessary
- [x] tests added for new combinations
- [x] whatsnew entry
This PR was suggested after review of #40060 as an alternative. It seems to be a simpler and more consistent solution for API. | {
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] | closed | false | null | [] | null | 2 | 2021-03-01T07:20:54Z | 2021-05-25T12:07:29Z | 2021-05-25T12:07:29Z | MEMBER | null | - [ ] closes #37654
- [ ] 1 tests added / 0 passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
WIP for #37654. Starting from scratch on this one. I will mark this ready for a review when it gets the job done. Apart from refactoring the code to rely only on the public interface of tzinfo independent of the implementation, the main worry is performance. I'll make sure to benchmark appopriately.
Apologies for the long absence. Had some health issues, but they are, thankfully, pinned down now, and I have mostly recovered. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40136 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40136/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40136/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40136/events | https://github.com/pandas-dev/pandas/pull/40136 | 818,565,073 | MDExOlB1bGxSZXF1ZXN0NTgxODA2MTk1 | 40,136 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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} | [] | closed | false | null | [] | null | 1 | 2021-03-01T07:25:19Z | 2021-03-01T09:17:59Z | 2021-03-01T09:17:52Z | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.10.1-py2.py3-none-any.whl (185 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting identify>=1.0.0
Downloading identify-1.6.1-py2.py3-none-any.whl (98 kB)
Collecting pyyaml>=5.1
Downloading PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.5.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.2-py2.py3-none-any.whl (7.2 MB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Installing collected packages: six, filelock, distlib, appdirs, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed appdirs-1.4.4 cfgv-3.2.0 distlib-0.3.1 filelock-3.0.12 identify-1.6.1 nodeenv-1.5.0 pre-commit-2.10.1 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/python/black ... already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
already up to date.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
updating v1.7.1 -> v1.8.0.
Updating https://github.com/asottile/yesqa ... already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pre-commit-hooks.
already up to date.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/MarcoGorelli/no-string-hints ... [INFO] Initializing environment for https://github.com/MarcoGorelli/no-string-hints.
already up to date.
Updating https://github.com/MarcoGorelli/abs-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/abs-imports.
Cannot update because the update target is missing these hooks:
abs-imports
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for https://github.com/MarcoGorelli/abs-imports.
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/no-string-hints.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/MarcoGorelli/abs-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
black..................................................................................................Passed
flake8.................................................................................................Passed
flake8 (cython)........................................................................................Passed
flake8 (cython template)...............................................................................Passed
isort..................................................................................................Passed
pyupgrade..............................................................................................Passed
rst ``code`` is two backticks..........................................................................Passed
rst directives end with two colons.....................................................................Passed
rst ``inline code`` next to normal text................................................................Passed
Generate pip dependency from conda.....................................................................Passed
flake8-rst.............................................................................................Passed
Check for non-standard imports.........................................................................Passed
Check for non-standard numpy.random-related imports excluding pandas/_testing.py.......................Passed
Check for non-standard imports in test suite...........................................................Passed
Check for incorrect code block or IPython directives...................................................Passed
Check for use of not concatenated strings..............................................................Passed
Check for strings with wrong placed spaces.............................................................Passed
Check for import of private attributes across modules..................................................Passed
Check for use of private functions across modules......................................................Passed
Check for use of bare pytest raises....................................................................Passed
Check for use of pytest.xfail..........................................................................Passed
Check for inconsistent use of pandas namespace in tests................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias..........................Passed
Check for use of foo.__class__ instead of type(foo)....................................................Passed
Check for outdated annotation syntax and missing error codes...........................................Passed
Check for use of np.bool instead of np.bool_...........................................................Passed
Check for use of np.object instead of np.object_.......................................................Passed
Check code for instances of os.remove..................................................................Passed
Check code for instances of pd.api.types...............................................................Passed
Validate correct capitalization among titles in documentation..........................................Passed
Strip unnecessary `# noqa`s............................................................................Passed
Fix End of Files.......................................................................................Passed
Trim Trailing Whitespace...............................................................................Passed
codespell..............................................................................................Passed
no-string-hints........................................................................................Passed
abs-imports............................................................................................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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} | 2 | 2021-03-01T08:25:54Z | 2021-03-02T07:31:26Z | 2021-03-02T07:30:15Z | MEMBER | null | {
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} | 1 | 2021-03-01T09:12:42Z | 2021-03-01T13:55:03Z | 2021-03-01T13:54:59Z | CONTRIBUTOR | null | - [x] closes #39988
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | 2021-03-01T09:54:02Z | 2021-03-05T21:43:36Z | null | NONE | null | #### Code Sample, a copy-pastable example
```python
df = pd.DataFrame({'birb':['Falcon', 'Falcon', 'Falcon', 'Falcon',
'Parrot', 'Parrot', 'Parrot', 'Parrot'],
'value': [390., 350.,390., 350.,
30., 20., 30., 20.]})
df.groupby('birb').aggregate({'value': ['count', 'mean', 'skew']})
```
#### Problem description
Code gives error: `AttributeError: 'SeriesGroupBy' object has no attribute 'kurtosis'` instead of correct group-wise kurtosis.
Skew works ok.
#### Expected Output
```
value
count mean kurtosis
birb
Falcon 4 370.0 -6.0
Parrot 4 25.0 -6.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-66-generic
Version : #74~18.04.2-Ubuntu SMP Fri Feb 5 11:17:31 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IE.UTF-8
LOCALE : en_IE.UTF-8
pandas : 1.2.2
numpy : 1.17.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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This ticket can be a starting point for proposed methods, although the old implementation was actually quite performant look at given tests in the discussion of 35244:
https://github.com/pandas-dev/pandas/blob/b5958ee1999e9aead1938c0bba2b674378807b3d/pandas/core/frame.py#L3848-L3861
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} | 0 | 2021-03-01T11:30:20Z | 2021-03-02T17:23:46Z | 2021-03-02T17:23:42Z | MEMBER | null | This moves the `astype` implementation (so the current logic of calling `EA.astype` or `astype_nansafe` depending on the array type, handling datetime and string special cases, handling error keyword, etc) from being defined on `Block` to the `array_algos/` submodule.
That's a useful clean-up anyway I think (it's not block specific), and this way it can then be reused in ArrayManager.
I currently put it in `core/array_algos/cast.py` since the other similar functions are also put in `array_algos` (`shift`, `putmask`, `replace`, `quantile` etc). But since this is casting specific and we already have most of the underlying pieces in `core/dtypes/cast.py`, could also just put it there (either is fine for me). | {
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} | 4 | 2021-03-01T12:38:29Z | 2021-03-01T19:45:36Z | 2021-03-01T19:32:52Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
As suggested on Gitter - this could help identify more untested parts of the codebase and hence uncover bugs
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} | 10 | 2021-03-01T13:13:59Z | 2021-03-05T15:49:50Z | 2021-03-05T01:09:48Z | MEMBER | null | - [ ] closes #39738
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 8 | 2021-03-01T13:36:34Z | 2021-03-04T03:22:13Z | 2021-03-04T03:22:13Z | CONTRIBUTOR | null | https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=55190&view=logs&j=eab14f69-13b6-5db7-daeb-7b778629410b&t=ce687173-08c6-5301-838d-71b2dda24510
```
__________________ TestGroupby.test_groupby_apply_identity ____________________
[gw0] linux -- Python 3.8.8 /home/vsts/miniconda3/envs/pandas-dev/bin/python
> raise TypeError("boolean value of NA is ambiguous")
E TypeError: boolean value of NA is ambiguous
pandas/_libs/missing.pyx:436: TypeError
The above exception was the direct cause of the following exception:
self = <pandas.tests.extension.test_boolean.TestGroupby object at 0x7f9eb07e2bb0>
data_for_grouping = <BooleanArray>
[True, True, <NA>, <NA>, False, False, True]
Length: 7, dtype: boolean
def test_groupby_apply_identity(self, data_for_grouping):
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1], "B": data_for_grouping})
result = df.groupby("A").B.apply(lambda x: x.array)
expected = pd.Series(
[
df.B.iloc[[0, 1, 6]].array,
df.B.iloc[[2, 3]].array,
df.B.iloc[[4, 5]].array,
],
index=pd.Index([1, 2, 3], name="A"),
name="B",
)
> self.assert_series_equal(result, expected)
pandas/tests/extension/test_boolean.py:335:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
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] | open | false | null | [] | null | 4 | 2021-03-01T13:57:49Z | 2021-08-23T22:54:34Z | null | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
print(pd.__version__)
idx = pd.PeriodIndex(['2020-01-01 11:00', '2020-01-01 12:00'], freq='H')
df = pd.DataFrame([1,2], index=idx)
df.loc['2020-01-01 11:00']
```
#### Problem description
It seems there has been a change in the behaviour of string slicing on a `PeriodIndex` in Pandas 1.1 onwards. Despite the exact value being present in the index, a `KeyError` is returned.
Output in 0.25.3:
```
0 1
Name: 2020-01-01 11:00, dtype: int64
```
Output in 1.0.1
```
0 1
Name: 2020-01-01 11:00, dtype: int6
```
Output in versions >= 1.1.0
```
KeyError: '2020-01-01 11:00'
```
Interestingly,
```
df.loc['2020-01-01 11']
```
still works and gives the expected answer.
Looks to be due to this assertion: https://github.com/pandas-dev/pandas/blob/8d2a57ecdbe2e45929605763c66cbfd8ec399e19/pandas/core/indexes/period.py#L581
#### Expected Output
```
0 1
Name: 2020-01-01 11:00, dtype: int6
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.19.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
</details>
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] | closed | false | null | [] | null | 10 | 2021-03-01T14:10:52Z | 2021-08-19T01:36:36Z | 2021-08-19T01:36:36Z | NONE | null | #### Code Sample
```python
# Platform: Ubuntu 18.04.1 LTS
# Python: Python 3.8.7 (default, Dec 21 2020, 20:10:35)
import pandas as pd
for i in range(100_000_000):
df = pd.DataFrame.from_records([{"A": 1}])
print(i, df)
df.A # Sometime it raises `AttributeError('DataFrame' object has no attribute 'A')` ?
```
#### Problem Description
Since we have updated to the last Pandas version 1.2.2 (from Pandas 0.25) we have been detecting some unexpected `AttributeError` exceptions when accessing `DataFrame` objects which do contain the accessed columns. We have detected this error in multiple projects, with different input data and with multiple column `dtype` types.
Also the errors seem to be non-deterministic and very difficult to reproduce (May be derived from some race condition? Memory issues?). I have tried multiple times to re-run the same exact process script with the same input data and environment and I could not replicate the error which seems to occur randomly. Since we have migrated to Pandas 1.2.2 a week ago we have detected this error 15 times (from the tens of thousands of DataFrame accesses we are doing daily in the processes).
#### Expected Output
Should not raise any `AttributeError` exception.
