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typing_extensions
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MEMBER
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ATM we vendor typing_extensions but mypy doesnt recognize it, so we can't actually use it. We should either remove it or make it a dependency.
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ERR: Removing quotation marks in error message
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| 2020-10-14T18:57:43Z
| 2020-10-15T00:48:34Z
| 2020-10-15T00:27:51Z
|
MEMBER
| null |
Follow-up to https://github.com/pandas-dev/pandas/pull/36852
When reviewing, I got confused and thought `iteration` was somehow an argument to `read_csv` (like `nrows`). Since it is now, the quotation marks don't make as much sense to have.
Doing this as a follow-up since the original PR was a pure refactoring.
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ENH: No need to call _choose_path in Dataframe groupby transform function
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| 2020-10-14T19:25:19Z
| 2020-10-16T15:21:22Z
| 2020-10-16T15:21:22Z
|
NONE
| null |
When every time the slow_path method run in self._choose_path, then why not we remove the fast_path code.
self._choose_path call
https://github.com/pandas-dev/pandas/blob/d7a5b838d8d6234f6bec5a30bfa33b24bd4afbd9/pandas/core/groupby/generic.py#L1313
Line to change
path, res = show_path, slow_path(group)
definition of self._choose_path method
https://github.com/pandas-dev/pandas/blob/d7a5b838d8d6234f6bec5a30bfa33b24bd4afbd9/pandas/core/groupby/generic.py#L1425
Execution time difference
----------------------------------
With fast_path method - 21 ms
Without fast_path method - 9ms
Used dataframe
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'], 'B': ['one', 'one', 'two', 'three', 'two', 'two'], 'C': [10, 5, 50, 2, 50, 5], 'D': [2.0, 5., 8., 1., 2., 9.]})
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Backport PR #35688 on branch 1.1.x: Fix GH-29442 DataFrame.groupby doesn't preserve _metadata
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| 1
| 2020-10-14T19:44:15Z
| 2020-10-15T12:02:59Z
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MEMBER
| null |
Backport PR #35688
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STY: avoid 1-letter variable names, especially pdb keywords
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| 2020-10-14T20:29:42Z
| 2020-10-15T16:22:28Z
| 2020-10-15T16:22:28Z
|
MEMBER
| null |
Every so often I use pdb and want to access a variable that happens to be a pdb keyword and its a hassle. Let's avoid these: ["h", "w", "u", "d", "b", "s", "n", "r", "c", "j", "l", "a", "p"]. Or simpler, just avoid 1-letter variable names altogether.
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REF: IntervalArray comparisons
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Add test_masking_duplicate_columns
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I don't know what an whatsnew entry is...
This PR adds a test in order to solve an issue
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[REDO] CLN: core/dtypes/cast.py::maybe_downcast_to_dtype
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- [ ] closes #xxxx
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xref https://github.com/pandas-dev/pandas/pull/37050#issuecomment-708487786
The combination of #37050 and #37024 generated a `mypy` complaint. This PR is resubmission of #37050 with the correct type hint.
cc @simonjayhawkins
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ENH: Aggregation with multiple columns in one function
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| 2020-10-15T03:43:51Z
| 2021-08-13T04:47:33Z
| 2021-08-13T04:47:33Z
|
NONE
| null |
I digged into the pandas.pivot_table and pandas.DataFrame.groupby + aggregate methods, it seems that the aggfunc (or the method in aggregate) doesn't support multiple columns' computation in one function.
Let me take an dataframe example:
```
data = {
'Col X': ['class 1', 'class 2', 'class 3', 'class 2'],
'Col Y': ['cat 1', 'cat 1', 'cat 2', 'cat 3'],
'count': [1, 2, 3, 4],
'count2': [1, 2, 3, 4]
}
df = pd.DataFrame(data=data)
```
The aggregation doesn't support directly "sum(count) + sum(count2)". What I have to do is first compute sum(count) and sum(count2), and then add the two sum results.
So do we plan to support the aggregation on multiple columns directly? Looks like:
```
df.groupby(['Col X','Col Y']).aggregate({
'New column': lambda x: sum(x['count']) + sum(x['count2'])
}).unstack('Col Y', fill_value=0)
```
or
```
pd.pivot_table(df, values=['count', 'count2'], index=['Col X'], columns=['Col Y'],
aggfunc=lambda x: sum(x['count']) + sum(x['count2']), fill_value=0)
```
Thanks!
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TYP: add Shape alias to pandas._typing
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| 2020-11-04T16:53:11Z
| 2020-11-04T16:53:07Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref https://github.com/pandas-dev/pandas/pull/37024#discussion_r503488032
cc @simonjayhawkins
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ENH: DataFrame.to_parquet() returns bytes if path_or_buf not provided
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| 2
| 2020-10-15T05:35:54Z
| 2020-10-21T19:14:15Z
| 2020-10-21T19:14:04Z
|
MEMBER
| null |
- [x] closes #37105
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Not sure if this is an API breaking change. Prior to this patch `path` was a required positional argument but it becomes optional here. It is now consistent with, for example, the csv writer.
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PERF: compare RangeIndex to equal RangeIndex
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| 1
| 2020-10-15T07:14:21Z
| 2021-06-13T10:11:15Z
| 2020-10-15T12:26:00Z
|
CONTRIBUTOR
| null |
Fastpath for RangeIndexes with equal `_range` attribute.
Notice this works even if the RangeIndexes are not identical.
Example:
```python
>>> n = 100_000
>>> rng1 = pd.RangeIndex(n)
>>> rng2 = pd.RangeIndex(n)
>>> %timeit rng1 == rng2
374 µs ± 15.1 µs per loop # master
10.4 µs ± 157 ns per loop # this PR
```
Somewhat related to #37109
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TST: test for expanding window with min_periods GH25857
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| 4
| 2020-10-15T07:43:15Z
| 2020-10-16T09:08:24Z
| 2020-10-16T01:16:23Z
|
CONTRIBUTOR
| null |
- [x] closes #25857
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Hi @mroeschke, here's the new test - please let me know if it needs any changes.
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BUG: raise when doing arithmetic on DataFrame and list of iterables
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| 10
| 2020-10-15T08:15:43Z
| 2021-07-11T17:45:12Z
| 2020-10-16T19:24:41Z
|
MEMBER
| null |
- [X] closes #36702
- [X] tests 1 added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## Problem
We currently allow doing arithmetic on a DataFrame, and, for example, a list of Series. The latter gets broadcast with `align_method_FRAME` and this leads to weirdness.
## Solution
We should raise. There is no definite answer to how such arithmetic should be handled, and the user should just make another DataFrame and add that to the frame instead of doing stuff like `[Series, Series]`.
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|
CI remove redundant checks covered by pyupgrade
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| 0
| 2020-10-15T10:20:32Z
| 2020-10-16T01:08:05Z
| 2020-10-16T01:08:05Z
|
MEMBER
| null |
Some of these checks, e.g.
```bash
MSG='Check for python2-style file encodings' ; echo $MSG
invgrep -R --include="*.py" --include="*.pyx" -E "# -\*- coding: utf-8 -\*-" pandas scripts
RET=$(($RET + $?)) ; echo $MSG "DONE"
MSG='Check for python2-style super usage' ; echo $MSG
invgrep -R --include="*.py" -E "super\(\w*, (self|cls)\)" pandas
RET=$(($RET + $?)) ; echo $MSG "DONE"
```
are already checked by pyupgrade.
Opening this as a placeholder to go through the checks and see what no longer needs to be there
EDIT
----
also, as a note, in `pandas/core/generic.py` there is an instance of `.. warning:`, but the `Check for incorrect sphinx directives` isn't picking it up. `pygrep` finds it though. I'm inclined to move all of these over to pre-commit so that they're cross-platform and can selectively be run only on staged files
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Remove the Label alias in pandas._typing.
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| 0
| 2020-10-15T11:20:19Z
| 2021-01-11T17:07:38Z
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|
MEMBER
| null |
in mypy 0.790 bugfix (open PR to update #37108)
> Make None compatible with Hashable (PR 9371)
we have an alias in pandas._typing for Label to work around this bug.
I propose that we now remove the Label alias for two reasons.
1. It has been the source of some confusion, especially when using Optional[Label]
2. for the API, users would know what Hashable is without needing to look up the alias.
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BUG:Installation problem in python 3.9
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| 4
| 2020-10-15T12:28:50Z
| 2021-03-28T20:37:02Z
| 2020-10-16T19:25:29Z
|
NONE
| null |
- [ v] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[When installing pandas, we have these messages:
Collecting pandas
Using cached pandas-1.1.3.tar.gz (5.2 MB)
Installing build dependencies ... error
ERROR: Command errored out with exit status 1:
command: 'c:\users\adihamdani\appdata\local\programs\python\python39\python.exe' 'c:\users\adihamdani\appdata\local\programs\python\python39\lib\site-packages\pip' install --ignore-installed --no-user --prefix 'C:\Users\adihamdani\AppData\Local\Temp\pip-build-env-r734sg_q\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- setuptools wheel 'Cython>=0.29.21,<3' 'numpy==1.15.4; python_version=='"'"'3.6'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.15.4; python_version=='"'"'3.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.6'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system=='"'"'AIX'"'"''
cwd: None
Complete output (20 lines):
Ignoring numpy: markers 'python_version == "3.6" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_system != "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.6" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version == "3.7" and platform_system == "AIX"' don't match your environment
Ignoring numpy: markers 'python_version >= "3.8" and platform_system == "AIX"' don't match your environment
Collecting setuptools
Using cached setuptools-50.3.1-py3-none-any.whl (785 kB)
Collecting wheel
Using cached wheel-0.35.1-py2.py3-none-any.whl (33 kB)
Collecting Cython<3,>=0.29.21
Using cached Cython-0.29.21-py2.py3-none-any.whl (974 kB)
Collecting numpy==1.17.3
Using cached numpy-1.17.3.zip (6.4 MB)
Using legacy 'setup.py install' for numpy, since package 'wheel' is not installed.
