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https://api.github.com/repos/pandas-dev/pandas/issues/27613 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27613/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27613/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27613/events | https://github.com/pandas-dev/pandas/issues/27613 | 473,437,178 | MDU6SXNzdWU0NzM0MzcxNzg= | 27,613 | assert_series_equal doesn't distinguish between different NA values | {
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] | closed | false | null | [] | null | 2 | 2019-07-26T16:21:22Z | 2020-10-09T15:47:12Z | 2020-10-09T15:47:12Z | MEMBER | null | The following, based on tests.indexing.test_datetime.TestDatetimeIndex.test_indexing_with_datetime_tz passes, I think incorrectly
```
now = pd.Timestamp.now()
left = pd.Series([now, pd.NaT, pd.NaT], dtype=object)
right = pd.Series([now, np.nan, np.nan], dtype=object)
tm.assert_series_equal(left, right)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/27614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27614/events | https://github.com/pandas-dev/pandas/issues/27614 | 473,454,626 | MDU6SXNzdWU0NzM0NTQ2MjY= | 27,614 | GroupBy(axis=1) Does Not Offer Implicit Selection By Columns Name(s) | {
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} | 5 | 2019-07-26T17:09:54Z | 2019-08-02T13:31:00Z | 2019-08-02T13:31:00Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20])
df.index.name = "y"
df.columns.name = "x"
print df
print
print "Grouped along index:"
print df.groupby(by="y").sum()
print
print "Grouped along columns:"
# The following raises a KeyError even though "x" is a column name
# (like "y" above, which is an index name):
df.groupby(by="x", axis=1).sum()
```
#### Problem description
The exception at the end is surprising: the intent is clearly to group by columns, on the "x" column label.
Furthermore, the documentation for `groupby()` seems to confirm this, as it states for the "by" argument that "A str or list of strs may be passed to group by the columns in ``self``".
#### Expected Output
A dataframe with index [0, 1, 0] but grouped (and summed) columns [10, 20].
I wasn't able to test with the latest Pandas version, sorry!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.15.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.21.1
pytest: 3.2.3
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.27.3
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.4
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.5
feather: None
matplotlib: 2.1.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details> | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27615 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27615/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27615/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27615/events | https://github.com/pandas-dev/pandas/pull/27615 | 473,455,400 | MDExOlB1bGxSZXF1ZXN0MzAxNjE5NDAx | 27,615 | Move json_normalize to pd namespace | {
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} | 30 | 2019-07-26T17:12:09Z | 2019-12-18T19:55:50Z | 2019-12-18T19:53:18Z | CONTRIBUTOR | null | - Added a whatsnew entry
- Imported pandas.io.json.json_normalize in __init__.py
- [x] closes #27586
- [x] whatsnew entry
cc: @WillAyd | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27616/events | https://github.com/pandas-dev/pandas/issues/27616 | 473,549,993 | MDU6SXNzdWU0NzM1NDk5OTM= | 27,616 | to_dict() on a boolean series sometimes returns numpy types instead of Python types | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 8 | 2019-07-26T21:55:33Z | 2021-07-10T19:12:22Z | null | NONE | null | #### Problem description
I construct a `Series` in several ways that should give the same output from `to_dict()`, but instead I get different output types. In my case, this breaks downstream JSON serializers.
The code sample below includes cases with correct output (`bool`) and incorrect (`numpy.bool_`) -- see inline comments.
Related issues, though none seem exactly the same: #13258, #13830, #16048, #17491, #19381, #20791, #23753, #23921, #24908, #25969
#### Code sample
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({ 'a': [True, False], 'b': [0, 1]} )
In [3]: df
Out[3]:
a b
0 True 0
1 False 1
In [27]: type(df['a'].iloc[0])
Out[27]: numpy.bool_
In [48]: type(df[['a']].iloc[0, 0])
Out[48]: numpy.bool_
In [33]: type(df.iloc[0,0])
Out[33]: numpy.bool_
In [24]: type(df.iloc[0]['a'])
Out[24]: numpy.bool_
# ----
In [4]: df[['a']].iloc[0].to_dict()
Out[4]: {'a': True}
# correct
In [5]: type(df[['a']].iloc[0].to_dict()['a'])
Out[5]: bool
In [6]: df.iloc[0][['a']].to_dict()
Out[6]: {'a': True}
# this one is incorrect, should return bool
In [7]: type(df.iloc[0][['a']].to_dict()['a'])
Out[7]: numpy.bool_
# ----
In [8]: df[['a', 'b']].to_dict(orient='records')[0]
Out[8]: {'a': True, 'b': 0}
# correct
In [9]: type(df[['a', 'b']].to_dict(orient='records')[0]['a'])
Out[9]: bool
In [10]: df[['a', 'b']].iloc[0].to_dict()
Out[10]: {'a': True, 'b': 0}
# this one is incorrect, should return bool
In [11]: type(df[['a', 'b']].iloc[0].to_dict()['a'])
Out[11]: numpy.bool_
```
This may explain what's going on:
```
In [54]: df.iloc[0][['a']]
Out[54]:
a True
Name: 0, dtype: object
In [56]: df[['a']].iloc[0]
Out[56]:
a True
Name: 0, dtype: bool
```
That relates to #25969, where @mroeschke commented about a similar dtype discrepancy:
> This probably occurs because `s2` is object dtype and it's trying to preserve the dtype of each input argument while the arguments in `s1` can both be coerced to `int64`.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
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 : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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"color": "53... | closed | false | null | [] | null | 11 | 2019-07-26T22:43:26Z | 2020-02-02T01:19:17Z | 2020-02-02T01:19:16Z | CONTRIBUTOR | null | - [ ] xref #26642
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I propose to deprecate (DataFrame|Series).filter. The method's functionality is already in the ``.loc``method, except the regex functionality, for which I've made a proposal adding regex functionality to ``.loc`` in #27363.
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} | 17 | 2019-07-26T23:35:30Z | 2019-08-23T22:38:22Z | 2019-08-23T22:38:19Z | MEMBER | null | - [X] closes #20500
In reviewing this module there is a shared function for object keys and values which encodes objects into a separate buffer and subsequently indexes off of that. Instead of encoding values in a buffer I've updated that function to be a char ** pointing to string representations of the labels (or index / columns, rather).
This is arguably a pre-cursor to:
1. #19486 to disentangle date time functions from JSON
2. #12004 to add indent support (tried this previously but vendoring ujson updates but didn't work because of this limitation)
3. #27164 because the various formats may need column / index labels at different points in time, so encoding up front makes that very difficult to reuse
The only downside here I haven't been able to figure out is how to deal with date formatting. Right now all labels are written as epochs. I'm sure there is a way to handle but I wasn't clear on what the best way to convert arbitrary input (i.e. object or datetime dtypes) into ISO formats by element where applicable.
cc @jbrockmendel in case you have insight on that | {
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} | 1 | 2019-07-27T01:39:24Z | 2019-07-27T15:11:12Z | 2019-07-27T14:09:37Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 30 | 2019-07-27T01:45:51Z | 2019-12-12T12:16:17Z | 2019-12-12T12:15:52Z | MEMBER | null | - [x] Needs release note
- [ ] Needs geopandas compat
- [x] Need to remove ix asvs | {
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- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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This fixes some docstring formatting issues: e.g. some deprecated directives do not appear in current docs because the two colons are missing `.. deprecated 0.25.0`. | {
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... | closed | false | null | [] | null | 1 | 2019-07-27T11:02:26Z | 2019-08-28T09:35:47Z | 2019-08-28T09:35:47Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```shell
python scripts/validate_docstrings.py pandas.Series.item
```
```
Traceback (most recent call last):
File "scripts/validate_docstrings.py", line 1047, in <module>
args.ignore_deprecated,
File "scripts/validate_docstrings.py", line 969, in main
result = validate_one(func_name)
File "scripts/validate_docstrings.py", line 836, in validate_one
errs, wrns, examples_errs = get_validation_data(doc)
File "scripts/validate_docstrings.py", line 760, in get_validation_data
if len(doc.returns) == 1 and doc.returns[0].name:
AttributeError: 'tuple' object has no attribute 'name'
```
#### Problem description
I get an AttributeError when running the script.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 11 | 2019-07-27T11:06:45Z | 2019-08-03T20:09:58Z | 2019-08-02T20:50:12Z | CONTRIBUTOR | null | - [X] closes #27622
- [ ] 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/27624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27624/events | https://github.com/pandas-dev/pandas/pull/27624 | 473,632,641 | MDExOlB1bGxSZXF1ZXN0MzAxNzU1NDM3 | 27,624 | BUG: cells are missing in the excel file when exporting excel using xlsxwriter with option constant_memory set to True (#15392) | {
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- [ ] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 1 | 2019-07-27T19:38:45Z | 2019-07-31T13:26:32Z | 2019-07-31T12:20:16Z | MEMBER | 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/27626 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27626/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27626/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27626/events | https://github.com/pandas-dev/pandas/pull/27626 | 473,671,735 | MDExOlB1bGxSZXF1ZXN0MzAxNzgxNDM1 | 27,626 | CLN: de-kludge quantile, make interpolate_with_fill understand datetime64 | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-27T19:49:34Z | 2019-07-28T01:25:01Z | 2019-07-28T01:24:56Z | MEMBER | null | 2 related-but-separate things here
-quantile: de-kludging the base class method and putting the datetimetz-specific stuff in a subclass method. This will allow us to implement the refactor suggested in #14562. If/when DTA supports 2D, we'll be able to get rid of the subclass override kludge altogether.
