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} | 4 | "2021-08-26T08:32:56Z" | "2021-10-16T19:30:31Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
I have observed a few issues, when constructing a dataframe from a list of series with multiindices, if any multiindex contains (NaN, NaN).
#### Code Sample, a copy-pastable example
1. In this case I would expect the columns of the dataframe to be the union of the indices of series1 and series2, but ("a", "b") is missing.
```python
import pandas as pd
series1 = pd.Series((1,), index=pd.MultiIndex.from_tuples(((None, None),)))
series2 = pd.Series((10, 20), index=pd.MultiIndex.from_tuples(((None, None), ("a", "b"))))
df = pd.DataFrame([series1, series2])
# NaN
# NaN
# 0 1
# 1 10
```
2. There is another issue, when series1 and series2 are flipped. The columns appear to be correct, but in this case I would expect the value NaN at index 1, column (a,b)):
```python
df = pd.DataFrame([series2, series1])
# a NaN
# b NaN
# 0 20 10
# 1 1 1
```
#### Problem description
#### Expected Output
```python
df = pd.DataFrame([series1, series2])
# a NaN
# b NaN
# 0 NaN 1
# 1 20 10
```
and the reverse case
```python
df = pd.DataFrame([series2, series1])
# a NaN
# b NaN
# 0 20 10
# 1 NaN 1
```
This behavior is not limited to both series containing the MultiIndex (NaN, NaN). Here is an example, where only one series is constructed with (None, None). The column ("a", "b") is missing.
```python
import pandas as pd
series1 = pd.Series((1,), index=pd.MultiIndex.from_tuples((("a", None),)))
series2 = pd.Series((10, 20), index=pd.MultiIndex.from_tuples(((None, None), ("a", "b"))))
df = pd.DataFrame([series1, series2])
# a NaN
# NaN NaN
# 0 1.0 NaN
# 1 NaN 10.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f346574c8bb8921664f6d47d2e4b0f1252e181b9
python : 3.9.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.59-1-MANJARO
Version : #1 SMP PREEMPT Sun Aug 15 13:11:32 UTC 2021
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 0+untagged.1.gf346574
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
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 : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43223 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43223/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43223/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43223/events | https://github.com/pandas-dev/pandas/issues/43223 | 980,064,497 | MDU6SXNzdWU5ODAwNjQ0OTc= | 43,223 | BUG: Slicing on Datetimeindex fails | {
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} | 3 | "2021-08-26T10:04:41Z" | "2021-09-09T13:51:01Z" | "2021-09-09T13:51:01Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(
{'col2': [i for i in range(4)]},
index=pd.DatetimeIndex(["11.01.2011 22:00", "11.01.2011 23:00", "12.01.2011 00:00", "2011-01-13 00:00"]))
df["2011-01-01": "2011-11-01"]
```
Error message:
```
File "...\pandas\core\frame.py", line 3438, in __getitem__
return self._slice(indexer, axis=0)
File "...\pandas\core\generic.py", line 3826, in _slice
assert isinstance(slobj, slice), type(slobj)
AssertionError: <class 'numpy.ndarray'>
```
#### Problem description
The execution of `df["2011-01-01": "2011-11-01"]` fails with the following exception: `AssertionError: <class 'numpy.ndarray'>`. So the first problem is that the error message is not helping to solve the issue. Moreover, I observed that at least in Pandas 1.0.0 the code snippet is executed successfully. Additionally, if the index is sorted the error also does not occur: `df.sort_index()["2011-01-01": "2011-11-01"]`
Note a similar closed issue exists: #35509. With the current version the code snippet from that issue works. However, mine fails.
#### Expected Output
col2
2011-01-13 00:00:00 3
2011-11-01 22:00:00 0
2011-11-01 23:00:00 1
or helpful error message
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : de_DE.cp1252
pandas : 1.3.2
numpy : 1.18.4
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 53.1.0
Cython : None
pytest : 6.2.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.18.2
xlrd : None
xlwt : None
numba : None
</details>
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] | closed | false | null | [] | null | 2 | "2021-08-26T10:15:04Z" | "2021-08-27T06:24:36Z" | "2021-08-27T06:24:36Z" | NONE | null | Hello,
I'm using changed to_csv method to write to correct:
- Raw string/bytes write with compression; when path_or_buf=None, it doesnt allow to compress it
- http(s) files with more options like method, headers (already exists) and other from requests module
If you think it can be added natively, would be nice
Br
Nicolas
```python
import typing, pandas, io
from pandas.core.generic import NDFrame
from urllib.parse import urlparse
def to_csv(
self, path_or_buf: typing.Any = None, sep=',', na_rep='', float_format=None, columns=None, header=True, index=None,
index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"',
line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.',
errors='strict', storage_options=None
):
if index is None:
index = self.index.names[0] is not None # infer index write
# Path/Buffer Check
if isinstance(path_or_buf, str):
url = urlparse(path_or_buf)
if url.scheme in ('http', 'https'):
# request method
if mode.upper() not in ('GET', 'PUT', 'POST', 'UPDATE'):
mode = 'UPDATE' if 'a' in mode else 'POST'
if storage_options is None:
storage_options = {
"method": mode, "verify": False, "headers": {}
}
else:
for k, v in [
('method', mode), ('verify', False), ('headers', {})
]:
storage_options[k] = storage_options.get(k, v)
if compression == 'infer': compression = None
if encoding is None: encoding = 'utf-8'
storage_options['headers']['Content-Type'] = 'text/csv' if compression is None else 'text/%s+csv' % compression
storage_options['headers']['Content-Sep'] = sep
storage_options['headers']['Content-Decimal'] = decimal
storage_options['headers']['Content-Encoding'] = encoding
if compression is not None:
storage_options['headers']['Content-Compression'] = compression
if line_terminator is not None:
storage_options['headers']['Content-Line-Terminator'] = line_terminator
storage_options['data'] = to_csv(
self, path_or_buf=None,
sep=sep, na_rep=na_rep, float_format=float_format, columns=columns, header=header,
index=index, index_label=index_label, mode='w', encoding=encoding, compression=compression,
quoting=quoting, quotechar=quotechar, line_terminator=line_terminator, chunksize=None,
date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal,
errors=errors, storage_options=None
)
if isinstance(storage_options['data'], str):
storage_options['data'] = storage_options['data'].encode(encoding)
with requests.request(
url=path_or_buf, **storage_options
) as r:
r.raise_for_status()
return path_or_buf
if '*' in path_or_buf:
path_or_buf = path_or_buf.replace('*', str(uuid.uuid4()))
if path_or_buf is None:
# RuntimeWarning: compression has no effect when passing a non-binary object as input.
if compression is None:
return NDFrame.to_csv(
self, path_or_buf=path_or_buf,
sep=sep, na_rep=na_rep, float_format=float_format, columns=columns, header=header,
index=index, index_label=index_label, mode=mode, encoding=encoding, compression=compression,
quoting=quoting, quotechar=quotechar, line_terminator=line_terminator, chunksize=chunksize,
date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal,
errors=errors, storage_options=storage_options
)
path_or_buf = io.BytesIO()
NDFrame.to_csv(
self, path_or_buf=path_or_buf,
sep=sep, na_rep=na_rep, float_format=float_format, columns=columns, header=header,
index=index, index_label=index_label, mode=mode, encoding=encoding, compression=compression,
quoting=quoting, quotechar=quotechar, line_terminator=line_terminator, chunksize=chunksize,
date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal,
errors=errors, storage_options=storage_options
)
path_or_buf.seek(0)
return path_or_buf.read()
NDFrame.to_csv(
self, path_or_buf=path_or_buf,
sep=sep, na_rep=na_rep, float_format=float_format, columns=columns, header=header,
index=index, index_label=index_label, mode=mode, encoding=encoding, compression=compression,
quoting=quoting, quotechar=quotechar, line_terminator=line_terminator, chunksize=chunksize,
date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal,
errors=errors, storage_options=storage_options
)
return path_or_buf
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} | 2 | "2021-08-26T14:05:03Z" | "2021-08-28T19:57:56Z" | "2021-08-28T19:56:49Z" | CONTRIBUTOR | null | - [ ] closes #42125
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
@MarcoGorelli | {
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} | 11 | "2021-08-26T14:15:56Z" | "2021-09-05T10:58:51Z" | "2021-09-05T09:34:06Z" | CONTRIBUTOR | null | - [x] closes #43080
- [x] tests added / passed
I have added a test function `test_series_tz_localize_matching_index` in `test_tz_localize.py`
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Index of the original series is preserved while converting `categorical` data to `datetime`
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Here I'm expecting GroupBy to return an identical structure but NaN groups get removed even with dropna=False. Providing the level argument as a keyword works as expected.
```python
mux = pd.MultiIndex.from_arrays([['a', 'a', np.nan, 'b', 'b'], ['t', 'u', np.nan, 'w', 'y']],
names=['level1', 'level2'])
df = pd.DataFrame({'col': [0, np.nan, np.nan, 3, 4]}, mux)
col
level1 level2
a t 0.0
u NaN
NaN NaN NaN
b w 3.0
y 4.0
df = df.groupby(by='level2', dropna=False).apply(lambda x: x)
col
level1 level2
a t 0.0
u NaN
b w 3.0
y 4.0
df = df.groupby(level='level2', dropna=False).apply(lambda x: x)
col
level1 level2
a t 0.0
u NaN
NaN NaN NaN
b w 3.0
y 4.0
```
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} | 2 | "2021-08-26T14:47:55Z" | "2021-08-29T06:29:05Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({"col1": list("ABBC"), "col2": list("ZZXY")}).astype("string")
conditions = [
(df["col2"] == "Z") & (df["col1"] == "A"),
(df["col2"] == "Z") & (df["col1"] == "B"),
(df["col1"] == "B"),
]
print((df["col2"] == "Z").dtype) #BooleanDtype
choices = ['yellow', 'blue', 'purple']
df['color'] = np.select(conditions, choices, default='black')
# TypeError: invalid entry 0 in condlist: should be boolean ndarray
```
#### Problem description
When this string dataframe is cast to dtype "string", it seems subsequent columns created from it default to pandas built in types. For instance, now a boolean field created from one of these columns is of type `BooleanDtype`, pandas nullable boolean type.
```python
print((df["col2"] == "Z").dtype) #BooleanDtype
```
However for some reason numpy is not seeing it as a boolean.
If all of the conditions are first cast to type `bool` then it works.
```python
conditions = [i.astype(bool) for i in conditions]
df['color'] = np.select(conditions, choices, default='black')
```
#### Expected Output
I would expect the `np.select` function to work, even with `BooleanDtype` columns.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.11.final.0
python-bits : 64
OS : Windows
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machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
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numpy : 1.21.2
pytz : 2021.1
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pip : 21.0.1
setuptools : 52.0.0.post20210125
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
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pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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Thanks
- [x] closes #32414 | {
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} | 1 | "2021-08-26T16:02:36Z" | "2021-08-26T20:27:16Z" | "2021-08-26T18:26:35Z" | CONTRIBUTOR | null | Closes #43214
These tests are not run in CI. I'll replace these with tests in the pandas-gbq repo (https://github.com/googleapis/python-bigquery-pandas/issues/379)
- [x] closes #xxxx
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"state": "closed",
"title": "1.3.4",
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} | 16 | "2021-08-26T16:07:12Z" | "2021-09-17T16:32:02Z" | "2021-09-17T16:32:02Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '1.3.2'
In [3]: sr = pd.Series(['a', 'b', 'c'], dtype='category')
In [4]: sr
Out[4]:
0 a
1 b
2 c
dtype: category
Categories (3, object): ['a', 'b', 'c']
In [5]: new = sr.cat.reorder_categories(['c', 'b', 'a'])
In [6]: new
Out[6]:
0 a
1 b
2 c
dtype: category
Categories (3, object): ['c', 'b', 'a']
In [7]: new.dtype.categories
Out[7]: Index(['c', 'b', 'a'], dtype='object')
In [8]: sr.cat.reorder_categories(['c', 'b', 'a'], inplace=True)
pandas/core/arrays/categorical.py:2630: FutureWarning: The `inplace` parameter in pandas.Categorical.reorder_categories is deprecated and will be removed in a future version. Removing unused categories will always return a new Categorical object.
res = method(*args, **kwargs)
In [9]: sr
Out[9]:
0 a
1 b
2 c
dtype: category
Categories (3, object): ['c', 'b', 'a']
In [10]: sr.dtype.categories
Out[10]: Index(['a', 'b', 'c'], dtype='object')
```
#### Problem description
When we perform `reorder_categories` operation as an inplace op, the `categories` attribute of `CategoricalDtype` is not updated.
#### Expected Output
```python
In [7]: sr.dtype.categories
Out[7]: Index(['c', 'b', 'a'], dtype='object')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.15.0
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 6 | "2021-08-26T16:23:51Z" | "2021-08-27T11:21:50Z" | "2021-08-27T10:56:35Z" | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
@MarcoGorelli
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The standard error of the mean is quite an interesting statistic. But many didn't hear about it.
It would be nice to add it to ``.describe()`` hoping that people would ask themself about the difference between ``.sem()`` and ``.std()``
```python
series = pd.Series([1,2,3,4])
print(series.sem())
print(series.describe())
```
If many people like the idea then I would create a PR to add this feature :) | {
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} | 6 | "2021-08-26T16:49:20Z" | "2021-09-07T09:00:31Z" | "2021-09-06T15:19:38Z" | CONTRIBUTOR | null | - [x] closes #43205
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 0 | "2021-08-26T17:02:57Z" | "2021-08-27T00:53:17Z" | "2021-08-27T00:04:00Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_ctor import *
self = FromDicts()
self.setup()
%timeit self.time_dict_of_categoricals()
1.48 ms ± 61.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- master
528 µs ± 2.75 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
``` | {
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} | 1 | "2021-08-26T20:28:29Z" | "2021-09-13T14:39:44Z" | "2021-09-13T13:18:32Z" | MEMBER | null | A step towards supporting arbitrary EA-backed Indexes | {
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} | 2 | "2021-08-26T22:29:29Z" | "2021-08-31T13:13:23Z" | "2021-08-31T13:13:23Z" | CONTRIBUTOR | null | - [x] closes #39167
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Follow up from https://github.com/pandas-dev/pandas/pull/35381 - looks like this was missed
Not sure if this is an enhancements or a bug given the docs already mention this arg is supported.