Installing collected packages: setuptools, wheel, Cython, numpy
Running setup.py install for numpy: started
Running setup.py install for numpy: still running...
Running setup.py install for numpy: finished with status 'done'
ERROR: Could not install packages due to an EnvironmentError: [WinError 123] The filename, directory name, or volume label syntax is incorrect: '"C:'
----------------------------------------
ERROR: Command errored out with exit status 1: 'c:\users\adihamdani\appdata\local\programs\python\python39\python.exe' 'c:\users\adihamdani\appdata\local\programs\python\python39\lib\site-packages\pip' install --ignore-installed --no-user --prefix 'C:\Users\adihamdani\AppData\Local\Temp\pip-build-env-r734sg_q\overlay' --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- setuptools wheel 'Cython>=0.29.21,<3' 'numpy==1.15.4; python_version=='"'"'3.6'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.15.4; python_version=='"'"'3.7'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system!='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.6'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.16.0; python_version=='"'"'3.7'"'"' and platform_system=='"'"'AIX'"'"'' 'numpy==1.17.3; python_version>='"'"'3.8'"'"' and platform_system=='"'"'AIX'"'"'' Check the logs for full command output.
C:\Users\adihamdani>pip install python==3.8.0
ERROR: Could not find a version that satisfies the requirement python==3.8.0 (from versions: none)
ERROR: No matching distribution found for python==3.8.0
]
#### 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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|
CI move check incorrect sphinx directives to pre-commit
|
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| 1
| 2020-10-15T13:17:33Z
| 2020-10-16T07:26:27Z
| 2020-10-16T01:23:04Z
|
MEMBER
| null |
Currently, the check (which uses `grep` with the return codes inverted) doesn't catch
```python
.. warning:
```
, while using `pygrep` from `pre-commit` (which is a Python implementation of `grep` with inverted return codes) does. I think this is because grep doesn't count the newline as a character
Advantages of moving this (and, later, other) `invgrep` checks to pre-commit are:
- they'll be cross-platform
- they'll be simpler to write/maintain because `pre-commit` comes with `pygrep` built-in, so there's no need to manually define something like `invgrep`
- they'll give devs faster feedback on when they introduce unwanted patterns
|
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MDExOlB1bGxSZXF1ZXN0NTA0MTI0MTM5
| 37,137
|
TYP: use overload to refine return type of reset_index
|
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| 2
| 2020-10-15T13:55:31Z
| 2020-10-24T21:02:24Z
| 2020-10-23T21:21:37Z
|
MEMBER
| null |
maybe alternative to #33519
with `from __future__ import annotations` on py3.7 the types in the function signature are not evaluated at run-time, so this shouldn't fail 🤞
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| 722,418,098
|
MDExOlB1bGxSZXF1ZXN0NTA0MTcyMDI0
| 37,138
|
Remove no-longer-necessary CI checks
|
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| 1
| 2020-10-15T15:01:11Z
| 2020-10-16T07:27:05Z
| 2020-10-16T01:08:05Z
|
MEMBER
| null |
xrange is no longer valid in Python3, so this check is unnecessary.
`# -\*- coding: utf-8 -\*-"`, `class\s\S*\((object)?\):`, and `"super\(\w*, (self|cls)\)` are fixed by pyupgrade
closes #37133
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| 37,139
|
BUG: Surprisingly large memory usage in groupby (but maybe I have the wrong mental model?)
|
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[] | null | 3
| 2020-10-15T15:03:02Z
| 2021-07-01T21:12:32Z
| null |
CONTRIBUTOR
| null |
Whilst teaching "more efficient Pandas" I dug into memory usage in a `groupby` with `memory_profiler` and was surprised by the output (below). For a 1.6GB DataFrame of random data with a indicator column (all random data), a groupby on the indicator generates a result that doubles the RAM usage. I was surprised that the groupby would take a further 1.6GB during the operation when the result is a tiny DataFrame.
In this case there are 20 columns by 1 million rows of random floats with a 21st column as an int indicator in the range [0, 9], a gropuby on this creates 10 groups resulting in a mean groupby result of 10 rows by 20 columns. This works as expected.
The groupby operation, shown further below with `memory_profiler`, seems to make a _copy_ of each group before performing a mean, so the total groupby costs a further 1.6GB. I'd have expected that a light reference was taken to the underlying data rather than (apparently, but maybe I read this incorrectly?) a copy being taken. I've also taken out a single group in further lines of code and each group costs 1/10th of the RAM (160-200MB) which gives some further evidence that a copy is being taken.
Is my mental model wrong? Is it expected that a copy is taken of each group? Is there a way to run this code with a smaller total RAM envelope?
```
$ python -m memory_profiler dataframe_example2.py
(10000000, 20) shape for our array
df.head():
0 1 2 3 4 5 6 ... 14 15 16 17 18 19 indicator
0 0.236551 0.588519 0.629860 0.736470 0.064391 0.755922 0.693302 ... 0.031612 0.593927 0.523154 0.704383 0.800547 0.730927 1
1 0.044981 0.305559 0.156594 0.014646 0.339585 0.177476 0.242033 ... 0.428930 0.099833 0.256720 0.326671 0.037584 0.435411 8
2 0.837702 0.246343 0.380937 0.990791 0.586211 0.155818 0.990258 ... 0.453055 0.363815 0.979012 0.220975 0.650783 0.338048 7
3 0.721275 0.327818 0.689749 0.715901 0.617750 0.550584 0.686884 ... 0.172825 0.083338 0.474990 0.213201 0.236640 0.962145 5
4 0.698709 0.998042 0.805397 0.971646 0.260935 0.602839 0.012762 ... 0.458625 0.248945 0.114550 0.212636 0.019970 0.159915 4
[5 rows x 21 columns]
# row by row memory usage clipped and shown further below as a detail
Mean shape: (10, 20)
(20,)
Filename: dataframe_example2.py
Line # Mem usage Increment Line Contents
================================================
9 76.633 MiB 76.633 MiB @profile
10 def run():
11 # make a big dataframe with an indicator column and lots of random data
12 # use 20 columns to make it clear we have "a chunk of data"
13 # float64 * 10M is 80M, for 20 rows this is 80M*20 circa 1,600MB
14 1602.570 MiB 1525.938 MiB arr = np.random.random((SIZE, 20))
15 1602.570 MiB 0.000 MiB print(f"{arr.shape} shape for our array")
16 1602.570 MiB 0.000 MiB df = pd.DataFrame(arr)
17 1602.570 MiB 0.000 MiB cols_to_agg = list(df.columns) # get [0, 1, 2,...]
18
19 # (0, 10] range for 10 indicator ints for grouping
20 1679.258 MiB 76.688 MiB df['indicator'] = np.random.randint(0, 10, SIZE)
21 1679.258 MiB 0.000 MiB print("df.head():")
22 1680.133 MiB 0.875 MiB print(df.head())
23 1680.133 MiB 0.000 MiB print("Memory usage:\n", df.memory_usage())
24
25 # calculate summary statistic across grouped rows by all columns
26 1680.133 MiB 0.000 MiB gpby = df.groupby('indicator')
27 3292.250 MiB 1612.117 MiB means = gpby.mean()
28 3292.250 MiB 0.000 MiB print(f"Mean shape: {means.shape}") # (10, 20) for 10 indicators and 20 columns
29
30 3454.648 MiB 162.398 MiB gp0_indexes = gpby.groups[0]
31 3470.438 MiB 15.789 MiB manual_lookup_mean = df.loc[gp0_indexes, cols_to_agg].mean()
32 3470.438 MiB 0.000 MiB print(manual_lookup_mean.shape)
33 3470.820 MiB 0.383 MiB np.testing.assert_allclose(manual_lookup_mean, means.loc[0])
34
35 3699.656 MiB 228.836 MiB gp0 = gpby.get_group(0)
36 3699.750 MiB 0.094 MiB manual_lookup_mean2 = gp0[cols_to_agg].mean()
37 3699.750 MiB 0.000 MiB np.testing.assert_allclose(manual_lookup_mean2, means.loc[0])
38 #breakpoint()
39 3699.750 MiB 0.000 MiB return df, gpby, means
# row by row memory usage clipped from above and pasted here as it is less relevant detail:
Memory usage:
Index 128
0 80000000
1 80000000
2 80000000
3 80000000
4 80000000
5 80000000
6 80000000
7 80000000
8 80000000
9 80000000
10 80000000
11 80000000
12 80000000
13 80000000
14 80000000
15 80000000
16 80000000
17 80000000
18 80000000
19 80000000
indicator 80000000
dtype: int64
```
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
# this assumes you have memory_profiler installed
#SIZE = 100_000 # quick run
SIZE = 10_000_000 # takes 20s or so & 4GB RAM
@profile
def run():
# make a big dataframe with an indicator column and lots of random data
# use 20 columns to make it clear we have "a chunk of data"
# float64 * 10M is 80M, for 20 rows this is 80M*20 circa 1,600MB
arr = np.random.random((SIZE, 20))
print(f"{arr.shape} shape for our array")
df = pd.DataFrame(arr)
cols_to_agg = list(df.columns) # get [0, 1, 2,...]
# (0, 10] range for 10 indicator ints for grouping
df['indicator'] = np.random.randint(0, 10, SIZE)
print("df.head():")
print(df.head())
print("Memory usage:\n", df.memory_usage())
# calculate summary statistic across grouped rows by all columns
gpby = df.groupby('indicator')
means = gpby.mean()
print(f"Mean shape: {means.shape}") # (10, 20) for 10 indicators and 20 columns
gp0_indexes = gpby.groups[0]
manual_lookup_mean = df.loc[gp0_indexes, cols_to_agg].mean()
print(manual_lookup_mean.shape)
np.testing.assert_allclose(manual_lookup_mean, means.loc[0])
gp0 = gpby.get_group(0)
manual_lookup_mean2 = gp0[cols_to_agg].mean()
np.testing.assert_allclose(manual_lookup_mean2, means.loc[0])
#breakpoint()
return df, gpby, means
if __name__ == "__main__":
df, gpby, means = run()
```
#### Output of ``pd.show_versions()``
<details>
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.1-050801-generic
Version : #202008111432 SMP Tue Aug 11 14:34:42 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : None
pytest : 6.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.2
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NTA0MjA2NDYx
| 37,140
|
BUG: algos.diff with datetimelike and NaT
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU3MjI1MTg3MjY=
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|
BUG: groupby.rolling.mean seems to roll over different groups when center=True
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| 3
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| 2020-10-16T14:13:22Z
| 2020-10-16T08:36:26Z
|
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.