#### Output of ``pd.show_versions()``
Extracted from the error log of a production server when one of the exceptions occurred:
<details>
| Package | Version |
| -------------|---------------|
aiohttp | 3.7.3
amightygirl.paapi5-python-sdk | 1.0.0
appdirs | 1.4.4
argon2-cffi | 20.1.0
arrow | 0.14.7
async-generator | 1.10
async-timeout | 3.0.1
attrs | 20.3.0
babel | 2.9.0
backcall | 0.2.0
beautifulsoup4 | 4.9.3
bleach | 3.3.0
boto3 | 1.17.14
botocore | 1.20.14
cached-property | 1.5.2
cachetools | 4.2.1
certifi | 2020.12.5
cffi | 1.14.5
chardet | 3.0.4
click | 7.1.2
click-plugins | 1.1.1
cligj | 0.7.1
cloudpickle | 1.6.0
configparser | 5.0.1
curlify | 2.2.1
cycler | 0.10.0
dask | 2.5.0
datatools | INTERNAL
decorator | 4.4.2
defusedxml | 0.6.0
elasticsearch | 7.11.0
entrypoints | 0.3
facebook-business | 9.0.3
fiona | 1.8.18
fsspec | 0.8.5
furl | 2.1.0
future | 0.18.2
geopandas | 0.8.2
google-api-core | 1.26.0
google-api-python-client | 1.12.8
google-auth | 1.27.0
google-auth-httplib2 | 0.0.4
google-auth-oauthlib | 0.4.2
googleads | 26.0.0
googleapis-common-protos | 1.52.0
httplib2 | 0.19.0
ics | 0.7
idna | 2.10
ipykernel | 5.4.3
ipython | 7.20.0
ipython-genutils | 0.2.0
ipywidgets | 7.6.3
isodate | 0.6.0
jedi | 0.18.0
jinja2 | 2.11.3
jmespath | 0.10.0
joblib | 1.0.1
jsonschema | 3.2.0
jupyter | 1.0.0
jupyter-client | 6.1.11
jupyter-console | 6.2.0
jupyter-core | 4.7.1
jupyterlab-pygments | 0.1.2
jupyterlab-widgets | 1.0.0
keplergl | 0.2.2
kiwisolver | 1.3.1
limits | 1.5.1
locket | 0.2.1
lxml | 4.6.2
markupsafe | 1.1.1
matplotlib | 3.3.4
mistune | 0.8.4
multi-key-dict | 2.0.3
multidict | 5.1.0
munch | 2.5.0
nbclient | 0.5.2
nbconvert | 6.0.7
nbformat | 5.1.2
nest-asyncio | 1.5.1
netaddr | 0.8.0
notebook | 6.2.0
numpy | 1.18.5
oauth2client | 4.1.3
oauthlib | 3.1.0
orderedmultidict | 1.0.1
packaging | 20.9
pandas | 1.2.2
pandocfilters | 1.4.3
parso | 0.8.1
partd | 1.1.0
pbr | 5.5.1
pexpect | 4.8.0
pickleshare | 0.7.5
pika | 1.2.0
pillow | 8.1.0
pip | 21.0.1
pkg-resources | 0.0.0
plotly | 3.10.0
polygon-geohasher | 0.0.1
prometheus-client | 0.9.0
prompt-toolkit | 3.0.16
protobuf | 3.14.0
psutil | 5.8.0
psycopg2-binary | 2.8.6
ptyprocess | 0.7.0
pyasn1 | 0.4.8
pyasn1-modules | 0.2.8
pycountry | 20.7.3
pycparser | 2.20
pycryptodome | 3.10.1
pygments | 2.8.0
pyhive | 0.6.3
pylogbeat | 2.0.0
pyodbc | 4.0.30
pyparsing | 2.4.7
pyproj | 3.0.0.post1
pyrsistent | 0.17.3
python-dateutil | 2.8.1
python-geohash | 0.8.5
python-jenkins | 1.7.0
python-logstash-async | 2.2.0
pytz | 2021.1
pyxdameraulevenshtein | 1.7.0
pyyaml | 5.4.1
pyzmq | 22.0.3
qtconsole | 5.0.2
qtpy | 1.9.0
redis | 3.5.3
requests | 2.25.1
requests-file | 1.5.1
requests-oauthlib | 1.3.0
requests-toolbelt | 0.9.1
requirements-parser | 0.2.0
retrying | 1.3.3
rsa | 4.7.1
rtree | 0.9.7
s3transfer | 0.3.4
scikit-surprise | 1.0.6
scipy | 1.5.4
seaborn | 0.11.1
send2trash | 1.5.0
sentry-sdk | 0.20.3
setuptools | 53.0.0
shapely | 1.7.1
six | 1.15.0
soupsieve | 2.2
sqlalchemy | 1.3.23
stringcase | 1.2.0
tatsu | 5.5.0
terminado | 0.9.2
testpath | 0.4.4
toolz | 0.11.1
tornado | 6.1
traitlets | 5.0.5
traittypes | 0.2.1
twitter-ads | 8.0.0
typing-extensions | 3.7.4.3
tzlocal | 2.1
uritemplate | 3.0.1
urllib3 | 1.26.3
wcwidth | 0.2.5
webencodings | 0.5.1
widgetsnbextension | 3.5.1
wordcloud | 1.8.1
xlrd | 2.0.1
xmltodict | 0.12.0
yarl | 1.6.3
zeep | 4.0.0
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} | 3 | 2021-03-01T14:12:51Z | 2021-03-03T08:22:26Z | 2021-03-02T21:08:13Z | MEMBER | null | Pre-cursor for https://github.com/pandas-dev/pandas/pull/39991
Currently we didn't really check that we were consistently storing datetimelike data as the EA (DatetimeArray, TimedeltaArray) or as ndarrray. Ensuring this in the ArrayManager constructor turns up a few failures.
I *think* it will be the easiest to always store them as EA and not as ndarray (eg for many other operations, we otherwise would wrap them in the EA anyway). | {
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} | 3 | 2021-03-01T14:51:36Z | 2021-03-25T01:52:30Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame({"str_col": ["a", "b", "c", "a"], "num_col": [1, 2, 3, 2]})
df["str_col"] = df["str_col"].astype("string")
print(df.dtypes)
avg = df.groupby("str_col", as_index=False).mean()
print(avg.dtypes)
```
#### Problem description
After grouping the string column loses it's string dtype and is object afterwards. This is rather unexpected.
When using a string col as grouping column one would have to manually change the dtypes back to string.
The output for avg.dtypes is:
```python
str_col object
num_col float64
dtype: object
```
#### Expected Output
```python
str_col string
num_col float64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 21 | 2021-03-01T15:55:44Z | 2021-05-17T19:22:05Z | 2021-05-17T19:17:35Z | MEMBER | null | Does what it says on the tin: `DatetimeBlock.values` is always `DatetimeArray`, and `dt64tzblock.shape == dt64tzblock.values` in all cases. Similarly `TimedeltaBlock.values` is always `TimedeltaArray`.
Notes:
- It is straightforward to extend this to work for PeriodDtype (i have a branch). Haven't tried it, but I expect it would be similarly easy to do the same for CategoricalDtype.
Things that im not yet fully happy with:
- fillna method on 2D (I think @simonjayhawkins commented on this in another branch recently),
- nargminmax with 2D and mask.any()
- pytables kludge
ASVs: run repeatedly (vs master from yesterday) with --record-samples --append-samples so im pretty confident these are stable (but still include some nonsense xref #40066)
```
before after ratio
[f4b67b5e] [65792836]
<master> <ref-hybrid-3>
+ 10.1±3ms 13.9±3ms 1.38 eval.Eval.time_add('python', 'all')
+ 2.06±0.02ms 2.40±0.06ms 1.16 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 1000000)
+ 227±2μs 263±2μs 1.15 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'transformation')
+ 228±2μs 261±2μs 1.15 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct')
+ 238±2μs 272±2μs 1.14 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'transformation')
+ 248±6μs 282±5μs 1.14 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'direct')
+ 3.92±0.03ms 4.37±0.01ms 1.11 rolling.Engine.time_rolling_apply('DataFrame', 'float', <function Engine.<lambda> at 0x7fb1c0b40670>, 'cython', 'median')
+ 2.83±0.02ms 3.14±0.06ms 1.11 io.hdf.HDFStoreDataFrame.time_store_info
- 275±4μs 248±4μs 0.90 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct')
- 1.41±0.05ms 1.27±0.01ms 0.90 stat_ops.FrameOps.time_op('sum', 'int', 1)
- 1.13±0.06ms 1.02±0.07ms 0.90 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function ne>)
- 271±2μs 242±2μs 0.89 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'transformation')
- 188±3μs 167±1μs 0.89 algos.isin.IsIn.time_isin_empty('datetime64[ns]')
- 192±2μs 170±2μs 0.89 algos.isin.IsIn.time_isin_mismatched_dtype('datetime64[ns]')
- 227±2μs 200±2μs 0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'direct')
- 226±2μs 199±1μs 0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'transformation')
- 227±2μs 199±1μs 0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'transformation')
- 895±60μs 785±80μs 0.88 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function ge>)
- 10.2±0.3ms 8.93±0.7ms 0.88 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 19, 'inside')
- 235±4μs 204±4μs 0.87 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'direct')
- 3.26±0.03μs 2.83±0.03μs 0.87 frame_methods.ToNumpy.time_to_numpy_tall
- 3.28±0.03μs 2.82±0.02μs 0.86 frame_methods.ToNumpy.time_to_numpy_wide
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- 2.94±0.05μs 2.52±0.02μs 0.86 frame_methods.ToNumpy.time_values_tall
- 2.95±0.03μs 2.52±0.02μs 0.85 frame_methods.ToNumpy.time_values_wide
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- 2.09±0.02ms 1.77±0.01ms 0.85 groupby.FillNA.time_df_bfill
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- 170±2μs 137±3μs 0.81 groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'direct')
- 170±2μs 137±4μs 0.80 groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'transformation')
- 29.1±3ms 22.9±0.4ms 0.79 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.uint64'>, 20, 'outside')
- 26.0±1ms 19.2±2ms 0.74 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.int64'>, 20, 'inside')
- 26.2±0.2ms 18.0±0.07ms 0.69 index_object.SetOperations.time_operation('date_string', 'symmetric_difference')
- 11.6±0.1ms 7.32±0.08ms 0.63 reshape.ReshapeExtensionDtype.time_stack('datetime64[ns, US/Pacific]')
- 40.2±0.5μs 25.0±0.3μs 0.62 ctors.SeriesDtypesConstructors.time_dtindex_from_index_with_series
- 3.77±0.03ms 2.08±0.03ms 0.55 reshape.ReshapeExtensionDtype.time_unstack_slow('datetime64[ns, US/Pacific]')
- 32.1±0.5μs 17.0±0.2μs 0.53 ctors.SeriesDtypesConstructors.time_dtindex_from_series
- 1.11±0.03ms 408±7μs 0.37 categoricals.Constructor.time_datetimes
- 14.1±0.1μs 1.26±0.02μs 0.09 attrs_caching.SeriesArrayAttribute.time_extract_array_numpy('datetime64')
- 13.7±0.1μs 1.04±0.03μs 0.08 attrs_caching.SeriesArrayAttribute.time_extract_array('datetime64')
- 13.0±0.2μs 455±10ns 0.04 attrs_caching.SeriesArrayAttribute.time_array('datetime64')
- 73.8±1ms 1.66±0.03ms 0.02 reshape.ReshapeExtensionDtype.time_unstack_fast('datetime64[ns, US/Pacific]')
- 64.3±0.9ms 258±2μs 0.00 reshape.ReshapeExtensionDtype.time_transpose('datetime64[ns, US/Pacific]')
```
IIRC the groupby.GroupByMethods.time_dtype_as_field were heavily influenced by constructor overhead, which motivated #40054. Still need to try out @jorisvandenbossche's suggestion of non-cython optimization there. | {
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} | 7 | 2021-03-01T18:05:21Z | 2021-03-02T06:43:14Z | 2021-03-01T21:41:50Z | MEMBER | null | Found that grepping for uses of pd.array is a PITA, so went through and changed them all (inside core) to pd_array. @MarcoGorelli would it be feasible to make a code check for this? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40151 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40151/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40151/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40151/events | https://github.com/pandas-dev/pandas/issues/40151 | 819,212,390 | MDU6SXNzdWU4MTkyMTIzOTA= | 40,151 | BUG: MonthEnd(), April 31, 2020 is not a date | {
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] | closed | false | null | [] | null | 2 | 2021-03-01T20:11:39Z | 2021-03-01T21:38:25Z | 2021-03-01T21:38:25Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
from pandas.tseries.offsets import MonthEnd
pd.Timestamp('2020-05-01') + MonthEnd(0)
>>> Timestamp('2020-05-31 00:00:00')
```
#### Problem description
There are not 31 days in April... Or am I missing an April Fools joke?
I have tried this code above in multiple environments, however I keep getting April 31, 2020.
There is no error, I just think this is a bug and wanted to report it.
#### Expected Output
>>> Timestamp('2020-05-30 00:00:00')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | 2021-03-01T20:45:53Z | 2021-03-05T07:14:46Z | 2021-03-05T00:55:16Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146#issuecomment-786741804
This implements a `SingleArrayManager` class for backing a Series, consistent with how we have a SingleBlockManager.
cc @jreback @jbrockmendel | {
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] | closed | false | null | [] | null | 1 | 2021-03-01T20:46:10Z | 2021-03-01T21:54:45Z | 2021-03-01T21:54:45Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
@simonjayhawkins I think we only need `=...` in one of the two overloads, then the error goes away and all passes:
```console
$ mypy pandas
Success: no issues found in 1244 source files
``` | {
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} | 0 | 2021-03-01T21:09:45Z | 2021-03-02T17:29:16Z | 2021-03-02T17:27:55Z | MEMBER | null | ultimate goal is to condense the several datetime casting/inference functions we use down to just one. | {
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] | closed | false | null | [] | null | 1 | 2021-03-01T21:47:23Z | 2021-10-29T02:30:11Z | 2021-10-29T02:30:11Z | MEMBER | null | In principle if a user passes dt64tz data and tznaive dtype we should raise (like we do for DatetimeIndex) and tell the user to use `dt.tz_localize`.