https://pandas.pydata.org/docs/reference/api/pandas.read_table.html | {
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} | 1 | "2021-08-26T23:32:11Z" | "2021-08-27T18:16:24Z" | "2021-08-27T18:16:24Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> pd.__version__
'1.3.2'
>>> s = pd.RangeIndex(0, 5)
>>> s
RangeIndex(start=0, stop=5, step=1)
>>> import numpy as np
>>> v = np.array([False, False, True, True, True])
>>> s.where(v, 10)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4472, in where
return self.putmask(~cond, other)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 4727, in putmask
return type(self)._simple_new(values, name=self.name)
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.8/site-packages/pandas/core/indexes/range.py", line 164, in _simple_new
assert isinstance(values, range)
AssertionError
```
#### Problem description
pandas RangeIndex is not allowing a `where` operation to be performed which was working in older versions of pandas i.e., `1.2.5`
#### Expected Output
```python
>>> import pandas as pd
>>> pd.__version__
'1.2.5'
>>> s = pd.RangeIndex(0, 5)
>>> s
RangeIndex(start=0, stop=5, step=1)
>>> import numpy as np
>>> v = np.array([False, False, True, True, True])
>>> s.where(v, 10)
Int64Index([10, 10, 2, 3, 4], dtype='int64')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.15.0
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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} | 1 | "2021-08-27T00:17:37Z" | "2021-08-27T15:28:49Z" | "2021-08-27T13:51:09Z" | MEMBER | null | - [ ] closes #42745
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 7 | "2021-08-27T00:24:53Z" | "2021-09-10T13:45:47Z" | "2021-09-10T13:45:47Z" | MEMBER | null | I'm pretty sure you can do this directly with a .squeeze() afterwards.
The code does look pretty similar
squeeze()
https://github.com/pandas-dev/pandas/blob/5f648bf1706dd75a9ca0d29f26eadfbb595fe52b/pandas/core/generic.py#L979-L985
vs
read_csv(squeeze=True)
https://github.com/pandas-dev/pandas/blob/15302106ef41e562d2f80c6d1ecffdd4aea7158a/pandas/io/parsers/readers.py#L1063-L1064 | {
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} | 0 | "2021-08-27T02:53:04Z" | "2021-08-30T15:16:32Z" | "2021-08-30T15:14:33Z" | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self = GroupByMethods()
self.setup("int", "skew", "direct", 2)
%timeit self.time_dtype_as_group("int", "skew", "direct", 2)
434 ms ± 17.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
367 ms ± 8.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- PR
``` | {
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} | 3 | "2021-08-27T03:26:17Z" | "2021-10-31T00:52:19Z" | "2021-10-31T00:52:18Z" | CONTRIBUTOR | null | Column names should consistently be retained when using df.groupby().agg()
- [x] closes #42332
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Whats new:
Add a test to `pandas/tests/groupby/aggregate/test_aggregate.py`
The issue appears to be fixed now:
Tested on 1.4.0.dev0+508.g5fe02971f8
Output of `pd.show_versions()`
INSTALLED VERSIONS
------------------
commit : 5fe02971f8bc40a6bbb742f1f376b3650c3c8eaa
python : 3.9.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-31-generic
Version : #33-Ubuntu SMP Wed Aug 11 13:19:04 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8
pandas : 1.4.0.dev0+508.g5fe02971f8
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.14.9
sphinx : 4.1.2
blosc : 1.10.4
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.05.0
fastparquet : 0.7.1
gcsfs : 2021.05.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.05.0
scipy : 1.7.1
sqlalchemy : 1.4.23
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
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I wish I could use pandas in a way that does not cause bug-hunting for hours to look for a specific spot where nonsensical behavior occurs.
#### Describe the solution you'd like
The iterrow function (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html) and a few other ways of access do not preserve data types, this is very counterintuitive behavior (unless reading the docs, and making a comment about it for the next guy that maintain the code to see).
Fixing this would involve making sure they return the correct types *and* potentially adding a `breaking_iterrow` function that can be used for more efficient type-unaware iteration.
#### API breaking implications
Would involve changing the behavior of iterrows, might break a few pieces of code that rely upon the arbitrary types this generates (likely without the author realizing)
#### Describe alternatives you've considered
Leaving comments every time I use pandas iterrows.
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} | 2 | "2021-08-27T08:03:16Z" | "2021-08-27T08:45:44Z" | "2021-08-27T08:09:21Z" | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
From #28602:
```
pandas.Series.to_hdf: Parameter "format" description should finish with "."
pandas.DataFrame.to_hdf: Parameter "format" description should finish with "."
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- [ ] tests added / passed
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- [x] tests added / passed
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-08-27T14:31:54Z" | "2021-08-27T18:17:33Z" | "2021-08-27T18:16:25Z" | MEMBER | null | - [x] closes #43240
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
cc @jbrockmendel
2 options here:
- calling ``_shallow_copy`` which handles the cast to Int64Index in theses cases
- implementing a similar logic in ``_simple_new`` passing the call through to Int64Index or Float64Index depending on the dtype
But not sure if this would be counterintuitive for ``_simple_new``. | {
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- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
I'm not sure how to test this on the CI - pyright is happy on my machine:
```py
import pandas as pd
index: pd.IntervalIndex = pd.IntervalIndex.from_arrays([1], [2])
reveal_type(index)
reveal_type(index.right)
```
```
info: Type of "index" is "IntervalIndex"
info: Type of "index.right" is "Index"
``` | {
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} | 2 | "2021-08-27T16:44:41Z" | "2021-09-04T14:33:30Z" | "2021-09-04T14:33:27Z" | MEMBER | null | - [x] closes #40682
- [x] tests added / passed
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https://github.com/pandas-dev/pandas/blob/b9e90dc8acf003588678196e7dc8294114918bb2/web/pandas/config.yml#L65-L89
we need to add:
- @attack68
- @fangchenli
- @twoertwein
- @tswast
Anyone want to open a PR to add them?
----
For anyone wondering about the process behind joining the core team, see https://github.com/pandas-dev/pandas-governance/blob/master/governance.md#core-team | {
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} | 1 | "2021-08-27T16:56:15Z" | "2021-08-27T18:40:03Z" | "2021-08-27T18:40:02Z" | CONTRIBUTOR | null | Added *attack68, fangchenli, twoertwein, tswast* to the list of active maintainers.
- [X] closes #43254
- [X] tests added / passed
- [X] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 2 | "2021-08-27T17:27:56Z" | "2021-08-31T20:56:45Z" | "2021-08-31T20:03:48Z" | CONTRIBUTOR | null | - [x] adds to #41395
- [x] add options
- [x] add tests
- [x] update `Styler.format` docstring
- [x] update `options.rst`
- [x] update `Styler` init docstring
- [x] whatsnew
update user guide **will be a follow-on when all options are merged**
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The current min version is 0.46.0 which came out 2019-10-10.
Bumping to at least 0.49.0 (which came out 2020-04-17) would allow this version check to be removed:
https://github.com/pandas-dev/pandas/blob/b9e90dc8acf003588678196e7dc8294114918bb2/pandas/core/util/numba_.py#L90
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} | 1 | "2021-08-27T18:11:18Z" | "2021-08-30T07:52:37Z" | "2021-08-30T07:52:26Z" | CONTRIBUTOR | null | - [x] closes #41174
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Provided tests for `to_json` when called on complex data on both `Series` and `DataFrame`. Coefficients are both `int` and `float`.
Tests and linter pass locally.