---
To reproduce:
```python
import pandas as pd
# Create a DF with a group column and more than 1 group (10 rows toy data)
dfBug = pd.DataFrame(data={
'Date': pd.date_range('2020-01-01', '2020-01-10'),
'gb': ['group_1'] * 6 + ['group_2'] * 4,
'value': range(10),
})
center = True # Problem only occurs when center=True
# the apply produces the expected result
via_apply = dfBug.groupby('gb').apply(lambda df: \
df.set_index('Date').rolling(6, center=center, min_periods=1).value.mean())
# the groupby.rolling... produces a different result
via_groupbyrolling = dfBug.groupby('gb') \
.rolling(6, on='Date', center=center, min_periods=1).value.mean()
print(pd.concat([via_apply.rename('apply'),
via_groupbyrolling.rename('groupbyrolling'),
(via_apply == via_groupbyrolling).rename('is-equal'),
], axis=1))
assert (abs(via_apply - via_groupbyrolling) < 1e-4).all()
```
#### Problem description
The code via apply and via groupbyrolling should produce identical results, yet the groupbyrolling version seems to ignore the grouping for the mean. Strange enough, when setting center to False, both variants produce identical results.
Output is the following (I expect the is-equal column to be True in all rows):
```
apply groupbyrolling is-equal
gb Date
group_1 2020-01-01 1.0 1.0 True
2020-01-02 1.5 1.5 True
2020-01-03 2.0 2.0 True
2020-01-04 2.5 2.5 True
2020-01-05 3.0 6.0 False
2020-01-06 3.5 6.5 False
group_2 2020-01-07 7.0 7.0 True
2020-01-08 7.5 7.5 True
2020-01-09 7.5 NaN False
2020-01-10 7.5 NaN False
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-51-generic
Version : #56-Ubuntu SMP Mon Oct 5 14:28:49 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.1.3
numpy : 1.17.4
pytz : 2020.1
dateutil : 2.7.3
pip : 20.0.2
setuptools : 45.2.0
Cython : 0.29.21
pytest : 4.6.9
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.1.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTA0Mjc1NDUw
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|
CLN: indexing
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| 0
| 2020-10-15T17:50:27Z
| 2020-10-16T01:09:22Z
| 2020-10-16T01:05:18Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Working on refactoring the indexing setitem code so that we go down the same path regardless of consolidation status. This splits off a small piece of that as a prelim.
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DOC: Remove redundant table in Series docs
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| 1
| 2020-10-15T18:00:42Z
| 2020-10-16T01:21:23Z
| 2020-10-16T01:21:15Z
|
CONTRIBUTOR
| null |
The attribute `Series.index` was separated in another table, which makes
the rendering of the documentation slightly inconsistent. This commit
consolidates the tables together to make the documentation consistent
and aligned.
**Live site** (https://pandas.pydata.org/docs/reference/series.html):
**Locally rendered change**:
I'm not sure why the color changed. My guess is because I just rendered that page with `python make.py --single reference/series.rst`, so the links to those pages are nonexistent.
If this separate table was intentional, feel free to close this PR. Thanks for considering this!
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BLD: update build requirement for py39 #37135
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| 2020-10-17T11:28:25Z
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MEMBER
| null |
- [x] closes #37135
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BUG: Fix bug in quantile() for resample and groupby with Timedelta
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| 2020-10-15T20:38:45Z
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MEMBER
| null |
- [x] closes #29485
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DOC: capitalize Python as proper noun
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| 2020-10-16T01:21:57Z
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CONTRIBUTOR
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MDExOlB1bGxSZXF1ZXN0NTA0Mzc5NDQ2
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CLN: sync min versions
|
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| 1
| 2020-10-15T20:49:08Z
| 2020-10-21T00:29:38Z
| 2020-10-16T01:18:30Z
|
MEMBER
| null |
The minimum version of pytables and pytest was not synchronized.
|
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MDExOlB1bGxSZXF1ZXN0NTA0MzgxNDU0
| 37,148
|
BUG: DataFrame[td64].sum(skipna=False)
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| 2020-10-15T20:53:27Z
| 2020-10-24T03:08:28Z
| 2020-10-24T02:47:51Z
|
MEMBER
| null |
- [x] closes #17235
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The same underlying problem affects mean, so this fixes that too.
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MDExOlB1bGxSZXF1ZXN0NTA0NDMwMjcy
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|
BUG: GroupBy().fillna() performance regression
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| 10
| 2020-10-15T22:52:55Z
| 2020-10-19T09:50:03Z
| 2020-10-18T14:56:32Z
|
MEMBER
| null |
- [x] closes #36757
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A performance regression was introduced with #30679 in handling grouping on a duplicate axis. The regression can be avoided by skipping the call to `get_indexer_non_unique` if the axis is unchanged (i.e. like it is in `fillna`).
I don't know how to write a test for this fix since it is just an issue of speed. If there is a test pattern or other check that should be included please highlight and I'm happy to add it.
In lieu of a test, running the minimal example from the issue report on the fixed branch shows the performance fix:
```
In [5]: import pandas as pd
...: import numpy as np
...:
...: N = 2000
...: df = pd.DataFrame({"A": [1] * N, "B": [np.nan, 1.0] * (N // 2)})
...: df = df.sort_values("A").set_index("A")
...:
...: %time df.groupby("A")["B"].fillna(method="ffill")
...:
Wall time: 1.03 ms
Out[5]:
A
1 NaN
1 1.0
1 1.0
1 1.0
1 1.0
...
1 1.0
1 1.0
1 1.0
1 1.0
1 1.0
Name: B, Length: 2000, dtype: float64
```
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MDExOlB1bGxSZXF1ZXN0NTA0NDU5MTMx
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BUG: indexing bugs #26490, #13691
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| 2020-10-15T23:48:49Z
| 2020-10-16T15:31:47Z
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|
MEMBER
| null |
- [x] closes #26490
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MDU6SXNzdWU3MjI3NjAxNjg=
| 37,151
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API/BUG: empty sum with td64 NaT or timedelta(0)?
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| 1
| 2020-10-15T23:53:04Z
| 2020-10-26T12:06:46Z
| 2020-10-26T12:06:46Z
|
MEMBER
| null |
```
ser = pd.Series([], dtype="m8[ns]")
>>> ser.sum()
NaT
>>> pd.Index(ser).sum()
NaT
>>> ser.array.sum()
NaT
>>> ser.to_frame().sum()
0 0 days
dtype: timedelta64[ns]
```
I think zero makes more sense. Easy fix if we have consensus.
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BUG: RecursionError when selecting single column from IntervalIndex #26490
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| 0
| 2020-10-16T01:36:41Z
| 2020-10-17T01:54:05Z
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|
MEMBER
| null |
- [x] closes #26490
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
#37150 is failing, this is the first of two orthogonal bugfixes in that PR
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BUG: rolling.median/quantile does not work with (some) BaseIndexer subclasses
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| 2020-10-16T03:48:16Z
| 2020-10-30T23:11:48Z
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|
MEMBER
| 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.
---
Using the example from `computation.rst`.
```
In [1]: In [70]: use_expanding = [True, False, True, False, True]
...:
In [2]: In [72]: df = pd.DataFrame({'values': range(5)})
...:
In [3]: In [2]: from pandas.api.indexers import BaseIndexer
...:
In [4]: ...: class CustomIndexer(BaseIndexer):
...: ...:
...: ...: def get_window_bounds(self, num_values, min_periods, center, closed):
...: ...: start = np.empty(num_values, dtype=np.int64)
...: ...: end = np.empty(num_values, dtype=np.int64)
...: ...: for i in range(num_values):
...: ...: if self.use_expanding[i]:
...: ...: start[i] = 0
...: ...: end[i] = i + 1
...: ...: else:
...: ...: start[i] = i
...: ...: end[i] = i + self.window_size
...: ...: return start, end
...:
In [5]: indexer = CustomIndexer(window_size=1, use_expanding=use_expanding)
# Each window
In [7]: for window in df.rolling(indexer):
...: print(window)
...:
values
0 0
values
1 1
values
0 0
1 1
2 2
values
3 3
values
0 0
1 1
2 2
3 3
4 4
# Should be [0, 1, 1, 3, 2]
In [8]: df.rolling(indexer).median()
Out[8]:
values
0 0.0
1 1.0
2 1.5
3 NaN
4 3.0
# Should be [0, 1, 1, 3, 2]
In [10]: df.rolling(indexer).quantile(0.5)
Out[10]:
values
0 0.0
1 1.0
2 1.5
3 NaN
4 3.0
```
The cython aggregations probably assume some sort of property of the windows that may not hold with the custom indexers
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TST: RollingGroupby(..., center=True, on=...)
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| 2020-10-16T04:10:02Z
| 2020-10-16T16:16:14Z
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MEMBER
| null |
- [x] closes #37141
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Didn't look like we had a test case that exactly covered this.
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| 722,969,170
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MDExOlB1bGxSZXF1ZXN0NTA0NjQwMjcx
| 37,155
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REF: avoid access to _mgr.blocks -> _mgr._is_single_block
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| 2
| 2020-10-16T07:09:09Z
| 2020-10-16T17:27:38Z
| 2020-10-16T17:27:33Z
|
MEMBER
| null |
@jbrockmendel a small follow-up on https://github.com/pandas-dev/pandas/pull/36873. It keeps the same spirit of that PR (I think), but just replacing the access of `len(self._mgr.blocks)` with an existing attribute on the manager (could also remove the leading underscore on the attribute).
