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https://api.github.com/repos/pandas-dev/pandas/issues/28513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28513/events | https://github.com/pandas-dev/pandas/pull/28513 | 495,495,314 | MDExOlB1bGxSZXF1ZXN0MzE5MDMzNDE2 | 28,513 | CLN: dont catch Exception when calling maybe_convert_numeric | {
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} | 2 | 2019-09-18T23:07:38Z | 2019-09-20T14:16:27Z | 2019-09-20T12:35:26Z | MEMBER | null | and parametrized one of the tests for maybe_convert_numeric | {
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} | 4 | 2019-09-18T23:47:04Z | 2019-09-19T16:05:23Z | 2019-09-19T15:49:21Z | MEMBER | null | So we don't have to scroll through tons of
```
pandas/tests/io/excel/test_xlrd.py::test_excel_table_sheet_by_index[.xlsm]
/usr/local/lib/python3.7/site-packages/xlrd/xlsx.py:312: PendingDeprecationWarning: This method will be removed in future versions. Use 'tree.iter()' or 'list(tree.iter())' instead.
for elem in self.tree.iter() if Element_has_iter else self.tree.getiterator():
``` | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-19T00:40:34Z | 2019-09-19T16:53:20Z | 2019-09-19T14:32:40Z | MEMBER | null | Let me know if I'm mistaken here, but I think we would prefer parametrization over for loops inside tests (e.g., so `pytest` flags which case is failing), and this should be equivalent. | {
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} | 11 | 2019-09-19T07:20:38Z | 2019-09-27T14:59:19Z | 2019-09-27T14:58:18Z | MEMBER | null | Updating our internal imports to use the path were they actually live, not the old `core.sparse.api` one.
While doing this, I was thinking now that for the tests, we could actually also use the top-level `pd.` imports.
@TomAugspurger in the `core/sparse/` module, the only actual code living there is the scipy_sparse code. I moved that to `arrays` module (but could also be moved within the sparse.py file, or create a sparse subdirectory, ..).
Do we want to raise a deprecation warning on pandas.core.sparse.api? | {
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} | 1 | 2019-09-19T07:41:39Z | 2019-09-19T14:17:29Z | 2019-09-19T14:17:25Z | MEMBER | null | I noticed that when you modified `pandas/__init__.py`, isort actually completely reordered it when using pre-commit hook. Apparantly, isort ignores the skip config when you explicitly pass a path to isort (pre-commit basically does `isort pandas/__init__.py` when that file changed).
See https://github.com/pre-commit/mirrors-isort/issues/9, which suggested to add this exclude to the pre-commit config (although that duplicates the "skip" information from setup.cfg) | {
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} | 0 | 2019-09-19T09:34:33Z | 2019-10-05T23:09:55Z | 2019-10-05T23:09:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index_tuples=[]
for word_group in ["a", "b", "c", "d"]:
for correctness in ["1", "2", "3"]:
index_tuples.append([word_group, correctness])
index = pd.MultiIndex.from_tuples(index_tuples, names=["outer", "inner"])
frame_x = pd.DataFrame(columns = index)
frame_x["id"]=""
frame_y = pd.DataFrame(columns = index)
frame_y["id"]=""
print(frame_x.merge(frame_y, on="id").columns)
```
#### Problem description
I'm trying to merge to dataframes. Both have a multiindex and an "id" column. The merge happens on "id", the outer layer of the multiindex should receive suffixes. Depending on the number of indices in the multiindex this doesn't work. Only some of the multiindex columns receive suffixes, other's don't. For the codesample, I've set up an empty dataframe, the behaviour is the same when it is filled with data.
The issue seems non-deterministic. Sometimes it happens, sometimes it doesn't. Here is a video:
https://imgur.com/a/rbSvuSl
I'm using pandas version 0.25.1
This is a conda environment, here is its yml file: https://gist.github.com/lhk/ab3cf1f95be37a23789792fd75beef93
#### Expected Output
All columns in the multiindex should receive either _x or _y as suffix.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-21-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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{... | open | false | null | [] | null | 2 | 2019-09-19T09:42:56Z | 2019-09-23T15:25:05Z | null | MEMBER | null | The css of the new website needs some care. I think there are two separate things:
- General styles (headers, paragraphs, code blocks...)
- Home page
And I see the next options to move forward:
- We do it ourselves
- The designer donated by Indeed propose a style (send us an image on how the website needs to look like) and we implement it
- We hire a designer to implement the changes to the css (they can implement the design, or we can still ask the designer from Indeed to propose one) | {
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{... | closed | false | null | [] | null | 0 | 2019-09-19T09:50:40Z | 2019-09-26T06:42:27Z | 2019-09-26T06:42:27Z | MEMBER | null | What do we want in the new website footer? Will be visible in all the pages (including the documentation I guess)?
Ideas:
- Copyright note
- NumFOCUS note (see Dask website)
- Social media links (GitHub and Twitter I think)
- Back to top link
- Note about sponsors (like the providers of hosting and the design, see Django website)
- Project license
- Trademark note (see Spark website)
- Terms and conditions, privacy note (see Tensorflow page)
- Note link to donate
- ...
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{... | closed | false | null | [] | null | 2 | 2019-09-19T09:55:07Z | 2021-03-25T12:55:30Z | 2021-03-25T12:55:30Z | MEMBER | null | List of issues that we must have implemented before we're happy to move the new website to production (so far the plan is to deploy the new website when we release pandas 1.0). xref #27870
- [x] New logo
- [ ] Improve home page #28168
- [ ] Fix styles #28519
- [x] Footer with the right content #28520
- [ ] Change the layout of the docs to match the one of the website #28580
- [ ] Deployment of the new website #28528
Anything else? | {
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] | closed | false | null | [] | null | 3 | 2019-09-19T10:06:12Z | 2021-07-21T04:37:34Z | 2021-07-21T04:37:34Z | NONE | null | #### Sample code
```python
# Your code here
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1,2,3,4], 'B': [1,2,3,4]})
df1 = pd.DataFrame({'A': [1,2, 3, 3, 4], 'B': [1,2,3, np.NaN, np.NaN],'C': ['a','b','a','c','d']})
df2=df.merge(df1, on=['A','B'], how='left')
df3=pd.DataFrame({'A': [1,2, 3, 3, 4], 'B': [1,2,3, np.NaN, np.NaN],'C': ['a','b','a', 'c','d']})
```
#### Problem description
Just want an option on merge function, which would take df and df1 to get df3 as well.
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} | 4 | 2019-09-19T10:55:36Z | 2021-03-31T00:58:13Z | 2021-03-31T00:58:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar'], 'B' : [1, 2, 3, 4]})
grouped = df.groupby('A')
print(grouped.apply(lambda x: x))
grouped.first()
print(grouped.apply(lambda x: x))
```
Output:
```
A B
0 foo 1
1 bar 2
2 foo 3
3 bar 4
B
0 1
1 2
2 3
3 4
```
#### Problem description
The invoking method, without re-assignment of result or specifying that it should work in place, should not affect the object upon which it is being called.
#### Expected Output
```
A B
0 foo 1
1 bar 2
2 foo 3
3 bar 4
A B
0 foo 1
1 bar 2
2 foo 3
3 bar 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-62-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 40.6.3
Cython : 0.27.3
pytest : 3.0.7
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : None
pymysql : None
psycopg2 : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 2.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 13 | 2019-09-19T12:01:09Z | 2019-09-20T15:56:19Z | 2019-09-20T12:43:54Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/27900
I didn't yet add any tests, because I am not fully sure how to write them (the IPython tests we already have clearly don't work, I suppose the programmatic `ip.Completer.completions(..)` doesn't go through jedi)
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https://api.github.com/repos/pandas-dev/pandas/issues/28525 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28525/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28525/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28525/events | https://github.com/pandas-dev/pandas/issues/28525 | 495,798,993 | MDU6SXNzdWU0OTU3OTg5OTM= | 28,525 | BUG: groupby quantile fails with multiple quantiles | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-19T13:26:40Z | 2019-09-19T14:40:03Z | 2019-09-19T14:14:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'grp': [1, 1, 2, 2],
'x': [1, 2, 3, 4]})
print(df.groupby(['grp']).quantile([.2, .4]))
```
#### Problem description
The quantile aggregation accepts a parameter `q` which which be a single quantile, or an array of multiple quantiles. According to the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.quantile.html) q should be "float or array-like".
Since the update to 0.25.0, I get a TypeError when q is a list or array.
#### Expected Output ( 0.24.2 )
```
x
grp
1 0.2 1.2
0.4 1.4
2 0.2 3.2
0.4 3.4
```
#### Error traceback ( 0.25.0 )
```
TypeErrorTraceback (most recent call last)
<ipython-input-1-b8366902885c> in <module>
4 'x': [1, 2, 3, 4]})
5
----> 6 print(df.groupby(['grp']).quantile([.2, .4]))
~/.conda/envs/bifrost-test-pandas25/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1906 post_processing=post_processor,
1907 q=q,
-> 1908 interpolation=interpolation,
1909 )
1910
~/.conda/envs/bifrost-test-pandas25/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, grouper, aggregate, cython_dtype, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2246 func = partial(func, ngroups)
2247
-> 2248 func(**kwargs) # Call func to modify indexer values in place
2249
2250 if result_is_index:
pandas/_libs/groupby.pyx in pandas._libs.groupby.group_quantile()
TypeError: must be real number, not list
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.14.4.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
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 : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-09-19T13:54:28Z | 2019-09-19T16:27:52Z | 2019-09-19T16:27:51Z | NONE | null | My issue is partially reported here:
https://stackoverflow.com/questions/57993351/unicodedecodeerror-cant-decode-byte-solved-by-writing-and-reading-with-panda
Ultimately I would like to use pandas.read_excel on already opened data (without having to save on the disk and re-open).
Tracing it into panda's depth I found that adding a test in excel.ExcelFile __init__ method was doing the trick.
```python
if engine == 'xlrd' and isinstance(io, xlrd.Book):
self.book = io
elif not isinstance(io, xlrd.Book) and hasattr(io, "read"):
# N.B. xlrd.Book has a read attribute too
data = io.read()
self.book = xlrd.open_workbook(file_contents=data)
**elif not isinstance(io, xlrd.Book) and not hasattr(io, "read"):
# N.B. xlrd.Book has no read attribute
self.book = xlrd.open_workbook(file_contents=io)**
elif isinstance(io, compat.string_types):
self.book = xlrd.open_workbook(io)
else:
raise ValueError('Must explicitly set engine if not passing in'
' buffer or path for io.')
```
Is there any other way to do it?
Thanks in advance for your help. | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 5 | 2019-09-19T14:25:52Z | 2021-07-21T04:41:46Z | null | CONTRIBUTOR | null | What's the expected behavior here?
```python
In [9]: a = pd.array([1, None, 2, 3], dtype='Int64')
In [10]: df = pd.DataFrame([[1, 2, 3, 4]])
In [11]: (a + df).dtypes
Out[11]:
0 int64
1 float64
2 int64
3 int64
dtype: object
```
Would we expect the eventual op to dispatch to the ExtensionArray, preserving the Int64Dtype for each?
I believe this is different from https://github.com/pandas-dev/pandas/issues/28506 | {
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{... | closed | false | null | [] | null | 16 | 2019-09-19T14:55:52Z | 2020-04-07T11:57:42Z | 2020-04-07T11:57:42Z | MEMBER | null | Until now, we've been deploying the website manually afaik, from the separate repo.
What I would do with the new website is next (open to discussion):
- Have a job running to automatically deploy the website from master, possible frequencies:
- Every commit
- Daily
- Weekly
- Build the website normally, as we do in https://github.com/pandas-dev/pandas/blob/master/azure-pipelines.yml#L122
- Deploy it to the server with rsync, something like: `rsync avz --delete --exclude="docs" ~/web/build pandas_ci@pandas.io:/var/www/pandas`
For the docs, I see two main options:
- Inside `docs/` have a directory per language (currently English), and inside them one per version (with a symlink `stable/ -> 0.25.1`), so the urls would be something like pandas.io/docs/en/stable or pandas.io/docs/en/0.24.0 (and redirect pandas.io/docs/ to pandas.io/docs/en/stable)
- Copy the docs directly in pandas.io/docs/ and keep the old version in directories there pandas.io/docs/0.24.0/
In both cases, we can deploy the master docs into a `dev/` directory together with the versions.
