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} | 12 | 2019-08-29T04:09:10Z | 2020-04-09T02:37:34Z | 2019-09-20T03:36:07Z | MEMBER | null | Fixes a bug where the return value for certain functions was hard-coded as `np.nan` when operating on an empty `DataFrame`.
Before
```python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(columns=["a", "b", "c"])
>>> df.apply(np.sum)
a NaN
b NaN
c NaN
dtype: float64
>>> df.apply(np.prod)
a NaN
b NaN
c NaN
dtype: float64
```
After
```python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(columns=["a", "b", "c"])
>>> df.apply(np.sum)
a 0.0
b 0.0
c 0.0
dtype: float64
>>> df.apply(np.prod)
a 1.0
b 1.0
c 1.0
dtype: float64
```
**Edit**: Closes #28202 and closes #21959 after https://github.com/pandas-dev/pandas/pull/28213/commits/cb68153f4b6fca19440ec6b79a0d1128c002ec11. The issue was that the arguments of `self.f` were already unpacked here:
https://github.com/pandas-dev/pandas/blob/cb68153f4b6fca19440ec6b79a0d1128c002ec11/pandas/core/apply.py#L112
and then we tried to do this again inside `apply_empty_result` which was raising an error and causing the unusual output. | {
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} | [] | closed | false | null | [] | null | 1 | 2019-08-29T04:18:35Z | 2019-08-29T06:07:29Z | 2019-08-29T06:07:28Z | NONE | null | I am trying to upgrade the versus of BeautifulSoup4 from 4.6.0 to 4.8.0 and pip from 18.0 to 19.2.3 which are newest versions.
So via the terminal, I used `python -m pip install -U bs4` and `pip install --upgrade pip`. The thing is these two packages are actually installed successfully, but the upgrading is failed.
As the terminal showed, the old version is already installed in the specific location and cannot be simply uninstalled:
>Found existing installation: pip 18.0
Not uninstalling pip at /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6, as it is in the standard library.
Can't uninstall 'pip'. No files were found to uninstall.
>Found existing installation: beautifulsoup4 4.6.0
Not uninstalling beautifulsoup4 at /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6, outside environment /Users/niejiaqi/PycharmProjects/URLDemo2/venv
Can't uninstall 'beautifulsoup4'. No files were found to uninstall.
How can I update those packages? | {
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} | 40 | 2019-08-29T10:25:38Z | 2020-01-05T13:32:27Z | 2020-01-04T19:07:05Z | CONTRIBUTOR | null | Changed the backtick quoting functions so that you can also use backtick quoting to use invalid Python identifiers like ones that start with a digit, start with a number, or are separated by operators instead of spaces.
- [x] closes #27017
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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As this builds upon #24955 I think @jreback would be again the right person for the code review. | {
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The documentation ([https://dev.pandas.io/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet](https://dev.pandas.io/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet)) says that for `DataFrame.to_parquet` when the value for the index parameter is `None`, the behavior will depend on the engine. However if we try this both engines, `pyarrow` and `fastparquet`, will keep the index. This PR proposes to set the default value of the index parameter as `index=True` instead of `index=None` since both engines keep the index anyway. Would appreciate any discussion/feedback :)
Sample code to demonstrate how both engines treat the index:
```
import pandas as pd
data = {'name':['a', 'b', 'c', 'd']}
df = pd.DataFrame(data, index =['rank_1', 'rank_2', 'rank_3', 'rank_4'])
df.info()
```
Output:
```
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, rank_1 to rank_4
Data columns (total 1 columns):
name 4 non-null object
dtypes: object(1)
memory usage: 64.0+ bytes
```
```
# pyarrow
df.to_parquet("test_pyarrow.parquet", engine="pyarrow")
df_read_pyarrow = pd.read_parquet("test_pyarrow.parquet", engine="pyarrow")
df_read_pyarrow.info()
```
Output:
```
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, rank_1 to rank_4
Data columns (total 1 columns):
name 4 non-null object
dtypes: object(1)
memory usage: 64.0+ bytes
```
```
# fastparquet
df.to_parquet("test_fastparquet.parquet", engine="fastparquet")
df_read_fastparquet = pd.read_parquet("test_fastparquet.parquet", engine="fastparquet")
df_read_fastparquet.info()
```
Output:
```
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, rank_1 to rank_4
Data columns (total 1 columns):
name 4 non-null object
dtypes: object(1)
memory usage: 64.0+ bytes
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"description": "read_html, to_html, S... | open | false | null | [] | null | 3 | 2019-08-29T13:39:24Z | 2021-09-09T14:05:43Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame(data={
'test': ['abcdefghijklmnopqrstuvwxyz', 'aaaabbbbbbccccccddddddeeeeefffff'],
})
pd.set_option('display.max_colwidth', 10)
df.to_html(escape=False, notebook=False)
```
#### Problem description
While we are working on issue https://github.com/pandas-dev/pandas/issues/9690 and trying to reproduce the bug we found what could be a possible bug, when using `pd.set_option('display.max_colwidth', 10)`, we expect `df.to_html(escape=False, notebook=False)` to show truncated values, but this does not occurs.
We already know that when using `notebook=True` the truncating occurs, but in our mind that to should working too when `notebook=False`. Should this be this way?
#### Output
```
'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>test</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>abcdefghijklmnopqrstuvwxyz</td>\n </tr>\n <tr>\n <th>1</th>\n <td>aaaabbbbbbccccccddddddeeeeefffff</td>\n </tr>\n </tbody>\n</table>'
```
#### Expected Output
```
'<table border="1" class="dataframe">\n <thead>\n <tr style="text-align: right;">\n <th></th>\n <th>test</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>abcdef...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>aaaabb...
</td>\n </tr>\n </tbody>\n</table>'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f34933848d7daa129651a53158cb94367bacbcd
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : pt_BR.UTF-8
LOCALE : pt_BR.UTF-8
pandas : 0.25.0+258.g5f3493384
numpy : 1.16.2
pytz : 2018.3
dateutil : 2.8.0
pip : 9.0.1
setuptools : 40.8.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.1
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.3.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.0.2
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.4.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
@elisamalzoni | {
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} | [] | closed | false | null | [] | null | 12 | 2019-08-29T13:40:07Z | 2021-11-20T07:43:47Z | 2019-08-29T20:18:54Z | NONE | null | `/usr/local/lib/python3.7/site-packages/pandas/compat/__init__.py:84: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.`
I get this error when I run the code and I don't know how to fix it
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} | 8 | 2019-08-29T14:26:55Z | 2019-09-28T21:13:23Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
X = pd.DataFrame({ "count": [1, 2] }, index=["A", "B"])
Y = pd.DataFrame({"name": ["A", "C"], "value": [100, 200]})
Z = pd.merge(X, Y, left_index=True, right_on="name", how='left')
print(Z.to_string())
# in 0.23.4
# count name value
# 0 1 A 100.0
# 1 2 B NaN
# in 0.24.2
# count name value
# 0 1 A 100.0
# 1 2 B NaN
# in 0.25.1
# count name value
# 0.0 1 A 100.0
# NaN 2 B NaN
assert isinstance(Z.index, pd.Int64Index)
```
#### Problem description
I looked on the GitHub tracker for similar issues but the closest I found was #24897. In previous versions of pandas it would return a Int64Index but now returns a Float64Index. I didn't see this behaviour documented in the release notes of 0.25, but please let me know if I've missed it. This bug is easy to reproduce in a virtualenv.
#### Expected Output
```
count name value
0 1 A 100.0
1 2 B NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.utf-8
LANG : en_US.utf-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-08-29T14:50:35Z | 2019-08-30T17:08:34Z | 2019-08-30T17:08:33Z | CONTRIBUTOR | null | When created by python 2.7, the "tz" attribute will be created with CSET
H5T_CSET_ASCII instead of H5T_CSET_UTF8, therefore it is read as bytes
when string is expected.
- [ X] closes #26443
- [ 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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https://api.github.com/repos/pandas-dev/pandas/issues/28222 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28222/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28222/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28222/events | https://github.com/pandas-dev/pandas/issues/28222 | 487,062,989 | MDU6SXNzdWU0ODcwNjI5ODk= | 28,222 | Implement str.contains for Dataframes so it can be used on multiple columns at once | {
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I would like to be able to utilise str.contains to return a boolean of all of the cells in a DataFrame that at least partially match a certain string. Currently this can only be applied for a single column at a time as it is only implemented for Series, and my searching has not found an elegant solution for this over multiple columns simultaneously.
Thanks! | {
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```
2887,2888d2886
< pandas\io\formats\format.py:626: error: Expression has type "Any"
< pandas\io\formats\format.py:651: error: Expression has type "Any"
2907,2909d2904
< pandas\io\formats\format.py:786: error: Expression has type "Any"
< pandas\io\formats\format.py:789: error: Expression has type "Any"
< pandas\io\formats\format.py:790: error: Expression has type "Any"
2922d2916
< pandas\io\formats\format.py:838: error: Expression has type "Any"
2944d2937
< pandas\io\formats\format.py:948: error: Expression has type "Any"
2970a2964,2979
> pandas\io\formats\format.py:976: error: Expression has type "Any"
> pandas\io\formats\format.py:978: error: Expression type contains "Any" (has type "Iterator[Tuple[Any, Any]]")
> pandas\io\formats\format.py:978: error: Expression has type "Any"
> pandas\io\formats\format.py:978: error: Expression type contains "Any" (has type "Iterator[Any]")
> pandas\io\formats\format.py:978: error: Expression type contains "Any" (has type "Callable[[Any], Any]")
> pandas\io\formats\format.py:980: error: Expression type contains "Any" (has type "Tuple[int, Tuple[Any, Any]]")
> pandas\io\formats\format.py:980: error: Expression type contains "Any" (has type "List[Any]")
> pandas\io\formats\format.py:980: error: List comprehension has incompatible type List[List[Any]]; expected List[Tuple[Any, ...]]
> pandas\io\formats\format.py:980: error: Expression has type "Any"
> pandas\io\formats\format.py:980: error: Expression type contains "Any" (has type "Union[bool, Any]")
> pandas\io\formats\format.py:980: error: Expression type contains "Any" (has type "Optional[Callable[..., Any]]")
> pandas\io\formats\format.py:980: error: Expression type contains "Any" (has type "Dict[Any, Any]")
> pandas\io\formats\format.py:981: error: Expression type contains "Any" (has type "Tuple[Any, Any]")
> pandas\io\formats\format.py:981: error: Expression has type "Any"
> pandas\io\formats\format.py:981: error: Expression type contains "Any" (has type "enumerate[Tuple[Any, Any]]")
> pandas\io\formats\format.py:981: error: Expression type contains "Any" (has type "Iterator[Tuple[Any, Any]]")
2973,2976d2981
< pandas\io\formats\format.py:988: error: Expression has type "Any"
< pandas\io\formats\format.py:992: error: Expression has type "Any"
< pandas\io\formats\format.py:1006: error: Expression has type "Any"
< pandas\io\formats\format.py:1007: error: Expression has type "Any"
2981a2987,2989
> pandas\io\formats\format.py:1018: error: Expression type contains "Any" (has type "List[Any]")
> pandas\io\formats\format.py:1018: error: Expression has type "Any"
> pandas\io\formats\format.py:1018: error: Expression type contains "Any" (has type "Optional[Callable[..., Any]]")
2989d2996
< pandas\io\formats\format.py:1044: error: Expression has type "Any"
3875d3881
< pandas\core\frame.py:2792: error: Expression has type "Any"
3929d3934
< pandas\core\frame.py:6255: error: Expression has type "Any"
4655,4657d4659
< pandas\io\formats\latex.py:56: error: Expression has type "Any"
< pandas\io\formats\latex.py:59: error: Expression has type "Any"
< pandas\io\formats\latex.py:60: error: Expression has type "Any"
4719d4720
< pandas\io\formats\html.py:93: error: Expression has type "Any"
4732d4732
< pandas\io\formats\html.py:201: error: Expression has type "Any"
5059d5058
< pandas\util\_doctools.py:184: error: Expression has type "Any"
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] | closed | false | null | [] | null | 3 | 2019-08-29T20:57:08Z | 2019-09-02T21:34:28Z | 2019-09-02T21:34:28Z | NONE | null | - [x] closes #28188
- Short summary
If we have enough information to know the shape of the final
dataframe, we set the value of data to np.nan if not set.
- Longer explanation
If I understand correctly the `__init__` of DataFrame, with this change
when the first if evaluates True then all following ifs evaluate False
until we enter the else in [frame.py:459](https://github.com/pandas-dev/pandas/blob/03b3c8fc82b3a18a3ddcad1b3b26d601467fc74c/pandas/core/frame.py#L459), and we are done because now the dataframe is initialized with a ndarray.
With the former behavior `data` defaults always to None,
hence the output dataframe has all dtypes object and its creation
is slow.
With the proposed code the output dataframe has all dtypes float and is
faster to create than before, but only when columns and index are given.
I don't know how to loose the current restriction of `dtype=None`. It would be nice to have a function that verifies that a type is compatible with nan.
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} | 4 | 2019-08-29T22:03:24Z | 2020-04-29T23:16:03Z | 2019-09-11T19:26:20Z | CONTRIBUTOR | null | When Series or DataFrames was empty (no cells) after removing columns
without a suitable type (.select_dtypes), pandas was throwing a "no
numeric data to plot" exception.
This can happen:
1) either because there is no column with a suitable type;
2) or if there is no row.
Raising an exception in the first case makes sense but we should
probably avoid throwing an exception in the second case.
- [X] closes #27758
- [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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} | 16 | 2019-08-29T23:16:02Z | 2019-09-05T16:02:56Z | 2019-09-05T15:52:22Z | MEMBER | null | by avoiding/lazifying expensive stdlib imports.
Exact timings are tough because repeated runs of `python3 -X importtime -c "import pandas as pd"` are really high variance, but my general read is that this trims about 35 ms out of about 650 ms.
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} | 6 | 2019-08-29T23:35:37Z | 2019-08-30T17:10:07Z | 2019-08-30T16:38:40Z | MEMBER | null | Shaves on the order of 5% off of import time.
Unrelated docstring fixups, improve typing on parse_timezone_directive and _calc_julian_from_V.