(this makes it easier to override this attribute in the ArrayManager, and limits the access to the blocks outside of the internals)
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MDExOlB1bGxSZXF1ZXN0NTA0NjUwNjc3
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CLN/REF: Refactor min_periods calculation in rolling
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| 2
| 2020-10-16T07:30:53Z
| 2020-10-16T19:20:30Z
| 2020-10-16T19:09:58Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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BUG: pd.read_sql_table() raises unknown column error when column name of a table contains `%`
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| 4
| 2020-10-16T08:27:57Z
| 2020-12-22T13:57:17Z
| 2020-12-22T13:57:17Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
###### Mysql Create Table
```
DROP TABLE IF EXISTS test.my_table;
CREATE TABLE test.my_table
(
`id` VARCHAR(3),
PRIMARY KEY(id),
`price` DOUBLE NULL,
`%_variation` DOUBLE NULL
);
INSERT INTO test.my_table VALUES('101', 25.2, 0.1);
INSERT INTO test.my_table VALUES('102', 40.2, -0.5);
INSERT INTO test.my_table VALUES('103', 55.2, 0.9);
```
```python
import pandas as pd
from sqlalchemy import create_engine
DB_PW = 'My_password'
DB_PORT = '3306'
DB_NAME = 'Test'
db_uri = f'mysql+mysqldb://root:{DB_PW}@localhost:{DB_PORT}'
engine = create_engine(f'{db_uri}/{DB_NAME}')
df = pd.read_sql_table(table_name='my_table', con=engine, schema=DB_NAME)
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
As you can see above, the `%_variation` column is read as `%%_variation` in the SELECT statement. <br>
But the `%%_variation` column is not present in the database and thus reading this table from the database provides an error.
#### Expected Output
I expect pandas to read the table from the database. <br>
I tried reading with pandas version 1.0.5 and I was able to read the table from the database without any problem. <br>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3MjMwMzExMzU=
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BUG: unexpected behaviour using `.sample` without replacement and with weights as a series
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| 2020-10-16T08:48:11Z
| 2020-11-17T04:24:28Z
| 2020-11-17T04:24:27Z
|
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
```python
import pandas as pd
elems = [0, 1, 2, 3, 4]
series_test = pd.Series(elems)
long_bad_weights = [0.1, 0.2] * 3
long_bad_weights_series = pd.Series([0.1, 0.2] * 3)
short_bad_weights_series = pd.Series([0.1, 0.2])
### Case 1: Weights passed in as a list
# throws pandas error - if weight list is too long or short, also throws with replace=True and replace=False
series_test.sample(weights=long_bad_weights)
### Case 2: Weights passed in as a series, with replace=True
series_test.sample(weights=long_bad_weights_series) # throws no error
series_test.sample(n=3, weights=short_bad_weights_series, replace=True) # throws no error
series_test.sample(n=3, weights=long_bad_weights_series, replace=True) # throws no error
### Case 3: Weights passed in as a series, with replace=False
series_test.sample(n=3, weights=long_bad_weights_series, replace=False) # throws no error
series_test.sample(n=3, weights=short_bad_weights_series, replace=False) # throws numpy error
```
#### Problem description
Passing in weights that do not match the length of the axis to be sampled throws a pandas error (Case 1). However, if passing in weights as a series, `.sample` accepts it despite unequal lengths.
This behaviour seems rather inconsistent to me. It results an error possibly being thrown by numpy, _if the weights are too short_, depending on whether `replace=True` or `replace=False` (Case 2, Case 3).
In the code for `.sample` with weights passed in as a series, we reindex the weights, leading to `np.nan`s if the weights were too short.
https://github.com/pandas-dev/pandas/blob/c2a96c644e51f4d164b657240b7bed5f39fd349c/pandas/core/generic.py#L5200-L5202
This can later throw an error from numpy, since random.choice doesn't accept sampling with replacement if `np.nan` is in the weights.
**Case 1 pandas error**
```
Traceback (most recent call last):
File "/python-scratchpad/main.py", line 11, in <module>
df_test.sample(weights=long_bad_weights)
File "/python-scratchpad/venv/lib/python3.8/site-packages/pandas/core/generic.py", line 4950, in sample
raise ValueError(
ValueError: Weights and axis to be sampled must be of same length
```
**Case 3 numpy error**
```
Traceback (most recent call last):
File "/repositories/python-scratchpad/main.py", line 20, in <module>
series_test.sample(n=3, weights=short_bad_weights_series, replace=False) # throws numpy error
File "/repositories/python-scratchpad/venv/lib/python3.8/site-packages/pandas/core/generic.py", line 4993, in sample
locs = rs.choice(axis_length, size=n, replace=replace, p=weights)
File "mtrand.pyx", line 961, in numpy.random.mtrand.RandomState.choice
ValueError: Fewer non-zero entries in p than size
```
#### Expected Output
I'd expect pandas to throw a weight length error, no matter the dtype the weights are passed in as. Is there a reason for accepting weights as a series regardless of length?
#### Output of ``pd.show_versions()``
<details>
```INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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BUG: Fix parsing of ISO8601 durations
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| 1
| 2020-10-16T11:29:05Z
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| 2020-10-17T00:07:08Z
|
CONTRIBUTOR
| null |
This PR fixes the following behavior:
- Adds "W" as a valid weeks designator
- Fixes parsing of durations with empty periods (e.g. "PT10M")
- Fixes partial parsing of durations (e.g. "P1DT1H")
Fixes #29773.
Fixes #36204.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MEMBER
| null |
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Gets rid of an unnecessary special case.
Could do this as a deprecation first.
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Broken off from #37150
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BUG: Bug in quantile() and median() returned wrong result for non monotonic window borders
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cc @mroeschke
Values were not added to the window again if starting point was moved back and values were not removed from the window, when ending point was moved back
Should I create functions for the deletion and addition?
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MDU6SXNzdWU3MjM0NzE5MTc=
| 37,167
|
BUG: Legend is wrong when plotting line over bar plot on same axis
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| 11
| 2020-10-16T19:21:46Z
| 2021-09-13T09:43:26Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import numpy as np
import pandas as pd
np.random.seed(123456)
d1 = ((np.random.randn(8)*3) + 200).astype(np.int)
d2 = ((np.random.randn(8)*3) - 150).astype(np.int)
df = pd.DataFrame({'Accrued': d1, 'Spent': d2})
df.loc[:, 'Net'] = df['Accrued'] + df['Spent']
# First plot step
ax = df.plot.bar(y=['Accrued', 'Spent'], stacked=True)
# Second plot step
df.plot.line(y='Net', marker='s', ms=3, color='0.3', ax=ax)
```
#### Problem description
I'm plotting a line on top of a stacked bar plot, but when I do the legend ends up incorrect - somehow the label _appends_ to the legend while the actual marker _prepends_ to the legend. The first plot is what shows after the first plot step. The second plot is after the second plot step. The dark line should be associated with 'Net', but instead it's associated with 'Accrued', and the whole legend is wrong.
It may be worth noting that if I replace `.bar(` with `.line(` in the first step, the legend stays correct. I usually run from inside PyCharm but this particular behavior is the same whether from PyCharm or directly from the command line.
#### Expected Output
I expect that the legend should a) update properly, or b) at the very least not update at all.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0.post20201006
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : 0.11.3
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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|
NIT: Format text.rst doc code
|
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| 1
| 2020-10-16T20:01:58Z
| 2020-10-17T00:03:23Z
| 2020-10-16T23:47:09Z
|
MEMBER
| null |
Minor formatting mistake I made in another PR
|
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CI: Windows PY38 build RuntimeError: can't start new thread
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| 2020-10-16T20:30:29Z
| 2020-11-18T16:28:56Z
| null |
MEMBER
| null |
I'm seeing this build fail a lot with low-level-looking errors, e.g.