I think the first option is a bit simpler to maintain, and the second makes the url a bit simpler. I don't have a strong preference.
I think this is very simple, but **requires that all pages that we don't want to version are in the web, and not in the docs**. So, if the roadmap is in the docs, the version we will have will be the stable version (0.25.1) and not master.
@jorisvandenbossche you proposed to have the next pages in the docs:
- Roadmap
- Ecosystem
- Contributing to pandas
Do you have a proposal for how to serve or deploy these? Or are you ok having the stable version of those? I thought about it, and I couldn't find any option I liked to keep those in the docs, that's why I'm proposing to move them to the website. | {
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This updates the directory, the deployment should work again.
1. https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=17721 | {
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} | 3 | 2019-09-19T15:08:46Z | 2019-09-19T21:10:10Z | 2019-09-19T20:33:52Z | MEMBER | null | With that fixed, we can clean up an Exception in tseries.frequencies. | {
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} | 16 | 2019-09-19T15:32:56Z | 2019-11-17T18:08:03Z | 2019-11-17T14:20:08Z | MEMBER | null | Another try to what was tried in #26949. Before this PR the tests are called twice, once in a core for the tests that affect shared results, and once in parallel for the rest.
This PR makes a single call, and tests in the same scope (class or module) are granted to run in the same core, so no shared data problems should happen.
The tests being very slow was possibly caused by the proxy env variables, and not `--dist=loadscope`. But please check how long tests took before merging.
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} | 3 | 2019-09-19T17:45:11Z | 2019-09-25T22:54:55Z | 2019-09-25T22:47:46Z | MEMBER | null | Adds tests for `astype_nansafe` in situations where we can't perform a datetime casting because of a precision mismatch:
```
arr
# array(['2018-01-01'], dtype='datetime64[D]')
arr.view(np.int32)
# array([17532, 0], dtype=int32)
arr.view(np.float32)
# array([2.4568e-41, 0.0000e+00], dtype=float32)
```
Related to https://github.com/pandas-dev/pandas/pull/28492 | {
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} | 3 | 2019-09-19T19:36:21Z | 2019-09-20T15:52:13Z | 2019-09-20T15:52:13Z | CONTRIBUTOR | null | Solves:
- Unknown parameters {*args, **kwargs :}
pandas.io.formats.style.Styler.pipe: Unknown parameters {*args, **kwargs :} in #27976
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} | 13 | 2019-09-19T19:51:37Z | 2019-10-11T22:10:18Z | 2019-10-11T22:10:18Z | CONTRIBUTOR | null | Solves:
- Unknown parameters {kwargs}
pandas.io.formats.style.Styler.apply: Unknown parameters {kwargs} in #27976 | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-19T19:56:47Z | 2019-09-19T20:52:52Z | 2019-09-19T20:52:52Z | NONE | null | On https://pandas.pydata.org under the `v0.25.1 Final (August 22, 2019)` heading, the PyPi installation link is
`python -m pip install –upgrade pandas`
The double hyphen in front of `upgrade` got converted to `'\u2013'` and pasting that into a terminal does not work. | {
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} | 4 | 2019-09-19T20:03:19Z | 2019-09-20T14:42:03Z | 2019-09-20T14:27:59Z | MEMBER | null | Parametrizes the for loop in `test_series_groupby_value_counts`. As a side note, this test seems to run for a pretty long time (a minute and a half); should it operate on less data perhaps? | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-19T20:19:42Z | 2019-09-19T22:30:56Z | 2019-09-19T20:56:08Z | NONE | null | This is a change in behavior between 0.23 and 0.25.
On `0.23.0`
```
In [10]: s=pd.Series(['a','b','c'])
: s.to_string(index=False)
Out[10]: 'a\nb\nc'
```
On `0.25.0`
```
In [2]: s=pd.Series(['a','b','c'])
: s.to_string(index=False)
Out[2]: ' a\n b\n c'
```
Particularly strange with a series of length 1
```
In [8]: s=pd.Series(['a'])
: s.to_string(index=False)
Out[8]: ' a'
```
There is always exactly 1 leading space regardless of how many characters the index would have taken. The same behavior appears in `DataFrame.to_string`.
Is this an intended change in behavior? I'm happy to look into identifying the cause if it is not intended. | {
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} | 2 | 2019-09-19T20:27:30Z | 2019-09-20T12:46:28Z | 2019-09-20T12:45:27Z | CONTRIBUTOR | null | Solves:
- Unknown parameters {kwargs} in apply method in Styler class
- Unknown parameters {kwargs} in applymap method in Styler class
- Unknown parameters {kwargs} in where method in Styler class
- Unknown parameters {low, high} in background_gradient method in Styler class
- Unknown parameters {kwargs} in set_properties method in Styler class
- Unknown parameters {*args, **kwargs :} in pipe method in Styler class
for issues:
pandas.io.formats.style.Styler.apply: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.applymap: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.where: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.set_properties: Unknown parameters {kwargs}
pandas.io.formats.style.Styler.pipe: Unknown parameters {*args, **kwargs :}
pandas.io.formats.style.Styler.background_gradient: Unknown parameters {low, high}
all in #27976
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"de... | closed | false | null | [] | null | 8 | 2019-09-19T20:33:41Z | 2019-12-18T11:16:38Z | 2019-12-17T17:39:34Z | CONTRIBUTOR | null | this gets the ball rolling on #18030 as it is very easy to fix, but requires some decisions, mostly for structure and naming
the basic options as I see them:
0 - this PR as is (maybe some name change to this 26 character monster)
1 - Outright changing the `dropna=False` default behavior I thought would be a bad idea as it breaks backwards compatibility, but this solution is quite clunky.
2 - Change `dropna=True` behavior to satisfy the test added here, but again that is not backwards compatible, but seems reasonable.
Also, I've noticed that taking out the line
https://github.com/pandas-dev/pandas/blob/3f8c0c4f002b6bcc69e0e534cb0c502fbab00373/pandas/core/reshape/pivot.py#L177
doesn't break any of the tests, so there is that. (this helps 2)
- [x] closes #18030
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-09-19T21:09:13Z | 2019-12-19T22:29:34Z | 2019-09-20T12:40:13Z | CONTRIBUTOR | null | Add requirements for reading 119 format files
- [X] closes #28250
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"description": ... | closed | false | null | [] | null | 1 | 2019-09-19T21:25:40Z | 2019-09-19T23:23:40Z | 2019-09-19T22:24:48Z | MEMBER | null | http://dev,pandas.io is currently not serving the website (http://dev,pandas.io/docs)
In the deployment of the new website, when building the web, the target default path `build` is not working as expected. The path is later expected to be `web/build`, but since the script is called from the root and not from inside web, the target path is `build/`, and the website is not copied.
This PR fixes the target path to be the correct one, and should fix http://dev,pandas.io
@TomAugspurger if you don't mind having a quick look. | {
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} | 0 | 2019-09-19T21:44:32Z | 2019-09-20T14:18:34Z | 2019-09-20T06:14:59Z | MEMBER | null | AFAICT these are artifacts of pre-pytest usage where we needed to manually add info to the traceback.
Adds a code_check to make sure we aren't catching BaseException anywhere. BaseException includes KeyboardInterrupt, which we shouldn't be catching in general.
Saving parametrization in test_nanops for a separate pass, as the diff will get big. | {
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] | closed | false | null | [] | null | 6 | 2019-09-19T22:05:49Z | 2020-09-27T14:10:20Z | 2020-09-27T14:10:20Z | MEMBER | null | I think this makes sense, and it will simplify things having a single CI system.
If I'm not missing anything, there are few challenges:
- The number of jobs (I think we should get 10 extra concurrent jobs in Azure, since the CI is ok but will be too slow)
- Setting up databases in Azure (I think we currently set up Postgres and MySQL, and may be in the future we want SQL server too #9097)
- I think we have some encrypted stuff in azure for GBQ (probably can be easily moved to Azure secrets)
If there is any objection, let me know, otherwise I'll move forward with this.
CC: @bhavaniravi | {
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} | 24 | 2019-09-20T03:21:10Z | 2021-02-02T02:29:20Z | 2021-02-02T02:29:20Z | MEMBER | null | Right now if you don't have `xlrd` installed and use `read_excel` without specifying the `engine` keyword you get the following message:
```python-traceback
>>> pd.read_excel("test.xlsx")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/util/_decorators.py", line 208, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 310, in read_excel
io = ExcelFile(io, engine=engine)
File "/Users/williamayd/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/io/excel/_base.py", line 819, in __init__
self._reader = self._engines[engine](self._io)
File "/Users/williamayd/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/io/excel/_xlrd.py", line 20, in __init__
import_optional_dependency("xlrd", extra=err_msg)
File "/Users/williamayd/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/compat/_optional.py", line 93, in import_optional_dependency
raise ImportError(message.format(name=name, extra=extra)) from None
ImportError: Missing optional dependency 'xlrd'. Install xlrd >= 1.0.0 for Excel support Use pip or conda to install xlrd.
```
This is in spite of the fact that the user may have openpyxl installed and could do something like `pd.read_excel(..., engine="openpyxl")` to get the same code to work
Two issues need to be addressed here:
- The default `read_excel` call with no `engine` argument should fall back to `openpyxl`, if installed
- The default error message should direct the user to install openpyxl first and foremost, as xlrd is unmaintained | {
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} | 32 | 2019-09-20T03:25:00Z | 2020-12-12T14:57:21Z | 2020-12-01T23:27:29Z | MEMBER | null | xlrd is unmaintained and the previous maintainer has asked us to move towards openpyxl. xlrd works now, but *might* have some issues when Python 3.9 or later gets released and changes some elements of the XML parser, as default usage right now throws a `PendingDeprecationWarning`
Considering that I think we need to deprecate using `xlrd` in favor of `openpyxl`. We might not necessarily need to remove the former and it does offer some functionality the latter doesn't (namely reading `.xls` files) but should at the very least start moving towards the latter | {
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} | [] | closed | false | null | [] | null | 4 | 2019-09-20T12:31:13Z | 2019-11-03T00:56:19Z | 2019-11-03T00:56:19Z | CONTRIBUTOR | null | I'm using tab completion and wildcard search in IPython to find the right method usually. It's very common: https://jakevdp.github.io/PythonDataScienceHandbook/01.01-help-and-documentation.html
This used to work fine with pandas also in the past, but now I noticed that wildcard search is broken, and that tab completion spits a bunch of deprecation warnings for things I actually don't want to call, i.e. in this context garbage.
I.e. currently Pandas is much less IPython help friendly?
Can this be improved?
```
$ ipython
Python 3.7.3 (default, Mar 27 2019, 16:54:48)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.8.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.25.1'
In [3]: import numpy as np
In [5]: n = np.array([1, 2, 3])
In [6]: n.*sum*?
n.cumsum
n.sum
In [7]: s = pd.Series([1, 2, 3])
In [8]: s.*sum*?
---------------------------------------------------------------------------
AbstractMethodError Traceback (most recent call last)
~/software/anaconda3/lib/python3.7/site-packages/IPython/core/oinspect.py in psearch(self, pattern, ns_table, ns_search, ignore_case, show_all, list_types)
1024 namespaces_seen.add(id(ns))
1025 tmp_res = list_namespace(ns, type_pattern, filter,
-> 1026 ignore_case=ignore_case, show_all=show_all)
1027 search_result.update(tmp_res)
1028
~/software/anaconda3/lib/python3.7/site-packages/IPython/utils/wildcard.py in list_namespace(namespace, type_pattern, filter, ignore_case, show_all)
104 results = {}
105 for name, obj in filtered.items():
--> 106 ns = list_namespace(dict_dir(obj), type_pattern,
107 ".".join(pattern_list[1:]),
108 ignore_case=ignore_case, show_all=show_all)
~/software/anaconda3/lib/python3.7/site-packages/IPython/utils/wildcard.py in dict_dir(obj)
68 # thing. In particular, Traits use this pattern
69 try:
---> 70 ns[key] = getattr(obj, key)
71 except AttributeError:
72 pass
~/software/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in _constructor_sliced(self)
255 original, such as DataFrame single columns slicing.