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} | 3 | "2021-08-27T18:41:19Z" | "2021-08-28T22:34:47Z" | "2021-08-28T22:34:44Z" | MEMBER | null | - [x] closes #43168
This should be fixed now.
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} | 0 | "2021-08-27T19:31:50Z" | "2021-09-07T05:18:03Z" | "2021-09-07T05:18:00Z" | MEMBER | null | - [x] closes #21157
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/docs/dev/reference/api/pandas.Series.str.replace.html
### Documentation problem
The method description lists regex=None as the default, while the argument and examples list regex=True. In addition, the argument description does not list None as a possibility.
### Suggested fix for documentation
Change method description to regex=True as that seems to be accurate behavior. | {
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- [ ] whatsnew entry
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 8 | "2021-08-28T02:45:58Z" | "2021-10-16T20:26:52Z" | "2021-10-16T20:26:52Z" | NONE | null | - [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
Here is a replicate code:
df = pd.DataFrame({'B': [np.nan, 1, 2, np.nan, 4,5,6,7,8,9,10,11,12,13,14,15]})
df['A']=1
indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=12) # forward 12 period
# First time is OK
df['mean']=df.groupby('A')['B'].rolling(window=indexer, min_periods=1).mean().reset_index().loc[:,'B']
# Second time would crash!
df['mean']=df.groupby('A')['B'].rolling(window=indexer, min_periods=1).mean().reset_index().loc[:,'B']
If don't have groupby, it won't crash.
If don't forward window indexer, it won't crash.
If in the first time, it won't crash.
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
When you use df.groupby.rolling.mean with a forward-looking period, it would crash if you run the df.groupby.rolling.mean again.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.8.11.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19042
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : zh_CN
LOCALE : Chinese (Simplified)_China.936
pandas : 1.3.1
numpy : 1.20.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.2
setuptools : 52.0.0.post20210125
Cython : 0.29.24
pytest : 6.2.4
hypothesis : None
sphinx : 4.0.2
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : 3.4.2
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : 1.4.22
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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} | 1 | "2021-08-28T07:56:35Z" | "2021-08-30T13:48:35Z" | "2021-08-30T13:12:40Z" | CONTRIBUTOR | null | - [x] closes #42606
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
```python
#PR
In [3]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint64"), unit="s", utc=True)
CPU times: user 20.6 ms, sys: 19.9 ms, total: 40.5 ms
Wall time: 39.2 ms
In [4]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint32"), unit="s", utc=True)
CPU times: user 16.3 ms, sys: 6.58 ms, total: 22.8 ms
Wall time: 21.2 ms
#master
In [3]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint64"), unit="s", utc=True)
CPU times: user 12.6 s, sys: 44.3 ms, total: 12.7 s
Wall time: 12.7 s
In [4]: %time index = pd.to_datetime(np.arange(1_000_000).astype("uint32"), unit="s", utc=True)
CPU times: user 5.88 s, sys: 21.2 ms, total: 5.9 s
Wall time: 5.92 s
``` | {
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} | 0 | "2021-08-28T11:16:16Z" | "2021-09-01T10:09:36Z" | "2021-08-31T23:29:59Z" | MEMBER | null | doesn't need backport since benchmarks on master are used for comparing branches and finding performance regressions
xref #43047 | {
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} | 2 | "2021-08-28T16:21:54Z" | "2021-08-31T23:32:37Z" | "2021-08-31T23:32:33Z" | CONTRIBUTOR | null | - [x] closes #37553
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
Provided tests for type cast `ExtensionDtypes` -> `DatetimeTZDtype`.
Tests and linter pass locally. | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] modified `azure-pipelines.yml`
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 11 | "2021-08-28T18:07:09Z" | "2021-08-30T15:49:36Z" | "2021-08-30T15:48:51Z" | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
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Added a check as suggested in https://github.com/pandas-dev/pandas/pull/39280#issuecomment-764753400
```python
In [1]: from asv_bench.benchmarks.indexing import InsertColumns
In [2]: self = InsertColumns()
In [3]: self.setup()
In [4]: %timeit self.time_assign_with_setitem()
27.6 ms ± 68.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) #master
In [4]: %timeit self.time_assign_with_setitem()
8.6 ms ± 6.43 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) #PR
```
Edit:
```
before after ratio
[2b3a9b16] [fa9c7140]
<master> <indexing.InsertColumns.time_assign_with_setitem>
- 179±4μs 163±0.6μs 0.91 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
- 838±50μs 759±7μs 0.90 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
- 37.7±4μs 34.0±0.5μs 0.90 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
- 2.91±0.09ms 2.61±0.1ms 0.90 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
- 60.1±2μs 54.0±0.5μs 0.90 indexing.NumericSeriesIndexing.time_iloc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
- 1.56±0.03ms 1.40±0.04ms 0.90 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 183±20μs 164±1μs 0.89 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
- 2.39±0.05ms 2.14±0ms 0.89 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
- 879±30μs 780±50μs 0.89 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_incr')
- 184±20μs 163±1μs 0.88 indexing.NumericSeriesIndexing.time_iloc_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 131±7μs 115±4μs 0.87 indexing.NumericSeriesIndexing.time_loc_scalar(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 3.02±0.03ms 2.63±0.09ms 0.87 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 3.19±0.04ms 2.78±0.04ms 0.87 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'nonunique_monotonic_inc')
- 11.7±0.2ms 10.2±0.2ms 0.87 indexing.NumericSeriesIndexing.time_loc_array(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 1.89±0.04ms 1.65±0ms 0.87 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
- 4.63±0.1ms 3.99±0.02ms 0.86 indexing.NumericSeriesIndexing.time_getitem_array(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
- 1.82±0.04ms 1.56±0.01ms 0.86 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 3.69±0.1ms 3.14±0.1ms 0.85 indexing.NumericSeriesIndexing.time_loc_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 4.08±0.06ms 3.43±0.02ms 0.84 indexing.NumericSeriesIndexing.time_getitem_list_like(<class 'pandas.core.indexes.numeric.Float64Index'>, 'unique_monotonic_inc')
- 150±30μs 120±3μs 0.80 indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
- 961±30μs 762±20μs 0.79 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr')
- 295±30μs 231±3μs 0.78 indexing.NumericSeriesIndexing.time_loc_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
- 1.11±0.3ms 825±20μs 0.74 indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.UInt8Engine'>, <class 'numpy.uint8'>), 'monotonic_incr')
- 741±80μs 288±9μs 0.39 indexing.AssignTimeseriesIndex.time_frame_assign_timeseries_index
- 49.3±2ms 18.2±0.2ms 0.37 indexing.InsertColumns.time_assign_with_setitem
- 54.3±20ms 18.5±1ms 0.34 indexing.InsertColumns.time_assign_list_like_with_setitem
```
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- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html
### Documentation problem
The limitations section is out of date given many of the PRs submitted in last few months.
### Suggested fix for documentation
Update it. | {
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} | 1 | "2021-08-28T18:51:27Z" | "2021-08-30T14:43:15Z" | "2021-08-30T13:27:32Z" | MEMBER | null | ```
from asv_bench.benchmarks.io.stata import *
self = StataMissing()
self.setup("td")
%timeit self.time_read_stata("td")
113 ms ± 1.44 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
94.4 ms ± 1.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
```
cc @bashtage | {
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Reprises #42824. | {
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] | null | 15 | "2021-08-29T01:18:43Z" | "2021-09-30T23:09:41Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> from datetime import datetime
>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0 1629590400000000000
dtype: int64
>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
>>>
>>> pd.to_numeric(pd.NaT, errors="coerce")
nan
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0 -9223372036854775808
dtype: int64
>>> pd.Series([pd.NaT]).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
```
#### Problem description
When using `pd.to_numeric` to convert a `pd.Series` with dtype `datetime64[ns]`, it returns different values than converting the series *value by value*
#### Expected Output
Converting a `pd.Series` as a *whole* should be the same than converting it *value by value*.
