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This PR fixes grammatical errors.
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NONE
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This PR fixes grammatical errors.
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Backport PR #36464: BUG: Fix astype from float32 to string
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Backport of https://github.com/pandas-dev/pandas/pull/36464
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TST: check inequality by comparing categorical with NaN ( #28384 )
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CONTRIBUTOR
| null |
- [x] closes #28384
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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BUG: pandas.DataFrame.where doesn't work when cond is of Pandas Dtype
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| null |
[] | null | 2
| 2020-09-21T08:09:50Z
| 2020-09-21T13:51:23Z
| 2020-09-21T13:51:23Z
|
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.
---
**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
df = pd.DataFrame()
df["input_col"] = ["a", "b", "c"]
df["is_valid_row"] = pd.Series([1, 1, 0], dtype="Int64")
df["output_col"] = df[["input_col"]].where(df["is_valid_row"] == 1) # AssertionError is thrown here
```
#### Problem description
The code snippet is simplified to show that when using pandas.DataFrame.where in combination with a Pandas Dtype (Int64) an AssertionError is raised.
Note that when df["input_col"] (= pandas.Series.where) or dtype="int64" (instead of "Int64") is used, the code behaves as expected.
This behaviour is broken since Pandas >= 1.1 (still an issue in 1.1.2).
#### Expected Output
output_col
a
b
NaN
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.2.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 46.2.0
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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| 705,406,883
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MDU6SXNzdWU3MDU0MDY4ODM=
| 36,522
|
BUG: Different behavior of .replace() method between pandas version 1.0.5 and 1.1.2
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| 2020-09-21T08:46:31Z
| 2021-08-13T03:07:06Z
| 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.
- [ ] (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
o1 = object()
o2 = object()
o3 = object()
df = pd.DataFrame([{"a": o1, "b": o2}, {"a": o2, "b": o1}])
df.replace(to_replace={"a": o1}, value={"a": o3})
```
#### Problem description
In pandas 1.0.5
```
In [1]: import pandas as pd
...:
...: o1 = object()
...: o2 = object()
...: o3 = object()
...:
...: df = pd.DataFrame([{"a": o1, "b": o2}, {"a": o2, "b": o1}])
...: df.replace(to_replace={"a": o1}, value={"a": o3})
...:
Out[1]:
a b
0 <object object at 0x7fcc03c86a10> <object object at 0x7fcc03c86a00>
1 <object object at 0x7fcc03c86a00> <object object at 0x7fcc04b2c040>
```
In pandas 1.1.2
```
In [1]: import pandas as pd
...:
...: o1 = object()
...: o2 = object()
...: o3 = object()
...:
...: df = pd.DataFrame([{"a": o1, "b": o2}, {"a": o2, "b": o1}])
...: df.replace(to_replace={"a": o1}, value={"a": o3})
...:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
contrib/python/ipython/py3/IPython/__init__.py in <module>
6
7 df = pd.DataFrame([{"a": o1, "b": o2}, {"a": o2, "b": o1}])
----> 8 df.replace(to_replace={"a": o1}, value={"a": o3})
contrib/python/pandas/py3/pandas/core/frame.py in replace(self, to_replace, value, inplace, limit, regex, method)
4376 method="pad",
4377 ):
-> 4378 return super().replace(
4379 to_replace=to_replace,
4380 value=value,
contrib/python/pandas/py3/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6513 if col in value.keys() and col in self
6514 }
-> 6515 return self._replace_columnwise(mapping, inplace, regex)
6516
6517 # {'A': NA} -> 0
contrib/python/pandas/py3/pandas/core/frame.py in _replace_columnwise(self, mapping, inplace, regex)
4412
4413 target, value = mapping[ax[i]]
-> 4414 newobj = ser.replace(target, value, regex=regex)
4415
4416 res.iloc[:, i] = newobj
contrib/python/pandas/py3/pandas/core/series.py in replace(self, to_replace, value, inplace, limit, regex, method)
4561 method="pad",
4562 ):
-> 4563 return super().replace(
4564 to_replace=to_replace,
4565 value=value,
contrib/python/pandas/py3/pandas/core/generic.py in replace(self, to_replace, value, inplace, limit, regex, method)
6437 or is_list_like(to_replace)
6438 ):
-> 6439 raise TypeError(
6440 "Expecting 'to_replace' to be either a scalar, array-like, "
6441 "dict or None, got invalid type "
TypeError: Expecting 'to_replace' to be either a scalar, array-like, dict or None, got invalid type 'object'
```
Is this expected behavior or bug?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.171-70.1
Version : #1 SMP Thu Jan 31 10:46:27 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.16.6
pytz : 2020.1
dateutil : 2.8.1
pip : None
setuptools : 44.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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MDExOlB1bGxSZXF1ZXN0NDkwMzE4ODA0
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|
DOC: a few sphinx fixes in release notes
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| 1
| 2020-09-21T13:58:43Z
| 2020-09-23T11:37:14Z
| 2020-09-23T11:16:22Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST: remove xfails with strict=False
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/pull/35772#issuecomment-675558375
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BUG: formatters ignored in to_html
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| 2
| 2020-09-21T15:02:11Z
| 2020-11-21T00:01:07Z
| 2020-11-21T00:01:07Z
|
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
df = pd.DataFrame(dict(c1=pd.Series(range(3),dtype='int64'),
c2=pd.Series(range(3),dtype='Int64'),
c3=pd.Series(range(3),dtype='float64'),
c4=pd.Series(range(3),dtype='string'),
c5=pd.Series([0,1,0],dtype='bool'),
c6=pd.Series([0,1,0],dtype='boolean'),
))
formatter = {c:lambda x: f"[{x}]" for c in df.columns}
display(HTML(df.to_html(formatters=formatter)))
```
| c1 | c2 | c3 | c4 | c5 | c6
-- | -- | -- | -- | -- | -- | --
_| [0] | 0 | [0.0] | 0 | [False] | False
_| [1] | 1 | [1.0] | 1 | [True] | True
_| [2] | 2 | [2.0] | 2 | [False] | False
#### Problem description
DataFrame.to_html(formatters=formatters) is not working for MaskedArrays (new arrays with missing values)
#### Expected Output
formatters should also be applied to MaskedArrays.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
Version : #1 SMP Tue May 26 11:42:35 UTC 2020
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 : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 49.6.0.post20200917
Cython : 0.29.21
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.3.4
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.2.2
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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MDU6SXNzdWU3MDU3NTMzMzE=
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|
BUG: modulo of pd.Int64Index returns negative values under some conditions
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| 2020-09-21T16:38:02Z
| 2020-09-22T19:37:12Z
| 2020-09-22T19:20:45Z
|
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.Int64Index(list(range(0,24))*500) - 5) % 24
# returns Int64Index([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4,
# ...
# 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
# dtype='int64', length=12000)
# note: (pd.Int64Index(list(range(0,24))*400) - 5) % 24 does not return negative values
```
#### Problem description
In Pandas 1.1.2, taking the modulo of an Int64Index can return negative values depending on the values in the index and on its length. This problem was not present in pandas 1.0.5. It is also inconsistent with the generally expected behaviour of modulo in python and numpy.
#### Expected Output
```
Int64Index([19, 20, 21, 22, 23, 0, 1, 2, 3, 4,
...
9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
dtype='int64', length=9600)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 6.0.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.10.1
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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MDU6SXNzdWU3MDU3NzA5NzA=
| 36,527
|
BUG: Different behaviour for index after assign dataframe new column by series
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| 5
| 2020-09-21T17:06:02Z
| 2020-09-22T09:12:40Z
| 2020-09-21T21:42:49Z
|
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.
---
#### Code Sample, a copy-pastable example
With pandas version 1.1.1
```python
import pandas as pd
df = pd.DataFrame()
series = pd.Series(1.23, index=pd.RangeIndex(4, name="series_index"))
df['series'] = series
print(df)
```
#### Problem description
With pandas version 1.1.1, output:
```
series
series_index
0 1.23
1 1.23
2 1.23
3 1.23
```
We perceive index from series
With pandas version 1.1.2, output:
```
series
0 1.23
1 1.23
2 1.23
3 1.23
```
We lost the index from series
#### Expected Output
I am not sure about it. Could you explain is bug or feature of the latest version?
We used to perceive index from series and it worked before.
#### Output of ``pd.show_versions()``
<details>
[INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : 6.0.2
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 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
]
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MDExOlB1bGxSZXF1ZXN0NDkwNDQ2Mjk0
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|
REF: test_to_latex
|
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| 3
| 2020-09-21T17:19:02Z
| 2020-09-28T18:53:19Z
| 2020-09-22T22:34:30Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Refactor/clean-up ``test_to_latex.py``.
- Split big test functions with multiple assertions into multiple functions
- Make readable expected strings by first indenting for the good visual appearance and then dedenting by the leading whitespace for the assertion.
|
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MDU6SXNzdWU3MDU4MTQ5OTY=
| 36,529
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add generate_pip_deps_from_conda.py to pre-commit
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| 0
| 2020-09-21T18:17:39Z
| 2020-09-26T01:19:12Z
| 2020-09-26T01:19:04Z
|
MEMBER
| null |
This could be a local hook. I'll have a go at this later this week if no one takes it
|
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MDExOlB1bGxSZXF1ZXN0NDkwNTE0NjE5
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REF: Categorical.fillna match patterns in other methods
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| 5
| 2020-09-21T19:34:52Z
| 2020-09-22T22:23:11Z
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|
MEMBER
| null |
Also:
- always return a copy even if there is nothing to fill
- validate the fill_value even if there is nothing to fill
|
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MDExOlB1bGxSZXF1ZXN0NDkwNTE3NTk5
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Add generate pip dependency's from conda to pre-commit
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| 24
| 2020-09-21T19:41:11Z
| 2020-10-07T16:12:02Z
| 2020-09-26T01:19:04Z
|
CONTRIBUTOR
| null |
closes: #36529
First try, not sure yet how to activate local virtual env, error message right now is:
```
black................................................(no files to check)Skipped
flake8...............................................(no files to check)Skipped
flake8-pyx...........................................(no files to check)Skipped
flake8-pxd...........................................(no files to check)Skipped
isort................................................(no files to check)Skipped
Generate pip dependency from conda.......................................Failed
- hook id: pip_to_conda
- exit code: 1
Traceback (most recent call last):
File "scripts/generate_pip_deps_from_conda.py", line 20, in <module>
import yaml
ModuleNotFoundError: No module named 'yaml'
```
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| 705,885,969
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MDExOlB1bGxSZXF1ZXN0NDkwNTM0NTUx
| 36,532
|
BUG: Fix issue in preserving index name on empty DataFrame
|
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| 3
| 2020-09-21T20:15:35Z
| 2020-09-22T11:59:24Z
| 2020-09-21T21:42:49Z
|
CONTRIBUTOR
| null |
- [x] closes #36527
- [x] tests added / passed
- `tests/indexing/test_partial.py:tesst_index_name_empty`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
- v1.1.3
|
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MDExOlB1bGxSZXF1ZXN0NDkwNTM5OTYz
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|
CLN: remove not existing argument
|
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| 1
| 2020-09-21T20:26:27Z
| 2020-09-21T22:10:22Z
| 2020-09-21T21:24:09Z
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CONTRIBUTOR
| null |
Small clean up of argument in docstrings which is not present in function.
|
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MDExOlB1bGxSZXF1ZXN0NDkwNTcyNjYy
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CLN: clean up blocks.py
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| 2020-09-21T21:34:38Z
| 2020-09-24T08:07:25Z
| 2020-09-24T01:12:35Z
|
CONTRIBUTOR
| null |
Clean up while going through code. Will probably check more files.
|
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MDExOlB1bGxSZXF1ZXN0NDkwNTgyNTQ0
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|
Regr/period range large value/issue 36430
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| 5
| 2020-09-21T21:59:47Z
| 2020-09-23T10:34:58Z
| 2020-09-22T22:08:22Z
|
CONTRIBUTOR
| null |
# Checklist
- [x] closes #36430
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
# Solution
Culprit was the multiplication `unix_date * 24 * 3600 * 10**9 / factor`, for
```
unix_date = 106752
np.log2(unix_date * 24 * 3600 * 10**9)
# 63.00000011936912
```
That probably lead to an integer overflow somewhere and the observed behaviours.
Splitting the multiplication did the trick.
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CLN: Break up wrap applied output
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| 2
| 2020-09-21T22:50:51Z
| 2020-10-11T13:22:08Z
| 2020-09-22T22:45:23Z
|
MEMBER
| null |
Splitting off the Series case seemed to be a natural way to break up this method. Always open to other thoughts.
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| 706,019,922
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MDExOlB1bGxSZXF1ZXN0NDkwNjQzMDI5
| 36,537
|
Revert "ENH: Optimize nrows in read_excel"
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MEMBER
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Reverts pandas-dev/pandas#35974
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validate fill_value in IntervalArray.take unconditionally
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| 1
| 2020-09-22T01:42:55Z
| 2020-09-22T14:54:53Z
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MEMBER
| null |
xref #36466
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|
DOC: Links to Scipy window functions are out of date
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| 2020-09-22T03:24:40Z
| 2020-09-22T07:22:11Z
| 2020-09-22T07:22:11Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
- https://github.com/pandas-dev/pandas/blob/master/doc/source/user_guide/computation.rst
- https://github.com/pandas-dev/pandas/blob/00a510bfc0416d890a385697a8a0e5127602f9ec/pandas/core/window/rolling.py#L943
#### Documentation problem
Current link goes to https://docs.scipy.org/doc/scipy/reference/signal.html#window-functions which simply points users to https://docs.scipy.org/doc/scipy/reference/signal.windows.html#module-scipy.signal.windows.
