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BUG:ValueError: Invalid frequency with date
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| 2021-04-13T06:01:51Z
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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.
---
#### Code Sample
```python
import pandas as pd
df = pd.read_hdf('metr-la.h5')
print(df)
```
metr-la.h5
https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX
It's index is DataTime type, data is float64 type.
#### Output
```python
Traceback (most recent call last):
File "C:\Test.py", line 5, in <module>
print(pandas_df)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\frame.py", line 751, in __repr__
show_dimensions=show_dimensions,
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\frame.py", line 881, in to_string
line_width=line_width,
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\io\formats\format.py", line 630, in __init__
self._chk_truncate()
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\io\formats\format.py", line 716, in _chk_truncate
frame = concat((frame.iloc[:row_num, :], frame.iloc[-row_num:, :]))
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 284, in concat
sort=sort,
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 454, in __init__
self.new_axes = self._get_new_axes()
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 521, in _get_new_axes
for i in range(ndim)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 521, in <listcomp>
for i in range(ndim)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 574, in _get_concat_axis
concat_axis = _concat_indexes(indexes)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\reshape\concat.py", line 592, in _concat_indexes
return indexes[0].append(indexes[1:])
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexes\base.py", line 4153, in append
return self._concat(to_concat, name)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\indexes\base.py", line 4161, in _concat
result = _concat.concat_compat(to_concat)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\dtypes\concat.py", line 162, in concat_compat
return concat_datetime(to_concat, axis=axis, typs=typs)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\dtypes\concat.py", line 392, in concat_datetime
result = type(to_concat[0])._concat_same_type(to_concat, axis=axis)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\arrays\datetimelike.py", line 695, in _concat_same_type
if all(pair[0][-1] + obj.freq == pair[1][0] for pair in pairs):
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\arrays\datetimelike.py", line 695, in <genexpr>
if all(pair[0][-1] + obj.freq == pair[1][0] for pair in pairs):
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\arrays\datetimelike.py", line 540, in __getitem__
return self._box_func(result)
File "C:\Program Files (x86)\Microsoft Visual Studio\Shared\Python37_64\lib\site-packages\pandas\core\arrays\datetimes.py", line 476, in <lambda>
return lambda x: Timestamp(x, freq=self.freq, tz=self.tz)
File "pandas\_libs\tslibs\timestamps.pyx", line 1083, in pandas._libs.tslibs.timestamps.Timestamp.__new__
File "pandas\_libs\tslibs\offsets.pyx", line 3580, in pandas._libs.tslibs.offsets.to_offset
ValueError: Invalid frequency: b"ccopy_reg\n_reconstructor\np1\n(cpandas.tseries.offsets\nMinute\np2\nc__builtin__\nobject\np3\nNtRp4\n(dp5\nS'normalize'\np6\nI00\nsS'_offset'\np7\ncdatetime\ntimedelta\np8\n(I1\nI0\nI0\ntRp9\nsS'_use_relativedelta'\np10\nI00\nsS'kwds'\np11\n(dp12\nsS'n'\nI5\nsb."
```
#### Problem description
Pandas seems to work incorrect with hdf5 file created by old version pandas.
The file work well with pandas 0.19.2. Now, many method will raise ValueError: Invalid frequency.
I am not sure what causes this error, but I found that the DataFrame's index is DatetimeArray type instead of DatetimeIndex type which may be the reason.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.1
numpy : 1.19.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.2
setuptools : 47.1.0
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : None
fsspec : 0.7.2
fastparquet : None
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pyxlsb : None
s3fs : 0.2.2
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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REF: window/test_dtypes.py with pytest idioms
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- [x] tests added / passed
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Backport PR #35794: BUG: issubclass check with dtype instead of type,…
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xref #35794
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Inconsistent Behavior in DataFrame.plot for kind='line' and kind='scatter'
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| 3
| 2020-08-27T12:13:49Z
| 2020-09-09T19:25:55Z
| null |
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
<!-- **Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. -->
#### Code Sample, a copy-pastable example
```python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
columns = ['mai_mass', 'a', 'b', 'c', 'd', 'e', 'f']
coefficients = pd.DataFrame(np.array(
[[ 2.38000000e+02, 2.83321374e-05,
-2.50365061e-03, 8.30752483e-02, -1.31248716e+00,
1.30955584e+01, 9.95461098e-01],
[ 2.32000000e+02, 2.79293189e-05,
-2.46649285e-03, 8.17323741e-02, -1.28814623e+00,
1.28178227e+01, 1.01163885e+00],
[ 2.10000000e+02, 2.56529877e-05,
-2.26170242e-03, 7.48763149e-02, -1.17863331e+00,
1.17456694e+01, 1.13733660e+00],
[ 2.09000000e+02, 2.61992434e-05,
-2.30290780e-03, 7.58678030e-02, -1.18582975e+00,
1.17216659e+01, 1.10922453e+00],
[ 2.08000000e+02, 2.48331770e-05,
-2.20267481e-03, 7.33166999e-02, -1.15820137e+00,
1.15702725e+01, 1.15312639e+00],
[ 2.05000000e+02, 2.45927176e-05,
-2.17930444e-03, 7.24894127e-02, -1.14523212e+00,
1.14529544e+01, 1.14653358e+00],
[ 2.02000000e+02, 2.57966338e-05,
-2.25854266e-03, 7.41400587e-02, -1.15423938e+00,
1.13598402e+01, 1.10762225e+00],
[ 1.97000000e+02, 2.40017298e-05,
-2.12125930e-03, 7.03851343e-02, -1.10885141e+00,
1.10811860e+01, 1.19365483e+00],
[ 1.95000000e+02, 2.46969467e-05,
-2.16678796e-03, 7.13037966e-02, -1.11347385e+00,
1.10318337e+01, 1.16592199e+00],
[ 1.93000000e+02, 2.36723928e-05,
-2.09471471e-03, 6.94883903e-02, -1.09206240e+00,
1.08695266e+01, 1.18271973e+00]]), columns=columns)
# this works fine
coefficients.plot(x='mai_mass')
plt.show()
# this raises an error
coefficients.plot(x='mai_mass', kind='scatter')
plt.show()
```
#### Problem description
`DataFrame.plot` raises a `ValueError` with message "scatter requires an x and y column" when trying to make a scatter plot.
