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https://api.github.com/repos/pandas-dev/pandas/issues/27713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27713/events | https://github.com/pandas-dev/pandas/issues/27713 | 476,234,175 | MDU6SXNzdWU0NzYyMzQxNzU= | 27,713 | DOC: Fix docstring quotes in pandas.PeriodIndex | {
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] | closed | false | null | [] | null | 15 | 2019-08-02T15:22:57Z | 2019-08-05T13:41:29Z | 2019-08-02T21:12:01Z | CONTRIBUTOR | null | I am trying to work on fixing the docstring errors GL01 and GL02. The `validate_docstrings.py` gave a lot of them so I picked `pandas.PeriodIndex`:
```
pandas.PeriodIndex.day: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.day: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.dayofweek: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.dayofweek: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.dayofyear: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.dayofyear: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.days_in_month: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.days_in_month: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.daysinmonth: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.daysinmonth: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.hour: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.hour: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.minute: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.minute: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.month: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.month: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.quarter: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.quarter: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.second: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.second: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.week: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.week: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.weekday: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.weekday: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.weekofyear: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.weekofyear: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
pandas.PeriodIndex.year: Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)
pandas.PeriodIndex.year: Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)
```
However I need some help navigating to the related docstrings for each of these attributes. I thought I could find the `PeriodIndex` class at `pandas.core.indexes.period` which I did but I can't find any of the attributes (so the docstrings) in that file.
I am fairly new to contributing to Pandas so maybe I am making a dumb mistake or looking at the wrong place so I would appreciate some guidance.
Thanks!
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} | 1 | 2019-08-02T15:34:09Z | 2019-08-04T23:12:38Z | 2019-08-04T21:52:45Z | MEMBER | null | Separate out a numeric-only maybe_downcast_to_dtype_numeric from maybe_downcast_to_dtype. Avoid a giant try/except by checking the correct things up front.
The non-numeric portion of maybe_downcast_to_dtype casts to datetime64, datetime64tz, or PeriodArray. Following #27683 the next step in cleaning up internals is going to be getting rid of _try_cast_result, which will involve replacing a usage of maybe_cast_to_dtype with the less-broadly-scoped maybe_cast_to_dtype_numeric. | {
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} | 2 | 2019-08-02T16:11:27Z | 2019-08-02T21:12:03Z | 2019-08-02T21:12:01Z | CONTRIBUTOR | null | - [x] closes #27713
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-08-02T16:16:54Z | 2019-08-12T21:24:56Z | 2019-08-05T13:32:32Z | CONTRIBUTOR | null | - [ ] closes #27707
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-08-02T17:28:33Z | 2019-12-04T01:10:59Z | 2019-12-04T01:10:54Z | NONE | null | By default, the optional "index" parameter of the "to_csv" method is set to True. However, I would argue this violates the principal of least surprise. That is to say, when calling this method I, as a user, would expect that my data frame is converted into a CSV format as is, with no data added or removed that I did not explicitly add or remove myself.
Adding an index column without the user specification is kind of weird, unexpected behavior. Not unlike adding a column for type of reference hashing, or dropping a random column from the data frame without the user specifying this in advance. Thus, I would argue that the "index" parameter should be set to False by default. | {
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} | 4 | 2019-08-02T17:54:39Z | 2020-01-16T00:35:08Z | 2019-08-25T16:03:22Z | MEMBER | null | follow up to #27598 to simplify things a bit. I think it was a mistake to add `IO[AnyStr]` as `AnyStr` is a TypeVar and not something to actually parametrize with. The source for `IO` already does this:
https://github.com/python/cpython/blob/8990ac0ab0398bfb9c62031288030fe7c630c2c7/Lib/typing.py#L1452
I'm not sure why mypy doesn't error on this.
@simonjayhawkins | {
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} | 3 | 2019-08-02T19:01:24Z | 2019-08-05T14:44:19Z | 2019-08-05T11:58:49Z | MEMBER | null | - [x] tests added / passed
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} | 4 | 2019-08-02T21:08:27Z | 2021-04-21T21:55:33Z | 2021-04-21T21:55:33Z | NONE | null | #### Example
```python
df = pd.concat({
'a': pd.DataFrame({
'col1': [1, 2],
'col2': [3, 4]
}),
'b': pd.DataFrame({
'col3': [5, 6],
'col4': [7, 8]
}),
}, axis=1)
```
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="2" halign="left">a</th>
<th colspan="2" halign="left">b</th>
</tr>
<tr>
<th></th>
<th>col1</th>
<th>col2</th>
<th>col3</th>
<th>col4</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>3</td>
<td>5</td>
<td>7</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>4</td>
<td>6</td>
<td>8</td>
</tr>
</tbody>
</table>
```python
df.groupby(level=0, axis=1).rank(axis=1, ascending=False, method='first')
```
**_ValueError: Shape of passed values is (4, 2), indices imply (2, 2)_**
#### Problem description
Should return a data frame with values indicating the rank for each row and within each upper level column. This works in 0.22.0 but in 0.24.2 it fails with the ValueError. It fails in both py 2.7 and py 3.68.
#### Expected Output
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="2" halign="left">a</th>
<th colspan="2" halign="left">b</th>
</tr>
<tr>
<th></th>
<th>col1</th>
<th>col2</th>
<th>col3</th>
<th>col4</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2.0</td>
<td>1.0</td>
<td>2.0</td>
<td>1.0</td>
</tr>
<tr>
<th>1</th>
<td>2.0</td>
<td>1.0</td>
<td>2.0</td>
<td>1.0</td>
</tr>
</tbody>
</table>
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.4.2
numexpr: 2.6.1
feather: None
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 1.0.1
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.4
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27722/events | https://github.com/pandas-dev/pandas/issues/27722 | 476,362,890 | MDU6SXNzdWU0NzYzNjI4OTA= | 27,722 | What to use instead of pd.read_msgpack/df.to_msgpack | {
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FutureWarning: to_msgpack is deprecated and will be removed in a future version.
It is recommended to use pyarrow for on-the-wire transmission of pandas objects.
```
Is there a link/pointer for how to do this? | {
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} | 0 | 2019-08-02T22:19:00Z | 2019-08-04T21:38:53Z | 2019-08-04T21:38:53Z | MEMBER | null | We use `flake8-comprehensions` as part of the CI process when linting code, and it's included in our `environment.yml` file, but it's not run as part of `pre-commit`.
For a simple example, add the following line of code to a file in the codebase:
```python
sum([x + 1 for x in range(3)])
```
Then in the command line run:
```shell
git add .
pre-commit run flake8
```
And the `pre-commit` check passes.
However, running `flake8` from the command line (like what is done in CI) fails due to the `flake8-comprehensions` plugin being included:
```shell
flake8 path/to/changed/file.py
```
Gives me the error:
```shell
C407 Unnecessary list comprehension - 'sum' can take a generator.
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} | 1 | 2019-08-02T22:19:58Z | 2019-08-05T18:39:39Z | 2019-08-04T21:38:54Z | MEMBER | null | - [x] closes #27724
Running `pre-commit` with this change catches the error described in the issue.
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https://api.github.com/repos/pandas-dev/pandas/issues/27726 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27726/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27726/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27726/events | https://github.com/pandas-dev/pandas/pull/27726 | 476,388,181 | MDExOlB1bGxSZXF1ZXN0MzAzOTU2NDQw | 27,726 | BUG: fix+test DTA/TDA/PA add/sub Index | {
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} | 4 | 2019-08-02T23:27:33Z | 2019-08-05T14:28:54Z | 2019-08-05T11:45:25Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Looking forward to doing a couple of test-cleanup passes. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27727 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27727/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27727/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27727/events | https://github.com/pandas-dev/pandas/pull/27727 | 476,408,002 | MDExOlB1bGxSZXF1ZXN0MzAzOTcxMjc4 | 27,727 | REF: define concat classmethods in the appropriate places | {
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} | 2 | 2019-08-03T02:44:17Z | 2019-08-05T14:30:58Z | 2019-08-05T11:54:25Z | MEMBER | null | cc @jorisvandenbossche we briefly discussed at the sprint the idea that `dtype.concat` is a weird place to define these functions.
This PR takes the subset of `dtypes.concat` methods that are a) private and b) equivalent to `klass._concat_same_dtype` for some `klass` and moves the implementation to the appropriate class.
The categorical one is left in place for now because a) it is public and b) it'd be a pretty big move in and of itself. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27728/events | https://github.com/pandas-dev/pandas/issues/27728 | 476,437,326 | MDU6SXNzdWU0NzY0MzczMjY= | 27,728 | Unable to change normalize property of objects from to_offset() | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-03T10:09:38Z | 2019-08-03T16:37:51Z | 2019-08-03T16:37:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from datetime import date
from pandas.tseries.frequencies import to_offset
from pandas.tseries.offsets import DateOffset
dt = date.today()
offset = to_offset("52W")
print(f"type: {type(offset)}\ndict: {offset.__dict__}")
print(f"{dt} -> {dt + offset}")
try:
offset.normalize = True
except AttributeError as ex:
print(f"\n{ex}")
offset = DateOffset(normalize=True, **offset.kwds)
print(f"\ntype: {type(offset)}\ndict: {offset.__dict__}")
print(f"{dt} -> {dt + offset}")
```
Yields
```
type: <class 'pandas.tseries.offsets.Week'>
dict: {'n': 52, 'normalize': False, '_cache': {}, 'weekday': 6}
2019-08-03 -> 2020-07-26 00:00:00
DateOffset objects are immutable.
type: <class 'pandas.tseries.offsets.DateOffset'>
dict: {'n': 1, 'normalize': True, '_cache': {}, '_offset': relativedelta(weekday=SU), '_use_relativedelta': True, 'weekday': 6}
2019-08-03 -> 2019-08-04 00:00:00
```
#### Problem description
Since Pandas `0.24` the `DateOffset` objects are immutable. Previously it was possible to change the `normalize` property after receiving an object from `to_offset()`, but now there doesn't seem to be any mechanism for doing this. While it seems possible to create a new instance of `DateOffset` with `normalize = True`, the type of the object changes and doesn't work correctly.