- interpolate_2d: have the core.missing function understand datetime64 dtypes so the blocks don't have to implement special handling. There are only a couple other places where this is needed before we can get rid of the datetimetz-specific try_coerce_result altogether. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27627 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27627/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27627/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27627/events | https://github.com/pandas-dev/pandas/pull/27627 | 473,693,256 | MDExOlB1bGxSZXF1ZXN0MzAxNzk2Mjkz | 27,627 | CLN: de-kludge Block.quantile | {
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```python
python scripts/validate_docstrings.py pandas.Series.item
```
#### Problem description
As pointed out by PR #27621, `scripts/validate_docstrings.py` does not detect dosctrings containing a reST directive without two colons.
#### Expected Output
The script should report an error in this case.
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} | 3 | 2019-07-28T15:16:26Z | 2019-08-03T11:44:30Z | 2019-07-29T16:55:40Z | MEMBER | null | - [x] closes #18303
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Notice that I profited to make the error message a bit more understandable. | {
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} | 17 | 2019-07-29T02:44:22Z | 2019-08-09T14:34:27Z | 2019-08-09T08:00:54Z | MEMBER | null | Broken off from #27142, plus some type annotations | {
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] | closed | false | null | [] | null | 12 | 2019-07-29T07:37:42Z | 2020-05-07T23:08:25Z | 2020-05-07T23:08:25Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
"""Lorem ipsum"""
import pandas as pd
def demo():
"""Demo for the false positive"""
with pd.ExcelWriter("demo.xlsx") as writer:
print(writer)
```
#### Problem description
The code shown above gives pylint: ``abstract-class-instantiated(E0110): test.py:8:9: demo: Abstract class 'ExcelWriter' with abstract methods instantiated``
#### Expected Output
No pylint issue: no abstract method left
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-54-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 : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 40.8.0
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27635/events | https://github.com/pandas-dev/pandas/issues/27635 | 473,903,632 | MDU6SXNzdWU0NzM5MDM2MzI= | 27,635 | export result as csv file | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-29T07:52:58Z | 2019-07-29T11:18:48Z | 2019-07-29T11:18:47Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**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]
</details>
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} | 9 | 2019-07-29T08:54:57Z | 2019-08-19T17:20:24Z | 2019-08-19T17:20:24Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
ind = list(range(0, 100))
cols = list(range(0, 300))
df = pd.DataFrame(index=ind, columns=cols, data=1.0)
series = pd.Series(index=cols, data=cols)
print(df.multiply(series, axis=1).head()) # Works fine
ind = list(range(0, 100000))
cols = list(range(0, 300))
df = pd.DataFrame(index=ind, columns=cols, data=1.0)
series = pd.Series(index=cols, data=cols)
print(df.add(series,axis=1).head())
```
#### Code Output:
```
0 1 2 3 4 5 ... 294 295 296 297 298 299
0 0.0 1.0 2.0 3.0 4.0 5.0 ... 294.0 295.0 296.0 297.0 298.0 299.0
1 0.0 1.0 2.0 3.0 4.0 5.0 ... 294.0 295.0 296.0 297.0 298.0 299.0
2 0.0 1.0 2.0 3.0 4.0 5.0 ... 294.0 295.0 296.0 297.0 298.0 299.0
3 0.0 1.0 2.0 3.0 4.0 5.0 ... 294.0 295.0 296.0 297.0 298.0 299.0
4 0.0 1.0 2.0 3.0 4.0 5.0 ... 294.0 295.0 296.0 297.0 298.0 299.0
[5 rows x 300 columns]
Traceback (most recent call last):
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-25-4d9165e5df4a>", line 15, in <module>
print(df.add(series,axis=1).head())
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 1499, in f
self, other, pass_op, fill_value=fill_value, axis=axis, level=level
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 1388, in _combine_series_frame
return self._combine_match_columns(other, func, level=level)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\frame.py", line 5392, in _combine_match_columns
return ops.dispatch_to_series(left, right, func, axis="columns")
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 596, in dispatch_to_series
new_data = expressions.evaluate(column_op, str_rep, left, right)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 220, in evaluate
return _evaluate(op, op_str, a, b, **eval_kwargs)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 126, in _evaluate_numexpr
result = _evaluate_standard(op, op_str, a, b)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 70, in _evaluate_standard
return op(a, b)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 584, in column_op
return {i: func(a.iloc[:, i], b.iloc[i]) for i in range(len(a.columns))}
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 584, in <dictcomp>
return {i: func(a.iloc[:, i], b.iloc[i]) for i in range(len(a.columns))}
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\ops\__init__.py", line 1473, in na_op
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 220, in evaluate
return _evaluate(op, op_str, a, b, **eval_kwargs)
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 101, in _evaluate_numexpr
if _can_use_numexpr(op, op_str, a, b, "evaluate"):
File "C:\dev\bin\anaconda\envs\py36\lib\site-packages\pandas\core\computation\expressions.py", line 84, in _can_use_numexpr
s = o.dtypes.value_counts()
AttributeError: 'numpy.dtype' object has no attribute 'value_counts'
```
#### Problem description
I think this is a regression somewhere between pandas 0.19.2 and 0.25. If you multiply or use any other operator function such as add/divide on a DataFrame by a Series where axis=1 pandas will crash in the `_can_use_numexpr` functon when the DataFrame/Series becomes very large. This is presumably down to check of the size of the objects being operated on not passing for small datasets but for larger ones it gets to the failing line.
```python
#pandas/core/computation/expressions.py : 73
def _can_use_numexpr(op, op_str, a, b, dtype_check):
""" return a boolean if we WILL be using numexpr """
if op_str is not None:
# required min elements (otherwise we are adding overhead)
if np.prod(a.shape) > _MIN_ELEMENTS:
# check for dtype compatibility
dtypes = set()
for o in [a, b]:
if hasattr(o, "dtypes"):
s = o.dtypes.value_counts() # Fails here
```
In pandas 0.19.2 the function uses the get_dtype_counts() method instead to inspect if the dtype is uniform in the object:
```python
def _can_use_numexpr(op, op_str, a, b, dtype_check):
""" return a boolean if we WILL be using numexpr """
if op_str is not None:
# required min elements (otherwise we are adding overhead)
if np.prod(a.shape) > _MIN_ELEMENTS:
# check for dtype compatiblity
dtypes = set()
for o in [a, b]:
if hasattr(o, 'get_dtype_counts'):
s = o.get_dtype_counts()
```
I have a workaround which is to transpose the dataframe and use axis=0:
```python
df.T.add(series,axis=0).T.head()
```
I noticed get_dtype_counts() is deprecated ( #27145 ) which appears to be the PR that has caused this regression as a Series only returns a single numpy dtype which does not have a value_counts() method.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.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 : 0.25.0
numpy : 1.16.4
pytz : 2018.4
dateutil : 2.7.3
pip : 10.0.1
setuptools : 39.1.0
Cython : None
pytest : 3.5.1
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.1.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 2.2.2
numexpr : 2.6.5
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.8
tables : 3.5.2
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.0.4
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27637 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27637/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27637/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27637/events | https://github.com/pandas-dev/pandas/issues/27637 | 473,993,909 | MDU6SXNzdWU0NzM5OTM5MDk= | 27,637 | Add comment char parameter to to_csv method | {
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... | open | false | null | [] | null | 6 | 2019-07-29T11:17:50Z | 2021-07-10T19:13:58Z | null | NONE | null | #### Code Sample
```
import pandas as pd
pd.DataFrame( ["a'", 'a"', "a,", "a#"], columns=["Column"]).to_csv("my.csv", index=False)
```
results in my.csv:
```
Column
a'
"a"""
"a,"
a#
```
(a# is not quoted)
#### Problem description
We would like to use the "#" character as comment indicator such that lines that start with the character are automatically ignored. However when fields contain the "#" character and the `pd.read_csv("my.csv", comment="#")` is used to read the CSV, then those fields are read as nan. When those fields are quoted, then they are read as literal strings (which is the behavior we want). So we want to automatically quote fields containing the comment character "#" in `to_csv`.