See traceback:
```python
~\Miniconda3\envs\simtra\lib\site-packages\pandas\plotting\_core.py in __call__(self, *args, **kwargs)
892 if kind in self._dataframe_kinds:
893 if isinstance(data, ABCDataFrame):
--> 894 return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)
895 else:
896 raise ValueError(f"plot kind {kind} can only be used for data frames")
~\Miniconda3\envs\simtra\lib\site-packages\pandas\plotting\_matplotlib\__init__.py in plot(data, kind, **kwargs)
58 ax = plt.gca()
59 kwargs["ax"] = getattr(ax, "left_ax", ax)
---> 60 plot_obj = PLOT_CLASSES[kind](data, **kwargs)
61 plot_obj.generate()
62 plot_obj.draw()
~\Miniconda3\envs\simtra\lib\site-packages\pandas\plotting\_matplotlib\core.py in __init__(self, data, x, y, s, c, **kwargs)
972 elif is_hashable(s) and s in data.columns:
973 s = data[s]
--> 974 super().__init__(data, x, y, s=s, **kwargs)
975 if is_integer(c) and not self.data.columns.holds_integer():
976 c = self.data.columns[c]
~\Miniconda3\envs\simtra\lib\site-packages\pandas\plotting\_matplotlib\core.py in __init__(self, data, x, y, **kwargs)
897 MPLPlot.__init__(self, data, **kwargs)
898 if x is None or y is None:
--> 899 raise ValueError(self._kind + " requires an x and y column")
900 if is_integer(x) and not self.data.columns.holds_integer():
901 x = self.data.columns[x]
ValueError: scatter requires an x and y column
```
#### Expected Output
No error raised as in the first example.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.1.1
numpy : 1.18.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.4.0.post20200518
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 : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU2ODcyNDEzNzg=
| 35,921
|
test_datetime64_factorize failing on MacPython windows py_3.8_32/linux py_3.8_32
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| 0
| 2020-08-27T13:25:44Z
| 2020-09-07T19:08:52Z
| 2020-09-07T19:05:51Z
|
MEMBER
| null |
test added in #35775
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=40968&view=results
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BUG: Index.sort_values and Series.sort_values reverse duplicate order when ascending=False
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| 2020-10-31T14:49:14Z
| 2020-10-31T14:49:14Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
IN:
import pandas as pd
ind = pd.Index([1, 1, 2, 3])
ind, indexer = ind.sort_values(ascending=False, return_indexer=True)
indexer
OUT:
array([3, 2, 1, 0], dtype=int64)
```
#### Problem description
Came across this one while fixing #35584 in #35604. `sort_values` reverses duplicate order when `ascending=False`. This is clearest when calling `Index.sort_values`, because it can return an indexer, but it's also true for `Series.sort_values` and it propagates to `DataFrame.sort_values`.
#35604 will make sorting in descending order stable for most `Index` types (leveraging nargsort from `sorting.py`), but the problem will remain for datetime-like index types and for Series and will require fixing.
#### Expected Output
```python
array([3, 2, 0, 1], dtype=int64)
```
Duplicates should maintain order when `descending=False`. This will also let us leverage the same sorting algorithm both for `Index` and `Series`.
#### Additional use cases
Some additional use cases from the PR.
```python
s = pd.Series(["A", "AA", "BB", "CAC"], dtype="object")
s.sort_values(ascending=False, key=lambda ser: ser.str.len())
OUT:
3 CAC
2 BB
1 AA
0 A
dtype: object
```
I don't think that swapping is expected here.
Then consider that you might be sorting a DataFrame with several columns, and a column with duplicates might be the first one. In this case you likely wouldn't expect a descending sort to change duplicate order. Or you could be using something like `nlargest` and get weirdness because there is a descending sort in there and it swaps elements.
Obviously, we could get by with a convention that we always revert duplicate order with a descending sort by being careful, but I believe keeping duplicate order is cleaner. In cases where it doesn't matter, it's the same, and when it does matter (as in `nlargest` and the like), you don't need to remember that you need extra reversals.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d0ca4b347b060bc4a49b2d31a818d5a28d1e665f
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : ru_RU.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+4054.gd0ca4b347
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.2.0.post20200714
Cython : 0.29.21
pytest : 5.4.3
hypothesis : 5.23.3
sphinx : 3.1.1
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : 0.6.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.3.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
</details>
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MDU6SXNzdWU2ODcyOTkxNzE=
| 35,923
|
BUG: Inconsistent results using pd.json_normalize() on a generator object versus list (off by one)
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| 6
| 2020-08-27T14:41:34Z
| 2020-12-30T14:08:34Z
| 2020-12-30T14:08:34Z
|
NONE
| null |
- [ x] I have checked that this issue has not already been reported.
- [ x] I have confirmed this bug exists on the latest version of pandas.
---
#### Code Sample, a copy-pastable example
Only one value is returned with this:
```python
def gen():
test = [{'created_at': '2020-08-24T09:30:05Z',
'_id': '5f43889de6a98fd57afce7be'},
{'created_at': '2020-08-23T11:16:09Z',
'_id': '5f44b03799944352493d9317'},
]
for val in test:
yield val
results = gen()
pd.json_normalize(results)
```
This returns all values though:
```
results = gen()
list_ = [x for x in results]
pd.json_normalize(list_)
```
And so does this:
```python
def list_():
final = []
test = [{'created_at': '2020-08-24T09:30:05Z',
'_id': '5f43889de6a98fd57afce7be'},
{'created_at': '2020-08-23T11:16:09Z',
'_id': '5f44b03799944352493d9317'},
]
for val in test:
final.append(val)
return final
results = list_()
pd.json_normalize(results)
```
#### Problem description
Using pd.json_normalize() on a generator always seems to reduce the expected results by 1. I first noticed this on a REST API where a column informed me that I should expect 901 results but I kept getting 900 results each time. When I tried to append the results to a list and normalize that, I got the expected 901 results.
#### Expected Output
Perhaps this is an expected output. It just caused me some headaches earlier and it was not immediately obvious that I was missing one record. I would expect that my example above would result in the same 2 row DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-1028-azure
Version : #29~18.04.1-Ubuntu SMP Fri Jun 5 14:32:34 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 : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 1.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDc0NzQ4ODE5
| 35,924
|
REF: use BlockManager.apply for DataFrameGroupBy.count
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| 2
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| 2020-09-04T20:50:50Z
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MDU6SXNzdWU2ODczNTQ2NTc=
| 35,925
|
CLN remove unnecessary trailing commas to get ready for new version of black
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| 20
| 2020-08-27T15:52:10Z
| 2020-09-22T13:39:07Z
| 2020-09-22T13:37:59Z
|
MEMBER
| null |
The new version of `black` is consistent in how it handles the magic trailing commas. So if we upgrade `black` and apply it, lots of files will be changed. However, the diff needn't be so large if we remove unnecessary trailing commas before upgrading.
E.g. in pandas/core/aggregation.py there is
```python
def reconstruct_func(
func: Optional[AggFuncType], **kwargs,
) -> Tuple[
bool, Optional[AggFuncType], Optional[List[str]], Optional[List[int]],
]:
```
which has an unnecessary trailing comma.
The new version of `black` would transform this as
```python
def reconstruct_func(
func: Optional[AggFuncType],
**kwargs,
) -> Tuple[bool, Optional[AggFuncType], Optional[List[str]], Optional[List[int]],]:
```
However, if we instead remove the trailing comma and write it as
```python
def reconstruct_func(
func: Optional[AggFuncType], **kwargs
) -> Tuple[bool, Optional[AggFuncType], Optional[List[str]], Optional[List[int]]]:
```
then both the current and the new versions of black will be OK with it.
So, PRs to remove some unnecessary trailing commas would be welcome - perhaps keep each PR limited to 5-10 files changed.
