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} | 3 | "2021-04-06T23:34:18Z" | "2021-04-15T07:21:18Z" | "2021-04-13T17:12:17Z" | MEMBER | null | - [x] closes #37506
- [x] closes #40585
- [x] closes #33449
- [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
Built on #40811
ASV's don't look affected:
<details>
```
before after ratio
[3b4b193d] [423f43fc]
<master> <enh/any_all_kleene>
216±8ms 205±4ms 0.95 groupby.GroupByCythonAgg.time_frame_agg('float64', 'all')
208±8ms 197±3ms 0.95 groupby.GroupByCythonAgg.time_frame_agg('float64', 'any')
162±10μs 139±4μs ~0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'direct')
161±20μs 138±9μs ~0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'transformation')
159±10μs 142±5μs ~0.90 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'direct')
160±10μs 140±5μs ~0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'transformation')
109±8μs 113±10μs 1.04 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct')
103±10μs 109±9μs 1.06 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'transformation')
113±20μs 132±10μs ~1.17 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'direct')
138±10μs 124±20μs ~0.90 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'transformation')
110±9μs 112±5μs 1.02 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct')
115±9μs 109±10μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation')
123±10μs 115±7μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct')
125±10μs 114±8μs 0.92 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation')
449±30μs 452±20μs 1.01 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'direct')
442±30μs 445±20μs 1.01 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'transformation')
470±10μs 455±30μs 0.97 groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'direct')
466±30μs 406±4μs ~0.87 groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'transformation')
118±8μs 118±7μs 1.00 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct')
134±9μs 132±20μs 0.99 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation')
131±20μs 115±9μs ~0.88 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct')
121±10μs 118±5μs 0.97 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation')
111±10μs 111±4μs 1.00 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct')
109±10μs 120±10μs ~1.11 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation')
110±9μs 127±10μs ~1.16 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct')
112±10μs 122±10μs 1.09 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation')
130±10μs 115±6μs ~0.89 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct')
133±7μs 115±7μs ~0.86 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation')
117±5μs 123±10μs 1.05 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct')
133±10μs 118±20μs ~0.89 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation')
107±9μs 122±10μs ~1.14 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct')
113±10μs 107±5μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation')
135±10μs 108±4μs ~0.80 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct')
114±6μs 105±2μs 0.92 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation')
```
</details>
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] | closed | false | null | [] | null | 7 | "2021-04-07T04:05:18Z" | "2021-08-19T04:04:59Z" | "2021-08-19T04:04:58Z" | NONE | null | #### Is your feature request related to a problem?
Yes, `pd.concat([])` currently raises the exception, ValueError("No objects to concatenate").
#### Describe the solution you'd like
To be correct on all input sizes, it should return an empty DataFrame.
#### API breaking implications
Obviously, concat will no longer detect empty data. However, empty data will detect itself.
#### Additional context
The concatenation operation is for lists what addition is for numbers. Concatenation of an empty list should produce an empty dataframe, because the empty dataframe represents `0`. Consider
```
sum([])
```
which python evaluates to 0.
This makes concatenation and sum commute, `sum([1] + []) == sum([1]) + sum([])`.
This already works for an empty dataframe:
```
pd.concat([pd.DataFrame({'A':[1]}), pd.DataFrame()])
```
if is very odd for concat([]) not to produce a DataFrame, since its input has the correct type.
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} | 6 | "2021-04-07T06:08:20Z" | "2021-04-26T12:34:15Z" | "2021-04-26T12:16:07Z" | CONTRIBUTOR | null | closes #40124
Proposal to deprecate the method: `Styler.where`, since `Styler.applymap` and `Styler.where` are so similar in function, and return signatures, that it is almost confusing to have 2 methods for the same thing.
Note that `Styler.where` offers no advantage over `applymap` since internally it just restructures the callable and uses `applymap` itself:
```
def where(..):
return self.applymap(lambda val: value if cond(val, **kwargs) else other, subset=subset)
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} | 3 | "2021-04-07T08:22:34Z" | "2021-04-08T14:36:35Z" | "2021-04-08T14:36:30Z" | 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
When looking through the enhancement xref #40820,
I noticed that there was no test checking that the ValueError is raised when concatenating an empty sequence.
Added this test. | {
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} | 7 | "2021-04-07T09:29:37Z" | "2021-06-02T13:24:14Z" | "2021-06-02T13:24:14Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df1 = pd.DataFrame(
{
"col1": [pd.NA, "foo", "bar"]
}, index=["id1", "id2", "id3"], dtype=pd.StringDtype()
)
df2 = pd.DataFrame(
{
"id": ["id4", "id2", "id1"]
}, dtype=pd.StringDtype()
)
df2["col1"] = df2["id"].map(df1["col1"])
print(df2.head())
```
#### Problem description
Traceback:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm 2020.3.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/XXX/AppData/Roaming/JetBrains/PyCharm2020.3/scratches/scratch_22.py", line 16, in <module>
df2["col1"] = df2["id"].map(df1["col1"])
File "C:\UsersXXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\series.py", line 3909, in map
new_values = super()._map_values(arg, na_action=na_action)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\base.py", line 908, in _map_values
new_values = algorithms.take_1d(mapper._values, indexer)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\algorithms.py", line 1699, in take_nd
return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\_mixins.py", line 78, in take
return self._from_backing_data(new_data)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\numpy_.py", line 190, in _from_backing_data
return type(self)(arr)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\string_.py", line 195, in __init__
self._validate()
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\string_.py", line 200, in _validate
raise ValueError("StringArray requires a sequence of strings or pandas.NA")
ValueError: StringArray requires a sequence of strings or pandas.NA
This issue can be circumvented if I do:
```python
df2["col1"] = df2["id"].map(df1["col1"].astype("object"))
print(df2.head())
```
Gives:
id col1
0 id4 NaN
1 id2 foo
2 id1 \<NA\>
Which is a combination of np.nan and pd.NA
If I do then a conversion back to pd.StringDtype:
```python
df2["col1"] = df2["id"].map(df1["col1"].astype("object")).astype(pd.StringDtype())
print(df2.head())
```
I get the expected output:
id col1
0 id4 \<NA\>
1 id2 foo
2 id1 \<NA\>
But this is ugly as hell.
#### Expected Output
id col1
0 id4 \<NA\>
1 id2 foo
2 id1 \<NA\>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.4.3
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 8 | "2021-04-07T12:50:34Z" | "2021-04-16T16:14:42Z" | "2021-04-16T16:14:42Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{
"A": ["foo", "bar", "baz", pd.NA]
}, index=["id1", "id2", "id3", "id4"], dtype=pd.StringDtype()
)
filtered_df = pd.DataFrame(
{
"A": ["this", "that"]
}, index=["id2", "id3"], dtype=pd.StringDtype()
)
filter = pd.Series([False, True, True, False])
new_df = df.mask(filter, filtered_df)
```
#### Problem description
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "C:\Program Files\JetBrains\PyCharm 2020.3.2\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.3.2\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/XXX/AppData/Roaming/JetBrains/PyCharm2020.3/scratches/scratch_24.py", line 19, in <module>
new_df = df.mask(filter, filtered_df)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 9317, in mask
return self.where(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 9286, in where
return self._where(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 9056, in _where
_, other = self.align(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\frame.py", line 4102, in align
return super().align(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 8813, in align
return self._align_frame(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 8880, in _align_frame
right = other._reindex_with_indexers(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\generic.py", line 4877, in _reindex_with_indexers
new_data = new_data.reindex_indexer(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\internals\managers.py", line 1311, in reindex_indexer
new_blocks = [
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\internals\managers.py", line 1312, in <listcomp>
blk.take_nd(
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\internals\blocks.py", line 1863, in take_nd
new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\_mixins.py", line 78, in take
return self._from_backing_data(new_data)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\numpy_.py", line 190, in _from_backing_data
return type(self)(arr)
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\string_.py", line 195, in __init__
self._validate()
File "C:\Users\XXX\AppData\Local\pypoetry\Cache\virtualenvs\basel-w19Rd76b-py3.9\lib\site-packages\pandas\core\arrays\string_.py", line 200, in _validate
raise ValueError("StringArray requires a sequence of strings or pandas.NA")
ValueError: StringArray requires a sequence of strings or pandas.NA
In generic.py starting on line 9053, there is this code block:
```python
# align with me
if other.ndim <= self.ndim:
_, other = self.align(
other, join="left", axis=axis, level=level, fill_value=np.nan
)
```
In case that the filtered_df has fewer rows than the original df, this code block is executed and it uses np.nan as a fill_value for the align method. This fill_value will propagate until the error is raised as shown above in the stack trace. I think that this issue can be easily solved if the fill_value is a mutable parameter for the .mask or .where method
#### Expected Output
"""
----| A
id1 | \<NA\>
id2 | this
id3 | that
id4 | \<NA\>
"""
or
"""
----| A
id1 | foo
id2 | this
id3 | that
id4 | \<NA\>
"""
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : 1.4.3
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 8 | "2021-04-07T15:07:48Z" | "2021-04-07T16:09:32Z" | "2021-04-07T16:09:32Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
(I tried searching all issues for SADeprecationWarning but didn't find this particular issue)
- [x] I have confirmed this bug exists on the latest version of pandas.