(the work-around we have now is set `quoting=2` (non-numeric) so by default all strings are quoted)
#### Expected Output
```
import pandas as pd
pd.DataFrame( ["a'", 'a"', "a,", "a#"], columns=["Column"]).to_csv("my.csv", index=False, commentchar="#")
```
results in my.csv:
```
Column
a'
"a"""
"a,"
"a#"
```
A more universal solution could be to allow passing a list of characters to quote for the `quoting` parameter. E.g. `df.to_csv("my.csv", quoting=['"', '#', ","]`.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-25-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: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.11
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.6.0
sphinx: 2.1.1
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: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: 0.9.3
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/27638 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27638/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27638/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27638/events | https://github.com/pandas-dev/pandas/issues/27638 | 474,030,215 | MDU6SXNzdWU0NzQwMzAyMTU= | 27,638 | ravel missing for IntegerArray Series | {
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} | [] | closed | false | null | [] | null | 1 | 2019-07-29T12:43:51Z | 2019-07-29T12:45:53Z | 2019-07-29T12:45:53Z | NONE | null | pandas version `0.24.2` on Linux.
```
import pandas
s1 = pandas.Series([1])
s2 = pandas.Series([1], dtype='Int64')
s1.ravel()
s2.ravel()
```
gives:
```
array([1])
Traceback (most recent call last):
File "/home/gijs/Work/alliander/rpy2/.venv/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3325, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-82-c5c390b559b3>", line 1, in <module>
s2.ravel()
File "/home/gijs/Work/alliander/rpy2/.venv/lib/python3.6/site-packages/pandas/core/series.py", line 520, in ravel
return self._values.ravel(order=order)
AttributeError: 'IntegerArray' object has no attribute 'ravel'
```
thanks! | {
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"id... | open | false | null | [] | null | 0 | 2019-07-29T14:50:29Z | 2020-04-26T21:07:52Z | null | CONTRIBUTOR | null | #### Code Sample
After some hypothesis testing on our codebase we noticed that pd.eval can cause a max recursion limit when run on an empty dataframe or an all NaN dataframe:
```python
import pandas as pd
df = pd.DataFrame(index=list(range(0,1)), columns=list('abc'))
df.eval('a*b') # Max recursion limit
```
And:
```python
import pandas as pd
df = pd.DataFrame(columns=list('abc'))
df.eval('a*b') # Max recursion limit
```
Interestingly enough using numpy.nan works correctly so the error is caused if the data is native python nan:
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(index=list(range(0,1)), columns=list('abc'), data=np.nan)
df.eval('a*b')
```
This seems to happen on 0.19.2 so I don't think this is a regression.
A similar issue was raised in 2013 #5198
INSTALLED VERSIONS
<details>
------------------
commit : None
python : 3.6.5.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 : 0.25.0
numpy : 1.16.4
pytz : 2018.4
dateutil : 2.7.3
pip : 10.0.1
setuptools : 39.1.0
Cython : None
pytest : 3.5.1
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.1.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 2.2.2
numexpr : 2.6.5
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.8
tables : 3.5.2
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.0.4
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] | closed | false | null | [] | null | 1 | 2019-07-29T17:49:55Z | 2019-07-29T19:23:03Z | 2019-07-29T19:22:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
A = pd.DataFrame(data = {'variable':[{'k0':'v0'}, {'k1':'v1'}]})
B = A.copy()
B.loc[0,'variable']['k0'] = 'v2'
A
## variable
## 0 {'k0': 'v2'}
## 1 {'k1': 'v1'}
B
## variable
## 0 {'k0': 'v2'}
## 1 {'k1': 'v1'}
```
#### Problem description
When modifying specific values of a dictionary type variable copy() method doesn't work as expected. It modifies both, the object's copy and the original object, instead of just modifying the copy.
#### Expected Output
```python
A
## variable
## 0 {'k0': 'v0'}
## 1 {'k1': 'v1'}
B
## variable
## 0 {'k0': 'v2'}
## 1 {'k1': 'v1'}
#### Output of ``pd.show_versions()``
<details>
pandas: 0.24.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27642 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27642/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27642/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27642/events | https://github.com/pandas-dev/pandas/issues/27642 | 474,183,308 | MDU6SXNzdWU0NzQxODMzMDg= | 27,642 | merge_asof(): cannot use tolerance flag when the index is a TimedeltaIndex | {
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} | 4 | 2019-07-29T18:03:54Z | 2019-08-22T13:09:49Z | 2019-08-22T13:09:49Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
import numpy as np
print(
"""
\nPandas merge_asof() bug:
\tUnimplemented error?
\tcannot use tolerance flag when my index is a timedelta (not a timestamp)
\tjust documenting so I can try to add this functionality
""")
print(f"pandas version: {pd.__version__}")
print(f"numpy version: {np.__version__}")
delta_300 = pd.timedelta_range(start='0 minutes', freq='3333334 N', periods=301, name='Time')
delta_120 = pd.timedelta_range(start='0 minutes', freq='8333334 N', periods=301, name='Time')
df_300hz = pd.DataFrame({'my300hz_data': np.arange(301)}, index=delta_300)
df_120hz = pd.DataFrame({'my120hz_data': np.arange(301)}, index=delta_120)
print(df_300hz)
print(df_120hz)
### this throws error "pandas.errors.MergeError: key must be integer, timestamp or float"
merged = pd.merge_asof(df_120hz, df_300hz, on='Time', direction='nearest', tolerance=pd.Timedelta('15 ms'))
### The line below works, but output is not what I want
# merged = pd.merge_asof(df_120hz, df_300hz, on='Time', direction='nearest')
merged.set_index('Time', inplace=True)
print(merged)
```
#### Problem description
I need to see NaNs when I merge and there is a gap in my data, without begin able to use the tolerance flag my data gets smoothed.
Current work around, convert all my TimedeltaIndex's to a time stamp. Since I do not have a date for this data I am using unix time. This feels bulky since I am going to drop the date when I save the file anyway.
#### Expected Output
[301 rows x 1 columns]
my120hz_data my300hz_data
Time
00:00:00 0 0
00:00:00.008333 1 2
00:00:00.016666 2 5
00:00:00.025000 3 7
00:00:00.033333 4 10
... ... ...
00:00:02.466666 296 NaN
00:00:02.475000 297 NaN
00:00:02.483333 298 NaN
00:00:02.491666 299 NaN
00:00:02.500000 300 NaN
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None | {
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} | 1 | 2019-07-29T18:58:46Z | 2019-07-29T19:05:59Z | 2019-07-29T19:05:52Z | MEMBER | null | When i am following the instruction and installing pandas packages listed in `requirement-dev.txt` on my new laptop, i get an error: `Could not find a version that satisfied the requirement pyqt (from -r requirement-dev.txt (line 48)). No matching distribution found for pyqt.`
And I need to change the package name to `pyqt5` to succeed the installation. Is it something that should also be changed in `requirement-dev` or it's due to issue of my virtualenv which i don't think it is though? | {
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Best, Patrik | {
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} | 7 | 2019-07-29T19:32:54Z | 2019-08-07T21:10:05Z | 2019-08-07T21:09:55Z | MEMBER | null | - [ ] closes #26838
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-07-29T21:26:06Z | 2019-07-30T01:56:45Z | 2019-07-29T22:03:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
my_list = [["654645", "54854"], ["1654151", "1564484"]]
df = pd.DataFrame(my_list, columns=["ds", "y"])
df = df.apply(pd.to_numeric, args=({"errors": "coerce"}))
print(df)
```
#### Problem description
Running the above code in v0.25.0 raises an exception like:
```
Traceback (most recent call last):
File "trash.py", line 5, in <module>
df = df.apply(pd.to_numeric, args=({"errors":"coerce"}))
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/frame.py", line 6906, in apply
return op.get_result()
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/apply.py", line 186, in get_result
return self.apply_standard()
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/apply.py", line 292, in apply_standard
self.apply_series_generator()
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/apply.py", line 321, in apply_series_generator
results[i] = self.f(v)
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/apply.py", line 112, in f
return func(x, *args, **kwds)
File "/home/anand/miniconda3/envs/prophet-env/lib/python3.6/site-packages/pandas/core/tools/numeric.py", line 114, in to_numeric
raise ValueError("invalid error value specified")
ValueError: ('invalid error value specified', 'occurred at index ds')
```
The error persists for any value for the `errors` arg.