#### Suggested fix for documentation
Pandas should link directly to the latter documentation.
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Link to new location for scipy.window documentation
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| 1
| 2020-09-22T03:24:55Z
| 2020-09-22T07:22:12Z
| 2020-09-22T07:22:11Z
|
CONTRIBUTOR
| null |
- [x] closes #36539
- [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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| 36,541
|
BUG: ValueError: cannot convert float NaN to integer when resetting MultiIndex with NaT values
|
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| 1
| 2020-09-22T05:09:50Z
| 2020-10-07T02:43:42Z
| 2020-10-07T02:43:42Z
|
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
In [18]: ix = pd.MultiIndex.from_tuples([(pd.NaT, 1), (pd.NaT, 2)], names=['a', 'b'])
In [19]: ix
Out[19]:
MultiIndex([('NaT', 1),
('NaT', 2)],
names=['a', 'b'])
In [20]: d = pd.DataFrame({'x': [11, 12]}, index=ix)
In [21]: d
Out[21]:
x
a b
NaT 1 11
2 12
In [22]: d.reset_index()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-22-4653618060e8> in <module>
----> 1 d.reset_index()
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in reset_index(self, level, drop, inplace, col_level, col_fill)
4851 name = tuple(name_lst)
4852 # to ndarray and maybe infer different dtype
-> 4853 level_values = _maybe_casted_values(lev, lab)
4854 new_obj.insert(0, name, level_values)
4855
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in _maybe_casted_values(index, labels)
4784 dtype = index.dtype
4785 fill_value = na_value_for_dtype(dtype)
-> 4786 values = construct_1d_arraylike_from_scalar(
4787 fill_value, len(mask), dtype
4788 )
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in construct_1d_arraylike_from_scalar(value, length, dtype)
1556
1557 subarr = np.empty(length, dtype=dtype)
-> 1558 subarr.fill(value)
1559
1560 return subarr
ValueError: cannot convert float NaN to integer
```
#### Problem description
With the introduction and use of `groupby(..., dropna=False)` multiindex with NaT values are more likely to occur which exhibits a few issues that previously went undetected. This issue was discovered when finding a workaround for another `dropna=False` related issue (https://github.com/pandas-dev/pandas/issues/36060#issuecomment-695869490)
Further investigation shows that this may be an issue with numpy not accepting pd.NaT. The following code reproduces the issue in `construct_1d_arraylike_from_scalar`:
```
In [33]: a = np.empty(2, dtype='datetime64[ns]')
In [34]: a.fill(pd.NaT)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-34-9fa9ff66da99> in <module>
----> 1 a.fill(pd.NaT)
ValueError: cannot convert float NaN to integer
```
which led me to propose this fix:
```diff
--- a/pandas/core/dtypes/cast.py
+++ b/pandas/core/dtypes/cast.py
@@ -1559,6 +1559,12 @@ def construct_1d_arraylike_from_scalar(
dtype = np.dtype("object")
if not isna(value):
value = ensure_str(value)
+ elif isinstance(dtype, np.dtype) and dtype.kind == "M" and value is NaT:
+ # can't fill sub array directly with pandas' NaT:
+ #
+ # > a.fill(pd.NaT)
+ # ValueError: cannot convert float NaN to integer
+ value = np.datetime64("NaT")
subarr = np.empty(length, dtype=dtype)
subarr.fill(value)
```
the `value is NaT` check could possibly be extended to `isna(value)`...
#### Expected Output
No ValueError being raised
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 15539fa6217039c2e5cb70b1d942caec9c5a2de3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_AU.UTF-8
pandas : 1.2.0.dev0+453.g15539fa62.dirty
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
Cython : 0.29.21
pytest : 6.0.2
hypothesis : 5.35.3
sphinx : 3.2.1
blosc : 1.9.2
feather : None
xlsxwriter : 1.3.4
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.2
fastparquet : 0.4.1
gcsfs : 0.7.1
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.5.1
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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MDU6SXNzdWU3MDYyMjA5ODg=
| 36,542
|
QST: Deadlock when using df.to_sql for none-related tables
|
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| 1
| 2020-09-22T09:31:38Z
| 2020-09-22T11:27:43Z
| 2020-09-22T11:27:43Z
|
NONE
| null |
I have a multithreaded ETL that occasionally fails due to an issue I can't understand and would like an explanation.
I have asked [the following question](https://stackoverflow.com/questions/63940226/mysql-deadlock-when-using-dataframe-to-sql-in-multithreaded-environment) but did not get a clear answer.
Basically my ETL uses the load method defined below in different instances, each instance on a different thread, and occasionally I get a deadlock error.
```class Query(abc.ABC):
def __init__(self):
self.connection = sqlalchemy.create_engine(MYSQL_CONNECTION_STR)
def load(self, df: pd.DataFrame) -> None:
df.to_sql(
name=self.table, con=self.connection, if_exists="replace", index=False,
)
@abc.abstractmethod
def transform(self, data: object) -> pd.DataFrame:
pass
@abc.abstractmethod
def extract(self) -> object:
pass
# other methods...
class ComplianceInfluxQuery(Query):
# Implements abstract methods... load method is the same as Query class
ALL_QUERIES = [ComplianceInfluxQuery("cc_influx_share_count"),ComplianceInfluxQuery("cc_influx_we_count")....]
while True:
with ThreadPoolExecutor(max_workers=8) as pool:
for query in ALL_QUERIES:
pool.submit(execute_etl, query) # execute_etl function calls extract, transform and load
```
This is the error log:
```
2020-09-17 09:48:28,138 | INFO | root | query | 44 | 1 | ComplianceInfluxQuery.load()
2020-09-17 09:48:28,160 | INFO | root | query | 44 | 1 | ComplianceInfluxQuery.load()
2020-09-17 09:48:28,241 | ERROR | root | calculate | 124 | 1 | Failed to execute query ComplianceInfluxQuery
Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context
self.dialect.do_execute(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
cursor.execute(statement, parameters)
File "/usr/local/lib/python3.8/site-packages/pymysql/cursors.py", line 163, in execute
result = self._query(query)
File "/usr/local/lib/python3.8/site-packages/pymysql/cursors.py", line 321, in _query
conn.query(q)
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 505, in query
self._affected_rows = self._read_query_result(unbuffered=unbuffered)
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 724, in _read_query_result
result.read()
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 1069, in read
first_packet = self.connection._read_packet()
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 676, in _read_packet
packet.raise_for_error()
File "/usr/local/lib/python3.8/site-packages/pymysql/protocol.py", line 223, in raise_for_error
err.raise_mysql_exception(self._data)
File "/usr/local/lib/python3.8/site-packages/pymysql/err.py", line 107, in raise_mysql_exception
raise errorclass(errno, errval)
pymysql.err.OperationalError: (1213, 'Deadlock found when trying to get lock; try restarting transaction')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "calculate.py", line 119, in execute_etl
query.run()
File "/chil_etl/query.py", line 45, in run
self.load(df)
File "/chil_etl/query.py", line 22, in load
df.to_sql(
File "/usr/local/lib/python3.8/site-packages/pandas/core/generic.py", line 2653, in to_sql
sql.to_sql(
File "/usr/local/lib/python3.8/site-packages/pandas/io/sql.py", line 512, in to_sql
pandas_sql.to_sql(
File "/usr/local/lib/python3.8/site-packages/pandas/io/sql.py", line 1316, in to_sql
table.create()
File "/usr/local/lib/python3.8/site-packages/pandas/io/sql.py", line 649, in create
self._execute_create()
File "/usr/local/lib/python3.8/site-packages/pandas/io/sql.py", line 641, in _execute_create
self.table.create()
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/schema.py", line 927, in create
bind._run_visitor(ddl.SchemaGenerator, self, checkfirst=checkfirst)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2097, in _run_visitor
conn._run_visitor(visitorcallable, element, **kwargs)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1656, in _run_visitor
visitorcallable(self.dialect, self, **kwargs).traverse_single(element)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/visitors.py", line 145, in traverse_single
return meth(obj, **kw)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/ddl.py", line 827, in visit_table
self.connection.execute(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1011, in execute
return meth(self, multiparams, params)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/sql/ddl.py", line 72, in _execute_on_connection
return connection._execute_ddl(self, multiparams, params)
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1068, in _execute_ddl
ret = self._execute_context(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1316, in _execute_context
self._handle_dbapi_exception(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1510, in _handle_dbapi_exception
util.raise_(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 182, in raise_
raise exception
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1276, in _execute_context
self.dialect.do_execute(
File "/usr/local/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 593, in do_execute
cursor.execute(statement, parameters)
File "/usr/local/lib/python3.8/site-packages/pymysql/cursors.py", line 163, in execute
result = self._query(query)
File "/usr/local/lib/python3.8/site-packages/pymysql/cursors.py", line 321, in _query
conn.query(q)
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 505, in query
self._affected_rows = self._read_query_result(unbuffered=unbuffered)
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 724, in _read_query_result
result.read()
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 1069, in read
first_packet = self.connection._read_packet()
File "/usr/local/lib/python3.8/site-packages/pymysql/connections.py", line 676, in _read_packet
packet.raise_for_error()
File "/usr/local/lib/python3.8/site-packages/pymysql/protocol.py", line 223, in raise_for_error
err.raise_mysql_exception(self._data)
File "/usr/local/lib/python3.8/site-packages/pymysql/err.py", line 107, in raise_mysql_exception
raise errorclass(errno, errval)
sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError) (1213, 'Deadlock found when trying to get lock; try restarting transaction')
[SQL:
CREATE TABLE cc_influx_share_count (
unique_identifier TEXT,
nfs_share_count FLOAT(53),
smb_share_count FLOAT(53),
s3_bucket_count FLOAT(53)
)
]
(Background on this error at: http://sqlalche.me/e/13/e3q8)
```
As you can see, when two threads use the `DataFrame.to_sql()` method at roughly the same time this could cause a deadlock.
The deadlock could occur on any of the classes.
I know I can call load again until it succeeds, but I want to understand why this happens in the first place.
I tried debugging this issue first through MySQL DB but when I run `SHOW ENGINE INNODB STATUS` no deadlocks show up, which leads me here.
My question is why would two none-related tables could have a deadlock?
what does pandas df.to_sql() does that could cause this?
I assumed df.to_sql() simply executes an INSERT statement but when I checked the logs I saw it does many more things I do not understand:
`SELECT * FROM mysql.general_log WHERE event_time >= "2020-09-17 09:48:27" AND event_time <= "2020-09-17 09:48:29" ORDER BY event_time ASC;`
Result:
```
Time: 2020-09-17 09:48:27.010747. Event: COMMIT
Time: 2020-09-17 09:48:27.011075. Event: ROLLBACK
Time: 2020-09-17 09:48:27.012042. Event: CREATE TABLE cc_influx_last_metric_time (
unique_identifier TEXT,
timestamp TIMESTAMP NULL,
uptime BIGINT
)
Time: 2020-09-17 09:48:27.033973. Event: COMMIT
Time: 2020-09-17 09:48:27.034327. Event: ROLLBACK
Time: 2020-09-17 09:48:27.053837. Event: INSERT INTO cc_influx_last_metric_time (unique_identifier, timestamp, uptime) VALUES (...)
Time: 2020-09-17 09:48:27.066930. Event: COMMIT
Time: 2020-09-17 09:48:27.068657. Event: ROLLBACK
Time: 2020-09-17 09:48:27.887579. Event: DESCRIBE `container_post_deployments`
Time: 2020-09-17 09:48:27.889705. Event: ROLLBACK
Time: 2020-09-17 09:48:27.890186. Event: DESCRIBE `container_post_deployments`
Time: 2020-09-17 09:48:27.892125. Event: ROLLBACK
Time: 2020-09-17 09:48:27.892619. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:27.894964. Event: SHOW CREATE TABLE `container_post_deployments`
Time: 2020-09-17 09:48:27.896491. Event: ROLLBACK
Time: 2020-09-17 09:48:27.897097. Event: DROP TABLE container_post_deployments
Time: 2020-09-17 09:48:27.907816. Event: COMMIT
Time: 2020-09-17 09:48:27.908322. Event: ROLLBACK
Time: 2020-09-17 09:48:27.909890. Event: CREATE TABLE container_post_deployments (
image TEXT,
`clientId` TEXT,
message TEXT,
timestamp TIMESTAMP NULL,
status_code BIGINT,
something TEXT,
user_agent TEXT
)
Time: 2020-09-17 09:48:27.928665. Event: COMMIT
Time: 2020-09-17 09:48:27.929089. Event: ROLLBACK
Time: 2020-09-17 09:48:27.932310. Event: INSERT INTO container_post_deployments (image, `clientId`, message, timestamp, status_code, something, user_agent) VALUES (...)