The important thing here is setting the "Z" key dynamically in `__getitem__` instead of in `__init__` (since an instance is created in the module namespace at importt) | {
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} | 1 | 2019-08-30T00:25:14Z | 2019-08-30T17:15:32Z | 2019-08-30T14:32:28Z | MEMBER | null | - [X] closes #28210
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 6 | 2019-08-30T10:40:51Z | 2019-09-05T17:11:04Z | 2019-09-05T17:10:54Z | CONTRIBUTOR | null | - [x] closes #27485
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AFAIK there's no writer for OpenDocument spreadsheet, so I came up with the minimal amount of code to generate the spreadsheet `odf` engine can read. | {
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} | 1 | 2019-08-30T12:51:23Z | 2019-09-05T18:03:57Z | 2019-09-05T18:03:57Z | NONE | null | ```python
class ExcelFile:
"""
Class for parsing tabular excel sheets into DataFrame objects.
Uses xlrd. See read_excel for more documentation
Parameters
----------
io : string, path object (pathlib.Path or py._path.local.LocalPath),
file-like object or xlrd workbook
If a string or path object, expected to be a path to xls or xlsx file.
engine : string, default None
If io is not a buffer or path, this must be set to identify io.
Acceptable values are None or ``xlrd``.
"""
from pandas.io.excel._odfreader import _ODFReader
from pandas.io.excel._openpyxl import _OpenpyxlReader
from pandas.io.excel._xlrd import _XlrdReader
_engines = {"xlrd": _XlrdReader, "openpyxl": _OpenpyxlReader, "odf": _ODFReader}
```
#### Problem description
I was searching for support for reading _.ods_ files, and upon reading the documentation for _read_excel_ and IO related functions, I found nothing.
When I was about to search for another library, I discovered that support had already been added to the _read_excel_ function (as of #2311), but the docs weren't updated, either in _ExcelFile_, or in the _read_excel_ function, and as suggested when creating the issue https://pandas-docs.github.io/pandas-docs-travis/ also wasn't. I also didn't find any related issue regarding this.
I might be missing something, since it is my first issue, sorry about that if it's the case.
I'd suggest even something along the lines of:
```
Parameters
----------
io : string, path object (pathlib.Path or py._path.local.LocalPath),
file-like object or xlrd workbook
If a string or path object, expected to be a path to xls or xlsx file.
engine : string, default None
If io is not a buffer or path, this must be set to identify io.
Acceptable values are None, ``xlrd``, ``openpyxl`` or ``odf``.
Note that ``odf`` reads tables out of OpenDocument formatted files.
"""
```
for both _ExcelFile_ and _read_excel_. That would make visible that support for other engines is available.
Thanks.
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```
# df.to_csv('output_file.csv')
```
#### Problem description
In Jupyter notebook, the to_csv method does not work anymore since I upgraded pandas to version 0.25.1
It gives the following error message:
> TypeError: __init__() got an unexpected keyword argument 'tupleize_cols'
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2018.9
dateutil : 2.6.1
pip : 19.2.3
setuptools : 41.1.0
Cython : None
pytest : 3.3.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-08-30T19:14:39Z | 2020-10-01T17:52:14Z | 2020-10-01T17:52:14Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
from pandas.util.testing import assert_almost_equal
e1 = np.array([0.5831076580182805, -0.9083518696779991], 'd')
e2 = np.array([0.5831076580182805, -0.9083518696779992], 'd')
e1z = np.asanyarray(e1, np.complex128)
e2z = np.asanyarray(e2, np.complex128)
assert_almost_equal(pd.Series(e1), pd.Series(e2)) # gives True as expected
assert_almost_equal(pd.Series(e1z), pd.Series(e2z)) # unexpectedly fails
# both of equivalent tests on NumPy side pass
np.testing.assert_almost_equal(e1, e2)
np.testing.assert_almost_equal(e1z, e2z)
```
#### Problem description
When running tests on MacOSX with Pandas 0.25.1, the test `pandas.tests.computation.test_eval.TestMathNumExprPandas::test_result_complex128` fails because of this issue.
#### Expected Output
It is expected that `pandas._lib.testing.assert_almost_equal` would use `np.testing.assert_almost_equal` for all `np.floating` and `np.complexfloating` dtypes.
#### Output of ``pd.show_versions()``
<details>
```
In [4]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 15.4.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1.post20190716
Cython : None
pytest : 3.8.1
hypothesis : 3.68.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 6.3.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
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
```
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https://api.github.com/repos/pandas-dev/pandas/issues/28236 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28236/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28236/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28236/events | https://github.com/pandas-dev/pandas/issues/28236 | 487,642,971 | MDU6SXNzdWU0ODc2NDI5NzE= | 28,236 | seed isort known_third_party pre-commit hook | {
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} | 0 | 2019-08-30T19:42:38Z | 2019-09-04T11:26:12Z | 2019-09-04T11:26:12Z | CONTRIBUTOR | null | @basnijholt question on this. When making changes in https://github.com/pandas-dev/pandas/pull/28164, seed_isort_known_third_party wants to make the following changes.
```
diff --git a/setup.cfg b/setup.cfg
index 43dbac15f5..227defcfe8 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -116,7 +116,7 @@ known_dtypes = pandas.core.dtypes
known_post_core = pandas.tseries,pandas.io,pandas.plotting
sections = FUTURE,STDLIB,THIRDPARTY,PRE_LIBS,PRE_CORE,DTYPES,FIRSTPARTY,POST_CORE,LOCALFOLDER
known_first_party = pandas
-known_third_party = _pytest,announce,dateutil,docutils,flake8,git,hypothesis,jinja2,lxml,matplotlib,numpy,numpydoc,pkg_resources,pyarrow,pytest,pytz,requests,scipy,setuptools,sphinx,sqlalchemy,validate_docstrings,yaml
+known_third_party = announce,dateutil,docutils,flake8,git,hypothesis,jinja2,lxml,matplotlib,numpy,numpydoc,pkg_resources,pyarrow,pytest,pytz,requests,scipy,setuptools,sphinx,sqlalchemy,validate_docstrings,yaml
multi_line_output = 3
include_trailing_comma = True
force_grid_wrap = 0
```
Is that expected? It also takes quite a while to run (5ish seconds). Is that expected? | {
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} | 1 | 2019-08-30T19:56:23Z | 2019-08-30T21:04:20Z | 2019-08-30T20:54:02Z | CONTRIBUTOR | null | [DatetimeIndex.tz_localize](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DatetimeIndex.tz_localize.html#pandas.DatetimeIndex.tz_localize) doc string has improperly formatted examples near the bottom. Trivial fix, so didn't think it needed an issue or whatsnew entry, but happy to add if needed. Now looks like:
<img width="765" alt="Screen Shot 2019-08-30 at 12 55 16 PM" src="https://user-images.githubusercontent.com/4383303/64048047-73e31080-cb25-11e9-9266-ef721bc2600b.png">
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28238 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28238/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28238/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28238/events | https://github.com/pandas-dev/pandas/issues/28238 | 487,691,194 | MDU6SXNzdWU0ODc2OTExOTQ= | 28,238 | pd.to_datetime() fails for KeyError when raising monotonic increasing index ValueError | {
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} | 8 | 2019-08-30T22:24:55Z | 2021-07-25T14:07:02Z | 2021-07-25T14:07:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from datetime import datetime
times = pd.date_range(datetime.now(), periods=1000, freq='h')
times = times.to_frame(index=False, name='DT').sample(1000)
times.index = times.index.to_series().astype(float)/1000
pd.to_datetime(times.iloc[:, 0]) # <-- Fails
pd.to_datetime(times.reset_index(drop=True).iloc[:, 0]) # <-- Reset index to sorted int works
```
#### Problem description
Sometimes during data processing after pivoting or sampling data you may end up with a float index that is not sorted. When you try to convert a series which happens to have an unsorted float index to a DateTime, a ValueError followed by KeyError occurs. Also, this only happens for a series with 60 elements or more. There may be differences in code path for larger frames.
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, label, side, kind)
5159 try:
-> 5160 return self._searchsorted_monotonic(label, side)
5161 except ValueError:
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in _searchsorted_monotonic(self, label, side)
5120
-> 5121 raise ValueError("index must be monotonic increasing or decreasing")
5122
ValueError: index must be monotonic increasing or decreasing
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-67-ffc7a0542f03> in <module>
----> 1 pd.to_datetime(times.iloc[:, 0])
D:\Python37\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
D:\Python37\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)
769 result = result.tz_localize(tz)
770 elif isinstance(arg, ABCSeries):
--> 771 cache_array = _maybe_cache(arg, format, cache, convert_listlike)
772 if not cache_array.empty:
773 result = arg.map(cache_array)
D:\Python37\lib\site-packages\pandas\core\tools\datetimes.py in _maybe_cache(arg, format, cache, convert_listlike)
149 if cache:
150 # Perform a quicker unique check
--> 151 if not should_cache(arg):
152 return cache_array
153
D:\Python37\lib\site-packages\pandas\core\tools\datetimes.py in should_cache(arg, unique_share, check_count)
119 assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
120
--> 121 unique_elements = unique(arg[:check_count])
122 if len(unique_elements) > check_count * unique_share:
123 do_caching = False
D:\Python37\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
1104 key = check_bool_indexer(self.index, key)
1105
-> 1106 return self._get_with(key)
1107
1108 def _get_with(self, key):
D:\Python37\lib\site-packages\pandas\core\series.py in _get_with(self, key)
1109 # other: fancy integer or otherwise
1110 if isinstance(key, slice):
-> 1111 indexer = self.index._convert_slice_indexer(key, kind="getitem")
1112 return self._get_values(indexer)
1113 elif isinstance(key, ABCDataFrame):
D:\Python37\lib\site-packages\pandas\core\indexes\numeric.py in _convert_slice_indexer(self, key, kind)
395
396 # translate to locations
--> 397 return self.slice_indexer(key.start, key.stop, key.step, kind=kind)
398
399 def _format_native_types(
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in slice_indexer(self, start, end, step, kind)
5025 slice(1, 3)
5026 """
-> 5027 start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
5028
5029 # return a slice
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in slice_locs(self, start, end, step, kind)
5245 end_slice = None
5246 if end is not None:
-> 5247 end_slice = self.get_slice_bound(end, "right", kind)
5248 if end_slice is None:
5249 end_slice = len(self)
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, label, side, kind)
5161 except ValueError:
5162 # raise the original KeyError
-> 5163 raise err
5164
5165 if isinstance(slc, np.ndarray):
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, label, side, kind)
5155 # we need to look up the label
5156 try:
-> 5157 slc = self.get_loc(label)
5158 except KeyError as err:
5159 try:
D:\Python37\lib\site-packages\pandas\core\indexes\numeric.py in get_loc(self, key, method, tolerance)
477 except (TypeError, NotImplementedError):
478 pass
--> 479 return super().get_loc(key, method=method, tolerance=tolerance)
480
481 @cache_readonly
D:\Python37\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2890 return self._engine.get_loc(key)
2891 except KeyError:
-> 2892 return self._engine.get_loc(self._maybe_cast_indexer(key))
2893 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2894 if indexer.ndim > 1 or indexer.size > 1:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Float64HashTable.get_item()
KeyError: 100.0
```
#### Expected Output
Ideally it could create the DateTime series regardless of the state of the index. Perhaps users want to keep an unsorted float index and just want to cast to DateTime.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 2008ServerR2
machine : AMD64
processor : Intel64 Family 6 Model 45 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.1.0
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : 1.8.1
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.6
tables : 3.5.2
xarray : 0.12.3
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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} | 1 | 2019-08-31T01:15:12Z | 2019-09-05T15:45:44Z | 2019-09-05T15:34:44Z | MEMBER | null | - [x] closes #26663
for py37 this uses `-X importtime` to get a more precise number without subprocess overhead. Doesn't actually do anything with it, but what the heck.
Couple of unrelated cleanups. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28240 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28240/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28240/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28240/events | https://github.com/pandas-dev/pandas/issues/28240 | 487,751,302 | MDU6SXNzdWU0ODc3NTEzMDI= | 28,240 | Missing <th> for index in notebook display of pd.DataFrame in 0.25.1 | {
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] | closed | false | null | [] | null | 1 | 2019-08-31T09:30:32Z | 2019-08-31T10:48:58Z | 2019-08-31T10:48:41Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
#### Problem description
Since `0.25.1`, the row index ("A" and "B" in [2]) is not shown in boldface any more.
#### Expected Output
Same as in [3] (pre `0.25.1` output)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : 0.11.0
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-08-31T13:51:23Z | 2021-06-27T18:50:43Z | 2019-09-25T15:50:32Z | CONTRIBUTOR | null | - [x] closes #28098
- [x] tests 1 / 1
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/28242 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28242/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28242/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28242/events | https://github.com/pandas-dev/pandas/issues/28242 | 487,774,702 | MDU6SXNzdWU0ODc3NzQ3MDI= | 28,242 | to_record fails on aggregate DataFrame | {
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} | [] | closed | false | null | [] | null | 2 | 2019-08-31T14:17:52Z | 2019-08-31T15:36:17Z | 2019-08-31T15:36:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
import numpy as np
df = pd.DataFrame([{
'uid': '1345a',
'category': 'Automobile',
'amount': 40050.12
}, {
'uid': 'b7345',
'category': 'Hovercraft',
'amount':279215.00
}, {
'uid': '0u1325',
'category': 'Automobile',
'amount': 124895.12
}])
agg_df = df.groupby(['category', 'uid']).agg({
'category': 'first',
'uid': 'first',
'amount': np.sum
})
agg_df.to_records()
```
#### Problem description
The `to_records` call should output a proper data structure. Instead, it fails with the following stacktrace:
```python
ValueError Traceback (most recent call last)
<ipython-input-8-39fc977374b6> in <module>
22 })
23
---> 24 agg_df.to_records()
~/Studio/Local/myapp/venv/lib/python3.7/site-packages/pandas/core/frame.py in to_records(self, index, convert_datetime64, column_dtypes, index_dtypes)
1822 raise ValueError(msg)
1823
-> 1824 return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
1825
1826 @classmethod
~/Studio/Local/myapp/venv/lib/python3.7/site-packages/numpy/core/records.py in fromarrays(arrayList, dtype, shape, formats, names, titles, aligned, byteorder)
616
617 if dtype is not None:
--> 618 descr = sb.dtype(dtype)
619 _names = descr.names
620 else:
ValueError: name already used as a name or title
```
However, `agg_df.to_dict(orient='records')` works returning a list of dicts.