I am not sure about what the correct output should be, but IMO the output should be consistent in these two scenarios.
What I suggest:
- For no-null values, returns the same value. Maybe the integer?
- For `pd.NaT`, always returns `np.NaN`
#### Output of ``pd.show_versions()``
I am using the latest version of `master` until today
<details>
INSTALLED VERSIONS
------------------
commit : e39ea3024cebb4e7a7fd35972a44637de6c41650
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jun 22 19:49:55 PDT 2021; root:xnu-6153.141.35~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.4.0.dev0+517.gc3761e24d8
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.9
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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} | 0 | "2021-08-29T03:39:09Z" | "2021-08-29T14:05:40Z" | "2021-08-29T09:02:00Z" | MEMBER | null | ```
from asv_bench.benchmarks.arithmetic import *
self = MixedFrameWithSeriesAxis()
self.setup("floordiv")
%timeit self.time_frame_op_with_series_axis1("floordiv")
27.9 ms ± 987 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- master
23.7 ms ± 630 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
``` | {
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https://github.com/pandas-dev/pandas/blob/e39ea3024cebb4e7a7fd35972a44637de6c41650/.pre-commit-config.yaml#L42-L45
https://github.com/pandas-dev/pandas/blob/e39ea3024cebb4e7a7fd35972a44637de6c41650/.pre-commit-config.yaml#L79-L83
we would use a [yaml anchor](https://ktomk.github.io/writing/yaml-anchor-alias-and-merge-key.html)
See here for an example of how that works https://github.com/psf/black/pull/2459/files
---
This would also mean being able to get rid of a big part of https://github.com/pandas-dev/pandas/blob/e39ea3024cebb4e7a7fd35972a44637de6c41650/scripts/sync_flake8_versions.py | {
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} | 1 | "2021-08-29T07:57:36Z" | "2021-09-01T17:57:37Z" | "2021-09-01T17:50:00Z" | CONTRIBUTOR | null | This pandas option is added to allow the `Styler` options to be independent from the others. See #41395
- [x] add option in config
- [x] add tests
- [x] whats new
- [x] `options.rst`
will update styler user guide as a follow on when all options PRs merged.
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} | 2 | "2021-08-29T10:49:31Z" | "2021-09-01T21:50:56Z" | "2021-09-01T19:36:08Z" | CONTRIBUTOR | null | adds item to #41395
- [x] added to config
- [x] method and docstrings added
- [x] options.rst docs
- [x] adds tests
- [x] whatsnew entry
will update user guide as a follow on after all options added.
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} | 2 | "2021-08-29T14:35:11Z" | "2021-08-30T14:42:34Z" | "2021-08-30T13:10:30Z" | MEMBER | null | ```
from asv_bench.benchmarks.io.style import *
self = Render()
self.setup(36, 12)
%timeit self.setup(36, 12); self.time_apply_render(36, 12)
29.2 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
9.98 ms ± 706 µs per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- PR
```
cc @attack68 | {
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} | 1 | "2021-08-29T17:07:11Z" | "2021-08-31T04:48:29Z" | "2021-08-30T22:58:12Z" | CONTRIBUTOR | null | extends the idea pushed by @jbrockmendel in #43285
```python
>>> df = pd.DataFrame(np.random.randn(10000, 1))
>>> % timeit df.style.applymap_index(lambda v: "color: blue;")._compute()
29.7 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # Master
24.4 ms ± 347 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # PR
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43288 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43288/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43288/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43288/events | https://github.com/pandas-dev/pandas/pull/43288 | 982,169,545 | MDExOlB1bGxSZXF1ZXN0NzIxOTk3ODUw | 43,288 | DOC: Updated doc with imp note(#39130) | {
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- [x] closes #39130
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43289 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43289/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43289/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43289/events | https://github.com/pandas-dev/pandas/pull/43289 | 982,203,582 | MDExOlB1bGxSZXF1ZXN0NzIyMDE4ODg5 | 43,289 | modified changes for datetime and NaT Objects Behavior closes #43280 | {
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- [ #] tests added / passed
**Modified datatime object 👍 **
i have changed view values to numeric of date and missing data found in pd series is returns Nan when coerce errors .
**Example for solved changes 💯 **
```
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
[OUT]
0 20210822
dtype: int64
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
[OUT]
0 NaN
dtype: float64
>>> s = pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
0 20210822
dtype: Int64
>>> s = pd.to_numeric(pd.NaT, errors="coerce")
nan
```
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 16 | "2021-08-30T01:02:42Z" | "2021-10-16T20:39:23Z" | "2021-10-16T20:26:52Z" | CONTRIBUTOR | null | - [x] closes https://github.com/pandas-dev/pandas/issues/43267
This PR addresses three issues, two major and one minor:
(1) If you repeated the use of a FixedForwardWindowIndexer, the indexer would be mutated and a window_size of 0 would be used. This would lead to an empty end array which unsurprisingly led to segfaults on the second run. Similar issues could be produced with other BaseIndexer subclasses, this one was simply the loudest.
(2) The end array generated by FixedForwardWindowIndexer.get_window_bounds was the wrong length, leading to longer arrays than necessary. When there was only one involved, it didn't matter much: the extra ones were simply ignored. But in the case of a groupby, these end arrays were concatenated, meaning that end values for the first group could leak into the second, and so on.