This worked fine in v0.24.2
#### Expected Output
```
ds y
0 654645 54854
1 1654151 1564484
```
#### Output of ``pd.show_versions()``
<details>
Working setup:
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 5.1.18-300.fc30.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.11
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
Breaking setup:
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 5.1.18-300.fc30.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 : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.11
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 : 7.6.1
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 : 1.3.5
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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Some random thoughts
1. Focus on "user API for working with tabular data" (de-emphasize things like in-memory format, array library, algorithms)
2. Focus on "spirit" rather than strict rules.
3. Single-machine (but not necessarily single-core?)
4. In-memory only?
5. Visualization?
6. What readers / writers are in-scope?
7. What future extension types are in scope? | {
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} | 7 | 2019-07-29T21:35:20Z | 2021-06-27T18:50:43Z | 2019-08-22T13:09:50Z | CONTRIBUTOR | null | - [X] closes #27642
- [x] tests 2 / 1 [added / passed]
- [X] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- [ ] \(Optional) allow users to pass datetime.timedelta objects for tolerance | {
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} | 1 | 2019-07-29T22:57:10Z | 2019-08-21T21:09:15Z | 2019-07-30T09:10:49Z | CONTRIBUTOR | null | - [x] closes #27610
- [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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"description": "Related ... | open | false | null | [] | null | 10 | 2019-07-30T02:29:40Z | 2021-07-10T19:15:04Z | null | CONTRIBUTOR | null | I would like to propose that any pandas API that allows specification of a column name also works when specifying an index level name. Today, this works in some places, but not all. Here is a list of places where things work, and where things don't work and could be improved. (The list is most likely incomplete, so additions are welcome). References to existing issues are given when they already exist (and if I knew about them):
Hoping to include this in the roadmap #27478
Things that work:
- `DataFrame.query()` allows queries to use column names and index level names
- `DataFrame.merge()` and `pd.merge()` allow both column names and index level names to be specified as key fields to use in the merge (but see below)
- `DataFrame.groupby()` allows both column names and index level names to be mixed in the groupby list
Things that could be improved:
- Allow `.loc()` and `.getitem()` to specify index level names wherever it allows column names.
- Modify `DataFrame.rename()` to allow renaming of index levels with a dict argument. (#20421)
- Allow index names to be specified like columns in `pd.Grouper()` (#19542)
- When merging on a subset of `MultiIndex` levels, preserve the levels not included in the merge. (#13371 is somewhat related)
- Have `.itertuples()` return a named tuple that includes index names (#27407)
- Allow `.assign` to refer to columns that correspond to level names (although the `.loc` and `.getitem()` suggestion above might handle this
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} | 2 | 2019-07-30T02:31:56Z | 2019-08-01T14:07:24Z | 2019-08-01T12:56:49Z | MEMBER | null | - [x] closes #27571
- [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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"description": "Interval... | open | false | null | [] | null | 4 | 2019-07-30T07:19:40Z | 2021-07-10T19:16:00Z | null | NONE | null | Due to #23980 the following code now raises a ValueError since 0.25:
#### Code Sample, a copy-pastable example if possible
```python
ii = pd.IntervalIndex.from_tuples([(0, 10), (2, 12), (4, 14)])
pd.cut([5, 6], bins=ii)
```
#### Problem description
Before #23980 an IntervalIndex with overlapping columns could be used. It would return every Interval which is valid for the required data, which is obviously the correct solution.
In #23980 it was stated that this doesn't make sense in the context of cut. Unfortunately I missed the discussion over there (there really was None). I argue that by raising a value error we unnecessarily remove a valid feature: I use cut frequently as kind of a more versatile replacement to `pd.rolling` for overlapping non-equal sized custom windows.
If there is a smarter way to do this I am happy to learn about it. Otherwise we should at least give the option to use overlapping indices in cut. Thus I would recommend to raise a warning instead of an error here:
https://github.com/pandas-dev/pandas/blob/0fd888c8b318d4041b0ae53c64715eca0d345dd3/pandas/core/reshape/tile.py#L247-L249
#### Expected Output
Raise a warning maybe (I am still not sure if this is necessary) and return:
```python
[(0, 10], (2, 12], (4, 14], (0, 10], (2, 12], (4, 14]]
Categories (3, interval[int64]): [(0, 10] < (2, 12] < (4, 14]]
```
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} | 8 | 2019-07-30T08:05:08Z | 2019-09-08T18:05:46Z | 2019-09-05T16:00:52Z | NONE | null | #### Code Sample
```python
#code snippet
...
elif filename.endswith('.csv'):
file_df = pd.read_csv(filed)
....
```
#### Automating pandas csv read into a pandas dataframe.
I am trying to build a Flask app that automates reading of files into a dataframe once they are uploaded. The issue is with CSV files which keep throwing UnicodeDecodeError. Now, setting encoding as follows:
```
elif filename.endswith('.csv'):
file_df = pd.read_csv(filed, encoding='utf-8')
```
will only cover for csv files with that particular encoding. I have tried the following script that checks for the encodings for a csv file, especially, once the error is generated:
```
import pkgutil
import encodings
import os
def all_encodings():
modnames = set([modname for importer, modname, ispkg in pkgutil.walk_packages(
path=[os.path.dirname(encodings.__file__)], prefix='')])
aliases = set(encodings.aliases.aliases.values())
return modnames.union(aliases)
text = b'\xa0' #given by the error message
for enc in all_encodings():
try:
msg = text.decode(enc)
except Exception:
continue
if msg == 'ñ':
print('Decoding {t} with {enc} is {m}'.format(t=text, enc=enc, m=msg))
else:
print('Decoding {t} with {enc} is {m}'.format(t=text, enc=enc, m=msg))
```
and it works well to give the encoding needed for that particular file.
Is there a way pandas can read a csv file and find out the encoding automatically . Or is there a fix to this? Maybe to be a feature (if it does not exist yet) in a future release?
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} | 4 | 2019-07-30T09:50:33Z | 2021-07-10T19:16:56Z | null | MEMBER | null | From https://github.com/pandas-dev/pandas/issues/27553 and https://github.com/pandas-dev/pandas/issues/27522, I quickly looked at how consistent we are with defining errors and warnings in `pandas.errors`.
So we have a `pandas.errors` module which is meant to publicly expose our custom exceptions and warnings.
Some are defined there such as PerformanceWarning, UnsortedIndexError, ParserError, ... (https://github.com/pandas-dev/pandas/blob/bb6135880e5e453d7701764b9f2e4ad3356a68d7/pandas/errors/__init__.py).
But many are not:
* tslibs.parsing.DateParseError
* tslibs.period.IncompatibleFrequency
* pandas/_config/config.py: OptionError
* pandas/core/base.py: DataError, GroupByError, SpeciicationError
* pandas/core/common.py: SettingWithCopyError/Warning
* pandas/core/computation/common/py: NameResolutionError
* pandas/core/computation/engine.py: NumExprClobberingError
* pandas/core/computation/ops.py: UndefinedVariableError
* pandas/core/indexes/base.py: InvalidIndexError
* pandas/coreindexing.py: IndexingError
* pandas/io/clipboard/exceptions.py: PyperclipException
* pandas/io/formats/css.py: CSSWarning
* pandas/io/msgpack: several exceptions
* pandas/io/pytables.py: lots of exceptions
* pandas/io/sql.py: SQLALchemyRequired, DatabaseError
* pandas/io/stata.py: several warnings
Do we want to move them all to the public `pandas.errors` module?
Are there reasons that some can / should not be moved? Eg should the cython ones be defined in the cython file for performance?
Are there certain custom exceptions/warnings that we rather convert in a built-in one instead of publicly exposing them?
Do we want to move all the io related ones? (those modules are somewhat public) | {
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```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**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]
</details>
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} | 1 | 2019-07-30T11:30:45Z | 2019-07-30T14:59:22Z | 2019-07-30T14:59:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import random as rd
df_single = pd.DataFrame(data={"Col1": rd.sample(range(1, 100), 25)}, index=rd.sample(range(1, 100), 25))
single_slice = df_single[:9]
single_slice.index
df_multi = pd.DataFrame(data={"Col1": rd.sample(range(1, 100), 25)}, index=[["a" for _ in range(10)] + ["b" for _ in range(15)], rd.sample(range(1, 100), 25)])
multi_slice = df_multi[:9]
multi_slice.index
```
#### Problem description
When slicing the single index df, the index of the slice is returned in the order it appears in the single df. However, when slicing the multi index df, the index attribute of the slice will return the index of the complete (unsliced) df.