In practice, we cast in some but not all cases. The first two cases here we specifically test for (xref #25843):
```
ts = pd.Timestamp("2019", tz="US/Eastern")
ts_naive = pd.Timestamp("2019")
exp_df = pd.DataFrame({"d": [ts_naive]})
exp_ser = pd.Series([ts_naive])
# These first two we explicitly test for
df = pd.DataFrame({"d": [ts]}, dtype="datetime64[ns]")
tm.assert_equal(df, exp_df)
ser = pd.Series([ts], dtype="datetime64[ns]")
tm.assert_equal(ser, exp_ser)
# The next three we don't explicitly test for, but works
df = pd.DataFrame([ts], columns=["d"], dtype="datetime64[ns]")
tm.assert_equal(df, exp_df)
ser = pd.Series({0: ts}, dtype="datetime64[ns]")
tm.assert_equal(ser, exp_ser)
ser = pd.Series(ts, dtype="datetime64[ns]")
tm.assert_equal(ser, exp_ser)
# The the rest do cast to tznaive, but are off by 5 hours
df = pd.DataFrame({"d": ts}, index=[0], dtype="datetime64[ns]")
tm.assert_equal(df, exp_df) # <--raises
df = pd.DataFrame(ts, index=[0], columns=["d"], dtype="datetime64[ns]")
tm.assert_equal(df, exp_df) # <--raises
ser = pd.Series(ts, index=[0], dtype="datetime64[ns]")
tm.assert_equal(df, exp_df) # <--raises
# If we wrap in a tz-aware Series first we get a FutureWarning bc of astype deprecation, followed by being off by 5 hours
aware = pd.Series([ts])
ser = pd.Series(aware, dtype="datetime64[ns]")
tm.assert_equal(df, exp_df) # <--raises
```
The "working" cases are due to a check in `maybe_cast_to_datetime`:
```
if is_datetime64: # <-- i.e. requested dtype is tznaive
dti = to_datetime(value, errors="raise")
# GH 25843: Remove tz information since the dtype
# didn't specify one
if dti.tz is not None:
dti = dti.tz_localize(None)
```
If these were all consistent, I'd want to deprecate just like we have for astype. But with the inconsistency, we might want to call it a bugfix. | {
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} | [] | closed | false | null | [] | null | 3 | 2021-03-01T23:13:27Z | 2021-03-02T01:56:38Z | 2021-03-02T01:56:25Z | MEMBER | null | cc @seberg was there a change in the ufunc code in the last day or so that might lead to a TypeError getting turned into a SystemError? | {
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} | 2 | 2021-03-02T00:15:55Z | 2021-03-02T08:09:02Z | 2021-03-02T08:09:01Z | CONTRIBUTOR | null | - [x] xref #39992
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 0 | 2021-03-02T01:03:26Z | 2021-08-19T01:38:02Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(np.arange(12).reshape(3, 4),
columns=['A', 'B', 'C', 'D'])
df.drop(columns=['B1', 'C'])
```
#### Problem description
When trying to drop a non existing column in pandas dataframe it throw a KeyError stating that "['B1'] not found in axis" It is better if the wording was more specific like : should be "['B1'] not found in columns" because I specified that I want to delete columns. The same thing is applicable for rows drop. Here you can see the result error:
KeyError Traceback (most recent call last)
<ipython-input-3-e63273db05c2> in <module>
----> 1 df.drop(columns=['B1', 'C'])
D:\Python\My_Anaconda\lib\site-packages\pandas\core\frame.py in drop(self, labels, axis, index, columns, level, inplace, errors)
4303 weight 1.0 0.8
4304 """
-> 4305 return super().drop(
4306 labels=labels,
4307 axis=axis,
D:\Python\My_Anaconda\lib\site-packages\pandas\core\generic.py in drop(self, labels, axis, index, columns, level, inplace, errors)
4150 for axis, labels in axes.items():
4151 if labels is not None:
-> 4152 obj = obj._drop_axis(labels, axis, level=level, errors=errors)
4153
4154 if inplace:
D:\Python\My_Anaconda\lib\site-packages\pandas\core\generic.py in _drop_axis(self, labels, axis, level, errors)
4185 new_axis = axis.drop(labels, level=level, errors=errors)
4186 else:
-> 4187 new_axis = axis.drop(labels, errors=errors)
4188 result = self.reindex(**{axis_name: new_axis})
4189
D:\Python\My_Anaconda\lib\site-packages\pandas\core\indexes\base.py in drop(self, labels, errors)
5589 if mask.any():
5590 if errors != "ignore":
-> 5591 raise KeyError(f"{labels[mask]} not found in axis")
5592 indexer = indexer[~mask]
5593 return self.delete(indexer)
KeyError: "['B1'] not found in axis"
#### OUTPUT EXPECTED:
"['B1'] not found in columns"
#### Output of ``1.2.2``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
None
</details>
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} | 1 | 2021-03-02T01:25:25Z | 2021-03-02T08:10:38Z | 2021-03-02T08:10:30Z | CONTRIBUTOR | null | - [x] xref #39992
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | 2021-03-02T03:44:35Z | 2021-03-04T22:51:41Z | 2021-03-03T03:09:05Z | CONTRIBUTOR | null | …ree parser
- [X] references IO XML tracker #40131
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry (not necessary, module releases: v1.3.0)
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} | 2 | 2021-03-02T03:45:54Z | 2021-03-02T23:37:52Z | 2021-03-02T23:25:54Z | MEMBER | null | DataFrame(set) raises, and Series raises for both. This unifies the 2 places this is checked.
Started out as a split-big-tests branch, so some of that is left over. | {
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} | 3 | 2021-03-02T05:30:25Z | 2021-03-03T20:23:41Z | 2021-03-03T13:06:59Z | MEMBER | null | - [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Also somewhat ameliorates the performance hit in https://github.com/pandas-dev/pandas/pull/40072#issuecomment-787456038
```
dtype="float"
window=1000
N = 10 ** 5
arr = (100 * np.random.random(N)).astype(dtype)
ewm = pd.Series(arr).ewm(halflife=window)
%timeit ewm.mean()
# Master
2.59 ms ± 25.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# PR
2.5 ms ± 31.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
``` | {
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For the rest it still updates the Series' values in place, preserving the performance trick.
cc @jbrockmendel @rhshadrach | {
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} | 3 | 2021-03-02T08:46:50Z | 2021-03-03T08:54:37Z | 2021-03-03T00:51:31Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Creating some df
df = pd.DataFrame({'idx': [1,1,1,2,2],
'ds': [20210101, 20210108, 20210115, 20210101, 20210108],
'units':[1,2,3, 10,20]})
def grp_apply_bug(part_df):
part_df.sort_values("ds", inplace=True)
return part_df.units.sum()
print(df.groupby("idx").apply(lambda x: grp_apply_bug(x)))
>>> idx
1 6
2 6
# expected result
>>> idx
1 6
2 6
## If I modify the function to either copy the data or dont use `inplace=True`, apply works perfectly
def grp_apply_bug(part_df):
part_df = part_df.sort_values("ds")
return part_df.units.sum()
print(df.groupby("idx").apply(lambda x: grp_apply_bug(x)))
>>> idx
1 6
2 30
```
#### If the function which is called in the apply post groupby we use `inplace=True`, then apply ends up using the first group data for all the groups
[aggregation is wrong which will lead to unecessary bug]
#### Expected Output
```
>>> idx
1 6
2 30
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d32926db8f7541c356066dcadabf854487738de
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.2.2
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : 0.29.14
pytest : 5.4.2
hypothesis : None
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 15 | 2021-03-02T11:45:22Z | 2021-04-20T08:58:49Z | 2021-04-20T08:58:49Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146 and follow-up on https://github.com/pandas-dev/pandas/pull/40050#discussion_r583042707
While getting groupby working for ArrayManager in https://github.com/pandas-dev/pandas/pull/40050, I didn't yet use `fast_apply` / `libreduction.apply_frame_axis0` because that relies on BlockManager internals. But, `libreduction` is faster than the python fallback (luckily, otherwise we would have it for nothing ;)).
So this PR is adding a version of the `BlockSlider` but then for a frame backed by an ArrayManager.
I didn't combine `BlockSlider` / `ArraySlider` in a single class for now (first wanted to get it working). I assume it would be "possible" with some gymnastics (there are several details that are different, such as different strides/shape index being used to move, access to `blklocs`/`blklocs`, etc).
So personally I would prefer keeping it as a separate class (certainly given that we don't intend to keep both long term, I think)
For benchmarks, we have `groupby.Apply.time_scalar_function_single/multi_col` that exercises this fast path, and this PR brings the ArrayManager code path back on par with the BlockManager version (before this PR, that benchmark saw a 3x slowdown because of using the python fallback).
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https://api.github.com/repos/pandas-dev/pandas/issues/40173 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40173/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40173/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40173/events | https://github.com/pandas-dev/pandas/issues/40173 | 819,983,007 | MDU6SXNzdWU4MTk5ODMwMDc= | 40,173 | BUG: read_parquet empty DataFrame with MultiIndex column fails with "AttributeError: 'dict' object has no attribute 'dtype'" | {
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] | open | false | null | [] | null | 0 | 2021-03-02T12:43:09Z | 2021-08-19T01:38:45Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = pd.DataFrame(columns=pd.MultiIndex(levels=[['abc'], []], codes=[[], []]))
data.to_parquet('test.parquet')
pd.read_parquet('test.parquet')
```
raises following Error
```
Traceback (most recent call last):
File "C:\Python38\lib\site-packages\IPython\core\interactiveshell.py", line 3427, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-3-de80afa86dfa>", line 5, in <module>
pd.read_parquet(fpath)
File "C:\Python38\lib\site-packages\pandas\io\parquet.py", line 459, in read_parquet
return impl.read(
File "C:\Python38\lib\site-packages\pandas\io\parquet.py", line 221, in read
return self.api.parquet.read_table(
File "pyarrow\array.pxi", line 751, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow\table.pxi", line 1668, in pyarrow.lib.Table._to_pandas
File "C:\Python38\lib\site-packages\pyarrow\pandas_compat.py", line 791, in table_to_blockmanager
columns = _deserialize_column_index(table, all_columns, column_indexes)
File "C:\Python38\lib\site-packages\pyarrow\pandas_compat.py", line 903, in _deserialize_column_index
columns = _reconstruct_columns_from_metadata(columns, column_indexes)
File "C:\Python38\lib\site-packages\pyarrow\pandas_compat.py", line 1097, in _reconstruct_columns_from_metadata
levels_dtypes = [
File "C:\Python38\lib\site-packages\pyarrow\pandas_compat.py", line 1098, in <listcomp>
(level, col_index.get('pandas_type', str(level.dtype)),
AttributeError: 'dict' object has no attribute 'dtype'
```
#### Problem description
The dataframe could be written to parquet, but raises an error when reading it back.
#### Expected Output
The same DataFrame as written to parquet, not an error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United Kingdom.1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 47.1.0
Cython : None
pytest : 6.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.20.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.1
scipy : 1.6.1
sqlalchemy : 1.3.23
tables : 3.6.1
tabulate : None
xarray : 0.16.2
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 2 | 2021-03-02T15:30:38Z | 2021-03-03T01:59:30Z | 2021-03-03T01:59:30Z | NONE | null | When transforming multiple `ndarray` to a `df` as per the code below
import numpy as np
import pandas as pd
ls_a = ['TA', 'BAT', 'T']
xxx = ['xx', 'cc']
feature_no = len(ls_a)
windows_no = len(xxx)
sub_iti = np.repeat([['s1']], (feature_no * windows_no), axis=0).reshape(-1, 1)
tw = np.repeat([xxx], feature_no, axis=1).reshape(-1, 1)
col_iti = np.repeat([ls_a], windows_no, axis=0).reshape(-1, 1)
df=pd.DataFrame ({'sub_iti': sub_iti,'tw': tw,'col_iti': col_iti})
, the compiler return an error
> ValueError: If using all scalar values, you must pass an index
Based on [OP][1], the argument `index` was inputed as below
df=pd.DataFrame (
{'sub_iti': sub_iti,
'tw': tw,
'col_iti': col_iti},index=range(0,3*2) )
However, the compiler return diff rent error
> Exception: Data must be 1-dimensional
May I know how to address this issue?