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} | 9 | "2021-08-30T03:10:28Z" | "2021-10-29T15:07:32Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
>>> df = pd.DataFrame({'a': ['hello', 'hello', 'world', 'world'], 'b': [np.inf, 10, np.nan, 10]})
>>> gb = df.groupby('a')
>>> gb.sum()
b
a
hello NaN
world 10.0
>>> gb.get_group('hello').b.sum()
inf
```
#### Problem description
while directly doing `groupby.sum`, the group with `inf` will return `np.nan`, but if we get the group and do `sum`, it returns `np.inf`
#### Expected Output
`groupby.sum` should also return `np.inf` if the values contian `np.inf`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.2.0
Version : Darwin Kernel Version 18.2.0: Thu Dec 20 20:46:53 PST 2018; root:xnu-4903.241.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.3.1
numpy : 1.21.1
pytz : 2021.1
dateutil : 2.8.2
pip : 21.1.2
setuptools : 57.0.0
Cython : None
pytest : 4.5.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.7.11.None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.26.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.3
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.24
tables : 3.5.2
tabulate : 0.7.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43295 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43295/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43295/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43295/events | https://github.com/pandas-dev/pandas/pull/43295 | 982,468,615 | MDExOlB1bGxSZXF1ZXN0NzIyMjI2NTM0 | 43,295 | ⬆️ UPGRADE: Autoupdate pre-commit config | {
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} | [] | closed | false | null | [] | null | 1 | "2021-08-30T07:10:56Z" | "2021-08-30T07:17:03Z" | "2021-08-30T07:17:03Z" | CONTRIBUTOR | null | <!-- START pr-commits -->
<!-- END pr-commits -->
## Base PullRequest
default branch (https://github.com/pandas-dev/pandas/tree/master)
## Command results
<details>
<summary>Details: </summary>
<details>
<summary><em>add path</em></summary>
```Shell
/home/runner/work/_actions/technote-space/create-pr-action/v2/node_modules/npm-check-updates/bin
```
</details>
<details>
<summary><em>pip install pre-commit</em></summary>
```Shell
Collecting pre-commit
Downloading pre_commit-2.14.1-py2.py3-none-any.whl (189 kB)
Collecting nodeenv>=0.11.1
Using cached nodeenv-1.6.0-py2.py3-none-any.whl (21 kB)
Collecting pyyaml>=5.1
Using cached PyYAML-5.4.1-cp39-cp39-manylinux1_x86_64.whl (630 kB)
Collecting toml
Using cached toml-0.10.2-py2.py3-none-any.whl (16 kB)
Collecting cfgv>=2.0.0
Downloading cfgv-3.3.1-py2.py3-none-any.whl (7.3 kB)
Collecting virtualenv>=20.0.8
Downloading virtualenv-20.7.2-py2.py3-none-any.whl (5.3 MB)
Collecting identify>=1.0.0
Downloading identify-2.2.13-py2.py3-none-any.whl (98 kB)
Collecting filelock<4,>=3.0.0
Using cached filelock-3.0.12-py3-none-any.whl (7.6 kB)
Collecting platformdirs<3,>=2
Downloading platformdirs-2.2.0-py3-none-any.whl (13 kB)
Collecting backports.entry-points-selectable>=1.0.4
Downloading backports.entry_points_selectable-1.1.0-py2.py3-none-any.whl (6.2 kB)
Collecting six<2,>=1.9.0
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting distlib<1,>=0.3.1
Downloading distlib-0.3.2-py2.py3-none-any.whl (338 kB)
Installing collected packages: six, platformdirs, filelock, distlib, backports.entry-points-selectable, virtualenv, toml, pyyaml, nodeenv, identify, cfgv, pre-commit
Successfully installed backports.entry-points-selectable-1.1.0 cfgv-3.3.1 distlib-0.3.2 filelock-3.0.12 identify-2.2.13 nodeenv-1.6.0 platformdirs-2.2.0 pre-commit-2.14.1 pyyaml-5.4.1 six-1.16.0 toml-0.10.2 virtualenv-20.7.2
```
</details>
<details>
<summary><em>pre-commit autoupdate || (exit 0);</em></summary>
```Shell
Updating https://github.com/MarcoGorelli/absolufy-imports ... already up to date.
Updating https://github.com/python/black ... [INFO] Initializing environment for https://github.com/python/black.
updating 21.7b0 -> 21.8b0.
Updating https://github.com/codespell-project/codespell ... [INFO] Initializing environment for https://github.com/codespell-project/codespell.
already up to date.
Updating https://github.com/pre-commit/pre-commit-hooks ... already up to date.
Updating https://github.com/cpplint/cpplint ... [INFO] Initializing environment for https://github.com/cpplint/cpplint.
already up to date.
Updating https://gitlab.com/pycqa/flake8 ... already up to date.
Updating https://github.com/PyCQA/isort ... [INFO] Initializing environment for https://github.com/PyCQA/isort.
already up to date.
Updating https://github.com/asottile/pyupgrade ... [INFO] Initializing environment for https://github.com/asottile/pyupgrade.
updating v2.24.0 -> v2.25.0.
Updating https://github.com/pre-commit/pygrep-hooks ... [INFO] Initializing environment for https://github.com/pre-commit/pygrep-hooks.
already up to date.
Updating https://github.com/asottile/yesqa ... already up to date.
```
</details>
<details>
<summary><em>pre-commit run -a || (exit 0);</em></summary>
```Shell
[INFO] Initializing environment for local:pyyaml,toml.
[INFO] Installing environment for https://github.com/MarcoGorelli/absolufy-imports.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/python/black.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/codespell-project/codespell.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/pre-commit/pre-commit-hooks.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/cpplint/cpplint.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://gitlab.com/pycqa/flake8.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/PyCQA/isort.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/pyupgrade.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for https://github.com/asottile/yesqa.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
[INFO] Installing environment for local.
[INFO] Once installed this environment will be reused.
[INFO] This may take a few minutes...
absolufy-imports................................................................................................Passed
black...........................................................................................................Passed
codespell.......................................................................................................Passed
Debug Statements (Python).......................................................................................Passed
Fix End of Files................................................................................................Passed
Trim Trailing Whitespace........................................................................................Passed
cpplint.........................................................................................................Passed
flake8..........................................................................................................Passed
flake8 (cython).................................................................................................Passed
flake8 (cython template)........................................................................................Passed
isort...........................................................................................................Passed
pyupgrade.......................................................................................................Passed
rst ``code`` is two backticks...................................................................................Passed
rst directives end with two colons..............................................................................Passed
rst ``inline code`` next to normal text.........................................................................Passed
Strip unnecessary `# noqa`s.....................................................................................Passed
flake8-rst......................................................................................................Passed
Unwanted patterns...............................................................................................Passed
Check Cython casting is `<type>obj`, not `<type> obj`...........................................................Passed
Check for backticks incorrectly rendering because of missing spaces.............................................Passed
Check for unnecessary random seeds in asv benchmarks............................................................Passed
Check for invalid EA testing....................................................................................Passed
Generate pip dependency from conda..............................................................................Passed
Check flake8 version is synced across flake8, yesqa, and environment.yml........................................Passed
Validate correct capitalization among titles in documentation...................................................Passed
Import pandas.array as pd_array in core.........................................................................Passed
Use bool_t instead of bool in pandas/core/generic.py............................................................Passed
Ensure pandas errors are documented in doc/source/reference/general_utility_functions.rst.......................Passed
```
</details>
</details>
## Changed files
<details>
<summary>Changed file: </summary>
- .pre-commit-config.yaml
</details>
<hr>
[:octocat: Repo](https://github.com/technote-space/create-pr-action) | [:memo: Issues](https://github.com/technote-space/create-pr-action/issues) | [:department_store: Marketplace](https://github.com/marketplace/actions/create-pr-action) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43296 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43296/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43296/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43296/events | https://github.com/pandas-dev/pandas/pull/43296 | 982,504,094 | MDExOlB1bGxSZXF1ZXN0NzIyMjU1NTcw | 43,296 | Update range.py | {
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- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/43297 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43297/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43297/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43297/events | https://github.com/pandas-dev/pandas/pull/43297 | 982,549,311 | MDExOlB1bGxSZXF1ZXN0NzIyMjkxMDkx | 43,297 | 1.1.x | {
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
found a small typo in the README | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43298 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43298/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43298/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43298/events | https://github.com/pandas-dev/pandas/pull/43298 | 982,578,063 | MDExOlB1bGxSZXF1ZXN0NzIyMzEyOTE2 | 43,298 | PERF: use _get_axis_number instead of if axis in [...]: #43048 | {
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```
if axis in ["index", 0]: #<- master
expected = ts.loc[indices, :]
elif axis in ["columns", 1]:
expected = ts.loc[:, indices]
axis = self.data._get_axis_number(axis) #<- PR
expected = ts.loc[indices, :] if axis == 0 else ts.loc[:, indices]
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/43299 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43299/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43299/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43299/events | https://github.com/pandas-dev/pandas/pull/43299 | 982,585,182 | MDExOlB1bGxSZXF1ZXN0NzIyMzE4MzE2 | 43,299 | ENH: Added multi index functionality (new level kwarg) to Dataframe.filter | {
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- [x] tests added / passed
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} | 4 | "2021-08-30T10:20:01Z" | "2021-09-01T18:08:28Z" | "2021-09-01T18:08:25Z" | CONTRIBUTOR | null | - [x] closes #35394
- [x] tests added / passed
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Provided tests for `to_string` when `max_rows=0`.