This seems inconsistent to me and is quite inconvenient in several different cases. Is this intentional? (if yes, what is the reasoning?)
**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]
</details>
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} | 2 | 2019-07-30T15:41:11Z | 2019-08-27T14:09:42Z | 2019-08-27T14:09:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"col":[False, True]})
df.replace({"col": {False:0, True:1}})
```
#### Problem description
The previous code triggers the following error:
ValueError: Replacement not allowed with overlapping keys and values
Though, there is no overlapping and the replacement should be done properly.
#### Expected Output
col
0 0
1 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr
LOCALE: None.None
pandas: 0.22.0
pytest: 3.3.2
pip: 19.0.2
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.14.2
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: 2.2.2
openpyxl: 2.4.10
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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} | 2 | 2019-07-30T19:50:58Z | 2020-02-03T09:29:21Z | 2019-08-01T20:42:19Z | MEMBER | null | Using a more recent snippet (following their current docs), and adding anonymization of the IP (according to their docs, this should only "slightly reduce the accuracy of geolocation").
If we do this, should do the same for main website as well. | {
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} | 19 | 2019-07-30T20:25:02Z | 2019-08-20T14:00:16Z | 2019-08-20T14:00:09Z | CONTRIBUTOR | null | - [x] closes #27482
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 13 | 2019-07-30T21:05:20Z | 2020-01-12T21:49:40Z | 2019-08-12T19:00:03Z | MEMBER | null | - [ ] closes #23719
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 2 | 2019-07-30T21:11:25Z | 2019-08-01T13:35:00Z | 2019-08-01T13:35:00Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
N/A
#### Problem description
The cheat sheet link at https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf is broken
#### Expected Output
The cheat sheet should show up.
#### Output of ``pd.show_versions()``
v0.25 docs. Link to it is at https://pandas.pydata.org/pandas-docs/version/0.25/getting_started/tutorials.html
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"description": "read_json, to_json, json... | closed | false | null | [] | null | 7 | 2019-07-31T03:14:30Z | 2019-12-02T00:57:36Z | 2019-12-02T00:57:36Z | CONTRIBUTOR | null | - [x] closes #27220
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This PR attempts to solve the missing value problems when using nested meta with nested record path.
The assumption is that we want to get the value of nested meta field even it is not on the record path. For example, in the example of GH 27220, ['info', 'governor'] and ['counties', 'name'] are not on the same path. The current behavior is to raise key error. I change the behavior to return the value of the key if we can find it in the json.
Let me know how we want to move forward with this. Thanks~ | {
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} | 0 | 2019-07-31T03:46:08Z | 2019-07-31T12:05:34Z | 2019-07-31T12:05:34Z | MEMBER | null | When using `cut` with an `IntervalIndex` for `bins` the result of the `cut` is first materialized as an `IntervalIndex` and then converted to a `Categorical`:
https://github.com/pandas-dev/pandas/blob/143bc34aa8f068b18e7137df7ca91b9929cc1389/pandas/core/reshape/tile.py#L373-L378
It seems like it'd be more performant from a computational and memory standpoint to bypass the intermediate construction of an `IntervalIndex` via `take_nd` and instead directly construct the `Categorical` via `Categorical.from_codes`.
Some ad hoc measurements on `master`:
```python
In [3]: ii = pd.interval_range(0, 20)
In [4]: values = np.linspace(0, 20, 100).repeat(10**4)
In [5]: %timeit pd.cut(values, ii)
7.69 s ± 43.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]: %memit pd.cut(values, ii)
peak memory: 278.39 MiB, increment: 130.76 MiB
```
And the same measurements with the `Categorical.from_codes` fix:
```python
In [3]: ii = pd.interval_range(0, 20)
In [4]: values = np.linspace(0, 20, 100).repeat(10**4)
In [5]: %timeit pd.cut(values, ii)
1.02 s ± 18.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [6]: %memit pd.cut(values, ii)
peak memory: 145.81 MiB, increment: 15.98 MiB
``` | {
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"updated_at": "2020-01-30T12:18:05Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/16"
} | 1 | 2019-07-31T04:06:28Z | 2019-07-31T13:32:56Z | 2019-07-31T12:05:34Z | MEMBER | null | - [X] closes #27668
- [X] benchmarks added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
ASV output:
```
before after ratio
[143bc34a] [d531e7b0]
<master> <perf-cut-ii>
- 198M 122M 0.62 reshape.Cut.peakmem_cut_interval(1000)
- 197M 122M 0.62 reshape.Cut.peakmem_cut_interval(4)
- 197M 122M 0.62 reshape.Cut.peakmem_cut_interval(10)
- 2.11±0.02s 910±2ms 0.43 reshape.Cut.time_cut_interval(1000)
- 1.95±0.01s 761±2ms 0.39 reshape.Cut.time_cut_interval(4)
- 1.95±0.03s 763±10ms 0.39 reshape.Cut.time_cut_interval(10)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27670 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27670/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27670/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27670/events | https://github.com/pandas-dev/pandas/issues/27670 | 474,954,841 | MDU6SXNzdWU0NzQ5NTQ4NDE= | 27,670 | `value_counts` does not respect ordered categoricals | {
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] | closed | false | null | [] | null | 1 | 2019-07-31T06:12:54Z | 2019-07-31T23:21:06Z | 2019-07-31T23:21:05Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
cat = pd.Series(pd.Categorical(
['a', 'c', 'a', 'b', 'a'],
categories=['a', 'b', 'c'],
ordered=True))
cat.value_counts()
```
#### Problem description
This outputs the value counts in the order 'a', 'c', 'b' when the categorical is ordered 'a', 'b', 'c'. `value_counts` should respect ordered categoricals and display them appropriately.
#### Expected Output
`value_counts` outputs in the specified order: 'a', 'b', 'c'
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.7.5
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 8 | 2019-07-31T12:55:37Z | 2021-07-10T19:18:27Z | null | NONE | null | The following code replace NaN values from a dataframe and works perfectly:
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'key': ['01', '01', '01', '03', '04', '05'], 'A': [np.nan, 'A1', 'A2', 'A3', np.nan, np.nan], 'B': [1, 2, 3, np.nan, 5, 6]})
df2 = pd.DataFrame({'key': ['01', '03', '04', '05', '08', '99'], 'A': ['OK1', 'KO3', 'OK4', 'OK5', 'KO8', 'K99'], 'B': [91, 92, 93, 94, 95, 12]})
df.set_index('key').fillna(df2.set_index('key')).reset_index()
```
We obtain:
```
df
key A B
0 01 NaN 1.0
1 01 A1 2.0
2 01 A2 3.0
3 03 A3 NaN
4 04 NaN 5.0
5 05 NaN 6.0
df2
key A B
0 01 OK1 91
1 03 KO3 92
2 04 OK4 93
3 05 OK5 94
4 08 KO8 95
5 99 K99 12
res
key A B
0 01 OK1 1.0
1 01 A1 2.0
2 01 A2 3.0
3 03 A3 92.0
4 04 OK4 5.0
5 05 OK5 6.0
```
However, the following minor change breaks everything for no apparent reason. When computing df3, we obtain an InvalidIndexError:
```
df.at[3, 'key'] = '99'
df_res = df.set_index('key').fillna(df2.set_index('key')).reset_index()
```
Here is the updated dataframe.
```
df
key A B
0 01 NaN 1.0
1 01 A1 2.0
2 01 A2 3.0
3 99 A3 NaN
4 04 NaN 5.0
5 05 NaN 6.0
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27673 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27673/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27673/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27673/events | https://github.com/pandas-dev/pandas/issues/27673 | 475,132,815 | MDU6SXNzdWU0NzUxMzI4MTU= | 27,673 | String-based ExtensionArrays force cast to numpy in list based construction | {
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} | [] | closed | false | null | [] | null | 0 | 2019-07-31T13:04:36Z | 2019-08-02T11:36:40Z | 2019-08-02T11:36:40Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Series(['E'], dtype=FletcherDType(pa.string())
```
#### Problem description
This leads to the following traceback but should actually create a Series object of a string-based type.
<details>
```
def test_constructor_sanitation():
> data = pd.Series(["E"], index=["F"], dtype=ArrowStringDtype())
pandas/tests/extension/arrow/test_string.py:11:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/core/series.py:310: in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
data = ['E'], index = Index(['F'], dtype='object'), dtype = <pandas.tests.extension.arrow.arrays.ArrowStringDtype object at 0x120913cf8>, copy = False, raise_cast_failure = True
def sanitize_array(data, index, dtype=None, copy=False, raise_cast_failure=False):
"""
Sanitize input data to an ndarray, copy if specified, coerce to the
dtype if specified.