[1]: https://stackoverflow.com/questions/17839973/constructing-pandas-dataframe-from-values-in-variables-gives-valueerror-if-usi
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} | 15 | 2021-03-02T18:43:20Z | 2021-04-08T14:59:56Z | 2021-04-08T14:43:39Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Currently, lots of methods use the `bool` annotation in that file. I'm not sure it affects `mypy` (some experiments with `reveal_type` suggest it doesn't) - however, it does confuse VSCode, which takes you to the method `bool` if you hover over one of these annotations.
The file already contains
```python
bool_t = bool # Need alias because NDFrame has def bool:
```
(added in #26024), but it's not always used | {
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} | 2 | 2021-03-02T19:34:57Z | 2021-03-02T21:42:33Z | 2021-03-02T21:34:15Z | MEMBER | null | See https://github.com/pandas-dev/pandas/pull/40171#issuecomment-789120453 for context, noticed that we were calling `splitter._get_sorted_data()` twice when using the non-fast_apply fallback.
Using the benchmark case from `groupby.Apply.time_scalar_function_single/multi_col` (like in https://github.com/pandas-dev/pandas/pull/40171#issuecomment-789116039), but then with bigger data (`10 ** 6` instead of `10 ** 4`):
```
N = 10 ** 6
labels = np.random.randint(0, 2000, size=N)
labels2 = np.random.randint(0, 3, size=N)
df = DataFrame(
{
"key": labels,
"key2": labels2,
"value1": np.random.randn(N),
"value2": ["foo", "bar", "baz", "qux"] * (N // 4),
}
)
df_am = df._as_manager("array")
```
I get
```
In [2]: %timeit df_am.groupby("key").apply(lambda x: 1)
252 ms ± 17.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) <-- master
166 ms ± 5.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- PR
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] | closed | false | null | [] | null | 0 | 2021-03-02T19:38:05Z | 2021-03-12T03:43:31Z | 2021-03-12T03:43:31Z | NONE | null | Changed "not found in axis" to "not found in columns"
- [x] closes #40160
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} | 1 | 2021-03-02T20:03:53Z | 2021-03-03T08:38:09Z | 2021-03-03T08:38:05Z | MEMBER | null | Looking up the exact cython function is relatively costly, and is also something that is done repeatedly for a limited number of possible cases, so a good target for caching.
Using the `GroupManyLabels` benchmark we have:
https://github.com/pandas-dev/pandas/blob/b835ca2fc2f772c27c914ae532cd32f8db69724a/asv_bench/benchmarks/groupby.py#L133-L145
```python
ncols = 1000
N = 1000
data = np.random.randn(N, ncols)
labels = np.random.randint(0, 100, size=N)
df = pd.DataFrame(data)
df_am = df._as_manager('array')
```
I get
```
In [2]: %timeit df_am.groupby(labels).sum()
72.6 ms ± 1.24 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
66.4 ms ± 1.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- PR
```
(the difference is not huge, but I am going to do several PRs with small changes that give a few ms improvement. And with all those together I hope to get the total time down to around 20ms, and in that case 5ms is significant)
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} | 4 | 2021-03-02T20:07:10Z | 2021-03-02T21:20:56Z | 2021-03-02T21:20:23Z | NONE | null | An empty dataframe can't be created because this error is raised when I try it:
AttributeError: type object 'object' has no attribute 'dtype'
```python
column_names = ["a", "b", "c"]
df = pd.DataFrame([], columns=column_names)
```
Based on [this](https://stackoverflow.com/questions/65998646/pandas-error-for-creating-an-emptydataframe/66086904), it's caused by numpy version. If I install numpy 1.19.5, empty dataframe is created but if I install numpy 1.20.0 (or newer), the error above is raised.
Can this bug be fixed in pandas or in numpy?
Thank you. | {
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} | 2 | 2021-03-02T20:15:00Z | 2021-03-05T01:28:55Z | 2021-03-05T01:28:49Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Due to a bug in my own code, I discovered that `pandas.Index` objects cannot be passed to the `numpy.all` function:
```python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: idx = pd.Int64Index([1,2,3,4,5])
In [4]: np.all(idx)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-597ac8c43e48> in <module>
----> 1 np.all(idx)
<__array_function__ internals> in all(*args, **kwargs)
~/venvs/funpack.venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py in all(a, axis, out, keepdims, where)
2437 """
2438 return _wrapreduction(a, np.logical_and, 'all', axis, None, out,
-> 2439 keepdims=keepdims, where=where)
2440
2441
~/venvs/funpack.venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs)
83 return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)
84 else:
---> 85 return reduction(axis=axis, out=out, **passkwargs)
86
87 return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
TypeError: all() got an unexpected keyword argument 'axis'
```
Using [this PR](https://github.com/pandas-dev/pandas/pull/26324/) as precedent, the fix is quite straightforward.
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} | 1 | 2021-03-02T20:36:54Z | 2021-03-04T21:56:07Z | 2021-03-04T18:09:41Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Deleted a few duplicates and parametrized | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40182/events | https://github.com/pandas-dev/pandas/pull/40182 | 820,377,658 | MDExOlB1bGxSZXF1ZXN0NTgzMzMxODIw | 40,182 | API: pseudo-public internals API for downstream libraries | {
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} | 2 | 2021-03-02T20:45:58Z | 2021-03-05T15:03:46Z | 2021-03-05T13:55:04Z | MEMBER | null | Downstream libraries accessing core.internals causes headaches pretty regularly (https://github.com/pandas-dev/pandas/pull/40149#discussion_r585699450, #38134,). The proposal: define a public-ish stable-ish API to expose for these libraries, leaving us free-er to make changes in internals.
This exposes `make_block`. I expect we'll also need to expose one or both of `create_block_manager_from_arrays`/`create_block_manager_from_blocks` (comment in `internals.__init__` says they are there for downstream xref #33892)
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] | open | false | null | [] | null | 0 | 2021-03-02T21:22:01Z | 2021-06-06T20:52:45Z | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
Although, this issue seems relevant: https://github.com/pandas-dev/pandas/issues/6796
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
>>> from sqlalchemy import create_engine
>>> eng = create_engine(postgesql-dsn)
>>> pd.DataFrame({'(abc]': [1,2,3]}).to_sql('tbl', eng, if_exists='replace')
KeyError: '(abc])s'
>>> pd.DataFrame({'(abc)': [1,2,3]}).to_sql('tbl', eng, if_exists='replace')
KeyError: '(abc'
>>> pd.DataFrame({'[abc)': [1,2,3]}).to_sql('tbl', eng, if_exists='replace')
KeyError: '[abc'
```
#### Problem description
I noticed that `to_sql` throws an error when column names have a combination of `(` and `]`s in them. I didn't try this across all sql flavors, but I didn't run into issues with sqlite so I'm guessing this is tied to psycopg2 variable identifier substitution.
#### Expected Output
No KeyErrors
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 2 | 2021-03-02T21:49:54Z | 2021-03-03T00:49:41Z | 2021-03-03T00:49:37Z | NONE | null | - [ x ] I have checked that this issue has not already been reported.
- [ x ] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> x = pd.Series([1, 2, 3])
>>> y = pd.Series([4, 5, 6], index=[2, 1, 0])
>>> x.iloc[:] = y
>>> x
0 4
1 5
2 6
dtype: int64
```
#### Problem description
When assigning a series to another, the index of the RHS should be considered. In this example, the mapping of the RHS is {2: 4, 1: 5, 0: 6}, but such mapping is ignored in the final result.
#### Expected Output
```
>>> x
0 6
1 5
2 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
Version : #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.2
hypothesis : 5.28.0
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.3.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.2
xlrd : None
xlwt : None
numba : 0.52.0
</details>
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} | 2 | 2021-03-02T21:52:02Z | 2021-03-03T15:23:55Z | 2021-03-03T13:07:39Z | MEMBER | null | The idea is that to_datetime_with_format will eventually be moved into objects_to_datetime64ns, and most of _convert_listlike_datetimes will just be a call to sequence_to_dt64ns | {
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] | open | false | null | [] | null | 4 | 2021-03-02T21:58:01Z | 2021-07-15T23:56:32Z | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample
```python
import pandas as pd
import numpy as np
cols = pd.MultiIndex.from_product([[1], ['a', 'b']])
index = np.arange(2)
data = np.ones((len(index), len(cols)))
df = pd.DataFrame(data=data, index=index, columns=cols)
print(df)
df.loc[:, 1] = df.loc[:, 1]
print(df)
```
#### Problem description
When assigning to block of MultiIndex-ed dataframe (I tested with columnar multi-index), data becomes covered with NaNs instead. Same happens when doing right-assign arithmetic operations (`*=`, `+=` etc.). It is not required for RHS to point to same data storage - any dataframe with corresponding index/columns gives same effect.
#### Expected Output
```
1
a b
0 1.0 1.0
1 1.0 1.0
1
a b
0 1.0 1.0
1 1.0 1.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1251
pandas : 1.2.3
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
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 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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ideally we'll end up with only one place in the non-cython code calling into the array_to_datetime/datetime_to_datetime64 | {
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} | 3 | 2021-03-03T07:34:13Z | 2021-03-03T23:48:26Z | 2021-03-03T23:48:26Z | NONE | null | Hello! I am working in the scientific community with pandas, and I use it especially with fcs files, a more specialized scientific file format. I have been using my own scripts to convert from fcs to csv, but it would be much easier if I could load and export them directly. I was wondering, if I made a pull request that added this support, would it be accepted? If you guys think it wouldn't fit or be worth adding, then I understand.
#### Describe the solution you'd like
I was imagining:
`pandas.read_fcs(filepath_or_buffer)` that returns a new DataFrame
`DataFrame.to_fcs(filepath_or_buffer)` that writes it to the file or buffer.
#### API breaking implications
I don't see how this would break anything, as it is only adding new functions.
#### Describe alternatives you've considered
There are other libraries that support fcs files, but all the ones I looked at seem to be pretty big and don't work well with pandas or other libraries. I feel like it would be useful to have it as part of pandas.
#### Additional context
Again, I am willing to do all of the programming and changes to add this, I just wanted to see if it is a good idea and if it would be accepted. | {
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} | 12 | 2021-03-03T08:34:37Z | 2021-04-12T14:14:52Z | 2021-04-12T12:35:35Z | MEMBER | null | Hashing of the dtype is used in numerous places of the code base. For example it is used in concat operations to determine whether all objects have the same dtype. For `CategoricalDtype` this operation can be extremely costly since it requires hashing the array/list of categories.
The following example shows the impact of this caching operation nicely since `pd.concat` hashes all dtypes at least twice
```python
import string
import pandas as pd
def make_df():
return pd.DataFrame(
{
"A": [
string.ascii_letters[ix : (ix + 10) % len(string.ascii_letters)]
for ix in range(10000)
],
"B": [
string.ascii_lowercase[ix : (ix + 10) % len(string.ascii_lowercase)]
for ix in range(10000)
],
}
).astype({"A": "category", "B": "category"})
dfs = [make_df() for _ in range(100)]
```
```python
# without caching
%time pd.concat(dfs)
CPU times: user 74.1 ms, sys: 6.32 ms, total: 80.4 ms
Wall time: 80.2 ms
# with caching
%time pd.concat(dfs)
CPU times: user 46.1 ms, sys: 6.65 ms, total: 52.7 ms
Wall time: 52.1 ms
```
Note: On an earlier version (I believe it was 1.1.X but I need to check again) this caching brought this concat down to ~30ms but I haven't identified this _regression_, yet
- [x] closes N/A
- [x] N/A
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | 2021-03-03T08:55:27Z | 2021-03-03T14:59:49Z | 2021-03-03T13:04:51Z | MEMBER | null | Apparently calling `np.zeros_like` has quite some overhead:
```
In [7]: arr = np.random.randn(1000)
In [8]: %timeit np.zeros_like(arr)
3.14 µs ± 44.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [9]: %timeit np.zeros(arr.shape, dtype=arr.dtype)
662 ns ± 41.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
So this PR replaces a few (recently added) occurences in groupby.pyx.