What is the best way of getting an object from `to_offset()` with `normalize` set to `True`? From an end-user perspective, the best solution would probably be to add a normalize attribute to the function, like so:
```python
def to_offset(freq, normalize=False):
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.1
setuptools : 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 : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27729/events | https://github.com/pandas-dev/pandas/issues/27729 | 476,444,851 | MDU6SXNzdWU0NzY0NDQ4NTE= | 27,729 | use DataFrame.style and can't get [cellstyle] as expected | {
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"description": "__repr__ of pandas objec... | closed | false | null | [] | null | 2 | 2019-08-03T11:57:44Z | 2021-02-10T07:23:21Z | 2021-02-10T07:23:21Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
df = DataFrame(np.arange(60).reshape(15,4),columns=['a','b','c','d'])
df1 = df>50
def b_color(val):
color = 'red' if val ==True else ''
return 'color: %s' % color
h_df = df1.style.applymap(b_color)
h_df._translate()['cellstyle']
```
#### Problem description
i'm using DataFrame.style and i want to extract the 'cellstyle'
but the props are all showed empty
the output is like this:
'cellstyle': [{'props': [], 'selector': 'row0_col0'},
{'props': [], 'selector': 'row0_col1'},
{'props': [], 'selector': 'row0_col2'},
{'props': [], 'selector': 'row0_col3'},
however, the output of h_df is normal and the color is applyed at the right position
#### Expected Output
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: AMD64 Family 21 Model 16 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0.post20170921
Cython: 0.26.1
numpy: 1.15.1
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
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https://api.github.com/repos/pandas-dev/pandas/issues/27730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27730/events | https://github.com/pandas-dev/pandas/pull/27730 | 476,466,122 | MDExOlB1bGxSZXF1ZXN0MzA0MDA5OTQz | 27,730 | BUG: update dict of sheets before check | {
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] | closed | false | null | [] | null | 9 | 2019-08-03T16:14:33Z | 2019-11-07T21:09:43Z | 2019-11-07T21:09:42Z | NONE | null | ```python
import pandas as pd
df = pd.DataFrame(
{
"id": ["1", "2", "3", "4", "5"],
"Feature1": ["A", "C", "E", "G", "I"],
"Feature2": ["B", "D", "F", "H", "J"],
},
columns=["id", "Feature1", "Feature2"],
)
writer = pd.ExcelWriter(path="testOutput.xlsx", mode="a", engine="openpyxl")
df.to_excel(writer, sheet_name="Main")
writer.save()
writer.close()
```
#### Problem description
I have excel file with existing sheet "Main", and if I try to append some dataframe to this sheet, on first df.to_excel() pandas creates new sheet with name "Main1" and saves information to this new sheet. But if once to_excel(), than pandas add new sheet to dictionary and saves to existing sheet. So, on start appen pandas don't know nothing about existing sheets, and check in **_openpyxl.py** file don't realy work
```python
if sheet_name in self.sheets:
wks = self.sheets[sheet_name]
else:
wks = self.book.create_sheet()
wks.title = sheet_name
self.sheets[sheet_name] = wks
```
so I add new code to update sheetname-list before check.
- [x] without issue
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] remove the different (ambiguity) behavior of the function in the case of the existence of a sheet of the book. When the first time the DataFrame is written to the sheet, a new sheet is created (moreover, if we try to write to the sheet with the name "main" which alreary exist in the book, now sheet "main1" will be created. And we try to write another DataFrame inside the current function on the same sheet "main", function again wrote to the sheet "main1", although we asked it to write to the sheet "main". So now if sheet exist then it will write to existing sheet, if doesn't exist it will create sheet with name which we want.
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https://api.github.com/repos/pandas-dev/pandas/issues/27731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27731/events | https://github.com/pandas-dev/pandas/issues/27731 | 476,473,015 | MDU6SXNzdWU0NzY0NzMwMTU= | 27,731 | Object dtype for int64 and Int64 values in column | {
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```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({"id": ["1", "2", "3", "4", "5"]}, columns=["id"]).astype('float64').astype('int64')
df2 = pd.DataFrame({"id": ["1", "2", "3", "4", "5"]}, columns=["id"]).astype('float64').astype('Int64')
df3 = pd.concat([df1, df2], axis="index", sort=False)
for col in df3.columns:
for i in df3[col]:
print(f"[{i}] has type: {type(i)}")
print("df1: " + str(df1['id'].dtype))
print("df2: " + str(df2['id'].dtype))
print("df3: " + str(df3['id'].dtype))
```
#### Problem description
What is the difference between "Int64" and "int64"?
After concat it has "object" dtype.
#### Real Output
```python
[1] has type: <class 'int'>
[2] has type: <class 'int'>
[3] has type: <class 'int'>
[4] has type: <class 'int'>
[5] has type: <class 'int'>
[1] has type: <class 'int'>
[2] has type: <class 'int'>
[3] has type: <class 'int'>
[4] has type: <class 'int'>
[5] has type: <class 'int'>
df1: int64
df2: Int64
df3: object
```
#### Expected Output
```python
[1] has type: <class 'int'>
[2] has type: <class 'int'>
[3] has type: <class 'int'>
[4] has type: <class 'int'>
[5] has type: <class 'int'>
[1] has type: <class 'int'>
[2] has type: <class 'int'>
[3] has type: <class 'int'>
[4] has type: <class 'int'>
[5] has type: <class 'int'>
df1: int64
df2: int64
df3: int64
```
or
```python
[1] has type: <class 'numpy.int64'>
[2] has type: <class 'numpy.int64'>
[3] has type: <class 'numpy.int64'>
[4] has type: <class 'numpy.int64'>
[5] has type: <class 'numpy.int64'>
[1] has type: <class 'numpy.int64'>
[2] has type: <class 'numpy.int64'>
[3] has type: <class 'numpy.int64'>
[4] has type: <class 'numpy.int64'>
[5] has type: <class 'numpy.int64'>
df1: Int64
df2: Int64
df3: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, 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 : 19.0.3
setuptools : 41.0.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.3 (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 : 0.3.1
gcsfs : 0.3.0
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
If there are no difference between them, thed dtype recognize uncorrecter, but if it is different, then may be need to change "name", case sensitive type I think is very bugy. | {
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} | 2 | 2019-08-03T20:26:27Z | 2019-08-05T21:09:30Z | 2019-08-05T20:29:17Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
ATM `to_datetime(naive_dti, utc=True)` returns naive incorrect, same for naive `DatetimeArray` | {
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} | 2 | 2019-08-04T00:35:40Z | 2019-08-05T16:38:29Z | 2019-08-05T15:52:11Z | MEMBER | null | We moved ops.py to `ops.__init__` a while back, still need to get the bulk of it out of `__init__`. This separates out templated invalid operations, which are the main things that outside modules import (this helps us move towards getting rid of the `import pandas as pd` in the main file) | {
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} | 7 | 2019-08-04T05:33:41Z | 2019-08-07T16:53:20Z | 2019-08-07T13:32:48Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Splits `pandas/core/window.py` into more logical division of:
`pandas/core/window/rolling.py`
`pandas/core/window/expanding.py`
`pandas/core/window/ewm.py`
`pandas/core/window/common.py`
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} | 3 | 2019-08-04T10:10:59Z | 2020-12-13T16:46:32Z | 2020-12-13T16:45:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
dff = pd.DataFrame(data={'a':['a1\r','a2'],'b':['b1','b2']})
print("===BEFORE SAVE===")
print(dff)
dff.to_csv('test.csv', index=False)
print("===AFTER SAVE===")
print(pd.read_csv('test.csv'))
```
The result will be
```
===BEFORE SAVE===
a b
0 a1\r b1
1 a2 b2
===AFTER SAVE===
a b
0 a1 NaN
1 NaN b1
2 a2 b2
```
#### Problem description
The problem can be resolved by adding `quoting=csv.QUOTE_NONNUMERIC` option, but it seems like a bug under default parameters of `to_csv`.