"""
if dtype is not None:
dtype = pandas_dtype(dtype)
if isinstance(data, ma.MaskedArray):
mask = ma.getmaskarray(data)
if mask.any():
data, fill_value = maybe_upcast(data, copy=True)
data.soften_mask() # set hardmask False if it was True
data[mask] = fill_value
else:
data = data.copy()
# extract ndarray or ExtensionArray, ensure we have no PandasArray
data = extract_array(data, extract_numpy=True)
# GH#846
if isinstance(data, np.ndarray):
if dtype is not None and is_float_dtype(data.dtype) and is_integer_dtype(dtype):
# possibility of nan -> garbage
try:
subarr = _try_cast(data, dtype, copy, True)
except ValueError:
if copy:
subarr = data.copy()
else:
subarr = np.array(data, copy=False)
else:
# we will try to copy be-definition here
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
elif isinstance(data, ABCExtensionArray):
# it is already ensured above this is not a PandasArray
subarr = data
if dtype is not None:
subarr = subarr.astype(dtype, copy=copy)
elif copy:
subarr = subarr.copy()
return subarr
elif isinstance(data, (list, tuple)) and len(data) > 0:
if dtype is not None:
try:
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
except Exception:
if raise_cast_failure: # pragma: no cover
raise
subarr = np.array(data, dtype=object, copy=copy)
subarr = lib.maybe_convert_objects(subarr)
else:
subarr = maybe_convert_platform(data)
subarr = maybe_cast_to_datetime(subarr, dtype)
elif isinstance(data, range):
# GH#16804
arr = np.arange(data.start, data.stop, data.step, dtype="int64")
subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
else:
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
# scalar like, GH
if getattr(subarr, "ndim", 0) == 0:
if isinstance(data, list): # pragma: no cover
subarr = np.array(data, dtype=object)
elif index is not None:
value = data
# figure out the dtype from the value (upcast if necessary)
if dtype is None:
dtype, value = infer_dtype_from_scalar(value)
else:
# need to possibly convert the value here
value = maybe_cast_to_datetime(value, dtype)
subarr = construct_1d_arraylike_from_scalar(value, len(index), dtype)
else:
return subarr.item()
# the result that we want
elif subarr.ndim == 1:
if index is not None:
# a 1-element ndarray
if len(subarr) != len(index) and len(subarr) == 1:
subarr = construct_1d_arraylike_from_scalar(
subarr[0], len(index), subarr.dtype
)
elif subarr.ndim > 1:
if isinstance(data, np.ndarray):
raise Exception("Data must be 1-dimensional")
else:
subarr = com.asarray_tuplesafe(data, dtype=dtype)
# This is to prevent mixed-type Series getting all casted to
# NumPy string type, e.g. NaN --> '-1#IND'.
if issubclass(subarr.dtype.type, str):
import ipdb; ipdb.set_trace()
# GH#16605
# If not empty convert the data to dtype
# GH#19853: If data is a scalar, subarr has already the result
> if not lib.is_scalar(data):
E TypeError: data type not understood
pandas/core/construction.py:478: TypeError
```
</details>
Problem exists in 0.25 and master, I'm working on a fix and will submit a PR later. | {
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] | closed | false | null | [] | null | 7 | 2019-07-31T13:18:15Z | 2019-08-02T11:39:16Z | 2019-08-02T11:36:40Z | CONTRIBUTOR | null | I had to add a string-based Arrow Extension array to trigger the bug but did not run the same test suite as we do on the boolean array as I don't see it adding value but just runtime at the moment.
- [x] closes #27673
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/27676 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27676/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27676/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27676/events | https://github.com/pandas-dev/pandas/issues/27676 | 475,200,982 | MDU6SXNzdWU0NzUyMDA5ODI= | 27,676 | read_excel: sheet_name of non-existent sheet does not raise appropriate error for engine='odf' | {
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} | 2 | 2019-07-31T15:07:24Z | 2019-10-25T17:01:30Z | 2019-10-25T17:01:30Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
pandas.read_excel('mybook.ods', sheet_name='mysheet', engine='odf')
```
#### Problem description
If the sheet specified does not exist, an error is thrown:
```
---> 63 raise ValueError("sheet {name} not found".format(name))
64
65 def get_sheet_data(self, sheet, convert_float: bool) -> List[List[Scalar]]:
KeyError: 'name'
```
The problem is at https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_odfreader.py#L63
#### Expected Output
It should raise a `ValueError` with text:
> "sheet mysheet not found"
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-20-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
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 : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_odfreader.py#L63
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 16 | 2019-07-31T15:19:32Z | 2019-10-28T13:56:51Z | 2019-10-25T17:01:30Z | CONTRIBUTOR | null | Fixes a formatted message at [io.excel._odfreader line 62](https://github.com/pandas-dev/pandas/blob/master/pandas/io/excel/_odfreader.py#L63)
- [x] closes #27676
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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```python
In [0]:
base = pd.Series({4:400, 5:500, 6:600, 7:700})
print(base)
print(base.index)
4 400
5 500
6 600
7 700
dtype: int64
Int64Index([4, 5, 6, 7], dtype='int64')
In [0]:
addon = pd.Series({'6': '600'})
print(addon)
print(addon.index)
6 600
dtype: object
Index(['6'], dtype='object')
In [0]:
merged = base.append(addon, verify_integrity=True)
print(merged)
print(merged.index)
4 400
5 500
6 600
7 700
6 600
dtype: object
Index([4, 5, 6, 7, '6'], dtype='object')
```
https://gist.github.com/abitrolly/14318e107ea2d41eefe5616aec3d0cb8
#### Problem description
Data and index type autoconversion in append() breaks pipelines down the way. It should be possible to detect index and data type conversion in Seried with `verify_integrity` instead of checking manually.
#### Expected Output
```
base.append(addon, verify_integrity=True)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-15-76ef0359a5e4> in <module>()
----> 1 base.append(addon, verify_integrity=True)
TypeError: 'Series' index autoconversion: Int64Index became Index
```
#### Output of ``pd.show_versions()``
<details>
In [0]:
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.79+
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 3.6.4
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.3.0
pyarrow: 0.14.0
xarray: 0.11.3
IPython: 5.5.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.5.3
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: 0.4.0
matplotlib: 3.0.3
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.6
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: 0.3.0
fastparquet: None
pandas_gbq: 0.4.1
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 7 | 2019-07-31T15:43:21Z | 2020-05-26T09:30:28Z | 2020-04-21T12:39:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
try:
pd.DataFrame({'foo': [None, ['foo', 'bar']]}).to_parquet('s3://foo/bar')
except Exception as e:
print('Here')
```
#### Problem description
Without credentials configured, the above code does not write any output, but also doesn't end up in the exception handling code. Instead, on stdout or stderr, an exception is printed:
```
Exception ignored in: <function AbstractBufferedFile.__del__ at 0x7fe0ae8db440>
Traceback (most recent call last):
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/fsspec/spec.py", line 1137, in __del__
self.close()
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/fsspec/spec.py", line 1114, in close
self.flush(force=True)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/fsspec/spec.py", line 986, in flush
self._initiate_upload()
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/s3fs/core.py", line 951, in _initiate_upload
Bucket=self.bucket, Key=self.key, ACL=self.acl)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/s3fs/core.py", line 939, in _call_s3
**kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/s3fs/core.py", line 182, in _call_s3
return method(**additional_kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/client.py", line 357, in _api_call
return self._make_api_call(operation_name, kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/client.py", line 648, in _make_api_call
operation_model, request_dict, request_context)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/client.py", line 667, in _make_request
return self._endpoint.make_request(operation_model, request_dict)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/endpoint.py", line 102, in make_request
return self._send_request(request_dict, operation_model)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/endpoint.py", line 132, in _send_request
request = self.create_request(request_dict, operation_model)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/endpoint.py", line 116, in create_request
operation_name=operation_model.name)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/hooks.py", line 356, in emit
return self._emitter.emit(aliased_event_name, **kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/hooks.py", line 228, in emit
return self._emit(event_name, kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/hooks.py", line 211, in _emit
response = handler(**kwargs)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/signers.py", line 90, in handler
return self.sign(operation_name, request)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/signers.py", line 157, in sign
auth.add_auth(request)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/auth.py", line 425, in add_auth
super(S3SigV4Auth, self).add_auth(request)
File "/home/languitar/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/botocore/auth.py", line 357, in add_auth
raise NoCredentialsError
botocore.exceptions.NoCredentialsError: Unable to locate credentials
```
#### Expected Output
`Here`
#### 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 : Linux
OS-release : 4.19.61-1-lts
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
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 : 0.14.1
pytables : None
s3fs : 0.3.1
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27680 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27680/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27680/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27680/events | https://github.com/pandas-dev/pandas/issues/27680 | 475,237,555 | MDU6SXNzdWU0NzUyMzc1NTU= | 27,680 | Issue with to_clipboard(excel=True) with negative floats | {
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} | [] | closed | false | null | [] | null | 2 | 2019-07-31T16:17:27Z | 2019-08-01T10:31:33Z | 2019-08-01T10:25:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
pd.DataFrame({ 'A' : np.arange(-10, 10 + 1 ,1.1)}).to_clipboard(excel=True)
```
#### Problem description
When using to_clipboard(excel=True) with a negative float number, and pasting it on Excel, it shows
'#FIELD'! The reason is that it is adding ‘=’ to all negatives floats.