**Note**: Also fails on (along with the version in the details section below):
- pandas 0.24.2
- numpy 1.16.2
#### Expected Output
A python list of dict with aggregated data.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB
LOCALE : en_GB.utf8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2018.9
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.7
pytest : None
hypothesis : None
sphinx : 1.8.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.5.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 3 | 2019-08-31T16:14:16Z | 2020-05-29T21:45:31Z | null | MEMBER | null | ```python
import numpy as np
import pandas as pd
df_left = pd.DataFrame(index=["a", "b"])
df_right = pd.DataFrame({"x": ["a", "c"]})
pd.merge(df_left, df_right, left_index=True, right_on="x", how="left")
# x
# 0.0 a
# NaN b
```
#### Problem description
This is closely related to https://github.com/pandas-dev/pandas/issues/28220 but deals with the values of the `DataFrame` rather than the index itself. When left joining on an index and a column it looks like the value `"b"` from the index of `df_left` is somehow getting carried over to the column `x`, but `"a"` should be the only value in this column since it's the only one that matches the index from `df_left`. This is happening on 0.25.1 and master, and has been a bug for some time according to https://github.com/pandas-dev/pandas/issues/28220.
#### Expected Output
```python
x
a a
b NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
Seems related to this issue: https://github.com/pandas-dev/pandas/issues/17257 | {
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} | 3 | 2019-08-31T16:53:21Z | 2020-09-18T21:59:52Z | 2020-09-18T21:59:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
arr = [9.54e+08, 6.225e-01, np.nan, 0, 1.14, 0]
arr = pd.DataFrame(arr)
print(arr.rolling(5,3).std())
###output
0
0 NaN
1 NaN
2 NaN
3 5.507922e+08
4 4.770000e+08
5 0.000000e+00
#### Problem description
Expected output.iloc[5,0] = 0.551, as we can see the standeviation of last five elements shouldn't be zero. <br>
But if i change the first element from 9.54e+08 to 9.45e+07, the last standeviation is 0.816 which is still wrong. If i change the first element to 9.45e+5, everything is okay. <br>
The first element should not affect the result since the window=5, but there seems to be a bug with large elements, and a magnitude of e+08 definitely will not overflow. <br>
There must be some bugs.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.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: 0.24.2
pytest: 3.0.7
pip: 19.2.1
setuptools: 36.4.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
pyarrow: None
xarray: 0.12.3
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml.etree: 3.7.3
bs4: 4.6.0
</details> | {
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} | 2 | 2019-08-31T21:57:19Z | 2019-09-05T18:04:03Z | 2019-09-05T18:03:57Z | CONTRIBUTOR | null | Closes #28231.
First PR. Not sure if I'm doing anything wrong. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28246 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28246/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28246/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28246/events | https://github.com/pandas-dev/pandas/issues/28246 | 487,859,359 | MDU6SXNzdWU0ODc4NTkzNTk= | 28,246 | .transform inconsistent / error-prone behavior for list | {
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... | open | false | null | [] | null | 0 | 2019-09-01T08:51:57Z | 2020-09-22T01:33:43Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(data={'label': ['a', 'b', 'b', 'c'], 'wave': [1, 2, 3, 4], 'y': [0,0,0,0]})
###problem 1
df['n_tuple'] = df.groupby(['label'])[['wave']].transform(tuple)
df['n_set'] = df.groupby(['label'])[['wave']].transform(set)
df['n_frozenset'] = df.groupby(['label'])[['wave']].transform(frozenset)
df['n_dict'] = df.groupby(['label'])[['wave']].transform(dict)
df['n_list_problem'] = df.groupby(['label'])[['wave']].transform(list)
df['n_list_expected'] = df.groupby(['label'])[['wave']].transform(tuple)['wave'].apply(list)
###problem 2
df['n_sum'] = df.groupby(['label'])['wave'].transform(sum)
df['n_tuple_problem'] = df.groupby(['label'])['wave'].transform(tuple)
df['n_tuple'] = df.groupby(['label'])[['wave']].transform(tuple)
```
#### Problem description
I was hinted that it could be a bug: https://stackoverflow.com/questions/57743798/pandas-transform-inconsistent-behavior-for-list
There are two things, but let focus on **1** first:
Result of
`df['n_list_problem'] = df.groupby(['label'])[['wave']].transform(list)
`is not consistent with similar operations like `df['n_tuple'], df['n_set']` etc.
On stackoverflow I was pointed to issues regarding series lenght for rationale, but consensus was that, this behavior is confusing and error-prone.
The **2** problem:
`df['n_sum'] = df.groupby(['label'])['wave'].transform(sum)` works as expected, but
`df['n_tuple_problem'] = df.groupby(['label'])['wave'].transform(tuple)` gives unexpected result.
To get proper one, you need to coerce series into dataframe by `[[]]` instead of `[]` for `wave`.
`df['n_tuple'] = df.groupby(['label'])[['wave']].transform(tuple)
`
#### Expected Output
`df['n_list_expected'] = df.groupby(['label'])[['wave']].transform(tuple)['wave'].apply(list)
`
```
label wave y n_tuple n_list_problem n_list_expected n_tuple_problem
0 a 1 0 (1,) 1 [1] 1
1 b 2 0 (2, 3) 2 [2, 3] 2
2 b 3 0 (2, 3) 3 [2, 3] 3
3 c 4 0 (4,) 4 [4] 4
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-141-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-09-01T12:06:20Z | 2019-10-03T17:25:04Z | 2019-10-03T17:25:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime
def get_vals(x):
return pd.Series([0,1,2], index=[2000, 2001, 2002])
b = list(range(0,3))*2
y = list(range(2000,2003))*2
df = pd.DataFrame({'b':b,'y':y})
df['date'] = pd.to_datetime(df['y'].apply(lambda x: datetime.date(x, 1, 1)))
print(pd.__version__)
print(df)
df.groupby(['b']).apply(lambda x: get_vals(x))
```
#### Problem description
The above code gives an error (KeyError: 0) in pandas 0.25.1, while it runs as expected in pandas 0.24.2
The apply function returns a timeseries.
I found that triggering the error is related to having a datetime column in the dataframe. Without that column it does not throw an error.
#### Expected Output
No error and the following output, as can be verified with pandas version 0.24.2:
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
Environment of 0.25.1 that I tested on:
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
Environment of 0.24.2 that I tested on:
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 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.1.1
pip: 19.2.3
setuptools: 41.2.0
Cython: 0.29.13
numpy: 1.16.4
scipy: 1.3.1
pyarrow: None
xarray: None
IPython: 7.7.0
sphinx: 2.2.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.7.0
feather: None
matplotlib: 3.1.1
openpyxl: 2.6.3
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.2.0
lxml.etree: 4.4.1
bs4: 4.8.0
html5lib: 1.0.1
sqlalchemy: 1.3.7
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
gcsfs: None
</details>
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} | 11 | 2019-09-01T17:11:13Z | 2019-11-23T22:44:03Z | 2019-11-23T22:43:53Z | CONTRIBUTOR | null | - [x] closes #24893
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
# What's new
Use of `maybe_downcast_to_dtype` in `_add_margins` so it can resolve the dtype conversion, avoiding floats being converted to integers when the result of the `aggfunc` is a float.
For the new case, if after applying the aggfunc, if the margin result is not an integer, the whole column is converted to float:
Example:
```
_df
A B C D
0 2 1 1 X
1 4 4 3 X
2 6 5 4 Y
3 8 8 6 Y
```
Currently pandas does this:
```
print(pd.pivot_table(_df, index="D", margins=True))
A B C
D
X 3 2.5 2
Y 7 6.5 5
All 5 4.5 3
```
with the fix the result is:
```
print(pd.pivot_table(_df, index="D", margins=True))
A B C
D
X 3 2.5 2.0
Y 7 6.5 5.0
All 5 4.5 3.5
```
# Issues
There are test referencing that np.means of ints are casted back into ints. However, giving that for the aggregations in the rows, floats are kept when the np.mean of integers is a float, it does not make sense that this behavior does not hold for the margins. | {
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} | 7 | 2019-09-02T09:10:58Z | 2021-07-11T17:22:16Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import datetime as dt
df = pd.DataFrame({'Date': ['2017-01-02', '2017-01-03','2017-01-04'],
'T': [10, 11,12],
'RM': [28, 29,30]})
df['Date'] = pd.to_datetime(df.Date,infer_datetime_format=True)
df.set_index('Date', inplace=True)
df = df.asfreq('D')
print(df)
print('Dataframe index of dtype: {} and freq: {}'.format(df.index.dtype_str, df.index.freq))
print("Droping one row")
df = df.drop(df.index[1])
print(df)
print('The new index is of dtype: {} and freq: {}'.format(df.index.dtype_str, df.index.freq))
print('''Let's change in place the unexisting index: 2017-01-03''')
df.loc['2017-01-03', 'RM']=290
print(df)
print('''The dataframe has shape: {} and it's new index is of dtype: {}'''.format(df.shape, df.index.dtype_str))
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
According to Pandas documentation, the updates of a cell value based on index lookup (df.loc and df.at) should work correctly only when the index is existing within dataframe.
The problem I encountered happens when I try to update some cells accessed by DateTime index, in case the index (which is actually a date) does not exist in the dataframe. According to the documentation, an exception should be raised in this case.
What actually happens, is that without raising any exception: 1) Pandas transforms the DateTime index into an object index (thus making it unusable for timeseries processing), 2) insert new rows in the dataframe with the specified new object index and set all columns to Nan, except the updated one.
I solved the above problem, wrapping the update commands in conditional 'If' rules but according to the documentation it seems to be a misbehavior of Pandas.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-60-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.17.0
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.8
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml: 4.3.0
bs4: 4.7.1
html5lib: 0.9999999
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
</details>
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} | 7 | 2019-09-02T12:33:59Z | 2019-09-20T12:40:12Z | 2019-09-20T12:40:12Z | CONTRIBUTOR | null | Please add functionality to read format 119 `.dta` Stata files! See file format here: https://www.stata.com/help.cgi?dta#versions
Currently, even though Pandas can _write_ format 119 files (https://github.com/pandas-dev/pandas/blob/612d3b23da5b99f6c5642be574fb08713a45d7d1/pandas/io/stata.py#L2663), it seems unable to _read_ format 119 files (https://github.com/pandas-dev/pandas/blob/612d3b23da5b99f6c5642be574fb08713a45d7d1/pandas/io/stata.py#L49-L53). | {
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} | 24 | 2019-09-02T14:49:53Z | 2019-10-01T21:06:20Z | 2019-10-01T16:38:46Z | CONTRIBUTOR | null | A new version of the pull request (#27755), the other one was a bit behind. Original text below.
This fixes #17294, for more than three years now NumPy has not allowed the subtraction of boolean series.
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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} | 10 | 2019-09-02T15:52:59Z | 2019-09-09T13:56:28Z | 2019-09-09T13:56:20Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [63]: pd.DataFrame({'foo': [datetime(2019, 10, 1)], 'bar': ['test']}).to_parquet('/tmp/minimal.parquet', engine='fastparquet')
In [64]: pd.read_parquet('/tmp/minimal.parquet', engine='pyarrow')
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-64-d866d1c8df39> in <module>
----> 1 pd.read_parquet('/tmp/minimal.parquet', engine='pyarrow')
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pandas/io/parquet.py in read_parquet(path, engine, columns, **kwargs)
294
295 impl = get_engine(engine)
--> 296 return impl.read(path, columns=columns, **kwargs)
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pandas/io/parquet.py in read(self, path, columns, **kwargs)
123 kwargs["use_pandas_metadata"] = True
124 result = self.api.parquet.read_table(
--> 125 path, columns=columns, **kwargs
126 ).to_pandas()
127 if should_close:
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/array.pxi in pyarrow.lib._PandasConvertible.to_pandas()
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/table.pxi in pyarrow.lib.Table._to_pandas()
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/pandas_compat.py in table_to_blockmanager(options, table, categories, ignore_metadata)
642 column_indexes = pandas_metadata.get('column_indexes', [])
643 index_descriptors = pandas_metadata['index_columns']
--> 644 table = _add_any_metadata(table, pandas_metadata)
645 table, index = _reconstruct_index(table, index_descriptors,
646 all_columns)
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/pandas_compat.py in _add_any_metadata(table, pandas_metadata)
965 raw_name = 'None'
966
--> 967 idx = schema.get_field_index(raw_name)
968 if idx != -1:
969 if col_meta['pandas_type'] == 'datetimetz':
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/types.pxi in pyarrow.lib.Schema.get_field_index()
~/.pyenv/versions/analytics-3.7/lib/python3.7/site-packages/pyarrow/lib.cpython-37m-x86_64-linux-gnu.so in string.from_py.__pyx_convert_string_from_py_std__in_string()
TypeError: expected bytes, dict found
```
#### Problem description
A parquet file written with the `fastparquet` backend cannot be converted back to a pandas data frame when read with the `pyarrow` implementation. Conversion fails with the shown Exception, which seems to be part of applying the pandas meta data.
#### Expected Output
Successful conversion.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.69-1-lts
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.0
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.0
matplotlib : None
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : 0.3.1
scipy : None
sqlalchemy : 1.2.19
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : 1.1.8
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28253 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28253/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28253/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28253/events | https://github.com/pandas-dev/pandas/issues/28253 | 488,256,580 | MDU6SXNzdWU0ODgyNTY1ODA= | 28,253 | Fix PR06 errors in docstrings ('Parameter "{param_name}" type should use "{right_type}" instead of "{wrong_type}"') | {
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} | 7 | 2019-09-02T16:11:02Z | 2021-11-20T05:51:53Z | 2021-11-20T05:51:53Z | CONTRIBUTOR | null | [link to original issue](https://github.com/pandas-dev/pandas/issues/27977 (27977))
The output of `python scripts/validate_docstrings.py --errors=PR06` returns 257 instances of 'Parameter "{param_name}" type should use "{right_type}" instead of "{wrong_type}'
Given the large number of cases, it probably makes sense to break them out by class:
- [x] pandas.Series
- [ ] pandas.Timestamp
- [ ] pandas.Timedelta
- [ ] pandas.Period
- [ ] pandas.arrays
- [ ] pandas.Categorical
- [ ] pandas.SparseArray
- [ ] pandas.read* (inc. all 'read' methods e.g. read_sql, read spss etc.)