```
FATAL ERROR: Committing semi space failed. Allocation failed - process out of memory
```
```
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 257, in wrap_session
INTERNALERROR> session.exitstatus = doit(config, session) or 0
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 313, in _main
INTERNALERROR> config.hook.pytest_runtestloop(session=session)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286, in __call__
INTERNALERROR> return self._hookexec(self, self.get_hookimpls(), kwargs)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84, in <lambda>
INTERNALERROR> self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 208, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 80, in get_result
File "C:\Miniconda\envs\pandas-dev\Scripts\pytest-script.py", line 10, in <module>
sys.exit(main())
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\config\__init__.py", line 164, in main
ret = config.hook.pytest_cmdline_main(
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286, in __call__
return self._hookexec(self, self.get_hookimpls(), kwargs)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93, in _hookexec
return self._inner_hookexec(hook, methods, kwargs)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84, in <lambda>
self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 208, in _multicall
return outcome.get_result()
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 80, in get_result
raise ex[1].with_traceback(ex[2])
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187, in _multicall
res = hook_impl.function(*args)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 306, in pytest_cmdline_main
return wrap_session(config, _main)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 294, in wrap_session
config.hook.pytest_sessionfinish(
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286, in __call__
return self._hookexec(self, self.get_hookimpls(), kwargs)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93, in _hookexec
return self._inner_hookexec(hook, methods, kwargs)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84, in <lambda>
self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 203, in _multicall
gen.send(outcome)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\terminal.py", line 794, in pytest_sessionfinish
outcome.get_result()
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 80, in get_result
raise ex[1].with_traceback(ex[2])
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187, in _multicall
res = hook_impl.function(*args)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\dsession.py", line 86, in pytest_sessionfinish
nm.teardown_nodes()
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\workermanage.py", line 78, in teardown_nodes
self.group.terminate(self.EXIT_TIMEOUT)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\multi.py", line 215, in terminate
safe_terminate(
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\multi.py", line 308, in safe_terminate
reply = workerpool.spawn(termkill, termfunc, killfunc)
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 315, in spawn
self.execmodel.start(self._perform_spawn, (reply,))
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 93, in exec_start
self._start_new_thread(func, args)
RuntimeError: can't start new thread
```
```
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 257, in wrap_session
INTERNALERROR> session.exitstatus = doit(config, session) or 0
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 313, in _main
INTERNALERROR> config.hook.pytest_runtestloop(session=session)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286, in __call__
INTERNALERROR> return self._hookexec(self, self.get_hookimpls(), kwargs)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84, in <lambda>
INTERNALERROR> self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 208, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 80, in get_result
INTERNALERROR> raise ex[1].with_traceback(ex[2])
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\dsession.py", line 112, in pytest_runtestloop
INTERNALERROR> self.loop_once()
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\dsession.py", line 135, in loop_once
INTERNALERROR> call(**kwargs)
INTERNALERROR> File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\dsession.py", line 174, in worker_workerfinished
INTERNALERROR> assert not crashitem, (crashitem, node)
INTERNALERROR> AssertionError: ('pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_hist_weights[weights0]', <WorkerController gw1>)
INTERNALERROR> assert not 'pandas/tests/plotting/test_frame.py::TestDataFramePlots::test_hist_weights[weights0]'
```
```
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...................................................ss.....................................................................................................................................x....................................................................x.........................ssssss.................................................................................................................................................................................................................ss.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ss.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................s...................................x............................................................................x.x.....x..x.x.......................................................................................................................................................................................................................................................................................x.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................x...x........x.x.x.x.................x..xxxxxxx.............xx.....xx...........s.......s...................xxxxx....x......................................xxxxxx.xx......................................ss...................s.sss...............................................................................................................................................................................................................................................x........................................................................x................................................................................................................................................................................................................................................................................................................s...............................................x........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................s...........................................................................................................................................................................................................................................................................................................................................................................................................s...............s.................s.......s..s...ss...........s..........................................................................................................................................................................................................................................................................................................sssssssssss.s.sssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss...................................s...s.........................................................................ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss.....................................ssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssssss.............................sssssssss..s......................................................................................................s......s............................................ss................ss...............ss..........................x......................x............ssssssssssss....................ssss.........ss.........ssx..........ss.........ss...........ss...............s.......ssss......s........ss......s.......ss...s........ss..........s.....ss.......s........ss.....sss.s.ss..........ssss.ss..........s.ss..........ss..........s....ss.......................sssss........X..s.ssssssss.sssssss.s.sssss........s..ss..........ss...........ss......s.ss.ss.s.ss...........ss......x.x...ss..ssss.ss..x..x.....ss......s......ss.........ss........x...s...sssssssss.sss.............ss.....s.......s.s..........ss.............s..ss......................s....s.......s......s....s.....s....ss.......s.......ss.............................ss.............ss.s..ss......ss.....s...........................ss.s..................................................ss......s..................................ss.........................ss.s............s...ss..........s.......s........s......s.........s.......s......s.....s.......s........s........................s...ss............ss...................s...ss........s................s.............s.ssssss.........s...................s........ss.............s...ss......s........s.s....s......ss.........ss..........s...ss.............ss......................s...................ss.ss...............s.s.....s.............s.............s........................s....................s...........................................................s........................................s...........s....................s.............................s........................................s.........s.......s.sss.............s.........s..............s..........s........................s..............................s.............................x......................ss.ss...s..............................................................s.......................s....................s.....................s............................s.............................................s.............................s......................................s................................s..................................s....s....s....s....s..................s.................................s..........................s...ssss........................s..................................................s.ss......s...s..............................s...................s..........................s.....................................s..........................s.............................................s..................................s........................................s..............................................s...........................................s....................s..x...........x....s.........x..........s.x..............x.........x........x....s.......x.........s.....x...s....................s................s..........s.............s....................s..........................s..............................s...............s..........................s..............................s........................s.............s....................s..................s..........................s...............................................s....................s..........s........s.......s......s......x........s................s...x.....s...........s........................s....s....xs......s....s......s......s..............xs............s....................s..............s...............s.........s...x.........s............ss........xs..........ss..s................s...................s................s...........s...............s..x..................sx.......s.......s............xx..xxxxxxxx.x...x...................s.......................................................................xx.....................................s...........................................................................s.s......s..................s..s.s.x.....x....s.....s..s..x....x....s..s....sx..x...............................................................................................................................................................................................................................................................................................................................................sss................................................................s........s................................................ss.........................................ss.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................s..................................................................................................................................................................................................................................ss..ss....s.....s.ss.sssss.s.s...............................................................s....................................................................................................................................................................................................x.....x...........................x...................x..............x...................x.................................................................................................................................................................s....................................................................................................................................................................................................................x...........................................................................s...............................................................x..........x...........................s................s............................s..........s.............................................................................s................s.......................................s.s.....................................x..........................s..................................................................................x............................s....................................................................ssss..............................................................................................................................................................................................................FF..................................................................................................................................................Fx..................................................s..................................................................................................................................s..s..s.......s..s.s.sss......................................................................................................Windows fatal exception:
stack overflow
Thread 0x00001184 (most recent call first):
<no Python frame>
Current thread 0x00001f04 (most recent call first):
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 180 in Event
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 197 in __init__
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 309 in spawn
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\multi.py", line 300 in termkill
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 220 in run
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 285 in _perform_spawn
Thread 0x00001fd8 (most recent call first):
<no Python frame>
Thread 0x00000eb0 (most recent call first):
<no Python frame>
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 285 in _perform_spawn
Thread 0x000011e0 (most recent call first):
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 400 in read
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 432 in from_io
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 967 in _thread_receiver
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 220 in run
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 285 in _perform_spawn
Thread 0x00001fd8 (most recent call first):
File "C:\Miniconda\envs\pandas-dev\lib\threading.py", line 302 in wait
File "C:\Miniconda\envs\pandas-dev\lib\threading.py", line 558 in wait
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 213 in waitfinish
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\gateway_base.py", line 206 in get
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\multi.py", line 311 in safe_terminate
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\execnet\multi.py", line 215 in terminate
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\workermanage.py", line 78 in teardown_nodes
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\xdist\dsession.py", line 86 in pytest_sessionfinish
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84 in <lambda>
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93 in _hookexec
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 294 in wrap_session
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\main.py", line 306 in pytest_cmdline_main
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\callers.py", line 187 in _multicall
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 84 in <lambda>
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\manager.py", line 93 in _hookexec
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\pluggy\hooks.py", line 286 in __call__
File "C:\Miniconda\envs\pandas-dev\lib\site-packages\_pytest\config\__init__.py", line 164 in main
File "C:\Miniconda\envs\pandas-dev\Scripts\pytest-script.py", line 10 in <module>
The STDIO streams did not close within 10 seconds of the exit event from process 'C:\Program Files\Git\bin\bash.exe'. This may indicate a child process inherited the STDIO streams and has not yet exited.
```
Is this an upstream thing?
|
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BUG: MultiIndex comparison with tuple #21517
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- [x] closes #21517
- [x] closes #34180
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: ensure we pass correct type to DTI/TDI shallow_copy
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BUG: Handle negative sign (-) when parsing ISO 8601 durations
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| 2
| 2020-10-16T21:28:08Z
| 2021-02-10T23:15:25Z
| 2021-02-10T23:15:25Z
|
CONTRIBUTOR
| null |
Related to #37159, #29773, #36204, splitting out only dealing with the behavior of the negative sign when parsing ISO 8601 Durations.
The current behavior is somewhat counter intuitive:
`"P-6DT0H50M3.010010012S"` parses as `Timedelta(
days=-6,
minutes=50,
seconds=3,
milliseconds=10,
microseconds=10,
nanoseconds=12,
)`
, and the negative is only allowed right after the P descriptor. A negative in any other position will raise an error.
This [comment](https://github.com/moment/moment/issues/2408#issuecomment-110180010) notes that the original spec for 8601 doesn't mention negativity at all, but that some other "extensions" (e.g. usage of it in Java Duration) do support it. I have been unable to find the detailed ISO 8601 spec.
As far as I can tell, there are a few possibilities to deal with this here:
- explicitly drop support for the negative sign
- only support an overall "-" e.g. `"-P6DT1H" = Timedelta('-7 days +23:00:00')` and/or
- support positive/negative on each, e.g. `"P7DT-1H3M" = Timedelta('6 days 23:03:00')`
_Originally posted by @mgmarino in https://github.com/pandas-dev/pandas/pull/37159#discussion_r506726762_
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| 4
| 2020-10-16T21:40:31Z
| 2020-11-28T18:41:42Z
| 2020-10-26T13:27:02Z
|
CONTRIBUTOR
| null |
Currently tab completion doesn't work on attributes defined on the instance, only ones defined on the class. This fixes that.
Example:
```python
>>> class MyCls(pd.core.accessor.DirNamesMixin):
... x = 1
...
... def __init__(self):
... self.y = 2
...
>>> x = MyCls()
>>> "x" in dir(x)
True # master and this PR
>>> "y" in dir(x)
False # master
True # this PR
```
Also some type fixups.
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BUG: ValueError: buffer source array is read-only
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| 0
| 2020-10-16T21:42:35Z
| 2020-10-17T17:04:02Z
| 2020-10-17T17:04:02Z
|
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.
---
This seems similar to: https://github.com/pandas-dev/pandas/issues/31710
I am getting it in a dash application, so it's difficult to get an example from there.
However, isin() example from the above post also causes the error:
#### Code Sample, a copy-pastable example
```python
>>> import numpy as np # v1.16.4, then v1.18.1
>>> import pandas as pd # v1.0.1
>>>
>>> arr = np.array([1,2,3], dtype=np.int64) # works fine if I don't set the dtype!
>>>
>>> arr.setflags(write=False) # make it read-only
>>>
>>> df = pd.DataFrame({"col": [2,4,8]})
>>>
>>> test = df.col.isin(arr)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/z0022z7b/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/series.py", line 4685, in isin
result = algorithms.isin(self, values)
File "/home/z0022z7b/anaconda3/envs/mindsynchro/lib/python3.8/site-packages/pandas/core/algorithms.py", line 465, in isin
return f(comps, values)
File "pandas/_libs/hashtable_func_helper.pxi", line 566, in pandas._libs.hashtable.ismember_int64
File "stringsource", line 658, in View.MemoryView.memoryview_cwrapper
File "stringsource", line 349, in View.MemoryView.memoryview.__cinit__
ValueError: buffer source array is read-only
```
#### Problem description
The problem is this error. My application code worked fine before, but I had to set it up on a new machine with new pandas and cython a couple days ago, and ran into this.