Example: If value is '-10.0' it will pass '=-10.0'
With positive values or ints it won’t happen
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>

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} | 3 | 2019-07-31T17:12:25Z | 2019-08-01T10:33:15Z | 2019-07-31T17:50:08Z | NONE | null | Hi, been browsing through the source code of pandas for the last couple of days, looking for:
**How and where in the source code is `.loc` like in `df.loc['foo']` defined?**
I found out that the type of a Dataframe `.loc` attribute is `pandas.core.indexing._LocIndexer` but how and where is that implemented? | {
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} | 8 | 2019-07-31T20:14:26Z | 2019-11-09T17:15:26Z | 2019-11-08T14:45:02Z | CONTRIBUTOR | null | - [x] closes #26597
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Used West's online [algorithm](https://dl.acm.org/citation.cfm?id=359153). [Here](http://www.nowozin.net/sebastian/blog/streaming-mean-and-variance-computation.html) is an explanation of the algorithm and link to pdf of the paper. Tested implementation with `win_type="boxcar"` comparing it with the result of `Rolling.std`.
Additionaly, `_get_window` function is split into two. `_get_kwargs` function is used to split kwargs for window function and rolling function (since std and var takes an additional `ddof` argument).
Shared docs for `std` and `var` are moved to be used with `Window` functions
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} | 1 | 2019-07-31T20:44:49Z | 2019-08-05T14:33:16Z | 2019-08-05T11:57:08Z | MEMBER | null | Following #27628, we can now remove _try_coerce_result altogether.
The step after this removes _try_cast_result. | {
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"description": "to_sql, read_sql, read_sql_query",
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```python
""" Insert dataframe into table."""
if not df1.empty:
df1['source'].str.lower()
df1 = df1.replace(r'^\s*$', 0, regex=True)
df1.to_sql(config['table_name'], schema='ecs_marketing',
con=conn, chunksize=200, index=False, if_exists='replace',
dtype={'date': sqlalchemy.types.Date,
'cost': sqlalchemy.types.Numeric(8, 3)})
```
#### Problem description
I am importing data from Google Sheets.
So I create a dataframe with the spreadsheet content.
Then, I perform some data transformations. Until here, everything normal.
The problem appears when I try to load the data into Snowflake using to_sql command.
I receive the error:
```
(snowflake.connector.errors.ProgrammingError) 090105 (22000): Cannot perform SELECT. This session does not have a current database. Call 'USE DATABASE', or use a qualified name.
[SQL:
SELECT /* sqlalchemy:get_columns */
ic.table_name,
ic.column_name,
ic.data_type,
ic.character_maximum_length,
ic.numeric_precision,
ic.numeric_scale,
ic.is_nullable,
ic.column_default,
ic.is_identity,
ic.comment
FROM information_schema.columns ic
WHERE ic.table_schema=%(table_schema)s
AND ic.table_name=%(table_name)s
ORDER BY ic.ordinal_position
]
[parameters: {'table_schema': 'dev_raw.ecs_marketing', 'table_name': 'DIRECT_AFFILIATES'}]
(Background on this error at: http://sqlalche.me/e/f405)
```
I immediately noted that on error "parameters" dictionary, table_schema is in lower case and table_name is upper case. **Would that be a bug?**
Then I started to dig in. And figured out that to_sql looks for table DDL when `if_exists`='replace'.
If `if_exists`='append', I don't face the problem. Unfortunately, I need to replace.
If I call the function with schema=<database_name>.ecs_marketing also doesn't work.
So, **should to_sql accept also a database parameter, in case of snowflake?**
#### Expected Output
Able to insert my data into Snowflake, without snowflake errors.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-55-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
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 : 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 : 1.3.6
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27685 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27685/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27685/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27685/events | https://github.com/pandas-dev/pandas/pull/27685 | 475,445,186 | MDExOlB1bGxSZXF1ZXN0MzAzMTk2NjQ4 | 27,685 | CLN: Prune unnecessary internals | {
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} | 1 | 2019-08-01T03:00:28Z | 2019-08-01T14:09:08Z | 2019-08-01T12:15:17Z | MEMBER | null | Also some post-black cleanups in pandas.io (I think we're almost done with these)
Removing "pragma: no cover" in a couple places because I'm working in those areas and it would be easier if we caught more specific things so I want to see if they are ever reached. | {
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] | closed | false | null | [] | null | 10 | 2019-08-01T07:47:56Z | 2019-11-08T17:39:56Z | 2019-08-20T14:18:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('iris.csv')
dfg = df.groupby('species').mean()
dfg['swratio'] = dfg.sepal_width / dfg.sepal_length
fig, ax = plt.subplots(figsize=(10, 6))
dfg[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].plot.bar(ax=ax, rot=0, stacked=False)
dfg[['swratio']].plot.line(label='Sepal Width Ratio',
ax=ax, secondary_y=True, mark_right=False,
rot=0, marker='o', markersize=3, color='c')
ax.grid(True)
pngfile = 'Output.png'
fig = plt.gcf()
fig.savefig(pngfile, bbox_inches = 'tight', dpi=100)
```
#### Problem description
dataframe.plot with secondary_y=True makes different chart between 0.23.4 and 0.25.0 as attached.


**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]
</details>
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} | 4 | 2019-08-01T09:18:48Z | 2019-10-22T15:35:53Z | 2019-08-01T13:57:00Z | CONTRIBUTOR | null | #### Problem description
There is no content in the Contributors section of the pandas v0.25.0 Release Notes: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html#contributors
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```python
import pandas as pd
df = pd.DataFrame({'a': [1, 1, 1, 2], 'b': [11, 11, None, None]})
# a b
# 0 1 11.0
# 1 1 11.0
# 2 1 NaN
# 3 2 NaN
df.groupby('a').ffill()
# b
# 0 11.0
# 1 11.0
# 2 11.0
# 3 NaN
```
#### Problem description
Filling missing values in groups removes the column upon which DataFrame got grouped by.
Previously (0.20.2) list of columns was left intact after such operation.
#### Expected Output
DataFrame containing the same list of columns as before the operation.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-55-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 : 1.16.4
pytz : 2019.1
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 : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.1.18
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-08-01T12:07:54Z | 2019-10-22T12:10:11Z | 2019-08-01T13:57:00Z | CONTRIBUTOR | null | * fix tag for contributors
* fix 0.25.1 contributors
* remove orphan
Closes https://github.com/pandas-dev/pandas/issues/27687 | {
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} | 0 | 2019-08-01T12:24:09Z | 2019-10-29T06:51:25Z | 2019-08-01T13:56:20Z | CONTRIBUTOR | null | closes #27558
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} | 1 | 2019-08-01T12:57:07Z | 2020-07-01T01:43:26Z | 2020-07-01T01:43:26Z | NONE | null | #### Copy-pastable example
``` python
# input data
import pandas as pd
t1 = pd.DataFrame(index=[0], data={'x':[1]}, dtype='UInt8')
t2 = pd.DataFrame(index=[1], data={'y':[1]}, dtype='UInt8')
t3 = pd.concat([t1,t2], join='outer', sort=False)
'''actual result'''
print(t3.dtypes)
# x object
# y object
# dtype: object
```
#### Problem description
Data types are not preserved data type while concatenating DataFrames with nullable integers. Instead, the result of concatenation has mixed data types and so the column types are `object`:
``` python
>>> type(t3.at[0,'x'])
<class 'int'>
>>> type(t3.at[1,'x'])
<class 'float'>
```
#### Expected Output
``` python
'''expected result'''
print(t3.dtypes)
# x UInt8
# y UInt8
# dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.11.6.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.utf-8
LANG : en_US.utf-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.16.3
pytz : 2018.4
dateutil : 2.7.3
pip : 19.1.1
setuptools : 39.0.1
Cython : 0.29.12
pytest : 3.3.2
hypothesis : None
sphinx : 1.6.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : 0.9999999
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.2
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-08-01T14:18:28Z | 2019-08-01T14:32:40Z | 2019-08-01T14:32:33Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
dt=pd.date_range('2012','2013',freq='d')
df_test=pd.DataFrame(index=dt)
df_test['x']=123
df_test.loc['2012 2','x']
```
#### Problem description
`.loc` operation above used to return a `pd.Series` but in v0.25.0 returns a numpy array.