Time: 2020-09-17 09:48:27.934410. Event: COMMIT
Time: 2020-09-17 09:48:27.936774. Event: ROLLBACK
Time: 2020-09-17 09:48:28.140219. Event: DESCRIBE `cc_influx_share_count`
Time: 2020-09-17 09:48:28.163517. Event: DESCRIBE `cc_influx_we_count`
Time: 2020-09-17 09:48:28.166070. Event: ROLLBACK
Time: 2020-09-17 09:48:28.168159. Event: DESCRIBE `cc_influx_share_count`
Time: 2020-09-17 09:48:28.169895. Event: ROLLBACK
Time: 2020-09-17 09:48:28.170583. Event: DESCRIBE `cc_influx_we_count`
Time: 2020-09-17 09:48:28.174444. Event: ROLLBACK
Time: 2020-09-17 09:48:28.176339. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:28.177915. Event: ROLLBACK
Time: 2020-09-17 09:48:28.179331. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:28.182284. Event: SHOW CREATE TABLE `cc_influx_share_count`
Time: 2020-09-17 09:48:28.185154. Event: ROLLBACK
Time: 2020-09-17 09:48:28.192493. Event: SHOW CREATE TABLE `cc_influx_we_count`
Time: 2020-09-17 09:48:28.192887. Event: DROP TABLE cc_influx_share_count
Time: 2020-09-17 09:48:28.194530. Event: ROLLBACK
Time: 2020-09-17 09:48:28.195707. Event: DROP TABLE cc_influx_we_count
Time: 2020-09-17 09:48:28.207712. Event: COMMIT
Time: 2020-09-17 09:48:28.208141. Event: ROLLBACK
Time: 2020-09-17 09:48:28.210087. Event: CREATE TABLE cc_influx_share_count (
unique_identifier TEXT,
nfs_share_count FLOAT(53),
smb_share_count FLOAT(53),
s3_bucket_count FLOAT(53)
)
Time: 2020-09-17 09:48:28.215350. Event: COMMIT
Time: 2020-09-17 09:48:28.216115. Event: ROLLBACK
Time: 2020-09-17 09:48:28.217996. Event: CREATE TABLE cc_influx_we_count (
unique_identifier TEXT,
timestamp TIMESTAMP NULL,
`ANF` FLOAT(53),
`S3` FLOAT(53),
`CVO` FLOAT(53)
)
Time: 2020-09-17 09:48:28.240455. Event: ROLLBACK
Time: 2020-09-17 09:48:28.240908. Event: ROLLBACK
Time: 2020-09-17 09:48:28.244425. Event: COMMIT
Time: 2020-09-17 09:48:28.244965. Event: ROLLBACK
Time: 2020-09-17 09:48:28.249009. Event: INSERT INTO cc_influx_we_count (unique_identifier, timestamp, `ANF`, `S3`, `CVO`) VALUES (...)
Time: 2020-09-17 09:48:28.253638. Event: COMMIT
Time: 2020-09-17 09:48:28.256299. Event: ROLLBACK
Time: 2020-09-17 09:48:28.525814. Event: DESCRIBE `cc_influx_disk_usage`
Time: 2020-09-17 09:48:28.530211. Event: ROLLBACK
Time: 2020-09-17 09:48:28.532392. Event: DESCRIBE `cc_influx_disk_usage`
Time: 2020-09-17 09:48:28.539685. Event: ROLLBACK
Time: 2020-09-17 09:48:28.541868. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:28.560271. Event: SHOW CREATE TABLE `cc_influx_disk_usage`
Time: 2020-09-17 09:48:28.565451. Event: ROLLBACK
Time: 2020-09-17 09:48:28.569257. Event: DROP TABLE cc_influx_disk_usage
Time: 2020-09-17 09:48:28.585562. Event: COMMIT
Time: 2020-09-17 09:48:28.595193. Event: ROLLBACK
Time: 2020-09-17 09:48:28.598230. Event: CREATE TABLE cc_influx_disk_usage (
unique_identifier TEXT,
timestamp TIMESTAMP NULL,
total_gb FLOAT(53),
used_gb FLOAT(53)
)
Time: 2020-09-17 09:48:28.619580. Event: COMMIT
Time: 2020-09-17 09:48:28.620411. Event: ROLLBACK
Time: 2020-09-17 09:48:28.625385. Event: INSERT INTO cc_influx_disk_usage (unique_identifier, timestamp, total_gb, used_gb) VALUES (....)
Time: 2020-09-17 09:48:28.628706. Event: COMMIT
Time: 2020-09-17 09:48:28.631955. Event: ROLLBACK
Time: 2020-09-17 09:48:28.840143. Event: DESCRIBE `cc_influx_aws_subscription`
Time: 2020-09-17 09:48:28.844303. Event: ROLLBACK
Time: 2020-09-17 09:48:28.845637. Event: DESCRIBE `cc_influx_aws_subscription`
Time: 2020-09-17 09:48:28.848076. Event: ROLLBACK
Time: 2020-09-17 09:48:28.848646. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:28.851165. Event: SHOW CREATE TABLE `cc_influx_aws_subscription`
Time: 2020-09-17 09:48:28.852202. Event: ROLLBACK
Time: 2020-09-17 09:48:28.852691. Event: DROP TABLE cc_influx_aws_subscription
Time: 2020-09-17 09:48:28.861657. Event: COMMIT
Time: 2020-09-17 09:48:28.862099. Event: ROLLBACK
Time: 2020-09-17 09:48:28.863288. Event: CREATE TABLE cc_influx_aws_subscription (
unique_identifier TEXT,
timestamp TIMESTAMP NULL,
is_subscribed BIGINT
)
Time: 2020-09-17 09:48:28.878554. Event: COMMIT
Time: 2020-09-17 09:48:28.879113. Event: ROLLBACK
Time: 2020-09-17 09:48:28.881054. Event: INSERT INTO cc_influx_aws_subscription (unique_identifier, timestamp, is_subscribed) VALUES (....)
Time: 2020-09-17 09:48:28.882642. Event: COMMIT
Time: 2020-09-17 09:48:28.884614. Event: ROLLBACK
Time: 2020-09-17 09:48:28.918677. Event: DESCRIBE `hubspot_data`
Time: 2020-09-17 09:48:28.922938. Event: ROLLBACK
Time: 2020-09-17 09:48:28.923993. Event: DESCRIBE `hubspot_data`
Time: 2020-09-17 09:48:28.928181. Event: ROLLBACK
Time: 2020-09-17 09:48:28.928808. Event: SHOW FULL TABLES FROM `chil_etl`
Time: 2020-09-17 09:48:28.931225. Event: SHOW CREATE TABLE `hubspot_data`
Time: 2020-09-17 09:48:28.934269. Event: ROLLBACK
Time: 2020-09-17 09:48:28.934851. Event: DROP TABLE hubspot_data
Time: 2020-09-17 09:48:28.949309. Event: COMMIT
Time: 2020-09-17 09:48:28.949778. Event: ROLLBACK
Time: 2020-09-17 09:48:28.953829. Event: CREATE TABLE hubspot_data (...)
Time: 2020-09-17 09:48:28.973177. Event: COMMIT
Time: 2020-09-17 09:48:28.973652. Event: ROLLBACK
```
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MDU6SXNzdWU3MDYyMzI3NzY=
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QST: Subclassing DataFrame creates problems with assigning to self when changing size
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[] | null | 2
| 2020-09-22T09:49:14Z
| 2020-12-15T18:43:15Z
| 2020-12-15T18:43:15Z
|
NONE
| null |
I have successfully subclassed DataFrame (say into CustomDataFrame) according to the recommendations. I am now adding new custom methods to enhance my workflow. I have come across an issue which I have not found an answer to anywhere:
My CustomDataFrame has a DatetimeIndex set. When I manipulate the CustomDataFrame in my class, I am able to assign a _new_ DataFrame to _self_ without an error message, but _self_ does not update if _new_ has more, less or a different DatetimeIndex. I can drop rows using `inplace=True`. However, if I expand manually, I can only add one DatetimeIndex at a time in a loop: `self.loc[idx, :] = new.loc[idx, :]` (which is very slow). When _idx_ is an array, then Pandas throws an error: KeyError: "None of [DatetimeIndex([], dtype='datetime64[ns, pytz.FixedOffset(600)]', name='datetime', length=19998, freq=None)] are in the [index]"
Does anyone know why that is and how I can overcome this issue?
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MDU6SXNzdWU3MDYyOTM4NzA=
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BUG: str.format() raises keyerror
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NONE
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- [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
# Your code here
import pandas as pd
import numpy as np
data=pd.Series([1,2,3,4,5],index=['a', 'b', 'c', 'd', 'e'])
d=7
l=[11,12,13,14,15]
print(data['a']) #->prints 1, as expected
print("{s}".format(s=d)) #->prints 7, as expected
print("{s[0]}".format(s=l)) #->prints 11, as expected
print("{s}".format(s=data)) #->prints data, as expected
print("{s.a}".format(s=data)) #->prints 1, as expected
# combining above methods
print("{s['a']}".format(s=data)) #->returns keyError 'a', 1 expected
```
#### Problem description
While replacing placeholders with data from variables/ attributes into a text file, it works fine (see example with d=y). However, when doing the same for data in a series/dataframe, it cannot be found.
#### Expected Output
The data in the indexed row.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.8.3.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 : Dutch_Netherlands.1252
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : 1.3.17
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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Backport PR #36532 on branch 1.1.x (BUG: Fix issue in preserving index name on empty DataFrame)
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| 2020-09-22T12:49:36Z
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NONE
| null |
Backport PR #36532: BUG: Fix issue in preserving index name on empty DataFrame
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TST: add missing assert
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| 2020-09-22T11:59:14Z
| 2020-09-22T14:07:46Z
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MEMBER
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Small follow-up on https://github.com/pandas-dev/pandas/pull/36532
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Backport PR #36546 on branch 1.1.x (TST: add missing assert)
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| 1
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NONE
| null |
Backport PR #36546: TST: add missing assert
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MDU6SXNzdWU3MDY0Nzc3ODg=
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|
BUG: Indexing Pandas dataframe via timestamps - inclusive at either end of range
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| 2020-09-22T15:20:03Z
| 2021-03-26T06:18:50Z
| 2021-03-26T06:18:49Z
|
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
# Integer indexing, non-inclusive i1 <= i < i2
dfi = pd.DateFrame(np.cumsum(np.ones(3))
print(dfi[0:2])
"""
0
0 1.0
0 2.0
"""
# Indexing via timestamp inclusive at both ends t1 <= t <= t2
idx = pd.date_range('2020/1/1','2020/1/3')
df = pd.DataFrame(np.cumsum(np.ones(3)),index = idx)
print(df[idx[0]:idx[2])
"""
0
2020-01-01 1.0
2020-01-02 2.0
2020-01-03 3.0
"""
```
#### Problem description
If I have a pandas dataframe that is indexed by timestamps, and I specify a slice based on two timestamps (t1,t2) eg:
df[t1:t2] then I find that the slice is inclusive of both t1 and t2, ie it selects all items for which t1 <= t <= t2
However if it is indexed by integers, i1, i2, then df[i1:i2] selects all i where t1<=i<t2 ie is inclusive at the higher end
Is the behaviour for timestamps by design or a bug? It leads to incorrect code, for example if I have two adjacent time windows (t1,t2) and (t2,t3) then if one of the items in the dataframe is indexed at exactly t2, that item appears in both windows.
#### Expected Output
Example:
I have a Pandas dataframe df, where the index is a timestamp.
If I then do
len(df[df.index[0]:df.index[2]],
then the result is three, having selected the elements at index[0] index[1] and index[2]
But if it is indexed by an integer, then the result is 2 as I expect, so points at the boundary aren't double counted.
I do not know if this is designed behaviour
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MDExOlB1bGxSZXF1ZXN0NDkxMDE5MjYz
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CI: fix failing pre-commit
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BUG: Categorical series `.isin({pd.NaT, ...})` gives all-False
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| 2
| 2020-09-22T15:44:57Z
| 2021-08-13T03:08:15Z
| 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.
- [ ] (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.Series([pd.NaT, ''], dtype='category').isin({pd.NaT, ''}) # [True, True]
pd.Series([''], dtype='category').isin({''}) # [True] because '' is in series
pd.Series([''], dtype='category').isin({None, ''}) # [True] because '' is in series
pd.Series([''], dtype='category').isin({pd.NaT, ''}) # [False] even though '' is in series
```
#### Problem description
When searching for empty values in a categorical full of empty values, searching for `pd.NaT` will prevent other terms from being found.
(Perhaps that's because Pandas casts all input values to Timedelta?)
This behavior is unexpected. It makes me feel like I must be crazy. If I'm not meant to search for `pd.NaT`, I'd feel very happy with an error message when I try this.