- [ ] pandas.HDFStore
- [ ] pandas.io.stata
- [ ] pandas.core.resample
- [ ] pandas.testing
- [ ] pandas.api
- [ ] pandas.melt
- [ ] pandas.pivot
- [ ] pandas.pivot_table
- [ ] pandas.qcut
- [ ] pandas.merge_asof
- [ ] pandas.to_numeric
- [ ] pandas.to_datetime
- [ ] pandas.date_range
- [ ] pandas.bdate_range
- [ ] pandas.period_range
- [ ] pandas.timedelta_range
- [ ] pandas.infer_freq
- [ ] pandas.interval_range
- [ ] pandas.eval
- [ ] pandas.util
- [ ] pandas.Index
- [ ] pandas.CategoricalIndex
- [ ] pandas.IntervalIndex
- [ ] pandas.MultiIndex
- [ ] pandas.DatetimeIndex
- [ ] pandas.TimedeltaIndex
- [ ] pandas.PeriodIndex
- [ ] pandas.Grouper
- [ ] pandas.core
- [ ] pandas.io
- [ ] pandas.DataFrame | {
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] | closed | false | null | [] | null | 4 | 2019-09-02T16:18:53Z | 2021-07-11T17:24:38Z | 2021-07-11T17:24:38Z | NONE | null | #### Feature Request
I often find myself writing code like the following to create a pipeline:
```python
# specific function
def astype_int(df, cols):
"""Convert columns to integer."""
df = df.copy()
df[cols] = df[cols].astype('int')
return df
# more general
def myfun(df, cols):
"""Custom transformation function."""
df = df.copy()
def my_transform(col):
"""Do something."""
return 100 * col
df[cols] = df[cols].apply(my_transform)
return df
# the pipeline
df = (pd.read_csv(my_csv)
.rename(columns=column_names)
.pipe(astype_int, cols=['a', 'b'])
.pipe(myfun, cols=['c', 'd', 'e'])
)
```
where I end up applying a function to a subset of columns. It is cumbersome to write an additional function that ends up in the pipeline, or to write separate lines outside of the pipeline such as:
```python
df[cols] = df[cols].apply(my_transform)
```
It would be much more convenient if `df.pipe` accepted a `columns` keyword argument that effectively does behind the scenes what we currently have to write explicitly. Other functions, such as `astype` would need to support this capability as well. The above code would then become:
```python
def my_transform(col):
"""Do something."""
return 100 * col
# the pipeline
df = (pd.read_csv(my_file)
.rename(columns=column_names)
.astype('int', columns=['a', 'b'])
.pipe(my_transform, columns=['c', 'd', 'e'])
)
```
which is much cleaner. I have started digging into the source code myself, but figured I would put this request out there in case there are any fundamental issues or someone beats me to it.
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} | 2 | 2019-09-02T19:31:29Z | 2019-09-03T05:04:49Z | 2019-09-03T05:04:46Z | MEMBER | null | ```
$ asv dev -b rolling
[ 20.00%] ··· rolling.Methods.peakmem_rolling ok
[ 20.00%] ··· ============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
-- method
----------------------------- ------------------------------------------------------------------------
contructor window dtype median mean max min std count skew kurt sum
============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
DataFrame 10 int 69.4M 67.7M 67.8M 67.7M 68.5M 68.6M 67.8M 67.7M 67.7M
DataFrame 10 float 69.4M 67.8M 67.9M 67.7M 68.5M 68.6M 67.8M 67.8M 67.7M
DataFrame 1000 int 69.6M 67.8M 67.9M 67.7M 68.6M 68.7M 67.8M 67.8M 67.7M
DataFrame 1000 float 70.4M 67.7M 67.8M 67.8M 68.6M 68.5M 67.8M 67.8M 67.7M
Series 10 int 72.5M 70.1M 70M 70.1M 71.7M 73.4M 70M 70.1M 70M
Series 10 float 72.5M 70.1M 70.1M 70M 71.7M 73.4M 70M 70M 70.1M
Series 1000 int 72.6M 70M 70.2M 70.1M 71.7M 73.4M 70M 70.1M 70M
Series 1000 float 72.6M 70.1M 70M 70.1M 71.6M 73.3M 70M 70M 70.1M
============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
[ 45.00%] ··· rolling.VariableWindowMethods.peakmem_rolling ok
[ 45.00%] ··· ============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
-- method
----------------------------- ------------------------------------------------------------------------
contructor window dtype median mean max min std count skew kurt sum
============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
DataFrame 50s int 69.5M 69.6M 69.6M 69.6M 70.3M 69.5M 69.6M 69.5M 69.6M
DataFrame 50s float 69.6M 69.6M 69.6M 69.6M 70.3M 69.5M 69.5M 69.6M 69.5M
DataFrame 1h int 69.7M 69.5M 69.6M 69.6M 70.4M 69.5M 69.6M 69.6M 69.6M
DataFrame 1h float 69.6M 69.5M 69.6M 69.5M 70.3M 69.6M 69.5M 69.6M 69.6M
DataFrame 1d int 74.4M 69.5M 69.7M 69.7M 70.4M 69.5M 69.6M 69.6M 69.6M
DataFrame 1d float 73.7M 69.6M 69.7M 69.8M 70.4M 69.5M 69.6M 69.5M 69.5M
Series 50s int 71.1M 71.2M 71.1M 71.1M 71.9M 71M 71M 71.1M 71.1M
Series 50s float 71.1M 71.1M 71.1M 71.1M 71.8M 71M 71.1M 71M 71.1M
Series 1h int 71.1M 71.1M 71.1M 71.1M 71.9M 71.1M 71.1M 71.1M 71.1M
Series 1h float 71.2M 71M 71.1M 71.1M 71.9M 71M 71.1M 71.1M 71.1M
Series 1d int 73.9M 71.1M 71.2M 71.2M 71.8M 71.1M 71.2M 71M 71M
Series 1d float 73M 71M 71.2M 71.2M 71.9M 71M 71M 71M 71.1M
============ ======== ======= ======== ======= ======= ======= ======= ======= ======= ======= =======
```
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} | 7 | 2019-09-02T21:04:56Z | 2020-03-19T00:57:22Z | 2020-03-19T00:57:22Z | MEMBER | null | Intertwined with #15137 though a slight different issue.
`date_format="iso"` has different behavior for Timedeltas depending on whether or not the Timedelta is in a DTA or an object array. To illustrate:
```python
# Wrong format ref 15137, but at least tries to do some formatting
>>> pd.DataFrame([[pd.Timedelta("1D")]]).to_json(date_format="iso")
'{"0":{"0":"1970-01-02T00:00:00.000Z"}}'
# Object array has no formatting
>>> pd.DataFrame([[pd.Timedelta("1D")]]).astype(object).to_json(date_format="iso")
'{"0":{"0":86400000}}'
```
By contrast the same issue does not appear with datetimes
```python
>>> pd.DataFrame([[pd.Timestamp(1)]]).to_json(date_format="iso")
'{"0":{"0":"1970-01-01T00:00:00.000Z"}}'
# Below still formats as iso in spite of being object array
>>> pd.DataFrame([[pd.Timestamp(1)]]).astype(object).to_json(date_format="iso")
'{"0":{"0":"1970-01-01T00:00:00.000Z"}}'
``` | {
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} | 6 | 2019-09-02T22:12:25Z | 2019-10-16T12:52:58Z | 2019-10-16T12:52:55Z | CONTRIBUTOR | null | For consistency with normal indexes, `CategoricalIndex.reindex` should allow targets to be duplicated but not the sources. However, currently in allows the source to be duplicated but not the targets, which is exactly the wrong behaviour.
Most of the work here is fixing the tests, which in many cases explicitly check for the incorrect behaviour.
Fixes #25459
(I can't run the full testsuite on my machine but the relevant categorical tests do seem to pass. Hoping that the GitHub CI infrastructure will pick up any other failures.) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28258 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28258/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28258/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28258/events | https://github.com/pandas-dev/pandas/pull/28258 | 488,337,114 | MDExOlB1bGxSZXF1ZXN0MzEzMzgwNTI0 | 28,258 | Revert #27959 | {
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} | 2 | 2019-09-02T23:31:09Z | 2019-09-03T01:31:07Z | 2019-09-03T00:07:27Z | MEMBER | null | On Sparse tests it is causing recursion errors. Revert and I'll revisit | {
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} | 3 | 2019-09-03T00:04:39Z | 2020-01-16T00:35:11Z | 2019-09-04T11:56:26Z | MEMBER | null | Inspired by @mroeschke I noticed the JSON tests weren't actually measuring anything because you need to return something for the `mem_` tests which these weren't. In any case probably better served as `peakmem_`
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https://api.github.com/repos/pandas-dev/pandas/issues/28260 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28260/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28260/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28260/events | https://github.com/pandas-dev/pandas/pull/28260 | 488,354,174 | MDExOlB1bGxSZXF1ZXN0MzEzMzkyNjQ3 | 28,260 | re-implement #27959 | {
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} | 2 | 2019-09-03T01:39:37Z | 2019-09-03T19:04:23Z | 2019-09-03T18:53:53Z | MEMBER | null | Previous version broke because a different branch changed the behavior of when extract_array is called. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28261 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28261/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28261/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28261/events | https://github.com/pandas-dev/pandas/issues/28261 | 488,423,322 | MDU6SXNzdWU0ODg0MjMzMjI= | 28,261 | TypeError: can only concatenate list (not "int") to list | {
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 4 | 2019-09-03T06:51:14Z | 2021-07-11T17:25:42Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
a = pd.Series([[12]])
b = a + [222]
```
#### Problem description
1. in pandas version 0.24, the code went well
2. in pandas version 0.25.1, the code raise TypeError can only concatenate list (not "int") to list
```shell
~\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\pandas\core\ops\__init__.py in na_op(x, y)
967 try:
--> 968 result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
969 except TypeError:
~\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr, **eval_kwargs)
220 if use_numexpr:
--> 221 return _evaluate(op, op_str, a, b, **eval_kwargs)
222 return _evaluate_standard(op, op_str, a, b)
~\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b, **eval_kwargs)
69 with np.errstate(all="ignore"):
---> 70 return op(a, b)
71
TypeError: can only concatenate list (not "int") to list
```
#### Expected Output
no error should be raised
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
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/28262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28262/events | https://github.com/pandas-dev/pandas/issues/28262 | 488,456,795 | MDU6SXNzdWU0ODg0NTY3OTU= | 28,262 | Non-silently handle duplicate column names | {
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} | [] | closed | false | null | [] | null | 1 | 2019-09-03T08:14:12Z | 2019-09-03T11:33:37Z | 2019-09-03T11:33:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame([[1, 2, 3]], columns=["x", "y", "x"])
>>> df['x']
x x
0 1 3
>>> df.loc[:, 'x']
x x
0 1 3
>>> df['x'].between(1,2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "[...]/python3.7/site-packages/pandas/core/generic.py", line 5179, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'between'
```
Hopefully:
```python
>>> df.join(pd.DataFrame([[4]], columns=["x"]))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "[...]/python3.7/site-packages/pandas/core/frame.py", line 7246, in join
other, on=on, how=how, lsuffix=lsuffix, rsuffix=rsuffix, sort=sort
File "[...]/python3.7/site-packages/pandas/core/frame.py", line 7269, in _join_compat
sort=sort,
File "[...]/python3.7/site-packages/pandas/core/reshape/merge.py", line 83, in merge
return op.get_result()
File "[...]/python3.7/site-packages/pandas/core/reshape/merge.py", line 648, in get_result
ldata.items, lsuf, rdata.items, rsuf
File "[...]/python3.7/site-packages/pandas/core/reshape/merge.py", line 2011, in _items_overlap_with_suffix
"{rename}".format(rename=to_rename)
ValueError: columns overlap but no suffix specified: Index(['x', 'x'], dtype='object')
```
But...
```python
>>> pd.concat([df, pd.DataFrame([[4]], columns=["x"])], axis=1)
x y x x
0 1 2 3 4
```
#### Problem description
At this moment, Pandas allows duplicate column names. This can potentially lead to problems, when user expects to receive `pd.Series` when asking for a single column, if they are not aware that the column names are duplicated.
This is especially problematic with automatic processing, where column duplication can easily appear as a consequence of some bug.
#### Expected Output
**Better solution:** at the time of creating the `pd.DataFrame` _or_ adding new column to `pd.DataFrame`, when the column name is duplicated, the code fails with an error message informing about the duplicated name. This is easy to implement and failing fast is usually a good practice.
**Worse solutions:**
1. Similar as above, but instead of crashing, the warning message is displayed _both_: (a) when initializing duplicated column, and (b) during _any_ operation that calls the duplicated column. In such case user would be aware that the behaviour of Pandas may give different results then if he was calling by the non-duplicated column name.
2. Silently add suffixes to duplicated column names when they appear, as for example, R does. Best to be done with some warning or info message.
_Examples:_
* Python's [DataTable](https://datatable.readthedocs.io/en/latest/quick-start.html) by default silently overwrites the duplicated column (bad):
```python
>>> dt.Frame({"x" : [1], "y": [2], "x": [3]})
x y
-- -- --
0 3 2
```
* R by default renames the duplicated column (little better):
```R
> data.frame(x=1, y=2, x=3)
x y x.1
1 1 2 3
```
* R's dplyr fails fast with error:
```R
> tibble(x=1, y=2, x=3)
Error: Column name `x` must not be duplicated.
Use .name_repair to specify repair.
Call `rlang::last_error()` to see a backtrace
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-25-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28263/events | https://github.com/pandas-dev/pandas/issues/28263 | 488,500,002 | MDU6SXNzdWU0ODg1MDAwMDI= | 28,263 | pandas.read_parquet cause OOM in a 16GB machine, it's a 200MB parquet file, 6M rows. | {
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] | closed | false | null | [] | null | 6 | 2019-09-03T09:41:57Z | 2019-09-03T15:44:18Z | 2019-09-03T15:28:28Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# query from db
datalist = ... # 6M rows, 4 fields(string string string datetime) every row.
df = pd.from_dict(datalist)
df.to_parquet("test.pq")
df = pd.read_parquet("test.pq") # OOM
```
#### Problem description
> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6741512 entries, 0 to 6741511
Data columns (total 5 columns):
index int64
_id object
wxOpenID object
channel object
createdAt datetime64[ns]
dtypes: datetime64[ns](1), int64(1), object(3)
memory usage: 257.2+ MB
It have 6M rows writed to disk(a parquet file), and the file size was 200MB.
then, I read it from the parquet file, it causes a OOM.