We pin to: `"pandas>=1.2.0, <2.0.0"`
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
Yes it's on master: https://github.com/pandas-dev/pandas/blob/master/pandas/io/sql.py#L1644
---
#### Code Sample, a copy-pastable example
It is hard to show a pastable example because it relies on a connection to a database. Basically any usage of:
```
with get_engine(dbname).connect() as conn:
df = pd.read_sql(stmt, conn, **kwargs)
return df
```
Note that the code surrounding pandas is using a context manager (conn)
#### Problem description
It reports:
```
/Users/tommycarpenter/Development/python-indexapi/.tox/py37/lib/python3.7/site-packages/pandas/io/sql.py:1430: SADeprecationWarning: The Connection.run_callable() method is deprecated and will be removed in a future release. Invoke the callable function directly, passing the Connection. (deprecated since: 1.4)
self.connectable.dialect.has_table, name, schema or self.meta.schema
```
Looking at that file, at that line, shows:
```
# pandas/io/sql.py
def has_table(self, name, schema=None):
return self.connectable.run_callable(
self.connectable.dialect.has_table, name, schema or self.meta.schema
)
```
Which is a different line number than master, bnut its still there on master: https://github.com/pandas-dev/pandas/blob/master/pandas/io/sql.py#L1644
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https://api.github.com/repos/pandas-dev/pandas/issues/40826 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40826/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40826/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40826/events | https://github.com/pandas-dev/pandas/issues/40826 | 852,650,379 | MDU6SXNzdWU4NTI2NTAzNzk= | 40,826 | STYLE pandas-dev flake8 plugin | {
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} | 11 | "2021-04-07T17:49:01Z" | "2021-04-16T01:06:48Z" | "2021-04-16T01:06:48Z" | MEMBER | null | Currently, there are a lot of style checks which are specific to this repo, the result of which is a ton of CI checks and occasionally quite a bit of extra code.
The main repo is already huge, so I'd like to suggest making a `pandas-dev/pandas-style-guide` repo, which would be a `flake8` plugin, with all the custom linting rules we currently have.
Some advantages:
- the current gigantic repo would become a bit smaller, as many style checks could be moved over to the `pandas-dev/pandas-style-guide` one
- the plugin could easily be tested on multiple OSs and versions of Python, which is currently difficult with all the stylistic checks living in the main repo. The `ast` module often changes a bit between versions, so it would be good to test the ast-based checks on different Python versions
- linting failures would be reported with the familiar flake8 syntax we're all accustomed to by now
One disadvantage I see is that there might be less visibility if this is in a separate repo.
@pandas-dev/pandas-core @pandas-dev/pandas-triage anyone have any thoughts/objections? | {
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} | 2 | "2021-04-07T18:08:17Z" | "2021-08-19T04:07:03Z" | null | NONE | null | - [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
My question is essentially the same as the StackOverflow question [“Unexpected behavior in assigning 2d numpy array to pandas DataFrame”](https://stackoverflow.com/q/52142475), which, although it has an accepted answer, states in an edit to the question text:
> Since the bounty is going to expiry [sic], I accepted the most voted answer. But it does not provide a answer to the above questions.
I agree, and still don’t understand the behavior described in the question, which I will restate here:
Given the following code:
```
import pandas
import numpy
df = pandas.DataFrame(numpy.zeros((4, 1)), columns=['A'])
y = numpy.random.randn(4, 2)
df['A'] = y
```
which results in (e.g.)
```
df
# A
# 0 0.994449
# 1 -0.280376
# 2 -0.993288
# 3 0.009625
y
# array([[ 0.99444899, -0.80571928],
# [-0.28037615, -0.47071158],
# [-0.99328776, -0.15674664],
# [ 0.00962502, 1.24896221]])
```
> I expect it to throw an exception because of shape mismatch. But pandas silently accepted the assignment: y's first column is assigned to df.
>
> Is this an intentional design? If yes, what is the rationale behind?
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} | 1 | "2021-04-07T18:49:18Z" | "2021-04-09T01:40:51Z" | "2021-04-09T01:40:51Z" | CONTRIBUTOR | null | #### Location of the documentation
**Note**: You can check the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/).
#### Documentation problem
`web/README.md` states
`Directory containing the pandas website (hosted at https://pandas.io).
`
However, [pandas.io](https://pandas.io) is not reachable.
#### Suggested fix for documentation
We can replace it with the following:
`Directory containing the pandas website (hosted at https://pandas.pydata.org/).
` | {
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Return type should be `Optional[FrameOrSeriesUnion]`
It could also do with being overloaded according to the value of `inplace` - see `set_axis` and `DataFrame.reset_index` for examples | {
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- [X] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
```python
import os
import pandas as pd
import numpy as np
file_contents = ("914793802660000000001000AAAAAAAAA0 "
+ "000000-00-000000-00-0000000000-00-0000000 "
+ " 0000000000-00-000000-00-00 000000000000000000.0000 0000000000000.00 00000\n")
header = [
'A',
'B',
'C',
'D',
'E',
'F',
'G',
'H',
'I',
'J',
'K',
'L',
'M',
'N',
'O',
'P',
'Q',
'R',
'S',
'T',
'U',
'V'
]
colspec_good = [
(0, 13),
(13, 23),
(23, 34),
(34, 45),
(45, 47),
(47, 57),
(57, 67),
(67, 71),
(71, 81),
(81, 86),
(86, 97),
(97, 103),
(103, 113),
(113, 123),
(123, 147),
(147, 164),
(164, 166),
(166, 168),
(168, 170),
(170, 172),
(172, 174),
(174, None)
]
print(len(header), len(colspec_good))
with open("bug.txt", "w") as foo:
foo.write(file_contents)
df = pd.read_fwf("bug.txt", names=header, colspecs=colspec_good, dtype=str)
print(df.head())
colspec_bad = [
(0, 13),
(13, 23),
(23, 34),
(34, 45),
(45, 47),
(47, 57),
(57, 67),
(67, 71),
(71, 81),
(81, 86),
(86, 97),
(97, 103),
(103, 113),
(113, 123),
(123, 147),
(147, 164),
(164, 166),
(166, 168),
(168, 170),
(170, 172),
(172, 174),
(174, 189),
(189, None)
]
print(len(header), len(colspec_bad))
with open("bug.txt", "w") as foo:
foo.write(file_contents)
df = pd.read_fwf("bug.txt", names=header, colspecs=colspec_bad, dtype=str)
with open("bug.txt", "w") as foo:
foo.write(file_contents * 2)
df = pd.read_fwf("bug.txt", names=header, colspecs=colspec_bad, dtype=str)
print(df.head())
```
#### Problem description
When passing both the "names" and "colspecs" parameters, pandas.read_fwf fails to raise a warning/error when the length of the lists passed to these parameters does not match. In the given example, colspec_good has one tuple for every header name; colspec_bad has an extra 23rd tuple with no matching name. When the given file contains one row, a ParserError is raised with colspec_bad; but if given more than one row, no such warning/error is raised.
I propose that pandas.read_fwf should raise a warning/error when the length of the list passed to "names" doesn't match the length of the list passed to "colspecs" (when both parameters are used). Practically speaking, this tells the developer that they need to fix their header/colspecs. From a healthcare industry perspective, this has many nontrivial applications - e.g., the ingestion of standard, fixed-width CMS Medicare Advantage and CCLF (claim and claim line feed) files.
#### Expected Output
ParserError: Expected 22 fields in line 1, saw 23
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.10.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.2.3
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 : 6.2.3
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.5
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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#### Problem description
I'm use to fix pandas lib up to the minor number (i.e pandas=1.1).
All my work is on Google Cloud Storage so I use a lot
`df = pd.read_csv(gcs+path)`
My old script use a mix of `pandas=1.1` and `gcsfs=0.7`
On a previous version of a script I work with pandas 1.1.5 and gcsfs 0.7.1
After an update the script was build (docker image) with gcsfs 0.7.2 where I got this error on read_csv
```
Traceback (most recent call last):
File "/.../python3.7/site-packages/aiohttp/client.py", line 326, in __del__
if not self.closed:
File "/.../python3.7/site-packages/aiohttp/client.py", line 963, in closed
return self._connector is None or self._connector.closed
AttributeError: 'ClientSession' object has no attribute '_connector'
```
I couldn't fix it but a simple revert back to 0.7.1 make it work.
I also tried to use the last version 0.8.0 and I needed to install manually a dependency after a fail. It was `ujson`.
I don't know if it's a pandas or a gcsfs regression but at least I wanted to write it somewhere if anyone face the same issue. | {
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} | 2 | "2021-04-08T03:52:11Z" | "2021-04-13T14:22:55Z" | "2021-04-13T11:36:23Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 6 | "2021-04-08T14:02:33Z" | "2021-04-15T04:17:10Z" | "2021-04-15T04:16:50Z" | MEMBER | null | - [x] closes #40014
- [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/40836 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40836/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40836/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40836/events | https://github.com/pandas-dev/pandas/pull/40836 | 853,596,052 | MDExOlB1bGxSZXF1ZXN0NjExNjU5OTgw | 40,836 | Dark background | {
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} | 11 | "2021-04-08T15:31:29Z" | "2021-05-06T10:45:02Z" | "2021-05-06T10:45:02Z" | CONTRIBUTOR | null |
- [*] 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
- [*] Color in boxplot.py should be a hex value. Letter based color will be at matplotlib level. Using same color as that of boxes for the caps as well. The color will be fetched based on the selected theme. We are over riding color with "k" , rather need to use the one set at matplotlib level.
Before Fix :
> Caps for the box plot is not visible :
After Fix :
> Caps for the box plow will be visible in dark background mode :
Whats New : Box Plot's Caps will have same color as boxes unless color is explicitly specified by user arguments. Hence theme changes will not adversly effect caps color.
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} | 1 | "2021-04-08T18:08:44Z" | "2021-04-13T16:53:51Z" | "2021-04-13T15:50:21Z" | MEMBER | null | Saw this failure the other day:
```
pandas/tests/io/test_gcs.py:137:
71
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
72
73
result = b'PK\x03\x04\x14\x00\x00\x00\x08\x00\xe0\xa1\x87R3ET~\x18\x06\x00\x00\x8c\n\x00\x00\x03\x00\x00\x00zip-\x96]WT;\x0c\x8...x00\x00\x00\x00\x80\x01\x00\x00\x00\x00zipPK\x05\x06\x00\x00\x00\x00\x01\x00\x01\x001\x00\x00\x009\x06\x00\x00\x00\x00'
74
expected = b'PK\x03\x04\x14\x00\x00\x00\x08\x00\xdd\xa1\x87R3ET~\x18\x06\x00\x00\x8c\n\x00\x00\x03\x00\x00\x00zip-\x96]WT;\x0c\x8...x00\x00\x00\x00\x80\x01\x00\x00\x00\x00zipPK\x05\x06\x00\x00\x00\x00\x01\x00\x01\x001\x00\x00\x009\x06\x00\x00\x00\x00'
```
The failing byte was the 10th, based on the linked wikipedia page in `assert_equal_zip_safe`, looks like 10th and 11th are last modified timestamp related, so may as well not check the 10th byte as well.