#### Expected Output
```
===BEFORE SAVE===
a b
0 a1\r b1
1 a2 b2
===AFTER SAVE===
a b
0 a1\r b1
1 a2 b2
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-151-generic
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: 4.6.3
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.13
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.2
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: 4.8.0
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
```
</details>
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} | 1 | 2019-08-04T11:08:54Z | 2019-08-05T09:35:04Z | 2019-08-04T21:28:23Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27598#discussion_r310128723 | {
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} | 1 | 2019-08-04T16:05:31Z | 2019-08-04T23:11:29Z | 2019-08-04T21:40:45Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
In conjunction with one more branch coming up, we're going to simplify the tar out of Series arithmetic ops. | {
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} | 4 | 2019-08-04T16:28:31Z | 2019-08-04T23:08:43Z | 2019-08-04T23:01:03Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
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After this bugfix, we can change all existing uses of `dispatch_to_index_op` in `core.ops.__init__` to use `dispatch_to_extension_op`. Once we do that, we can collapse ~50 lines of typechecking-dispatching code to all use `dispatch_to_extension_op` | {
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} | 4 | 2019-08-04T20:06:22Z | 2019-08-04T21:26:47Z | 2019-08-04T21:26:43Z | CONTRIBUTOR | null | Some clean-up, as these functions don't need to start with a "_". | {
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} | 3 | 2019-08-04T23:17:33Z | 2019-08-05T13:25:11Z | 2019-08-05T11:41:53Z | CONTRIBUTOR | null | ``StringMixin`` should be removed, but I can't figure out how to remove it without breaking tests, so this moves ``StringMixin`` to core.computation, which is the only module, where it is currently used.
This makes ``core.base.py`` a bit cleaner. | {
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} | 1 | 2019-08-04T23:48:15Z | 2019-08-05T14:29:57Z | 2019-08-05T11:44:37Z | MEMBER | null | Took a couple of preliminaries, but now we can merge these two together and we're within striking distance of being able to collapse all of the Series ops into much simpler functions. | {
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] | closed | false | null | [] | null | 3 | 2019-08-05T00:05:01Z | 2020-01-22T08:43:23Z | 2020-01-22T08:43:23Z | NONE | null | The web page
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html
is essentially empty. It should, instead, include the content from
https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.DataFrame.plot.html
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} | 2 | 2019-08-05T03:49:19Z | 2019-08-08T11:54:06Z | 2019-08-08T11:54:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import csv
import pandas as pd
data_dict = {'visitor_id': [123, 456],
'name': ['John Doe', 'Jane Doe']}
data_df = pd.DataFrame(data_dict)
data_df['visitor_id'] = data_df['visitor_id'].astype(str)
# Original dtype for visitor_id is object
print(data_df.dtypes)
# dtype for visitor_id is int64 when data file is read back using to_csv()
data_df.to_csv('./data_file.csv', index=False)
read_data_df = pd.read_csv('./data_file.csv')
print(read_data_df.dtypes)
# dtype for visitor_id is float64 when data file is read back using to_csv()
# with csv.QUOTE_NONNUMERIC
data_df.to_csv('./data_file.csv', index=False, quoting=csv.QUOTE_NONNUMERIC)
read_data_df = pd.read_csv('./data_file.csv', quoting=csv.QUOTE_NONNUMERIC)
print(read_data_df.dtypes)
```
#### Problem description
When a Pandas dataframe has a column with integer values but whose dtype is actually `object`, using `to_csv()` and then reading back the csv file using `read_csv()` does not preserve the column's dtype.
I have not seen a similar issue posted before (may have missed in my search). I am on Pandas 0.23 and upgrading to pandas 0.25 does not solve the issue.
#### Expected Output
The dtype for a column should be preserved for columns which have integer values but with dtype `object`.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.3.1
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.28.2
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: None
IPython: 7.2.0
sphinx: 1.7.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.4
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: 0.9.3
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.1
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-08-05T09:46:01Z | 2021-09-12T15:08:02Z | 2021-09-12T15:08:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# error pattern
import pandas as pd
unicode_data = [["key", "\u070a"]]
df = pd.DataFrame(unicode_data)
df.to_csv("./test.csv", encoding="cp932") # UnicodeEncodeError: 'cp932' codec can't encode character '\u070a' in position 6: illegal multibyte sequence
```
```python
# good pattern
import pandas as pd
unicode_data = [["key", "\u070a"]]
df = pd.DataFrame(unicode_data)
with open("./test.csv", mode="w", encoding="cp932", errors="ignore") as f:
df.to_csv(f)
```
#### Problem description
UnicodeEncodeError occurs when executing to_csv with eoncode parameter SHIFT-JIS or cp932.
We are able to avoid this error using `with open`(good pattern), this code is redundant.
So I want to suppress UnicodeEncodeError with to_csv's parameter.
#### Expected Output
```python
# good pattern
import pandas as pd
unicode_data = [["key", "\u070a"]]
df = pd.DataFrame(unicode_data)
df.to_csv("./test.csv", encoding="cp932", ignore_error=True)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : ja_JP.UTF-8
LOCALE : ja_JP.UTF-8
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : 4.3.1
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 : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.1
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 8 | 2019-08-05T11:31:46Z | 2019-08-05T15:34:52Z | 2019-08-05T12:55:35Z | NONE | null | ```python
df = pd.DataFrame(0, index=[1,2,3,4], columns=['a', 'b', 'c'])
# Starting from 0.25 groupby pad behaves like other groupby functions in removing the grouping column
df.groupby(df.a).mean()
b c
a
0 0 0
df.groupby(df.a).pad()
b c
1 0 0
2 0 0
3 0 0
4 0 0
# However for other functions it is possible to keep the group column using as_index=False
df.groupby(df.a, as_index=False).mean()
a b c
0 0 0 0
# For ffill/bfill/pad instead the keyword as_index does not help as the column is not being used as the index anyway
df.groupby(df.a, as_index=False).pad()
b c
1 0 0
2 0 0
3 0 0
4 0 0
# There is no way of keeping the column, except creating a useless copy of the column series
df.groupby(df.a.copy()).pad()
a b c
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
```
#### Problem description
Starting from 0.25 ffill behaves like other groupby functions in removing the group column. As far as I understand there is no non-hacky way of getting the old behaviour back. This is not consistent with what happens with other functions where the keyword argument as_index can be used to keep the grouping column.
The only way of having the old behaviour is to create a copy of the column. However this whole thing of view-dependent-behaviour is not only very confusing (at least to me) but it is also inefficient as it requires a useless copy. Moreover it is not a backward compatible solution.
I would suggest to either add a keep_group_columns that works consistently with all groupy functions or to add a special keyword only for groupby functions that keep the original index. | {
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} | 0 | 2019-08-05T11:55:58Z | 2019-08-05T12:56:32Z | 2019-08-05T12:56:32Z | NONE | null | Backport PR #27715: TST: troubleshoot inconsistent xfails | {
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} | 2 | 2019-08-05T12:15:09Z | 2019-08-05T13:29:11Z | 2019-08-05T13:27:42Z | CONTRIBUTOR | null | As a follow-up to #27594 (which I couldn't review in due time), here are some minor improvements for the SPSS user guide text.
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TypeError Traceback (most recent call last)
<ipython-input-46-3da3b949c6bd> in <module>
1 data = pd.Series([0,-1,-2,-3,-4,-3,-2,-1,0,-1,-1,0,-1,-2,-3,-2,0])
2 filtered = data.between(-2,0, inclusive = True)
----> 3 filtered.diff()
4 print(filtered)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\series.py in diff(self, periods)
2191 dtype: float64
2192 """
-> 2193 result = algorithms.diff(com.values_from_object(self), periods)
2194 return self._constructor(result, index=self.index).__finalize__(self)
2195
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\algorithms.py in diff(arr, n, axis)
1817 out_arr[res_indexer] = result
1818 else:
-> 1819 out_arr[res_indexer] = arr[res_indexer] - arr[lag_indexer]
1820
1821 if is_timedelta:
TypeError: numpy boolean subtract, the `-` operator, is deprecated, use the bitwise_xor, the `^` operator, or the logical_xor function instead.
- [x] closes #17294
- [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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} | 13 | 2019-08-05T13:17:11Z | 2019-08-12T19:10:50Z | 2019-08-12T19:10:50Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.read_parquet('s3://my-bucket/df.parquet')
```
Raises
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/.../pandas/io/parquet.py", line 294, in read_parquet
return impl.read(path, columns=columns, **kwargs)
File "/.../pandas/io/parquet.py", line 192, in read
parquet_file = self.api.ParquetFile(path, open_with=s3.s3.open)
AttributeError: 'S3File' object has no attribute 's3'
```
#### Problem description
In version 0.3.0 s3fs removed the `S3File.s3` attribute. It is replaced by `S3File.fs` (which is inherited from `fsspec.AbstractBufferedFile.fs`.
Should pandas check the s3fs version and call the right attribute based on that?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.1
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.3.1
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27757 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27757/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27757/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27757/events | https://github.com/pandas-dev/pandas/pull/27757 | 476,873,607 | MDExOlB1bGxSZXF1ZXN0MzA0MzEzMzg4 | 27,757 | Added diff testing file | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-05T13:55:19Z | 2019-08-05T14:05:53Z | 2019-08-05T14:05:53Z | CONTRIBUTOR | null | Should test the diff for a specific series.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-08-05T14:17:05Z | 2019-09-11T19:26:19Z | 2019-09-11T19:26:19Z | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
import matplotlib.pyplot as plt
ax = plt.subplots(1)[1]
ax.set_xlim((0, 10))
ax.set_ylim((0, 10))
x = pd.Series([], index=pd.Index([], dtype=float), dtype=float)
x.plot(ax=ax)
```
#### Problem description
It is not possible to `.plot()` an empty `Series` or `DataFrame`. When `.plot()` is called on an empty `Series` or `DataFrame`, an exception is raised:
<pre>
~/software/venv-scipy/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py in _compute_plot_data(self)
412 # no non-numeric frames or series allowed
413 if is_empty:
--> 414 raise TypeError("no numeric data to plot")
415
416 # GH25587: cast ExtensionArray of pandas (IntegerArray, etc.) to
TypeError: no numeric data to plot
</pre>
This is problematic because code path which are supposed to work can suddenly fail when the data to plot is empty (for example, there was no data for the requested time-range).