#### Expected Output
```python
pd.Series([''], dtype='category').isin({pd.NaT, ''}) # [True] because '' is in series
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.4-200.fc32.x86_64
Version : #1 SMP Wed Aug 26 22:28:08 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 45.2.0
Cython : None
pytest : 5.4.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : 4.9.1
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : 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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PERF: TimedeltaArray.__iter__
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MEMBER
| null |
- [x] closes #26713
- [x] tests added / passed
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Also 2d compat for TDA/DTA `__iter__`
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MEMBER
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- [ ] closes #36047
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MDExOlB1bGxSZXF1ZXN0NDkxMTUzMDQ4
| 36,553
|
Call finalize in Series.__array_ufunc__
|
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CONTRIBUTOR
| null |
xref #28283
|
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MDExOlB1bGxSZXF1ZXN0NDkxMTUzODI3
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|
Call finalize in Series.dt
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| 2020-09-22T19:39:41Z
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CONTRIBUTOR
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xref #28283
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MDExOlB1bGxSZXF1ZXN0NDkxMTYyOTY2
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DOC: Fix missing spaces in whatsnew 1.1.3
|
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| 2020-09-22T19:58:54Z
| 2020-09-22T21:54:21Z
| 2020-09-22T21:54:21Z
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CONTRIBUTOR
| null |
Fix missing spaces preventing correct display of the issues.
|
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MDExOlB1bGxSZXF1ZXN0NDkxMTY2MTkx
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[TST]: Add test for duplicate keys in concat
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| 2020-09-22T20:05:24Z
| 2020-11-27T11:55:16Z
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MEMBER
| null |
- [x] closes #20816
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Added a test
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[BUG]: Fix bug with pre epoch normalization
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| 2
| 2020-09-22T20:23:57Z
| 2020-09-29T21:08:14Z
| 2020-09-26T01:22:29Z
|
MEMBER
| null |
- [x] closes #36294
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The fix propsed by @jbrockmendel worked.
Hope the test is in the right place
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REF: de-duplicate Categorical validators
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| 0
| 2020-09-22T20:48:48Z
| 2020-09-22T22:18:07Z
| 2020-09-22T22:17:16Z
|
MEMBER
| null |
- [ ] closes #xxxx
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TYP/CLN: exclusions in BaseGroupBy
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| 1
| 2020-09-22T20:58:50Z
| 2020-09-26T12:00:16Z
| 2020-09-22T22:16:05Z
|
MEMBER
| null |
Part of the code had exclusions as a list of hashables, change this to a set.
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[BUG]: Fix regression in read_table with delim_whitespace=True
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| 20
| 2020-09-22T21:07:21Z
| 2020-09-29T21:07:59Z
| 2020-09-26T10:31:11Z
|
MEMBER
| null |
- [x] closes #35958
- [x] tests added / passed
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We could write a private function which is called from read_csv and read_table with the appropriate default_set alternatively.
Edit: Black_pandas formats files not touched by myself. Was there an update or change of guidelines?
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REF: share _reduce
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BUG: TypeError: '<' not supported between instances of 'float' and 'str' when using `.combine_first` with categorical index containing nan
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| 2020-11-04T01:55:12Z
| 2020-11-04T01:55:12Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
In [79]: x = ['b', 'b', 'c', 'a', 'b', np.nan]
...: y = ['a', 'b', 'c', 'a', 'b', 'd']
...: mi1 = pd.MultiIndex.from_arrays(
...: [x, [1, 2, 3, 4, 5, 6]],
...: names=['a', 'b']
...: )
...: df = pd.DataFrame({'c': [1, 1, 1, 1, 1, 1]}, index=mi1)
...: mi2 = pd.MultiIndex.from_arrays(
...: [y, [1, 1, 1, 1, 1, 1]],
...: names=['a', 'b']
...: )
...: s = pd.Series([1, 2, 3, 4, 5, 6], index=mi2)
...: df.combine_first(pd.DataFrame({'some_col': s}))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/algorithms.py in safe_sort(values, codes, na_sentinel, assume_unique, verify)
2060 try:
-> 2061 sorter = values.argsort()
2062 ordered = values.take(sorter)
TypeError: '<' not supported between instances of 'float' and 'str'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-79-de018ddfae29> in <module>
11 )
12 s = pd.Series([1, 2, 3, 4, 5, 6], index=mi2)
---> 13 df.combine_first(pd.DataFrame({'some_col': s}))
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in combine_first(self, other)
6239 return expressions.where(mask, y_values, x_values)
6240
-> 6241 return self.combine(other, combiner, overwrite=False)
6242
6243 def update(
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in combine(self, other, func, fill_value, overwrite)
6104 other_idxlen = len(other.index) # save for compare
6105
-> 6106 this, other = self.align(other, copy=False)
6107 new_index = this.index
6108
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
3955 broadcast_axis=None,
3956 ) -> "DataFrame":
-> 3957 return super().align(
3958 other,
3959 join=join,
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/generic.py in align(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)
8542 axis = self._get_axis_number(axis)
8543 if isinstance(other, ABCDataFrame):
-> 8544 return self._align_frame(
8545 other,
8546 join=join,
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/generic.py in _align_frame(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)
8589 if axis is None or axis == 0:
8590 if not self.index.equals(other.index):
-> 8591 join_index, ilidx, iridx = self.index.join(
8592 other.index, how=join, level=level, return_indexers=True
8593 )
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/indexes/base.py in join(self, other, how, level, return_indexers, sort)
3491 )
3492 else:
-> 3493 return self._join_non_unique(
3494 other, how=how, return_indexers=return_indexers
3495 )
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/indexes/base.py in _join_non_unique(self, other, how, return_indexers)
3618 rvalues = other._get_engine_target()
3619
-> 3620 left_idx, right_idx = _get_join_indexers(
3621 [lvalues], [rvalues], how=how, sort=True
3622 )
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/reshape/merge.py in _get_join_indexers(left_keys, right_keys, sort, how, **kwargs)
1326 for n in range(len(left_keys))
1327 )
-> 1328 zipped = zip(*mapped)
1329 llab, rlab, shape = [list(x) for x in zipped]
1330
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/reshape/merge.py in <genexpr>(.0)
1323 # get left & right join labels and num. of levels at each location
1324 mapped = (
-> 1325 _factorize_keys(left_keys[n], right_keys[n], sort=sort, how=how)
1326 for n in range(len(left_keys))
1327 )
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/reshape/merge.py in _factorize_keys(lk, rk, sort, how)
1978 if sort:
1979 uniques = rizer.uniques.to_array()
-> 1980 llab, rlab = _sort_labels(uniques, llab, rlab)
1981
1982 # NA group
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/reshape/merge.py in _sort_labels(uniques, left, right)
2003 labels = np.concatenate([left, right])
2004
-> 2005 _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1)
2006 new_labels = ensure_int64(new_labels)
2007 new_left, new_right = new_labels[:llength], new_labels[llength:]
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/algorithms.py in safe_sort(values, codes, na_sentinel, assume_unique, verify)
2063 except TypeError:
2064 # try this anyway
-> 2065 ordered = sort_mixed(values)
2066
2067 # codes:
~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/algorithms.py in sort_mixed(values)
2046 # order ints before strings, safe in py3
2047 str_pos = np.array([isinstance(x, str) for x in values], dtype=bool)
-> 2048 nums = np.sort(values[~str_pos])
2049 strs = np.sort(values[str_pos])
2050 return np.concatenate([nums, np.asarray(strs, dtype=object)])
<__array_function__ internals> in sort(*args, **kwargs)
~/envs/pandas-test/lib/python3.8/site-packages/numpy/core/fromnumeric.py in sort(a, axis, kind, order)
989 else:
990 a = asanyarray(a).copy(order="K")
--> 991 a.sort(axis=axis, kind=kind, order=order)
992 return a
993
TypeError: '<' not supported between instances of 'float' and 'str'
```
#### Problem description
I use `df.combine_first(...)` to add a column to a dataframe while extending the index in case an index value does not exist in the target dataframe. However, if the MultIindex of the dataframe/series contains mixed np.nan/str values in their index value, the above `TypeError` is raised. I originally noticed this for categorical types (`x` and `y`), but the problem can be simplified to plain string types.
The reproduction of this error also seems to depend on the length/order of `x` and `y` (I tried to reduce it to fewer elements and kept the `np.nan`, but that didn't reproduce the error).
#### Expected Output
No exception and the dataframe containing the new column of series.
#### Output of ``pd.show_versions()``
<details>
In [84]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.4.0
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 : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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Closes #36541 (BUG: ValueError: cannot convert float NaN to integer when resetting MultiIndex with NaT values)
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| 2020-09-23T01:33:27Z
| 2020-10-07T02:43:48Z
| 2020-10-07T02:43:42Z
|
CONTRIBUTOR
| null |
- [x] closes #36541
- [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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ENH: return RangeIndex from difference, symmetric_difference
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MEMBER
| null |
- [x] closes #12034
- [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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BUG: visit_Constant() got an unexpected keyword argument 'side'
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| 2
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| 2020-09-23T03:16:12Z
| 2020-09-23T03:16:12Z
|
NONE
| null |
- [ ] 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
this is a simple example for me to show the error
```python
# Your code here
import pandas as pd
p = pd.DataFrame([[1,2,3],[2,3,4]], columns=['a','b','c'] )
p.query('a<1.2 and a>1')
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
in the code of [code](https://github.com/pandas-dev/pandas/blob/master/pandas/core/computation/expr.py#L442)
```python
def _maybe_transform_eq_ne(self, node, left=None, right=None):
if left is None:
left = self.visit(node.left, side="left")
if right is None:
right = self.visit(node.right, side="right")
op, op_class, left, right = self._rewrite_membership_op(node, left, right)
return op, op_class, left, right
```
there is side kwargs, however, when call the ```self.visit``` methods, it tells
```
TypeError: visit_Constant() got an unexpected keyword argument 'side'
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-115-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 5.4.3
pip: 20.1.1
setuptools: 49.2.0.post20200714
Cython: 0.29.21
numpy: 1.18.5
scipy: 1.5.0
pyarrow: None
xarray: None
IPython: 7.16.1
sphinx: 3.1.2
patsy: 0.5.1
dateutil: 2.8.1
pytz: 2020.1
blosc: None
bottleneck: 1.3.2
tables: 3.6.1
numexpr: 2.7.1
feather: None
matplotlib: 3.2.2
openpyxl: 3.0.4
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.2.9
lxml.etree: 4.5.2
bs4: 4.9.1
html5lib: 1.1
sqlalchemy: 1.3.18
pymysql: 0.10.0
psycopg2: None
jinja2: 2.11.2
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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MDU6SXNzdWU3MDcwNTQ0NDI=
| 36,566
|
BUG: TypeError on Series(dtype='string').value_counts().idxmax()
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| 2
| 2020-09-23T05:16:43Z
| 2021-01-01T21:38:40Z
| 2021-01-01T21:38:40Z
|
NONE
| null |
# Expected output
Obtain the most frequent string in a series:
```pycon
>>> pd.Series('a').value_counts().idxmax()
'a'
```
# Problem
Changing the dtype on the second example from `'object'` to `'string'` breaks this idiom:
```python-traceback
>>> pd.Series('a', dtype='string').value_counts().idxmax()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/core/series.py", line 2168, in idxmax
i = nanops.nanargmax(self._values, skipna=skipna)
File "pandas/core/nanops.py", line 71, in _f
return f(*args, **kwargs)
File "pandas/core/nanops.py", line 924, in nanargmax
result = values.argmax(axis)
TypeError: argmax() takes 1 positional argument but 2 were given
```
The problem is evidently not due to a `'string'` index:
```pycon
>>> pd.Series([1], index=pd.Series(['a'], dtype='string')).idxmax()
'a'
```
# Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.9.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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REF: Remove rolling window fixed algorithms
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| 10
| 2020-09-23T06:33:13Z
| 2020-10-09T23:34:10Z
| 2020-10-09T23:16:36Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Pros:
* Less code to maintain + makes internals simplier
* Fixed a bug: https://github.com/pandas-dev/pandas/pull/36567/files#diff-7656adc204bcddba0534588cabdab62eR770
Cons:
* A performance hit
```
before after ratio
[d9722efe] [198ab9e8]
<clean/rolling_aggregations^2> <clean/rolling_aggregations>
+ 1.27±0.02ms 4.05±1ms 3.19 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'linear')
+ 1.27±0.01ms 3.67±0.7ms 2.88 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'nearest')
+ 1.27±0.01ms 3.22±0.3ms 2.54 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'higher')
+ 1.27±0.01ms 2.99±0.08ms 2.36 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'midpoint')
+ 1.29±0ms 2.92±0.01ms 2.26 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'nearest')
+ 1.29±0ms 2.91±0.01ms 2.26 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'midpoint')
+ 1.29±0.08ms 2.91±0.01ms 2.25 rolling.Methods.time_rolling('Series', 10, 'float', 'max')
+ 1.29±0.01ms 2.91±0ms 2.25 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'higher')
+ 1.29±0.01ms 2.90±0.02ms 2.24 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'linear')