256 """
--> 257 raise AbstractMethodError(self)
258
259 @property
AbstractMethodError: This method must be defined in the concrete class Series
```
and for tab completion
```
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: `real` is deprecated and will be removed in a future version. To eliminate this warning for a Series `ser`, use `np.real(ser.to_numpy())` or `ser.to_numpy().real`.
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.strides is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.base is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.data is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.flags is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.ftype is deprecated and will be removed in a future version. Use Series.dtype instead.
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.ftypes is deprecated and will be removed in a future version. Use Series.dtype instead.
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: `imag` is deprecated and will be removed in a future version. To eliminate this warning for a Series `ser`, use `np.imag(ser.to_numpy())` or `ser.to_numpy().imag`.
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.itemsize is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning:
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing
See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: `real` is deprecated and will be removed in a future version. To eliminate this warning for a Series `ser`, use `np.real(ser.to_numpy())` or `ser.to_numpy().real`.
return_obj = getattr(self._obj, name)
/Users/deil/software/anaconda3/lib/python3.7/site-packages/jedi/evaluate/compiled/access.py:347: FutureWarning: Series.strides is deprecated and will be removed in a future version
return_obj = getattr(self._obj, name)
In [9]: s.abs
abs() agg() any() argmin() asfreq() at_time() between() clip()
add() aggregate() append() argsort() asof() autocorr() between_time() clip_lower()
add_prefix() align() apply() array astype() axes bfill() clip_upper() >
``` | {
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} | 2 | 2019-09-20T15:49:23Z | 2020-05-22T03:37:21Z | null | CONTRIBUTOR | null | Discovered while fixing an issue in #28541
A large number of tests relating to `DataFrameGroupBy.apply` depend on a bug in `apply` where the grouped column is not removed from the returned DataFrame.
Here are some examples:
- [`test_apply_chunk_view`](https://github.com/pandas-dev/pandas/blob/4edf938aedf55b9e6fbfb3199f70f857e8ec7e41/pandas/tests/groupby/test_apply.py#L366)
- [`test_apply_multiindex_fail`](https://github.com/pandas-dev/pandas/blob/4edf938aedf55b9e6fbfb3199f70f857e8ec7e41/pandas/tests/groupby/test_apply.py#L366)
There are around ~20 cases of this.
---
In general, they expect the result of something like this:
```
df = pd.DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
df.groupby('key').apply(lambda x: x.sum())
```
To include the "key" column in the result, which should not be the case.
```
key value
key
1 3 3
2 6 12
3 9 21
```
The underlying issue with `apply` is a straightforward fix, all that needs to be done is change [this return](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/groupby.py#L729) to use the `_group_selection_context`, but the PR will have to include updates to many tests.
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} | 0 | 2019-09-20T15:55:55Z | 2019-09-21T11:36:06Z | 2019-09-21T11:36:03Z | MEMBER | null | Backport for #28524
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```
OptionError Traceback (most recent call last)
<ipython-input-157-342bfcd26ea6> in <module>()
----> 1 plot_confusion_matrix(y_test, predicted_y)
<ipython-input-156-5c1d0cc2db4c> in plot_confusion_matrix(test_y, predict_y)
35 cmap=sns.light_palette("blue")
36 plt.subplot(1, 3, 1)
---> 37 sns.heatmap(C)
38 plt.xlabel('Predicted Class')
39 plt.ylabel('Original Class')
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/seaborn/matrix.py in heatmap(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)
515 plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,
516 annot_kws, cbar, cbar_kws, xticklabels,
--> 517 yticklabels, mask)
518
519 # Add the pcolormesh kwargs here
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/seaborn/matrix.py in __init__(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)
111
112 # Validate the mask and convet to DataFrame
--> 113 mask = _matrix_mask(data, mask)
114
115 plot_data = np.ma.masked_where(np.asarray(mask), plot_data)
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/seaborn/matrix.py in _matrix_mask(data, mask)
90 # This works around an issue where `plt.pcolormesh` doesn't represent
91 # missing data properly
---> 92 mask = mask | pd.isnull(data)
93
94 return mask
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/core/ops.py in f(self, other, axis, level, fill_value)
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/core/frame.py in _combine_frame(self, other, func, fill_value, level)
4749
4750 Returns
-> 4751 -------
4752 DataFrame
4753 DataFrame with NA entries dropped from it.
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/core/frame.py in _arith_op(left, right)
4730 .. deprecated:: 0.23.0
4731
-> 4732 Pass tuple or list to drop on multiple axes.
4733 Only a single axis is allowed.
4734
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/core/ops.py in na_op(x, y)
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/core/computation/expressions.py in <module>()
166
167 # turn myself on
--> 168 set_use_numexpr(get_option("compute.use_numexpr"))
169
170
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/_config/config.py in __call__(self, *args, **kwds)
231
232 def __call__(self, *args, **kwds):
--> 233 return self.__func__(*args, **kwds)
234
235 @property
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/_config/config.py in _get_option(pat, silent)
100
101 def _get_option(pat, silent=False):
--> 102 key = _get_single_key(pat, silent)
103
104 # walk the nested dict
~/.pyenv/versions/anaconda3-5.2.0/lib/python3.6/site-packages/pandas/_config/config.py in _get_single_key(pat, silent)
86 if not silent:
87 _warn_if_deprecated(pat)
---> 88 raise OptionError("No such keys(s): {pat!r}".format(pat=pat))
89 if len(keys) > 1:
90 raise OptionError("Pattern matched multiple keys")
OptionError: "No such keys(s): 'compute.use_numexpr'"
```
Seaborn and pandas version -
pandas==0.25.1
seaborn==0.9.0
The code I am trying is -
labels = [1,2]
C = confusion_matrix(test_y, predict_y)
#[[9475 9411]
#[5490 5624]]
sns.heatmap(C, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.title("Precision matrix")
I have tried reinstalling pandas as suggested here https://github.com/mwaskom/seaborn/issues/1789 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28552 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28552/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28552/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28552/events | https://github.com/pandas-dev/pandas/issues/28552 | 496,446,021 | MDU6SXNzdWU0OTY0NDYwMjE= | 28,552 | DataFrame[list[Series[datetime64ns, tz]]] drops timezone information | {
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} | 5 | 2019-09-20T16:36:45Z | 2020-05-12T13:32:09Z | 2020-05-12T13:32:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
s = pd.Series([pd.to_datetime('2018-10-08 13:36:45+00:00')])
Output:
0 2018-10-08 13:36:45+00:00
dtype: datetime64[ns, UTC]
```
```
pd.DataFrame([s]).min()
Output:
0 2018-10-08 13:36:45
dtype: datetime64[ns]
```
#### Problem description
When using `DataFrame().min()` method timezone information gets removed but it should remain untouched just like when using `Series().combine(...,min)`. Example:
```
s.combine(s, min)
Output:
0 2018-10-08 13:36:45+00:00
dtype: datetime64[ns, UTC]
```
#### Expected Output
```
0 2018-10-08 13:36:45+00:00
dtype: datetime64[ns, UTC]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-64-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 40.6.3
Cython : 0.27.3
pytest : 3.0.7
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : None
pymysql : None
psycopg2 : 2.7.3.2 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 2.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-09-20T19:07:57Z | 2019-09-23T12:47:18Z | 2019-09-23T12:01:39Z | MEMBER | null | These are each going to need at least one more pass after this. | {
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} | [] | closed | false | null | [] | null | 0 | 2019-09-20T21:10:37Z | 2019-09-20T21:40:17Z | 2019-09-20T21:40:17Z | CONTRIBUTOR | null | - [x] closes #28549
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
There were lots of inconsistencies among tests about whether or not `apply` should return the grouper column(s), and almost no testing on the impact of `as_index` on `apply` operations. This hopefully should provide better coverage, and also normalize the behavior everywhere. | {
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} | 6 | 2019-09-21T00:04:56Z | 2019-09-27T22:47:24Z | 2019-09-27T22:47:24Z | MEMBER | null | A lot of the tests in that module repeat the following pattern:
```python
if orient == "records" or orient == "values":
expected = expected.reset_index(drop=True)
if orient == "values":
expected.columns = range(len(expected.columns))
assert_frame_equal(result, expected)
```
This is because the `records` and `values` formats intentionally do not completely preserve the axes labels. It would be nice to refactor this into a dedicated helper function (`assert_json_roundtrip_equal`?) | {
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} | 7 | 2019-09-21T00:14:06Z | 2021-01-04T23:29:00Z | 2021-01-04T23:29:00Z | MEMBER | null | ```python
>>> df = pd.DataFrame(range(3), index=list("123"))
>>> df.to_json(orient="split")
'{"columns":[0],"index":["1","2","3"],"data":[[0],[1],[2]]}'
>>> pd.read_json(df.to_json(orient="split"), orient="split").index
Int64Index([1, 2, 3], dtype='int64')
```
Note that the string nature of the values should be preserved via roundtrip here, but ends up being lossy anyway. Noted during refactor of #28510 | {
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} | 4 | 2019-09-21T00:26:07Z | 2019-10-09T14:50:46Z | 2019-10-09T14:50:46Z | MEMBER | null | Note that the order of input is not maintained with orient="index"
```python
>>> df = pd.DataFrame(range(3), index=range(3, 0, -1))
>>> df
0
3 0
2 1
1 2
>>> df.to_json(orient="index")
'{"3":{"0":0},"2":{"0":1},"1":{"0":2}}'
>>> pd.read_json(df.to_json(orient="index"), orient="index")
0
1 2
2 1
3 0
```
This is not necessarily a problem as by definition JSON objects are unordered, but preserving the order here would be a logical follow up to #27309 | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 0 | 2019-09-21T00:35:07Z | 2020-05-08T21:15:22Z | null | MEMBER | null | Another edge case brought up by #28510 . I'm not sure if we have a definite stance on this and there are probably a few other issues that are similar, though couldn't find anything exactly matching this
Note the index types for an empty data frame
```python
>>> df = pd.DataFrame()
>>> df.index
Index([], dtype='object')
>>> df.columns
Index([], dtype='object')
```
In `read_json` with `convert_axes` the axes labels end up with a different dtype
```python
>>> for orient in ["columns", "index", "records", "values", "split"]:
... print(pd.read_json("{}", orient=orient, convert_axes=True).columns)
...
Float64Index([], dtype='float64')
Float64Index([], dtype='float64')
Float64Index([], dtype='float64')
Float64Index([], dtype='float64')
Float64Index([], dtype='float64')
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28559 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28559/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28559/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28559/events | https://github.com/pandas-dev/pandas/issues/28559 | 496,593,108 | MDU6SXNzdWU0OTY1OTMxMDg= | 28,559 | read_json with orient="values" and numpy=True provides strange column | {
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] | closed | false | null | [] | null | 1 | 2019-09-21T00:38:38Z | 2020-01-09T03:34:38Z | 2020-01-09T03:34:38Z | MEMBER | null | Another edge case revealed in #28510 . Perhaps won't be relvant if we go through with #28512 but opening for posterity
For whatever reason, this particular combination of values produces a RangeIndex for the columns.