(Previously I had
- cython=0.29.14=py37he1b5a44_0
- pandas=0.24.2=py37he6710b0_0
Don't know if the isin() example would have thrown an error with that)
#### Output of ``pd.show_versions()``
'''
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-118-generic
Version : #119-Ubuntu SMP Tue Sep 8 12:30:01 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20201009
Cython : 0.29.21
lxml.etree : 4.5.2
jinja2 : 2.11.2
IPython : 7.18.1
fsspec : 0.8.4
fastparquet : 0.4.1
matplotlib : 3.3.2
pyarrow : 0.17.1
scipy : 1.5.2
xarray : 0.16.1
numba : 0.51.2
'''
</details>
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DOC: Same description of `Returns` in `isna` and `notna`
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| 1
| 2020-10-16T21:48:10Z
| 2020-10-17T16:00:53Z
| 2020-10-17T16:00:53Z
|
NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isna.html
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.notna.html
#### Documentation problem
There seems to be a typo in `Returns` section where both `isna` and `notna` are having this same description:
_Mask of bool values for each element in DataFrame that indicates whether an element is **not an NA value.**_
#### Suggested fix for documentation
I think `isna` doc should be corrected with:
_Mask of bool values for each element in DataFrame that indicates whether an element is **an NA value.**_
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BUG: infer_dtype with decimal/complex
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| 1
| 2020-10-16T21:59:49Z
| 2020-10-18T17:39:28Z
| 2020-10-18T14:58:45Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Does this need a whatsnew?
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REF: Simplify creation of rolling window indexers internally
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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|
TST: suppress matplotlib warnings (or determine if they represent a bug)
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MEMBER
| null |
There are a bunch of these in the test logs:
```
pandas/tests/plotting/test_hist_method.py::TestDataFramePlots::test_hist_column_order_unchanged[None-expected0]
pandas/tests/plotting/test_hist_method.py::TestDataFramePlots::test_hist_column_order_unchanged[column1-expected1]
pandas/tests/plotting/test_hist_method.py::TestDataFramePlots::test_hist_with_legend[None-None]
pandas/tests/plotting/common.py:556: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
ret = f(**kwargs)
```
Do these represent problems we need to do something about? cc @tacaswell
If not, we should suppress them. My attempts to do so with `pytestmark = pytest.mark.filterwarnings` have failed, could use another pair of eyeballs.
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API: tighten DTA/TDA _from_sequence signature
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MEMBER
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cc @jorisvandenbossche
need to decide how to handle test_from_2d_ndarray_with_dtype which this currently xfails
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BUG: use cls instead of MultiIndex in MultiIndex classmethods
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| 2020-10-17T01:51:49Z
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MEMBER
| null |
- [x] closes #11267
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This doesn't go through all the places where MultiIndex is hard-coded, just the classmethods where in `cls` should be used as a matter of principle.
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BUG: Fix isin with read-only target
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Tests for where() with categorical
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| 2020-10-20T01:37:40Z
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| null |
- [X] closes #18888
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Hey everyone,
I wrote some tests for issue #18888. I don't know exactly if the format is correct but if any change is needed I'm happy to make it right.
Which 'whatsnew entry' should I report for both new tests?
Also, if you think this is a decent contribution and if there's no problem with doing that, it would be nice to tag this PR with hacktoberfest-accepted label :)
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CLN: core/dtypes/cast.py::maybe_casted_values
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- [ ] closes #xxxx
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xref https://github.com/pandas-dev/pandas/pull/36985#issuecomment-705804862
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BUG: Series.loc[timestamp] with MultiIndex with DTI first-level
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The underlying problem here is that `MultiIndex._get_level_number` is not idempotent.
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DOC: Add example to Series.divmod
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CONTRIBUTOR
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- [x] closes #24589
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
-----
[Current doc](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.divmod.html); Rendered doc:
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| 723,627,283
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MDExOlB1bGxSZXF1ZXN0NTA1MTc5NTc1
| 37,186
|
call __finalize__ in more methods
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| 6
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| 2020-10-20T18:01:30Z
| 2020-10-20T18:01:26Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The goal here is to make progress on some of the more uncontroversial/easier content of #28283. I'll split off into another PR if this gets too big
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|
BUG:When SeriesGroupBy.apply to return DataFrame for each group, the result is incorrect
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| 2021-01-27T12:11:26Z
| 2021-01-27T12:11:25Z
|
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.
- [ ] (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
import numpy as np
df = pd.DataFrame(
{
'name':'a a b b'.split(),
'n1': [1,20,1,30],
})
def ap_gp(s):
des = np.where(s>10,'over','not over')
res = s.to_frame()
res['des']=des
return res
df.groupby('name')['n1'].apply(ap_gp)
```
output:
| | a | des | b |
|---:|----:|:---------|----:|
| 0 | 1 | not over | nan |
| 1 | 20 | over | nan |
| 2 | nan | not over | 1 |
| 3 | nan | over | 30 |
#### Expected Output
| | n1 | des |
|---:|-----:|:---------|
| 0 | 1 | not over |
| 1 | 20 | over |
| 2 | 1 | not over |
| 3 | 30 | over |
#### Problem description
I think the reason for this error is to modify the name attribute of Series before concat
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NTA1MjAyNTQ2
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|
CI: Check for inconsistent pandas namespace usage
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| 7
| 2020-10-17T03:51:42Z
| 2020-10-21T00:54:43Z
| 2020-10-21T00:52:16Z
|
MEMBER
| null |
Adding a CI check that we aren't (for instance) using Series(...) and pd.Series(...) in the same file. ~This is kept intentionally small in scope (checking only DataFrame and Series for one file name right now) since this is very common in the code base and I'm not sure if this is something we'd actually want to enforce.~
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Backport PR #37144 on branch 1.1.x: BLD: update build requirement for py39 #37135
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| 0
| 2020-10-17T11:27:04Z
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MEMBER
| null |
Backport PR #37144 on branch 1.1.x
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MDU6SXNzdWU3MjM3NDc2Mzg=
| 37,190
|
weird behaviours with tuples in index
|
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[] | null | 1
| 2020-10-17T12:32:06Z
| 2021-08-13T04:57:57Z
| null |
NONE
| null |
I'm not submitting this strictly as a bug because this is so messed up that I seriously consider tuples in indexes just simply never work, but I don't think the docs are clear about it.
I'm using python 3.6.9 and pandas==1.1.3.
Example 1:
```
import pandas as pd
data = {
'first': [[(0,), 0, 0], [(1,), 1, 1], [(2,), 2, 2]],
'second': [[(0,), 0, 0], [(1,), 1, 1], [(1,), 2, 2]],
}
for name, d in data.items():
df = pd.DataFrame(d, columns=['a', 'b', 'c']).set_index('a')
print(name)
print(df)
print(df.index.get_loc((0,)))
```
works for `first` and not for `second`:
```
first
b c
a
(0,) 0 0
(1,) 1 1
(2,) 2 2
0
second
b c
a
(0,) 0 0
(1,) 1 1
(1,) 2 2
Traceback (most recent call last):
File "pandas/_libs/index.pyx", line 112, in pandas._libs.index.IndexEngine._get_loc_duplicates
TypeError: '<' not supported between instances of 'tuple' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 2895, in get_loc
return self._engine.get_loc(casted_key)
File "pandas/_libs/index.pyx", line 70, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 96, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 116, in pandas._libs.index.IndexEngine._get_loc_duplicates
KeyError: (0,)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "t3.py", line 10, in <module>
print(df.index.get_loc((0,)))
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 2897, in get_loc
raise KeyError(key) from err
KeyError: (0,)
```
Example 2:
```
import pandas as pd
data = [[0, (1,2), (3,)], [1, (2,3), (4,5)]]
a = pd.DataFrame(data, columns=['a', 'b', 'c']).set_index(['b', 'c'])
print(a)
print(a.loc[[a.index.values[0]]])
print(a.loc[[a.index.values[1]]])
```
works for the first row only:
```
a
b c
(1, 2) (3,) 0
(2, 3) (4, 5) 1
a
b c
(1, 2) (3,) 0
Traceback (most recent call last):
File "t2.py", line 8, in <module>
print(a.loc[[a.index.values[1]]])
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 879, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 1099, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 1037, in _getitem_iterable
keyarr, indexer = self._get_listlike_indexer(key, axis, raise_missing=False)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py", line 1240, in _get_listlike_indexer
indexer, keyarr = ax._convert_listlike_indexer(key)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/multi.py", line 2388, in _convert_listlike_indexer
_, indexer = self.reindex(keyarr, level=level)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/multi.py", line 2302, in reindex
target = ensure_index(target)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 5618, in ensure_index
return Index(index_like)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py", line 385, in __new__
return Int64Index(data, copy=copy, dtype=dtype, name=name)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/numeric.py", line 76, in __new__
raise ValueError("Index data must be 1-dimensional")
ValueError: Index data must be 1-dimensional
```
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MDExOlB1bGxSZXF1ZXN0NTA1Mjc3NTI2
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BUG: Regression in Resample.apply raised error when apply affected only a Series
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| 2020-10-17T12:54:13Z
| 2020-10-17T16:12:45Z
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|
MEMBER
| null |
- [x] closes #36951
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Error was no longer caught as before.
|
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MDExOlB1bGxSZXF1ZXN0NTA1Mjc4OTM4
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DOC: fix doc isna same as notna
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| 1
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| 2020-10-17T16:01:50Z
| 2020-10-17T16:00:54Z
|
CONTRIBUTOR
| null |
- [x] closes #37175
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BUG: record warnings related to DatetimeIndex
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| 5
| 2020-10-17T14:22:59Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR handles warnings emitted when running tests in ``pandas/tests/plotting/test_datetimelike.py``.