+ 1.30±0.02ms 2.91±0.04ms 2.24 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'lower')
+ 1.30±0.03ms 2.90±0.01ms 2.23 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'lower')
+ 1.32±0.08ms 2.93±0.02ms 2.22 rolling.Methods.time_rolling('Series', 10, 'float', 'min')
+ 1.33±0.03ms 2.95±0ms 2.21 rolling.Methods.time_rolling('Series', 10, 'int', 'max')
+ 1.44±0.01ms 3.11±0.03ms 2.16 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'lower')
+ 1.37±0.05ms 2.96±0.01ms 2.16 rolling.Methods.time_rolling('Series', 10, 'int', 'min')
+ 1.44±0.01ms 3.10±0.02ms 2.15 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint')
+ 1.44±0.01ms 3.07±0.01ms 2.14 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'higher')
+ 1.44±0ms 3.08±0.01ms 2.14 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'nearest')
+ 1.45±0.01ms 3.07±0.01ms 2.13 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'max')
+ 1.46±0.01ms 3.07±0.01ms 2.10 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear')
+ 1.47±0ms 3.09±0.02ms 2.10 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'linear')
+ 1.48±0.02ms 3.10±0.02ms 2.10 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'higher')
+ 1.47±0ms 3.08±0ms 2.09 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'min')
+ 1.47±0.01ms 3.07±0.01ms 2.09 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'midpoint')
+ 1.48±0.2ms 3.10±0.02ms 2.09 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'nearest')
+ 1.47±0.04ms 3.05±0.02ms 2.08 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'linear')
+ 1.52±0.01ms 3.15±0.02ms 2.07 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'max')
+ 1.54±0.01ms 3.17±0.03ms 2.06 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'min')
+ 1.49±0ms 3.05±0.06ms 2.04 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'nearest')
+ 1.49±0ms 3.03±0.03ms 2.03 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'lower')
+ 1.49±0ms 2.98±0.03ms 1.99 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'midpoint')
+ 1.40±0.01ms 2.75±0.01ms 1.97 rolling.ExpandingMethods.time_expanding('Series', 'float', 'max')
+ 1.49±0ms 2.93±0.02ms 1.96 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'higher')
+ 1.44±0.01ms 2.81±0.02ms 1.96 rolling.ExpandingMethods.time_expanding('Series', 'int', 'max')
+ 1.59±0.2ms 3.11±0.03ms 1.95 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'lower')
+ 930±3μs 1.80±0.01ms 1.94 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'higher')
+ 1.52±0ms 2.94±0.02ms 1.93 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'linear')
+ 1.51±0.2ms 2.92±0.01ms 1.93 rolling.Methods.time_rolling('Series', 1000, 'float', 'max')
+ 933±4μs 1.80±0.01ms 1.93 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'nearest')
+ 931±4μs 1.79±0.01ms 1.93 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
+ 1.69±0.01ms 3.26±0.01ms 1.93 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear')
+ 933±4μs 1.80±0.01ms 1.93 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'midpoint')
+ 1.52±0.2ms 2.93±0.01ms 1.93 rolling.Methods.time_rolling('Series', 1000, 'float', 'min')
+ 937±10μs 1.80±0.01ms 1.92 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'lower')
+ 1.56±0ms 2.99±0.05ms 1.92 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'max')
+ 940±30μs 1.80±0.01ms 1.91 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
+ 1.56±0.02ms 2.98±0.01ms 1.91 rolling.Methods.time_rolling('Series', 1000, 'int', 'max')
+ 935±3μs 1.79±0.01ms 1.91 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
+ 934±7μs 1.78±0.01ms 1.91 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'linear')
+ 931±3μs 1.78±0.01ms 1.91 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'lower')
+ 1.45±0.01ms 2.76±0ms 1.91 rolling.ExpandingMethods.time_expanding('Series', 'float', 'min')
+ 1.63±0ms 3.11±0.01ms 1.90 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest')
+ 937±4μs 1.78±0.01ms 1.90 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
+ 961±2μs 1.82±0.04ms 1.90 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'midpoint')
+ 1.49±0.02ms 2.82±0.01ms 1.89 rolling.ExpandingMethods.time_expanding('Series', 'int', 'min')
+ 964±9μs 1.82±0.02ms 1.88 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
+ 963±7μs 1.81±0.05ms 1.88 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
+ 962±8μs 1.81±0.01ms 1.88 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
+ 967±3μs 1.81±0.01ms 1.88 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
+ 966±3μs 1.81±0.02ms 1.87 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'linear')
+ 964±1μs 1.80±0.02ms 1.87 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
+ 1.66±0.03ms 3.10±0.01ms 1.87 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest')
+ 964±4μs 1.80±0ms 1.87 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
+ 1.12±0ms 2.08±0.01ms 1.87 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+ 1.67±0.01ms 3.10±0.01ms 1.86 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint')
+ 1.63±0.02ms 3.02±0.03ms 1.86 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'max')
+ 1.68±0.01ms 3.11±0.01ms 1.86 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'lower')
+ 969±3μs 1.80±0.02ms 1.85 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'midpoint')
+ 1.68±0.01ms 3.11±0.02ms 1.85 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'higher')
+ 1.61±0.09ms 2.97±0.01ms 1.85 rolling.Methods.time_rolling('Series', 1000, 'int', 'min')
+ 970±7μs 1.79±0.01ms 1.84 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
+ 1.72±0.01ms 3.16±0.1ms 1.84 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear')
+ 1.71±0.02ms 3.16±0.07ms 1.84 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'max')
+ 1.70±0.08ms 3.13±0.02ms 1.84 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'min')
+ 1.70±0.07ms 3.11±0.01ms 1.83 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'lower')
+ 1.71±0.01ms 3.13±0.02ms 1.83 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'higher')
+ 1.65±0.01ms 3.00±0.02ms 1.82 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'min')
+ 1.15±0.01ms 2.09±0.01ms 1.82 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear')
+ 1.71±0.02ms 3.11±0.01ms 1.82 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'max')
+ 1.71±0ms 3.11±0.01ms 1.82 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint')
+ 1.62±0.01ms 2.94±0.03ms 1.82 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'min')
+ 1.12±0ms 2.02±0.05ms 1.80 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+ 1.76±0.08ms 3.15±0.01ms 1.80 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'min')
+ 1.12±0.01ms 1.99±0.03ms 1.78 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
+ 1.12±0.01ms 1.99±0.02ms 1.78 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+ 1.14±0ms 2.03±0.03ms 1.78 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'higher')
+ 1.12±0ms 1.99±0.01ms 1.78 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher')
+ 1.15±0ms 2.03±0.05ms 1.77 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+ 1.13±0.01ms 2.00±0.05ms 1.77 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+ 1.12±0ms 1.98±0.04ms 1.77 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower')
+ 1.12±0ms 1.97±0.01ms 1.76 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
+ 1.13±0.01ms 1.99±0.01ms 1.76 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
+ 1.12±0ms 1.97±0.01ms 1.76 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
+ 1.14±0.01ms 1.98±0.01ms 1.74 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'midpoint')
+ 1.14±0.02ms 1.97±0ms 1.74 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower')
+ 1.14±0.01ms 1.97±0ms 1.73 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher')
+ 1.14±0.01ms 1.97±0.01ms 1.73 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint')
+ 1.14±0ms 1.97±0.01ms 1.72 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
+ 1.14±0.01ms 1.97±0.01ms 1.72 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower')
+ 1.46±0.07ms 2.48±0.01ms 1.70 rolling.Methods.time_rolling('Series', 10, 'float', 'std')
+ 1.21±0.09ms 2.00±0.02ms 1.66 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear')
+ 1.54±0.03ms 2.54±0.01ms 1.65 rolling.Methods.time_rolling('Series', 1000, 'int', 'std')
+ 1.62±0.04ms 2.66±0.01ms 1.65 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'std')
+ 1.68±0.01ms 2.75±0.02ms 1.64 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'std')
+ 1.56±0.1ms 2.53±0.01ms 1.63 rolling.Methods.time_rolling('Series', 10, 'int', 'std')
+ 1.66±0.03ms 2.67±0.01ms 1.61 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'std')
+ 1.71±0.03ms 2.73±0.01ms 1.59 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'std')
+ 913±40μs 1.42±0ms 1.56 rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
+ 1.62±0.2ms 2.49±0.01ms 1.54 rolling.Methods.time_rolling('Series', 1000, 'float', 'std')
+ 933±100μs 1.43±0.01ms 1.53 rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
+ 980±30μs 1.48±0.01ms 1.51 rolling.Methods.time_rolling('Series', 10, 'int', 'mean')
+ 1.07±0.01ms 1.60±0.01ms 1.50 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'mean')
+ 985±60μs 1.47±0.01ms 1.50 rolling.Methods.time_rolling('Series', 1000, 'int', 'mean')
+ 1.21±0ms 1.81±0.01ms 1.49 rolling.ExpandingMethods.time_expanding('Series', 'float', 'kurt')
+ 1.43±0ms 2.11±0.01ms 1.47 rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
+ 1.14±0ms 1.68±0.01ms 1.47 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'mean')
+ 1.26±0ms 1.86±0ms 1.47 rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
+ 1.40±0.08ms 2.06±0.01ms 1.47 rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
+ 1.14±0.01ms 1.67±0ms 1.46 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'mean')
+ 1.12±0.01ms 1.63±0.01ms 1.45 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'mean')
+ 1.55±0.01ms 2.24±0.01ms 1.44 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'kurt')
+ 846±4μs 1.22±0ms 1.44 rolling.ExpandingMethods.time_expanding('Series', 'float', 'mean')
+ 1.38±0.01ms 1.98±0.01ms 1.43 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'kurt')
+ 1.62±0ms 2.30±0.01ms 1.42 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'kurt')
+ 1.27±0.02ms 1.80±0.02ms 1.41 rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
+ 1.57±0.02ms 2.22±0.06ms 1.41 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+ 1.32±0.08ms 1.86±0.01ms 1.41 rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
+ 1.44±0.01ms 2.03±0.01ms 1.41 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+ 895±3μs 1.26±0ms 1.40 rolling.ExpandingMethods.time_expanding('Series', 'int', 'mean')
+ 1.32±0.06ms 1.84±0ms 1.40 rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
+ 1.64±0.04ms 2.29±0.01ms 1.40 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+ 1.42±0.01ms 1.97±0ms 1.39 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'skew')
+ 900±50μs 1.25±0.02ms 1.38 rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
+ 2.12±0.07ms 2.92±0.01ms 1.38 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'lower')
+ 1.49±0.01ms 2.04±0ms 1.37 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'skew')
+ 1.45±0.04ms 1.98±0.03ms 1.37 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+ 1.51±0.02ms 2.05±0.01ms 1.36 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'skew')
+ 1.51±0.2ms 2.05±0.01ms 1.36 rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
+ 1.02±0.01ms 1.38±0.01ms 1.36 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'mean')
+ 1.08±0.01ms 1.46±0.02ms 1.36 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'mean')
+ 962±60μs 1.28±0ms 1.34 rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
+ 841±5μs 1.12±0.01ms 1.33 rolling.ExpandingMethods.time_expanding('Series', 'float', 'sum')
+ 1.26±0.01ms 1.66±0.02ms 1.33 rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
+ 969±80μs 1.28±0.01ms 1.32 rolling.Methods.time_rolling('Series', 1000, 'int', 'sum')
+ 1.20±0.02ms 1.58±0.01ms 1.31 rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
+ 1.14±0.01ms 1.48±0.01ms 1.30 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'sum')
+ 899±5μs 1.17±0ms 1.30 rolling.ExpandingMethods.time_expanding('Series', 'int', 'sum')
+ 1.08±0.04ms 1.40±0.01ms 1.30 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'sum')
+ 1.09±0.04ms 1.41±0.01ms 1.29 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'sum')
+ 1.36±0ms 1.75±0.01ms 1.29 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'skew')
+ 1.15±0.06ms 1.47±0.01ms 1.28 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'sum')
+ 1.01±0ms 1.28±0ms 1.27 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'sum')
+ 1.43±0ms 1.81±0.01ms 1.27 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'skew')
+ 1.22±0.03ms 1.54±0.01ms 1.26 rolling.Methods.time_rolling('Series', 10, 'float', 'count')
+ 1.10±0ms 1.38±0.01ms 1.26 rolling.ExpandingMethods.time_expanding('Series', 'int', 'count')
+ 1.07±0.01ms 1.35±0ms 1.26 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'sum')
+ 1.36±0ms 1.71±0.02ms 1.26 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
+ 1.40±0.01ms 1.74±0.01ms 1.24 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'count')
+ 1.14±0.01ms 1.40±0ms 1.23 rolling.ExpandingMethods.time_expanding('Series', 'float', 'count')
+ 1.22±0.1ms 1.49±0ms 1.22 rolling.Methods.time_rolling('Series', 10, 'int', 'count')
+ 1.40±0.04ms 1.70±0.01ms 1.21 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'count')
+ 1.43±0.09ms 1.73±0.01ms 1.21 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'count')
+ 1.29±0ms 1.56±0.01ms 1.21 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'count')
+ 1.33±0.01ms 1.60±0.01ms 1.20 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'count')
+ 1.38±0ms 1.66±0.01ms 1.20 rolling.ExpandingMethods.time_expanding('Series', 'float', 'std')
+ 1.44±0.01ms 1.72±0.01ms 1.19 rolling.ExpandingMethods.time_expanding('Series', 'int', 'std')
+ 1.56±0.01ms 1.84±0ms 1.18 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'std')
+ 1.62±0.01ms 1.89±0.01ms 1.17 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'std')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
|
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|
https://api.github.com/repos/pandas-dev/pandas/issues/36568
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https://api.github.com/repos/pandas-dev/pandas
|
https://api.github.com/repos/pandas-dev/pandas/issues/36568/labels{/name}
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https://api.github.com/repos/pandas-dev/pandas/issues/36568/comments
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https://api.github.com/repos/pandas-dev/pandas/issues/36568/events
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https://github.com/pandas-dev/pandas/issues/36568
| 707,111,217
|
MDU6SXNzdWU3MDcxMTEyMTc=
| 36,568
|
BUG: Return DataFrame could not preserve index dtype when using `.combine_first` with categorical index
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| 2021-08-13T03:09:07Z
| null |
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.