```python
>>> pd.read_json("[]", orient="values", numpy=True).columns
RangeIndex(start=0, stop=1, step=1)
```
Nothing else appears to do that | {
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"color": "207... | closed | false | null | [] | null | 1 | 2019-09-21T00:43:55Z | 2020-01-09T03:34:30Z | 2020-01-09T03:34:30Z | MEMBER | null | I don't have access to a PC so cannot replicate directly (if anyone else can would be appreciated) but going through #28510 there were a few Windows with dtype preservation, which would look something as follows:
```python
>>> df = pd.DataFrame(range(3))
>>> df.dtypes
0 int64
dtype: object
>>> pd.read_json(df.to_json()).dtypes
0 int32
dtype: object
```
Note that this may also be irrelevant if #28512 happens | {
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"d... | closed | false | null | [] | null | 2 | 2019-09-21T02:35:24Z | 2019-09-23T12:55:46Z | 2019-09-23T12:55:41Z | MEMBER | null | xref #25425. Working on fixing the xfailed tests in this file. This is going to be a marathon. | {
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- [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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... | closed | false | null | [] | null | 6 | 2019-09-22T01:50:38Z | 2021-07-21T04:46:13Z | 2021-07-21T04:46:12Z | NONE | null | #### Brief Description
I'm getting a KeyError when trying to access a row by string in a DateTimeIndex. This only happens if I use 'YYYY-MM-DD HH:MM:SS' format. If I use 'YYYY-MM-DD' it works. Also if I add another element before accessing using the more complete format the exception vanishes. This will get more clear with the codes below.
#### Testing Code
```python
import pandas as pd
example = pd.DataFrame(columns=['Date', 'Price', 'Amount'])
example = example.set_index('Date')
example.loc[pd.to_datetime('2019-09-19 23:00:10')] = [100, 200]
print('Checking the value was added')
print(example)
print('\nNow I try to access by index')
print(example['2019-09-19 23:00:10'])
```
#### Expected Output
```bash
Checking the value was added
Price Amount
Date
2019-09-19 23:00:10 100 200
Now I try to access by index
Price Amount
Date
2019-09-19 23:00:10 100 200
```
#### Error Output
```bash
Checking the value was added
Price Amount
Date
2019-09-19 23:00:10 100 200
Now I try to access by index
Traceback (most recent call last):
File "/home/rpa/anaconda3/envs/datas/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2897, in get_loc
return self._engine.get_loc(key)
File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '2019-09-19 23:00:10'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "testing_error_pandas.py", line 10, in <module>
print(example['2019-09-19 23:00:10'])
File "/home/rpa/anaconda3/envs/datas/lib/python3.7/site-packages/pandas/core/frame.py", line 2980, in __getitem__
indexer = self.columns.get_loc(key)
File "/home/rpa/anaconda3/envs/datas/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 2899, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/_libs/index.pyx", line 107, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 131, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 1607, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 1614, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: '2019-09-19 23:00:10'
```
However if I use 'YYYY-MM-DD' like in the code below it works fine.
```python
import pandas as pd
example = pd.DataFrame(columns=['Date', 'Price', 'Amount'])
example = example.set_index('Date')
example.loc[pd.to_datetime('2019-09-19 23:00:10')] = [100, 200]
print('Checking the value was added')
print(example)
print('\nNow I try to access by index')
print(example['2019-09-19'])
```
Also if I add another entry before checking the first condition, it also works fine like in the code below.
```python
import pandas as pd
example = pd.DataFrame(columns=['Date', 'Price', 'Amount'])
example = example.set_index('Date')
example.loc[pd.to_datetime('2019-09-19 23:00:10')] = [100, 200]
example.loc[pd.to_datetime('2019-09-19 17:45:20')] = [50, 75]
print('Checking the value was added')
print(example)
print('\nNow I try to access by index\n')
print(example['2019-09-19 23:00:10'])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-53-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 5 | 2019-09-22T10:36:22Z | 2019-09-27T07:00:24Z | 2019-09-27T07:00:09Z | CONTRIBUTOR | null | - Documentation update fixing some of the methods with a PR-2 error code which involved updating **kwds to **kwargs
- No tests required
- The data frame.plot methods now have the docstring arguments updated from **kwds to **kwargs to match the method signature and exclude them from the PR02 errors:
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"de... | open | false | null | [] | null | 6 | 2019-09-22T11:19:37Z | 2021-07-11T17:45:08Z | null | CONTRIBUTOR | null | I was wondering if there has been discussion about having dedicated RFC / Proposals for larger changes to pandas?
For example, after a rather heated discussion in #24046, @wesm [asked](https://github.com/pandas-dev/pandas/issues/24046#issuecomment-515492965):
> I question whether GitHub issues was the appropriate venue for this discussion compared with some form of RFC / design document.
I wanted to come back to this and maybe write such a design document, and therefore wanted to check what the thoughts of the core team are about this? Needless to say, the obvious candidates to model this after would be Python's [PEPs](https://www.python.org/dev/peps/) or NumPy's [NEPs](https://numpy.org/neps/), but maybe there could/should be pandas-specific departures.
I think this could benefit several high-impact design questions (e.g. around extension arrays or release plans). Actually, I have 2-3 other bigger PRs that eventually stalled in design discussion (and a few ideas of similar magnitude), where I could imagine that such a vessel would help move the discussion forward. | {
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} | 2 | 2019-09-22T18:14:59Z | 2020-01-18T00:29:08Z | 2019-10-03T17:25:04Z | CONTRIBUTOR | null | - [x] closes #28247
- [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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from functools import partial
import pandas as pd
import numpy as np
quant50 = partial(np.percentile, q=50)
quant70 = partial(np.percentile, q=70)
test = pd.DataFrame({'col1': ['a', 'a', 'b', 'b', 'b'], 'col2': [1,2,3,4,5]})
test.groupby('col1').agg({'col2': [quant50, quant70]})
However, quant50 result is rewritten by quant70, so will get the same output on both columns.
output:
| col1 | percentile | percentile |
|---|---|---|
| a | 1.7 | 1.7 |
| b | 4.4 | 4.4 |
expected output:
| col1 | percentile | percentile |
|---|---|---|
| a | 1.5 | 1.7 |
| b | 4.0 | 4.4 |
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} | 2 | 2019-09-22T23:41:47Z | 2019-10-01T18:51:29Z | 2019-10-01T04:08:13Z | CONTRIBUTOR | null | This relates to: [27977](https://github.com/pandas-dev/pandas/issues/27977). I have fixed the doc PR08 formatting issues for:
```
pandas.tseries.offsets.CustomBusinessDay: Parameter "weekmask" description should start with a capital letter
pandas.tseries.offsets.CustomBusinessDay: Parameter "holidays" description should start with a capital letter
pandas.tseries.offsets.WeekOfMonth: Parameter "week" description should start with a capital letter
pandas.tseries.offsets.WeekOfMonth: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.LastWeekOfMonth: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253: Parameter "variation" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "weekday" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "qtr_with_extra_week" description should start with a capital letter
pandas.tseries.offsets.FY5253Quarter: Parameter "variation" description should start with a capital letter
pandas.tseries.offsets.CDay: Parameter "weekmask" description should start with a capital letter
pandas.tseries.offsets.CDay: Parameter "holidays" description should start with a capital letter
```
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Will continue to work through all PR08 docstring errors. | {
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} | 8 | 2019-09-23T01:30:15Z | 2021-11-03T16:28:59Z | null | NONE | null | #### Problem description
I wish to get the number of sales for each month, even when the product is missing sales figures for a certain period. Consider the following example:
```python
import pandas as pd
import numpy as np
np.random.seed(42)
dates = pd.date_range('1/1/2001','31/12/2001', freq = 'd')
sales = [np.random.randint(100) for _ in range(len(dates))]
product = [['A', 'B', 'C'][np.random.randint(3)] for _ in range(len(dates))]
df = pd.DataFrame({'Dates': dates,
'Sales': sales,
'Product': product
})
march = df.Dates.dt.month == 3
df = df[~march]
```
All the march data is deleted. I wish to get those sales to show zero when I print out this:
```
monthly = pd.Grouper(key='Dates', freq='M')
sum_sales = df.groupby(['Product', monthly])['Sales'].sum()
```
where `sum_sales` just for product A is as follows (notice the lack of March time step):
```
Product Dates
A 2001-01-31 658
2001-02-28 460
2001-04-30 541
2001-05-31 701
2001-06-30 517
2001-07-31 596
2001-08-31 802
2001-09-30 654
2001-10-31 561
2001-11-30 473
2001-12-31 605
```
However, if I simply do `df.groupby(monthly)['Sales'].sum()` without grouping by product, I get the zero as expected.
```
Dates
2001-01-31 1616
2001-02-28 1256
2001-03-31 0
2001-04-30 1555
2001-05-31 1384
2001-06-30 1451
2001-07-31 1677
2001-08-31 1472
2001-09-30 1535
2001-10-31 1316
2001-11-30 1573
2001-12-31 1403
```
So just wondering how do I get pandas as to show the missing dates as zero sales when using multiple things inside `groupby`.
The solution according to this [answer](https://stackoverflow.com/a/57752676/2530674) was to use `sum_sales = df.set_index('Dates').groupby('Product').resample('M')['Sales'].sum()`. However, I feel like groupby should be doing this anyway? Thoughts?
pandas version: 0.24.2
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- [x] closes #28330
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-09-23T05:06:40Z | 2019-10-10T02:10:06Z | 2019-10-10T02:06:39Z | CONTRIBUTOR | null | - [x] closes #9299
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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```python
import pandas as pd
a = {'abc':[1,2,3],'xy (z)':[4,5,6]}
df = pd.DataFrame(a)
query = "`xy (z)` == 2"
new_df = df.query(query)
```
#### Error :
xy_(_z_)_BACKTICK_QUOTED_STRING ==2
SyntaxError: invalid syntax
#### Problem description
df.query is not able to parse the query where column name of df is not valid python identifier.
Workaround: https://stackoverflow.com/questions/40045545/pandas-query-string-where-column-name-contains-special-characters
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.187-1.el7.elrepo.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 40.6.2
Cython : 0.28.2
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.0.4
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28577 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28577/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28577/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28577/events | https://github.com/pandas-dev/pandas/pull/28577 | 497,035,331 | MDExOlB1bGxSZXF1ZXN0MzIwMjQyODA4 | 28,577 | DOC: Add scaling to large datasets section | {
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} | 11 | 2019-09-23T11:15:58Z | 2019-10-01T12:09:30Z | 2019-10-01T11:59:04Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/28315 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28578 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28578/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28578/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28578/events | https://github.com/pandas-dev/pandas/issues/28578 | 497,136,128 | MDU6SXNzdWU0OTcxMzYxMjg= | 28,578 | Feature Request: Left join conditionally on timestamps in pd.merge_asof(…) | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-23T14:33:02Z | 2019-10-07T21:07:08Z | 2019-10-07T21:07:08Z | NONE | null | As the title states, it would great to have the option to left-join in a pd.merge_asof(). Currently it only supports 1-1 joins.
See this post on stack overflow: https://stackoverflow.com/questions/57825101/left-join-conditionally-on-timestamps-in-pd-merge-asof.
Lets say I have two dataframes that I would like left join based on timestamp in a range based on the left table's timestamp. I would like to use pd.merge_asof(...) to do this but it seems like that function only joins to the nearest row in the right dataframe, and I would like for each row in the right dataframe to join to the left based on proximity to the left dataframe's timestamp.