Using the same benchmarks case as in https://github.com/pandas-dev/pandas/pull/40178#issue-583301313, this gives:
```
In [2]: %timeit df_am.groupby(labels).sum()
66.5 ms ± 876 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- master
54.6 ms ± 958 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- PR
```
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```
start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
rng = pd.date_range(start, end, freq='7min')
ts = pd.DataFrame(data = {'timestamp': rng, 'col2': ['cat', 'dog','cat', 'dog','cat', 'dog','cat', 'dog','cat']})
ts.groupby(['col2', pd.Grouper(key="timestamp", freq='60min', `origin='start')]).cumcount()
ts
```
0 0
1 0
2 1
3 1
4 2
5 0
6 0
7 1
8 1
Expected behaviour:
0 0
1 0
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3 1
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5 2
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} | 7 | 2021-03-03T11:06:38Z | 2021-04-01T18:53:55Z | 2021-03-14T15:47:50Z | MEMBER | null | Trying to use overload so that mypy knows that the return type of `.set_axis` is depending on the value of `inplace`.
This seems to work - if I have
```python
# t.py
import pandas as pd
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4], axis=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4], axis=0, inplace=False))
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4], inplace=True))
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4], inplace=False))
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4], axis=0))
reveal_type(pd.DataFrame([1,2,3]).set_axis([2,3,4]))
```
then I get
```console
$ mypy t.py
t.py:3: note: Revealed type is 'None'
t.py:4: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:5: note: Revealed type is 'None'
t.py:6: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:7: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:8: note: Revealed type is 'pandas.core.frame.DataFrame'
```
which seems correct.
However, running `mypy pandas` throws some errors, which I've ignored (similarly to how this was done in #37137, though this time there's an extra error) and put the errors as comments. @simonjayhawkins @Dr-Irv sorry to ask for your help here, but do you know how to address this?
Reading through https://github.com/python/mypy/issues/6580 , my understanding is that we would need to provide exponentially many overloads for this to work without errors. However, by placing the `: Literal[False] = ...` overload first, then ties will be broken with that one and so the correct return type will be inferred (see the above `t.py` experiment) | {
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} | 6 | 2021-03-03T12:50:08Z | 2021-03-05T13:55:57Z | 2021-03-05T13:55:57Z | CONTRIBUTOR | null | https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=55400&view=logs&j=404760ec-14d3-5d48-e580-13034792878f&t=f81e4cc8-d61a-5fb8-36be-36768e5c561a
Something updated and left spam behind; only on the 3.8 build (its possible this is the only one we are testing).
```
2021-03-03T09:34:50.9393763Z ================================== FAILURES ===================================
2021-03-03T09:34:50.9394902Z ___________________________ test_bs4_version_fails ____________________________
2021-03-03T09:34:50.9396205Z [gw3] win32 -- Python 3.8.8 C:\Miniconda\envs\pandas-dev\python.exe
2021-03-03T09:34:50.9397221Z
2021-03-03T09:34:50.9398044Z monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x000002A71EF656D0>
2021-03-03T09:34:50.9399101Z datapath = <function datapath.<locals>.deco at 0x000002A71EEA7040>
2021-03-03T09:34:50.9400267Z
2021-03-03T09:34:50.9401214Z @td.skip_if_no("bs4")
2021-03-03T09:34:50.9402383Z def test_bs4_version_fails(monkeypatch, datapath):
2021-03-03T09:34:50.9403404Z import bs4
2021-03-03T09:34:50.9404409Z
2021-03-03T09:34:50.9405477Z monkeypatch.setattr(bs4, "__version__", "4.2")
2021-03-03T09:34:50.9406860Z with pytest.raises(ImportError, match="Pandas requires version"):
2021-03-03T09:34:50.9408079Z > read_html(datapath("io", "data", "html", "spam.html"), flavor="bs4")
2021-03-03T09:34:50.9408759Z
2021-03-03T09:34:50.9409527Z pandas\tests\io\test_html.py:77:
2021-03-03T09:34:50.9410691Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9411453Z
2021-03-03T09:34:50.9412292Z args = ('D:\\a\\1\\s\\pandas\\tests\\io\\data\\html\\spam.html',)
2021-03-03T09:34:50.9413367Z kwargs = {'flavor': 'bs4'}, arguments = " except for the argument 'io'"
2021-03-03T09:34:50.9414395Z num_allow_args = 1
2021-03-03T09:34:50.9415166Z
2021-03-03T09:34:50.9416110Z @wraps(func)
2021-03-03T09:34:50.9417000Z def wrapper(*args, **kwargs):
2021-03-03T09:34:50.9418076Z arguments = _format_argument_list(allow_args)
2021-03-03T09:34:50.9420427Z if isinstance(allow_args, (list, tuple)):
2021-03-03T09:34:50.9422235Z num_allow_args = len(allow_args)
2021-03-03T09:34:50.9423173Z else:
2021-03-03T09:34:50.9424186Z num_allow_args = allow_args
2021-03-03T09:34:50.9425230Z if len(args) > num_allow_args:
2021-03-03T09:34:50.9426200Z msg = (
2021-03-03T09:34:50.9427274Z f"Starting with Pandas version {version} all arguments of "
2021-03-03T09:34:50.9428386Z f"{func.__name__}{arguments} will be keyword-only"
2021-03-03T09:34:50.9429355Z )
2021-03-03T09:34:50.9430499Z warnings.warn(msg, FutureWarning, stacklevel=stacklevel)
2021-03-03T09:34:50.9431542Z > return func(*args, **kwargs)
2021-03-03T09:34:50.9432167Z
2021-03-03T09:34:50.9432866Z pandas\util\_decorators.py:310:
2021-03-03T09:34:50.9434201Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9435043Z
2021-03-03T09:34:50.9436688Z io = 'D:\\a\\1\\s\\pandas\\tests\\io\\data\\html\\spam.html', match = '.+'
2021-03-03T09:34:50.9437750Z flavor = 'bs4', header = None, index_col = None, skiprows = None, attrs = None
2021-03-03T09:34:50.9439069Z parse_dates = False, thousands = ',', encoding = None, decimal = '.'
2021-03-03T09:34:50.9440479Z converters = None, na_values = None, keep_default_na = True
2021-03-03T09:34:50.9441401Z displayed_only = True
2021-03-03T09:34:50.9442035Z
2021-03-03T09:34:50.9443107Z @deprecate_nonkeyword_arguments(version="2.0")
2021-03-03T09:34:50.9443927Z def read_html(
2021-03-03T09:34:50.9444910Z io: FilePathOrBuffer,
2021-03-03T09:34:50.9446104Z match: Union[str, Pattern] = ".+",
2021-03-03T09:34:50.9447121Z flavor: Optional[str] = None,
2021-03-03T09:34:50.9448233Z header: Optional[Union[int, Sequence[int]]] = None,
2021-03-03T09:34:50.9449370Z index_col: Optional[Union[int, Sequence[int]]] = None,
2021-03-03T09:34:50.9450592Z skiprows: Optional[Union[int, Sequence[int], slice]] = None,
2021-03-03T09:34:50.9453243Z attrs: Optional[Dict[str, str]] = None,
2021-03-03T09:34:50.9454443Z parse_dates: bool = False,
2021-03-03T09:34:50.9455453Z thousands: Optional[str] = ",",
2021-03-03T09:34:50.9456466Z encoding: Optional[str] = None,
2021-03-03T09:34:50.9457526Z decimal: str = ".",
2021-03-03T09:34:50.9458519Z converters: Optional[Dict] = None,
2021-03-03T09:34:50.9459455Z na_values=None,
2021-03-03T09:34:50.9460540Z keep_default_na: bool = True,
2021-03-03T09:34:50.9461533Z displayed_only: bool = True,
2021-03-03T09:34:50.9462491Z ) -> List[DataFrame]:
2021-03-03T09:34:50.9463397Z r"""
2021-03-03T09:34:50.9464413Z Read HTML tables into a ``list`` of ``DataFrame`` objects.
2021-03-03T09:34:50.9465322Z
2021-03-03T09:34:50.9466210Z Parameters
2021-03-03T09:34:50.9467115Z ----------
2021-03-03T09:34:50.9468087Z io : str, path object or file-like object
2021-03-03T09:34:50.9469308Z A URL, a file-like object, or a raw string containing HTML. Note that
2021-03-03T09:34:50.9470751Z lxml only accepts the http, ftp and file url protocols. If you have a
2021-03-03T09:34:50.9471778Z URL that starts with ``'https'`` you might try removing the ``'s'``.
2021-03-03T09:34:50.9473219Z
2021-03-03T09:34:50.9474209Z match : str or compiled regular expression, optional
2021-03-03T09:34:50.9475628Z The set of tables containing text matching this regex or string will be
2021-03-03T09:34:50.9476778Z returned. Unless the HTML is extremely simple you will probably need to
2021-03-03T09:34:50.9477931Z pass a non-empty string here. Defaults to '.+' (match any non-empty
2021-03-03T09:34:50.9479073Z string). The default value will return all tables contained on a page.
2021-03-03T09:34:50.9480641Z This value is converted to a regular expression so that there is
2021-03-03T09:34:50.9482369Z consistent behavior between Beautiful Soup and lxml.
2021-03-03T09:34:50.9483359Z
2021-03-03T09:34:50.9484366Z flavor : str, optional
2021-03-03T09:34:50.9486070Z The parsing engine to use. 'bs4' and 'html5lib' are synonymous with
2021-03-03T09:34:50.9487175Z each other, they are both there for backwards compatibility. The
2021-03-03T09:34:50.9488508Z default of ``None`` tries to use ``lxml`` to parse and if that fails it
2021-03-03T09:34:50.9489964Z falls back on ``bs4`` + ``html5lib``.
2021-03-03T09:34:50.9490925Z
2021-03-03T09:34:50.9491964Z header : int or list-like, optional
2021-03-03T09:34:50.9493314Z The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to
2021-03-03T09:34:50.9496285Z make the columns headers.
2021-03-03T09:34:50.9497205Z
2021-03-03T09:34:50.9498236Z index_col : int or list-like, optional
2021-03-03T09:34:50.9499348Z The column (or list of columns) to use to create the index.
2021-03-03T09:34:50.9500559Z
2021-03-03T09:34:50.9501458Z skiprows : int, list-like or slice, optional
2021-03-03T09:34:50.9502617Z Number of rows to skip after parsing the column integer. 0-based. If a
2021-03-03T09:34:50.9503782Z sequence of integers or a slice is given, will skip the rows indexed by
2021-03-03T09:34:50.9504958Z that sequence. Note that a single element sequence means 'skip the nth
2021-03-03T09:34:50.9506054Z row' whereas an integer means 'skip n rows'.
2021-03-03T09:34:50.9507075Z
2021-03-03T09:34:50.9508036Z attrs : dict, optional
2021-03-03T09:34:50.9509131Z This is a dictionary of attributes that you can pass to use to identify
2021-03-03T09:34:50.9510472Z the table in the HTML. These are not checked for validity before being
2021-03-03T09:34:50.9511547Z passed to lxml or Beautiful Soup. However, these attributes must be
2021-03-03T09:34:50.9512772Z valid HTML table attributes to work correctly. For example, ::
2021-03-03T09:34:50.9514058Z
2021-03-03T09:34:50.9515226Z attrs = {'id': 'table'}
2021-03-03T09:34:50.9516136Z
2021-03-03T09:34:50.9517184Z is a valid attribute dictionary because the 'id' HTML tag attribute is
2021-03-03T09:34:50.9518485Z a valid HTML attribute for *any* HTML tag as per `this document
2021-03-03T09:34:50.9519874Z <https://html.spec.whatwg.org/multipage/dom.html#global-attributes>`__. ::
2021-03-03T09:34:50.9520961Z
2021-03-03T09:34:50.9521954Z attrs = {'asdf': 'table'}
2021-03-03T09:34:50.9524613Z
2021-03-03T09:34:50.9527108Z is *not* a valid attribute dictionary because 'asdf' is not a valid
2021-03-03T09:34:50.9528585Z HTML attribute even if it is a valid XML attribute. Valid HTML 4.01
2021-03-03T09:34:50.9529710Z table attributes can be found `here
2021-03-03T09:34:50.9531196Z <http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A
2021-03-03T09:34:50.9532780Z working draft of the HTML 5 spec can be found `here
2021-03-03T09:34:50.9534290Z <https://html.spec.whatwg.org/multipage/tables.html>`__. It contains the
2021-03-03T09:34:50.9535412Z latest information on table attributes for the modern web.