Warnings looked like this:
```
pandas/tests/plotting/test_datetimelike.py::TestTSPlot::test_irreg_dtypes
/workspaces/pandas/pandas/core/frame.py:3216: FutureWarning: Automatically casting object-dtype Index of datetimes to DatetimeIndex is deprecated and will be removed in a future version. Explicitly cast to DatetimeIndex instead.
return klass(values, index=self.index, name=name, fastpath=True)
```
Presumably it started after implementing this: https://github.com/pandas-dev/pandas/pull/36697/
The reason for not using ``tm.assert_produces_warning`` is that when I use it,
then I get an error that the warning is raised with the incorrect stacklevel.
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BUG: .loc with MultiIndex with names[1] = 0
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CLN: parametrize test_nat_comparisons
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| 2020-10-17T15:35:50Z
| 2020-11-06T15:36:53Z
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|
MEMBER
| null |
Parametrize ``TestDatetime64SeriesComparison.test_nat_comparisons``
using ``operator`` module.
If this kind of cleanup is considered reasonable,
then I will go through the remaining tests in this test module.
- [ ] closes #xxxx
- [ ] tests added / passed
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BUG: JoinUnit.is_na wrong for CategoricalDtype
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| 1
| 2020-10-17T15:38:41Z
| 2020-10-20T01:48:35Z
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MEMBER
| null |
- [x] closes #20833
- [ ] tests added / passed
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- [ ] whatsnew entry
JoinUnit.is_na is basically checking `isna(self.block.values).all()`. The check for is_categorical is an attempted optimization bc values.categories is often much smaller than values. But Categorical represents its NAs in its codes, not in its categories. So this will incorrectly always return False in the status quo.
Having trouble coming up with a useful test. I can adapt a test from test_concat that returns an incorrect answer from is_na, but that does not appear to affect the result of the higher-level pd.concat call.
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| 2020-10-17T15:43:55Z
| 2020-10-19T07:24:23Z
| 2020-10-19T07:24:23Z
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NONE
| null |
version
* python 3.8 64bit for windows
* pandas 1.1.3
code
```python
import pandas as pd
path = 'tmp.txt'
df = pd.read_csv(path, sep='\s+', index_col=0)
print(df)
```
Below are two `tmp.txt` files with different contents but the same format.
The 1st one works with `read_csv`.
The 2nd one doesn't, error message:
```
pandas.errors.ParserError: Error tokenizing data. C error: Expected 4 fields in line 3, saw 5
```
Why? How to fix?
tmp.txt (in utf-8)
```
0 1 2 3
0 项目 附注 2019年12月31日 2018年12月31日
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20 合同资产
21 持有待售资产
22 一年内到期的非流动资产 七（12） 190,000,000
23 其他流动资产 七（13） 320,404,434 313,634,314
24 流动资产合计 17,774,363,635 15,581,139,669
25 非流动资产：
26 发放贷款和垫款
27 债权投资
28 可供出售金融资产
29 其他债权投资
30 持有至到期投资
31 长期应收款 七（16） 180,000,000
32 长期股权投资 七（17） 199,805,151 205,738,050
33 其他权益工具投资
34 其他非流动金融资产
35 投资性房地产
36 固定资产 七（21） 14,520,366,436 13,629,887,296
37 在建工程 七（22） 2,901,032,823 2,936,812,592
38 生产性生物资产
39 油气资产
40 使用权资产 七（25） 701,329,178
41 无形资产 七（26） 1,337,282,523 1,219,578,721
```
tmp.txt (in utf-8)
```
0 1 2 3
0 开发支出
1 商誉 七（28） 154,940,513 153,707,174
2 长期待摊费用 七（29） 538,654,362 510,271,130
3 递延所得税资产 七（30） 518,504,986 252,461,078
4 其他非流动资产 七（31） 842,960
5 非流动资产合计 21,051,915,972 18,909,299,001
6 资产总计 38,826,279,607 34,490,438,670
7 流动负债：
8 短期借款 七（32） 8,491,599,785 5,567,436,870
9 向中央银行借款
10 拆入资金
11 交易性金融负债
12 以公允价值计量且其变动计入当期损益的金融负债
13 衍生金融负债 七（34） 3,795,000 3,077,741
14 应付票据 七（35） 860,739,543 1,164,568,692
15 应付账款 七（36） 1,236,580,062 1,300,781,289
16 预收款项
17 合同负债 七（38） 695,400,166 594,503,112
18 卖出回购金融资产款
19 吸收存款及同业存放
20 代理买卖证券款
21 代理承销证券款
22 应付职工薪酬 七（39） 473,972,041 483,015,711
23 应交税费 七（40） 344,357,096 449,716,675
24 其他应付款 七（41） 1,555,660,964 1,177,199,622
25 其中：应付利息
26 应付股利
27 应付手续费及佣金
28 应付分保账款
29 持有待售负债
30 一年内到期的非流动负债 七（43） 1,123,793,163 1,315,823,251
31 其他流动负债 300,984,971
32 流动负债合计 14,785,897,820 12,357,107,934
33 非流动负债：
34 保险合同准备金
35 长期借款 七（45） 1,193,000,000 1,246,875,075
36 应付债券
37 其中：优先股
38 永续债
39 租赁负债 七（47） 571,281,590
40 长期应付款 七（48） 72,490,512
41 长期应付职工薪酬
42 预计负债
43 递延收益 七（51） 673,448,373 536,834,206
44 递延所得税负债 七（30） 161,079,790 159,748,849
45 其他非流动负债
46 非流动负债合计 2,671,300,265 1,943,458,130
```
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BUG: Regression in Resample.apply raised error when apply affected only a Series
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| 2020-10-17T16:14:41Z
| 2020-10-21T10:46:18Z
| 2020-10-20T01:30:23Z
|
MEMBER
| null |
- [x] closes #36951
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Error was no longer caught as before.
Sorry made a mistake when pushing...
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BUG: Non deterministic level order in MultiIndex with join
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| 2020-10-17T16:24:09Z
| 2020-10-25T19:19:14Z
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MEMBER
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This fixes the issue, if the number should be deterministic. If not, we should add a note in the docs
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DOC: excel format tag (binary) seems to be missing in the table output
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| 2
| 2020-10-17T16:24:30Z
| 2020-10-17T18:36:33Z
| 2020-10-17T18:36:33Z
|
CONTRIBUTOR
| null |
https://pandas.pydata.org/docs/user_guide/io.html
|
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| 37,201
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Backport PR #37181 on branch 1.1.x (BUG: Fix isin with read-only target)
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NONE
| null |
Backport PR #37181: BUG: Fix isin with read-only target
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DOC: update io-excel tag
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| 1
| 2020-10-17T17:14:41Z
| 2020-10-17T18:36:37Z
| 2020-10-17T18:36:33Z
|
CONTRIBUTOR
| null |
- [x] closes #37200
- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: clean color selection in _matplotlib/style
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| 6
| 2020-10-17T18:18:43Z
| 2020-11-04T02:18:10Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
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There was a comment in the top of the module that the code was a bit too dynamic.
The logic in the function was too complex.
I refactored the code
- Extract functions
- Simplify logic
- Add type annotations
Note: there is one issue with mypy, which I do not know how to handle. Currently I suggested to ignore the error.
|
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ENH: Support all Scipy window types in rolling(..., win_type)
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| 2020-10-17T18:49:01Z
| 2020-10-31T22:57:52Z
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MEMBER
| null |
- [x] closes #34556
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Additionally cleans up some helper methods
|
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MDExOlB1bGxSZXF1ZXN0NTA1MzQwOTIw
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BUG: Don't copy DataFrame columns as metadata
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| 3
| 2020-10-17T20:14:42Z
| 2020-10-28T11:33:02Z
| 2020-10-20T00:26:53Z
|
CONTRIBUTOR
| null |
See https://github.com/pandas-dev/pandas/pull/37186/files#r506978889.
Basically, we don't want to accidentally do `series.name =
dataframe.name` and try to set a column.
cc @jorisvandenbossche. This could conceivably affect geopandas, since geodataframe has different metadata than geoseries: https://github.com/geopandas/geopandas/blob/924cdf65c7c15b01749d1cdd036c5c291e87b0f4/geopandas/geodataframe.py#L83
But I don't think it'll actually break anything for you.
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MDExOlB1bGxSZXF1ZXN0NTA1MzQ0MzU2
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|
DOC: remove debug messages by adding semicolons
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| 1
| 2020-10-17T20:42:33Z
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|
CONTRIBUTOR
| null |
remove last few remaining debug messages that slipped through the cracks by adding semicolons after plot functions
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 2
| 2020-10-17T20:45:53Z
| 2020-10-22T04:41:30Z
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|
MEMBER
| null |
- [x] closes #34315
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
BUG: Join did not work correctly when one MultiIndex had only one level
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| 4
| 2020-10-17T21:51:17Z
| 2021-01-03T22:26:06Z
| 2021-01-03T22:22:53Z
|
MEMBER
| null |
- [x] closes #36909
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
``droplevel`` for ``MultiIndex`` returns an ``Index`` when the resulting ``MultiIndex`` had only one level. But if the input had only one level and no level should be dropped, the return was a ``MultiIndex``. This did not seem consistent, so I changed it, that in this case an Index would be returned too. If this is not the desired behavior, we could fix the ``join`` problems after calling this functions.
If this is not desired, we should add a note to the docstring, that the return for ``droplevel`` is a MultiIndex, if no level is dropped.
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DOC: add value counts as related method to count
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| 1
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| 2020-11-09T05:09:36Z
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CONTRIBUTOR
| null |
Counting values using `.value_counts()`, more generally, is a counting task, so it felt natural to link the `.count()` page to `.value_counts()`.
I copied the description for `.value_counts()` from the "See also" box in [`.drop_duplicates()`](https://github.com/pandas-dev/pandas/blob/9fed16cd4c302e47383480361260d63dc23cbefc/pandas/core/frame.py#L5102).
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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ENH: DataFrame.to_sql with if_exists='replace' should do truncate table instead of drop table
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| 2021-09-22T15:16:52Z
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NONE
| null |
#### Is your feature request related to a problem?
Dropping table when if_exists=‘replace’ will fail if the table has any objects, like a view, depending on it. Also, some databases like Oracle will end a transaction implicitly when a DLL statement like 'drop table' is issued.