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} | 2 | "2021-04-08T21:44:57Z" | "2021-04-09T07:28:42Z" | "2021-04-09T01:40:51Z" | CONTRIBUTOR | null |
- [x] closes #40828
- [ ] 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 | 1 | "2021-04-08T21:54:42Z" | "2021-08-19T04:08:24Z" | null | MEMBER | null | #### Is your feature request related to a problem?
Currently, StringArray can only be instantiated directly with a ndarray with strings or NA values represented by pd.NA. The only way to instantiate a StringArray with other missing value indicators(like ``np.nan`` and ``None``) is to use pandas.array, which has a side effect of casting non-string elements to strings instead of erroring.
The proposed solution would allow StringArray instantiation from a numpy array containing np.nan/None without casting non-strings. This is useful if you want the StringArray constructor to validate that inputs are strings and also accepts other missing values other than pd.NA. At the very least, it should support np.nan since StringArray is created from a numpy array, and np.nan is the missing value indicator for numpy.
#### Describe the solution you'd like
Either accept nan-likes in the constructor directly(breaking change) or add a parameter to the constructor allowing other na_values, maybe something like the na_values parameter from read_csv.
#### API breaking implications
Either breaking change or new parameter.
#### Describe alternatives you've considered
You'd have to do the validation yourself and validating yourself and then having StringArray validate again is not good for perf.
cc @jorisvandenbossche
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} | 0 | "2021-04-08T22:27:24Z" | "2021-04-14T14:10:05Z" | "2021-04-14T13:04:21Z" | MEMBER | null | This is a reboot of #40054, implements NDArrayBacked in a new arrays.pyx rather than in lib.pyx. The perf benefits have improved since #40054, as there is now the prospect of implementing a NDArrayBackedExtensionBlock in cython which will appreciably improve constructor and slicing performance. | {
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} | 9 | "2021-04-08T23:20:20Z" | "2021-04-14T12:53:21Z" | "2021-04-14T12:53:21Z" | 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
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: right = pd.DataFrame(data={'C': [0.5274]}, index=pd.DatetimeIndex(['2021-04-08 21:21:14+00:00'], dtype='datetime64[ns, UTC]', name='Time (UTC)', freq=None))
In [4]: left = pd.DataFrame(data={'A': [None], 'B': [np.nan]}, index=pd.Index([None], dtype='object', name='Maybe Time (UTC)'))
In [5]: pd.concat([left, right], axis='columns')
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-5-27d2416870b3> in <module>
----> 1 pd.concat([left, right], axis='columns')
~/.miniconda/lib/python3.9/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
283 ValueError: Indexes have overlapping values: ['a']
284 """
--> 285 op = _Concatenator(
286 objs,
287 axis=axis,
~/.miniconda/lib/python3.9/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)
465 self.copy = copy
466
--> 467 self.new_axes = self._get_new_axes()
468
469 def get_result(self):
~/.miniconda/lib/python3.9/site-packages/pandas/core/reshape/concat.py in _get_new_axes(self)
535 def _get_new_axes(self) -> List[Index]:
536 ndim = self._get_result_dim()
--> 537 return [
538 self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)
539 for i in range(ndim)
~/.miniconda/lib/python3.9/site-packages/pandas/core/reshape/concat.py in <listcomp>(.0)
536 ndim = self._get_result_dim()
537 return [
--> 538 self._get_concat_axis() if i == self.bm_axis else self._get_comb_axis(i)
539 for i in range(ndim)
540 ]
~/.miniconda/lib/python3.9/site-packages/pandas/core/reshape/concat.py in _get_comb_axis(self, i)
542 def _get_comb_axis(self, i: int) -> Index:
543 data_axis = self.objs[0]._get_block_manager_axis(i)
--> 544 return get_objs_combined_axis(
545 self.objs,
546 axis=data_axis,
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/api.py in get_objs_combined_axis(objs, intersect, axis, sort, copy)
90 """
91 obs_idxes = [obj._get_axis(axis) for obj in objs]
---> 92 return _get_combined_index(obs_idxes, intersect=intersect, sort=sort, copy=copy)
93
94
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/api.py in _get_combined_index(indexes, intersect, sort, copy)
143 index = index.intersection(other)
144 else:
--> 145 index = union_indexes(indexes, sort=sort)
146 index = ensure_index(index)
147
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/api.py in union_indexes(indexes, sort)
215 else:
216 for other in indexes[1:]:
--> 217 result = result.union(other)
218 return result
219 elif kind == "array":
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in union(self, other, sort)
2698
2699 if not self._can_union_without_object_cast(other):
-> 2700 return self._union_incompatible_dtypes(other, sort=sort)
2701
2702 result = self._union(other, sort=sort)
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in _union_incompatible_dtypes(self, other, sort)
2616 # cast to Index for when `other` is list-like
2617 other = Index(other).astype(object, copy=False)
-> 2618 return Index.union(this, other, sort=sort).astype(object, copy=False)
2619
2620 def _can_union_without_object_cast(self, other) -> bool:
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in union(self, other, sort)
2702 result = self._union(other, sort=sort)
2703
-> 2704 return self._wrap_setop_result(other, result)
2705
2706 def _union(self, other, sort):
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in _wrap_setop_result(self, other, result)
2786 return result
2787 else:
-> 2788 return self._shallow_copy(result, name=name)
2789
2790 # TODO: standardize return type of non-union setops type(self vs other)
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in _shallow_copy(self, values, name)
524
525 if values is not None:
--> 526 return self._simple_new(values, name=name)
527
528 result = self._simple_new(self._values, name=name)
~/.miniconda/lib/python3.9/site-packages/pandas/core/indexes/base.py in _simple_new(cls, values, name)
431 Must be careful not to recurse.
432 """
--> 433 assert isinstance(values, np.ndarray), type(values)
434
435 result = object.__new__(cls)
AssertionError: <class 'pandas.core.arrays.datetimes.DatetimeArray'>
```
#### Problem description
This appears to be a regression when concatenating DataFrames with partially null DatetimeIndices. I don't know precisely in which version it was introduced, but it worked with v0.25.3 and python 3.6.
#### Expected Output
Concatenated DataFrame from pandas v1.0.5:
```
A B C
NaN None NaN NaN
2021-04-08 21:21:14+00:00 None NaN 0.5274
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
Version : Darwin Kernel Version 16.7.0: Thu Dec 20 21:53:35 PST 2018; root:xnu-3789.73.31~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.17.0
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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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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} | 1 | "2021-04-09T07:31:48Z" | "2021-04-12T18:45:03Z" | "2021-04-09T16:29:18Z" | CONTRIBUTOR | null | Simple bug fix. `kwargs` not passed in correct place.
partially closes #40124 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40846 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40846/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40846/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40846/events | https://github.com/pandas-dev/pandas/issues/40846 | 854,388,227 | MDU6SXNzdWU4NTQzODgyMjc= | 40,846 | QST: Is differenence in frame __setitem__() and __getitem__() intended? | {
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- [X] I have discussed my usage related question on [StackOverflow](https://stackoverflow.com).
---
@kristiancanler came across different behavior between assignment and look-ups when using a `dictionary.values()` view object as slice, as documented in his [StackOverflow question](https://stackoverflow.com/questions/66961614/why-does-pandas-recognize-dictionary-values-as-a-list-of-column-names-when-used/67001369#67001369).
We do know how to work around this - as is documented in the question, as well as answers - we just want to know if it is intended.
Creating a DataFrame + dictionary, using `dictionary.values()` as slice in `print()` simply prints the correct column:
```python
import pandas as pd
cols_dict = {'X': 'col1'}
df = pd.DataFrame({'col1': range(2),
'col2': range(3, 5)})
print(df[cols_dict.values()])
col1
0 0
1 1
```
however using dictionary.values() on assignment yielded unexpected results:
```python
df[cols_dict.values()] = df[cols_dict.values()]
print(df)
col1 col2 (col1)
0 0 3 0
1 1 4 1
```
Is this expected behavior?
If not, is the reason for this behavior the difference in this part of frame's [`__setitem__()`](https://github.com/pandas-dev/pandas/blob/822db7a53fdcf8d860aaa8b51da4b767b3f56fad/pandas/core/frame.py#L3528-L3536) and [`__getitem__()`](https://github.com/pandas-dev/pandas/blob/822db7a53fdcf8d860aaa8b51da4b767b3f56fad/pandas/core/frame.py#L3368-L3388)?
```python
# Do we have a slicer (on rows)?
indexer = convert_to_index_sliceable(self, key)
if indexer is not None: #!
if isinstance(indexer, np.ndarray): #!
indexer = lib.maybe_indices_to_slice( #!
indexer.astype(np.intp, copy=False), len(self) #!
) #!
# either we have a slice or we have a string that can be converted
# to a slice for partial-string date indexing
return self._slice(indexer, axis=0)
```
The part with #! is only in `__getitem__()`, not in `__setitem__()`, even though both parts of code start with `indexer = convert_to_index_sliceable(self, key)` and the pre-return comments come to the exact same conclusion as to what indexer could be. | {
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} | 0 | "2021-04-09T12:19:27Z" | "2021-04-10T21:18:44Z" | null | NONE | null | #### Is your feature request related to a problem?
Right now when you do `pandas.read_sql` without chunking, memory usage appears to be `O(4N)` where N is number of rows. For example, if the rows can be represented in ~1GB of RAM, approximately 4GB of RAM will be used.
By using chunking + server-side cursors (https://github.com/pandas-dev/pandas/issues/35689) internally, this could be changed to `O(N)`, or ~1GB of RAM usage in our example.
In particular, it appears that the rows get represented in 4 different forms as part of constructing the DataFrame, as you can see in the example code + memory usage report in "Iteration \#1" section of https://pythonspeed.com/articles/pandas-sql-chunking/.
#### Describe the solution you'd like
Pandas could internally load the SQL in chunks + streaming/server-side-cursors (see the linked article above for details, or https://github.com/pandas-dev/pandas/pull/40796), and construct the dataframe with appending. The 4× would then only be on chunk size, i.e. memory usage would be `O(N + 4C)`.