In order to be robust to this, the code needs to include a lot of things such as:
```python
if not x.empty:
x.plot(ax=ax)
``
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-5-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : fr_FR.utf8
LOCALE : fr_FR.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
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 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-08-05T14:20:16Z | 2019-08-05T16:44:44Z | 2019-08-05T15:03:45Z | MEMBER | null | pointed out by @simonjayhawkins | {
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"description": "Concat, ... | open | false | null | [] | null | 0 | 2019-08-05T17:41:39Z | 2019-11-02T21:59:25Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
INDEX_COLS = ['group', 'timestamp']
left = pandas.DataFrame({
'group': ['a', 'a', 'b', 'b'],
'timestamp': [0, 1, 2, 5],
'obs1': [10, 11, 12, 15]
}).set_index(INDEX_COLS)
right = pandas.DataFrame({
'group': ['a', 'a', 'b', 'b'],
'timestamp': [0, 1, 3, 4],
'obs2': [20, 21, 23, 24]
}).set_index(INDEX_COLS)
pandas.merge_ordered(left, right)
# Traceback (most recent call last):
# File "<stdin>", line 1, in <module>
# File "python3.6/site-packages/pandas/core/reshape/merge.py", line 226, in merge_ordered
# result = _merger(left, right)
# File "python3.6/site-packages/pandas/core/reshape/merge.py", line 212, in _merger
# how=how)
# File "python3.6/site-packages/pandas/core/reshape/merge.py", line 1252, in __init__
# sort=True # factorize sorts
# File "python3.6/site-packages/pandas/core/reshape/merge.py", line 524, in __init__
# self._validate_specification()
# File "python3.6/site-packages/pandas/core/reshape/merge.py", line 1033, in _validate_specification
# lidx=self.left_index, ridx=self.right_index))
# pandas.errors.MergeError: No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False
```
#### Problem description
The error message above implies that I should be able to merge these two timeseries DataFrames using `left_index=True` and `right_index=True`. But these are not arguments to the `merge_ordered()` function like they are to other merging functions.
I could work around this by doing the following:
```python
pandas.merge_ordered(left, right, left_on=INDEX_COLS, right_on=INDEX_COLS).set_index(INDEX_COLS)
```
but that seems unnecessarily verbose, and possibly less performant than if I could do
```python
pandas.merge_ordered(left, right, left_index=True, right_index=True)
```
#### Proposal
It looks like `merge_ordered()` uses `_OrderedMerge` under the hood which can take the `left_index` and `right_index` arguments (and the code example above works as expected if those arguments are passed through). Would you consider adding these arguments? I can create a PR if there's appetite.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-08-05T18:07:09Z | 2019-10-24T00:04:13Z | 2019-10-24T00:04:13Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
# Labor Day calendar
import pandas as pd
from pandas.tseries.holiday import AbstractHolidayCalendar, USLaborDay
class testCalendar(AbstractHolidayCalendar):
rules = [
USLaborDay,
]
cal = testCalendar()
workDay = pd.offsets.CustomBusinessDay(calendar=cal)
# Print dates of Labor Day
for yr in [2021, 2032]:
print("Labor day", yr, "is on ", cal.holidays(pd.to_datetime(str(yr-1)+'-12-31'),
pd.to_datetime(str(yr) + '-12-31')))
print("\n")
# Check next working day for Saturday before Labor Day
for dateText in ['2021-09-04', '2032-09-04']:
date = pd.to_datetime(dateText)
print(dateText, "is a", date.strftime('%A') + ". Roll forward to", date + 0 * workDay)
```
#### Problem description
Here's the output:
Labor day 2021 is on DatetimeIndex(['2021-09-06'], dtype='datetime64[ns]', freq='WOM-1MON')
Labor day 2032 is on DatetimeIndex(['2032-09-06'], dtype='datetime64[ns]', freq='WOM-1MON')
2021-09-04 is a Saturday. Roll forward to 2021-09-07 00:00:00
2032-09-04 is a Saturday. Roll forward to 2032-09-06 00:00:00
The calendar knows that Labor day is on Monday, Sept. 6 in both 2021 and 2032. However, when I ask to move to the next working day from Saturday, Sept. 4, I get the (correct) answer Sept.7 for 2021, but the (wrong) answer Sept 6 (which is Labor Day) for 2032.
Is this a bug, or am I doing something wrong?
Thanks.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-08-05T20:29:30Z | 2019-08-06T15:33:38Z | 2019-08-06T15:33:38Z | NONE | null | Backport PR #27733: BUG: fix to_datetime(dti, utc=True) | {
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} | 1 | 2019-08-05T21:37:00Z | 2019-08-06T16:03:44Z | 2019-08-06T15:40:40Z | MEMBER | null | This finishes getting the post-call casting/coersion out of the Blocks.
Pre-call coercion is still in there, exclusively for TimedeltaBlock and DatetimeBlock, i.e. will not be necessary if/when those are backed by EA. | {
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} | 3 | 2019-08-05T21:37:03Z | 2019-08-24T07:45:54Z | 2019-08-23T22:36:59Z | MEMBER | null | xref #27568
```
$ mypy pandas/io/formats/printing.py --check-untyped-defs --follow-imports skip
pandas\io\formats\printing.py:194: error: "str" has no attribute "decode"; maybe "encode"?
```
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} | 0 | 2019-08-06T00:38:54Z | 2019-08-06T15:39:20Z | 2019-08-06T15:39:20Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> arr = np.array([1, 3, np.nan, 3, 5])
>>> arr.setflags(write=False)
>>> pd.Series(arr).rolling(2).mean().values
---------------------------------------------------------------------------
ValueError: assignment destination is read-only
```
#### Expected Output
```py
>>> pd.Series(arr).rolling(2).mean().values
array([nan, 2., nan, nan, 4.])
```
#### Problem description
https://github.com/pandas-dev/pandas/pull/27391#discussion_r309941141 missed a copy call before writing into array, which is user's data and which may be read-only.
#### Output of ``pd.show_versions()``
pandas 0.25.0
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} | 2 | 2019-08-06T00:42:26Z | 2019-08-06T15:39:34Z | 2019-08-06T15:39:20Z | CONTRIBUTOR | null | - [x] closes #27766
- [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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} | 3 | 2019-08-06T02:22:00Z | 2019-08-14T21:37:58Z | 2019-08-13T12:05:44Z | MEMBER | null | Partially addresses #19513.
After this, CategoricalIndex will be defining comparison ops identically to DTA/TDA/PA, could share some code. | {
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} | 4 | 2019-08-06T03:53:13Z | 2019-08-07T13:27:04Z | 2019-08-07T13:27:04Z | CONTRIBUTOR | null | The Travis build process can fail in hard to decipher ways if git tags are not synced between `upstream` and a developer's repo. This failure mode would occur in the main repo if we go 2000 commits (current clone depth, ~9 months) without tagging a release. This PR explicitly fails the build with instructions of how to resolve if this situation is encountered.
The underlying issue is that `versioneer` and the underlying `git describe` rely on tags to exist in the local repo for the purposes of creating a version description like:
```
v0.25.0-116-gee54d95952
```
As Travis makes a shallow clone, these tags are will not exist if:
- A developer has not explicitly synced tags between repos (the default git behavior)
- `pandas` goes 2000 commits without tagging a commit
If tags do not exist, we get something like:
```
0+untagged.2000.g66ada8c
```
Which causes causes tests involving downstream libraries like `pyarrow`, `statsmodels`, etc to fail their internal version checks in sometimes indecipherable ways like this:
```
=================================== FAILURES ===================================
____________________________ TestFeather.test_error ____________________________
[gw0] linux -- Python 3.7.3 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.io.test_feather.TestFeather object at 0x7f353ab95c18>
def test_error(self):
for obj in [
pd.Series([1, 2, 3]),
1,
"foo",
pd.Timestamp("20130101"),
np.array([1, 2, 3]),
]:
> self.check_error_on_write(obj, ValueError)
pandas/tests/io/test_feather.py:49:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/io/test_feather.py:27: in check_error_on_write
to_feather(df, path)
pandas/io/feather_format.py:24: in to_feather
from pyarrow import feather
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
from distutils.version import LooseVersion
import os
import six
import pandas as pd
import warnings
> from pyarrow.compat import pdapi
E ImportError: cannot import name 'pdapi' from 'pyarrow.compat' (/home/travis/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/pyarrow/compat.py)
```
With some digging, you can eventually find that the root cause is:
```
self = LooseVersion ('0+untagged.2000.g66ada8c'), other = LooseVersion ('0.21')
def _cmp (self, other):
if isinstance(other, str):
other = LooseVersion(other)
if self.version == other.version:
return 0
> if self.version < other.version:
E TypeError: '<' not supported between instances of 'str' and 'int'
```
Syncing tags between repos resolves the issue.