2021-03-03T09:34:50.9536751Z
2021-03-03T09:34:50.9537819Z parse_dates : bool, optional
2021-03-03T09:34:50.9539089Z See :func:`~read_csv` for more details.
2021-03-03T09:34:50.9540539Z
2021-03-03T09:34:50.9541838Z thousands : str, optional
2021-03-03T09:34:50.9543300Z Separator to use to parse thousands. Defaults to ``','``.
2021-03-03T09:34:50.9544304Z
2021-03-03T09:34:50.9545341Z encoding : str, optional
2021-03-03T09:34:50.9546733Z The encoding used to decode the web page. Defaults to ``None``.``None``
2021-03-03T09:34:50.9548178Z preserves the previous encoding behavior, which depends on the
2021-03-03T09:34:50.9549386Z underlying parser library (e.g., the parser library will try to use
2021-03-03T09:34:50.9550583Z the encoding provided by the document).
2021-03-03T09:34:50.9551571Z
2021-03-03T09:34:50.9552794Z decimal : str, default '.'
2021-03-03T09:34:50.9553961Z Character to recognize as decimal point (e.g. use ',' for European
2021-03-03T09:34:50.9555156Z data).
2021-03-03T09:34:50.9556075Z
2021-03-03T09:34:50.9557126Z converters : dict, default None
2021-03-03T09:34:50.9558260Z Dict of functions for converting values in certain columns. Keys can
2021-03-03T09:34:50.9559448Z either be integers or column labels, values are functions that take one
2021-03-03T09:34:50.9561145Z input argument, the cell (not column) content, and return the
2021-03-03T09:34:50.9562185Z transformed content.
2021-03-03T09:34:50.9563144Z
2021-03-03T09:34:50.9564371Z na_values : iterable, default None
2021-03-03T09:34:50.9565328Z Custom NA values.
2021-03-03T09:34:50.9566234Z
2021-03-03T09:34:50.9567466Z keep_default_na : bool, default True
2021-03-03T09:34:50.9568610Z If na_values are specified and keep_default_na is False the default NaN
2021-03-03T09:34:50.9569944Z values are overridden, otherwise they're appended to.
2021-03-03T09:34:50.9571026Z
2021-03-03T09:34:50.9572069Z displayed_only : bool, default True
2021-03-03T09:34:50.9573777Z Whether elements with "display: none" should be parsed.
2021-03-03T09:34:50.9574758Z
2021-03-03T09:34:50.9575740Z Returns
2021-03-03T09:34:50.9576684Z -------
2021-03-03T09:34:50.9577583Z dfs
2021-03-03T09:34:50.9578572Z A list of DataFrames.
2021-03-03T09:34:50.9579501Z
2021-03-03T09:34:50.9580619Z See Also
2021-03-03T09:34:50.9581611Z --------
2021-03-03T09:34:50.9584490Z read_csv : Read a comma-separated values (csv) file into DataFrame.
2021-03-03T09:34:50.9585488Z
2021-03-03T09:34:50.9586627Z Notes
2021-03-03T09:34:50.9587704Z -----
2021-03-03T09:34:50.9589093Z Before using this function you should read the :ref:`gotchas about the
2021-03-03T09:34:50.9590662Z HTML parsing libraries <io.html.gotchas>`.
2021-03-03T09:34:50.9591679Z
2021-03-03T09:34:50.9593980Z Expect to do some cleanup after you call this function. For example, you
2021-03-03T09:34:50.9595393Z might need to manually assign column names if the column names are
2021-03-03T09:34:50.9596531Z converted to NaN when you pass the `header=0` argument. We try to assume as
2021-03-03T09:34:50.9597875Z little as possible about the structure of the table and push the
2021-03-03T09:34:50.9598988Z idiosyncrasies of the HTML contained in the table to the user.
2021-03-03T09:34:50.9600154Z
2021-03-03T09:34:50.9601555Z This function searches for ``<table>`` elements and only for ``<tr>``
2021-03-03T09:34:50.9602693Z and ``<th>`` rows and ``<td>`` elements within each ``<tr>`` or ``<th>``
2021-03-03T09:34:50.9603978Z element in the table. ``<td>`` stands for "table data". This function
2021-03-03T09:34:50.9605142Z attempts to properly handle ``colspan`` and ``rowspan`` attributes.
2021-03-03T09:34:50.9606361Z If the function has a ``<thead>`` argument, it is used to construct
2021-03-03T09:34:50.9607735Z the header, otherwise the function attempts to find the header within
2021-03-03T09:34:50.9611377Z the body (by putting rows with only ``<th>`` elements into the header).
2021-03-03T09:34:50.9612659Z
2021-03-03T09:34:50.9613857Z Similar to :func:`~read_csv` the `header` argument is applied
2021-03-03T09:34:50.9615108Z **after** `skiprows` is applied.
2021-03-03T09:34:50.9616445Z
2021-03-03T09:34:50.9617649Z This function will *always* return a list of :class:`DataFrame` *or*
2021-03-03T09:34:50.9618836Z it will fail, e.g., it will *not* return an empty list.
2021-03-03T09:34:50.9620175Z
2021-03-03T09:34:50.9621156Z Examples
2021-03-03T09:34:50.9622483Z --------
2021-03-03T09:34:50.9623759Z See the :ref:`read_html documentation in the IO section of the docs
2021-03-03T09:34:50.9625741Z <io.read_html>` for some examples of reading in HTML tables.
2021-03-03T09:34:50.9626654Z """
2021-03-03T09:34:50.9629961Z _importers()
2021-03-03T09:34:50.9630986Z
2021-03-03T09:34:50.9632375Z # Type check here. We don't want to parse only to fail because of an
2021-03-03T09:34:50.9636697Z # invalid value of an integer skiprows.
2021-03-03T09:34:50.9645805Z if isinstance(skiprows, numbers.Integral) and skiprows < 0:
2021-03-03T09:34:50.9647398Z raise ValueError(
2021-03-03T09:34:50.9648490Z "cannot skip rows starting from the end of the "
2021-03-03T09:34:50.9649597Z "data (you passed a negative value)"
2021-03-03T09:34:50.9650845Z )
2021-03-03T09:34:50.9651870Z validate_header_arg(header)
2021-03-03T09:34:50.9655846Z
2021-03-03T09:34:50.9657475Z io = stringify_path(io)
2021-03-03T09:34:50.9658425Z
2021-03-03T09:34:50.9659385Z > return _parse(
2021-03-03T09:34:50.9660473Z flavor=flavor,
2021-03-03T09:34:50.9661514Z io=io,
2021-03-03T09:34:50.9662746Z match=match,
2021-03-03T09:34:50.9665347Z header=header,
2021-03-03T09:34:50.9666332Z index_col=index_col,
2021-03-03T09:34:50.9667304Z skiprows=skiprows,
2021-03-03T09:34:50.9668269Z parse_dates=parse_dates,
2021-03-03T09:34:50.9669246Z thousands=thousands,
2021-03-03T09:34:50.9670791Z attrs=attrs,
2021-03-03T09:34:50.9671907Z encoding=encoding,
2021-03-03T09:34:50.9673510Z decimal=decimal,
2021-03-03T09:34:50.9674463Z converters=converters,
2021-03-03T09:34:50.9675720Z na_values=na_values,
2021-03-03T09:34:50.9676696Z keep_default_na=keep_default_na,
2021-03-03T09:34:50.9677853Z displayed_only=displayed_only,
2021-03-03T09:34:50.9678795Z )
2021-03-03T09:34:50.9679392Z
2021-03-03T09:34:50.9680319Z pandas\io\html.py:1101:
2021-03-03T09:34:50.9681587Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9682233Z
2021-03-03T09:34:50.9683012Z flavor = ('bs4',), io = 'D:\\a\\1\\s\\pandas\\tests\\io\\data\\html\\spam.html'
2021-03-03T09:34:50.9684431Z match = '.+', attrs = None, encoding = None, displayed_only = True
2021-03-03T09:34:50.9685691Z kwargs = {'converters': None, 'decimal': '.', 'header': None, 'index_col': None, ...}
2021-03-03T09:34:50.9686805Z compiled_match = re.compile('.+'), retained = None, flav = 'bs4'
2021-03-03T09:34:50.9687627Z
2021-03-03T09:34:50.9688479Z def _parse(flavor, io, match, attrs, encoding, displayed_only, **kwargs):
2021-03-03T09:34:50.9689643Z flavor = _validate_flavor(flavor)
2021-03-03T09:34:50.9691093Z compiled_match = re.compile(match) # you can pass a compiled regex here
2021-03-03T09:34:50.9692126Z
2021-03-03T09:34:50.9693385Z retained = None
2021-03-03T09:34:50.9694360Z for flav in flavor:
2021-03-03T09:34:50.9697075Z > parser = _parser_dispatch(flav)
2021-03-03T09:34:50.9697778Z
2021-03-03T09:34:50.9698570Z pandas\io\html.py:905:
2021-03-03T09:34:50.9699858Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9700874Z
2021-03-03T09:34:50.9701578Z flavor = 'bs4'
2021-03-03T09:34:50.9702089Z
2021-03-03T09:34:50.9703080Z def _parser_dispatch(flavor):
2021-03-03T09:34:50.9703985Z """
2021-03-03T09:34:50.9705294Z Choose the parser based on the input flavor.
2021-03-03T09:34:50.9706453Z
2021-03-03T09:34:50.9707647Z Parameters
2021-03-03T09:34:50.9708798Z ----------
2021-03-03T09:34:50.9710072Z flavor : str
2021-03-03T09:34:50.9711281Z The type of parser to use. This must be a valid backend.
2021-03-03T09:34:50.9712730Z
2021-03-03T09:34:50.9713616Z Returns
2021-03-03T09:34:50.9714697Z -------
2021-03-03T09:34:50.9715908Z cls : _HtmlFrameParser subclass
2021-03-03T09:34:50.9717015Z The parser class based on the requested input flavor.
2021-03-03T09:34:50.9717942Z
2021-03-03T09:34:50.9718843Z Raises
2021-03-03T09:34:50.9719952Z ------
2021-03-03T09:34:50.9721500Z ValueError
2021-03-03T09:34:50.9722526Z * If `flavor` is not a valid backend.
2021-03-03T09:34:50.9723578Z ImportError
2021-03-03T09:34:50.9725002Z * If you do not have the requested `flavor`
2021-03-03T09:34:50.9725921Z """
2021-03-03T09:34:50.9726921Z valid_parsers = list(_valid_parsers.keys())
2021-03-03T09:34:50.9728115Z if flavor not in valid_parsers:
2021-03-03T09:34:50.9729121Z raise ValueError(
2021-03-03T09:34:50.9730409Z f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
2021-03-03T09:34:50.9731612Z )
2021-03-03T09:34:50.9732646Z
2021-03-03T09:34:50.9733997Z if flavor in ("bs4", "html5lib"):
2021-03-03T09:34:50.9735079Z if not _HAS_HTML5LIB:
2021-03-03T09:34:50.9736256Z > raise ImportError("html5lib not found, please install it")
2021-03-03T09:34:50.9739210Z E ImportError: html5lib not found, please install it
2021-03-03T09:34:50.9741569Z
2021-03-03T09:34:50.9743398Z pandas\io\html.py:854: ImportError
2021-03-03T09:34:50.9744168Z
2021-03-03T09:34:50.9745062Z During handling of the above exception, another exception occurred:
2021-03-03T09:34:50.9745611Z
2021-03-03T09:34:50.9746799Z monkeypatch = <_pytest.monkeypatch.MonkeyPatch object at 0x000002A71EF656D0>
2021-03-03T09:34:50.9748701Z datapath = <function datapath.<locals>.deco at 0x000002A71EEA7040>
2021-03-03T09:34:50.9749285Z
2021-03-03T09:34:50.9750140Z @td.skip_if_no("bs4")
2021-03-03T09:34:50.9752292Z def test_bs4_version_fails(monkeypatch, datapath):
2021-03-03T09:34:50.9753395Z import bs4
2021-03-03T09:34:50.9754430Z
2021-03-03T09:34:50.9755519Z monkeypatch.setattr(bs4, "__version__", "4.2")
2021-03-03T09:34:50.9757033Z with pytest.raises(ImportError, match="Pandas requires version"):
2021-03-03T09:34:50.9758425Z > read_html(datapath("io", "data", "html", "spam.html"), flavor="bs4")
2021-03-03T09:34:50.9759758Z E AssertionError: Regex pattern 'Pandas requires version' does not match 'html5lib not found, please install it'.