For example:
df1:
TS1 id1
1 2019-08-15 16:45:00 1
2 2019-08-15 17:00:00 2
3 2019-08-15 17:15:00 3
df2:
TS2 id2
1 2019-08-15 16:45:00 1
2 2019-08-15 16:45:00 2
3 2019-08-15 16:46:00 3
4 2019-08-15 17:00:00 4
5 2019-08-15 17:01:00 5
6 2019-08-15 17:15:00 6
What I have tried:
test = pd.merge_asof(
df1.sort_values('TS1'),
df2.sort_values('TS2'),
left_on='TS1',
right_on='TS2',
direction='forward'
)
Which gives:
TS1 TS2 id1 id2
1 2019-08-15 16:45:00 2019-08-15 16:45:00 1 1
2 2019-08-15 17:00:00 2019-08-15 17:00:00 2 4
3 2019-08-15 17:15:00 2019-08-15 17:15:00 3 6
What I would like to see is:
TS1 TS2 id1 id2
1 2019-08-15 16:45:00 2019-08-15 16:45:00 1 1
2 2019-08-15 16:45:00 2019-08-15 16:45:00 1 2
3 2019-08-15 16:45:00 2019-08-15 16:46:00 1 3
4 2019-08-15 17:00:00 2019-08-15 17:00:00 2 4
5 2019-08-15 17:00:00 2019-08-15 17:01:00 2 5
6 2019-08-15 17:15:00 2019-08-15 17:15:00 3 6
It would also be great if you could somehow specify a window based on another timestamp column from the same row in the left table. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28579 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28579/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28579/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28579/events | https://github.com/pandas-dev/pandas/pull/28579 | 497,144,836 | MDExOlB1bGxSZXF1ZXN0MzIwMzMyODM4 | 28,579 | Fix typo | {
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} | 5 | 2019-09-23T14:47:24Z | 2019-09-23T22:05:02Z | 2019-09-23T15:21:25Z | 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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... | open | false | null | [] | null | 10 | 2019-09-23T16:10:58Z | 2019-11-01T20:27:50Z | null | MEMBER | null | With the new markdown based website (https://github.com/pandas-dev/pandas/pull/28014) and the new sphinx documentation theme (https://github.com/pandas-dev/pandas/issues/15556, https://dev.pandas.io/pandas-sphinx-theme/), we need to see how to integrate both.
Roughly speaking, I can currently think of three options:
- Have them separate.
- This is like we have it now, but of course styling-wise they can *look* unified. But the button for the docs on the main website goes to the doc website without actual integration of menus of both.
- Also show the website navbar on the documentation site:
- This would mean that when landing on the home page of the docs, you see two navbars: one of the main website, one of the docs. Once you start to scroll down, the navbar of the main website can eg disappear
- An example of this is the docker docs (https://docs.docker.com/), where there are links from the main website fully at the top, which disappear when scrolling down (then only the navbar menu of the docs is kept).
I did a quick experiment for this: https://dev.pandas.io/pandas-sphinx-theme/pr-integration-website-double-navbar/. Right now this looks ugly of course due to the non-matching styles and alignment, but to give the idea and I think this can be made look nice with matching styles.
- Have a single shared navbar with menu items of both main website and the sphinx documentation site:
- Just combining both as they are right now will probably give a too crowded header navbar, so we might need to make some choices what to put there. Currently we would have: Home, Install, Getting Started, User Guide, API Reference, Developer, Release Notes, Community, Donate. Probably Developer and Community could be combined, Release Notes is maybe also not needed on this level (the question is then where to put this though).
(TODO: will also do an experiment for this option)
- Having the top-level navigation of the docs in a dropdown (so "docs" itself is only 1 item in the top navbar), as it is now at https://dev.pandas.io/, is also an option to reduce the number of items on the navbar. But personally, I think those should not be hidden in a dropdown, and one of the goals of the new sphinx theme was actually to make this top-level navigation more accessible.
Short term we can use the first option (eg if we want to start using the new theme on the dev docs this week), but I think ideally we want to go to a more integrated option?
---
Besides the choice on how we want it to look, there is also a technical issue to solve: how to put this common navbar in the docs?
We can hardcode this in the html (when building it can eg be included in the layout from a file shared with the website), but this means that this navbar is fixed for older versions of the documentation, and if we do updates to the website this might not match with the header navbar of the released documentation. | {
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"color": "f2... | closed | false | null | [] | null | 1 | 2019-09-23T17:17:43Z | 2021-07-21T04:53:33Z | 2021-07-21T04:53:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import os, io, math
import numpy as np, pandas as pd
performance_df = pd.DataFrame()
performance_df["GAP"] = np.random.randn(75)
performance_df["SETTINGS"] = [['default', 'MLrank', 'MLselect'][int(i/25)] for i in range(75)]
performance_df["INSTANCE_NAME"] = [i%25 for i in range(75)]
performance_df = pd.read_csv("/home/twang/Ausarbeitung/rootperformance.csv", header=0)
default_df = performance_df[performance_df["SETTINGS"].str.match('default')]
rank_df = performance_df[performance_df["SETTINGS"].str.match('MLrank')]
select_df = performance_df[performance_df["SETTINGS"].str.match('MLselect')]
compare_df = default_df.drop(["SETTINGS"], axis=1).merge(
rank_df.drop(["SETTINGS"], axis=1), on=["INSTANCE_NAME"], validate="1:1", suffixes=("","_MLrank")).merge(
select_df.drop(["SETTINGS"], axis=1), on=["INSTANCE_NAME"], validate="1:1", suffixes=("_default","_MLselect") ).copy(deep=True)
gap_compare = compare_df.query("GAP_default<=9e19 and GAP_MLrank<=9e19", inplace=False)#.copy(deep=True)
gap_compare.loc[:,("GAP_MLselect")] = gap_compare.loc[:,["GAP_MLselect"]].where(gap_compare["GAP_MLselect"].values.reshape(-1,1) <= 9e19, np.random.randn(gap_compare.shape[0], 1), inplace=False, axis=0)
```
#### Problem description
I receive a SettingWithCopyWarning when loading the dataframe from my csv. But creating the df doesn't trigger a warning. I would expect it to be indifferent whether I load a csv or create df in python. Using deep copy at the second place, prevents the warning. But the first deep copy doesn't.
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] | closed | false | null | [] | null | 12 | 2019-09-23T18:33:21Z | 2019-09-26T13:22:31Z | 2019-09-26T06:42:27Z | MEMBER | null | xref #28519
closes #28520
You can see the result rendered in: https://datapythonista.github.io/pandas-web/ (the new logo is not in the PR).
Been discussing with NumPy the navigation, and what is proposed here is with minimal differences agreed with them. See https://github.com/numpy/numpy.org/issues/43
I don't touch the home page here, I think we need to remove almost everything we've got so far (based on the current), and move more in the direction of Dask or Jupyter. Discussions about it better in #28168 than here. | {
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] | closed | false | null | [] | null | 4 | 2019-09-23T19:38:24Z | 2020-04-05T17:45:11Z | 2019-11-10T17:01:52Z | MEMBER | null | One of four cases we'll need to implement (the others being Series-align-index, Series-align-columns, and DataFrame).
~670x speedup on the fastest ops, ~8x on the slower end.
```
In [3]: arr = np.arange(10**5).reshape(100, 1000)
In [4]: df = pd.DataFrame(arr)
In [5]: %timeit df + 1
198 ms ± 2.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
294 µs ± 3.25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
In [8]: ts = pd.Timestamp.now("UTC")
In [9]: df2 = pd.DataFrame(arr.view("timedelta64[ns]"))
In [10]: %timeit ts - df2
319 ms ± 2.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <-- master
40.2 ms ± 622 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- PR
```
The 2D DTA and TDAs that get created exist only briefly, and the changes to these classes are about as minimal as I can make them. | {
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} | 3 | 2019-09-23T20:20:36Z | 2020-03-11T03:06:03Z | 2020-03-11T03:06:03Z | CONTRIBUTOR | null | ```python
>>> idx = pd.Index(np.arange(100_000))
>>> %timeit idx.get_loc(99_999)
774 ns ± 26 ns per loop
>>> %timeit idx._shallow_copy().get_loc(99_999)
3.57 ms ± 56.8 µs per loop
```
The same performance issue can be seen on other index types, e.g. CategoricalIndex and MultiIndex.
#### Problem description
The reason for the above diferences is that ``_shallow_copy`` does not copy over the ``._engine`` attribute to the new index and the ``_engine`` is expensive to recreate.
Indexes are immutable, and likewise - to my understanding - are the ``._engine`` attribute of indexes. The ``._engine`` is quite expensive to create and if it has been created on the original index, I think it should be possible to reuse it on the new index, saving ther time needed to create a new and identical ``._engine``.
``_shallow_copy`` is used in a few places internally in pandas, so there seems to be potential for some speedups for several pandas merhods by copying the _engine over to newly-copied indexes.
Possibly I'm missing some finer details here, e.g. don't know what the ``._engine.clear_mappings`` is for and it seems from its name to be destructive, but overall it seems to be possible to make a change to copy this over.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 79663fb669cce436c6379c83c887755780b84770
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0.dev0+1363.g79663fb66
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : 4.28.2
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 2 | 2019-09-23T21:52:31Z | 2020-03-26T20:55:22Z | 2020-03-26T20:55:22Z | MEMBER | null | With the type hints that are being added in a lot of places, the question comes up if we want to have them in the docs.
Currently, this actually happens. See eg https://dev.pandas.io/docs/reference/api/pandas.DataFrame.to_string.html#pandas.DataFrame.to_string
But personally, I am not sure the having the type hints in the signature like above is an improvement. It makes the signature very hard to read and interpret, while we (in principle) already have the type information in textual form in the Parameter listing.
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is basically a version of #28579 for the master branch. | {
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} | 1 | 2019-09-24T01:37:31Z | 2019-09-24T15:41:49Z | 2019-09-24T12:06:59Z | MEMBER | null | These were there for compat with the SparseDataFrame._construct_result signature, which is no longer relevant.
Also got rid of _combine_const; that was also just waiting for the SparseDataFrame version to be gone. | {
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} | 6 | 2019-09-24T01:40:48Z | 2019-09-26T14:42:48Z | 2019-09-26T12:30:40Z | MEMBER | null | ATM we have three slightly different usages for the arithmetic, comparison and logical ops, with no clear reason for the differences. This changes that to make the arithmetic the one true version, which has the added benefit of letting us share _construct_result code across these three functions.
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} | [] | closed | false | null | [] | null | 5 | 2019-09-24T06:28:55Z | 2019-11-03T01:05:07Z | 2019-11-03T01:05:06Z | NONE | null | #### Problem description
Hi everyone,
I'm here because I want to achieve some stuff, but I don't know how to achive it in the best way. I browse a lot of topics on stackoverflow without a clue. Here is the thing, I want to achieve some utilities function for machine learning stuff.
I want to make several function that add new data to an existing dataframe, and manage some filtering of it, without making any data assignement.
Idea #1
# Method call
transform(inputs[mask], {'datum': 'Data'}, 'PCA', PCA())
# Method definition
def transform(dataframe, tags, out, model):
# Check mandatory fields
mandatory = ['datum']
if not isinstance(tags, dict) or not all(elem in mandatory for elem in tags.keys()):
raise Exception(f'Not a dict or missing tag: {mandatory}.')
features = model.transform(dataframe.loc[tags['datum']].to_numpy())
dataframe.loc[out] = features.tolist()
Idea #2
# Method call
transform(inputs, {'datum': 'Data'}, 'PCA', PCA(), mask)
# Method definition
def transform(dataframe, tags, out, model, mask=None):
# Check mandatory fields
mandatory = ['datum']
if not isinstance(tags, dict) or not all(elem in mandatory for elem in tags.keys()):
raise Exception(f'Not a dict or missing tag: {mandatory}.')
# Mask creation (see pandas view / copy mechanism)
if mask is None:
mask = [True] * len(dataframe.index)
features = model.transform(dataframe.loc[mask, tags['datum']].to_numpy())
dataframe.loc[mask, out] = features.tolist()
#### Input
Row ; Data ; Label
0 ; [61953.017837947686, 9.505037089204054, 74.585... ] ;0
1 ; [80832.69302693632, 9.524642547991316, 83.9228... ] ;1
#### Expected Output
Row ; Data ; Label ; PCA
0 ; [61953.017837947686, 9.505037089204054, 74.585... ] ;0 ; [74.585... ]
1 ; [80832.69302693632, 9.524642547991316, 83.9228... ] ;1 ; [92.578... ]
I'm doing 'features = model.transform(dataframe.loc[mask, tags['datum']].to_numpy())' to manage my data as a matrix and not a line by line operation by use of apply method().