Files that (may) need changing are:
- [ ] /asv_bench/benchmarks/arithmetic.py
- [ ] /pandas/core/array_algos/replace.py
- [x] /doc/make.py
- [ ] /doc/source/conf.py
- [ ] /pandas/core/aggregation.py
- [ ] /pandas/core/algorithms.py
- [ ] /pandas/_vendored/typing_extensions.py
- [ ] /pandas/core/util/numba_.py
- [ ] /pandas/core/sorting.py
- [ ] /pandas/io/formats/latex.py
- [ ] /pandas/core/series.py
- [ ] /pandas/io/formats/format.py
- [ ] /pandas/core/frame.py
- [ ] /pandas/tests/arrays/sparse/test_array.py
- [ ] /pandas/tests/frame/test_analytics.py
- [x] /pandas/tests/indexes/base_class/test_indexing.py
- [x] /pandas/tests/indexes/test_common.py
- [x] /pandas/tests/indexes/test_numeric.py
- [ ] /pandas/tests/io/test_gcs.py
- [ ] /pandas/tests/io/test_parquet.py
- [ ] /pandas/tests/scalar/timestamp/test_constructors.py
- [ ] /pandas/tests/series/test_operators.py
- [ ] /scripts/tests/test_validate_docstrings.py
EDIT
----
updated list of files which need changing
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MDExOlB1bGxSZXF1ZXN0NDc0ODAyMDM4
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remove unnecessary trailing commas
|
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xref #35925
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MDExOlB1bGxSZXF1ZXN0NDc0ODAzNTE3
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|
CLN remove unnecessary trailing commas
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MEMBER
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xref #35925
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MDExOlB1bGxSZXF1ZXN0NDc0ODA0Njkz
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|
Inconsistencies between python/cython groupby.agg behavior
|
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| 2020-09-17T20:56:49Z
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MEMBER
| null |
This is pretty ugly, but tentatively is sufficient to make #34997 pass.
The upshot is that we have two problems:
1) in libreduction setting `setattr(cached_ityp, '_index_data', islider.buf)` silently does the wrong thing for EA-backed indexes
2) when we go through the non-libreduction path, we do things slightly differently, which requires more patches to get tests passing.
cc @WillAyd
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MDExOlB1bGxSZXF1ZXN0NDc0ODEwODUy
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CLN remove unnecessary trailing commas
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MEMBER
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xref #35927
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MDExOlB1bGxSZXF1ZXN0NDc0ODEyMTU1
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CLN remove unnecessary trailing commas from aggregation
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MEMBER
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xref #35925
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MDU6SXNzdWU2ODczODk3OTc=
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BUG: breaking change in df.replace() from 1.0.5 to 1.1.0
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| 4
| 2020-08-27T16:45:23Z
| 2020-09-01T14:56:22Z
| 2020-09-01T14:56:22Z
|
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
# Your code here
df = pd.DataFrame({"age": range(1,100)})
df["age"] = pd.cut(df["age"], [0, 50, 65, 99], right=False)
df.replace(to_replace={
"age":{
pd.Interval(0, 50, closed='left'): "Ages < 50",
pd.Interval(50, 65, closed='left'): "Ages 50-64",
pd.Interval(65, 99, closed='left'): "Ages 65+"
}
})
```
#### Problem description
The above sample code works in 1.0.5 but error out in 1.1.0 and above with error msg:
```
TypeError: Cannot compare types 'ndarray(dtype=object)' and 'Interval'
```
It only happens with pd.Interval dtype, nested dict of string to string mapping works fine.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : e3bcf8d1949833793ff50cca393240dab0f0d461
python : 3.7.6.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+165.ge3bcf8d19
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 49.6.0.post20200814
Cython : 0.29.21
pytest : 6.0.1
hypothesis : 5.29.0
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.3.3
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : 0.2.0
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.1
</details>
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BUG:Installation errors on aarch64
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| 2020-08-27T16:49:33Z
| 2020-09-03T10:49:57Z
| 2020-08-29T08:16:59Z
|
NONE
| null |
- [+] I have checked that this issue has not already been reported.
- [+] I have confirmed this bug exists on the latest version of pandas.
- [+] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
pip3 install pandas
or
pip3 install pandas --no-build-isolation --no-binary :all:
or
python setup.py build_ext --inplace --force
or
LDFLAGS=" -lm -lcompiler_rt" pip3 install pandas --no-build-isolation --no-binary :all: --no-use-pep517
```
#### Problem description
```https://termbin.com/wzsr```
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### Expected Output
#### Output of ``pd.show_versions()``
When installed from source instead of pip it installs pandas but no submodules
Output of pd.show_versions()
```AttributeError: module 'pandas' has no attribute 'show_versions'```
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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TYP: Annotations
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| 2020-08-30T15:07:35Z
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MDU6SXNzdWU2ODc0MjQ3MzI=
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BUG: Inconsistency in `pd.to_datetime` with `orig` parameter according to docs.
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| 2021-04-20T04:00:43Z
| 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
>>> pd.to_datetime([1, 2, 3])
DatetimeIndex(['1970-01-01 00:00:00.000000001',
'1970-01-01 00:00:00.000000002',
'1970-01-01 00:00:00.000000003'],
dtype='datetime64[ns]', freq=None)
>>> pd.to_datetime([1, 2, 3], unit='D') # Expected
DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
>>> pd.to_datetime(['1', '2', '3'], unit='D') # Expected
DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
>>> pd.to_datetime(['1', '2', '3'], format='%d') # Inconsistent, was expecting this to be equivalent of above.
DatetimeIndex(['1900-01-01', '1900-01-02', '1900-01-03'], dtype='datetime64[ns]', freq=None)
```
#### Problem description
According to the `pd.to_datetime` docs, the default `orig` parameter is `unix` where the time starts at `1970-01-01`.
In which case I would be expecting `pd.to_datetime(['1', '2', '3'], format='%d')` to return `1970` years dates instead of treating `1900` as origin.
#### Expected Output
```python
>>> pd.to_datetime(['1', '2', '3'], unit='D')
DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
>>> pd.to_datetime(['1', '2', '3'], format='%d')
DatetimeIndex(['1970-01-02', '1970-01-03', '1970-01-04'], dtype='datetime64[ns]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
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 : 1.1.0
numpy : 1.19.0
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.1.0
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 : 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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TYP: annotations in pandas.plotting
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| 4
| 2020-08-28T01:34:17Z
| 2020-08-31T14:44:24Z
| 2020-08-31T10:16:16Z
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MEMBER
| null |
I'm still seeing a couple of mypy complaints, suggestions @simonjayhawkins ?
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BUG:Pandas 1.0.3 → 1.1.1 behavior change on DataFrame.apply() whith raw option and func returning string
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| 3
| 2020-08-28T01:42:52Z
| 2020-09-24T23:42:19Z
| 2020-09-24T23:42:19Z
|
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.
---
```python
df_1 = pd.DataFrame({'A': ["aa","bbb"]})
df_2 = pd.DataFrame({'A': ["bbb","aa"]})
def get_value(array):
return array[0]
r_1 = df_1.apply(get_value, axis=1, raw=True)
r_2 = df_2.apply(get_value, axis=1, raw=True)
print(r_1)
print(r_2)
```
#### Output
0 aa
1 bb
dtype: object
0 bbb
1 aa
dtype: object
#### Problem description
The results are truncated when the smallest strings is first. However, when the result (eg. array[0]) is printed before the return of the func, it's displays the correct value.