#### Code Sample, a copy-pastable example
```
# Step1: create 2 categorical series
x1 = pd.Categorical(['a', 'b', 'd'])
x2 = pd.Categorical(['a', 'b', 'c'])
# Step2: create `df` with multi index. Note here the multi index of
# df is `[x1, [1, 2, 3]]`
df = pd.DataFrame({'x': x1, 'a': [1, 2, 3], 'value': [55, 66, 77]})
df = df.set_index(['x', 'a'], drop=True)
# Step3: create a multi indexed series `s`. Note here the multi index
# of series `s` is `[x2, [1, 2, 3]]`. This multi index is different from
# the multi index of `df`.
mi = pd.MultiIndex.from_arrays(
[
x2,
[1, 2, 3]
],
names=['x', 'a']
)
s = pd.Series([11, 22, 33], index=mi)
# Step4: Use combine_first to extend `df` with a new multi indexed series `s`
df2 = df.combine_first(pd.DataFrame({'some_col': s}))
df3 = df2.reset_index(drop=False)
# result comparison:
# The `dtype` of level 0 index of original `df` is `CategoricalDtype` which
# is what we we expected. `df2` is the return value of `combine_first`.
# Its 'dtype' of level 0 index is `dtype('O')` as shown below which is not
# what we expected. We expect `df2` level 0 index `dtype` to be
# `CategoricalDtype` too.
>>> df.index.get_level_values(0).dtype
CategoricalDtype(categories=['a', 'b', 'd'], ordered=False)
>>> df2.index.get_level_values(0).dtype
dtype('O')
```
#### Problem description
I use df.combine_first(...) to add a column to an existing dataframe while extending the index in case an index value does not exist in the target dataframe. One level of indexes is `Categorical` Dtype which came from `df = df.set_index(['x', 'a'], drop=True)` . I expected the `Categorical` dtype index could be restored by `df3 = df2.reset_index(drop=False)` after `combine_first`. However, if the MultIindex of the dataframe uses `Categorical` values in their index, the `Categorical` dtype info in the return dataframe lost .
#### Expected Output
df2 level 0 index has`CategoricalDtype`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.1.1
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.3
setuptools : 47.3.1
Cython : 0.29.17
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 1.8.6
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NDkxNjE2ODA4
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CLN: clean up pandas core arrays
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| 2020-09-24T08:07:12Z
| 2020-09-24T06:44:53Z
|
CONTRIBUTOR
| null |
Some clean up in `pandas/core/arrays/array.py`.
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MDExOlB1bGxSZXF1ZXN0NDkxNjI0NDEz
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CLN clean ups in code
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| 2020-09-23T08:59:14Z
| 2020-10-07T01:06:22Z
| 2020-10-07T01:05:30Z
|
CONTRIBUTOR
| null |
Started with whitespaces before colons, ended up doing some other clean ups as well.
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BUG: df1.values is df1_shallow_copy.values returns false
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| 2020-09-23T13:19:20Z
| 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.
- [ ] (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
r = pd.DataFrame({"a": [1,2,3], "b": [3,4,5]})
shallow = r.copy(deep=False)
r.values is shallow.values
```
#### Problem description
I am making a shallow copy, hence I expect r and shallow's values to point to the same underlying data (as it is described here:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.equals.html). Instead it is returning False
#### Expected Output
r.values is shallow.values should return True.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.2
numpy : 1.18.1
pytz : 2019.1
dateutil : 2.7.5
pip : 19.3.1
setuptools : 49.3.1
Cython : 0.29.14
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.0.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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Backport PR #36535 on branch 1.1.x (Regr/period range large value/issue 36430)
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NONE
| null |
Backport PR #36535: Regr/period range large value/issue 36430
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Backport PR #36523: DOC: a few sphinx fixes in release notes
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Backport PR #36523
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QST: what are the semantics of the SettingWithCopyWarning?
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| 2020-09-23T12:10:05Z
| 2020-09-23T12:31:36Z
| 2020-09-23T12:31:35Z
|
NONE
| null |
Hi, on pandas `1.0.5` I get `SettingWithCopyWarning` when I do the following
```python
df['x'] = df['y'] - df['z']
```
1. Is the warning correct in this case?
2. The suggestion is `Try using .loc[row_indexer,col_indexer] = value instead`
If I do something like
```
df.loc[~df.x.isna() , 'x'] = df['y'] - df['z']
```
I still get the same warning. I am not sure what is the proper way to do it?
Thanks
Prokopis
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|
BUG: `tz_localize` drops `freq` from `DatetimIndex`
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| false
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[] | null | 5
| 2020-09-23T13:34:16Z
| 2021-08-13T03:10:27Z
| null |
NONE
| 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.
- [ ] (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
s = pd.Series(
[1, 2, 3, 4, 5], index=pd.date_range("2020", periods=5, freq="D")
)
print(s.index.freq) # <Day>
print(s.tz_localize("europe/london").index.freq) # None
```
#### Problem description
The index should retain the `freq` attribute despite localization.
#### Output of ``pd.show_versions()``
<details>
It raises
```python-traceback
ImportError: Can't determine version for hypothesis
```
</details>
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|
REF: refactor/cleanup of CSSToExcelConverter
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| 0
| 2020-09-23T15:23:03Z
| 2020-10-04T13:24:27Z
| 2020-09-24T23:57:04Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Refactor/clean up of ``CSSToExcelConverter`` in module ``pandas.io.formats.excel``.
- Move class variables to the top of the class
- Add font family mapping
- Extract methods
- Add missing typing
- Make color parsing cleaner
|
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QST: pyinstaller pandas
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| 2020-09-23T15:32:49Z
| 2020-09-24T00:30:37Z
| 2020-09-24T00:30:37Z
|
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [x] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
```python
# Your code here, if applicable
File "", line 219, in call_with_frames_removed
File "c:\users\leroy\anaconda3\lib\site-packages_pyinstaller_hooks_contrib\hooks\stdhooks\hook-IPython.py", line 34, in
datas += collect_data_files('IPython.extensions', include_py_files=True)
File "c:\users\leroy\anaconda3\lib\site-packages\PyInstaller\utils\hooks_init.py", line 712, in collect_data_files
pkg_base, pkg_dir = get_package_paths(package)
File "c:\users\leroy\anaconda3\lib\site-packages\PyInstaller\utils\hooks_init_.py", line 506, in get_package_paths
file_attr = get_module_file_attribute(package)
File "c:\users\leroy\anaconda3\lib\site-packages\PyInstaller\utils\hooks_init_.py", line 308, in get_module_file_attribute
raise ImportError('Unable to load module attribute') from e
ImportError: Unable to load module attribute
```
error on the below when compling py file on pyinstaller. command is pyinstaller --noconsole --onefile filename.py
```
python code as per below.
import pandas
import tkinter as tk
from tkinter import filedialog
import os
def analysis():
path = filedialog.askopenfilename()
data = pandas.read_excel(path)
groups = data.groupby('Group')
table = groups.agg({'Revenue':sum, 'Group': len})
savename = filedialog.asksaveasfilename()
savename = savename.split('.')[0] + '.csv'
table.to_csv(savename)
if name == "main":
window = tk.Tk()
window.title('Analysis')
window.geometry("200x150")
frame = tk.Frame(window)
frame.grid(row=0, column=0)
button = tk.Button(frame, text='Generate Table', command=analysis)
button.grid(row=0, column=1)
window.mainloop()
```
full running codes here: https://pastebin.com/0z34GfNW
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MDU6SXNzdWU3MDc0ODE3MTk=
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|
ENH: change display.width to None by default
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[] | null | 3
| 2020-09-23T15:47:56Z
| 2021-06-06T19:10:23Z
| null |
CONTRIBUTOR
| null |
#### Describe the solution you'd like
Currently `display.width` is set to 80 char on a terminal as far as I understand, which often leads to output being truncated. It would be nicer if the terminal width was auto-detected, which would avoid repr truncation for some fraction of those cases. This can be achieved by setting `display.width=None` by default.
#### API breaking implications
This would likely be an issue for doctests. I imagine there should be a way to keep `display.width` inside a test session where doctests are checked. Though then it would mean that running some code in a REPL and in the doctest might not produce exactly the same output.
Overall, this would need more careful consideration but I'm wondering if there is any way to improve the usability for the interactive use-case.
#### Describe alternatives you've considered
Remembering which option needs to be set to avoid repr truncation and setting it. I keep forgetting which one it is, and have to spend a couple of minutes finding it again.
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TST: dtype related test failures on 32bit system
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| 2021-08-13T03:11:07Z
| null |
MEMBER
| null |
1. `tests/test_algos.py:TestHashTable:test_lookup_nan` #36584
2. `tests/test_algos.py:TestHashTable:test_lookup_overflow` #36584
3. `tests/indexes/test_base.py:test_get_indexer_non_unique_wrong_dtype` #36584
4. `tests/indexes/period/test_indexing.py:TestGetIndexer:test_get_indexer_non_unique` #36584
```
E AssertionError: numpy array are different
E
E Attribute "dtype" are different
E [left]: int32
E [right]: int64
```
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MDExOlB1bGxSZXF1ZXN0NDkxOTM2NTE0
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BUG: use cmath to test complex number equality in pandas._testing
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MEMBER
| null |
- [x] closes #28235
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds `cmath`-based equality testing for complex numeric types (`complex`, `np.complex64` and `np.complex128`)
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REF: refactor/cleanup CSSResolver
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- [ ] closes #xxxx
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Refactor ``__call__`` method of ``CSSResolver``.
- Extract methods
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[BUG]: Fix regression when adding timeldeta_range to timestamp
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| 8
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| 2020-09-29T21:07:39Z
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| null |
- [x] closes #35897
- [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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MDU6SXNzdWU3MDc2MDY5MDc=
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BUG: Read_Table and Read_Csv does not raise when delim_whitespace=True and sep=default is given
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| 6
| 2020-09-23T18:59:31Z
| 2020-10-08T00:46:50Z
| 2020-10-08T00:46:50Z
|
MEMBER
| 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 io
f = io.StringIO("a b c\n1 -2 -3\n4 5 6")
df = pd.read_table(f, sep="\t", delim_whitespace=True)
df = pd.read_csv(f, sep=",", delim_whitespace=True)
```
#### Problem description
Both statement should raise a ``ValueError``
#### Expected Output
``ValueError``
Must also edit docs. See #36560 for discussion.
cc @simonjayhawkins
#### Output of ``pd.show_versions()``
<details>
master
</details>
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MDExOlB1bGxSZXF1ZXN0NDkyMDE1OTY3
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TST: 32bit dtype compat #36579
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Part of #36579
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MDU6SXNzdWU3MDc2NTMyODE=
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BUG: `DataFrame.mean` fails but `Series.mean` works incase of pandas Nullable integer dtype
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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
>>> df = pd.DataFrame({"a":[1,2,3, 4], "b":pd.Series([1, 2, 4, None], dtype=pd.Int64Dtype())})
>>> df
a b
0 1 1
1 2 2
2 3 4
3 4 <NA>
>>> df.mean()
a 2.500000
b 2.333333
dtype: float64
>>> df.mean(skipna=False)
a 2.5
b <NA>
dtype: object
>>> df.iloc[3]
a 4
b <NA>
Name: 3, dtype: Int64
>>> df.iloc[3].mean(skipna=True)
4.0
>>> df.iloc[3].mean(skipna=False)
<NA>
>>> df.mean(axis=1, skipna=False)
Traceback (most recent call last):
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 1415, in _ensure_numeric
x = x.astype(np.complex128)
TypeError: must be real number, not NAType
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/frame.py", line 8649, in _reduce
result = func(values)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/frame.py", line 8570, in func
return op(values, axis=axis, skipna=skipna, **kwds)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 71, in _f
return f(*args, **kwargs)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 129, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 563, in nanmean
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 1418, in _ensure_numeric
x = x.astype(np.float64)
TypeError: float() argument must be a string or a number, not 'NAType'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 1415, in _ensure_numeric
x = x.astype(np.complex128)
TypeError: must be real number, not NAType
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/generic.py", line 11466, in stat_func
func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/frame.py", line 8660, in _reduce
result = func(values)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/frame.py", line 8570, in func
return op(values, axis=axis, skipna=skipna, **kwds)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 71, in _f
return f(*args, **kwargs)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 129, in f
result = alt(values, axis=axis, skipna=skipna, **kwds)
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 563, in nanmean
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_sum))
File "/Users/pgali/PycharmProjects/del/venv1/lib/python3.7/site-packages/pandas/core/nanops.py", line 1418, in _ensure_numeric
x = x.astype(np.float64)
TypeError: float() argument must be a string or a number, not 'NAType'
```
#### Problem description
When there is a row-wise mean calculation of a nullable interger dtype the operation fails. But the same operation works correctly on a series by doing an iloc on the row.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.17.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
Cython : None
pytest : None
hypothesis : 5.29.0
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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MDU6SXNzdWU3MDc2NzQzOTM=
| 36,586
|
BUG: CategoricalDtype does not support pd.NA
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| 2020-09-23T20:57:18Z
| 2021-08-13T03:12:58Z
| 2021-08-13T03:12:58Z
|
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 numpy as np
import pandas as pd
s = pd.Series([1, 2, pd.NA], dtype='category')
```
```
Out[3]:
0 1
1 2
2 NaN
dtype: category
Categories (2, int64): [1, 2]
```
#### Problem description
- `pd.NA` is inputted into the Series, but it is changed to `np.nan`.