- [ ] 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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```python
df1 = pd.read_csv("./data/test.csv")
print(df1)
df1.to_csv("./data/test1.csv")
test.csv has only one column "a" and one number is "656.964"，when I prepare to use "to_csv" then it has changed to 656.9639999.
I hope you can help solve this problem.
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} | 24 | 2019-08-06T09:06:37Z | 2021-09-23T09:05:13Z | null | NONE | null | #### Code Sample
```python
data = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
multi_index = pd.MultiIndex.from_product([['a', 'b'], ['one', 'two', 'three']])
df = pd.DataFrame(data, columns=multi_index, index=[1, 2])
df.to_excel('test_files/test.xlsx')
```
#### Problem description
When exporting an xlsx file from a dataframe with MultiIndex, a blank line is added.
Here a screenshot representing the problem:
This is happening with pandas 0.25.0.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, 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 : 19.2
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : 0.2.3
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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} | 9 | 2019-08-06T10:27:57Z | 2019-08-19T17:20:29Z | 2019-08-19T17:20:24Z | CONTRIBUTOR | null | This fixes a regression introduced in #27145 where _can_use_numexpr would fail if passed a Series and not a DataFrame. I decided not to use run_arithmetic in the test_suite because there is a separate issue when running floordiv that is out of the scope of the fix. I will open a separate issue in improving the test coverage of "test_expressions.py" as it currently does not check any of the arguments you can pass to the operators, such as 'axis', 'level' and 'fill_value'.
- [x] closes #27636
- [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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} | 0 | 2019-08-06T10:31:33Z | 2019-08-08T14:20:03Z | 2019-08-07T06:23:26Z | MEMBER | null | and revert #17143, see https://github.com/pandas-dev/pandas/pull/17143#issuecomment-505080265 | {
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} | 13 | 2019-08-06T10:56:09Z | 2019-08-09T07:36:58Z | 2019-08-09T07:36:58Z | NONE | null | Root cause (in both cases using `df = pd.DataFrame({'a': [1, 2, 3]})`):
```
In [71]: pd.__version__
Out[71]: '0.25.0'
In [73]: df.index[:, None]
Out[73]: Int64Index([0, 1, 2], dtype='int64')
In [74]: df.index[:, None].shape
Out[74]: (3,)
```
vs
```
In [10]: pd.__version__
Out[10]: '0.24.2'
In [13]: df.index[:, None]
Out[13]: Int64Index([0, 1, 2], dtype='int64')
In [14]: df.index[:, None].shape
Out[14]: (3, 1)
```
So before, indexing with `[:, None]` (in numpy a way to add a dimension to get 2D array) actually resulting in Index with ndim of 2 (but which is of course inconsistent state of the Index object)
Matplotlib relied on this fact when an Index is passed to `plt.plot`, as reported in https://github.com/matplotlib/matplotlib/issues/14992
---
I have explained the issue [here ](https://stackoverflow.com/questions/57372557/matplotlib-drops-indexerror-tuple-index-out-of-range-after-upgrade)and [here ](https://github.com/matplotlib/matplotlib/issues/14992#issue-477267556)in details. Basically, after upgrading to the version `0.25` I got the error:
> IndexError: tuple index out of range
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"description": "Related to indexing on s... | open | false | null | [] | null | 1 | 2019-08-06T12:14:21Z | 2019-11-02T22:00:20Z | null | NONE | null | ```python
import pandas as pd
pd.set_option('display.max_columns', None)
idx = pd.IndexSlice
df = pd.DataFrame(index=pd.Index([pd.Timestamp('20080101 23:00'),
pd.Timestamp('20080101 23:01')]), columns = pd.MultiIndex.from_product([[1,2],['A','B']]))
print(df)
df.loc[:,idx[:,'A']]=0
print(df)
df.loc[:,idx[:,'B']] = df.loc[:,idx[:,'A']]
print(df)
```
```
1 2
A B A B
2008-01-01 23:00:00 NaN NaN NaN NaN
2008-01-01 23:01:00 NaN NaN NaN NaN
1 2
A B A B
2008-01-01 23:00:00 0 NaN 0 NaN
2008-01-01 23:01:00 0 NaN 0 NaN
1 2
A B A B
2008-01-01 23:00:00 0 NaN 0 NaN
2008-01-01 23:01:00 0 NaN 0 NaN
```
I believe I use the correct Pandas idioms. But Column B is still NaN after assignment. If you initialize column B first, it also destroys the values with NaNs afterwards.
Sorry for the formatting, for some reason forum will not do what it is supposed to do for code blocks.
It may be related to but not the same:
#10440
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} | 9 | 2019-08-06T12:48:03Z | 2019-08-12T19:10:55Z | 2019-08-12T19:10:50Z | CONTRIBUTOR | null | When reading from S3 using fastparquet. This attribute was removed in
s3fs 0.3.0. This change avoids accessing it by using a new method
`get_file_and_filesystem` which returns the filesystem in addition to the
file.
- [x] closes #27756
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 3 | 2019-08-06T12:51:50Z | 2019-08-09T18:32:22Z | 2019-08-09T18:32:21Z | MEMBER | null | Right now, for arithmetic operations like `add`, `div`, `pow` etc, the default is to rewrite input columns with new values (which are processed by those arithmetic operations), e.g.
```python
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df.div(10)
```
And this will return something like
```
| a | b |
|----|----|
| 0.1 | 0.4 |
| 0.2 | 0.5 |
| 0.3 | 0.6 |
```
And we could also assign it to new cols by:
```python
df[['c', 'd']] = df.div(10)
```
But it's gonna be good to have something like
```python
df = df.div(10, new_cols=['c', 'd'])
```
So that the chaining can be used afterwards and in practice I feel very useful to have this enhancement.
If it's okay, I would be very glad to implement this feature.
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https://api.github.com/repos/pandas-dev/pandas/issues/27779 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27779/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27779/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27779/events | https://github.com/pandas-dev/pandas/issues/27779 | 477,400,086 | MDU6SXNzdWU0Nzc0MDAwODY= | 27,779 | Give more precise warning when passing `header=-1` in `pandas.read_csv`, or don't throw error | {
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} | 3 | 2019-08-06T14:02:00Z | 2019-08-15T20:27:18Z | 2019-08-15T20:27:18Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
from io import StringIO
import pandas as pd
print(f"Using pandas version {pd.__version__}")
pd.read_csv(StringIO('spam'), header=-1)
```
> Using pandas version 0.25.0
>
>---------------------------------------------------------------------------
>OverflowError
#### Problem description
In previous versions of pandas (e.g. 0.24.2), this would have worked fine. Instead, now, one has to pass `header=None`. However, when working with old code after having updated pandas, it may be unclear where the problem lies.
#### Expected Output
```
0
0 spam
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-55-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 : 0.29.12
pytest : 4.5.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
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 : 0.13.0
pytables : None
s3fs : 0.2.1
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-08-06T14:26:35Z | 2019-08-10T07:52:36Z | 2019-08-10T04:17:14Z | CONTRIBUTOR | null | - [ ] closes # xxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 17 | 2019-08-06T15:00:41Z | 2021-07-10T19:27:45Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df1 = pd.DataFrame({"A": pd.SparseArray([0, 0, 0]), 'B': [1,2,3]})
# df1_filtered will have NAs in column A
df1_filtered = df1.loc[df1['B'] != 2]
df2 = pd.DataFrame({"A": pd.SparseArray([0, 1, 0]), 'B': [1,2,3]})
# df2_filtered has no NAs in column A
df2_filtered = df2.loc[df2['B'] != 2]
```
where `df1_filtered` will look like
```
A B
0 NaN 1
2 NaN 3
```
and `df2_filtered` like
```
A B
0 0 1
2 0 3
```
#### Problem description
Filtering a dataframe with an all-zero sparse column can lead to NAs in the sparse column.
#### Expected Output
Both data frames should be the same, as filtering a dataframe with non-missing data should not lead to missing data.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 39.1.0
Cython : None
pytest : 4.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : 0.2.1
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.2.0
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-08-06T18:38:31Z | 2019-08-07T17:51:34Z | 2019-08-07T17:51:33Z | MEMBER | null | When doing an indexing operation on an EA column that results in a single row, the resulting SingleBlockManager / Block has an inconsistent internal state:
```
In [62]: df = pd.DataFrame({'a': pd.Series([1, 2, 3], dtype='Int64'), 'b': [1, 2, 3]})
In [63]: df.loc[[0], 'a']._data
Out[63]:
SingleBlockManager
Items: Int64Index([0], dtype='int64')
ExtensionBlock: slice(0, 1, 1), 1 x 1, dtype: Int64
In [64]: df.loc[[0], 'a']._data.ndim
Out[64]: 1
In [65]: df.loc[[0], 'a']._data._block.ndim
Out[65]: 2
In [66]: df.loc[[0], 'b']._data
Out[66]:
SingleBlockManager
Items: Int64Index([0], dtype='int64')
IntBlock: 1 dtype: int64
In [67]: df.loc[[0], 'b']._data._block.ndim
Out[67]: 1
```
So `df.loc[[0], 'a']` gives a Series as result that holds a 2d block, which leads to other bugs (although not directly visible in the repr. Example: https://github.com/geopandas/geopandas/issues/1078)
The reason for this is that `Block.take_nd` does not specify the ndim of the resulting new Block, and thus this is inferred in the `NonConsolidatableBlock.__init__`:
https://github.com/pandas-dev/pandas/blob/640d9e1f5fe8ab64d1f6496b8216c28185e53225/pandas/core/internals/blocks.py#L1593-L1598
But, this `len(placement)` is not only 1 in case of a 2D block, but also if you have a 1D block with values of len 1 ...