(This issue occurred when using apply with the raw option for a function using several columns)
#### Expected Output
0 aa
1 bbb
dtype: object
0 bbb
1 aa
dtype: object
#### Output of ``pd.show_versions()``
<details>
Pandas 1.1.1
Numpy 1.19.1
</details>
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DOC: complement the documentation for pandas.DataFrame.agg #35912
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CONTRIBUTOR
| null |
- [x] closes #35912
- [x] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: DeprecationWarning in `_testing`
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MEMBER
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```
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-DatetimeIndex-datetime64[ns]-ctor0-0]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-DatetimeIndex-datetime64[ns]-ctor0-0]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-DatetimeIndex-datetime64[ns]-ctor0-1]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-DatetimeIndex-datetime64[ns]-ctor0-1]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-TimedeltaIndex-timedelta64[ns]-ctor1-0]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-TimedeltaIndex-timedelta64[ns]-ctor1-0]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-TimedeltaIndex-timedelta64[ns]-ctor1-1]
pandas/tests/indexes/test_index_new.py::TestIndexConstructorInference::test_constructor_infer_nat_dt_like[<NA>-TimedeltaIndex-timedelta64[ns]-ctor1-1]
/home/fangchenli/Workspace/pandas-VirosaLi/pandas/_testing.py:790: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
diff = np.sum((left.values != right.values).astype(int)) * 100.0 / len(left)
```
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CLN: resolve DeprecationWarning in `pandas/_testing.py` #35942
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- [x] closes #35942
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Version Number correction in to_json table
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| 2020-08-28T05:21:36Z
| 2020-08-29T18:45:42Z
| 2020-08-29T18:44:13Z
|
NONE
| null |
Added a line of code to obtain version of Pandas and display it in Schema of a table json when to_json(orient='table') is used.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: UserWarning in `pandas/plotting/_matplotlib/core.py`
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MEMBER
| null |
```
pandas/tests/plotting/test_frame.py: 10 warnings
pandas/tests/plotting/test_series.py: 5 warnings
/home/fangchenli/Workspace/pandas-VirosaLi/pandas/plotting/_matplotlib/core.py:1235: UserWarning: FixedFormatter should only be used together with FixedLocator
ax.set_xticklabels(xticklabels)
```
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CLN: resolve UserWarning in `pandas/plotting/_matplotlib/core.py` #35945
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MEMBER
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- [x] closes #35945
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paste_windows() wrong arg for c_wchar_p
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NONE
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paste_windows() now directly used handle return from safeGetClipboardData(CF_UNICODETEXT) as argument for c_wchar_p, which should used safeGlobalLock(handle) instead, while copy_windows(text) doing the right thing.
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DOC: "engine=odf" option should ask you to install pyodf instead of odf in pandas.read_excel
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| 5
| 2020-08-28T06:44:41Z
| 2020-09-07T11:16:12Z
| 2020-09-07T11:16:12Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
"pandas.read_excel"
#### Documentation problem
When `engine="odf"` is used and `pyodf` is not installed it gives an error that you should install optional dependency odf as `pip or conda install odf`. This pip/conda command fails because there's no module called `odf`. This could also be a security vulnerability if someone puts a malicious "odf" module on pypi.
#### Suggested fix for documentation
The error should ask you to install odf using `pip or conda install pyodf`
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MDExOlB1bGxSZXF1ZXN0NDc1MjEyNDk1
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|
CLN remove unnecessary trailing commas to get ready for new version of black: _testing -> generic
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xref #35925
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CLN remove unnecessary trailing commas to get ready for new version of black: generic -> blocks
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MDExOlB1bGxSZXF1ZXN0NDc1Mjk3MjMy
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BUG: Fix DataFrame.groupby().apply() for NaN groups with dropna=False
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| 3
| 2020-08-28T09:33:10Z
| 2020-09-05T03:15:11Z
| 2020-09-05T03:15:05Z
|
CONTRIBUTOR
| null |
- [X] closes #35889
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry in `v1.1.2.rst`
|
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MDU6SXNzdWU2ODgwMTk5OTg=
| 35,952
|
BUG: Series.fillna(..., inplace=True) causes subsequent df.sort_values() to crash for categorical dtype
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| 2020-08-28T12:40:30Z
|
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.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
# Fillna without inplace - everything works OK.
df = pd.DataFrame({"a": [1, 5, 7, 3, 7], "b": ["a", "b", "c", "d", np.nan]})
df["b"] = df["b"].astype("category")
df["b"] = df["b"].fillna("a")
df = df.sort_values(by="a")
print(df)
# As above, but the fillna() is done inplace
df = pd.DataFrame({"a": [1, 5, 7, 3, 7], "b": ["a", "b", "c", "d", np.nan]})
df["b"] = df["b"].astype("category")
df["b"].fillna("a", inplace=True)
# df looks normal here - the np.nan has been replaced
print(df)
df = df.sort_values(by="a")
# Crash
```
Traceback:
<details>
```
Traceback (most recent call last):
File "test.py", line 17, in <module>
df = df.sort_values(by="a")
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/frame.py", line 5301, in sort_values
indexer, axis=self._get_block_manager_axis(axis), verify=False
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/internals/managers.py", line 1415, in take
new_axis=new_labels, indexer=indexer, axis=axis, allow_dups=True
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/internals/managers.py", line 1259, in reindex_indexer
for blk in self.blocks
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/internals/managers.py", line 1259, in <listcomp>
for blk in self.blocks
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/internals/blocks.py", line 1720, in take_nd
new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/core/series.py", line 829, in take
nv.validate_take(tuple(), kwargs)
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/compat/numpy/function.py", line 68, in __call__
validate_kwargs(fname, kwargs, self.defaults)
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/util/_validators.py", line 148, in validate_kwargs
_check_for_invalid_keys(fname, kwargs, compat_args)
File "/home/jbanorthwest.co.uk/danielevans/venvs/farmcat3/lib64/python3.6/site-packages/pandas/util/_validators.py", line 122, in _check_for_invalid_keys
raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'")
TypeError: take() got an unexpected keyword argument 'allow_fill'
```
</details>
#### Problem description
If a dataframe contains a categorical column, `df["col"].fillna(..., inplace=True)` is called on that column, and the dataframe is subsequently sorted by a different column, Pandas will crash due to an internal error.
This error only occurs in Pandas 1.1.0/1.1.1 (latest at time of writing); Pandas 1.0.5 and below behaves correctly.
Briefly poking around the internals of Pandas, the difference seems to be that `self.values.take` in `take_nd()` is operating on a `pandas.core.series.Series` object in the latest Pandas (i.e. `self.values` is a Series), but was formerly working with a `pandas.core.arrays.categorical.Categorical` object in 1.0.5 and below. As the traceback states, `Series` does not support `allow_fill` in its `take()` function, while most other implementations of `take()` in Pandas do allow it.