#### API breaking implications
This should be completely transparent in most ways, except perhaps for same sort of issues you get with read CSV in terms of guessing dtypes? Although perhaps the SQL can use column types for that?
#### Describe alternatives you've considered
Users can use chunking themselves, but it's non-obvious that you're getting 4× the RAM usage over what you'd actually need, so people end up both duplicating the idiom resulting in bugs, and might actually be able to do the simpler all-in-RAM thing if Pandas did it right.
In general, memory usage is quite opaque (I had to write a whole new profiler to get the graphs in the article linked above) so putting burden on users is not ideal.
#### Additional context
It's possible to use [the open source Fil memory profiler](https://pythonspeed.com/fil) to measure peak memory usage in order to validate an improvement. To unit test memory usage could maybe look at https://docs.python.org/3/library/resource.html#resource.RLIMIT_RSS of a subprocess. | {
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] | open | false | null | [] | null | 25 | "2021-04-09T14:04:01Z" | "2021-04-11T15:44:20Z" | null | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ X ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 3 | "2021-04-09T15:17:09Z" | "2021-05-26T02:10:39Z" | null | NONE | null | python 3.7
pd.__version__ '1.1.4' and '1.2.3'
running the following code:
```
df_top=pd.DataFrame([["i1_top","i2_top",1,2]], columns=["index1","index2","value1","value2"])
df_top.set_index(["index1","index2"],inplace=True)
df_bottom=pd.DataFrame([["i1_bottom","i2_bottom",2,1]], columns=["index1","index2","value2","value1"])
df_bottom.set_index(["index2","index1"],inplace=True)
print("df_top")
print(df_top)
print("df_bottom")
print(df_bottom)
print("pd.concat([df_top,df_bottom])")
print(pd.concat([df_top,df_bottom]))
```
gives the following output:
```
df_top
value1 value2
index1 index2
i1_top i2_top 1 2
df_bottom
value2 value1
index2 index1
i2_bottom i1_bottom 2 1
pd.concat([df_top,df_bottom])
value1 value2
index1 index2
i1_top i2_top 1 2
i2_bottom i1_bottom 1 2
```
Apparently the concatenation function does not match the index names, and sets the index names of the concatenated data frame in the order of the first dataframe passed to it.
In this example pd.concat (incorrectly) concatenates index1 to index2 and index2 to index1, disregarding the names of the indices...
Is this the expected behaviour? I would expect the index names to be matched in the concatenation, as with the column names..
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} | 2 | "2021-04-09T15:29:40Z" | "2021-04-11T12:49:42Z" | "2021-04-11T12:39:10Z" | MEMBER | null | - [x] closes #40024
- [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 a regression caused by #38759 | {
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} | 2 | "2021-04-09T16:41:52Z" | "2021-04-13T14:17:08Z" | "2021-04-13T13:21:10Z" | MEMBER | null | Makes it feasible to annotate a Manager as specifically-2D, which I find makes it easier to reason about some of the groupby code.
@jorisvandenbossche any objection to a refactor of ArrayManager to make SingleArrayManager share a parent class with ArrayManager, but not subclass it? See https://github.com/pandas-dev/pandas/compare/master...jbrockmendel:typ-mgr-2d?expand=1#diff-80af4c7d6ece8cfeec6401a5f5babe27f19a7fc7476ad9148dca02f58a07a8dcR191
@simonjayhawkins the annotation for obj_with_exclusions https://github.com/pandas-dev/pandas/compare/master...jbrockmendel:typ-mgr-2d?expand=1#diff-f1ec980d06b0b54c8263f663f767636c3f5921f04f8a7ee91dfc71a2100b05cdR934 is a bit sketchy since it is a cache_readonly, not an attribute. Thoughts on how best to handle this? (Also, annotating it incorrectly as DataFrame in SeriesGroupBy doesn't get mypy to complain, which seems like it should?) | {
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Given a datafame with a `MultiIndex` axis, it would be nice to be able to perform a mathematical operation based on the outer axis (or axes) and obtain a result that maintained the number of dimensions in the index. For example:
```python
cols = [(x, y) for p in ["r", "s"] for n in ["a","b","c"]]
dct = {col: [0, 1, 2] for col in cols}
df = pd.DataFrame(dct)
# r s
# a b c a b c
#0 0 0 0 0 0 0
#1 1 1 1 1 1 1
#2 2 2 2 2 2 2
type(df.columns)
# <class 'pandas.core.indexes.multi.MultiIndex'>
diff = df["r"] - df["s"]
type(diff.columns)
# <class 'pandas.core.indexes.base.Index'>
```
This would allow for expressions like the following:
```python
df["diff"] = df["r"] - df["s"] # THIS DOES NOT WORK
```
At the moment, the only way to add a new set of columns for `"diff"` is to create them one at a time.
#### Describe the solution you'd like
I know it's tricky, but it would be nice if "inner" data frames were aware that they were part of a `MultiIndex`.
#### API breaking implications
Note sure...
#### Describe alternatives you've considered
As stated before, the goal can be achieved by iterating over columns.
#### Additional context
I was not able to find duplicate issues, but feel free to close if there is one. | {
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} | 1 | "2021-04-09T18:13:39Z" | "2021-09-14T16:35:21Z" | "2021-09-14T16:35:21Z" | NONE | null |
When attempting to export a dataframe to a Stata data file, I hit the following error due to my dataframe including variables of the Int64 dtype.
```python
import pandas as pd
# CREATE DATAFRAME
s1 = pd.Series(2**33, dtype='Int64')
s2 = pd.Series("ABC", dtype=object)
df = pd.DataFrame({'col1': s1, 'col2': s2})
df = df.append({'col1': None, 'col2': "DEF"}, ignore_index=True)
# EXPORT TO STATA
df.to_stata('df.dta', version=117)
```
Error message:
> NotImplementedError: Data type Int64 not supported.
My expectation is columns of dtype Int64 (i.e., a nullible INT) would end up as byte, int or float in the dta file depending on values therein.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 61 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : English_United States.1252
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-09T19:29:24Z" | "2021-04-10T01:31:24Z" | "2021-04-10T01:25:32Z" | 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
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https://api.github.com/repos/pandas-dev/pandas/issues/40858 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40858/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40858/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40858/events | https://github.com/pandas-dev/pandas/issues/40858 | 854,814,443 | MDU6SXNzdWU4NTQ4MTQ0NDM= | 40,858 | QST: hi guys i have an question on creating a series in pandas diamonds['Quality–color'] = diamonds.cut + ', ' + diamonds.color after running this code why iam getting two columns as 'Quality–color' | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40859 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40859/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40859/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40859/events | https://github.com/pandas-dev/pandas/issues/40859 | 854,826,234 | MDU6SXNzdWU4NTQ4MjYyMzQ= | 40,859 | QST: hi guys i have an doubt in pandas i created series as ['Quality–color'] in my data frame by adding Cut & Color variable into that series as "diam['Quality-color']=diam.cut + ', ' + diam.color" my query was why im getting two colums as 'Quality-color' for your reference i have attached the image | {
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} | 7 | "2021-04-09T20:16:04Z" | "2021-04-14T14:56:45Z" | "2021-04-11T11:13:41Z" | MEMBER | null | - [x] closes #40829
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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https://api.github.com/repos/pandas-dev/pandas/issues/40861 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40861/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40861/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40861/events | https://github.com/pandas-dev/pandas/pull/40861 | 854,885,664 | MDExOlB1bGxSZXF1ZXN0NjEyNzU1Mjky | 40,861 | Fix fillna accepting downcast as dict | {
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} | 8 | "2021-04-09T22:15:49Z" | "2021-04-16T20:37:01Z" | "2021-04-13T23:22:59Z" | MEMBER | null | - [x] closes #40809
- [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 fix makes sense in this case I think, but this does not fix the case for non-list like values, which raises a NotImplementedError
The docstring for Series.fillna says, that downcast may be a dict too, but I don't think that this makes sense, so we should maybe raise an error there? | {
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
The previous fix missed one case where other has dups which are unique in self
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} | 0 | "2021-04-09T23:18:32Z" | "2021-04-14T14:06:40Z" | "2021-04-14T13:05:49Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
ArrayManager analogue to #40843, discussed briefly in #40853 | {
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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
- [ ] whatsnew entry
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} | 4 | "2021-04-10T12:59:10Z" | "2021-05-03T15:12:33Z" | "2021-05-03T15:12:33Z" | MEMBER | null | Currently we first remove nans, then use `len` on the result of `Series.unique`. Except for Series that are mostly null values, it is more performant to switch the order of these operations:
```
n = 100_000
part_nan = 10
ser = pd.Series(n * (part_nan * [np.nan] + list(range(100)))).astype(float)
%timeit ser.nunique()
%timeit (~np.isnan(ser.unique())).sum()
```
gives
```
104 ms ± 273 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
67 ms ± 567 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
Changing part_nan to 100 gives
```
126 ms ± 141 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
96.5 ms ± 431 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
On my machine, they are about equal when part_nan is 250 (~70% null values). | {
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} | 1 | "2021-04-10T15:11:13Z" | "2021-04-12T13:35:28Z" | "2021-04-12T13:31:43Z" | CONTRIBUTOR | null | - [ ] closes #17130
- [ ] 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
Adds tests for using iloc on an interval as index.
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} | 6 | "2021-04-10T19:15:52Z" | "2021-05-11T08:27:12Z" | "2021-04-12T08:11:23Z" | CONTRIBUTOR | null | - [ ] closes part of https://github.com/MarcoGorelli/pyladies-sprint-April-2021/issues/29
- [ ] tests added / passed
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} | 4 | "2021-04-10T20:16:59Z" | "2021-04-15T07:33:45Z" | "2021-04-13T15:55:37Z" | MEMBER | null | - [x] closes #36719
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Seems to have gotten build from > 1 hr to ~ 38 min. | {
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This would deprecate the level keyword if we want to move forward with this | {
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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
- [ ] whatsnew entry
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} | 1 | "2021-04-11T00:30:41Z" | "2021-04-11T06:02:44Z" | "2021-04-11T06:02:43Z" | CONTRIBUTOR | null | I fixed the missing space that made the documentation render "isthe" instead of "is the".
Before the change:
- This **isthe** equivalent of the numpy.ndarray method argmax.