This bug shows with any EA, so also eg Categorical. And it seems to go back to pandas 0.23, and still manifests on master.
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} | 5 | 2019-08-06T18:56:26Z | 2019-08-07T17:50:41Z | 2019-08-07T13:25:37Z | MEMBER | null | - [x] closes #27785
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] passes `black pandas`
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} | 2 | 2019-08-06T20:05:29Z | 2019-08-07T13:57:41Z | 2019-08-07T13:57:34Z | MEMBER | null | This is xref https://github.com/pandas-dev/pandas/pull/27700#issuecomment-517703401
- [x] closes #13420
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} | 0 | 2019-08-06T20:24:36Z | 2019-08-08T11:43:18Z | 2019-08-08T11:42:54Z | MEMBER | null | The docstring of `to_latext` shows a small example (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_latex.html) as
```
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']})
>>> df.to_latex(index=False) # doctest: +NORMALIZE_WHITESPACE
'\\begin{tabular}{lll}\n\\toprule\n name & mask & weapon
\\\\\n\\midrule\n Raphael & red & sai \\\\\n Donatello &
purple & bo staff \\\\\n\\bottomrule\n\\end{tabular}\n'
```
I think it would be nice to also add a print statement like `print(df.to_latex(index=False))`(typically you want to be able to copy it to your tex file, and so you first need to print it to have something you can copy paste) | {
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} | 28 | 2019-08-06T20:39:12Z | 2019-10-24T01:58:44Z | 2019-10-24T00:04:13Z | CONTRIBUTOR | null | - [x] closes #27761
- [x] tests added / passed
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} | 2 | 2019-08-06T23:23:59Z | 2019-08-08T13:33:19Z | 2019-08-08T12:37:41Z | MEMBER | null | - make PTA/DTA strftime() return ndarray instead of Index
- fix a xfailed Series[Period] test
- avoid imports of Index subclass in arrays.categorical and arrays.datetimelike
- some typing | {
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} | 3 | 2019-08-07T02:24:52Z | 2019-08-07T17:18:46Z | 2019-08-07T16:54:43Z | MEMBER | null | The removed code is not covered in the test suite, so this makes for a nice little simplification. Moreover, since the condition above on 1369-1373 is an ugly one, de-nesting this function will give us a good shot and further simplifying this code.
That said, this could be considered an API change, since now the "errors" kwarg doesn't actually do anything. | {
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} | 1 | 2019-08-07T05:36:32Z | 2019-08-07T14:35:51Z | 2019-08-07T13:16:50Z | MEMBER | null | Didn't see any references to `IntervalIndex.from_intervals` in the docs.
We deprecated this before `IntervalArray` was implemented, so no `from_intervals` to worry about there; `IntervalArray` file just has a "shared" docstring for `from_intervals` that isn't actually shared. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27794 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27794/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27794/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27794/events | https://github.com/pandas-dev/pandas/issues/27794 | 477,735,755 | MDU6SXNzdWU0Nzc3MzU3NTU= | 27,794 | Min/max does not work for dates with timezones if there are missing values in the data frame | {
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... | open | false | null | [] | null | 9 | 2019-08-07T06:41:53Z | 2021-07-10T19:28:04Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.concat([
pd.date_range(start='1/1/2018', end='1/30/2018').to_series().dt.tz_localize("UTC"),
pd.date_range(start='1/1/2018', end='1/20/2018').to_series().dt.tz_localize("UTC")
], axis=1)
df.min(axis=1)
```
#### Problem description
Without `tz_localize`, everything works as expected. Also, if both columns have the same length and time zones, it works just fine. However, the combination of time zones and different lengths results in:
```
2018-01-01 NaN
2018-01-02 NaN
2018-01-03 NaN
2018-01-04 NaN
2018-01-05 NaN
2018-01-06 NaN
2018-01-07 NaN
2018-01-08 NaN
2018-01-09 NaN
2018-01-10 NaN
2018-01-11 NaN
...
```
of type float64.
#### Expected Output
List of dates
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-17134-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.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 : 0.29.10
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
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 : 1.3.3
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
I have found some related fixed issues, but not exactly this one. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27795/events | https://github.com/pandas-dev/pandas/issues/27795 | 477,767,057 | MDU6SXNzdWU0Nzc3NjcwNTc= | 27,795 | df.astype(dict) drops 'names' on a multiindex | {
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} | 2 | 2019-08-07T07:58:57Z | 2019-08-07T19:01:01Z | 2019-08-07T19:00:39Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(
data=[1., 2., 3.],
columns=pd.MultiIndex.from_tuples(
[('A', 'X')],
names=['Top', 'Bottom'],
)
)
df2 = df.astype({('A', 'X'): 'int'})
print(df.columns.names)
print(df2.columns.names)
```
#### Problem description
`df2` should have the same `column.names` as `df`. What actually happens is that the `column.names` become `[None, None]`.
If instead the definition of `df2` becomes:
```python
df2 = df.astype('int')
```
Then the expected behavior is observed.
#### Expected Output
`['Top', 'Bottom']`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.36.3.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: 4.6.2
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.28.5
numpy: 1.14.5
scipy: 1.0.1
pyarrow: None
xarray: 0.11.3
IPython: 5.8.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml: 4.2.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27796/events | https://github.com/pandas-dev/pandas/issues/27796 | 477,774,239 | MDU6SXNzdWU0Nzc3NzQyMzk= | 27,796 | Missing spaces between lines and y-axis | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-07T08:14:23Z | 2019-08-08T16:23:06Z | 2019-08-08T16:23:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import matplotlib.pyplot as plt
plt.plot([1,2],[2,4])
pd.DataFrame([2,4],index=[1,2]).plot()
```
#### Problem description
Plots generated by df.plot() and plt.plot() are different and I expected them exactly the same.
As you can see, all the spaces between y-axis and lines are missing when I use pd.DataFrame.plot().
When you have a large dataset and there's a rapid change at the end, the change might be covered by the border, makes the plot hard to read.
pandas version:0.25.0
matplotlib version:3.1.1
Latest version that works:
pandas:0.23.x
matplotlib 3.0.3
#### Expected Output
Two figures should look exactly the same.