#### Expected Output
output:
```
array([123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123,
123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123, 123,
123, 123, 123])
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
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} | 18 | 2019-08-01T14:26:59Z | 2019-08-27T14:09:55Z | 2019-08-27T14:09:42Z | MEMBER | null | - [x] closes #27660
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-08-01T15:11:45Z | 2019-08-02T18:14:02Z | 2019-08-02T18:13:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# create a dataframe containing a nan value
inputs = [{'a': i} for i in range(2)]
inputs.append({'a':None})
df = pd.DataFrame(inputs)
def buggy_function(row):
if not pd.isnull(row.a):
print(f'foo {row.a}')
else:
raise Exception()
df.apply(buggy_function, axis=1)
```
#### Problem description
Before the exception is raised, buggy_function is applied a second time. Not just on the first row, as described in the documentation, but to all rows until the exception.
Actual output:
foo 0.0
foo 1.0
foo 0.0
foo 1.0
-> exception raised
#### Expected Output
foo 0.0
foo 1.0
-> exception raised
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: 1.2.16
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 4 | 2019-08-01T18:16:28Z | 2019-08-02T16:03:45Z | 2019-08-02T15:59:17Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-08-01T23:15:43Z | 2019-08-04T23:12:01Z | 2019-08-04T21:58:00Z | MEMBER | null | The initial impetus for this was avoiding two try/excepts in Block methods (i.e. the diff in internals.blocks). This uncovered the bug in DTA/TDA, which accounts for the rest of the diff. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27705/events | https://github.com/pandas-dev/pandas/issues/27705 | 475,949,153 | MDU6SXNzdWU0NzU5NDkxNTM= | 27,705 | 0.22.0: 3 failed tests on ubuntu 16.04 (xenial) with numpy 1.13.3 | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-02T00:57:11Z | 2019-08-02T01:07:43Z | 2019-08-02T01:07:43Z | NONE | null | In #19865, there were several unit test failures reported. The issue was closed after blaming it on [numpy being less than version 1.13]( https://github.com/pandas-dev/pandas/issues/19865#issuecomment-504276463_). However, even _with_ numpy upgraded to 1.13.3, the same three units (`TestnanopsDataFrame::test_nansem`, `TestnanopsDataFrame::test_nanskew`, and `TestnanopsDataFrame::test_nankurt`) fail with the same `AssertionError` as they did before in numpy:
```
self = <pandas.tests.test_nanops.TestnanopsDataFrame object at 0x7f98b2b79810>
def test_nansem(self):
tm.skip_if_no_package('scipy', min_version='0.17.0')
from scipy.stats import sem
with np.errstate(invalid='ignore'):
self.check_funs_ddof(nanops.nansem, sem, allow_complex=False,
allow_str=False, allow_date=False,
> allow_tdelta=False, allow_obj='convert')
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:399:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:316: in check_funs_ddof
allow_tdelta, allow_obj, ddof=ddof)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:253: in check_funs
**kwargs)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:242: in check_fun
**kwargs)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:199: in check_fun_data
check_dtype=check_dtype)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/tests/test_nanops.py:147: in check_results
tm.assert_almost_equal(targ, res, check_dtype=check_dtype)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py:220: in assert_almost_equal
assert_class_equal(left, right, obj=obj)
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py:969: in assert_class_equal
repr_class(right))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
obj = 'numpy array', message = 'numpy array classes are different'
left = 'float', right = 'ndarray', diff = None
def raise_assert_detail(obj, message, left, right, diff=None):
if isinstance(left, np.ndarray):
left = pprint_thing(left)
elif is_categorical_dtype(left):
left = repr(left)
if isinstance(right, np.ndarray):
right = pprint_thing(right)
elif is_categorical_dtype(right):
right = repr(right)
msg = """{obj} are different
{message}
[left]: {left}
[right]: {right}""".format(obj=obj, message=message, left=left, right=right)
if diff is not None:
msg += "\n[diff]: {diff}".format(diff=diff)
> raise AssertionError(msg)
E AssertionError: ('numpy array are different\n\nnumpy array classes are different\n[left]: float\n[right]: ndarray', 'axis: 0 of 2', 'skipna: False', "kwargs: {'ddof': 0}", 'testar: arr_float_nan', 'targar: arr_float', 'targarnan: arr_float_nan', 'ddof 0')
../debian/tmp/usr/lib/python2.7/dist-packages/pandas/util/testing.py:1093: AssertionError
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-157-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.22.0
pytest: 3.1.3
pip: None
setuptools: 20.7.0
Cython: 0.29.2
numpy: 1.13.3
scipy: 0.17.0
pyarrow: None
xarray: None
IPython: 2.4.1
sphinx: 1.3.6
patsy: None
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
feather: None
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
============================= test session starts ==============================
platform linux2 -- Python 2.7.12, pytest-3.1.3, py-1.4.34, pluggy-0.4.0 -- /usr/bin/python2.7
``` | {
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} | 6 | 2019-08-02T01:12:20Z | 2019-08-05T14:32:37Z | 2019-08-05T06:31:49Z | MEMBER | null | The current name makes it difficult to grep for.
Assorted cleanups; added type annotations in a few places. | {
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} | 12 | 2019-08-02T02:50:39Z | 2019-08-05T13:32:31Z | 2019-08-05T13:32:31Z | CONTRIBUTOR | null | #### Excerpt from [Contributing to pandas](https://dev.pandas.io/development/contributing.html#installing-a-c-compiler)
>Pandas uses C extensions (mostly written using Cython) to speed up certain operations. To install pandas from source, you need to compile these C extensions, which means you need a C compiler. This process depends on which platform you’re using. Follow the [CPython contributing guide](https://devguide.python.org/setup/#compile-and-build) for getting a compiler installed. You don’t need to do any of the ./configure or make steps; you only need to install the compiler.
#### Problem description
The link to CPython contributing guide doesn't explain how to set up a C Compiler.
What is needed is an OS-specific explanation for how to set up a C Compiler, or an explanation for how to check that one already has a compiler. | {
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} | 11 | 2019-08-02T07:59:37Z | 2019-08-04T11:33:26Z | 2019-08-03T16:05:38Z | CONTRIBUTOR | null | https://pandas.pydata.org/pandas-docs/stable/reference/series.html
```html
<table><td>
...
<tr class="row-even"><td><a class="reference internal" href="api/pandas.Series.T.html#pandas.Series.T" title="pandas.Series.T"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Series.T</span></code></a></td>
<td>Return the transpose, which is by</td>
</tr>
...
</td>
</table>
```
And looks weird on https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.T.html#pandas.Series.T
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-08-02T08:16:41Z | 2021-05-06T01:35:18Z | 2021-05-06T01:35:18Z | CONTRIBUTOR | null | On implementing a boolean based ExtensionArray I stumbled on the case that boolean arrays with missing values (which can only be `object`-typed in `pandas`) are kind-of undefined behaviour in Pandas reductions with `skipna=False`:
The following case should return `True` according to the docstring of `Series.any(skipna=False)`:
```python
pd.Series([False, None]).any(skipna=False)
# None
pd.Series([None, False]).any(skipna=False)
# False
pd.Series([False, np.nan]).any(skipna=False)
# nan
pd.Series([np.nan, False]).any(skipna=False)
# nan
```
Whereas when you do the same operation on `float` columns the behaviour is as documented:
```python
pd.Series([np.nan, 0.]).any(skipna=False)
# True
pd.Series([0, np.nan]).any(skipna=False)
# True
```
As I have not found a unit test for the above mentioned case with a boolean object column, I suspect that this is rather undefined behaviour then intended.
Three solutions come to my mind:
1. Document this behaviour in the `Series.any()` docstring.
2. Align the behaviour of `pd.Series(booleans, dtype=object).any(…)` with `pd.Series(booleans, dtype=object).astype(float).any(…)`.
3. Raise an error when calling `any/all` on a mixed typed boolean series. | {
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} | 2 | 2019-08-02T14:22:02Z | 2019-08-04T21:55:20Z | 2019-08-04T21:54:57Z | CONTRIBUTOR | null | Closes #27516
cc @jreback if you have thoughts on this approach. | {
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