#### Expected Output
The two versions of the code both run, producting the sorted, na-filled output dataframe:
```
a b
0 1 a
3 3 d
1 5 b
2 7 c
4 7 a
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : d9fff2792bf16178d4e450fe7384244e50635733
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1127.13.1.el7.x86_64
Version : #1 SMP Tue Jun 23 10:32:27 CDT 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 45.2.0
Cython : 0.29.14
pytest : 5.4.3
hypothesis : 5.16.0
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext)
jinja2 : 2.11.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.6.2
fastparquet : None
gcsfs : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.2
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.0
</details>
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TYP: misc typing cleanups for #29116
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pandas\io\excel\_odswriter.py:62:35: error: Argument 1 to "_validate_freeze_panes" has incompatible type "Optional[List[Any]]"; expected "Optional[Tuple[int, int]]" [arg-type]
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MEMBER
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pandas\core\groupby\generic.py:610: error: Too many arguments [call-arg]
pandas\core\groupby\generic.py:1212: error: "create_series_with_explicit_dtype" gets multiple values for keyword argument "dtype_if_empty" [misc]
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Issue35925 remove trailing commas
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| 1
| 2020-08-28T16:42:01Z
| 2020-08-28T18:06:28Z
| 2020-08-28T18:06:09Z
|
CONTRIBUTOR
| null |
xref #35925
|
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MDExOlB1bGxSZXF1ZXN0NDc1NTMxMTA1
| 35,957
|
TYP: Remove NDFrame._add_series_or_dataframe_operations
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| 2
| 2020-08-28T17:04:32Z
| 2020-08-30T12:54:09Z
| 2020-08-30T12:00:25Z
|
CONTRIBUTOR
| null |
Refactoring ``NDFrame._add series or dataframe`` class method helps with typing.
|
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BUG: read_table raises ValueError when delim_whitespace is set to True
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| 4
| 2020-08-28T17:34:40Z
| 2020-09-26T10:31:10Z
| 2020-09-26T10:31:10Z
|
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 io
import pandas as pd
# Build a simple space delimited file buffer to read
f = io.StringIO("a b c\n1 -2 -3\n4 5 6")
# This raises an error
df = pd.read_table(f, delim_whitespace=True)
# Setting the "sep" parameter as suggested in the docs works
# df = pd.read_table(f, sep='\s+')
# print(df)
# Not setting the delim_whitespace parameter or setting it to False works,
# but without correct formatting.
# df = pd.read_table(f)
# print(df)
```
#### Problem description
If you set the delim_whitespace to True when calling read_table() on a space delimited file, I get the following exception:
```
ValueError: Specified a delimiter with both sep and delim_whitespace=True; you can only specify one.
```
#### Expected Output
I would expect the same output as you get if the sep='\s+' parameter is set (as suggested in the docs).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.6.10.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Thu Jun 18 20:50:10 PDT 2020; root:xnu-4903.278.43~1/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 : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 49.6.0.post20200814
Cython : None
pytest : 5.4.3
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
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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MDExOlB1bGxSZXF1ZXN0NDc1NTU1OTEx
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|
Issue35925 remove more trailing commas
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| 1
| 2020-08-28T17:55:58Z
| 2020-08-28T18:40:00Z
| 2020-08-28T18:39:49Z
|
CONTRIBUTOR
| null |
xref #35925
|
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TYP: annotate plotting based on _get_axe_freq
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| 1
| 2020-08-28T18:20:48Z
| 2020-08-30T15:06:52Z
| 2020-08-30T11:59:01Z
|
MEMBER
| null |
In some places in plotting `ax` is an Axes object and in other its an Axis object. Current goal is to pin these down.
in timeseries._get_ax_freq we call `ax.get_shared_x_axes()`, which is an Axes method that does not exist on Axis. This annotates that usage and annotates all the other places where we can infer Axes from that.
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MDU6SXNzdWU2ODgyNzU2MDk=
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|
BUG: Unable to add np.timedelta64 + pd.Interval(pd.Timestamp, pd.Timestamp)
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| 2020-08-28T18:26:08Z
| 2020-08-30T02:41:13Z
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|
MEMBER
| null |
Note: this is currently _not_ a bug on master but will be after https://github.com/pandas-dev/pandas/pull/35938, which reverts a change that caused other regressions.
```python
import numpy as np
import pandas as pd
delta = np.timedelta64(0)
interval = pd.Interval(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-02-01"))
delta + interval
```
```python
UFuncTypeError Traceback (most recent call last)
<ipython-input-1-2c3b3a0b4fb8> in <module>
4 delta = np.timedelta64(0)
5 interval = pd.Interval(pd.Timestamp("2020-01-01"), pd.Timestamp("2020-02-01"))
----> 6 delta + interval
UFuncTypeError: ufunc 'add' cannot use operands with types dtype('<m8') and dtype('O')
```
Flipping the operands however works
```python
interval + delta
```
```python
Interval('2020-01-01', '2020-02-01', closed='right')
```
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MDU6SXNzdWU2ODgyNzc0MzA=
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|
ENH: Shuffle DataFrame
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| 2
| 2020-08-28T18:29:24Z
| 2020-08-28T18:54:01Z
| 2020-08-28T18:53:48Z
|
NONE
| null |
#### Is your feature request related to a problem?
Multiple inquiries on Stackoverflow regarding how to shuffle/permutation of dataframes.
https://stackoverflow.com/questions/15772009/shuffling-permutating-a-dataframe-in-pandas
https://stackoverflow.com/questions/24701217/randomizing-shuffling-rows-in-a-dataframe-in-pandas
https://stackoverflow.com/questions/55538496/shuffling-a-dataframe
#### Describe the solution you'd like
DataFrame.shuffle(self, shuffle_amount, labels=None, groupe_tuples: bool: = False, inplace : bool = False)
Shuffles values in dataframe (along an axis).
Returns a new shuffled DataFrame if `inplace` argument is
``false``, otherwise updates the original DataFrame and returns None.
Parameters
----------
shuffle_amount : double
The percentage of each column to be shuffled
labels : single label or list-like
Index or column labels to shuffle.
group_tuples : bool, default False
Whether to shuffle labels independently or together
inplace : bool, default False
If true, perform operation in-place
Returns
-------
DataFrame
Shuffled values
#### API breaking implications
No backwards compatibility issues
#### Describe alternatives you've considered
Users could create custom functions to achieve desired results. All examples listed above have such functions
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TYP: misc cleanup in core\generic.py
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| 2
| 2020-08-28T19:43:38Z
| 2020-08-30T11:17:04Z
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MEMBER
| null |
pandas\core\generic.py:4707: error: Unsupported operand types for in ("Optional[str]" and "str") [operator]
pandas\core\generic.py:6559: error: 'builtins.object' object is not iterable [misc]
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| 4
| 2020-08-28T20:33:13Z
| 2020-12-03T21:44:40Z
| 2020-09-12T21:36:52Z
|
MEMBER
| null |
- [x] closes #35811
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
First step toward #35725. Currently `transform` just calls `aggregate`, and so if we are to forbid `aggregate` from transforming, these need to be decoupled. Other than the bugfix (#35811), the only other behavioral change is in the error messages.
Assuming the bugfix #35811 is the correct behavior, docs/whatsnew also needs to be updated.
I wasn't sure if tests should be marked with #35725 or perhaps this PR #. Any guidance here?
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MDU6SXNzdWU2ODgzNTY4NDU=
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|
BUG: DataFrame creates an object-dtype array for extension type scalars in dictionaries
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| 2
| 2020-08-28T21:10:02Z
| 2020-09-11T13:03:03Z
| 2020-09-11T13:03:03Z
|
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
df = pd.DataFrame({
'a': [pd.Period('2020-01')],
'b': pd.Period('2020-01')
})
df.dtypes
```
#### Problem description
In the example above, column `a` will be correctly created as a `period[m]` column, but column `b` will be an `object` type. This is similar to an [issue 34832](https://github.com/pandas-dev/pandas/issues/34832), but for a different DF instantiation method.