Tests and linter pass locally. | {
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The initial issue #22435 (check that there is no conflicting names failing) was actually hiding a deeper one leading to a failure when calling `numexpr.evaluate()`.
The commit proposed here solves both issues by adding a temporary variable to the scope for the results of function / method calls when using numexpr engine. | {
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} | 0 | "2021-08-30T15:49:24Z" | "2021-08-30T18:13:46Z" | "2021-08-30T18:13:45Z" | NONE | null | Backport PR #43274: PERF: indexing | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
pd.json_normalize(test_data['mycol'])
import pandas as pd
test_data = pd.DataFrame([[{'a':1,'b':4,'c':0}],[ {'a':10,'b':10,'c':10}],[{'a':0,'b':0,'c':0}]], index=[5,2,3], columns=['mycol'])
pd.json_normalize(test_data['mycol'])
a b c
0 1 4 0
1 10 10 10
2 0 0 0
```
#### Problem description
When a Series is passed to json_normalize the function returns a re-indexed dataframe without taking into account the index passed through the Series. This beahaviour is absolutely unexpected while working with Series
#### Expected Output
```python
a b c
5 1 4 0
2 10 10 10
3 0 0 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 3e89b4c4b1580aa890023fc550774e63d499da25
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Italian_Italy.1252
pandas : 1.2.0
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.2
pip : 20.3.3
setuptools : 51.1.2.post20210112
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 3.0.1
IPython : 7.26.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.5
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : None
odfpy : None
openpyxl : 3.0.6
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.0
sqlalchemy : 1.3.22
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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} | 0 | "2021-08-30T17:03:25Z" | "2021-08-31T14:59:10Z" | "2021-08-31T14:59:10Z" | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
```python
df = pd.DataFrame()
midx = pd.MultiIndex.from_arrays([["A", "B"], [1, 2]], names=["zero", "one"])
df.style # OK
df.index=[0,1]
df.style # OK
df.index=midx
df.style # IndexError
```
The error here was not the use of MultiIndex but of the index name display row when there are no columns. | {
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} | 0 | "2021-08-30T18:34:41Z" | "2021-08-30T19:40:13Z" | "2021-08-30T19:36:39Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_ctor import *
self = FromNDArray()
self.setup()
%timeit self.time_frame_from_ndarray()
37.9 µs ± 1.17 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
29.8 µs ± 549 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
``` | {
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} | 0 | "2021-08-30T20:16:05Z" | "2021-08-30T23:47:42Z" | "2021-08-30T23:28:30Z" | MEMBER | null | ```
from asv_bench.benchmarks.period import *
self = DataFramePeriodColumn()
self.setup()
%timeit self.time_setitem_period_column()
59.8 µs ± 2.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
33 µs ± 933 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
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} | 0 | "2021-08-30T20:30:04Z" | "2021-08-30T21:54:38Z" | "2021-08-30T21:45:33Z" | MEMBER | null | ```
from asv_bench.benchmarks.groupby import *
self = GroupByMethods()
self.setup('int', 'cumsum', 'transformation', 2)
%timeit self.time_dtype_as_group('int', 'cumsum', 'transformation', 2)
128 µs ± 7.81 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- master
99.7 µs ± 1.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <- PR
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https://api.github.com/repos/pandas-dev/pandas/issues/43310 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43310/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43310/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43310/events | https://github.com/pandas-dev/pandas/issues/43310 | 983,181,634 | MDU6SXNzdWU5ODMxODE2MzQ= | 43,310 | BUG: `DataFrame.rank` & `Series.rank` results are inconsistent | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> import numpy as np
>>> values = [-np.inf, 0, np.inf, np.nan, 2, np.nan]
>>> s = pd.Series(values)
>>> s
0 -inf
1 0.0
2 inf
3 NaN
4 2.0
5 NaN
dtype: float64
>>> kwargs = {'method': 'dense', 'na_option': 'bottom', 'ascending': False, 'pct': False, 'numeric_only': False}
>>> s.rank(**kwargs)
0 4.0
1 3.0
2 1.0
3 5.0
4 2.0
5 5.0
dtype: float64
>>> pd.DataFrame({'a':s}).rank(**kwargs)
a
0 4.0
1 3.0
2 1.0
3 4.0
4 2.0
5 4.0
>>>
```
#### Problem description
The results being returned by `Series.rank` & `DataFrame.rank` seem to be inconsistent.
#### Expected Output
Output of `DataFrame.rank` must be returned correctly(as is being done by `Series.rank`)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.17.2
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/43311 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43311/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43311/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43311/events | https://github.com/pandas-dev/pandas/pull/43311 | 983,190,924 | MDExOlB1bGxSZXF1ZXN0NzIyODA5ODY3 | 43,311 | PERF: nanops | {
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} | 2 | "2021-08-30T20:49:49Z" | "2021-08-31T18:32:22Z" | "2021-08-31T18:31:30Z" | MEMBER | null | ```
from asv_bench.benchmarks.stat_ops import *
self = FrameOps()
self.setup("skew", "float", 0)
%timeit self.time_op("skew", "float", 0)
3.22 ms ± 235 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.06 ms ± 71.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
%timeit self.time_op("skew", "int", 0)
3.81 ms ± 294 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.3 ms ± 79.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
%timeit self.time_op("kurt", "float", 0)
2.79 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.2 ms ± 85.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <- PR
%timeit self.time_op("kurt", "int", 0)
3.29 ms ± 131 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.35 ms ± 69.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
%timeit self.time_op("prod", "float", 0)
2.9 ms ± 116 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.23 ms ± 40.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
%timeit self.time_op("sum", "float", 0)
2.9 ms ± 194 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- master
2.14 ms ± 130 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) # <- PR
```
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https://api.github.com/repos/pandas-dev/pandas/issues/43312 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/43312/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/43312/comments | https://api.github.com/repos/pandas-dev/pandas/issues/43312/events | https://github.com/pandas-dev/pandas/pull/43312 | 983,198,256 | MDExOlB1bGxSZXF1ZXN0NzIyODE1ODU3 | 43,312 | BUG: Fix some parse dates tests for the Arrow CSV reader | {
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} | 1 | "2021-08-30T21:00:27Z" | "2021-09-05T18:07:53Z" | "2021-09-03T00:01:07Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
Fixes around 21 tests. This fix was left out of the original PR in the interest of keeping that one small.
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] | open | false | null | [] | null | 4 | "2021-08-30T21:18:11Z" | "2021-09-02T01:31:25Z" | null | MEMBER | null | With the addition of the new pyarrow engine, we now have the option to use multiple threads to read a CSV file. (This is also controllable through the ``pyarrow.set_cpu_count`` option).