#### 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 : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
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 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27797/events | https://github.com/pandas-dev/pandas/issues/27797 | 477,782,632 | MDU6SXNzdWU0Nzc3ODI2MzI= | 27,797 | groupby().ffill() column disappeared in pandas==0.25.0 | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-07T08:31:02Z | 2019-08-07T10:21:46Z | 2019-08-07T10:14:49Z | MEMBER | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame([[1, 1], [2, 2], [1, None], [2, None]], columns=['a', 'b'])
df.groupby('a').ffill()
```
#### Problem description
Dataframe df itself:
```
a b
0 1 1.0
1 2 2.0
2 1 NaN
3 2 NaN
```
Output of ```df.groupby('a').ffill()``` in 0.25.0:
```
b
0 1.0
1 2.0
2 1.0
3 2.0
```
Output of ```df.groupby('a').ffill()``` in 0.24.2:
```
a b
0 1 1.0
1 2 2.0
2 1 1.0
3 2 2.0
```
The output of the above code in pandas==0.25.0 drops columns "a", which is kind of weird and also different from 0.24.2. I believe people would expected column "a" to be still there as a column or as an index. The behavior in 0.24.2 is intuitive, natural and easy to use.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
```
a b
0 1 1.0
1 2 2.0
2 1 1.0
3 2 2.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.0.1
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.19
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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} | 4 | 2019-08-07T10:05:05Z | 2019-08-08T11:43:06Z | 2019-08-08T11:42:55Z | CONTRIBUTOR | null | changed `df.to_latex` to `print(df.to_latex)`
closes #27789
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https://api.github.com/repos/pandas-dev/pandas/issues/27799 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27799/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27799/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27799/events | https://github.com/pandas-dev/pandas/issues/27799 | 477,834,340 | MDU6SXNzdWU0Nzc4MzQzNDA= | 27,799 | BUG: pivot_table fails with aggfunc='size' and margins=True | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 0 | 2019-08-07T10:10:49Z | 2021-07-10T19:29:18Z | null | CONTRIBUTOR | null | #### Code Sample: Calculating a simple cross table
```python
import pandas as pd
# prepare DataFrame
df = pd.DataFrame({
'nr': [1,2,3,4,5,6,7,8],
'nr2': [1,2,3,4,5,6,7,8],
'cl':list('aabbcccc')
})
df['cl_cat'] = df.cl.astype('category')
```
This works:
```python
df.pivot_table(index='nr', columns='nr2', aggfunc='size', fill_value=0, margins=False)
```
```
nr2 1 2 3 4 5 6 7 8
nr
1 1 0 0 0 0 0 0 0
2 0 1 0 0 0 0 0 0
3 0 0 1 0 0 0 0 0
4 0 0 0 1 0 0 0 0
5 0 0 0 0 1 0 0 0
6 0 0 0 0 0 1 0 0
7 0 0 0 0 0 0 1 0
8 0 0 0 0 0 0 0 1
```
This (`margins=True`) crashes with **KeyError: (1, 'All')** and **ValueError: Names should be list-like for a MultiIndex**:
```python
df.pivot_table(index='nr', columns='nr2', aggfunc='size', fill_value=0, margins=True)
```
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2889 try:
-> 2890 return self._engine.get_loc(key)
2891 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: (1, 'All')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in set(self, item, value)
1067 try:
-> 1068 loc = self.items.get_loc(item)
1069 except KeyError:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2891 except KeyError:
-> 2892 return self._engine.get_loc(self._maybe_cast_indexer(key))
2893 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: (1, 'All')
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-85-ddda268badda> in <module>
----> 1 df.pivot_table(index='nr', columns='nr2', aggfunc='size', fill_value=0, margins=True)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
6065 dropna=dropna,
6066 margins_name=margins_name,
-> 6067 observed=observed,
6068 )
6069
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed)
160 observed=dropna,
161 margins_name=margins_name,
--> 162 fill_value=fill_value,
163 )
164
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in _add_margins(table, data, values, rows, cols, aggfunc, observed, margins_name, fill_value)
229 observed,
230 grand_margin,
--> 231 margins_name,
232 )
233 if not isinstance(marginal_result_set, tuple):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in _generate_marginal_results(table, data, values, rows, cols, aggfunc, observed, grand_margin, margins_name)
312 piece = piece.copy()
313 try:
--> 314 piece[all_key] = margin[key]
315 except TypeError:
316
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3465 else:
3466 # set column
-> 3467 self._set_item(key, value)
3468
3469 def _setitem_slice(self, key, value):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
3543 self._ensure_valid_index(value)
3544 value = self._sanitize_column(key, value)
-> 3545 NDFrame._set_item(self, key, value)
3546
3547 # check if we are modifying a copy
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _set_item(self, key, value)
3380
3381 def _set_item(self, key, value):
-> 3382 self._data.set(key, value)
3383 self._clear_item_cache()
3384
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in set(self, item, value)
1069 except KeyError:
1070 # This item wasn't present, just insert at end
-> 1071 self.insert(len(self.items), item, value)
1072 return
1073
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals\managers.py in insert(self, loc, item, value, allow_duplicates)
1176
1177 # insert to the axis; this could possibly raise a TypeError
-> 1178 new_axis = self.items.insert(loc, item)
1179
1180 block = make_block(values=value, ndim=self.ndim, placement=slice(loc, loc + 1))
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\numeric.py in insert(self, loc, item)
144 if is_scalar(item) and isna(item):
145 item = self._na_value
--> 146 return super().insert(loc, item)
147
148 def _union(self, other, sort):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in insert(self, loc, item)
5302 """
5303 _self = np.asarray(self)
-> 5304 item = self._coerce_scalar_to_index(item)._ndarray_values
5305 idx = np.concatenate((_self[:loc], item, _self[loc:]))
5306 return self._shallow_copy_with_infer(idx)
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in _coerce_scalar_to_index(self, item)
4148 dtype = None
4149
-> 4150 return Index([item], dtype=dtype, **self._get_attributes_dict())
4151
4152 def _to_safe_for_reshape(self):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
531
532 return MultiIndex.from_tuples(
--> 533 data, names=name or kwargs.get("names")
534 )
535 # other iterable of some kind
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in from_tuples(cls, tuples, sortorder, names)
487 arrays = zip(*tuples)
488
--> 489 return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
490
491 @classmethod
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in from_arrays(cls, arrays, sortorder, names)
427 sortorder=sortorder,
428 names=names,
--> 429 verify_integrity=False,
430 )
431
C:\ProgramData\Anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
206 else:
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
210 return wrapper
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in __new__(cls, levels, codes, sortorder, names, dtype, copy, name, verify_integrity, _set_identity)
262 if names is not None:
263 # handles name validation
--> 264 result._set_names(names)
265
266 if sortorder is not None:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\multi.py in _set_names(self, names, level, validate)
1207 # Don't allow a single string for names in a MultiIndex
1208 if names is not None and not is_list_like(names):
-> 1209 raise ValueError("Names should be list-like for a MultiIndex")
1210 names = list(names)
1211
ValueError: Names should be list-like for a MultiIndex
```
These (index variable string/categorical) both crash the same way:
```python
df.pivot_table(index='cl', columns='nr2', aggfunc='size', fill_value=0, margins=True)
df.pivot_table(index='cl_cat', columns='nr2', aggfunc='size', fill_value=0, margins=True)
```
This (`aggfunc='sum'`) works, but does a different thing:
```python
df.pivot_table(index='nr', columns='nr2', aggfunc='sum', fill_value=0, margins=True)
```
#### Problem description
Margin sums cannot be calculated with pivot_table if `aggfunc='size'` is set (to calculate crosstables).
#### Expected Output
Margin sums like those resulting from
```python
pd.crosstab(index=df.nr, columns=df.nr2, margins=True)
```
```
nr2 1 2 3 4 5 6 7 8 All
nr
1 1 0 0 0 0 0 0 0 1
2 0 1 0 0 0 0 0 0 1
3 0 0 1 0 0 0 0 0 1
4 0 0 0 1 0 0 0 0 1
5 0 0 0 0 1 0 0 0 1
6 0 0 0 0 0 1 0 0 1
7 0 0 0 0 0 0 1 0 1
8 0 0 0 0 0 0 0 1 1
All 1 1 1 1 1 1 1 1 8
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 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 : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/27800 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27800/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27800/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27800/events | https://github.com/pandas-dev/pandas/issues/27800 | 477,921,074 | MDU6SXNzdWU0Nzc5MjEwNzQ= | 27,800 | BUG: aggregation on ordered categorical column drops grouping index or crashes, depending on context | {
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} | 14 | 2019-08-07T13:13:15Z | 2020-08-21T22:34:52Z | 2020-08-21T22:34:52Z | CONTRIBUTOR | null | ### Code Sample
Build the model data frame:
```python
df = pd.DataFrame({
'nr': [1,2,3,4,5,6,7,8],
'cat_ord': list('aabbccdd'),
'cat':list('aaaabbbb')
})
df = df.astype({'cat': 'category', 'cat_ord': 'category'})
df['cat_ord'] = df['cat_ord'].cat.as_ordered()
```
When grouping, single aggregations on a _numeric column_ work:
```python
df.groupby('cat').agg({'nr': 'min'})
```
```
nr
cat
a 1
b 5
```
**Single aggregations** on an _ordered categorical column_ work, but drop the grouping index:
```python
df.groupby('cat').agg({'cat_ord': 'min'})
```
```
cat_ord
0 a
1 c
```
**Combined single aggregations** on a _numeric_ and an _ordered categorical_ column work:
```python
df.groupby('cat').agg({'nr': 'min', 'cat_ord': 'min'})
```
```
nr cat_ord
cat
a 1 a
b 5 c
```
**Multiple aggregations** on an _ordered categorical_ column work, but drop the grouping index:
```python
df.groupby('cat').agg({'cat_ord': ['min', 'max']})
```
```
cat_ord
min max
0 a b
1 c d
```
**Combined aggregations** on a _numeric_ (single) and an _ordered categorical_ column (multiple) fail with a **TypeError**:
```python
df.groupby('cat').agg({'nr': 'min', 'cat_ord': ['min', 'max']})
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-148-b446de510106> in <module>
----> 1 df.groupby('cat').agg({'nr': 'min', 'cat_ord': ['min', 'max']})
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, arg, *args, **kwargs)
1453 @Appender(_shared_docs["aggregate"])
1454 def aggregate(self, arg=None, *args, **kwargs):
-> 1455 return super().aggregate(arg, *args, **kwargs)
1456
1457 agg = aggregate
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
227 func = _maybe_mangle_lambdas(func)
228
--> 229 result, how = self._aggregate(func, _level=_level, *args, **kwargs)
230 if how is None:
231 return result
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
528 # return a MI DataFrame
529
--> 530 return concat([result[k] for k in keys], keys=keys, axis=1), True
531
532 elif isinstance(self, ABCSeries) and is_any_series():
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
256 )
257
--> 258 return op.get_result()
259
260
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in get_result(self)
466 obj_labels = mgr.axes[ax]
467 if not new_labels.equals(obj_labels):
--> 468 indexers[ax] = obj_labels.reindex(new_labels)[1]
469
470 mgrs_indexers.append((obj._data, indexers))
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\category.py in reindex(self, target, method, level, limit, tolerance)
616 # coerce to a regular index here!