#### Expected Output
These two columns should have identical types. They should both be type `period[m]`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : c413df6d6a85eb411cba39b907fe7a74bdf4be1f
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Sun Dec 1 18:59:03 PST 2019; root:xnu-4903.278.19~1/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+173.gc413df6d6.dirty
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.3.1.post20200616
Cython : 0.29.20
pytest : 5.4.3
hypothesis : 5.16.2
sphinx : 3.1.1
blosc : 1.9.1
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fsspec : 0.7.4
fastparquet : 0.4.0
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.17
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.50.0
</details>
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MDExOlB1bGxSZXF1ZXN0NDc1NjQ0NTky
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BUG: instantiation using a dict with a period scalar
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| 6
| 2020-08-28T21:15:57Z
| 2020-10-10T15:07:02Z
| 2020-09-11T13:03:04Z
|
CONTRIBUTOR
| null |
- [x] closes #35965
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixing bug discussed in [issue 35965](https://github.com/pandas-dev/pandas/issues/35965) where `pd.DataFrame({'a': pd.Period('2020-01')})` created `a` as an object column instead of a `period[m]` column.
Changing the functionality of `infer_dtype_from_scalar` isn't necessarily required here, but the fact that `infer_dtype_from_scalar` would return the `period.ordinal` value seems inconsistent with the behavior for other dtypes in this function. Additionally, that functionality was only used in a single place within the code (`interval.py`), which I fixed accordingly.
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Deprecate groupby/pivot observed=False default
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| 2020-08-28T21:52:59Z
| 2021-07-11T20:27:45Z
| 2021-07-11T20:27:45Z
|
CONTRIBUTOR
| null |
- [x] Relates to #17594, Closes #30552
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Had a relatively small 70k data frame that I was trying to do a groupby sum on blow up on me today. This was the reason. I had something like zip codes and cities as categoricals, expected SQL-like groupby but instead got a cartesian product of 'cities' and 'zips'. Sounds like there was some previous desire to explore a new default.
Didn't try to do any wild stuff to keep up with the stacklevel depending on where this was called from.
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TYP: annotate plotting._matplotlib.tools
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| 2020-08-31T14:45:45Z
| 2020-08-31T10:15:05Z
|
MEMBER
| null |
Same idea as #35960, focused on clarifying Axis vs Axes
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QST: to_excel() deletes the existed sheets
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| 2020-08-28T23:11:06Z
| 2020-09-18T11:45:05Z
| 2020-09-18T11:45:05Z
|
NONE
| null |
- As per Pandas [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html), the to_excel() deletes the existed sheets in the excel document using the code below. I am not sure what is the issue.
```python
with pd.ExcelWriter('pay_past_5_years.xlsx', mode='a') as writer:
df.to_excel(writer, sheet_name='Sheet_name_3')
```
- The second question the mode is give error when using small 'a' char in mode. Here tip was found in [stackoverflow](https://stackoverflow.com/questions/54863238/pandas-excelwriter-valueerror-append-mode-is-not-supported-with-xlsxwriter/55683301)
```python
ValueError: Append mode is not supported with xlsxwriter!
````
**The Pandas version is 1.0.5**
Thank you
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Comma cleanup for Issue #35925
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| 2020-08-29T02:40:36Z
| 2020-08-29T03:31:50Z
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|
CONTRIBUTOR
| null |
- [x] contributes to #35925
- [x] tests added / passed
Tested with:
`./ci/code_checks.sh`
Files edited:
pandas/tests/frame/test_analytics.py,
pandas/tests/frame/test_constructors.py,
pandas/tests/frame/test_reshape.py,
pandas/tests/generic/test_finalize.py,
pandas/tests/generic/test_to_xarray.py,
pandas/tests/groupby/aggregate/test_numba.py
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Comma cleanup
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CONTRIBUTOR
| null |
Comma cleanup for #35925
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MDU6SXNzdWU2ODg0NjE5NjQ=
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BUG: rolling count does not respect the default behavior of min_periods
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|
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.
Per the documentation:
> For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.
Therefore these two statements should be equal
```
In [4]: pd.__version__
Out[4]: '1.2.0.dev0+160.gd90b73bde'
In [5]: pd.Series([np.nan] * 4).rolling(2).count()
Out[5]:
0 0.0
1 0.0
2 0.0
3 0.0
dtype: float64
In [6]: pd.Series([np.nan] * 4).rolling(2, min_periods=2).count()
Out[6]:
0 NaN
1 0.0
2 0.0
3 0.0
dtype: float64
```
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ENH: implement timeszones support for read_json(orient='table') and astype() from 'object'
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CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Currently timezones raise a `NotImplementedError` when using the `read_json(orient='table')` method.
This PR aims to fix what I believe is a fairly common request (numerous workarounds and questions exist on StackOverflow).
The PR aims to reconstitute DataFrames via json columns with timezones, Index with timezones or MultiIndex with timezones and/or combinations.
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ENH: Optimize nrows in read_excel
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| 2020-08-29T10:15:23Z
| 2020-09-22T17:00:12Z
| 2020-09-21T21:47:15Z
|
MEMBER
| null |
- [ ] closes #32727
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
based on #33281
----
output of asv benchmarks:
```
(pandas-dev) marco@marco-Predator-PH315-52:~/pandas-dev/asv_bench$ asv continuous -f 1.1 upstream/master optimise-nrows-excel -b excel.ReadExcel
· Creating environments..................................................................................................................................
· Discovering benchmarks
·· Uninstalling from conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
·· Building d0a8a687 <optimise-nrows-excel> for conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt....................................
·· Installing d0a8a687 <optimise-nrows-excel> into conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
· Running 4 total benchmarks (2 commits * 1 environments * 2 benchmarks)
[ 0.00%] · For pandas commit c413df6d <master> (round 1/2):
[ 0.00%] ·· Building for conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt....................................
[ 0.00%] ·· Benchmarking conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 12.50%] ··· Setting up io.excel:62 ok
[ 12.50%] ··· Running (io.excel.ReadExcel.time_read_excel--)..
[ 25.00%] · For pandas commit d0a8a687 <optimise-nrows-excel> (round 1/2):
[ 25.00%] ·· Building for conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
[ 25.00%] ·· Benchmarking conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 37.50%] ··· Setting up io.excel:62 ok
[ 37.50%] ··· Running (io.excel.ReadExcel.time_read_excel--)..
[ 50.00%] · For pandas commit d0a8a687 <optimise-nrows-excel> (round 2/2):
[ 50.00%] ·· Benchmarking conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 62.50%] ··· Setting up io.excel:62 ok
[ 62.50%] ··· io.excel.ReadExcel.time_read_excel ok
[ 62.50%] ··· ========== ============
engine
---------- ------------
xlrd 953±6ms
openpyxl 1.66±0.03s
odf 6.02±0.02s
========== ============
[ 75.00%] ··· io.excel.ReadExcel.time_read_excel_nrows ok
[ 75.00%] ··· ========== ============
engine
---------- ------------
xlrd 878±20ms
openpyxl 1.67±0.02s
odf 4.58±0.04s
========== ============
[ 75.00%] · For pandas commit c413df6d <master> (round 2/2):
[ 75.00%] ·· Building for conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt..