- This **isthe** equivalent of the numpy.ndarray method argmin.
After the change:
- This **is the** equivalent of the numpy.ndarray method argmax.
- This **is the** equivalent of the numpy.ndarray method argmin.
- [ ] 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
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} | 1 | "2021-04-11T11:54:11Z" | "2021-04-11T13:01:55Z" | "2021-04-11T13:01:50Z" | MEMBER | null | - [x] closes #39739
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 5 | "2021-04-11T11:57:32Z" | "2021-04-16T01:06:48Z" | "2021-04-16T01:06:48Z" | MEMBER | null | The docstring reads
https://github.com/pandas-dev/pandas/blob/9ab55b4aed9018b070c939ffbdf232f99bf50f1a/scripts/validate_unwanted_patterns.py#L355-L361
yet this file doesn't error if the script is run on it:
```console
$ cat t.py
rule = (
"We want the space at the begging of "
"the line if the previous line is ending with a \n "
"not at the end, like always"
)
$ python scripts/validate_unwanted_patterns.py -vt 'strings_with_wrong_placed_whitespace' t.py
```
Some other files which don't error, though I can't understand why:
```console
$ cat t.py
rule = (
" We want the space at the begging of"
" the line if the previous line is ending with a \n "
"not at the end, like always"
)
$ python scripts/validate_unwanted_patterns.py -vt 'strings_with_wrong_placed_whitespace' t.py
```
```console
$ cat t.py
rule = (
"We want the space at the begging of"
" the line if the previous line is ending with a "
"not at the end, like always"
)
$ python scripts/validate_unwanted_patterns.py -vt 'strings_with_wrong_placed_whitespace' t.py
```
Hi @ShaharNaveh @datapythonista - do you have any context you could provide on the trailing newline rule? I'm struggling to make sense of it - thanks :) | {
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} | 5 | "2021-04-11T12:02:54Z" | "2021-04-18T15:55:39Z" | "2021-04-18T15:55:39Z" | MEMBER | null | - [x] I have checked that this issue has not already been reported.
---
The weekly wheel of numpy changed and caused our ci to fail
Worked:
https://github.com/pandas-dev/pandas/runs/2314968903
First failure:
https://github.com/pandas-dev/pandas/runs/2315938439
Numpy wheels:
First:
https://pypi.anaconda.org/scipy-wheels-nightly/simple/numpy/1.21.0.dev0%2B1199.g623bc1fae/numpy-1.21.0.dev0%2B1199.g623bc1fae-cp38-cp38-manylinux2010_x86_64.whl
Second:
https://pypi.anaconda.org/scipy-wheels-nightly/simple/numpy/1.21.0.dev0%2B1229.g36eb76c9d/numpy-1.21.0.dev0%2B1229.g36eb76c9d-cp38-cp38-manylinux2010_x86_64.whl
cc @simonjayhawkins is this an issue for the 1.2.4 release? | {
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} | 12 | "2021-04-11T12:42:50Z" | "2021-07-04T10:06:53Z" | "2021-07-04T10:06:53Z" | MEMBER | null | This check is no longer relevant:
https://github.com/pandas-dev/pandas/blob/9ab55b4aed9018b070c939ffbdf232f99bf50f1a/.pre-commit-config.yaml#L83-L88
Since using PEP604 rewrites, this would be written as
```
x: Series | DataFrame
```
anyway.
```
x: FrameOrSeriesUnion
```
is actually just as long as the above, and arguably less explicit.
----
@simonjayhawkins thoughts on replacing all the `FrameOrSeriesUnion` occurrences, removing the alias, and removing the above check? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40877/events | https://github.com/pandas-dev/pandas/pull/40877 | 855,300,762 | MDExOlB1bGxSZXF1ZXN0NjEzMDY5ODQ1 | 40,877 | Skipt failing tests for numpy dev | {
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} | 9 | "2021-04-11T12:57:21Z" | "2021-04-11T18:34:13Z" | "2021-04-11T16:32:10Z" | MEMBER | null | - [x] xref #40874
- [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
Skipping for now.
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Had to add the is_numpy_dev import, we could otherwise import from other directory if this would be preferrable | {
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} | 13 | "2021-04-11T19:42:33Z" | "2021-05-05T18:38:43Z" | "2021-04-22T22:35:21Z" | CONTRIBUTOR | null | - [X] closes #40287
- [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
- [X] install.rst entry
- [X] io.rst entry
---
Note: This PR relies on a new dependency, [**`pyreadr`**](https://pypi.org/project/pyreadr/).
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} | 1 | "2021-04-11T19:54:18Z" | "2021-04-19T14:02:52Z" | "2021-04-19T14:02:52Z" | MEMBER | null | We have an ugly regression on master
#### Code Sample, a copy-pastable example
```python
df = DataFrame({"a": [1, 2], "b": [3, 4], "c": "a"})
indexer = Series([False, False], name="c")
rhs = DataFrame({"b": [5, 6]}, index=[0, 1])
df.loc[indexer, ["b"]] = rhs
```
We need more than one block to reproduce
#### Problem description
This returns
```
a b c
0 1 5 a
1 2 6 a
```
which is obviously wrong.
#### Expected Output
```
a b c
0 1 3 a
1 2 4 a
```
I think this was introduced with 80227439db6b90985b9fbcde2a8f8ea85a4569b2 cc @jbrockmendel
``_align_series`` previously returns the value, if the indexer is all False, which is not caught here anymore, since we run into
```
elif is_array_like(value) and is_exact_shape_match(ser, value):
ser = value
```
<details>
INSTALLED VERSIONS
------------------
commit : 895f0b4022562d33e485c5d4eab1014d18656e0c
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-48-generic
Version : #54~20.04.1-Ubuntu SMP Sat Mar 20 13:40:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.0.dev0+1287.g895f0b4022
numpy : 1.20.0
pytz : 2020.5
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20210108
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.2
sphinx : 3.4.3
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.5
fastparquet : 0.5.0
gcsfs : 0.7.1
matplotlib : 3.3.3
numexpr : 2.7.2
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : 1.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
None
</details>
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- [x] tests added / passed
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} | 6 | "2021-04-11T23:15:42Z" | "2021-04-15T21:21:57Z" | "2021-04-15T21:21:57Z" | CONTRIBUTOR | null | - [x] closes #40737
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them | {
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- [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: Adds a basic test for #32413.
Hey! This is my first PR with actual code: added a very basic test to validate #32413.
Questions:
* What is the best place for this test? (currently put it in `pandas/tests/indexes/test_indexing.py`)
* Should I try to create another test that uses the `index` input argument? | {
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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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} | 0 | "2021-04-12T07:20:11Z" | "2021-04-12T08:25:50Z" | "2021-04-12T08:25:46Z" | 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-2.12.0-py2.py3-none-any.whl (189 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting nodeenv>=0.11.1
Downloading nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.4.3-py2.py3-none-any.whl (7.2 MB)
Collecting cfgv>=2.0.0
Using cached cfgv-3.2.0-py2.py3-none-any.whl (7.3 kB)
Collecting identify>=1.0.0
Downloading identify-2.2.3-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 distlib<1,>=0.3.1
Using cached distlib-0.3.1-py2.py3-none-any.whl (335 kB)
Collecting six<2,>=1.9.0
Using cached six-1.15.0-py2.py3-none-any.whl (10 kB)
Collecting appdirs<2,>=1.4.3
Using cached appdirs-1.4.4-py2.py3-none-any.whl (9.6 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 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-2.2.3 nodeenv-1.6.0 pre-commit-2.12.0 pyyaml-5.4.1 six-1.15.0 toml-0.10.2 virtualenv-20.4.3
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... [INFO] Initializing environment for https://github.com/MarcoGorelli/absolufy-imports.
already up to date.
Updating https://github.com/python/black ... 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/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/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
=====> /home/runner/.cache/pre-commit/repo2ir9bkt3/.pre-commit-hooks.yaml does not exist
Updating https://gitlab.com/pycqa/flake8 ... [INFO] Initializing environment for 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.
updating v2.11.0 -> v2.12.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for https://github.com/cpplint/cpplint.
[INFO] Initializing environment for https://gitlab.com/pycqa/flake8:flake8-bugbear>=21.3.2,flake8-comprehensions>=3.1.0.
[INFO] Initializing environment for https://github.com/asottile/yesqa:flake8==3.9.0.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[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://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/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/cpplint/cpplint.
[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 https://github.com/asottile/yesqa.
[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...
absolufy-imports....................................................................................Passed
black...............................................................................................Passed
codespell...........................................................................................Passed
Fix End of Files....................................................................................Passed
Trim Trailing Whitespace............................................................................Passed
cpplint.............................................................................................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
Strip unnecessary `# noqa`s.........................................................................Passed
flake8-rst..........................................................................................Passed
Check for use of Union[Series, DataFrame] instead of FrameOrSeriesUnion alias.......................Passed
Check for inconsistent use of pandas namespace......................................................Passed
Check code for instances of os.remove...............................................................Passed
Unwanted patterns...................................................................................Passed
Unwanted patterns in tests..........................................................................Passed
Generate pip dependency from conda..................................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml............................Passed
Validate correct capitalization among titles in documentation.......................................Passed
Check for use of bare pytest raises.................................................................Passed
Check for use of private functions across modules...................................................Passed
Check for import of private attributes across modules...............................................Passed
Check for use of not concatenated strings...........................................................Passed
Check for strings with wrong placed spaces..........................................................Passed
Import pandas.array as pd_array in core.............................................................Passed
Use bool_t instead of bool in pandas/core/generic.py................................................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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- [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
dt_idx = pd.date_range(pd.Timestamp('2021-03-12 05:00:00', tz='US/Pacific'),
pd.Timestamp('2021-04-07 12:45:00', tz='US/Pacific'),
freq='1T',
)
series = dt_idx.to_series()
bar_df = series.resample('120T', label='right').last().dropna()
print(bar_df)
```
#### Problem description
After resampling a time-series with tz-aware DatetimeIndex I've found that at the point of daylight clock change `resample` begins to show shifted time-labels. For example, typical hours are 0,2,4,6,8,10 , but after DST shift at 2021-03-14 we get +1 hour i.e. 7,9,11,13 and so on.