Should we expose a keyword(such as ``num_threads`` maybe) to the user as a keyword, or just add an example in the docs(for this case, redirecting to ``pyarrow.set_cpu_count``? In the case of ``read_csv``, this keyword would probably only apply to the ``pyarrow`` engines, however it is worth noting that we have had multiple feature requests for parallel CSV reading (e.g. #37955), and it is probably worth it to be configure the number of threads used if we offer multithreading.
Personally, I would prefer having a keyword, as if we decide to add more I/O engines with multithreading capabilities, it would be more convenient to be able to control this option through a keyword.
cc @pandas-dev/pandas-core | {
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} | 2 | "2021-08-30T23:20:25Z" | "2021-09-30T12:54:17Z" | "2021-09-30T12:54:17Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({0: [[0, 1, 2], 'foo', [], [3, 4]],
... 1: 1,
... 2: [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
0 1 2
0 [0, 1, 2] 1 [a, b, c]
1 foo 1 NaN
2 [] 1 []
3 [3, 4] 1 [d, e]
>>> df.columns
Int64Index([0, 1, 2], dtype='int64')
>>> df.explode(0)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.8/site-packages/pandas/core/frame.py", line 8229, in explode
assert isinstance(column, (str, tuple))
AssertionError
>>> df.explode([0])
0 1 2
0 0 1 [a, b, c]
0 1 1 [a, b, c]
0 2 1 [a, b, c]
1 foo 1 NaN
2 NaN 1 []
3 3 1 [d, e]
3 4 1 [d, e]
>>>
```
#### Problem description
Looks like passing a scalar `int` to `column` is causing a failure in `DataFrame.explode`, but passing a list of int's is working.
#### Expected Output
```python
>>> df.explode(0)
0 1 2
0 0 1 [a, b, c]
0 1 1 [a, b, c]
0 2 1 [a, b, c]
1 foo 1 NaN
2 NaN 1 []
3 3 1 [d, e]
3 4 1 [d, e]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 5f648bf1706dd75a9ca0d29f26eadfbb595fe52b
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-27-generic
Version : #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : 0.29.24
pytest : 6.2.4
hypothesis : 6.17.2
sphinx : 4.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 2021.07.0
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : 0.53.1
</details>
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
`pd.to_numeric` was having an inconsistent behavior when using in datelike objects and was returning wrong result when using with `pd.NaT`
**In master:**
```python
>>> import pandas as pd
>>> from datetime import datetime
>>> from functools import partial
>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0 1629590400000000000
dtype: int64
>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
>>>
>>> pd.to_numeric(pd.NaT, errors="coerce")
nan
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0 -9223372036854775808
dtype: int64
>>> pd.Series(pd.NaT).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
```
**In this PR:**
```python
>>> import pandas as pd
>>> from datetime import datetime
>>> from functools import partial
>>> pd.to_numeric(datetime(2021, 8, 22), errors="coerce")
nan
>>> pd.to_numeric(pd.Series(datetime(2021, 8, 22)), errors="coerce")
0 NaN
dtype: float64
>>> pd.Series([datetime(2021, 8, 22)]).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
>>>
>>> pd.to_numeric(pd.NaT, errors="coerce")
nan
>>> pd.to_numeric(pd.Series(pd.NaT), errors="coerce")
0 NaN
dtype: float64
>>> pd.Series(pd.NaT).apply(partial(pd.to_numeric), errors="coerce")
0 NaN
dtype: float64
```
Please let me know if this approach makes sense so I can add the tests. | {
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} | 2 | "2021-08-31T01:29:17Z" | "2021-09-03T01:13:35Z" | "2021-09-02T23:55:01Z" | MEMBER | null | ```
from asv_bench.benchmarks.frame_methods import *
self5 = Fillna()
self5.setup(False, 'pad', 'object')
%timeit self5.time_frame_fillna(False, 'pad', 'object')
191 ms ± 5.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) # <- master
127 ms ± 5.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each) # <- PR
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} | 6 | "2021-08-31T04:29:59Z" | "2021-08-31T15:58:32Z" | "2021-08-31T15:44:12Z" | MEMBER | null | ATM almost half of the benchmark runtime is in the DataFrame constructor. | {
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} | 2 | "2021-08-31T05:07:44Z" | "2021-10-23T12:20:00Z" | "2021-10-23T12:19:57Z" | CONTRIBUTOR | null | - [x] closes #40675
- [x] tests added / passed
- [x] whatsnew entry
Finishes a series of PRs for the tracker
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- [X] I have checked that the issue still exists on the latest versions of the docs on `master` [here](https://pandas.pydata.org/docs/dev/)
### Location of the documentation
```
import pandas as pd
a = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("pyarrow"))})
a2 = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("python"))})
a.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
a2.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
```
### Documentation problem
```
import pandas as pd
a = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("pyarrow"))})
a2 = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("python"))})
a.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
a2.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
```
### Suggested fix for documentation
```
import pandas as pd
a = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("pyarrow"))})
a2 = pd.DataFrame({"A": pd.array(['a', 'b'], dtype=pd.StringDtype("python"))})
a.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
a2.to_parquet("test.parquet")
with pd.option_context("string_storage", "pyarrow"):
b = pd.read_parquet("test.parquet")
pd.testing.assert_frame_equal(b, a)
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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] | open | false | null | [] | null | 0 | "2021-08-31T09:44:24Z" | "2021-09-30T23:12:57Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
a = pd.DataFrame(
{2010: [1, 2, 3], 2020: [3, 4, 5]},
index=pd.MultiIndex.from_product(
[["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"]
),
)
# yes the example is just a scalar so this is overkill but
# it illustrates a bigger problem
b = pd.Series(
[0.4],
index=pd.MultiIndex.from_product(
[["b"], ["a"]], names=["mod", "scen"]
),
)
a.add(b, axis=0)
# result (below) is full of nans
2010 2020
scen mod id
a b 0 NaN NaN
1 NaN NaN
2 NaN NaN
```
#### Problem description
The output is all nans even though these two objects can be sensibly aligned (see workaround below).
#### Expected Output
```python
2010 2020
mod scen id
b a 0 1.4 3.4
1 2.4 4.4
2 3.4 5.4
```
There is a simple workaround, simply align before doing the operation (rather than letting the operation align for you).
```python
b_aligned, a_aligned = b.align(a)
a_aligned.add(b_aligned, axis=0)
# gives result above
```
Given that there's such a simple workaround, it could just be worth noting this in the docs (i.e. saying that alignment can be tricky and won't always work internally, in which case the user can align themselves before performing the operation) rather than changing the code. I'm happy to add such a line to the docs if helpful.
If code changes are a better route, then flipping the way alignment is done in https://github.com/pandas-dev/pandas/blob/e4a2196e83d61d14789edf44b0f135fcda4ec11e/pandas/core/ops/__init__.py#L306 (i.e. doing `right, left = right.align(left...)` rather than the current `left, right = left.align(right...`) fixes the problem but fails other tests so the simplest solution doesn't seem to be an acceptable one in this case.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : b577f0b4a6862dbb3329a23b5c2d6b6dc045996a
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Tue Jan 12 22:13:05 PST 2021; root:xnu-6153.141.16~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.4.0.dev0+538.gb577f0b4a6
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.1.0.post20200704
Cython : 0.29.21
pytest : 6.1.1
hypothesis : 5.37.1
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.6
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : 0.4.0
gcsfs : 0.7.1
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.17.1
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.1
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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