[ 75.00%] ·· Benchmarking conda-py3.8-Cython0.29.16-jinja2-matplotlib-numba-numexpr-numpy-odfpy-openpyxl-pytables-pytest-scipy-sqlalchemy-xlrd-xlsxwriter-xlwt
[ 87.50%] ··· Setting up io.excel:62 ok
[ 87.50%] ··· io.excel.ReadExcel.time_read_excel ok
[ 87.50%] ··· ========== ============
engine
---------- ------------
xlrd 941±5ms
openpyxl 1.69±0.02s
odf 6.15±0.04s
========== ============
[100.00%] ··· io.excel.ReadExcel.time_read_excel_nrows ok
[100.00%] ··· ========== ============
engine
---------- ------------
xlrd 971±20ms
openpyxl 1.69±0.01s
odf 6.07±0.03s
========== ============
before after ratio
[c413df6d] [d0a8a687]
<master> <optimise-nrows-excel>
- 971±20ms 878±20ms 0.90 io.excel.ReadExcel.time_read_excel_nrows('xlrd')
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE INCREASED.
```
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MDU6SXNzdWU2ODg1MzQxMTI=
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BUG: When the value in the data set has underscore i.e "_" and forward slash "/", the select query with where clause does not fetch actual result set.
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| 2020-08-31T13:31:13Z
| 2020-08-31T13:31:13Z
|
NONE
| null |
- [ ] I have checked that this issue has not already been reported.
- [ ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### 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>
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BUG:
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| 2020-08-29T14:33:42Z
| 2020-08-29T14:45:45Z
| 2020-08-29T14:45:44Z
|
NONE
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- [ ] 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.
---
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#### Code Sample, a copy-pastable example
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution]
#### 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>
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BUG: replacement works for object but not string dtype
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| 6
| 2020-08-29T14:35:09Z
| 2021-05-11T20:23:12Z
| 2021-05-11T20:23:12Z
|
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
```python
import pandas as pd
import numpy as np
a = pd.DataFrame({'a': ['a', 'b', 'c'], 'b': ['d', '', '']}, dtype='object')
b = pd.DataFrame({'a': ['a', 'b', 'c'], 'b': ['d', '', '']}, dtype='string')
print(a)
a.replace(r'^\s*$', pd.NA, regex=True, inplace=True)
print(a)
print(b)
b.replace(r'^\s*$', pd.NA, regex=True, inplace=True)
print(b)
```
#### Problem description
replace(r'^\s*$', pd.NA, regex=True, inplace=True) works on object dtype, but not on stringdtype
#### Expected Output
Same on both replacement
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here leaving a blank line after the details tag]
</details>
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TYP: annotate plotting._matplotlib.converter
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| 3
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| 2020-09-27T03:27:54Z
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|
MEMBER
| null |
- [x] closes https://github.com/pandas-dev/pandas/issues/35471
- [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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DOC: doc/source/getting_started/overview.rst
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| 0
| 2020-08-29T22:21:55Z
| 2020-08-31T18:31:36Z
| 2020-08-31T18:24:05Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
"https://pandas.pydata.org/docs/getting_started/overview.html"
#### Documentation problem
Unnecessary words and hyphens were missing.
#### Suggested fix for documentation
E.g. real world can be replaced by real-world
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DOC clean up doc/source/getting_started/overview.rst
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| 2
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CONTRIBUTOR
| null |
- [x] closes #35980
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CLN: window/rolling.py
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| 1
| 2020-08-30T03:24:03Z
| 2020-08-31T22:11:38Z
| 2020-08-31T20:42:06Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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ENH: pd.progress_concat
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| 5
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| 2020-09-03T10:48:11Z
| 2020-08-30T10:04:01Z
|
NONE
| null |
#### Is your feature request related to a problem?
I need to concat many thousands of fairly large dataframes, which can take 15-45mins, and I'm unable to get an idea of progress.
#### Describe the solution you'd like
I'd really like either a `pd.progress_concat` or ` pd.concat(progress=True)`
#### API breaking implications
I don't think this would break anything?
#### Describe alternatives you've considered
I've searched, but have been unable to find any alternative solution that does not seriously hamper speed.
#### Additional context
I have very little idea how `pd.concat` actually works internally, so I'm not sure if this is actually feasible.
|
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MDExOlB1bGxSZXF1ZXN0NDc1ODc0MzI1
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ENH: Add axis argument to Dataframe.corr
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| 2020-10-29T04:13:02Z
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NONE
| null |
- [x] closes #35002
- [x] tests added / passed
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DOC: make difference between numpy behaviour clearer in Dataframe.std() and Series.std()
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| 2020-09-03T16:58:36Z
| 2020-09-03T16:58:36Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.DataFrame.std.html
https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.std.html
#### Documentation problem
The `ddof` kwarg has a different default to numpy. This means using the `std()` method on a `Series` or `numpy.array` with the same values give different results:
```python
>>>df=pandas.Series([1,2,3])
>>>df.std()
Out[4]: 1.0
>>>df.values.std()
Out[5]: 0.816496580927726
```
#### Suggested fix for documentation
I assume the difference in behaviour is intentional, but I would suggest making this difference clearer in the documentation.
We could add the text: "Note that this normalization is different to numpy, which by default normalizes by N (equivalent to `ddof=0`)."
A similar string could be added in the kwarg description for ddof.
This would make it clear from reading the pandas docs that the behaviour is different to numpy, rather than having to compare the numpy docs side by side after noticing a discrepancy. It would also make users more likely to consider which ddof value they want for their use case.
If you agree, I am happy to make the PR to change this.
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| null |
- [x] closes #35985
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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TST: Verify operators with IntegerArray and list-likes (22606)
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CONTRIBUTOR
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- [x] closes #22606
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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|
TYP: mypy [attr-defined] error in `pandas/core/indexes/numeric.py`
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|
MEMBER
| null |
This is one of the errors after unpin mypy.
```
pandas/core/indexes/numeric.py:68: error: "Type[NumericIndex]" has no attribute "_default_dtype" [attr-defined]
pandas/core/indexes/numeric.py:69: error: "Type[NumericIndex]" has no attribute "_default_dtype" [attr-defined]
```
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|
DOC: DataFrame.query() are contradictory on whether Python keywords can used as identifiers
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| 2
| 2020-08-30T15:18:20Z
| 2020-09-12T21:31:59Z
| 2020-09-12T21:31:59Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html#pandas.DataFrame.query
#### Documentation problem
The `expr` parameter is described as follows:
> You can refer to column names that contain spaces or operators by surrounding them in backticks. This way you can also escape names that start with a digit, or those that are a Python keyword. Basically when it is not valid Python identifier. See notes down for more details.
However, the notes that follow indicate:
> Python keywords may not be used as identifiers.
So the first part suggests Python keywords can be used, while the second part indicates they cannot. Testing suggests they cannot currently be used.
#### Suggested fix for documentation
Change the `expr` description to read:
> You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus column names containing spaces or punctuation (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced as `Area (cm^2)`.) Column names which are Python keywords (like "list", "for", "import", etc) cannot be used.
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MDExOlB1bGxSZXF1ZXN0NDc1OTI1ODQ5
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TYP: misc typing fixes for pandas\core\frame.py
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|
MEMBER
| null |
pandas\core\frame.py:1091: error: namedtuple() expects a string literal as the first argument [misc]
pandas\core\frame.py:4594: error: Need type annotation for 'names' (hint: "names: List[<type>] = ...") [var-annotated]
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TYP: misc typing in core\indexes\base.py
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MDExOlB1bGxSZXF1ZXN0NDc1OTI2NDcz
| 35,992
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TYP: check_untyped_defs core.dtypes.cast
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| 688,751,204
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MDExOlB1bGxSZXF1ZXN0NDc1OTM0Njgx
| 35,993
|
Updating fork
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| 2020-08-30T16:44:16Z
| 2020-08-30T16:47:58Z
| 2020-08-30T16:44:36Z
|
CONTRIBUTOR
| null |
Updating fork (this PR was opened in the wrong repo by mistake, apologies for the same.)
|
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|
TYP: type error in `pandas/io/excel/_xlsxwriter.py`
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| 0
| 2020-08-30T17:16:05Z
| 2020-08-31T18:28:17Z
| 2020-08-31T18:22:35Z
|
MEMBER
| null |
Those are some of the typing errors generated by mypy after unpining it.