- Since the goal of pd.NA is to`provide a “missing” indicator that can be used consistently across data types`, the CategoricalDtype should support **pd.NA**
#### Expected Output
```
Out[3]:
0 1
1 2
2 <NA>
dtype: category
Categories (2, int64): [1, 2]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 4291973e52cd7d14ccaaf565dbf7a6b978d1595e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.0.dev0+482.g4291973e5
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.6.0
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 : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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|
BUG: Cannot use pd.NA in a series with float dtype
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| 2020-09-23T21:33:17Z
| 2020-09-24T12:38:00Z
| 2020-09-24T11:28:14Z
|
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.Series([1.1, pd.NA], dtype='float64')
```
#### Problem description
Similar to the issue documented in #36586, if the goal of `pd.NA` is to provide a missing indicator that can be used across datatypes, it should also work with a `float` datatype. Currently, if a user attempts to use a `pd.NA` value in a series with dtype of float, an error is raised: `TypeError: float() argument must be a string or a number, not 'NAType'`.
This also appears related to the issue mentioned in #36585
#### Expected Output
```python
import pandas as pd
pd.Series([1.1, pd.NA], dtype='float64')
0 1.1
1 <NA>
dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Mon Feb 10 21:08:45 PST 2020; root:xnu-4903.278.28~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 40.8.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
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xlwt : None
numba : None
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CLN: clean up new detected trailing whitespace
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CONTRIBUTOR
| null |
- [N/A] closes #xxxx (xref [#36386 (comment)](https://github.com/pandas-dev/pandas/pull/36386#discussion_r493865987))
- [N/A] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [N/A] whatsnew entry
Fixed the remaining trailing whitespace problems that weren't being found by `ci/code_checks.sh` because of the file type filters.
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BUG: nanoseconds argument for DateOffset results in wrong output
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CONTRIBUTOR
| null |
```python
>>>ts = pd.Timestamp('2020-1-1')
>>>ts + pd.DateOffset(nanoseconds=1000)
Timestamp('2020-01-02 00:00:00')
```
This is related to `offset = timedelta(1)` in `_determine_offset` from `pd._libs.tslibs.offsets`.
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MDU6SXNzdWU3MDc3ODc0NjQ=
| 36,590
|
BUG: add between DateOffsets raises error
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| 2020-09-24T01:39:34Z
| 2021-08-13T03:16:43Z
| null |
CONTRIBUTOR
| null |
```python
>>> pd.offsets.Day(1) + pd.offsets.Day(1)
<2 * Days>
>>> pd.offsets.DateOffset(days=1) + pd.offsets.DateOffset(days=1)
TypeError Traceback (most recent call last)
in
----> 1 pd.offsets.DateOffset(days=1) + pd.offsets.DateOffset(days=1)
pandas\_libs\tslibs\offsets.pyx in pandas._libs.tslibs.offsets.BaseOffset.__add__()
pandas\_libs\tslibs\offsets.pyx in pandas._libs.tslibs.offsets.apply_wraps.wrapper()
TypeError: Argument 'other' has incorrect type (expected datetime.datetime, got DateOffset)
```
I think they should give the same output.
```python
>>>pd.offsets.DateOffset(days=1) - pd.offsets.DateOffset(days=1)
<0 * DateOffsets: days=1>
```
And the substraction works fine.
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CI: Add rst backtick checker
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| 1
| 2020-09-24T02:08:34Z
| 2020-09-25T00:48:30Z
| 2020-09-25T00:48:27Z
|
MEMBER
| null |
Adding a pre-commit hook for detecting single backticks around code in RST files. Running for instance on the v1.2.0 whatsnew shows errors:
```
doc/source/whatsnew/v1.2.0.rst:120:- `Styler` now allows direct CSS class name addition to individual data cells (:issue:`36159`)
doc/source/whatsnew/v1.2.0.rst:225:- Performance improvements when creating DataFrame or Series with dtype `str` or :class:`StringDtype` from array with many string elements (:issue:`36304`, :issue:`36317`, :issue:`36325`, :issue:`36432`)
doc/source/whatsnew/v1.2.0.rst:230:- Performance improvement in :meth:`pd.to_datetime` with non-`ns` time unit for `float` `dtype` columns (:issue:`20445`)
doc/source/whatsnew/v1.2.0.rst:265:- Bug in :func:`date_range` was raising AmbiguousTimeError for valid input with `ambiguous=False` (:issue:`35297`)
doc/source/whatsnew/v1.2.0.rst:306:- Bug in :meth:`SeriesGroupBy.transform` now correctly handles missing values for `dropna=False` (:issue:`35014`)
doc/source/whatsnew/v1.2.0.rst:312:- Bug in :meth:`DataFrame.xs` when used with :class:`IndexSlice` raises ``TypeError`` with message `Expected label or tuple of labels` (:issue:`35301`)
doc/source/whatsnew/v1.2.0.rst:320:- In :meth:`read_csv` `float_precision='round_trip'` now handles `decimal` and `thousands` parameters (:issue:`35365`)
doc/source/whatsnew/v1.2.0.rst:322:- :meth:`to_csv` passes compression arguments for `'gzip'` always to `gzip.GzipFile` (:issue:`28103`)
doc/source/whatsnew/v1.2.0.rst:324:- :meth:`to_csv` and :meth:`read_csv` did not honor `compression` and `encoding` for path-like objects that are internally converted to file-like objects (:issue:`35677`, :issue:`26124`, and :issue:`32392`)
doc/source/whatsnew/v1.2.0.rst:327:- Bug in :meth:`read_csv` with `engine='python'` truncating data if multiple items present in first row and first element started with BOM (:issue:`36343`)
doc/source/whatsnew/v1.2.0.rst:328:- Removed ``private_key`` and ``verbose`` from :func:`read_gbq` as they are no longer supported in `pandas-gbq` (:issue:`34654` :issue:`30200`)
```
~This is going to fail for now so need to figure out how to make it pass.~
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ENH: replace method for more time-related type
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CONTRIBUTOR
| null |
Now we can only implement `replace` on `Timestamp`, I think `TimedeltaIndex`, `Timedelta`, `DatetimeIndex` and even Series with `datetime64[ns]` or `timedelta64[ns]` all need `replace` method (works like replace on Timestamp). Is there any plan to do this? (Maybe in pandas 2.0) Thanks!
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BUG: Wrong order and ambiguous start point for some period values
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| 2020-09-24T06:50:01Z
| 2020-09-24T08:13:44Z
| 2020-09-24T08:13:43Z
|
CONTRIBUTOR
| null |
```python
>>>pd.period_range('1350-01-01', '1350-01-02', freq='6h').sort_values()
PeriodIndex(['1350-01-01 23:00', '1350-01-01 05:00', '1350-01-01 11:00',
'1350-01-01 17:00', '1350-01-02 23:00'],
dtype='period[6H]', freq='6H')
```
Though sorted, '1350-01-01 23:00' still locates the first.
```python
>>>pd.period_range('2150-01-01', '2150-01-02', freq='6h')
PeriodIndex(['2150-01-01 00:00', '2150-01-01 06:00', '2150-01-01 12:00',
'2150-01-01 18:00', '2150-01-02 00:00'],
dtype='period[6H]', freq='6H')
```
And why this starts at 00:00, but the former one starts at 23:00?
Thanks!
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ENH: Expose type annotations declared in `_typing.py`
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[] | null | 2
| 2020-09-24T09:17:47Z
| 2021-07-01T21:09:27Z
| null |
NONE
| null |
I often use pandas type annotations found in the `_typing` module: `FrameOrSeries`, `FrameOrSeriesUnion`, ...
It feels a bit weird to import stuff from a private module though. Would it make sense to expose *some* annotations in a `typing.py` module?
#### API breaking implications
I wouldn't expect any, `typing.py` could be an additional module that imports from `_typing.py`
#### Describe alternatives you've considered
Alternatively, annotations could be added to the main `__init__.py` so they can be imported with `pandas.*`.
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MDExOlB1bGxSZXF1ZXN0NDkyMzE3NTcz
| 36,595
|
Partial Revert "ENH: infer freq in timedelta_range (#32377)"
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| 8
| 2020-09-24T09:37:11Z
| 2020-09-26T08:36:51Z
| 2020-09-26T01:31:39Z
|
MEMBER
| null |
closes #35897
maybe alternative to #36582 if no changes since #32377 now fail (not tested locally)
cc @phofl @jbrockmendel
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BUG: select_dtypes(include="int") has different behaviour on windows and linux
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| 3
| 2020-09-24T09:48:34Z
| 2021-02-07T17:06:43Z
| 2021-02-07T17:06:43Z
|
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 (1.1.2) of pandas.
---
#### Problem description
Using `DataFrame.select_dtypes(include='int')` on a DataFrame that contains an integer column returns an empty dataframe in windows, yet selects the correct columns in linux (Ubuntu 20.04).
`DataFrame.select_dtypes(include='integer')` works as expected in both environments.
In both cases the dtype of the column is 'int64'.
#### Code Sample, a copy-pastable example
```python
df = pd.DataFrame(columns=["Ints"], data=[1,2,3])
df.select_dtypes(include="int")
```
#### Output
on ubuntu (tested 16.04, 18.04, 20.04, python 3.6, 3.7, 3.8)
```
Ints
0 1
1 2
2 3
```
on windows 10 (python 3.6, 3.7. 3.8)
```
Empty DataFrame
Columns: []
Index: [0, 1, 2]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 126 Stepping 5, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Europe.1252
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.104-microsoft-standard
Version : #1 SMP Wed Feb 19 06:37:35 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : 5.4.3
hypothesis : 5.20.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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wheel build failing on MacPython.pandas-wheels
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| 2
| 2020-09-24T10:42:57Z
| 2020-09-25T11:54:53Z
| 2020-09-25T11:54:53Z
|
MEMBER
| null |
opening a new issue for visibility.
https://github.com/pandas-dev/pandas/pull/36393#issuecomment-694299430
> wheel build failing https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=42575&view=logs&j=a846d25a-e32c-5640-1b53-e815fab94407
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ENH: Add Option to `.plot.scatter` to Label the Points based on Index name or supplied sequence
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| 2020-09-24T11:20:49Z
| 2021-06-06T19:09:47Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
I just had to generate a scatter plot with labels for the points, and was surprised that there isn't an argument to `.plot.scatter` to allow this.
#### Describe the solution you'd like
An additional keyword argument to `df.plot.scatter` that adds labels to the points. This would be off by default because it is not helpful for plots with too many points. I am unsure about the type of the parameter that should be passed. A default case that uses the index (if available) as a label would be useful, otherwise something iterable that contains the value for each point, or a string reference to a column name to use as labels.
#### API breaking implications
This would only extend the available options for `.plot.scatter` and should lead to no API breaking changes as long as care is taken that the parameter name doesn't shadow some `kwarg` that would be handled by the underlying functions (incl. matplotlib) instead.
#### Describe alternatives you've considered
My current workaround is simply:
```python
plt = df.plot.scatter(x='x', y='y')
# Label the points
for row in df[['x', 'y']].itertuples():
plt.text(row.x, row.y, row.Index)
```
If this is a reasonable feature request, I will look into implementing it myself, but currently I don't have the time, and could not find any previous issue that addresses this feature.
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MDExOlB1bGxSZXF1ZXN0NDkyMzgwODQ4
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numpy version in py36_locale_slow_old_np on 1.1.x
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| 3
| 2020-09-24T11:32:56Z
| 2020-09-25T09:14:15Z
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|
MEMBER
| null |
PR against 1.1.x
not sure if important but numpy version in https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=43144&view=logs&jobId=a69e7846-138e-5465-0656-921e8964615b&j=a69e7846-138e-5465-0656-921e8964615b&t=56da51de-fd5a-5466-5244-b5f65d252624 is 1.19.2
xref #33729
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| 2020-09-24T12:38:42Z
| 2020-09-24T14:58:54Z
| 2020-09-24T14:58:54Z
|
CONTRIBUTOR
| null |
https://travis-ci.org/github/pandas-dev/pandas/jobs/729859284
well at least we detected this early. ideally can investigate and fix, but for now need a green CI.
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| 9
| 2020-09-24T14:59:34Z
| 2021-10-16T20:16:13Z
| 2021-10-16T20:16:13Z
|
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
def wtf(x):
x['baz'] = ''
return x.sort_values(by='boo')
df = pd.DataFrame({'boo': ['A', 'B'], 'grouper': ['foo', 'bar']})
df.groupby('grouper').apply(wtf)
```
#### Problem description
The output I get from above code is
````
boo grouper baz
grouper
bar 1 B bar
foo 0 B bar
````
whereas I would expect
````
boo grouper baz
grouper
bar 1 B bar
foo 0 A foo
````
Both steps are necessary. If we leave out entirely or apply `df['baz'] = ''` before executing the groupby-apply, the bug doesn't happen. If we leave out the `sort_values`, the bug doesn't happen. (Applying it outside of the groupby-apply is different semantically).
Order matters. If we first sort then modify, the bug doesn't happen.