- First idea doesn't seems to work, as it's no possible to pass as an argument a view and change data in it from a function, as the datframe would be convert in a get_item when I pass it to the function, it will became a copy of the dataframe.
- Second idea doesn't seems to work, as 'dataframe.loc[mask, out] = features.tolist()' is returning ValueError: 'Must have equal len keys and value when setting with an ndarray', as it seem to deal with the mask element by element...
EDIT:
I'm doing it in two steps, not the most intuitive way for now...
https://stackoverflow.com/questions/58064179/pandas-masked-dataframe-assign-2d-array
I don't have any ideas at this point...
If you have any advices, I would be thanful,
Best regards
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] | closed | false | null | [] | null | 3 | 2019-09-24T06:29:30Z | 2019-11-07T23:49:52Z | 2019-11-07T23:49:51Z | NONE | null | As referred to in https://github.com/pandas-dev/pandas/issues/28495.
- [x] closes #28495
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28593 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28593/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28593/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28593/events | https://github.com/pandas-dev/pandas/issues/28593 | 497,538,221 | MDU6SXNzdWU0OTc1MzgyMjE= | 28,593 | Dataframe.plot error (on version 0.25.1) | {
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... | open | false | null | [] | null | 0 | 2019-09-24T08:30:21Z | 2021-07-21T04:53:58Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
da = np.arange(12).reshape(6,2)
df=pd.DataFrame(da,columns=list('ab'))
fig, ax = plt.subplots()
df[['a']].plot(x=df['b'],ax=ax)
plt.show()
```
#### Problem description
these test code run ok on pandas version 0.20.0 ,but error on pandas version is 0.25.1.
the error info is:
KeyError: "None of [Int64Index([1, 3, 5, 7, 9, 11], dtype='int64')] are in the [columns]"
the detail trace info are:
File "E:/pytest/dfplot1.py", line 7, in
df[['a']].plot(x=df['b'],ax=ax)
File "C:\Program Files\Python37\lib\site-packages\pandas\plotting_core.py", line 759, in call
elif not isinstance(data[x], ABCSeries):
File "C:\Program Files\Python37\lib\site-packages\pandas\core\frame.py", line 2986, in getitem
indexer = self.loc._convert_to_indexer(key, axis=1, raise_missing=True)
File "C:\Program Files\Python37\lib\site-packages\pandas\core\indexing.py", line 1285, in _convert_to_indexer
return self._get_listlike_indexer(obj, axis, **kwargs)[1]
File "C:\Program Files\Python37\lib\site-packages\pandas\core\indexing.py", line 1092, in _get_listlike_indexer
keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
File "C:\Program Files\Python37\lib\site-packages\pandas\core\indexing.py", line 1177, in _validate_read_indexer
key=key, axis=self.obj._get_axis_name(axis)
KeyError: "None of [Int64Index([1, 3, 5, 7, 9, 11], dtype='int64')] are in the [columns]"
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28594 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28594/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28594/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28594/events | https://github.com/pandas-dev/pandas/issues/28594 | 497,595,218 | MDU6SXNzdWU0OTc1OTUyMTg= | 28,594 | DataFrameGroupBy.quantile returns -> IndexError: indices are out-of-bounds | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-24T10:18:41Z | 2019-09-24T19:13:36Z | 2019-09-24T19:13:36Z | NONE | null | #### Sample Code
```python
df1212 = pd.DataFrame([['a', 1, 1], ['a', 1, 2], ['a', 2, 8 ], ['b', 1, 9 ], ['b', 1, 7], ['b', 2, 1]], columns=['key', 'val', 'val1'])
df1212.groupby(['key','val']).quantile(q=[0.5,0.75])
```
#### Problem description
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-48-0067bad4e229> in <module>
----> 1 df1212.groupby(['key','val']).quantile(q=[0.5,0.75])
~/miniconda/envs/minimal_ds/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1951 indices = np.concatenate(arrays)
1952 assert len(indices) == len(result)
-> 1953 return result.take(indices)
1954
1955 @Substitution(name="groupby")
~/miniconda/envs/minimal_ds/lib/python3.7/site-packages/pandas/core/generic.py in take(self, indices, axis, is_copy, **kwargs)
3602
3603 new_data = self._data.take(
-> 3604 indices, axis=self._get_block_manager_axis(axis), verify=True
3605 )
3606 result = self._constructor(new_data).__finalize__(self)
~/miniconda/envs/minimal_ds/lib/python3.7/site-packages/pandas/core/internals/managers.py in take(self, indexer, axis, verify, convert)
1387 n = self.shape[axis]
1388 if convert:
-> 1389 indexer = maybe_convert_indices(indexer, n)
1390
1391 if verify:
~/miniconda/envs/minimal_ds/lib/python3.7/site-packages/pandas/core/indexers.py in maybe_convert_indices(indices, n)
199 mask = (indices >= n) | (indices < 0)
200 if mask.any():
--> 201 raise IndexError("indices are out-of-bounds")
202 return indices
203
IndexError: indices are out-of-bounds
#### Expected Output
Return a DF
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 12 | 2019-09-24T12:12:30Z | 2020-02-12T00:41:12Z | 2020-02-12T00:41:12Z | CONTRIBUTOR | null | - [x] closes #28256
- [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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"i... | open | false | null | [] | null | 0 | 2019-09-24T13:06:18Z | 2020-05-11T19:17:51Z | null | NONE | null | #### Code Sample, a copy-pastable example
```python
from io import StringIO
import pandas as pd
from scipy import interpolate
import matplotlib.pyplot as plt
csv = StringIO("""date_time,CUM_PROD,RATE
2012-05-03 00:00:00.000000000,21306.043,23.143911
2012-05-08 11:15:00.000000000,21432.611,23.143911
2012-05-14 09:10:22.252800000,21569.47,23.143911
2012-05-19 04:35:16.396800000,21680.768,23.143911
2012-05-24 00:00:00.000000000,21792.064,23.143911
2012-05-29 18:22:40.540800000,21925.506,23.143911
2012-06-04 13:36:30.268800000,22059.771,23.143911
2012-06-09 09:51:19.728000000,22171.87,23.143911
2012-06-15 00:00:00.000000000,22301.23,23.143911
2012-06-21 03:10:53.875200000,22443.162,23.143911
2012-06-23 04:02:13.603200000,22490.275,23.143911
2012-06-24 17:55:46.848000000,22526.816,23.143911
2012-06-27 05:00:35.136000000,22583.787,23.143911
2012-06-29 21:36:23.241600000,22646.08,23.143911
2012-07-04 07:18:34.415999999,22748.016,23.143911
2012-07-08 01:25:36.307200000,22834.916,23.143911
2012-07-12 06:10:53.875200000,22932.076,23.143911
2012-07-18 03:05:26.937600000,23067.959,23.143911
2012-07-24 00:00:00.000000000,23203.842,23.143911
2012-07-28 12:00:00.000000000,23307.99,23.143911
2012-08-02 00:00:00.000000000,23412.139,23.143911
2012-08-07 06:01:45.494400000,23533.672,23.143911
2012-08-09 16:24:33.062400000,23589.97,23.143911
2012-08-12 10:02:24.144000000,23653.26,23.143911
2012-08-15 01:19:27.206400000,23714.285,23.143911
2012-08-17 15:06:09.100800000,23773.863,23.143911
2012-08-20 11:47:41.712000000,23840.104,23.143911
2012-08-24 21:44:49.459200000,23942.275,23.143911
2012-08-29 08:03:55.584000000,24044.803,23.143911
2012-09-02 14:28:00.451200000,24143.549,23.143911
2012-09-08 00:00:00.000000000,24268.46,23.143911
2012-09-13 00:00:00.000000000,24384.182,23.143911
2012-09-17 22:28:14.505600000,24498.428,23.143911
2012-09-23 13:20:30.451200000,24628.488,23.143911
2012-09-28 06:40:15.225600000,24737.777,23.143911
2012-10-03 00:00:00.000000000,24847.062,23.143911
2012-10-07 00:00:00.000000000,24993.674,36.65348
2012-10-09 00:00:00.000000000,25066.982,36.653137
2012-10-11 00:00:00.000000000,25140.287,36.653137
2012-10-14 17:39:04.953600000,25277.205,36.65333
2012-10-17 10:10:50.361600000,25375.756,36.653137
2012-10-20 02:59:28.377600000,25474.734,36.653137
2012-10-22 13:29:49.459200000,25564.082,36.653137
2012-10-25 00:00:00.000000000,25653.434,36.653137
2012-10-28 14:44:10.809599999,25785.9,36.653137
2012-11-01 09:26:22.070400000,25924.42,36.653137
2012-11-05 12:10:11.712000000,26075.201,36.653137
2012-11-10 10:47:34.684800000,26256.365,36.653137
2012-11-16 04:29:38.918400000,26466.66,36.652935
2012-11-21 09:27:56.937600000,26657.523,36.653137
2012-11-26 10:33:30.902400000,26842.459,36.653137
2012-12-02 05:16:45.494400000,27054.312,36.653137
2012-12-08 00:00:00.000000000,27266.166,36.653137
2012-12-09 00:00:00.000000000,27302.822,36.653137
2012-12-11 19:51:37.296000000,27406.46,36.653137
2012-12-16 00:19:41.260800000,27559.896,36.653137
2012-12-19 05:32:24.144000000,27677.816,36.653137
2012-12-21 08:56:29.097599999,27756.316,36.653137
2012-12-24 03:38:19.190400000,27858.176,36.653137
2012-12-27 02:35:23.424000000,27966.533,36.653137
2012-12-30 05:08:40.272000000,28080.393,36.653137
2013-01-03 00:00:00.000000000,28219.15,36.653137
2013-01-08 11:45:45.676800000,28420.38,36.653137
2013-01-10 20:49:16.665600000,28507.523,36.653137
2013-01-13 18:02:06.576000000,28613.223,36.653137
2013-01-18 00:00:00.000000000,28768.95,36.653137
2013-01-22 03:18:59.097600000,28920.627,36.653587
2013-01-26 09:26:43.152000000,29076.598,36.653137
2013-01-30 14:13:35.587200000,29230.514,36.653137
2013-02-04 00:00:00.000000000,29392.055,36.653137
2013-02-06 20:17:27.657600000,29496.35,36.653137
2013-02-09 05:49:48.288000000,29584.223,36.653137
2013-02-11 10:37:33.513600000,29664.855,36.653137
2013-02-15 01:36:09.100800000,29797.668,36.64804
2013-02-19 07:32:59.280000000,29953.357,36.653046
2013-02-23 07:20:30.451200000,30099.637,36.64767
2013-02-26 03:40:15.225600000,30203.986,36.653137
2013-03-01 00:00:00.000000000,30308.34,36.653137
2013-03-05 09:00:10.540800000,30468.697,36.65226
2013-03-09 15:09:19.008000000,30624.703,36.652893
2013-03-13 13:05:23.423999999,30768.168,36.653137
2013-03-17 15:41:18.556800000,30918.746,36.653137
2013-03-21 15:24:04.953600000,31064.924,36.654118
2013-03-26 00:00:00.000000000,31224.668,36.653137
2013-03-30 00:00:00.000000000,31371.281,36.653137
2013-04-03 00:00:00.000000000,31517.893,36.653137
""")
#raw data
raw = pd.read_csv(csv, parse_dates=['date_time'], index_col=0)
#resampled cumulative data
resampled = pd.DataFrame(raw['CUM_PROD'].resample(rule='MS').first())
#calculate effective rate in period
resampled['days'] = resampled.index
resampled['days'] = (resampled['days'].shift(-1) - resampled.index) / pd.np.timedelta64(1, 'D')
resampled['RATE_EFF'] = (resampled['CUM_PROD'].shift(-1) - resampled['CUM_PROD']) / resampled['days']
#float index for scipy linear interpolation
elapsed = (raw.index - raw.index[0]) / pd.np.timedelta64(1, 'D')
#float index for resampled data
elapsed_resampled = (resampled.index - resampled.index[0]) / pd.np.timedelta64(1, 'D')
interp = interpolate.interp1d(elapsed, raw['CUM_PROD'], kind='linear')
#calculate values from interpolator
resampled['CUM_PROD_INTERP'] = interp(elapsed_resampled)
#calculate corresponding effective rate
resampled['RATE_INTERP'] = (resampled['CUM_PROD_INTERP'].shift(-1) - resampled['CUM_PROD_INTERP']) / resampled['days']
#plot things
fig, ax = plt.subplots(2)
#cumulative plot
ax[0].plot_date(raw.index, raw['CUM_PROD'], marker='o', label='Raw data')
ax[0].plot_date(resampled.index, resampled['CUM_PROD'], linestyle='--', label='Pandas resample')
ax[0].plot_date(resampled.index, resampled['CUM_PROD_INTERP'], linestyle='--', label='Scipy interpolate')
ax[0].legend()
#rate plot
ax[1].plot_date(raw.index, raw['RATE'], linestyle='-', marker='None', label='Raw data')
ax[1].plot_date(resampled.index, resampled['RATE_EFF'], linestyle='--', label='Pandas resample')
ax[1].plot_date(resampled.index, resampled['RATE_INTERP'], linestyle='--', label='Scipy interpolate')
ax[1].legend()
```
#### Problem description
I'm calculating effective production rate in period (week/month etc) from irregularly spaced cumulative production time series - see code. Using panda's `resample` produces noisy time series that doesn't match the original rate data. The workaround was to use linear interpolation from scipy (`interp1d`) and calculate the values from it. See figure below, where 'raw' rate data is compared with panda's and scipy's outputs:
As evident from the graph, data coming out of panda's `resample` has oscillations which aren't observed in the original data.