#### Describe the solution you'd like
Better alternative would be to issue 'truncate table' command instead.
#### API breaking implications
Should not have any changes on API
#### Describe alternatives you've considered
As above or a new flag if_exists=‘truncate’
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BUG: Join behaved like an inner join when only one side had MultiIndex
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MEMBER
| null |
- [x] closes #34292
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
We can use the regular path now, since ``droplevel`` can handle an ``Index``. Only difference is a resorted result, because ``Index.join()`` does not preserve the order of the leading input
```
left = pd.Index([0, 1, 2, 1], name="b")
right = pd.Index([1, 2, 5], name="b")
print(left.join(right, how="left"))
```
returns
```
Int64Index([0, 1, 1, 2], dtype='int64', name='b')
```
Edit: We may be able to delete the ``_join_level`` function, if we add a ``level`` keyword to the ``_join_multi`` function to determine the overlap.
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| 3
| 2020-10-18T00:53:38Z
| 2020-10-22T23:31:02Z
| 2020-10-22T23:31:02Z
|
CONTRIBUTOR
| null |
- [ ] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
In xarray we make use of an `Index` subclass for datetime-like indexing with non-standard calendars, which we call `CFTimeIndex`. A recent change, #37087, led to some test failures on our end. We currently do not define an `_id` attribute on our subclass, so we are vulnerable to the kinds of attribute errors illustrated below (note this example requires the [xarray](https://github.com/pydata/xarray) and [cftime](https://github.com/Unidata/cftime) libraries be installed, with the development version of pandas):
```
In [1]: import xarray as xr
In [2]: xr.cftime_range("2000", periods=2).view()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-5226cd5e3bc1> in <module>
----> 1 xr.cftime_range("2000", periods=2).view()
~/Software/pandas/pandas/core/indexes/base.py in view(self, cls)
630 result = self._shallow_copy()
631 if isinstance(result, Index):
--> 632 result._id = self._id
633 return result
634
AttributeError: 'CFTimeIndex' object has no attribute '_id'
```
Where possible, we try to avoid using private methods/attributes on our subclass. In this case would you recommend we go against that by setting the `_id` attribute, e.g. by using `_reset_identity` in our constructor, or would it be possible to continue to have `_id` be somewhat of an optional attribute of `Index` objects in pandas?
|
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DOC: macOS is the new name for Mac OS
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: de-duplicate in arithmetic/test_numeric
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- Move one test to another one parametrized.
- Address one part of TODO item
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BUG:cannot perform __truediv__ with this index type: DatetimeArray
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| 2020-10-19T02:23:55Z
| 2020-10-19T02:23:55Z
|
NONE
| null |
#### Code Sample, a copy-pastable example
```python
df_price_new.pct_change()
```
#### Problem description
Index is time, column is 300 stock names, calculating the growth rate, pandas reported an error
df_price_new.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 81 entries, 2019-09-02 to 2019-12-31
Columns: 300 entries, 000001.SZ to 603993.SH
dtypes: datetime64[ns](1), float64(299)
memory usage: 190.5 KB
#### Expected Output
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-de16c3a3e118> in <module>
----> 1 df_price_new.pct_change()
d:\Anaconda3\lib\site-packages\pandas\core\generic.py in pct_change(self, periods, fill_method, limit, freq, **kwargs)
10239 data = _data
10240
> 10241 rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
10242 if freq is not None:
10243 # Shift method is implemented differently when freq is not None
d:\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in f(self, other, axis, level, fill_value)
649 if isinstance(other, ABCDataFrame):
650 # Another DataFrame
--> 651 new_data = self._combine_frame(other, na_op, fill_value)
652
653 elif isinstance(other, ABCSeries):
d:\Anaconda3\lib\site-packages\pandas\core\frame.py in _combine_frame(self, other, func, fill_value)
5864 return func(left, right)
5865
-> 5866 new_data = ops.dispatch_to_series(self, other, _arith_op)
5867 return new_data
5868
d:\Anaconda3\lib\site-packages\pandas\core\ops\__init__.py in dispatch_to_series(left, right, func, axis)
273 # _frame_arith_method_with_reindex
274
--> 275 bm = left._mgr.operate_blockwise(right._mgr, array_op)
276 return type(left)(bm)
277
d:\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in operate_blockwise(self, other, array_op)
362 Apply array_op blockwise with another (aligned) BlockManager.
363 """
--> 364 return operate_blockwise(self, other, array_op)
365
366 def apply(self: T, f, align_keys=None, **kwargs) -> T:
d:\Anaconda3\lib\site-packages\pandas\core\internals\ops.py in operate_blockwise(left, right, array_op)
36 lvals, rvals = _get_same_shape_values(blk, rblk, left_ea, right_ea)
37
---> 38 res_values = array_op(lvals, rvals)
39 if left_ea and not right_ea and hasattr(res_values, "reshape"):
40 res_values = res_values.reshape(1, -1)
d:\Anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in arithmetic_op(left, right, op)
184 if should_extension_dispatch(lvalues, rvalues) or isinstance(rvalues, Timedelta):
185 # Timedelta is included because numexpr will fail on it, see GH#31457
--> 186 res_values = op(lvalues, rvalues)
187
188 else:
d:\Anaconda3\lib\site-packages\pandas\core\ops\invalid.py in invalid_op(self, other)
51 def invalid_op(self, other=None):
52 typ = type(self).__name__
---> 53 raise TypeError(f"cannot perform {name} with this index type: {typ}")
54
55 invalid_op.__name__ = name
TypeError: cannot perform __truediv__ with this index type: DatetimeArray
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.15063
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Chinese (Simplified)_China.936
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : 0.9.2
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: 0.9.0
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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| 2020-11-28T17:49:46Z
| 2020-11-28T17:49:46Z
|
CONTRIBUTOR
| null |
- [ y] I have checked that this issue has not already been reported. (Here - it [has in Debian](https://bugs.debian.org/972033))
- [ y] I have confirmed this bug exists on the latest version of pandas.
- [ y] (optional) I have confirmed this bug exists on the master branch of pandas. (Though with a mix of Debian and pip dependencies, as pip doesn't have them all for 3.9 yet.)
---
#### Code Sample, a copy-pastable example
The tests in tests/io/pytables/test_store.py, or
```python
import pandas as pd;from pandas.io.pytables import HDFStore;s1=HDFStore("tmp1.h5","w");df=pd.DataFrame([[1,2,3],[4,5,6]],columns=['A','B','C']);s1.append("d1",df,data_columns=["B"]);df2=s1.select("d1","index>df.index[0]");print(type(df2.index[0]))
```
#### Problem description
In Python 3.9, HDFStore.Select fails if it involves an index expression, with this traceback:
```
self = <DatetimeArray>
['2000-01-03 00:00:00', '2000-01-04 00:00:00', '2000-01-05 00:00:00',
'2000-01-06 00:00:00', '2000-01...2-08 00:00:00',
'2000-02-09 00:00:00', '2000-02-10 00:00:00', '2000-02-11 00:00:00']
Length: 30, dtype: datetime64[ns]
key = 4
def __getitem__(self, key):
if lib.is_integer(key):
# fast-path
result = self._ndarray[key]
if self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)
key = extract_array(key, extract_numpy=True)
key = check_array_indexer(self, key)
> result = self._ndarray[key]
E IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
pandas/core/arrays/_mixins.py:200: IndexError
```
The debugger says key is a pandas.core.computation.pytables.Constant, while in Python 3.8 (where this works) it is a plain int. The underlying cause may be [Python replacing ast.Index with bare values](https://docs.python.org/3/library/ast.html#ast.AST).
The CI may have missed this because it [skips optional dependencies on 3.9](https://github.com/pandas-dev/pandas/blob/master/.travis.yml#L97) (to avoid having to build them).
#### Possible fix
Warning: not fully tested.
```
--- a/pandas/core/computation/pytables.py
+++ b/pandas/core/computation/pytables.py
@@ -429,6 +429,10 @@ class PyTablesExprVisitor(BaseExprVisito
value = value.value
except AttributeError:
pass
+ try:
+ slobj = slobj.value
+ except AttributeError:
+ pass
try:
return self.const_type(value[slobj], self.env)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.9.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-11-amd64
Version : #1 SMP Debian 4.19.146-1 (2020-09-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : C
LANG : C
LOCALE : None.None
pandas : 0+unknown
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 50.3.0
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.32.1
sphinx : 3.2.1
blosc : 1.9.2
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.2.1
fsspec : 0.8.4
fastparquet : None
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
numba : None
</details>
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BUG: Wrong results and SegFaults when indexing with uint arrays
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| 0
| 2020-10-18T12:44:37Z
| 2020-10-30T22:45:57Z
| 2020-10-30T22:45:57Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
ser = pd.Series([1, 2, 3])
arr8 = np.array([4], dtype=np.uint8)
arr16 = np.array([4], dtype=np.uint16)
arr32 = np.array([4], dtype=np.uint32)
arr64 = np.array([4], dtype=np.uint64)
print(ser[arr8]) # prints random number, probably out-of-bounds memory address
print(ser[arr16]) # prints random number, probably out-of-bounds memory address
print(ser[arr32]) # segfault
print(ser[arr64]) # correctly returns a KeyError
```
#### Problem description
Indexing into a Series without `.iloc` or `.loc` using an unsigned integer that points outside of the Series sometimes results in pandas returning some bogus value, and sometimes causes a segfault. I wasn't able to reproduce this behavior with signed integer arrays.
#### Expected Output
I assume that these queries should return a KeyError.
#### Output of ``pd.show_versions()``
I confirmed the bug with the following two versions:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.147-1-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_IL.UTF-8
LOCALE : en_IL.UTF-8
pandas : 1.0.3
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.19
pytest : 5.1.2
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.1.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.45.1
</details>
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.147-1-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_IL.UTF-8
LOCALE : en_IL.UTF-8
pandas : 1.0.3
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.19
pytest : 5.1.2
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.1.2
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.45.1
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