Any pointers as to where to start debugging this?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.17763
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : 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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| 708,283,346
|
MDU6SXNzdWU3MDgyODMzNDY=
| 36,603
|
BUG: pd.cut fails when ordered is set to False and labels is set to a series
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| 1
| 2020-09-24T15:47:43Z
| 2020-09-25T01:52:59Z
| 2020-09-25T01:52:59Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
test = pd.DataFrame([['a', 5], ['b', 2], ['c', 6], ['d', 3], ['e', 8]],
columns=['row_name', 'row_value'])
cuts = pd.DataFrame([[1, 'odd'], [2, 'even'], [3, 'odd'], [4, 'even'], [5, 'odd'],
[6, 'even'], [7, 'odd'], [8, 'even'], [9, 'odd'], [10, '']],
columns=['cut_value', 'cut_label'])
print(pd.cut(test.row_value, cuts.cut_value, labels=cuts.cut_label[:-1], ordered=False))
```
#### Problem description
When a user wants to call pd.cut with a set of labels that includes duplicate values, they must set ordered to False and set labels to the series of strings to be used.
But running the sample code above, with ordered set to False and labels set to a series, gives this output:
```
Traceback (most recent call last):
File "/Users/mark/PycharmProjects/temp/bug1/cut_test.py", line 8, in <module>
print(pd.cut(test.row_value, cuts.cut_value, labels=cuts.cut_label[:-1], ordered=False))
File "/Users/mark/PycharmProjects/temp/bug1/venv/lib/python3.7/site-packages/pandas/core/reshape/tile.py", line 284, in cut
ordered=ordered,
File "/Users/mark/PycharmProjects/temp/bug1/venv/lib/python3.7/site-packages/pandas/core/reshape/tile.py", line 384, in _bins_to_cuts
if not ordered and not labels:
File "/Users/mark/PycharmProjects/temp/bug1/venv/lib/python3.7/site-packages/pandas/core/generic.py", line 1327, in __nonzero__
f"The truth value of a {type(self).__name__} is ambiguous. "
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Process finished with exit code 1
```
#### Expected Output
The expected output is a series of strings taken from the labels list, as documented.
You can see from the Traceback that the error occurs at line 384 of tile.py. which is
```
if not ordered and not labels:
```
If that line is changed to
```
if not ordered and labels is None:
```
then running the sample code again gives the correct output:
```
0 even
1 odd
2 odd
3 even
4 odd
Name: row_value, dtype: category
Categories (2, object): ['even', 'odd']
Process finished with exit code 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_CA.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.3
setuptools : 50.3.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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|
BUG: propagate dropna in pd.Grouper
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| 3
| 2020-09-24T15:59:41Z
| 2021-05-18T02:07:46Z
| 2020-09-26T01:18:00Z
|
MEMBER
| null |
- [x] closes #36620
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
A precursor to #35751
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BUG: handle multi-dimensional grouping better
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| 15
| 2020-09-24T16:08:07Z
| 2020-10-31T19:11:38Z
| 2020-10-31T19:11:35Z
|
CONTRIBUTOR
| null |
As described in issue #36158 multi-dimensional groups prepared with lambda or named function raise error on indices as isnan() is not defined for MultiIndex.
Grouping already has the check for MultiIndex on line 440, but it's not triggered on __init__ when grouper is represented by **lambda** function and gets casted to MultiIndex later where MultiIndex stays as is.
~~Tested on the code provided in #36158, yet not sure if significant testing is needed as this is a relatively small change to code.
Grouper code has not been changed for a while, should be save to merge.~~
Tests for this particular issue provided.
~~**Concerns**: probably it will be better to make `isinstance(grouper, MultiIndex):` a function as after this change this piece of code occurs twice in __init__.~~
Those operate on different grouper object. First one is passed on init, second one is `self`, where grouper is changed from lambda to MultiIndex and needs further change added in PR.
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CI: rerun flaky test in html #36467
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Part of #36467
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ERR: DataFrame.fillna with non-unique mapping
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| 2021-08-13T03:33:41Z
| null |
MEMBER
| 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.
---
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({"a": [1.0, np.nan]})
s = pd.Series([0.0, 2.0], index=["a", "a"])
df.fillna(s) # works silently on master or 1.1.2
# a
# 0 1.0
# 1 0.0
```
#### Problem description
DataFrame.fillna using a Series with a duplicated index is potentially ambiguous, but the filling works silently (apparently using the first value).
#### Expected Output
Maybe a warning that the operation is ambiguous?
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CLN: de-duplicate _local_timestamps
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REGR: DataFrame.apply() with raw option and func returning string
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MEMBER
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- [ ] closes #35940
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDExOlB1bGxSZXF1ZXN0NDkyNjc2ODE5
| 36,611
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PERF: Always using panda's hashtable approach, dropping np.in1d
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| 15
| 2020-09-24T20:16:34Z
| 2020-11-17T12:59:27Z
| 2020-11-17T12:59:22Z
|
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
numpy's `in1d` uses look-up in `O(log n)` in `in1d` compared to panda's `O(1)` so for a large number of unique elements in `values` pandas solution will become better.
Here is comparison in the performance benchmark:
```
before after ratio
[27aae225] [796a7dbc]
+ 11.4±0.07ms 48.8±0.3ms 4.29 series_methods.IsInLongSeries.time_isin('float64', 1)
+ 19.4±0.2ms 59.5±0.6ms 3.06 series_methods.IsInLongSeries.time_isin('float64', 2)
+ 13.1±0.1ms 39.2±0.5ms 2.98 series_methods.IsInLongSeries.time_isin('int64', 1)
+ 26.9±0.4ms 64.0±0.7ms 2.38 series_methods.IsInLongSeries.time_isin('float32', 1)
+ 34.5±0.2ms 74.9±0.9ms 2.17 series_methods.IsInLongSeries.time_isin('float32', 2)
+ 23.2±0.1ms 44.5±0.3ms 1.92 series_methods.IsInLongSeries.time_isin('int64', 2)
+ 28.1±0.4ms 53.8±0.4ms 1.91 series_methods.IsInLongSeries.time_isin('int32', 1)
+ 42.7±0.2ms 70.3±0.3ms 1.64 series_methods.IsInLongSeries.time_isin('float64', 5)
+ 38.2±0.3ms 59.1±0.5ms 1.55 series_methods.IsInLongSeries.time_isin('int32', 2)
+ 57.4±0.3ms 85.3±0.5ms 1.49 series_methods.IsInLongSeries.time_isin('float32', 5)
- 83.2±1ms 76.6±0.5ms 0.92 series_methods.IsInLongSeries.time_isin('float64', 10)
- 118±6ms 69.8±0.2ms 0.59 series_methods.IsInLongSeries.time_isin('int32', 10)
- 102±1ms 54.9±0.3ms 0.54 series_methods.IsInLongSeries.time_isin('int64', 10)
- 515±3ms 216±1ms 0.42 series_methods.IsInLongSeries.time_isin('int32', 100000)
- 498±3ms 201±1ms 0.40 series_methods.IsInLongSeries.time_isin('int64', 100000)
- 529±2ms 154±7ms 0.29 series_methods.IsInLongSeries.time_isin('float32', 100000)
- 529±3ms 131±0.7ms 0.25 series_methods.IsInLongSeries.time_isin('float64', 100000)
- 519±5ms 107±0.6ms 0.21 series_methods.IsInLongSeries.time_isin('int32', 1000)
- 499±3ms 91.3±0.3ms 0.18 series_methods.IsInLongSeries.time_isin('int64', 1000)
- 534±4ms 87.6±3ms 0.16 series_methods.IsInLongSeries.time_isin('float32', 1000)
- 519±2ms 72.6±0.9ms 0.14 series_methods.IsInLongSeries.time_isin('float64', 1000)
```
It looks as if for more than 10 `values`-elements, panda's approach is faster.
See also this comment (https://github.com/pandas-dev/pandas/issues/22205#issuecomment-410519409).
The question is whether it really makes sense to keep numpy's approach for less than 10 `values`-elements.
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CLN: Avoid importing Series in core.aggregation
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| 2020-09-24T21:18:22Z
| 2020-09-26T12:00:22Z
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MEMBER
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- [ ] closes #xxxx
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Breaks import cycles allowing aggregation to be imported at the top.
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MEMBER
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MDU6SXNzdWU3MDg1NDkzOTg=
| 36,614
|
QST: Why have my data values been replaced by “NaN” after using qcut?
|
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| 2020-09-25T00:15:19Z
| 2020-10-04T10:34:43Z
| 2020-10-04T10:34:06Z
|
NONE
| null |
- [ ] I have searched the [[pandas] cut, qcut](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ https://stackoverflow.com/questions/63929143/why-have-my-data-values-been-replaced-by-nan-after-using-qcut] I have asked my usage related question on [StackOverflow](https://stackoverflow.com).
---
#### Question about pandas
**Note**: If you'd still like to submit a question, 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 question.
I am working with pandas dataframe of 9000 rows and 6 columns. At this point, I am trying to convert the continuous variable 'Experience' years of a job into the categorical variable 'Level' of expertise (beginner - intermediate - advanced - expert) for each of the 4 jobs (Commercial Manager - Business Developer - Web Marketer - Traffic Manager).
Giving that years experience range are not the same for each job, I utilised "qcut" to divide data into 4 groups as follows:
(You can run the code below to get the dataframe sample)
```python
import pandas as pd
df = pd.DataFrame({'Job': ['Commercial Manager', 'Traffic Manager', 'Web Marketer', 'Commercial Manager', 'Commercial Manager', 'Web Marketer', 'Commercial Manager', 'Commercial Manager', 'Traffic Manager', 'Business Developer', 'Business Developer', 'Web Marketer', 'Traffic Manager', 'Traffic Manager', 'Commercial Manager', 'Business Developer', 'Traffic Manager', 'Commercial Manager', 'Business Developer', 'Business Developer', 'Web Marketer'],
'Experience': [1.00000, 3.00000, 3.00000, 1.50000, 2.00000, 6.00000, 0.00000, 4.00000, 8.00000, 5.00000, 0.50000, 3.00000, 3.00000, 0.00000, 2.00000, 3.00000, 0.50000, 3.00000, 3.00000, 8.00000, 3.50000]})
levels = ["beginner", "intermediate", "advanced", "expert"]
jobs = ["Commercial Manager", "Business Developer", "Web Marketer", "Traffic Manager"]
def convert(levels, jobs):
for j in jobs:
df["Level"] = pd.qcut(df.loc[df["Job"] == j, "Experience"].rank(method="first"), q = 4, labels = levels, duplicates = "drop")
return df
convert(levels, jobs)
```
It appears that it only worked for "Traffic Manager" and it replaced the other 'level' experience with NaN. I am really lost. Any help please?
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MDU6SXNzdWU3MDg1NzAxNTg=
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BUG: OutOfBoundsTimedelta not raised for out-of-bounds Timedelta
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| 4
| 2020-09-25T01:20:51Z
| 2021-07-13T21:17:24Z
| 2021-07-13T21:17:24Z
|
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
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: pd.Timedelta(np.timedelta64(200000, "D"))
Out[3]: Timedelta('-13504 days +00:25:26.290448384')
```
#### Problem description
Pandas provides a useful error message if one tries to create a `Timestamp` using a datetime that is out of bounds for nanosecond-precision times:
```
In [4]: pd.Timestamp(np.datetime64("0001-01-01", "us"))
---------------------------------------------------------------------------
OutOfBoundsDatetime Traceback (most recent call last)
<ipython-input-11-1bf9f0c9d954> in <module>
----> 1 pd.Timestamp(np.datetime64("0001-01-01", "us"))
~/Software/pandas/pandas/_libs/tslibs/timestamps.pyx in pandas._libs.tslibs.timestamps.Timestamp.__new__()
~/Software/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.convert_to_tsobject()
~/Software/pandas/pandas/_libs/tslibs/conversion.pyx in pandas._libs.tslibs.conversion.get_datetime64_nanos()
~/Software/pandas/pandas/_libs/tslibs/np_datetime.pyx in pandas._libs.tslibs.np_datetime.check_dts_bounds()
OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1-01-01 00:00:00
```
I was excited to see an exception along these lines added for out-of-bounds timedeltas, `OutOfBoundsTimedelta`:
https://github.com/pandas-dev/pandas/pull/34448; however, it does not seem to be very easy to trigger. Is the idea to eventually check this in more places?
#### Expected Output
I might expect this to raise the newly added `OutOfBoundsTimedelta` error.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.5.0
Version : Darwin Kernel Version 19.5.0: Tue May 26 20:41:44 PDT 2020; root:xnu-6153.121.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 19.2.2
setuptools : 49.6.0.post20200917
Cython : 0.29.19
pytest : 5.0.1
hypothesis : 5.6.0
sphinx : 3.0.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.10.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.2.1
fsspec : 0.8.2
fastparquet : None
gcsfs : 0.6.2+6.geca6dce
matplotlib : 3.3.0rc1.post439+g7e9530338
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.1
xlrd : None
xlwt : None
numba : 0.51.2
</details>
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MDExOlB1bGxSZXF1ZXN0NDkyNzgwNzQx
| 36,616
|
Revert "[BUG]: Fix regression when adding timeldeta_range to timestamp"
|
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| 2
| 2020-09-25T01:29:21Z
| 2020-09-25T08:52:06Z
| 2020-09-25T08:51:50Z
|
MEMBER
| null |
Reverts pandas-dev/pandas#36582
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