#### Expected Output
do not make OOM.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.5.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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} | [] | closed | false | null | [] | null | 1 | 2019-09-03T12:29:47Z | 2019-09-05T15:34:03Z | 2019-09-05T15:34:03Z | NONE | null | - [ ] closes #15580
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I'm new at contributing on Pandas and any project at all, and saw this generic "fix docs" issue. I made some very small changes that i think it helps users to understand better the `groupBy` usage. Please, feel free to judge my changes and request modifications. Thanks! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28265/events | https://github.com/pandas-dev/pandas/pull/28265 | 488,591,395 | MDExOlB1bGxSZXF1ZXN0MzEzNTgwNzU2 | 28,265 | BUG: pd.to_datetime fixed (#28238) | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-03T12:59:35Z | 2019-09-07T17:48:56Z | 2019-09-07T17:48:56Z | NONE | null | - [ ] closes #28238
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fix for KeyError when trying to convert a series with unsorted float index to a DateTime
@AntonioAndraues
@Vinigl
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} | 5 | 2019-09-03T14:30:54Z | 2020-10-26T03:18:18Z | 2020-10-26T03:18:18Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'B': [0, 1, 2, 4,3],'A':[7,4,6,9,3]},
index = [pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130102 09:00:02'),
pd.Timestamp('20130103 09:00:03'),
pd.Timestamp('20130105 09:00:05'),
pd.Timestamp('20130106 09:00:06')])
print(df.corr())
'''
df.corr()
Out[53]:
B A
B 1.00000 0.19868
A 0.19868 1.00000
'''
df.rolling(window='3d').corr()
'''
B A
2013-01-01 09:00:00 B NaN NaN
A NaN NaN
2013-01-02 09:00:02 B 1.000000 -1.000000
A -1.000000 1.000000
2013-01-03 09:00:03 B 1.000000 -0.327327
A -0.327327 1.000000
2013-01-05 09:00:05 B 1.000000 0.609449
A 0.609449 1.000000
2013-01-06 09:00:06 B 1.000000 0.198680
A 0.198680 1.000000
'''
```
#### Problem description
Due to some conflicts I'm not able to test it on pandas 0.25, so I'm not sure whether the problem is solved. You may find that the last coorelation value 0.198680 is exactly the same as perform corr() on the total dataframe. No matter how I change the timeoffset, namely the window parameter, the rolling(window='timeoffset string').corr() method returns cummulative correlation instead of correlation inside the window.
The reason why the expected output contains so many 'ones' is that rows which not fall in the time window should not be included into the calculation. For example, the 'ones' at the last row is calculated by the fourth row and the fifth row(the last row), because the indices is between 2013-01-03 09:00:06 and 2013-01-06 09:00:06 (the window parameter is 3d) and the correlation of these two point pairs is 1.
#### Expected Output
```pyhon
B A
2013-01-01 09:00:00 B NaN NaN
A NaN NaN
2013-01-02 09:00:02 B 1.000000 -1.000000
A -1.000000 1.000000
2013-01-03 09:00:03 B 1.000000 -0.327327
A -0.327327 1.000000
2013-01-05 09:00:05 B 1.000000 1.000000
A 1.000000 1.000000
2013-01-06 09:00:06 B 1.000000 1.000000
A 1.000000 1.000000
```
#### Output of ``pd.show_versions()``
<details>
------------------
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 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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https://api.github.com/repos/pandas-dev/pandas/issues/28267 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28267/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28267/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28267/events | https://github.com/pandas-dev/pandas/pull/28267 | 488,729,451 | MDExOlB1bGxSZXF1ZXN0MzEzNjkxNDY0 | 28,267 | BUG: Make sure correct values are passed to Rolling._on when axis=1 | {
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} | 3 | 2019-09-03T17:24:19Z | 2019-09-04T17:10:19Z | 2019-09-04T17:07:17Z | MEMBER | null | - [x] closes #28192
- [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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https://api.github.com/repos/pandas-dev/pandas/issues/28268 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28268/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28268/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28268/events | https://github.com/pandas-dev/pandas/pull/28268 | 488,741,692 | MDExOlB1bGxSZXF1ZXN0MzEzNzAwOTIy | 28,268 | BUG: Timestamp+int should raise NullFrequencyError, not ValueError | {
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} | 8 | 2019-09-03T17:53:21Z | 2019-09-08T17:12:39Z | 2019-09-08T17:09:55Z | MEMBER | null | Last prerequisite before we can fix #28080. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28269 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28269/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28269/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28269/events | https://github.com/pandas-dev/pandas/issues/28269 | 488,742,837 | MDU6SXNzdWU0ODg3NDI4Mzc= | 28,269 | Casting bool to object during join | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 2 | 2019-09-03T17:56:06Z | 2021-07-11T17:26:21Z | null | NONE | null | ```python
import pandas as pd
core_df = pd.DataFrame([
{'id': 1},
{'id': 2},
]).set_index('id')
df1 = pd.DataFrame([
{'id': 1, 'foo': True},
{'id': 2, 'foo': False},
]).set_index('id')
df2 = pd.DataFrame([
{'id': 1, 'bar': 3},
{'id': 2, 'bar': 4},
{'id': 3, 'bar': 4}, # Adding a new row in df2 is required to create the issue
]).set_index('id')
result = core_df.join([df1, df2])
print(result.dtypes)
# With pandas 0.24.2
# foo bool <- stays a bool
# bar int64
# dtype: object
#
# With pandas 0.25.1
# foo object <- Changed to object
# bar int64
# dtype: object
```
#### Problem description
This is a kind of subtle issue when upgrading from `0.24` to `0.25`. When joining dataframes onto an initial dataframe, if one of the dataframes being joined in has an extra row compared to the other it will cause any boolean columns to be cast to object columns. This is a change in behavior from 0.24 which would leave them as bools.
#### Expected Output
In the example above I would expect the `foo` column to remain of dtype `bool`.
#### 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 : 0.25.1
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.1.0
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : 2.0.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.3
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28270 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28270/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28270/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28270/events | https://github.com/pandas-dev/pandas/issues/28270 | 488,757,915 | MDU6SXNzdWU0ODg3NTc5MTU= | 28,270 | Issue with pandas delimiter | {
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} | [] | closed | false | null | [] | null | 7 | 2019-09-03T18:32:20Z | 2019-11-03T00:48:56Z | 2019-11-03T00:48:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from census import Census
import pandas as pd
from sqlalchemy import create_engine
import csv
import sys
filename = 'C:/Users/APIlist2.csv'
with open(filename) as f:
reader = csv.reader(f)
try:
for row in reader:
print(row)
except csv.Error as e:
sys.exit('file {}, line {}: {}'.format(filename, reader.line_num, e))
def getdata(mylist, tablename, schema):
# Create db engine# #
engine = create_engine('postgresql://blahblah/melissa', convert_unicode=True)
# Census parameters
# state_fips = '*'
state_fips = [str(i).zfill(2) for i in range(1, 56)]
county_fips = '*'
tract = '*'
c = Census('key', year=2017, session=None) # replace ca.... with your api key from census
# print(state_fips)
# Extract data
for s in state_fips:
x = c.acs5.state_county_tract(mylist, s, county_fips, tract) # mylist is a list of census variable names
df = pd.DataFrame(x) # convert data to data frame
df.to_sql(tablename, engine, schema=schema, if_exists='append')
def main():
# mylist = ['B01001_001E']
# above B01001 is the table is required by the Python census library;001 is the variable in the table; and E =estimate.
# two lines below will read from a CSV file. variable names must be stored in a column called Code
df = pd.read_csv('C:/Users/APIlist2.csv', header=0, encoding='unicode_escape', delimiter=',', lineterminator='\n')
mylist = df['Code'].values.tolist()
tablename = '2017acs5b'
getdata(mylist, tablename, 'census')
main()
```
#### Problem description
I am reading from column 'Code' and the line with more than one variable is not returning a delimiter with ',' instead each line is one variable therefore the variable is """"census.core.CensusException: error: error: unknown variable ''""""
[['Code', 'Description']
[**'C24060_003E'**, 'Administrative n emergency services']
[**'B99171_003E, B01003_001E'**, 'Percent living in poverty']
[**'B26103_005E, B01003_001E'**, 'Per capita residents in nursing homes,']
[**'B26103_005E**', 'Number of people living in group quarters']
[**'B26001_001E, B09001_001E'**, 'Percent population living group quarters']
['B25121_077E, B25122_001E', 'Percent of households earning more than 75000, ']
['B25032_013E, B00002_001E', 'Percent renter-occupied housing units']
['B25024_010E, B00002_001E', 'Percent of housing units that are mobile homes, ']
['B25024_007E, B25024_008E, B25024_009E, B25001_001E', 'Percent of housing units with 10 or more units']
['B25024_007E, B25024_008E, B25024_009E', 'Number of housing units with 10 or more units']
['B25003_003E, B00002_001E', 'Percent of renter-occupied housing units']]
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
['Code', 'Description']
['C24060_003E', 'Administrative n emergency services']
['B99171_003E', 'B01003_001E', 'Percent living in poverty']
['B26103_005E', 'B01003_001E', 'Per capita residents in nursing homes,']
['B26103_005E', 'Number of people living in group quarters']
['B26001_001E', 'B09001_001E', 'Percent population living group quarters']
['B25121_077E', 'B25122_001E', 'Percent of households earning more than 75000, ']
['B25032_013E', 'B00002_001E', 'Percent renter-occupied housing units']
['B25024_010E', 'B00002_001E', 'Percent of housing units that are mobile homes, ']
['B25024_007E', 'B25024_008E', 'B25024_009E', 'B25001_001E', 'Percent of housing units with 10 or more units']
['B25024_007E', 'B25024_008E', 'B25024_009E', 'Number of housing units with 10 or more units']
['B25003_003E', 'B00002_001E', 'Percent of renter-occupied housing units']
#######I need to have a delimiter after every comma
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.0.3
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https://api.github.com/repos/pandas-dev/pandas/issues/28271 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28271/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28271/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28271/events | https://github.com/pandas-dev/pandas/issues/28271 | 488,772,339 | MDU6SXNzdWU0ODg3NzIzMzk= | 28,271 | to_numeric(errors='ignore') doesn't work with leading zeros | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-03T19:05:44Z | 2019-09-26T19:49:32Z | 2019-09-26T19:49:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
s = pd.Series([1, 1.0, '1', '1.0', ' 1', ' 1.0', '01', '01.0'])
pd.to_numeric(s, errors='ignore')
# returns [1, 1, 1, 1.0, 1, 1.0, '01', '01.0']
# but expected [1, 1, 1, 1.0, 1, 1.0, 1, 1.0]
```
#### Problem description
The `to_numeric` function doesn't work as expected if the string contains leading zeros.
#### Expected Output
I would expect that `'01'` gets converted to `1`, just like in the leading whitespace case of `' 1'` converting to `1`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.0
pytz : 2018.5
dateutil : 2.7.3
pip : 18.1
setuptools : 40.2.0
Cython : 0.28.5
pytest : 3.8.0
hypothesis : None
sphinx : 1.7.9
blosc : None
feather : None
xlsxwriter : 1.1.0
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 6.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 2.2.3
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.6
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.2.11
tables : 3.4.4
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.1.0
</details>
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} | 2 | 2019-09-03T19:16:44Z | 2019-09-04T11:26:12Z | 2019-09-04T11:26:12Z | CONTRIBUTOR | null | This was causing issues for me locally. Anyone else?
It took a while to run, and didn't seem to give the same
output as others (depends on something peculiar to my environment)
which doesn't seem to be great for a pre-commit hook.
Closes https://github.com/pandas-dev/pandas/issues/28236 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28273 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28273/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28273/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28273/events | https://github.com/pandas-dev/pandas/issues/28273 | 488,838,931 | MDU6SXNzdWU0ODg4Mzg5MzE= | 28,273 | Address Untested to_json Extension Module Code | {
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"d... | open | false | null | [] | null | 2 | 2019-09-03T21:43:23Z | 2020-05-08T21:13:14Z | null | MEMBER | null | There's an old TODO note about untested code in the extension module hanging around in objToJSON.c:
https://github.com/pandas-dev/pandas/blob/bfff080275b4456b28d71f0c7b4ec9e678d4270c/pandas/_libs/src/ujson/python/objToJSON.c#L504
Interestingly enough, this is actually tested by the `np.datetime64` parameter in `test_encode_as_null` as part of the `test_ujson.py` module:
https://github.com/pandas-dev/pandas/blob/bfff080275b4456b28d71f0c7b4ec9e678d4270c/pandas/tests/io/json/test_ujson.py#L388
The intent here is somewhat blurry. I think the `test_ujson.py` module was created when it was up for discussion if we wanted the JSON serializers to be publicly exposed at the top level (see #9147) which I don't believe is still something we want to do. At that time it would make sense to explicitly support the `np.datetime64` type.
The remaining question then is whether `np.datetime64` should be represented within a pandas container. It seems that this can be held in a Series with an object dtype but not a DataFrame, as shown below:
```python
>>> ser = pd.Series([np.datetime64("2000-01-01")], dtype=object)
>>> type(ser.iloc[0])
numpy.datetime64
>>> type(ser.to_frame().iloc[0, 0])
pandas._libs.tslibs.timestamps.Timestamp
```
If we don't want `np.datetime64` objects to be contained then we can delete this code altogether. If we do we should add test coverage for it, but then also address the inconsistency across container types above | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28274 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28274/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28274/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28274/events | https://github.com/pandas-dev/pandas/issues/28274 | 488,874,233 | MDU6SXNzdWU0ODg4NzQyMzM= | 28,274 | NDFrame.sum ignores min_count for object dtype | {
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"color": "d7e102",
"default": false,
"description": "np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate",
"id": 2822342,
"name": "Missing-data",
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"color": "006b75"... | closed | false | null | [] | null | 7 | 2019-09-03T23:52:37Z | 2021-07-11T17:31:00Z | 2021-07-11T17:31:00Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> index = pd.MultiIndex.from_product([['a'], ['foo', 'bar']])
>>> s = pd.Series([np.nan] * 2, index=index)
>>> s.astype('O').sum(level=1, min_count=1)
foo 0
bar 0
dtype: int64
```
#### Problem description
Using sum with a multiindex and dtype set to object will force all answers to 0, and not `nan` as is expected. This is confusing, since pandas automatically coerces np.nan when this isn't the case, see below.