The issue seem to affect non-standard timeframes, e.g. 2H, 4H, 90T. Others like 1H, 30min, 15min, 5min look good.
**IMPORTANT**
The problem looks to be dependent on start date:
```python
# If we include previous DST transition date, we'll get valid values (no issue at all)
dt_idx = pd.date_range(pd.Timestamp('2020-10-31 05:00:00', tz='US/Pacific'),
pd.Timestamp('2021-04-07 12:45:00', tz='US/Pacific'),
freq='1T',
)
....
>print(bar_df.tail())
2021-04-07 06:00:00-07:00 2021-04-07 05:59:00-07:00
2021-04-07 08:00:00-07:00 2021-04-07 07:59:00-07:00
2021-04-07 10:00:00-07:00 2021-04-07 09:59:00-07:00
2021-04-07 12:00:00-07:00 2021-04-07 11:59:00-07:00
2021-04-07 14:00:00-07:00 2021-04-07 12:45:00-07:00
# Jan 2020 has influence on timestamp of Apr 2021
dt_idx = pd.date_range(pd.Timestamp('2020-01-31 05:00:00', tz='US/Pacific'),
pd.Timestamp('2021-04-07 12:45:00', tz='US/Pacific'),
freq='1T',
)
....
>print(bar_df.tail())
2021-04-07 05:00:00-07:00 2021-04-07 04:59:00-07:00
2021-04-07 07:00:00-07:00 2021-04-07 06:59:00-07:00
2021-04-07 09:00:00-07:00 2021-04-07 08:59:00-07:00
2021-04-07 11:00:00-07:00 2021-04-07 10:59:00-07:00
2021-04-07 13:00:00-07:00 2021-04-07 12:45:00-07:00
```
#### Expected Output
```python
> print(bar_df)
# Prior DST time
2021-03-12 06:00:00-08:00 2021-03-12 05:59:00-08:00
2021-03-12 08:00:00-08:00 2021-03-12 07:59:00-08:00
2021-03-12 10:00:00-08:00 2021-03-12 09:59:00-08:00
2021-03-12 12:00:00-08:00 2021-03-12 11:59:00-08:00
2021-03-12 14:00:00-08:00 2021-03-12 13:59:00-08:00
...
# BUG new DST time timestamp hours are shifted
2021-04-07 05:00:00-07:00 2021-04-07 04:59:00-07:00
2021-04-07 07:00:00-07:00 2021-04-07 06:59:00-07:00
2021-04-07 09:00:00-07:00 2021-04-07 08:59:00-07:00
2021-04-07 11:00:00-07:00 2021-04-07 10:59:00-07:00
2021-04-07 13:00:00-07:00 2021-04-07 12:45:00-07:00
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-16-amd64
Version : #1 SMP Debian 4.19.181-1 (2021-03-19)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : 0.29.22
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.2.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.53.0
</details>
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] | open | false | null | [] | null | 2 | "2021-04-12T10:30:53Z" | "2021-08-19T04:39:40Z" | 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
tested = pd.Series(["key_1", "key_2"], dtype="category")
tested.map({"key_1": {"a": 1}, "key_2": {"a": 2}})
```
#### Problem description
The given code raises an exception but actually ignores it and returns the expected result. I think it would be better to ignore it silently. Note that it seems to happen only with categorical Series.
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.map_locations()
TypeError: unhashable type: 'dict'
Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'
Traceback (most recent call last):
File "pandas/_libs/hashtable_class_helper.pxi", line 4588, in pandas._libs.hashtable.PyObjectHashTable.map_locations
TypeError: unhashable type: 'dict'
Out[10]:
0 {'a': 1}
1 {'a': 2}
dtype: object
```
#### Expected Output
```python
Out[10]:
0 {'a': 1}
1 {'a': 2}
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-70-generic
Version : #78-Ubuntu SMP Fri Mar 19 13:29:52 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.23
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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] | open | false | null | [] | null | 0 | "2021-04-12T10:50:11Z" | "2021-08-19T04:50:42Z" | null | MEMBER | null | In general, we want to get rid of value-dependent behaviour in concat-operations: the resulting dtype of a concat-operation only depends on the input *dtypes*, and not on the exact content (the exact values) of the inputs.
This has been discussed in the past on general occasions, eg in https://github.com/pandas-dev/pandas/pull/33607 when adding the general EA interface for concat (there is still one value-dependent special case for Categorical involving integer categories / missing values, encoded in `core/dtypes/concat.py::cast_to_common_type`), or https://github.com/pandas-dev/pandas/issues/39122 about this issue when concerning empty series/dataframes.
But so one other case (which came up recently in eg https://github.com/pandas-dev/pandas/pull/39574 and https://github.com/pandas-dev/pandas/pull/39612) is related to all-NA/NaN objects.
For DataFrames, when there is all-missing column, its type gets ignored when determining the result dtype (which, however, requires inspecting the values of the column). Small example:
```python
>>> df_missing = pd.DataFrame({'a': [np.nan]})
>>> df_dt64 = pd.DataFrame({'a': [pd.Timestamp("2021-01-01")]}, dtype="datetime64[ns]")
>>> pd.concat([df_missing, df_dt64])
a
0 NaT
0 2021-01-01
>>> pd.concat([df_missing, df_dt64]).dtypes
a datetime64[ns]
dtype: object
```
This can be useful, as you can get such object/float dtype columns depending on how those "empty" all-NaN DataFrames are created (eg when constructing a DataFrame with given index/column but without data, or by reindexing the rows of an actual empty DataFrame, or reindexing the columns of a non-empty DataFrame).
However, it does introduce annoying value-dependent behaviour, and is also not very consistent throughout pandas. For example, Series does not check for this, and will actually result in object dtype:
```python
>>> pd.concat([df_missing['a'], df_dt64['a']])
0 NaN
0 2021-01-01 00:00:00
Name: a, dtype: object
```
Further, this is also not consistent across data types. For example, we don't check for all-NA for the new nullable dtypes.
For ArrayManager, I didn't yet implement any special case value-dependent behaviour (https://github.com/pandas-dev/pandas/pull/39612, so on this aspect it diverges from the BlockManager behaviour), as it would be good to first decide on the desired behaviour long term.
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} | 3 | "2021-04-12T15:29:52Z" | "2021-04-28T13:07:33Z" | "2021-04-28T13:07:33Z" | MEMBER | null | Report from https://issues.apache.org/jira/browse/ARROW-12336
The original reproducer is (but it would be good to see if we can find a simpler case for a test):
```
df = pd.DataFrame({"Int_col": [1, 2, 10], "str_col": ["A", "B", "Z"]})
df = df.astype({"Int_col": "Int64"})
table = pa.table(df)
path_1 = "./test_1.parquet"
pa.parquet.write_table(table, path_1)
schema = pa.parquet.read_schema(path_1)
ds = pa.dataset.FileSystemDataset.from_paths(
paths=[path_1],
filesystem=pa.fs.LocalFileSystem(),
schema=schema,
format=pa.dataset.ParquetFileFormat(),
)
table = ds.to_table(filter=(pa.dataset.field("str_col") == "C"))
print("Size of array: " + str(table.column(0).nbytes))
df = table.to_pandas()
```
which gives
```
Traceback (most recent call last):
File "/Users/xxx/empty_array_buffer_size.py", line 47, in <module>
df = table.to_pandas()
File "pyarrow/array.pxi", line 756, in pyarrow.lib._PandasConvertible.to_pandas
File "pyarrow/table.pxi", line 1740, in pyarrow.lib.Table._to_pandas
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 794, in table_to_blockmanager
blocks = _table_to_blocks(options, table, categories, ext_columns_dtypes)
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 1135, in _table_to_blocks
return [_reconstruct_block(item, columns, extension_columns)
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 1135, in <listcomp>
return [_reconstruct_block(item, columns, extension_columns)
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pyarrow/pandas_compat.py", line 753, in _reconstruct_block
pd_ext_arr = pandas_dtype.__from_arrow__(arr)
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pandas/core/arrays/integer.py", line 117, in __from_arrow__
data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=self.type)
File "/usr/local/mambaforge/envs/pa_nightly/lib/python3.9/site-packages/pandas/core/arrays/_arrow_utils.py", line 32, in pyarrow_array_to_numpy_and_mask
data = np.frombuffer(buflist[1], dtype=dtype)[arr.offset : arr.offset + len(arr)]
ValueError: buffer size must be a multiple of element size
``` | {
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} | 1 | "2021-04-12T15:47:25Z" | "2021-04-13T14:09:32Z" | "2021-04-13T14:09:32Z" | CONTRIBUTOR | null | https://pandas.pydata.org/pandas-docs/dev/whatsnew/v1.3.0.html#other
should create a new Styler section & move things to the relevant categories. | {
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} | 3 | "2021-04-12T16:08:10Z" | "2021-04-13T15:26:25Z" | "2021-04-13T15:26:21Z" | CONTRIBUTOR | null | This composites `Styler._translate()` into:
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def _translate():
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self._translate_header()
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self._translate_body()
...
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where the `_translate_header()` and `_translate_body()` methods are given some documentation explaining what they are building. The code is not fundamentally changed, but variable names are renamed to add clarity to match the documentation and loop comprehensions replace `for loops` where possible.
Some tests are minimally altered since the generic `_element` method now returns more dict keys for some elements. | {
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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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} | 1 | "2021-04-12T16:23:37Z" | "2021-04-13T14:21:50Z" | "2021-04-13T14:09:32Z" | CONTRIBUTOR | null | - [x] closes #40897
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 3 | "2021-04-12T19:28:38Z" | "2021-05-04T21:46:38Z" | "2021-04-16T01:11:25Z" | 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
df = pd.DataFrame([[0]*100] * 10)
print(df.to_string(index=False, max_cols=20))
```
Output
```
0 1 2 3 4 5 6 7 8 9 90 ... 91 92 93 94 95 96 97 98 99
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
```
#### Problem description
`to_string` is placing the truncation column (i.e. the column with the ellipses) one position too far to the right. This should not happen.