2021-03-03T09:34:50.9760716Z
2021-03-03T09:34:50.9762174Z pandas\tests\io\test_html.py:77: AssertionError
2021-03-03T09:34:50.9763450Z _____________________________ test_same_ordering ______________________________
2021-03-03T09:34:50.9764923Z [gw3] win32 -- Python 3.8.8 C:\Miniconda\envs\pandas-dev\python.exe
2021-03-03T09:34:50.9765551Z
2021-03-03T09:34:50.9766362Z datapath = <function datapath.<locals>.deco at 0x000002A71EEA7430>
2021-03-03T09:34:50.9766872Z
2021-03-03T09:34:50.9767640Z @td.skip_if_no("bs4")
2021-03-03T09:34:50.9768481Z @td.skip_if_no("lxml")
2021-03-03T09:34:50.9769582Z def test_same_ordering(datapath):
2021-03-03T09:34:50.9771581Z filename = datapath("io", "data", "html", "valid_markup.html")
2021-03-03T09:34:50.9772659Z dfs_lxml = read_html(filename, index_col=0, flavor=["lxml"])
2021-03-03T09:34:50.9773803Z > dfs_bs4 = read_html(filename, index_col=0, flavor=["bs4"])
2021-03-03T09:34:50.9774600Z
2021-03-03T09:34:50.9775542Z pandas\tests\io\test_html.py:94:
2021-03-03T09:34:50.9776496Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9777952Z pandas\util\_decorators.py:310: in wrapper
2021-03-03T09:34:50.9778968Z return func(*args, **kwargs)
2021-03-03T09:34:50.9780021Z pandas\io\html.py:1101: in read_html
2021-03-03T09:34:50.9781455Z return _parse(
2021-03-03T09:34:50.9782553Z pandas\io\html.py:905: in _parse
2021-03-03T09:34:50.9783499Z parser = _parser_dispatch(flav)
2021-03-03T09:34:50.9785072Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9785765Z
2021-03-03T09:34:50.9786505Z flavor = 'bs4'
2021-03-03T09:34:50.9786937Z
2021-03-03T09:34:50.9787755Z def _parser_dispatch(flavor):
2021-03-03T09:34:50.9788587Z """
2021-03-03T09:34:50.9789891Z Choose the parser based on the input flavor.
2021-03-03T09:34:50.9791126Z
2021-03-03T09:34:50.9792223Z Parameters
2021-03-03T09:34:50.9793762Z ----------
2021-03-03T09:34:50.9794765Z flavor : str
2021-03-03T09:34:50.9795933Z The type of parser to use. This must be a valid backend.
2021-03-03T09:34:50.9797081Z
2021-03-03T09:34:50.9798045Z Returns
2021-03-03T09:34:50.9799239Z -------
2021-03-03T09:34:50.9800943Z cls : _HtmlFrameParser subclass
2021-03-03T09:34:50.9801829Z The parser class based on the requested input flavor.
2021-03-03T09:34:50.9803173Z
2021-03-03T09:34:50.9804333Z Raises
2021-03-03T09:34:50.9805377Z ------
2021-03-03T09:34:50.9806666Z ValueError
2021-03-03T09:34:50.9807877Z * If `flavor` is not a valid backend.
2021-03-03T09:34:50.9808911Z ImportError
2021-03-03T09:34:50.9810250Z * If you do not have the requested `flavor`
2021-03-03T09:34:50.9811402Z """
2021-03-03T09:34:50.9812458Z valid_parsers = list(_valid_parsers.keys())
2021-03-03T09:34:50.9813575Z if flavor not in valid_parsers:
2021-03-03T09:34:50.9815089Z raise ValueError(
2021-03-03T09:34:50.9816319Z f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
2021-03-03T09:34:50.9817541Z )
2021-03-03T09:34:50.9818602Z
2021-03-03T09:34:50.9819657Z if flavor in ("bs4", "html5lib"):
2021-03-03T09:34:50.9820905Z if not _HAS_HTML5LIB:
2021-03-03T09:34:50.9822084Z > raise ImportError("html5lib not found, please install it")
2021-03-03T09:34:50.9823452Z E ImportError: html5lib not found, please install it
2021-03-03T09:34:50.9824265Z
2021-03-03T09:34:50.9825003Z pandas\io\html.py:854: ImportError
2021-03-03T09:34:50.9825973Z ____________________ TestReadHtml.test_to_html_compat[bs4] ____________________
2021-03-03T09:34:50.9827714Z [gw3] win32 -- Python 3.8.8 C:\Miniconda\envs\pandas-dev\python.exe
2021-03-03T09:34:50.9828410Z
2021-03-03T09:34:50.9829267Z self = <pandas.tests.io.test_html.TestReadHtml object at 0x000002A71EDF1850>
2021-03-03T09:34:50.9830475Z
2021-03-03T09:34:50.9831536Z def test_to_html_compat(self):
2021-03-03T09:34:50.9832727Z df = (
2021-03-03T09:34:50.9833922Z tm.makeCustomDataframe(
2021-03-03T09:34:50.9834959Z 4,
2021-03-03T09:34:50.9835999Z 3,
2021-03-03T09:34:50.9837656Z data_gen_f=lambda *args: np.random.rand(),
2021-03-03T09:34:50.9838744Z c_idx_names=False,
2021-03-03T09:34:50.9850315Z r_idx_names=False,
2021-03-03T09:34:50.9851844Z )
2021-03-03T09:34:50.9852936Z .applymap("{:.3f}".format)
2021-03-03T09:34:50.9854049Z .astype(float)
2021-03-03T09:34:50.9855186Z )
2021-03-03T09:34:50.9856391Z out = df.to_html()
2021-03-03T09:34:50.9857792Z > res = self.read_html(out, attrs={"class": "dataframe"}, index_col=0)[0]
2021-03-03T09:34:50.9858510Z
2021-03-03T09:34:50.9859338Z pandas\tests\io\test_html.py:132:
2021-03-03T09:34:50.9860736Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9861961Z pandas\util\_decorators.py:310: in wrapper
2021-03-03T09:34:50.9863212Z return func(*args, **kwargs)
2021-03-03T09:34:50.9864610Z pandas\io\html.py:1101: in read_html
2021-03-03T09:34:50.9865602Z return _parse(
2021-03-03T09:34:50.9866631Z pandas\io\html.py:905: in _parse
2021-03-03T09:34:50.9867680Z parser = _parser_dispatch(flav)
2021-03-03T09:34:50.9868829Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9869511Z
2021-03-03T09:34:50.9870601Z flavor = 'bs4'
2021-03-03T09:34:50.9871200Z
2021-03-03T09:34:50.9871985Z def _parser_dispatch(flavor):
2021-03-03T09:34:50.9872961Z """
2021-03-03T09:34:50.9874403Z Choose the parser based on the input flavor.
2021-03-03T09:34:50.9876648Z
2021-03-03T09:34:50.9877662Z Parameters
2021-03-03T09:34:50.9878646Z ----------
2021-03-03T09:34:50.9879598Z flavor : str
2021-03-03T09:34:50.9881204Z The type of parser to use. This must be a valid backend.
2021-03-03T09:34:50.9882129Z
2021-03-03T09:34:50.9883450Z Returns
2021-03-03T09:34:50.9884408Z -------
2021-03-03T09:34:50.9885677Z cls : _HtmlFrameParser subclass
2021-03-03T09:34:50.9887041Z The parser class based on the requested input flavor.
2021-03-03T09:34:50.9888150Z
2021-03-03T09:34:50.9889131Z Raises
2021-03-03T09:34:50.9890445Z ------
2021-03-03T09:34:50.9891252Z ValueError
2021-03-03T09:34:50.9892685Z * If `flavor` is not a valid backend.
2021-03-03T09:34:50.9893689Z ImportError
2021-03-03T09:34:50.9895137Z * If you do not have the requested `flavor`
2021-03-03T09:34:50.9896140Z """
2021-03-03T09:34:50.9897186Z valid_parsers = list(_valid_parsers.keys())
2021-03-03T09:34:50.9898667Z if flavor not in valid_parsers:
2021-03-03T09:34:50.9899580Z raise ValueError(
2021-03-03T09:34:50.9901350Z f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
2021-03-03T09:34:50.9903003Z )
2021-03-03T09:34:50.9904985Z
2021-03-03T09:34:50.9907240Z if flavor in ("bs4", "html5lib"):
2021-03-03T09:34:50.9908492Z if not _HAS_HTML5LIB:
2021-03-03T09:34:50.9909912Z > raise ImportError("html5lib not found, please install it")
2021-03-03T09:34:50.9911782Z E ImportError: html5lib not found, please install it
2021-03-03T09:34:50.9912325Z
2021-03-03T09:34:50.9913065Z pandas\io\html.py:854: ImportError
2021-03-03T09:34:50.9914414Z _______________________ TestReadHtml.test_spam_url[bs4] _______________________
2021-03-03T09:34:50.9915596Z [gw3] win32 -- Python 3.8.8 C:\Miniconda\envs\pandas-dev\python.exe
2021-03-03T09:34:50.9916260Z
2021-03-03T09:34:50.9917104Z self = <pandas.tests.io.test_html.TestReadHtml object at 0x000002A71F00E520>
2021-03-03T09:34:50.9917669Z
2021-03-03T09:34:50.9918797Z @tm.network
2021-03-03T09:34:50.9920021Z def test_spam_url(self):
2021-03-03T09:34:50.9920972Z url = (
2021-03-03T09:34:50.9922490Z "https://raw.githubusercontent.com/pandas-dev/pandas/master/"
2021-03-03T09:34:50.9923734Z "pandas/tests/io/data/html/spam.html"
2021-03-03T09:34:50.9925069Z )
2021-03-03T09:34:50.9926162Z > df1 = self.read_html(url, match=".*Water.*")
2021-03-03T09:34:50.9926769Z
2021-03-03T09:34:50.9927572Z pandas\tests\io\test_html.py:166:
2021-03-03T09:34:50.9928594Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9929920Z pandas\util\_decorators.py:310: in wrapper
2021-03-03T09:34:50.9931095Z return func(*args, **kwargs)
2021-03-03T09:34:50.9932295Z pandas\io\html.py:1101: in read_html
2021-03-03T09:34:50.9933330Z return _parse(
2021-03-03T09:34:50.9934390Z pandas\io\html.py:905: in _parse
2021-03-03T09:34:50.9935584Z parser = _parser_dispatch(flav)
2021-03-03T09:34:50.9936804Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2021-03-03T09:34:50.9937707Z
2021-03-03T09:34:50.9938466Z flavor = 'bs4'
2021-03-03T09:34:50.9938912Z
2021-03-03T09:34:50.9939676Z def _parser_dispatch(flavor):
2021-03-03T09:34:50.9940660Z """
2021-03-03T09:34:50.9942408Z Choose the parser based on the input flavor.
2021-03-03T09:34:50.9943364Z
2021-03-03T09:34:50.9944419Z Parameters
2021-03-03T09:34:50.9945528Z ----------
2021-03-03T09:34:50.9946538Z flavor : str
2021-03-03T09:34:50.9947615Z The type of parser to use. This must be a valid backend.
2021-03-03T09:34:50.9948607Z
2021-03-03T09:34:50.9950485Z Returns
2021-03-03T09:34:50.9951422Z -------
2021-03-03T09:34:50.9952595Z cls : _HtmlFrameParser subclass
2021-03-03T09:34:50.9953879Z The parser class based on the requested input flavor.