Zooming in on cumulative production curve, it appears that pandas resampling overpredicts the values in some places and underpredicts in others, whereas scipy's linear interpolation is systematically above the original curve. I guess this is the source of 'noise' in resampled data in the figure above.
#### Expected Output
Resampling should honour source data and be free of artefacts not present in data.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: English_United States.1252
pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 9 | 2019-09-24T15:38:25Z | 2020-04-17T22:08:51Z | 2020-04-17T22:08:51Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
test = pd.DataFrame(
{
'foo' : ['small', 'large', 'large', 'large', 'medium', 'large', 'large', 'medium'],
'bar' : ['C', 'A', 'A', 'C', 'A', 'C', 'A', 'C']
})
test['foo'] = test['foo'].astype('category').cat.set_categories(['tiny','small', 'medium', 'large'], ordered=True)
test.groupby(['bar', 'foo']).size().unstack()
# output
foo medium large small
bar
A 1.0 3.0 NaN
C 1.0 2.0 1.0
```
#### Problem description
I have a dataframe with an ordered category column `foo`. I want to group by both columns then take the size of the groups and unstack to get a summary table. If all of the values in my ordered category are in the data, then the result is as expected:
```python
test = pd.DataFrame(
{
'foo' : ['small', 'large', 'large', 'large', 'medium', 'large', 'large', 'medium'],
'bar' : ['C', 'A', 'A', 'C', 'A', 'C', 'A', 'C']
})
test['foo'] = test['foo'].astype('category').cat.set_categories(['small', 'medium', 'large'], ordered=True)
print(test.groupby(['bar', 'foo']).size().unstack())
# output
foo small medium large
bar
A NaN 1.0 3.0
C 1.0 1.0 2.0
```
My columns appear in the specified order. However, if for some reason I have categories that are listed but don't actually appear in the data (in this case, `'tiny'`) the order seems to be determined by the order that the categories appear in the series before stacking:
```python
test = pd.DataFrame(
{
'foo' : ['small', 'large', 'large', 'large', 'medium', 'large', 'large', 'medium'],
'bar' : ['C', 'A', 'A', 'C', 'A', 'C', 'A', 'C']
})
test['foo'] = test['foo'].astype('category').cat.set_categories(['small', 'medium', 'large'], ordered=True)
print(test.groupby(['bar', 'foo']).size())
print(test.groupby(['bar', 'foo']).size().unstack())
# output
bar foo
A medium 1
large 3
C small 1
medium 1
large 2
dtype: int64
foo medium large small
bar
A 1.0 3.0 NaN
C 1.0 2.0 1.0
```
I originally encountered this when using `pd.cut` to group rows into bins, but an explicitly ordered category I thought made a clearer example. It's also very easy to end up in this situation when filtering a large dataframe.
#### Expected Output
```python
foo small medium large
bar
A NaN 1.0 3.0
C 1.0 1.0 2.0
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-64-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 6 | 2019-09-24T16:57:25Z | 2019-10-11T22:16:32Z | 2019-10-11T22:16:32Z | 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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https://api.github.com/repos/pandas-dev/pandas/issues/28599 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28599/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28599/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28599/events | https://github.com/pandas-dev/pandas/issues/28599 | 497,814,547 | MDU6SXNzdWU0OTc4MTQ1NDc= | 28,599 | Series of object/strings cannot be converted to Int64Dtype() | {
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} | [] | closed | false | null | [] | null | 18 | 2019-09-24T17:06:54Z | 2019-11-19T00:30:29Z | 2019-11-03T01:11:36Z | NONE | null | **Edited to add information.**
#### Code Sample, a copy-pastable example if possible
```python
a = pd.Series(['123', '345', '456'])
a.astype(int) # works
a.astype('Int64') # doesn't work
```
#### Problem description
Currently, the conversion of object dtypes (containing strings) to Int64 doesn't work, even though it should be able to. It produces a long error (see at the end).
**Important to note:** the above is trying to convert to `Int64` with the capital I. Those are the new nullable-integer arrays that got added to `python`. `pandas` seems to support them, yet I think something inside `astype` wasn't update to reflect that.
In essence, the above should work; there is no reason why it should fail and it's quite simply a bug (in answer to some comments). Moreover, `to_numeric` is not a sufficient replacement here; it doesn't convert to `Int64` when there are missing datatypes, instead it converts to `float` automatically (this is actually a non-trivial problem when dealing with long integer identifiers, such as GAIA target identifiers).
**Traceback:**
<details>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-5778d49bfa2e> in <module>()
2 a = pd.Series(['123', '345', '456'])
3 print(a.astype(int))
----> 4 print(a.astype('Int64'))
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors, **kwargs)
5880 # else, only a single dtype is given
5881 new_data = self._data.astype(
-> 5882 dtype=dtype, copy=copy, errors=errors, **kwargs
5883 )
5884 return self._constructor(new_data).__finalize__(self)
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/internals/managers.py in astype(self, dtype, **kwargs)
579
580 def astype(self, dtype, **kwargs):
--> 581 return self.apply("astype", dtype=dtype, **kwargs)
582
583 def convert(self, **kwargs):
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/internals/managers.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
436 kwargs[k] = obj.reindex(b_items, axis=axis, copy=align_copy)
437
--> 438 applied = getattr(b, f)(**kwargs)
439 result_blocks = _extend_blocks(applied, result_blocks)
440
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors, values, **kwargs)
557
558 def astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
--> 559 return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
560
561 def _astype(self, dtype, copy=False, errors="raise", values=None, **kwargs):
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/internals/blocks.py in _astype(self, dtype, copy, errors, values, **kwargs)
641 # _astype_nansafe works fine with 1-d only
642 vals1d = values.ravel()
--> 643 values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
644
645 # TODO(extension)
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
648 # dispatch on extension dtype if needed
649 if is_extension_array_dtype(dtype):
--> 650 return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
651
652 if not isinstance(dtype, np.dtype):
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy)
321 @classmethod
322 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 323 return integer_array(scalars, dtype=dtype, copy=copy)
324
325 @classmethod
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/arrays/integer.py in integer_array(values, dtype, copy)
105 TypeError if incompatible types
106 """
--> 107 values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
108 return IntegerArray(values, mask)
109
/home/sestovic/anaconda3/lib/python3.6/site-packages/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
190 ]:
191 raise TypeError(
--> 192 "{} cannot be converted to an IntegerDtype".format(values.dtype)
193 )
194
TypeError: object cannot be converted to an IntegerDtype
```
</details>
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.16-041816-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.14.3
pytz : 2018.4
dateutil : 2.7.3
pip : 19.1.1
setuptools : 39.1.0
Cython : 0.28.2
pytest : 3.5.1
hypothesis : None
sphinx : 1.7.4
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.1.1
numexpr : 2.6.5
odfpy : None
openpyxl : 2.5.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.7
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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} | 2 | 2019-09-24T17:58:59Z | 2019-09-26T14:47:19Z | 2019-09-26T12:27:47Z | MEMBER | null | We have two special cases in `_combine_series_frame` that are never tested ATM. This adds tests for them, then notes that the special case handling code is unnecessary and this can now fall through to the general case code. In a follow-up, we'll be able to simplify _combine_series_frame further, but for now I want to keep the changed logic obvious.
Also note that the removed
```
if not len(other):
return self * np.nan
```
is actually wrong in the non-float test case in L681-685 in the test file. | {
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} | 8 | 2019-09-24T21:19:22Z | 2019-09-27T17:30:07Z | 2019-09-26T12:25:29Z | MEMBER | null | - [x] closes #28079
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
xref #28050.
To make the Series vs DataFrame behavior consistent, the two main options are a) change the DataFrame behavior (this PR) or b) change the Series behavior. The latter is complicated by the fact that for object dtypes the Series comparisons go through comp_method_OBJECT_ARRAY instead of the numpy op, so we would still have to change the complex-case test changed here. | {
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} | 8 | 2019-09-24T21:40:47Z | 2021-07-21T04:55:35Z | null | CONTRIBUTOR | null | Validation error `PR09 (Parameter description should end with ".")`. Removing this issue from the code base enables the addition to the CI for automated testing in the future.
If you are working on this issue, it probably makes sense to check for error `PR08` in issue #25232 as well since they require similar changes.
`PR09` has 445 occurrences (see #27977 )
Todo:
- [ ] get rid of the errors in the code base
- [ ] update the code_check.sh script to take into account the PR09 type of errors
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} | [] | closed | false | null | [] | null | 1 | 2019-09-24T22:21:26Z | 2019-09-24T22:36:50Z | 2019-09-24T22:35:53Z | NONE | null | Pandas 0.25.1
Python 3.6
I am attempting to use Pandas.drop_duplicates() in Anacadona, Jupyter Notebooks, by considering only a certain subset but am getting an error KeyError: Index(['days'], dtype='object')
The Index is as follows: `id, event_description, attribute1, attribute 2, attribute 3, days, days_supply, days_equivalent`
I want to ignore attribute 2 and attribute 3 so I have ran the following:
```python
df = df.drop_duplicates(subset=['id', 'event_description', 'attribute1', 'days', 'days_supply', 'days_equivalent'])
```
Which returns:
```python
eyError Traceback (most recent call last)
<ipython-input-4-3f7da32b380f> in <module>
7
8 df = df.drop_duplicates(subset=['id', 'event_description', 'attribute1', 'days',
-> 9 'days_supply', 'days_equivalent'])
10
11 print(df)
/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in drop_duplicates(self, subset, keep, inplace)
4892
4893 inplace = validate_bool_kwarg(inplace, "inplace")
-> 4894 duplicated = self.duplicated(subset, keep=keep)
4895
4896 if inplace:
/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in duplicated(self, subset, keep)
4949 diff = Index(subset).difference(self.columns)
4950 if not diff.empty:
-> 4951 raise KeyError(diff)
4952
4953 vals = (col.values for name, col in self.items() if name in subset)
KeyError: Index(['days'], dtype='object')
```
Once I remove days, the remove duplicates runs without flaw, but I do need to make sure I consider days. What does the error require that I fix?
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} | 1 | 2019-09-25T00:00:34Z | 2019-10-01T13:33:37Z | 2019-10-01T04:00:19Z | MEMBER | null | Broken off from abandoned local branches.