#### Expected Output
```
0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.4.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 : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 54.1.2
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
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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} | 10 | "2021-04-12T20:40:51Z" | "2021-04-16T07:17:01Z" | "2021-04-16T01:06:48Z" | MEMBER | null | - [ ] closes #40826
- [ ] closes #40873
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
----
This means that, just by running `flake8`, you'll be able to get all these custom linting errors alongside the regular `flake8` ones. E.g. if we make `pandas/tests/t.py` as follows:
```python
import pytest
import pandas as pd
from pandas import Categorical
def test_foo():
cat_0 = pd.Categorical([1])
with pytest.warns(FutureWarning):
cat_1 = pd.Categorical(1)
```
then we get:
```console
$ flake8 pandas/tests/t.py
pandas/tests/t.py:4:1: F401 'pandas.Categorical' imported but unused
pandas/tests/t.py:8:5: F841 local variable 'cat_0' is assigned to but never used
pandas/tests/t.py:8:13: PDF019 found both 'pd.Categorical' and 'Categorical' in the same file
pandas/tests/t.py:9:10: PDF011 found 'pytest.warns' (use 'pandas._testing.assert_produces_warning')
pandas/tests/t.py:10:9: F841 local variable 'cat_1' is assigned to but never used
pandas/tests/t.py:10:17: PDF019 found both 'pd.Categorical' and 'Categorical' in the same file
```
These can then easily be configured using `flake8`'s configuration options, like `# noqa` and `per-file-ignores` in `setup.cfg`.
This also brings down the number of pre-commit checks.
----
I this is something the team wants, and it's decided to keep it as a separate repo, then some outstanding things to do would be:
- transferring it to `pandas-dev`
- sorting out the license (it's currently in my name, and I presume that should change) | {
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} | 2 | "2021-04-12T21:02:05Z" | "2021-04-16T01:11:32Z" | "2021-04-16T01:11:25Z" | CONTRIBUTOR | null | - [x] closes #40904
- [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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} | 10 | "2021-04-12T22:18:39Z" | "2021-04-21T15:37:42Z" | "2021-04-21T12:53:28Z" | 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
Precursor to #40790
This adds support for e.g. `float32` NumPy dtypes to maybe_convert_object. If any non-NumPy scalar is hit, the behavior is the same as master. This is my first foray into the NumPy C-API, so any tips are appreciated. In particular, I couldn't figure out how to use the C API to do the cast:
result = result.astype(result.dtype.kind + str(itemsize))
Not sure if there should also be specific logic for EAs/nullable types.
From a full ASV run:
```
before after ratio
[7d4757b4] [c1288962]
<maybe_convert_object_itemsize~12> <maybe_convert_object_itemsize>
+ 768±8μs 1.01±0ms 1.32 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'float')
+ 779±9μs 1.02±0ms 1.31 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'float')
+ 579±5μs 710±20μs 1.23 arithmetic.NumericInferOps.time_divide(<class 'numpy.int8'>)
+ 608±30μs 715±20μs 1.18 arithmetic.NumericInferOps.time_divide(<class 'numpy.uint8'>)
+ 3.18±0.2μs 3.69±0.3μs 1.16 index_cached_properties.IndexCache.time_engine('UInt64Index')
+ 1.58±0.01ms 1.83±0.01ms 1.16 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f5964729820>, False, 'float')
+ 3.30±0.2μs 3.80±0.5μs 1.15 index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
+ 943±80ns 1.08±0.1μs 1.15 index_cached_properties.IndexCache.time_inferred_type('Float64Index')
+ 1.73±0.01ms 1.98±0ms 1.14 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
+ 1.72±0.01ms 1.96±0ms 1.14 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
+ 5.19±0.03μs 5.93±0.3μs 1.14 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 1.65±0.01ms 1.89±0.01ms 1.14 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f5964729820>, True, 'float')
+ 6.06±0.06μs 6.91±0.1μs 1.14 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, datetime.timezone(datetime.timedelta(seconds=3600)))
+ 7.46±0.04μs 8.47±0.5μs 1.14 tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessDay>)
+ 1.15±0.09μs 1.27±0.08μs 1.11 index_cached_properties.IndexCache.time_values('UInt64Index')
- 11.7±1ms 10.5±0.1ms 0.90 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.object_'>, 18, 'outside')
- 641±30ns 577±20ns 0.90 index_cached_properties.IndexCache.time_is_monotonic('RangeIndex')
- 641±20ns 576±20ns 0.90 index_cached_properties.IndexCache.time_shape('Int64Index')
- 3.37±0.03ms 3.02±0.02ms 0.90 timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
- 787±20ns 702±20ns 0.89 index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex')
- 1.25±0.04μs 1.12±0.04μs 0.89 index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
- 1.50±0.1ms 1.33±0.02ms 0.88 dtypes.SelectDtypes.time_select_dtype_string_exclude('float32')
- 1.19±0.09μs 1.05±0.08μs 0.88 index_cached_properties.IndexCache.time_inferred_type('UInt64Index')
- 57.6±1μs 50.3±0.2μs 0.87 frame_methods.Dtypes.time_frame_dtypes
- 168±3μs 146±0.6μs 0.87 algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 8000)
- 552±20ns 480±20ns 0.87 index_cached_properties.IndexCache.time_is_monotonic_increasing('RangeIndex')
- 1.50±0.1ms 1.30±0ms 0.87 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex64')
- 1.89±0ms 1.64±0.01ms 0.87 period.DataFramePeriodColumn.time_set_index
- 11.0±0.2μs 9.50±0.08μs 0.87 period.Indexing.time_series_loc
- 110±20ms 93.1±0.06ms 0.85 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'random_hits')
- 392±30ns 330±10ns 0.84 index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
- 4.45±0.4ms 3.68±0.02ms 0.83 algorithms.Factorize.time_factorize(True, True, 'Int64')
- 9.60±0.8ms 7.75±0.01ms 0.81 algorithms.Factorize.time_factorize(True, False, 'string')
- 11.2±0.8ms 8.90±0.1ms 0.80 algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.object_'>, 18, 'inside')
- 615±30ns 484±30ns 0.79 index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
- 73.8±20ms 55.4±0.5ms 0.75 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'monotone_hits')
- 180±0.1μs 128±0.5μs 0.71 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), 'monotonic_incr')
- 128±40ms 90.8±0.03ms 0.71 algos.isin.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_hits')
- 186±0.3μs 131±2μs 0.70 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), 'monotonic_incr')
- 1.72±0.4μs 1.17±0.1μs 0.68 index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex')
- 216±0.8μs 140±3μs 0.65 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.UInt32Engine'>, <class 'numpy.uint32'>), 'monotonic_incr')
- 1.55±0.01ms 972±7μs 0.63 algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 100000)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
Specific timings via %timeit on `maybe_convert_objects` directly:
```
np.array of python integers:
127 ms ± 80.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- PR
120 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- master
np.array of int32:
179 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- PR
133 ms ± 274 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- master
np.array of int32 with last one a python int:
178 ms ± 1.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- PR
133 ms ± 106 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <--- master
```
<details>
<summary>timeit code</summary>
```
values = np.array(range(1_000_000), dtype="object")
print('np.array of python integers:')
%timeit maybe_convert_objects(values)
print()
values = np.array([np.int32(1)] * 1_000_000, dtype="object")
print('np.array of int32:')
%timeit maybe_convert_objects(values)
print()
values = np.array([np.int32(1)] * 999_999 + [1], dtype="object")
print('np.array of int32 with last one a python int:')
%timeit maybe_convert_objects(values)
print()
```
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} | 1 | "2021-04-12T22:23:22Z" | "2021-04-13T16:54:33Z" | "2021-04-13T15:25:30Z" | 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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} | 5 | "2021-04-12T23:08:28Z" | "2021-05-22T16:04:59Z" | "2021-05-22T16:04:59Z" | CONTRIBUTOR | 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
I noticed in https://github.com/pandas-dev/pandas/pull/40867 that keep didn't specify a type, so adding it here.
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The behaviour of `pct_change` when there are negative numbers could be improved.
```{python}
In [3]: df = pd.DataFrame([10, -10, 10])
In [4]: df.pct_change()
Out[4]:
0
0 NaN
1 -2.0
2 -2.0
```
There are basically two ways of calculating %change with negative numbers, depending on what you want.
**1) Usual `(p_1 - p_0) / p_0` or `(p_1 / p_0) - 1`**
This results in %changes that have correct dimension but *wrong* direction; %change and difference have different signs.
On the other hand, these %changes can be compounded, as shown below:
```{python}
In [12]: (df.pct_change().replace(np.NaN, 0) + 1).cumprod() * 10
Out[12]:
0
0 10.0
1 -10.0
2 10.0
```
**2) Using absolute denominator `(p_1 - p_0) / abs(p_0)`**
This results in %changes that have correct dimension and direction, but that cannot be compounded.
This post explains it *really* well: https://math.stackexchange.com/a/3986352
Bloomberg, for example, reports %change like this.
### Suggestion
There is no "one size fits all" here. However, I feel there is space for `pandas` to be proactive and alert the user that negative prices have certain implications.
I would like to suggest the following small modification to `pct_change`:
* Add an argument `calc_type` with 3 possible values:
* `None`: current calculation + warning if negative values are found.
* `"signed"`: current calculation.
* `"absolute"`: calculation using absolute denominators.
The advantage of this? To alert users that the current calculation may not be what they expect if the data has a stray negative number, and to force them to be explicit when that happens.
One might say that "users should read the documentation" and that `pandas` should not validate your data for you, which is also correct. As a user, however, I would have been massively thankful if `pandas` had alerted me of the behaviour of `pct_change`.
I imagine many people out there don't really think about this if they expect their data to be strictly positive. I myself never gave this too much thought until crude oil turned negative on 20-Mar-2020.
### Output of ``pd.show_versions()``
```{python}
In [1]: import pandas as pd
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-71-generic
Version : #79-Ubuntu SMP Wed Mar 24 10:56:57 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2020.5
dateutil : 2.8.1
pip : 21.0.1
setuptools : 44.0.0
Cython : 0.29.21
pytest : 6.2.1
hypothesis : 6.0.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : 0.16.2
xlrd : None
xlwt : None
numba : 0.52.0
``` | {
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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
### whatsnew
Adds a very simple test to verify that `None`s are not casted to strings as `"None"` inside the DataFrame's constructor for string columns.