617 result = Index(np.array(self), name=self.name)
--> 618 new_target, indexer, _ = result._reindex_non_unique(np.array(target))
619 else:
620
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in _reindex_non_unique(self, target)
3434
3435 target = ensure_index(target)
-> 3436 indexer, missing = self.get_indexer_non_unique(target)
3437 check = indexer != -1
3438 new_labels = self.take(indexer[check])
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_indexer_non_unique(self, target)
4792 tgt_values = target._ndarray_values
4793
-> 4794 indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
4795 return ensure_platform_int(indexer), missing
4796
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_indexer_non_unique()
TypeError: '<' not supported between instances of 'str' and 'int'
```
**Combined aggregations** on a _numeric_ (multiple) and an _ordered categorical_ column (single) also fail with the same **TypeError**:
```python
df.groupby('cat').agg({'nr': ['min', 'max'], 'cat_ord': 'min'})
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-164-b1d70184bd81> in <module>
----> 1 df.groupby('cat').agg({'nr': ['min', 'max'], 'cat_ord': 'min'})
...
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_indexer_non_unique()
TypeError: '<' not supported between instances of 'str' and 'int'
```
### Problem description
Aggregations on ordered categoricals drop the grouping index, or crash, as shown above.
This makes it hard to calculate combined aggregations over big data sets correctly and efficiently.
### Expected Output
Aggregations on ordered categoricals should work as on non-categorical columns.
### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 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 : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 40 | 2019-08-07T14:45:47Z | 2020-07-08T14:47:45Z | 2020-07-08T14:38:16Z | CONTRIBUTOR | null | - closes #24382
- tests added
- passes `black pandas`
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- whatsnew entry
The methods added in this PR ignore `nan` ,i.e., `skipna=True`. The existing `categorical.min` return `nan` if `categorical` contain any `nan`. This behavior is expected in `test_min_max` (tests/arrays/categorical/test_analytics.py). | {
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} | 0 | 2019-08-07T15:48:41Z | 2019-08-09T12:55:23Z | 2019-08-09T12:55:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.25.0'
In [3]: s = pd.Series(["A1", "BC2", "DEF3"])
In [4]: s
Out[4]:
0 A1
1 BC2
2 DEF3
dtype: object
In [5]: s.str.slice(start=-1)
Out[5]:
0 1
1 2
2 3
dtype: object
In [6]: s.str[:-1]
Out[6]:
0 A
1 BC
2 DEF
dtype: object
```
#### Problem description
It is not apparent from the documentation at https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.slice.html that you can specify negative values for the `start` and `stop` arguments. So I think this should be stated, as well as used in some examples (as shown above).
#### Expected Output
N/A
#### Output of ``pd.show_versions()``
N/A | {
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} | 1 | 2019-08-07T16:04:41Z | 2019-08-09T15:39:54Z | 2019-08-08T12:35:31Z | MEMBER | null | - Do raising checks earlier on, matching how this is done elsewhere (moving towards #23853)
- Comment on line 1034 notes possibly-inconsistent behavior that we should either change or add an explanatory comment for.
- Ditto line 1058
- Before long we'll be able to collapse all of the dispatch_to_extension_op cases into one case; I'm still troubleshooting some corner cases of this on another branch.
- After this, `na_op` is only called in one place, meaning it will be easier to make further de-nesting/de-closuring simplifications in follow-ups.
- Putting all the finalize/_constructor business at the end will make it easier to separate out the array-specific middle and apply it to a) PandasArray and b) Block-wise. | {
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- [ ] 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 | 2019-08-07T17:58:13Z | 2019-08-11T23:15:22Z | 2019-08-11T23:15:22Z | CONTRIBUTOR | null | There seem to be many inconsistencies between the iterators of `Series` and `DataFrame`. I'll lay them out here. To address these, we'll probably need to open up several other issues. And before anyone reading this gets squeamish, **I'd like to offer my help in fixing these problems.**
`Series.items()` and `DataFrame.items()` don't have similar behavior and this is confusing, especially since the docs state `DataFrame.items()` is 'Equivalent to `Series.items()` for `DataFrame`.' Which is simply not true.
This is also an issue for `Series.iteritems()` and `DataFrame.iteritems()`:
`Series.iteritems()` is exactly the same as `Series.items()`, which both yield `(index, value)`
`DataFrame.iteritems()` is exactly the same as `DataFrame.items()` except for in the documentation `.items()` uses "Yields" in the docs while `.iteritems()` uses "Returns", another inconsistency. Both of these yield `(column, df[column])`.
On another note, `iter(DataFrame)` is functionally equivalent to `iter(DataFrame.columns)`, which is worth noting, and at least from my perspective, not an expected behavior. Under the current implementation, I'd expect the same results as `DataFrame.iteritems()` or, under my preferred implementation, I'd expect the same results as `DataFrame.iterrows()`.
`iter(Series)` is the one method out of all of these that makes the most sense to me as it simply yields `value`. Still, this behavior is dissimilar to `iter(DataFrame)`, which, in my opinion,results in unnecessary confusion.
There are other inconsistencies between `DataFrame.iterrows()`, `DataFrame.itertuples()`, and `Series.iteritems()`/`Series.items()`. All of them have similar functionality. The most similar functionality is between `DataFrame.iterrows()` and `Series.iteritems()`/`Series.items()`, which both return `(index, row/value)`
`DataFrame.itertuples()` is the only one that has an `index` parameter, which allows us to include or exclude the index in the tuple that's being iterated over. Itertuples does have a different behavior than the other iterators though since it includes the index in the tuple instead of outside the iterator. `Series.itertuples()` does not exist, but merits existence since with the indexes included, it would behave the same as `DataFrame.itertuples()` and return something like `(index, value)` or `(multiindex1, multiindex2, value)`.
Nonetheless, being able to decide whether to include the index in other situations would be useful so I could do things like what I would do now with `map(lambda x: x[1], my_series.iteritems())`.
In my situation, I'm most in need of a `Series.itertuples()` with a similar functionailty as the current `DataFrame.itertuples()`.
I understand that I can just do this simply with `my_series.to_frame().itertuples()` or `map(lambda x: (x[0][0], x[0][1], x[1]), my_series` (I have a multiindex) but it seems like there should be a better way and there are a lot of other inconsistencies that should be fixed before that happens.
I'm happy to do a lot of the work for this myself, but I would need guidance on how to go about this since implementing these changes involves some decision-making.
Also, with the breadth of this post, I might have missed something or made a mistake somewhere, but I think I've generally cataloged the majority of the problems. | {
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} | 1 | 2019-08-07T18:02:30Z | 2019-08-07T21:08:37Z | 2019-08-07T21:08:37Z | MEMBER | null | Manual backport of https://github.com/pandas-dev/pandas/pull/27786, meeseeksdev seemed to be inactive for a moment (PR was properly milestoned). | {
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} | 10 | 2019-08-07T20:10:10Z | 2021-01-31T10:02:37Z | 2019-08-16T11:38:47Z | MEMBER | null | I found out that the reason this happens is because `subplots` is not updated accordingly when `plt.subplots()` is used and assign to `ax`, e.g. `fig, ax = plt.subplots(nrows=1, ncols=3)`. I tested my solution and things all get solved, and figure looks very correct like below:
BTW, i am not very sure how to properly test this, and would be nice to know!
- [x] closes #18222
- [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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} | 1 | 2019-08-07T21:33:24Z | 2019-08-13T13:57:29Z | 2019-08-13T12:06:27Z | MEMBER | null | If running the tests without the branch installed, this test fails. Skip it instead. | {
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} | 1 | 2019-08-07T22:26:00Z | 2019-08-08T13:33:55Z | 2019-08-08T12:33:12Z | MEMBER | null | These are the ones that are executed at import-time and then never needed again. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/27812 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/27812/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/27812/comments | https://api.github.com/repos/pandas-dev/pandas/issues/27812/events | https://github.com/pandas-dev/pandas/issues/27812 | 478,211,779 | MDU6SXNzdWU0NzgyMTE3Nzk= | 27,812 | BUG:No xlabel for twinx axes in subploted figures | {
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... | open | false | null | [] | null | 6 | 2019-08-08T01:53:22Z | 2021-07-10T19:29:58Z | null | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
df=pd.DataFrame()
t1 = np.arange(1, 9.0, 1)
df['TIME'] = t1
df['DAT1'] = t1
df['DAT2'] = np.arange(9, 1.0, -1)
fig = plt.figure(constrained_layout=True)
gs = mpl.gridspec.GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])
ax3 = fig.add_subplot(gs[1, :])
color = 'tab:red'
ax1.set_xlabel('time (s)')
ax1.set_ylabel('exp', color=color)
df.plot(ax=ax1, x='TIME', y='DAT1')
ax1.tick_params(axis='y', labelcolor=color)
**ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = 'tab:blue'
ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1
df.plot(ax=ax1, x='TIME', y='DAT2')
ax2.tick_params(axis='y', labelcolor=color)**
fig.tight_layout() # otherwise the right y-label is slightly clipped
plt.show()
```
#### Problem description
When using GridSpec to set the layout, xlabels are not displayed if twinx is used
If the following code is commented out：
“”“
ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
color = 'tab:blue'
ax2.set_ylabel('sin', color=color) # we already handled the x-label with ax1
df.plot(ax=ax1, x='TIME', y='DAT2')
ax2.tick_params(axis='y', labelcolor=color)
”“”
You can see that xlabel can be displayed：
#### Expected Output
show xlabels
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: zh_CN
LOCALE: None.None
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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