Also fixes one of the problems currently afflicting the CI in tests.groupby.test_categorical | {
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} | 6 | 2019-09-25T01:29:14Z | 2019-10-11T04:12:27Z | 2019-10-09T14:50:47Z | CONTRIBUTOR | null | - [x] closes #28557
- [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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} | [] | closed | false | null | [] | null | 2 | 2019-09-25T03:37:44Z | 2019-09-25T05:17:39Z | 2019-09-25T03:55:23Z | CONTRIBUTOR | null | * If applying rep to recons_labels go fail, use ids which has no
consecutive duplicates instead.
- [x] closes #28479
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xuancong84 found that value_counts() crashes if `groupby` object contains empty groups.
However, even though I made the construction of DataFrame don't skip empty rows, it still crashed.
Till then, I already tried in many ways though, in this time I tried to correct the callee `self.grouper.recons_labels`. After several tests, I found that If freq of `Grouper` is too long so that it has empty groups in some periods then it crashes. And also have found that this is solved by using `ids` which has no consecutive duplicates instead of `self.grouper.recons_labels`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28608 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28608/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28608/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28608/events | https://github.com/pandas-dev/pandas/pull/28608 | 498,032,985 | MDExOlB1bGxSZXF1ZXN0MzIxMDQ4NDIw | 28,608 | BUG: value_counts can handle the case even with empty groups (#28479) | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-25T03:56:12Z | 2019-09-26T13:05:59Z | 2019-09-26T13:05:58Z | CONTRIBUTOR | null | * If applying rep to recons_labels go fail, use ids which has no
consecutive duplicates instead.
- [x] closes #28479
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
xuancong84 found that value_counts() crashes if `groupby` object contains empty groups.
However, even though I made the construction of DataFrame don't skip empty rows, it still crashed.
Till then, I already tried in many ways though, in this time I tried to correct the callee `self.grouper.recons_labels`. After several tests, I found that If freq of `Grouper` is too long so that it has empty groups in some periods then it crashes. And also have found that this is solved by using `ids` which has no consecutive duplicates instead of `self.grouper.recons_labels`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28609 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28609/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28609/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28609/events | https://github.com/pandas-dev/pandas/issues/28609 | 498,136,054 | MDU6SXNzdWU0OTgxMzYwNTQ= | 28,609 | OverflowError on using to_json to serialize NaN value with type Decimal. | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 1 | 2019-09-25T08:39:12Z | 2021-04-23T08:58:25Z | null | NONE | null | ```python
import pandas as pd
from decimal import Decimal
def handle_nan(value):
if isinstance(value, Decimal):
return float(value)
return value
temp_dict = {'a': [Decimal(1), Decimal('NaN')]}
df = pd.DataFrame(temp_dict)
df_json = df.to_json(default_handler=handle_nan)
```
#### Error
OverflowError: Invalid Nan value when encoding double.
#### Problem Description
Default handler not being called while getting error in serialization.
#### Expected Output
handle_nan should be called to return float NaN which is serializable.
<details>
INSTALLED VERSIONS
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.187-1.el7.elrepo.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 40.6.2
Cython : 0.28.2
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.0.4
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28610/events | https://github.com/pandas-dev/pandas/issues/28610 | 498,158,746 | MDU6SXNzdWU0OTgxNTg3NDY= | 28,610 | DatetimeIndex + DateOffset broken around DST switching time | {
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... | open | false | null | [] | null | 1 | 2019-09-25T09:20:52Z | 2020-04-02T05:12:06Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> idx = pd.DatetimeIndex(['2000-03-26 04:00'], tz='Europe/Berlin') # Switch to DST was on that day
>>> idx
DatetimeIndex(['2000-03-26 04:00:00+02:00'], dtype='datetime64[ns, Europe/Berlin]', freq=None)
>>> idx + pd.DateOffset(hours=-2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/indexes/datetimelike.py", line 510, in __add__
result = self._data.__add__(maybe_unwrap_index(other))
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 1212, in __add__
result = self._add_offset(other)
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 832, in _add_offset
result = result.tz_localize(self.tz)
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1151, in tz_localize
self.asi8, tz, ambiguous=ambiguous, nonexistent=nonexistent
File "pandas/_libs/tslibs/tzconversion.pyx", line 276, in pandas._libs.tslibs.tzconversion.tz_localize_to_utc
pytz.exceptions.NonExistentTimeError: 2000-03-26 02:00:00
>>> idx[0] + pd.DateOffset(hours=-2)
Timestamp('2000-03-26 01:00:00+0100', tz='Europe/Berlin')
```
#### Problem description
The vectorized operation `DatetimeIndex + DateOffset` is broken around the DST switching time. However, the same operation works element-wise (`Timestamp + DateOffset`), so I would expect either both versions to work (preferably) or both versions to fail with the same error.
#### Expected Output
```python
DatetimeIndex(['2000-03-26 01:00:00+01:00'], dtype='datetime64[ns, Europe/Berlin]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28611 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28611/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28611/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28611/events | https://github.com/pandas-dev/pandas/issues/28611 | 498,163,118 | MDU6SXNzdWU0OTgxNjMxMTg= | 28,611 | Misleading error message when Series.dt.tz_convert overflows | {
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... | open | false | null | [] | null | 0 | 2019-09-25T09:28:40Z | 2021-07-21T04:57:51Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> import datetime as dt
>>> pd.Series([dt.datetime.max.replace(tzinfo=dt.timezone.utc)]).tz_convert('Europe/Berlin')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'datetime.datetime' object is not callable
>>> pd.Series([dt.datetime.max.replace(tzinfo=dt.timezone.utc)]).tz_convert('Europe/Berlin')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/generic.py", line 9703, in tz_convert
ax = _tz_convert(ax, tz)
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/generic.py", line 9686, in _tz_convert
"%s is not a valid DatetimeIndex or " "PeriodIndex" % ax_name
TypeError: index is not a valid DatetimeIndex or PeriodIndex
>>> pd.Series([dt.datetime.max.replace(tzinfo=dt.timezone.utc)]).dt.tz_convert('Europe/Berlin')
Traceback (most recent call last):
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1979, in objects_to_datetime64ns
values, tz_parsed = conversion.datetime_to_datetime64(data)
File "pandas/_libs/tslibs/conversion.pyx", line 189, in pandas._libs.tslibs.conversion.datetime_to_datetime64
File "pandas/_libs/tslibs/conversion.pyx", line 399, in pandas._libs.tslibs.conversion.convert_datetime_to_tsobject
File "pandas/_libs/tslibs/np_datetime.pyx", line 118, in pandas._libs.tslibs.np_datetime.check_dts_bounds
pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 9999-12-31 23:59:59
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/accessor.py", line 93, in f
return self._delegate_method(name, *args, **kwargs)
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/indexes/accessors.py", line 106, in _delegate_method
values = self._get_values()
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/indexes/accessors.py", line 55, in _get_values
return DatetimeIndex(data, copy=False, name=self.name)
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/indexes/datetimes.py", line 334, in __new__
int_as_wall_time=True,
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 446, in _from_sequence
int_as_wall_time=int_as_wall_time,
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1866, in sequence_to_dt64ns
data, dayfirst=dayfirst, yearfirst=yearfirst
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1984, in objects_to_datetime64ns
raise e
File "/var/local/conda/envs/pandas-debug/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 1975, in objects_to_datetime64ns
require_iso8601=require_iso8601,
File "pandas/_libs/tslib.pyx", line 465, in pandas._libs.tslib.array_to_datetime
File "pandas/_libs/tslib.pyx", line 543, in pandas._libs.tslib.array_to_datetime
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
#### Problem description
The error message of the outer exception above is misleading. The inner exception has the right error message, but in many cases (e.g. log files) only the outer exception's message is displayed.
If I understand things correctly, the underlying issue is discussed in #12677. In contrast to that ticket I'm fine with getting the exception but would like a clearer message.
#### Expected Output
The inner exception message should be kept instead of being replaced with a misleading new message.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-29-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28612/events | https://github.com/pandas-dev/pandas/issues/28612 | 498,246,761 | MDU6SXNzdWU0OTgyNDY3NjE= | 28,612 | CI failures on master | {
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] | closed | false | null | [] | null | 4 | 2019-09-25T12:14:55Z | 2019-09-25T16:49:46Z | 2019-09-25T15:48:00Z | CONTRIBUTOR | null | I retriggered a passing build on master, and it failed: https://travis-ci.org/pandas-dev/pandas/builds/588926447.
Last passing build was https://travis-ci.org/pandas-dev/pandas/builds/588919832.
@jbrockmendel do you have time to look into this today?
```
=================================== FAILURES ===================================
2294____________________________ test_with_s3_url[gzip] ____________________________
2295[gw1] linux -- Python 3.7.4 /home/travis/miniconda3/envs/pandas-dev/bin/python
2296
2297compression = 'gzip', s3_resource = s3.ServiceResource()
2298
2299 @td.skip_if_not_us_locale
2300 def test_with_s3_url(compression, s3_resource):
2301 # Bucket "pandas-test" created in tests/io/conftest.py
2302
2303 df = pd.read_json('{"a": [1, 2, 3], "b": [4, 5, 6]}')
2304
2305 with tm.ensure_clean() as path:
2306 df.to_json(path, compression=compression)
2307 with open(path, "rb") as f:
2308 s3_resource.Bucket("pandas-test").put_object(Key="test-1", Body=f)
2309
2310> roundtripped_df = pd.read_json("s3://pandas-test/test-1", compression=compression)
2311
2312pandas/tests/io/json/test_compression.py:48:
2313_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2314pandas/io/json/_json.py:602: in read_json
2315 compression=compression,
2316pandas/io/json/_json.py:665: in __init__
2317 self.data = self._preprocess_data(data)
2318pandas/io/json/_json.py:676: in _preprocess_data
2319 data = data.read()
2320../../../miniconda3/envs/pandas-dev/lib/python3.7/gzip.py:276: in read
2321 return self._buffer.read(size)
2322_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2323
2324self = <gzip._GzipReader object at 0x7fb226df7450>, size = 8192
2325
2326 def read(self, size=-1):
2327 if size < 0:
2328 return self.readall()
2329 # size=0 is special because decompress(max_length=0) is not supported
2330 if not size:
2331 return b""
2332
2333 # For certain input data, a single
2334 # call to decompress() may not return
2335 # any data. In this case, retry until we get some data or reach EOF.
2336 while True:
2337 if self._decompressor.eof:
2338 # Ending case: we've come to the end of a member in the file,
2339 # so finish up this member, and read a new gzip header.
2340 # Check the CRC and file size, and set the flag so we read
2341 # a new member
2342 self._read_eof()
2343 self._new_member = True
2344 self._decompressor = self._decomp_factory(
2345 **self._decomp_args)
2346
2347 if self._new_member:
2348 # If the _new_member flag is set, we have to
2349 # jump to the next member, if there is one.
2350 self._init_read()
2351 if not self._read_gzip_header():
2352 self._size = self._pos
2353 return b""
2354 self._new_member = False
2355
2356 # Read a chunk of data from the file
2357 buf = self._fp.read(io.DEFAULT_BUFFER_SIZE)
2358
2359 uncompress = self._decompressor.decompress(buf, size)
2360 if self._decompressor.unconsumed_tail != b"":
2361 self._fp.prepend(self._decompressor.unconsumed_tail)
2362 elif self._decompressor.unused_data != b"":
2363 # Prepend the already read bytes to the fileobj so they can
2364 # be seen by _read_eof() and _read_gzip_header()
2365 self._fp.prepend(self._decompressor.unused_data)
2366
2367 if uncompress != b"":
2368 break
2369 if buf == b"":
2370> raise EOFError("Compressed file ended before the "
2371 "end-of-stream marker was reached")
2372E EOFError: Compressed file ended before the end-of-stream marker was reached
2373
2374../../../miniconda3/envs/pandas-dev/lib/python3.7/gzip.py:482: EOFError
2375____________________________ test_with_s3_url[bz2] _____________________________
2376
``` | {
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