Questions:
* Should the test be more general?
* What is the proper location for the test?
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] | open | false | null | [] | null | 6 | "2021-04-13T01:42:24Z" | "2021-08-19T04:52:32Z" | 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.
- [ ] (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.
# Update 2
- The issue seems to be Google Chrome and Microsoft Edge
- Jupyter Lab in Firefox correctly displays all of the styled rows and correctly renders an output HTML file.
- The question is then, what are the relevant settings in Chrome and Edge, or why doesn't this work in Chrome or Edge?
# Original
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
from faker import Faker # conda install -c conda-forge faker or pip install Faker
# for fake names
fake = Faker()
# test data
np.random.seed(365)
rows = 11000
# change 36 or 158 to test where the rows stop appearing
vals = {f'val{i}': np.random.randint(1, 11, size=(rows)) for i in range(1, 36)}
data = {'name': np.random.choice([fake.unique.name() for i in range(158)], size=rows),
'cat': np.random.randint(1, 4, size=(rows))}
data.update(vals)
df = pd.DataFrame(data)
# used to create the mask for the background color
mean = df.groupby('cat').mean().round(2)
# calculate the mean for each name and cat
cat_mean = df.groupby(['name', 'cat']).mean()
def color(x):
"""Function to apply background color"""
c1 = 'background-color: green'
c = ''
# compare columns
mask1 = x.gt(mean)
# DataFrame with same index and columns names as original filled empty strings
df1 = pd.DataFrame(c, index=x.index, columns=x.columns)
# modify values of df1 column by boolean mask
df1.iloc[mask1] = c1
display(df1)
return df1
# displays the notebook in Jupyter
cat_mean.style.apply(color, axis=None)
# In PyCharm saving rendered styler to file
cm = cat_mean.style.apply(color, axis=None).set_precision(3).render()
with open('cm_test.html', 'w') as f:
f.write(cm)
```
#### Problem description
- Given a large dataframe, in this case `474 rows x 35 columns`, the applied styling does not correctly display for all rows in Jupyter or if saving the file to HTML.
- [![enter image description here][1]][1]
- If the number of rows or columns increases beyond this size, then more rows aren't displayed properly
- We can see from the styling map, that the rows are correctly mapped with a background color, but it isn't displayed.
- [![enter image description here][2]][2]
- If the number of rows or columns is reduced, then all of the rows display the correct styling.
- `jupyterlab v3.0.11` and `pandas v1.2.3`
- In `PyCharm 2021.1 (Professional Edition) Build #PY-211.6693.115, built on April 6, 2021` saving the redendered styler to a file has the same result, so this isn't just an issue with Jupyter.
- This issue is reproducible on two different systems that I have tried.
- If the shape is reduced to `471 rows × 35 columns` or `474 rows × 34 columns`, then all rows correctly display the highlighting.
- Associated Stack Overflow [question][3].
#### Workaround
##### Split the DataFrame
- It's dissatisfying, but splitting the DataFrame, and applying the style will work, since if reduces the overall size.
```python
cat_mean.iloc[:237, :].style.apply(color, axis=None)
cat_mean.iloc[237:, :].style.apply(color, axis=None)
```
##### Save to Excel
- All rows are displayed correctly with the highlight color when saving to Excel
```python
test = cat_mean.style.apply(color, axis=None)
test.to_excel('test.xlsx', engine='openpyxl')
```
#### Expected Output
- All rows should display highlighting.
#### Output1 of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.3
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 : 6.2.3
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.9.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.5
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
# Update 1
- The following options produced the same results as the original attempts
- Standard python console creating HTML file
- PyCharm creating HTML file
- Jupyter Lab displaying styled dataframe in a cell
#### Output2 of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2cb96529396d93b46abab7bbc73a208e708c642e
python : 3.9.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.2.4
numpy : 1.20.0
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
[1]: https://i.stack.imgur.com/iaq8J.png
[2]: https://i.stack.imgur.com/3D0gy.png
[3]: https://stackoverflow.com/q/67065785/7758804 | {
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} | 3 | "2021-04-13T03:00:36Z" | "2021-05-29T17:55:11Z" | "2021-05-05T12:46:39Z" | MEMBER | null | Precursor for #40687
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] | open | false | null | [] | null | 2 | "2021-04-13T03:01:39Z" | "2021-04-13T05:57:52Z" | 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.
---
#### Code Sample, a copy-pastable example
```python
In [11]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/London', nonexistent='shift_forward')
Out[11]: Timestamp('2021-03-28 03:00:00+0100', tz='Europe/London')
In [12]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/London', nonexistent='shift_forward') - pd.Timedelta(60, 'm')
Out[12]: Timestamp('2021-03-28 02:00:00+0100', tz='Europe/London')
In [13]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/London', nonexistent='shift_forward') - pd.Timedelta(61, 'm')
Out[13]: Timestamp('2021-03-28 00:59:00+0000', tz='Europe/London')
```
#### Problem description
When attempting to localize a non-existent time in the Europe/London timezone using the nonexistent='shift_forward', parameter, the resulting time is 03:00 summer time-- even though the time transition skips from 01:00 AM UTC to 02:00 AM summer time.
The issue appears to occur in European timezones that are in UTC when the time is not summer time (i.e. London, Dublin, and Lisbon). Timezones that start at UTC+1 and have their summer time conversions from 2 AM-3 AM work properly.
```python
In [23]: pd.to_datetime('2021-03-28 02:30').tz_localize('Europe/Paris', nonexistent='shift_forward')
Out[23]: Timestamp('2021-03-28 03:00:00+0200', tz='Europe/Paris')
In [24]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/Dublin', nonexistent='shift_forward')
Out[24]: Timestamp('2021-03-28 03:00:00+0100', tz='Europe/Dublin')
In [25]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/Lisbon', nonexistent='shift_forward')
Out[25]: Timestamp('2021-03-28 03:00:00+0100', tz='Europe/Lisbon')
In [26]: pd.to_datetime('2021-03-28 02:30').tz_localize('Europe/Paris', nonexistent='shift_forward')
Out[26]: Timestamp('2021-03-28 03:00:00+0200', tz='Europe/Paris')
In [27]: pd.to_datetime('2021-03-28 02:30').tz_localize('Europe/Stockholm', nonexistent='shift_forward')
Out[27]: Timestamp('2021-03-28 03:00:00+0200', tz='Europe/Stockholm')
```
#### Expected Output
Per the documentation localizing with nonexistent='shift_forward' should "shift the nonexistent time forward to the closest existing time". In this case the closest existing time in Europe/London to 2021-03-28 01:30 would be 2021-03-28 02:00+0100.
```python
In [11]: pd.to_datetime('2021-03-28 01:30').tz_localize('Europe/London', nonexistent='shift_forward')
Out[11]: Timestamp('2021-03-28 02:00:00+0100', tz='Europe/London')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 7d4757b4deb851bb44ab6bb20cdc404fa13fffcf
python : 3.8.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.3.0.dev0+1297.g7d4757b4de
numpy : 1.20.2
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 : 6.8.3
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : 1.3.8
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : None
matplotlib : 3.4.0
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.2
sqlalchemy : 1.4.3
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
</details>
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} | 1 | "2021-04-13T03:24:52Z" | "2021-07-10T20:41:37Z" | null | MEMBER | null | #### Location of the documentation
https://pandas.pydata.org/docs/dev/development/contributing.html#updating-your-pull-request
#### Documentation problem
It is somewhat common to send in a large pull request for review and have a maintainer ask to split a large pull request up into several precursor pull requests. However, the process of doing so can be somewhat tricky, so it should be mentioned in the contributing docs.
#### Suggested fix for documentation
Add to the contributing docs how to split up a PR.
Maybe use this as an example. (Am writing this from memory, might have errors)
```
git branch
# * feature-1
# master
git checkout master
git checkout -b pre-cursor-to-feature-1
# on pre-cursor-to-feature-1 now
git checkout feature-1 /path/to/first/part/precursor/changes
git commit -m "Pre-cursor for feature-1
# After pre-cursor is merged(on branch feature-1 now)
git branch
# * feature-1
# master
git pull upstream master
# If there are conflicts and you want to take the version in master.
git checkout --theirs path/to/files
```
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} | 20 | "2021-04-13T08:08:09Z" | "2021-06-23T08:13:15Z" | "2021-06-23T08:13:15Z" | MEMBER | null | Tracking issue for the 1.2.5 release.
https://github.com/pandas-dev/pandas/milestone/85
Currently scheduled for May 10, 2021
List of open regressions: https://github.com/pandas-dev/pandas/issues?q=is%3Aopen+is%3Aissue+label%3ARegression | {
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} | 2 | "2021-04-13T08:54:22Z" | "2021-04-21T12:41:02Z" | "2021-04-21T12:41:02Z" | 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.
https://github.com/pandas-dev/pandas/blob/master/pandas/io/orc.py#L15-L54
---
#### Code Sample, a copy-pastable example
```python
...
import pandas as pd
orc_data = pd.read_orc(orc_file_path)
```
#### Problem description
Pandas uses [PyArrow](https://arrow.apache.org/docs/python) package to load ORC/Parquet data.
For the orc data format, it will use `pyarrow.orc.ORCFile` to read data ([orc.py](https://github.com/pandas-dev/pandas/blob/master/pandas/io/orc.py#L53)), but the PyArrow does not declare `orc` in [\_\_init\_\_.py](https://github.com/apache/arrow/blob/master/python/pyarrow/__init__.py) file, so pandas will raise an AttributeError: module 'pyarrow' has no attribute 'orc'
This bug will occur if the Pandas version is greater than v1.2.0.dev0(after [commit-6d1541e](https://github.com/pandas-dev/pandas/commit/6d1541e1782a7b94797d5432922e64a97934cfa4)). Before that, `pandas/io/orc.py` will declare `import pyarrow.orc` before uses pyarrow to load orc data([v1.1.5/pandas/io.orc.py/](https://github.com/pandas-dev/pandas/blob/v1.1.5/pandas/io/orc.py#L51)).
---
Testing environment:
- Ubuntu 18.04
- python 3.7
- pandas v1.2.1
- pyarrow v3.0.0 (install via pip)(I haven't installed pyarrow via Conda for testing yet.) | {
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