```python
>>> s = pd.Series([np.nan])
>>> s.astype('O').sum(min_count=1)
nan
```
<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 : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.3
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.6
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28275 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28275/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28275/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28275/events | https://github.com/pandas-dev/pandas/pull/28275 | 488,877,832 | MDExOlB1bGxSZXF1ZXN0MzEzODEwMDkz | 28,275 | BUG: passing DataFrame to make_block silently raises | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-04T00:09:06Z | 2020-04-05T17:35:48Z | 2019-09-16T21:04:22Z | MEMBER | null | ATM in the `except NotImplementedError:` branch of `_cython_agg_blocks`, we take a difference approach to the operation. if that approach succeeds, we end up passing a DataFrame to `make_block` on L189, which will raise `ValueError`. As a result, we'll end up falling back to python-space for the entire operation, which presumably entails a performance hit.
This fixes the incorrect passing of DataFrame, but the fix is kind of kludgy. Suggestions welcome on how to improve it. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28276 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28276/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28276/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28276/events | https://github.com/pandas-dev/pandas/pull/28276 | 488,911,873 | MDExOlB1bGxSZXF1ZXN0MzEzODM2MDQ2 | 28,276 | CLN: catch Exception in fewer places, assorted cleanups | {
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} | 8 | 2019-09-04T05:11:59Z | 2021-07-11T17:32:09Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
arr = ["AbC", "de", "FGHI", "j", "kLLLm"]
ps = pd.Series(arr)
expect = ps.str.cat(others=ps.str)
print(expect)
Out[16]:
0 NaN
1 NaN
2 NaN
3 NaN
4 kLLLmkLLLm
dtype: object
```
#### Problem description
`series.str.cat(series.str)` is concatenating only the largest string in the series but it should concatenate all the strings element wise.
#### Expected Output
```python
Out[18]:
0 AbCAbC
1 dede
2 FGHIFGHI
3 jj
4 kLLLmkLLLm
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
In [2]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.14.4.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.1.2
pip: 19.2.3
setuptools: 41.2.0
Cython: 0.29.13
numpy: 1.17.1
scipy: None
pyarrow: 0.14.1
xarray: None
IPython: 7.8.0
sphinx: 2.2.0
patsy: None
dateutil: 2.8.0
pytz: 2019.2
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: 2.10.1
s3fs: 0.3.4
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28278 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28278/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28278/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28278/events | https://github.com/pandas-dev/pandas/issues/28278 | 488,964,971 | MDU6SXNzdWU0ODg5NjQ5NzE= | 28,278 | pivot_table(..., values='a') and pivot_talbe(...)['a'] yield different output | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-04T06:21:27Z | 2019-11-03T00:49:47Z | 2019-11-03T00:49:46Z | NONE | null | #### Code Sample
```python
aaa = pd.pivot_table(data=human, index='patient_ID', columns='Hugo_Symbol', fill_value=0, values='RNA.alt.fra')
bbb = pd.pivot_table(data=human, index='patient_ID', columns='Hugo_Symbol', fill_value=0)['RNA.alt.fra']
print(aaa.shape)
>>> (39, 4553)
print(bbb.shape)
>>> (39, 4556)
```
#### Problem description
`pivot_table` with `values='a'` assigned works faster, but in my case, it drop off three columns.
Any ideas why this is happenning?
Thanks!
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} | 1 | 2019-09-04T06:27:51Z | 2019-11-27T20:47:44Z | 2019-11-27T20:47:44Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
bug = pd.Series([0, 1, 2, 3, 4], index=[7606741985629028552, 17876870360202815256, 13106359306506049338, 8991270399732411471, 8991270399732411472])
assert bug.loc[7606741985629028552]==0
assert bug.loc[17876870360202815256]==1
bug.loc[[7606741985629028552, 17876870360202815256]].index
```
#### Problem description
The above selection should not have the side effect of modifying data types. I noticed this while looking at #28023 but I think this is a separate issue, so I'm documenting it separately.
#### Output of ``pd.show_versions()``
[Same with #28023, I'll update this when I have my laptop at hand.]
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In the current stable pandas documentation both [DataFrame.plot()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) and [Series.plot()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html?highlight=plot#pandas.Series.plot) pages are broken, i.e. there is no content showing up.
Therefore, currently, one has to go back to an older version (e.g. [this](https://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.plot.html?highlight=plot#pandas.DataFrame.plot)) or a the latest [beta version](https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.plot.html?highlight=plot#pandas.DataFrame.plot) . | {
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} | 3 | 2019-09-04T11:25:42Z | 2019-09-05T07:49:56Z | 2019-09-04T16:13:12Z | CONTRIBUTOR | null | - Fix typo in example
- Regenerated output also seems to fix a bad output in the next line (out 32 in current online documentation) where df is truncated, when it shouldn't by according to the code
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} | 1 | 2019-09-04T11:43:15Z | 2020-01-02T00:58:16Z | 2020-01-02T00:58:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas, sys
>>> sys.version
'3.6.7 (default, Oct 25 2018, 09:16:13) \n[GCC 5.4.0 20160609]'
>>> pandas.__version__
'0.25.1'
>>> df = pandas.DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(255)}])
>>> df.itertuples(index=False)
...
SyntaxError: more than 255 arguments
```
The issue seems to have been caused/revealed by [this commit](https://github.com/pandas-dev/pandas/commit/81f5c0164e6cb26cc8fea00ebc22f4fe93771ff6#diff-1e79abbbdd150d4771b91ea60a4e1cc7R903) that removed the try-catch block around the namedtuple class creation.
FWIW, this issue is _not_ reproducible in version `0.24.2`, and is also not a problem in Python 3.7+, as the limit of the max number of arguments that can be passed to a function has been removed (AFAIK).
#### Problem description
The [condition](https://github.com/pandas-dev/pandas/blob/v0.25.1/pandas/core/frame.py#L970) in `itertuples()` method does not correctly determine when plain tuples should be used instead of named tuples.
This how the named tuple [class template](https://github.com/python/cpython/blob/3.6/Lib/collections/__init__.py#L301-L349) defines the `__new__()` method (in Python 3.6 at least):
```py
"""
...
def __new__(_cls, {arg_list}):
...
"""
```
If there are 255 column names given, the total number of arguments to `__new__()` will be 256, because of that extra `cls`, causing a syntax error.
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} | 34 | 2019-09-04T12:10:47Z | 2021-10-23T21:46:31Z | null | CONTRIBUTOR | null | Improve coverage of NDFrame.__finalize__
Pandas uses `NDFrame.__finalize__` to propagate metadata from one NDFrame to
another. This ensures that things like `self.attrs` and `self.flags` are not
lost. In general we would like that any operation that accepts one or more
NDFrames and returns an NDFrame should propagate metadata by calling
`__finalize__`.
The test file at
https://github.com/pandas-dev/pandas/blob/master/pandas/tests/generic/test_finalize.py
attempts to be an exhaustive suite of tests for all these cases. However there
are many tests currently xfailing, and there are likely many APIs not covered.
This is a meta-issue to improve the use of `__finalize__`. Here's a hopefully
accurate list of methods that don't currently call finalize.
Some general comments around finalize
1. We don't have a good sense for what should happen to `attrs` when there are
multiple NDFrames involved with differing attrs (e.g. in concat). The safest
approach is to probably drop the attrs when they don't match, but this will
need some thought.
2. We need to be mindful of performance. `__finalize__` can be somewhat expensive
so we'd like to call it exactly once per *user-facing* method. This can be tricky
for things like `DataFrame.apply` which is sometimes used internally. We may need
to refactor some methods to have a user-facing `DataFrame.apply` that calls an internal
`DataFrame._apply`. The internal method would *not* call `__finalize__`, just the
user-facing `DataFrame.apply` would.
If you're interested in working on this please post a comment indicating which method
you're working on. Un-xfail the test, then update the method to pass the test. Some of these
will be much more difficult to work on than others (e.g. groupby is going to be difficult). If you're
unsure whether a particular method is likely to be difficult, ask first.
- [x] `DataFrame.__getitem__` with a scalar
- [ ] `DataFrame.eval` with `engine="numexpr"`
- [x] `DataFrame.duplicated`
- [ ] `DataFrame.add`, `mul`, etc.
- [ ] `DataFrame.combine`, `DataFrame.combine_first`
- [x] `DataFrame.update`
- [x] `DataFrame.pivot`, `pivot_table`
- [x] `DataFrame.stack`
- [x] `DataFrame.unstack`
- [ ] `DataFrame.explode`
- [ ] `DataFrame.melt`
- [x] `DataFrame.diff`
- [x] `DataFrame.applymap`
- [x] `DataFrame.append`
- [ ] `DataFrame.merge`
- [ ] `DataFrame.cov`
- [ ] `DataFrame.corrwith`
- [ ] `DataFrame.count`
- [ ] `DataFrame.nunique`
- [ ] `DataFrame.idxmax`, `idxmin`
- [ ] `DataFrame.mode`
- [ ] `DataFrame.quantile` (scalar and list of quantiles)
- [ ] `DataFrame.isin`
- [ ] `DataFrame.pop`
- [ ] `DataFrame.squeeze`
- [ ] `Series.abs`
- [ ] `DataFrame.get`
- [ ] `DataFrame.round`
- [ ] `DataFrame.convert_dtypes`
- [ ] `DataFrame.pct_change`
- [ ] `DataFrame.transform`
- [ ] `DataFrame.apply`
- [ ] `DataFrame.any`, `sum`, `std`, `mean`, etdc.
- [x] `Series.str.` operations returning a Series / DataFrame
- [x] `Series.dt.` operations returning a Series / DataFrame
- [x] `Series.cat.` operations returning a Series / DataFrame
- [ ] All groupby operations
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https://api.github.com/repos/pandas-dev/pandas/issues/28284 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28284/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28284/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28284/events | https://github.com/pandas-dev/pandas/issues/28284 | 489,206,094 | MDU6SXNzdWU0ODkyMDYwOTQ= | 28,284 | Index.drop - add inplace option | {
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} | [] | closed | false | null | [] | null | 2 | 2019-09-04T14:43:56Z | 2019-09-04T15:13:04Z | 2019-09-04T15:13:04Z | NONE | null | I have a Pandas.Index object which i want to delete some of the indices stored in it.
Since currently inplace=True is not available, i am forced to declare a new variable to hold the result which seems cumbersome.
currently i need to write:
#drop all columns with only 1 unique value.
nunique=df.apply(pd.Series.nunique)
cols_to_delete=nunique[nunique == 1].index
#do not delete column named 'label' even if it has only 1 unique value
cols_to_delete=cols_to_delete.drop('label')
df.drop(cols_to_delete, axis=1,inplace=True)
desired syntax:
cols_to_delete.drop('label', inplace=True)
instead of:
cols_to_delete=cols_to_delete.drop('label')
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28285 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28285/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28285/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28285/events | https://github.com/pandas-dev/pandas/issues/28285 | 489,228,914 | MDU6SXNzdWU0ODkyMjg5MTQ= | 28,285 | Dataframe Groupby with Quantile Can't handle q=tuple | {
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} | [] | closed | false | null | [] | null | 3 | 2019-09-04T15:19:57Z | 2019-09-04T19:06:58Z | 2019-09-04T19:06:57Z | NONE | null | In version 0.24.1, a groupby with `quantile(q)`, where `q` is a tuple works. However, with 0.25, a `TypeErr` exception is thrown. This works as expected:
`pd.DataFrame([[0,1,2,3],[0,4,5,6],[1,7,8,9],[1,10,11,12]],columns=['a','b','c','d']).quantile((0.01,0.99))`
but this does not:
`pd.DataFrame([[0,1,2,3],[0,4,5,6],[1,7,8,9],[1,10,11,12]],columns=['a','b','c','d']).groupby(by=['a']).b.quantile((0.01,0.99))`
#### 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 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 : 2.7.6.1 (dt dec pq3 ext lo64)
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 : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 4 | 2019-09-04T16:51:55Z | 2019-09-07T19:26:36Z | 2019-09-07T19:21:09Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2019-09-04T19:50:41Z | 2020-06-20T13:46:25Z | 2020-06-20T13:35:18Z | NONE | null | ```python
import numpy as np
import pandas as pd
A = pd.DataFrame({'a': [0,1], 'b': pd.SparseArray([np.nan, 1])})
# Prints empty DataFrame
A.dropna()
```
Not sure if I'm using this correctly, but I'd expect only the first row to be dropped.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
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 : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-09-04T21:33:19Z | 2019-09-16T02:39:38Z | 2019-09-16T02:39:31Z | CONTRIBUTOR | null | First bit of work on #28253, fixes PR06 validation errors in Series docstrings.
Mostly just converting boolean -> bool, string -> str, integer -> ; re-worded a little where necessary.
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Ran validate_docstrings.py and confirmed all PR06 issues for Series are removed, and that no additional validation errors are added by this change.
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} | 13 | 2019-09-04T22:41:18Z | 2019-11-02T15:37:48Z | 2019-11-02T15:23:47Z | MEMBER | null | - [x] closes #18880
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-09-05T04:02:35Z | 2019-09-05T18:16:25Z | 2019-09-05T18:14:17Z | MEMBER | null | Series logical ops (`|`, `&`, `^`) have a bunch of inconsistencies. This partially addresses one of them, and does so in a way that makes the diff for the next one much more manageable than it would be without this. | {
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} | 1 | 2019-09-05T05:01:15Z | 2021-05-24T13:44:05Z | 2021-05-24T13:44:05Z | NONE | null | Please considering add a parameter to allow users to add `\hline` to the latex output so that each two rows will have a horizontal line between them.