2021-03-03T09:34:50.9954835Z
2021-03-03T09:34:50.9955766Z Raises
2021-03-03T09:34:50.9956889Z ------
2021-03-03T09:34:50.9957885Z ValueError
2021-03-03T09:34:50.9958927Z * If `flavor` is not a valid backend.
2021-03-03T09:34:50.9960085Z ImportError
2021-03-03T09:34:50.9961314Z * If you do not have the requested `flavor`
2021-03-03T09:34:50.9962245Z """
2021-03-03T09:34:50.9963716Z valid_parsers = list(_valid_parsers.keys())
2021-03-03T09:34:50.9964776Z if flavor not in valid_parsers:
2021-03-03T09:34:50.9965879Z raise ValueError(
2021-03-03T09:34:50.9967523Z f"{repr(flavor)} is not a valid flavor, valid flavors are {valid_parsers}"
2021-03-03T09:34:50.9968532Z )
2021-03-03T09:34:50.9969460Z
2021-03-03T09:34:50.9970634Z if flavor in ("bs4", "html5lib"):
2021-03-03T09:34:50.9971682Z if not _HAS_HTML5LIB:
2021-03-03T09:34:50.9973164Z > raise ImportError("html5lib not found, please install it")
2021-03-03T09:34:50.9974624Z E ImportError: html5lib not found, please install it
2021-03-03T09:34:50.9975686Z
2021-03-03T09:34:50.9976475Z pandas\io\html.py:854: ImportError
2021-03-03T09:34:50.9977489Z _________________________ TestReadHtml.test_spam[bs4] _________________________
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40199 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40199/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40199/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40199/events | https://github.com/pandas-dev/pandas/pull/40199 | 821,127,555 | MDExOlB1bGxSZXF1ZXN0NTgzOTcyMzYw | 40,199 | REF: remove block access in groupby libreduction Series(Bin)Grouper | {
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} | 2 | 2021-03-03T13:50:05Z | 2021-03-04T16:45:14Z | 2021-03-04T16:45:10Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146
The `Series(Bin)Grouper` still has some direct block access, which needs to be resolved to let it work for both Block and ArrayManager | {
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} | 6 | 2021-03-03T16:08:47Z | 2021-04-05T21:25:14Z | 2021-03-10T11:26:26Z | MEMBER | null | - [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Took me longer than I'm proud to admit, but I think I've finally understood the deal with overload and default types.
We need one overload for the default case (here, `inplace=False`), and then an extra overload of each combination of keyword arguments which _precede_ `inplace`.
So here, we need:
- one case for the defaults
- one case for when there all all arguments and `inplace=True`
- one case for when there's no `level`, and `inplace=True`
- one case for when there's no `drop`, and `inplace=True`
- one case for when there's no `level`, no `drop`, and `inplace=True`
Then, `mypy` will be able to correctly infer the return type for `reset_index` based on the value of `inplace`.
This kind of thing will help resolve the mypy issues seen in #38594, where the return type from `fillna` couldn't be determined. It also means not needing to add the ignore error from #37137
----
Some examples:
```python
# t.py
import pandas as pd
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=False))
reveal_type(pd.DataFrame([1,2,3]).reset_index(drop=True, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, drop=False, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=True, col_fill='a'))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, col_fill='a'))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=True))
```
```console
$ mypy t.py
t.py:3: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:4: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:5: note: Revealed type is 'None'
t.py:6: note: Revealed type is 'None'
t.py:7: note: Revealed type is 'None'
t.py:8: note: Revealed type is 'None'
t.py:9: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:10: note: Revealed type is 'None'
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40201/events | https://github.com/pandas-dev/pandas/issues/40201 | 821,259,633 | MDU6SXNzdWU4MjEyNTk2MzM= | 40,201 | BUG: read_csv header=0 does NOT infer column headers if the separator is a ';', i.e. non-standard | {
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- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
dataframe = pd.read_csv('semicolon_separated_values.csv', sep=';', header = 'infer')
```
#### Problem description
I am trying to import a CSV into pandas using the pandas.read_csv method and every time I have a CSV with a semi-colon ';' as a separator, it does not read the column headers, even if I add header=0 or header='infer'.
#### Expected Outcome
First row of the data should be treated as the header row
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Jan 12 22:04:47 PST 2021; root:xnu-4903.278.56~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.5.4
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : 0.10.1
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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This PR uses the a method of testing used (maybe developed?) by numpy.
IMO this is most useful for testing overloads, but could be extended to the complete public api.
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} | 10 | 2021-03-03T19:13:49Z | 2021-04-09T21:48:13Z | 2021-04-09T21:47:31Z | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> pd.__version__
'1.2.3'
>>> df = pd.DataFrame({"a": [1, 2, 3]})
>>> df[[True, False, True]] = pd.DataFrame({"a": [-1, -2]})
>>> df
a
0 -1.0
1 2.0
2 NaN
```
Whereas this was fixed in `1.2.2`:
```python
>>> import pandas as pd
>>> pd.__version__
'1.2.2'
>>> df = pd.DataFrame({"a": [1, 2, 3]})
>>> df[[True, False, True]] = pd.DataFrame({"a": [-1, -2]})
>>> df
a
0 -1
1 2
2 -2
```
#### Problem description
Setting a column based on boolean values seems to be broken in `1.2.3`. This previously was broken and fixed in `1.2.2`.
#### Expected Output
```python
>>> import pandas as pd
>>> df = pd.DataFrame({"a": [1, 2, 3]})
>>> df[[True, False, True]] = pd.DataFrame({"a": [-1, -2]})
>>> df
a
0 -1
1 2
2 -2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/40205 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40205/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40205/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40205/events | https://github.com/pandas-dev/pandas/issues/40205 | 821,412,270 | MDU6SXNzdWU4MjE0MTIyNzA= | 40,205 | BUG: Pandas will always attempt to fetch all rows, even from a ResultProxy that does not return rows | {
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] | closed | false | null | [] | null | 1 | 2021-03-03T19:23:16Z | 2021-04-30T00:58:33Z | 2021-04-30T00:58:33Z | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [X] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
unload_query = f"""
UNLOAD ('SELECT * FROM {schema}.{table}')
TO '{s3_path}'
IAM_ROLE '{iam_role}'
PARQUET
"""
query_results = pd.read_sql(unload_query, connector)
```
#### Problem description
`ResourceClosedError: This result object does not return rows. It has been closed automatically.`
The example above is specific to Redshift, but will probably occur in other DBs with similar queries. The issue seems to be that after executing the query, Pandas will always attempt to run `fetchall()` on the ResultProxy object without first checking if there are any rows to fetch.
https://github.com/pandas-dev/pandas/blob/440a7de1c328aede3f566830c989922d46c254a8/pandas/io/sql.py#L1480 The Redshift `UNLOAD` command ([docs](https://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html)) does not return rows, so this query will always result in an error being raised if executed with `pd.read_sql()`.
I suggest wrapping that line with `if result.returns_rows is True:`
#### Expected Output
Nothing. I'd expect `pd.read_sql()` to execute silently.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.5
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : 1.0.2
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 3.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | 2021-03-03T19:51:54Z | 2021-03-04T01:45:23Z | 2021-03-04T01:45:13Z | CONTRIBUTOR | null | The docstring for `read_hdf` claims it can read from URLs and file-like objects. Neither of these are supported by the underlying `PyTables` library.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | 2021-03-03T20:38:26Z | 2021-03-04T16:45:58Z | 2021-03-04T01:35:00Z | CONTRIBUTOR | null | - [X] xref #37875 (see https://github.com/pandas-dev/pandas/issues/37875#issuecomment-790026815)
- [ ] tests added / passed (n/a)
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry (n/a)
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} | 2 | 2021-03-03T20:47:46Z | 2021-03-05T06:36:30Z | 2021-03-05T01:34:07Z | MEMBER | null | Some of the example sections were not rendering correctly after a doc refactor I did a while back:
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.core.window.rolling.Rolling.mean.html
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} | 0 | 2021-03-03T22:50:35Z | 2021-03-05T01:37:37Z | 2021-03-05T01:29:46Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 3 | 2021-03-03T23:35:38Z | 2021-03-16T16:45:51Z | 2021-03-16T15:36:30Z | MEMBER | null | - [x] closes #24539
- [x] closes #24615
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | 2021-03-04T01:16:39Z | 2021-03-26T13:36:27Z | 2021-03-26T13:36:27Z | MEMBER | null | Synthetic example:
```
def foo(x):
if x.name == "a":
raise ValueError
return x
def bar(x):
if x.name == "a":
raise ValueError
return x.sum()
df = pd.DataFrame(
{'a': [1, 2, 3], 'b': [1, 2, 3]}
)
print(df.transform([foo]))
print(df.agg([bar]))
```
The transform call results in
```
b
foo
0 1
1 2
2 3
```
whereas the agg call fails outright due to the `raise ValueError`. Series/DataFrame `apply` just calls `agg`. I also tried but couldn't find an example similar to transform's behavior of partial failure using `DataFrame.groupby` with apply/agg/transform, but wasn't able to (the code paths are a bit complex here, so I've resorted to blackbox testing).
My thinking here is having `transform` fail outright in the example above is the good way to go. It would be simpler from a code perspective and avoids silent failure, although perhaps it would make a user drop nuisance columns. I'll also mention that one of the things I'd like to work toward is having e.g.
df.transform(['mean'])
have the same performance as
df.transform('mean')
for which having transform allow partial failure like this means there would need to be a fallback.
cc @jorisvandenbossche @jreback @jbrockmendel | {
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} | 1 | 2021-03-04T01:22:44Z | 2021-03-18T16:39:16Z | 2021-03-04T03:22:13Z | MEMBER | null | - [x] closes #40144
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} | 8 | 2021-03-04T04:02:14Z | 2021-03-09T23:55:22Z | 2021-03-09T23:45:23Z | CONTRIBUTOR | null | Ref #39808
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
This is the fix proposed in the discussion of #40095 | {
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} | 2 | 2021-03-04T06:40:53Z | 2021-03-13T04:39:50Z | 2021-03-08T13:04:40Z | NONE | null | #### Location of the documentation
https://pandas.pydata.org/docs/getting_started/index.html
#### Documentation problem
As part of the HackIllinois Open Source Fellowship and having never used pandas before, I have been reading through the pandas documentation to learn more about it and noticed several issues in the Getting Started page of the docs.
- Small punctuation errors
- The "to user guide" link under "How to manipulate textual data?" leads to the time-series section of the guide
- Under "How to combine data from multiple tables", the given explanation is hard to understand due to grammar issues
#### Suggested fix for documentation
- Under "What kind of data does pandas handle" add a comma after "clean"
- Under "How to calculate summary statistics" add a comma after "a sliding window of data"
- For the incorrect linking to time-series, have the user guide instead link to "Working with text data"
- Rephrase the synopsis under "How to combine data from multiple tables" to -> Multiple tables can be concatenated both column wise **and** row wise **as** database-like join/merge operations are provided to combine multiple tables of data.
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} | 5 | 2021-03-04T08:40:20Z | 2021-03-05T08:31:11Z | 2021-03-05T08:29:58Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/issues/39146/
This is on top of #40152, so only the second commit is relevant: https://github.com/pandas-dev/pandas/pull/40217/commits/e7209aaa7b85c206d3efcd446eaa54b34851cb98
But this basically adds the following to CI for ArrayManager:
```
pytest pandas/tests/api/ --array-manager
pytest pandas/tests/base/ --array-manager
pytest pandas/tests/computation/ --array-manager
pytest pandas/tests/config/ --array-manager
pytest pandas/tests/dtypes/ --array-manager
pytest pandas/tests/generic/ --array-manager
pytest pandas/tests/indexes/ --array-manager
pytest pandas/tests/libs/ --array-manager
pytest pandas/tests/plotting/ --array-manager
pytest pandas/tests/scalar/ --array-manager
pytest pandas/tests/strings/ --array-manager
pytest pandas/tests/tools/ --array-manager
pytest pandas/tests/tseries/ --array-manager
pytest pandas/tests/tslibs/ --array-manager
pytest pandas/tests/util/ --array-manager
``` | {
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} | 3 | 2021-03-04T09:02:54Z | 2021-03-04T13:06:40Z | 2021-03-04T12:02:18Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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