For example:
```python
import pandas as pd
df = pd.DataFrame(data={'col_1': [1, 2, 4],
'col_2': [4, 3, 2]})
print(df.to_latex(index=False))
```
It will give me:
```
\begin{tabular}{rr}
\toprule
col\_1 & col\_2 \\
\midrule
1 & 4 \\
2 & 3 \\
4 & 2 \\
\bottomrule
\end{tabular}
```
I wish the output could be
```
\begin{tabular}{rr}
\toprule
\hline
col\_1 & col\_2 \\
\hline
\midrule
1 & 4 \\ \hline
2 & 3 \\ \hline
4 & 2 \\ \hline
\bottomrule
\end{tabular}
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28292 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28292/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28292/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28292/events | https://github.com/pandas-dev/pandas/issues/28292 | 489,574,912 | MDU6SXNzdWU0ODk1NzQ5MTI= | 28,292 | df.style.highlight_max() crashes if DataFrame is empty, but has some metadata | {
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] | closed | false | null | [] | null | 5 | 2019-09-05T07:23:02Z | 2019-09-13T18:32:17Z | 2019-09-13T18:32:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
print(df.columns)
Index([], dtype='object', name='MONTH')
df.style.highlight_max(axis=1).format("{:.0f}")
```
#### Problem description
Pandas crashes with the trace shown below, when styler applied to an empty dataframe, which contains some meta-data. Expected output - empty output without crash.
```
IndexError Traceback (most recent call last)
c:\cygwin64\home\antongolubev\.venv\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
343 method = get_real_method(obj, self.print_method)
344 if method is not None:
--> 345 return method()
346 return None
347 else:
c:\cygwin64\home\antongolubev\.venv\lib\site-packages\pandas\io\formats\style.py in _repr_html_(self)
162 Hooks into Jupyter notebook rich display system.
163 """
--> 164 return self.render()
165
166 @Appender(
c:\cygwin64\home\antongolubev\.venv\lib\site-packages\pandas\io\formats\style.py in render(self, **kwargs)
518 self._compute()
519 # TODO: namespace all the pandas keys
--> 520 d = self._translate()
521 # filter out empty styles, every cell will have a class
522 # but the list of props may just be [['', '']].
c:\cygwin64\home\antongolubev\.venv\lib\site-packages\pandas\io\formats\style.py in _translate(self)
331 index_header_row.extend(
332 [{"type": "th", "value": BLANK_VALUE, "class": " ".join([BLANK_CLASS])}]
--> 333 * (len(clabels[0]) - len(hidden_columns))
334 )
335
IndexError: list index out of range
```
#### Expected Output
Expected empty output (no exception), like it is correctly happenes with freshly initialized dataframe.
#### 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 : en_US.utf8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.8.0
Cython : None
pytest : 5.0.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28293 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28293/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28293/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28293/events | https://github.com/pandas-dev/pandas/issues/28293 | 489,686,761 | MDU6SXNzdWU0ODk2ODY3NjE= | 28,293 | Different behavior of read_csv on Windows with Anaconda and Ubuntu 18 | {
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"descriptio... | closed | false | null | [] | null | 3 | 2019-09-05T11:14:01Z | 2020-01-16T22:38:33Z | 2020-01-16T22:38:32Z | NONE | null | Hello,
I'm experiencing different behavior of the following script:
https://stackoverflow.com/questions/57791115/forecast-produced-by-gluon-ts-example-is-around-0
on Windows with Anaconda and on Ubuntu 18. It works as expected on Windows, but doesn't on Ubuntu 18 (produces plot around 0)
This seems to have something to do with `df = pd.read_csv(url, header=0, index_col=0)` | {
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} | 8 | 2019-09-05T11:47:59Z | 2021-09-05T21:55:14Z | 2020-01-03T13:12:59Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(3, 3), columns=pd.MultiIndex.from_tuples([(1, 2), (1, 4), (5, 6)]))
df.columns = df.columns.set_levels([1, 1, 3], level=0)
```
Which raises a ```ValueError```:
```python
ValueError: Level values must be unique: [1, 1, 3] on level 0
```
#### Problem description
Despite a dataframe with non-unique MultiIndex can be created, they cannot be set using ```set_levels()```. Is this behaviour expected? I believe non-unique level values were not allowed for a period of time (#18882), but then they were allowed again (#21423), so I am not sure which is the current convention.
#### Expected Output
```python
1 3
2 4 6
0 0.317669 0.329142 0.056725
1 0.969472 0.340309 0.135204
2 0.242408 0.934748 0.683186
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.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 : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
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"id": 71268330,... | closed | false | null | [] | null | 1 | 2019-09-05T11:53:34Z | 2019-09-05T16:45:07Z | 2019-09-05T16:45:07Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.rand(3, 3), columns=pd.MultiIndex.from_tuples([(1, 2), (1, 4), (5, 6)]))
df.columns = df.columns.set_levels([1, 2, 3], level=0)
```
Output:
```python
1 2
2 4 6
0 0.729667 0.816939 0.763848
1 0.534183 0.571790 0.368467
2 0.983884 0.277658 0.256763
```
#### Problem description
Slightly different setup of #28294, but different issue. Here a level in a MultiIndex dataframe has duplicates to start with and a level with unique values is set afterwards, but the values are incorrect. It seems to work fine if the starting dataframe has unique level values.
#### Expected Output
```python
1 2 3
2 4 6
0 0.729667 0.816939 0.763848
1 0.534183 0.571790 0.368467
2 0.983884 0.277658 0.256763
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.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 : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
</details>
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"de... | closed | false | null | [] | null | 5 | 2019-09-05T14:01:21Z | 2019-12-17T17:42:55Z | 2019-12-17T17:42:54Z | NONE | null | - [x] closes #28189
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This modification resolves the error in issue #28189 but still not working as expected. It seems that there is a bug related with left-join, as you can see in issues #28220 and #28243.
I'm making this pull request for the #28189 in case you want to resolve this bug separately from the left join problem. On the other hand, I can work on this and help is welcome.
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} | 7 | 2019-09-05T14:26:07Z | 2020-03-01T16:01:26Z | 2019-10-22T01:35:37Z | CONTRIBUTOR | null | Fix Rolling operation for level of multi-index and descending time index (that is monotonic, but decreasing).
- [x] closes #19248
- [x] closes #15584
- [x] tests 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 | 8 | 2019-09-05T16:34:00Z | 2021-08-30T05:43:12Z | 2021-07-11T17:37:10Z | NONE | null | #### Problem description
The `get_group()` method supports getting **one group** from a grouped object by
```python
# Current syntax
grouped.get_group('name1')
```
but you can't get **multiple groups** simply by
```python
# Desired syntax
grouped.get_group(['name1', 'name2'])
```
This causes *"ValueError: must supply a tuple to get_group with multiple grouping keys"*
My workaround for now is using `concat` and list comprehension
```python
# Workaround
pd.concat([group for (name, group) in grouped if name in ['name1', 'name2']])
```
but this is a bit cumbersome and not Pythonic...
#### Expected Output
Could we modify `get_group()` to support the syntax shown in code snippet `# Desired syntax`? Or maybe implement another `get_groups()` method?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
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: 0.12.3
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: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-09-05T17:16:42Z | 2019-09-12T12:45:21Z | 2019-09-12T12:45:21Z | CONTRIBUTOR | null | #### Code Sample
```python
# this fails with ValueError in 0.25.1:
pandas.to_datetime('200622-12-31', errors='coerce')
# but returns Timestamp('2022-06-21 19:00:00') in pandas 0.23.4
# this also fails:
pandas.to_datetime('111111-24-11', errors='coerce')
# but this does not:
pandas.to_datetime('111111-23-11', errors='coerce')
```
#### Problem description
I have some text files with malformed dates, which at one point I will process with the above code. While trying to migrate my code from 23.4 to 25.1 I got the following:
```python
.../my_file.py in <module>
----> 1 pandas.to_datetime('200622-12-31', errors='coerce')
.../lib/python3.7/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
.../lib/python3.7/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)
794 result = convert_listlike(arg, box, format)
795 else:
--> 796 result = convert_listlike(np.array([arg]), box, format)[0]
797
798 return result
.../lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, box, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
461 errors=errors,
462 require_iso8601=require_iso8601,
--> 463 allow_object=True,
464 )
465
.../lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1982 return values.view("i8"), tz_parsed
1983 except (ValueError, TypeError):
-> 1984 raise e
1985
1986 if tz_parsed is not None:
.../lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1973 dayfirst=dayfirst,
1974 yearfirst=yearfirst,
-> 1975 require_iso8601=require_iso8601,
1976 )
1977 except ValueError as e:
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
ValueError: offset must be a timedelta strictly between -timedelta(hours=24) and timedelta(hours=24).
```
#### Expected Output
The main expectation is that an exception is not raised.
I would probably expect ` pandas.to_datetime('200622-12-31', errors='coerce')` to return NaT, but pandas 23.4 seems to parse it into `Timestamp('2022-06-21 19:00:00')`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 4.6.3
hypothesis : 4.7.3
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : 0.3.0
lxml.etree : 4.3.3
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.2.14
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 8 | 2019-09-05T17:28:44Z | 2019-11-12T11:25:23Z | 2019-11-02T20:19:35Z | MEMBER | null | - [x] closes #27952
- [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 | 2019-09-05T18:01:51Z | 2019-09-10T12:05:08Z | 2019-09-10T12:05:08Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
columns = pd.MultiIndex.from_product([['a', 'b'], [1, 2]])
index = [0, 0, 1, 1]
values = np.arange(16).reshape(-1, 4)
df = pd.DataFrame(values, columns=columns, index=index)
a b
1 2 1 2
0 0 1 2 3
0 4 5 6 7
1 8 9 10 11
1 12 13 14 15
df.stack(1)
a b
0 1 0 2
2 1 3
1 4 6
2 5 7
1 1 8 10
2 9 11
1 12 14
2 13 15
# ACTUAL BEHAVIOR
df.stack(1).groupby(level=[0, 1]).sum()
a b
0 1 0 2
2 1 3
1 4 6
2 5 7
1 1 8 10
2 9 11
1 12 14
2 13 15
# EXPECTED
a b
0 1 4 8
2 6 10
1 1 20 24
2 22 26
# WORKAROUND
df.stack(1).reset_index().groupby(['level_0', 'level_1']).sum().rename_axis([None, None])
```
#### Problem description
When grouping by levels after stacking, no grouping seems to be taking place. However, after resetting the index and using those series to group, the groupby works as expected
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.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 : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.0
numpy : 1.17.0
pytz : 2018.6
dateutil : 2.7.3
pip : 18.1
setuptools : 39.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.0.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-09-05T18:15:35Z | 2020-05-26T05:19:03Z | 2020-05-10T15:52:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.date_range(start="1/1/2018", end="1/2/2018", periods=1000).to_frame()
print(df.resample("1h", loffset="15min").last().index)
print(df.groupby(pd.Grouper(freq="1h", loffset="15min")).last().index)
```
#### Problem description
I thought the two calls should be equivalent. However, the output is:
```
DatetimeIndex(['2018-01-01 00:15:00', '2018-01-01 01:15:00',
'2018-01-01 02:15:00', '2018-01-01 03:15:00',
'2018-01-01 04:15:00', '2018-01-01 05:15:00',
'2018-01-01 06:15:00', '2018-01-01 07:15:00',
'2018-01-01 08:15:00', '2018-01-01 09:15:00',
'2018-01-01 10:15:00', '2018-01-01 11:15:00',
'2018-01-01 12:15:00', '2018-01-01 13:15:00',
'2018-01-01 14:15:00', '2018-01-01 15:15:00',
'2018-01-01 16:15:00', '2018-01-01 17:15:00',
'2018-01-01 18:15:00', '2018-01-01 19:15:00',
'2018-01-01 20:15:00', '2018-01-01 21:15:00',
'2018-01-01 22:15:00', '2018-01-01 23:15:00',
'2018-01-02 00:15:00'],
dtype='datetime64[ns]', freq='H')
DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 01:00:00',
'2018-01-01 02:00:00', '2018-01-01 03:00:00',
'2018-01-01 04:00:00', '2018-01-01 05:00:00',
'2018-01-01 06:00:00', '2018-01-01 07:00:00',
'2018-01-01 08:00:00', '2018-01-01 09:00:00',
'2018-01-01 10:00:00', '2018-01-01 11:00:00',
'2018-01-01 12:00:00', '2018-01-01 13:00:00',
'2018-01-01 14:00:00', '2018-01-01 15:00:00',
'2018-01-01 16:00:00', '2018-01-01 17:00:00',
'2018-01-01 18:00:00', '2018-01-01 19:00:00',
'2018-01-01 20:00:00', '2018-01-01 21:00:00',
'2018-01-01 22:00:00', '2018-01-01 23:00:00',
'2018-01-02 00:00:00'],
dtype='datetime64[ns]', freq='H')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: 0.7.4
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 13 | 2019-09-05T18:43:23Z | 2020-11-14T16:15:26Z | 2020-11-14T16:15:26Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from time import time
df = pd.date_range(start="1/1/2018", end="1/2/2018", periods=1e6).to_frame()
start = time()
dfr = df.resample("1s").last()
print(time() - start)
print("Length:", len(dfr))
print()
group_index = np.round(df.index.astype(int) / 1e9)
start = time()
dfr = df.groupby(group_index).last()
print(time() - start)
print("Length:", len(dfr))
```
#### Problem description
In my current project, I use groupby as well as resample for the same data frames with the same aggregations. I have noticed that resample is way quicker than groupby. While I understand that groupby is more flexible, it would still be nice if the performance was comparable. In the example above, resample is more than 50 times faster:
```
Length: 86401
0.023558616638183594
Length: 86401
1.264981746673584
```
I am aware that they don't result in the exact same data frames, but this does not matter for this discussion.
#### Expected Output
Better performance for groupby.
I haven't looked at the groupby implementation and therefore I don't know if there is a good reason for the difference. If there is a good reason, some common cases could still be improved a lot. For example, in this case, we could just check first if the by-argument is monotonic increasing or decreasing. In this case, the operation can be implemented even without a hash map.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.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 : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: 0.7.4
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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"de... | closed | false | null | [] | null | 3 | 2019-09-05T21:32:05Z | 2019-09-11T01:20:23Z | 2019-09-11T01:20:23Z | NONE | null |
- [ ] closes #xxxx
- [ ] 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 | 6 | 2019-09-05T21:39:19Z | 2020-04-01T14:02:05Z | 2020-04-01T14:02:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
#Sample residual pcts
residuals_pct =[0.044516001,0.031137117,1.06758E+64,0.003522454,0.065171486,0.06033751,0.01325514,-0.005620799,-0.006225719,0.045713825,0.039280786,0.000531307]
#Creating a dataframe with residual percent
d = pd.DataFrame(residuals, columns=['residual_pct'])
#Shifting the pct 1 row down.
d['residual_pct_shift'] = d.residual_pct.shift(1)
#Calculating the adjusted residual pct.
d['adj_residual_pct'] = d['residual_pct_shift'][3:].rolling(window=3).mean()
```
#### Problem description
I am trying to implement adjusted residual percent as the rolling average of the previous three residual percents.
Due to some data issue, model forecasted the target variable in the scale of billions, consequently effecting the residual percentage calculation.
However, this is irrelevant to the current problem, the issue is when doing the rolling average using .....rolling(window=3).mean(), **the average for the rows after the abnormal residual percent got affected**.
**If I do take those same numbers and do the normal way of averaging or use MS Excel, I got it right.**
Please see the image below:
#### 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 : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
Is there anything that I am missing or unaware. I tried to check in the pandas' source code and found nothing. Irrespective of the scale of the numbers, excel and the normal way of averaging works fine.
Please advise.
I greatly appreciate your help.
Thank you. | {
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} | 1 | 2019-09-05T22:43:48Z | 2020-03-26T20:08:08Z | 2020-03-26T20:08:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
data = {
('effect_size', 'cohen_d', 'mean'): {
('m1', 'P3', '222'): 0.52,
('m1', 'A5', '111'): -0.07,
('m2', 'P3', '222'): -0.53,
('m2', 'A5', '111'): 0.05,
},
('wilcoxon', 'z_score', 'stouffer'): {
('m1', 'P3', '222'): 2.2,
('m1', 'A5', '111'): -0.92,
('m2', 'P3', '222'): -2.0,
('m2', 'A5', '111'): -0.52,
}
}
df = pd.DataFrame(data)
df.index.rename(['metric', 'bar', 'foo'], inplace=True)
df.unstack(['foo', 'bar'])
```
#### Problem description
The `df` looks like this before unstacking:
```
effect_size wilcoxon
cohen_d z_score
mean stouffer
metric bar foo
m1 A5 111 -0.07 -0.92
P3 222 0.52 2.20
m2 A5 111 0.05 -0.52
P3 222 -0.53 -2.00
```
by unstacking `bar` and `foo`, I had expected to see them as column indices, but that's not what happens. Instead `foo` and `metric` are unstacked, and `bar` is left stacked as a row index:
```
> df.unstack(['foo', 'bar'])
effect_size wilcoxon
cohen_d z_score
mean stouffer
foo 111 222 111 222
metric m1 m2 m1 m2 m1 m2 m1 m2
bar
A5 -0.07 0.05 NaN NaN -0.92 -0.52 NaN NaN
P3 NaN NaN 0.52 -0.53 NaN NaN 2.2 -2.0
```
I got around the problem by doing the following, but I think the above behavior might be a bug.
Here's my workaround:
```
> print df.stack([0, 1, 2]).unstack(0).transpose()
bar A5 P3
foo 111 222
effect_size wilcoxon effect_size wilcoxon
cohen_d z_score cohen_d z_score
mean stouffer mean stouffer
metric
m1 -0.07 -0.92 0.52 2.2
m2 0.05 -0.52 -0.53 -2.0
```
#### 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: 4.19.37-5+deb10u1rodete2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: None
setuptools: unknown
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 2.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.10dev0
feather: None
matplotlib: 1.5.2
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0+unknown
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 4 | 2019-09-05T23:05:58Z | 2019-09-06T16:29:58Z | 2019-09-06T15:59:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [34]: pd.DataFrame({'x': [0, 1, 2], 'y': [0, 1, 2]}).groupby('x')['y'].quantile([0.5])
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-34-287422cf6b27> in <module>
----> 1 pd.DataFrame({'x': [0, 1, 2], 'y': [0, 1, 2]}).groupby('x')['y'].quantile([0.5])
~/.venv-3.7.2/p4p_backend-1ulE3AqG/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1951 indices = np.concatenate(arrays)
1952 assert len(indices) == len(result)
-> 1953 return result.take(indices)
1954
1955 @Substitution(name="groupby")
~/.venv-3.7.2/p4p_backend-1ulE3AqG/lib/python3.7/site-packages/pandas/core/series.py in take(self, indices, axis, is_copy, **kwargs)
4430
4431 indices = ensure_platform_int(indices)
-> 4432 new_index = self.index.take(indices)
4433
4434 if is_categorical_dtype(self):
~/.venv-3.7.2/p4p_backend-1ulE3AqG/lib/python3.7/site-packages/pandas/core/indexes/multi.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
2030 allow_fill=allow_fill,
2031 fill_value=fill_value,
-> 2032 na_value=-1,
2033 )
2034 return MultiIndex(
~/.venv-3.7.2/p4p_backend-1ulE3AqG/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _assert_take_fillable(self, values, indices, allow_fill, fill_value, na_value)
2058 taken = masked
2059 else:
-> 2060 taken = [lab.take(indices) for lab in self.codes]
2061 return taken
2062
~/.venv-3.7.2/p4p_backend-1ulE3AqG/lib/python3.7/site-packages/pandas/core/indexes/multi.py in <listcomp>(.0)
2058 taken = masked
2059 else:
-> 2060 taken = [lab.take(indices) for lab in self.codes]
2061 return taken
2062
IndexError: index 3 is out of bounds for size 3
```
#### Problem description
An exception is thrown above. This was not an issue in Pandas 0.24.2.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-27-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.0
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.0
```
</details>
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} | 4 | 2019-09-05T23:26:42Z | 2019-09-07T20:10:03Z | 2019-09-07T20:09:55Z | NONE | null | When selecting a row in a data frame that only contains numeric values, pandas coerces any integer values to floats. I assume this keeps the underlying data structure more contiguous and improves performance, but it's not really semantically correct, because floats can't do everything that integers can. For example, the problem I ran into today came about because I was trying to use values from an integer column to index into a string:
```pycon
>>> df = pd.DataFrame([[1, 2.0], [3, 4.0]], columns=['a' , 'b'])
>>> df
a b
0 1 2.0
1 3 4.0
>>> df.dtypes
a int64
b float64
dtype: object
>>> df.apply(lambda x: 'ABCD'[x.a], axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.7/site-packages/pandas/core/frame.py", line 6487, in apply
return op.get_result()
File "/usr/lib/python3.7/site-packages/pandas/core/apply.py", line 151, in get_result
return self.apply_standard()
File "/usr/lib/python3.7/site-packages/pandas/core/apply.py", line 257, in apply_standard
self.apply_series_generator()
File "/usr/lib/python3.7/site-packages/pandas/core/apply.py", line 286, in apply_series_generator
results[i] = self.f(v)
File "<stdin>", line 1, in <lambda>
TypeError: ('string indices must be integers', 'occurred at index 0')
```
Note that either (i) removing the float column or (ii) adding a non-numeric column resolves the error. This makes code that depends on true integer behavior fragile, because it could be broken as unrelated columns are added or removed.
```pycon
>>> df.drop('b', axis=1).apply(lambda x: 'ABCD'[x.a], axis=1)
0 B
1 D
dtype: object
>>> df.assign(c=['a', 'b']).apply(lambda x: 'ABCD'[x.a], axis=1)
0 B
1 D
dtype: object
```
Clearly pandas prefers if everything is the same data type, but also knows how to handle the case where columns have different types. When given integer and float columns, pandas just considers them all "numeric" and coerces the integers to floats so that everything can be the same type. I would argue that integers and floats should be considered different types, because integers can do things (e.g. indexing and slicing) that floats can't, but I'll admit that I have a very narrow view of what's going on here.
---
For what it's worth, a simpler way to see this same coercion is to just select a row:
```pycon
>>> df
a b
0 1 2.0
1 3 4.0
>>> df.iloc[0]
a 1.0
b 2.0
Name: 0, dtype: float64
>>> df.drop('b', axis=1).iloc[0]
a 1
Name: 0, dtype: int64
>>> df.assign(c=['a', 'b']).iloc[0]
a 1
b 2 # I double-checked that b is still a float here, even though it's shown without a decimal.
c a
Name: 0, dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Linux
OS-release: 5.2.5-arch1-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.6.3
pip: 19.2.3
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.1
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.4.0
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28311/events | https://github.com/pandas-dev/pandas/issues/28311 | 490,201,947 | MDU6SXNzdWU0OTAyMDE5NDc= | 28,311 | concat and append ignore level names and order in multi-level index DataFrames | {
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"description": "Concat, Merge/Join, Stac... | open | false | null | [] | null | 3 | 2019-09-06T08:37:14Z | 2021-07-11T17:38:20Z | null | NONE | null | #### Code Sample
```python
>>> tmp = pd.DataFrame( { 't': [0,0], 'h': [1,2], 'k': [3,4] } )
>>> tmp
t h k
0 0 1 3
1 0 2 4
>>> tmp2 = tmp.set_index( ['h','k'] )
>>> tmp2
t
h k
1 3 0
2 4 0
>>> tmp3 = tmp.set_index( ['k','h'] ) # swap levels
>>> tmp3
t
k h
3 1 0
4 2 0
>>> tmp2.append( tmp3, sort=False ) # k-level values are appended to h-level values and vice-versa
t
h k
1 3 0
2 4 0
3 1 0
4 2 0
>>> pd.concat( [ tmp2, tmp3 ], sort=False )
t
h k
1 3 0
2 4 0
3 1 0
4 2 0
>>> tmp4 = pd.DataFrame( { 't': [ 0,0], 'h': [1,2], 'p': [3,4] } ).set_index( ['p','h'] ) # change level name
>>> tmp4
t
p h
3 1 0
4 2 0
>>> tmp2.append( tmp4, sort=False ) # level name ignored altogether
t
h k
1 3 0
2 4 0
3 1 0
4 2 0
```
#### Problem description
The multiindex level names are ignored at concatenation (as described here: https://github.com/pandas-dev/pandas/issues/10187). In addition, even with common level names, the level order is also ignored.
I am not saying this is a bug, but since frame columns are aligned at concatenation it's a bit of a surprise to see index levels are not. Should a warning be added to the docs of both `concat` and `append` ?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.1
pytz : 2018.3
dateutil : 2.7.2
pip : 9.0.3
setuptools : 39.0.1
Cython : 0.28.1
pytest : 3.5.0
hypothesis : None
sphinx : 1.7.2
blosc : 1.5.1
feather : 0.4.0
xlsxwriter : 1.0.2
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : 0.1.4
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.0.2
numexpr : 2.6.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.9.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.5
tables : 3.4.2
xarray : 0.10.2
xlrd : 1.1.0
xlwt : None
xlsxwriter : 1.0.2
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28312/events | https://github.com/pandas-dev/pandas/issues/28312 | 490,240,575 | MDU6SXNzdWU0OTAyNDA1NzU= | 28,312 | GroupBySeries Quantile fails when there are 3 or more categories | {
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] | closed | false | null | [] | null | 1 | 2019-09-06T09:59:21Z | 2019-09-06T16:00:15Z | 2019-09-06T16:00:09Z | NONE | null | #### Code Sample
Using the latest version of pandas v0.25.1
```python
import numpy as np
import pandas as pd
np.random.seed(12345)
df1 = pd.DataFrame({
'category': ['A', 'A', 'A', 'A',
'B', 'B', 'B', 'B',
],
'value': np.random.randint(1, 10, 8)
})
df1.groupby("category").value.quantile([0.25, 0.75])
```
produces
```
category
A 0.25 2.75
0.75 5.25
B 0.25 2.75
0.75 6.25
Name: value, dtype: float64
```
as expected. However, running this
```python
np.random.seed(12345)
df2 = pd.DataFrame({
'category': ['A', 'A', 'A', 'A',
'B', 'B', 'B', 'B',
'C', 'C', 'C', 'C',
],
'value': np.random.randint(1, 10, 12)
})
df2.groupby("category").value.quantile([0.25, 0.75])
```
produces this error instead:
```
IndexError Traceback (most recent call last)
<ipython-input-60-12c4dbb665fc> in <module>
8 })
9
---> 10 df2.groupby("category").value.quantile([0.25, 0.75])
~/anaconda3/envs/dabest-dev-py3.7/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1951 indices = np.concatenate(arrays)
1952 assert len(indices) == len(result)
-> 1953 return result.take(indices)
1954
1955 @Substitution(name="groupby")
~/anaconda3/envs/dabest-dev-py3.7/lib/python3.7/site-packages/pandas/core/series.py in take(self, indices, axis, is_copy, **kwargs)
4430
4431 indices = ensure_platform_int(indices)
-> 4432 new_index = self.index.take(indices)
4433
4434 if is_categorical_dtype(self):
~/anaconda3/envs/dabest-dev-py3.7/lib/python3.7/site-packages/pandas/core/indexes/multi.py in take(self, indices, axis, allow_fill, fill_value, **kwargs)
2030 allow_fill=allow_fill,
2031 fill_value=fill_value,
-> 2032 na_value=-1,
2033 )
2034 return MultiIndex(
~/anaconda3/envs/dabest-dev-py3.7/lib/python3.7/site-packages/pandas/core/indexes/multi.py in _assert_take_fillable(self, values, indices, allow_fill, fill_value, na_value)
2058 taken = masked
2059 else:
-> 2060 taken = [lab.take(indices) for lab in self.codes]
2061 return taken
2062
~/anaconda3/envs/dabest-dev-py3.7/lib/python3.7/site-packages/pandas/core/indexes/multi.py in <listcomp>(.0)
2058 taken = masked
2059 else:
-> 2060 taken = [lab.take(indices) for lab in self.codes]
2061 return taken
2062
IndexError: index 6 is out of bounds for size 6
```
The expected output is produced with `pandas=0.24`:
```python
df2.groupby("category").value.quantile([0.25, 0.75])
```
```
category
A 0.25 2.75
0.75 5.25
B 0.25 2.75
0.75 6.25
C 0.25 1.75
0.75 7.25
```
Not exactly sure how to mitigate this?
I understand a related bug was patched with #28285 and #27526.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
pandas : 0.25.1
numpy : 1.16.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 4.3.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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