```
pandas/io/excel/_xlsxwriter.py:105: error: "object" has no attribute "__iter__"; maybe "__str__" or "__dir__"? (not iterable) [attr-defined]
pandas/io/excel/_xlsxwriter.py:196: error: "None" has no attribute "close" [attr-defined]
pandas/io/excel/_xlsxwriter.py:207: error: "None" has no attribute "add_worksheet" [attr-defined]
pandas/io/excel/_xlsxwriter.py:225: error: "None" has no attribute "add_format" [attr-defined]
```
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TYP: typing errors in _xlsxwriter.py #35994
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| 1
| 2020-08-30T17:18:37Z
| 2020-08-31T20:03:42Z
| 2020-08-31T18:22:35Z
|
MEMBER
| null |
- [x] closes #35994
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Issue35925 Remove trailing commas
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| 1
| 2020-08-30T17:56:06Z
| 2020-08-31T09:59:26Z
| 2020-08-31T09:59:18Z
|
CONTRIBUTOR
| null |
#35925
Files edited:
- pandas/tests/test_multilevel.py
- pandas/tests/test_nanops.py
- pandas/tests/window/moments/test_moments_consistency_rolling.py
- pandas/tests/window/moments/test_moments_ewm.py
- pandas/tests/window/moments/test_moments_rolling.py
- pandas/tests/window/test_base_indexer.py
- pandas/tests/window/test_pairwise.py
- pandas/tests/window/test_rolling.py
- pandas/tseries/frequencies.py
- pandas/util/_test_decorators.py
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MDU6SXNzdWU2ODg3NjYzNjg=
| 35,997
|
BUG: Reading from parquet throws UnknownTimeZoneError using timezone-aware date in index
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| 11
| 2020-08-30T18:19:25Z
| 2020-10-07T01:44:44Z
| 2020-10-07T01:44:44Z
|
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.
```python
import pandas as pd
from datetime import datetime, timezone
df = pd.DataFrame([[datetime.now(timezone.utc)]], columns=['date']).set_index('date')
df.to_parquet('out.parquet')
pd.read_parquet('out.parquet')
```
#### Problem description
The bug above happens with pandas 1.1.1 and pyarrow 1.0.1.
The timezone-aware date in the index should survive the parquet round trip.
If `date` is not index, or when I add parameter `ignore_metadata=True` to the `pyarrow.Table.to_pandas()` it works (but `date` won't be an index automatically)
#### Expected Output
A correct DataFrame
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.2.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 : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
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 : 2.11.2
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 1.0.1
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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|
QST: What is the use of cell_context in the Styler?
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|
NONE
| null |
- [X] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
- [ ] 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.
In pandas/io/formats/style.py (https://github.com/pandas-dev/pandas/blob/3ef3617c2e784b98db83ab437e7607459f68a1cc/pandas/io/formats/style.py#L265) the cell_context dict is created (empty). It is referenced twice (on line 318 and 382), where values are accessed, if they are present. The dict is however never updated.
What is the use of this variable? It seems to me to be quite useless as there is no way to set it's content. Is it purely there for future use? I use v0.23 where it was already like this, so I expected it to have changed in master, but this doesn't seem to be the case. Is there some way of setting it that I'm not aware of?
There are a few stack overflow questions (https://stackoverflow.com/questions/55236631/add-custom-css-class-to-column-or-cell-of-a-dataframe and https://stackoverflow.com/questions/62075616/add-style-class-to-pandas-dataframe-html) )by others,s not me) that ask how to set the class of a cell. These seem related to how this dict's values could be set. To "implement" support for classes, I simply overwrote the render method to call _translate and then to adjust the d["body']'s content where I added to the cell's class value. If cell_context could be set, then there would be no need to do this overriding after the fact (and actually benefit from the looking that is performed against cell_context)... (I would like to respond to these two questions, possibly with my monkey-patching, but preferably with what happens in _translate()).
Thanks,
Grant
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| 2
| 2020-08-30T23:24:59Z
| 2020-09-01T15:03:03Z
| 2020-09-01T01:20:41Z
|
MEMBER
| null |
- [x] closes #35788
- [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: vendor typing_extensions
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MEMBER
| null |
- [x] closes #34869
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
haven't figured out how to make isort and black ignore this file
|
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ENH: Arithmetic with Timestamp-based intervals
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|
CONTRIBUTOR
| null |
- [X] closes #35908
- [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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MDU6SXNzdWU2ODg4NzUyMTQ=
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|
DOC: Update documentation for pd.Interval if string endpoints are not allowed anymore
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| 1
| 2020-08-31T03:46:07Z
| 2020-09-01T17:02:04Z
| 2020-09-01T16:15:26Z
|
CONTRIBUTOR
| null |
#### Location of the documentation
[https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Interval.html](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Interval.html)
On `master`:
[https://pandas.pydata.org/docs/dev/reference/api/pandas.Interval.html](https://pandas.pydata.org/docs/dev/reference/api/pandas.Interval.html)
#### Documentation problem
The examples in the documentation for `pd.Interval` show in the end that Intervals with string endpoints can also be created. However, when the example is run on `master`, a `ValueError` is raised, saying:
"Only numeric, Timestamp and Timedelta endpoints are allowed when constructing an Interval."
#### Suggested fix for documentation
If Intervals with strings as endpoints are not allowed anymore, this example should be removed from the documentation.
I've confirmed that the docstring still contains this example, so I'd be happy to make a PR for this if we agree.
|
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|
REGR: Column with datetime values too big to be converted to pd.Timestamp leads to assertion error in groupby
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| 6
| 2020-08-31T07:03:11Z
| 2020-11-27T20:12:02Z
| 2020-11-27T20:12:02Z
|
NONE
| null |
- [X ] I have checked that this issue has not already been reported.
- [ X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
Two different dates, one within the range of what `pd.Timestamp` can handle, the other outside of that range:
```
import pandas as pd
import datetime
df = pd.DataFrame({'A': ['X', 'Y'], 'B': [datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
datetime.datetime(3005, 1, 1, 10, 30, 23, 540000)]})
print(df.groupby('A').B.max())
```
#### Problem description
`pd.Timestamp` can't deal with a too big date like the year 3005, so to represent such a date I need to use the `datetime.datetime` type. Before 1.1.1 (1.1.0?) this hasn't been an issue, but now this code throws an assertion error:
```
Traceback (most recent call last):
File "<ipython-input-38-8b8ec5e4e179>", line 5, in <module>
print(df.groupby('A').B.max())
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\groupby.py", line 1558, in max
numeric_only=numeric_only, min_count=min_count, alias="max", npfunc=np.max
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\groupby.py", line 1015, in _agg_general
result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\generic.py", line 261, in aggregate
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\groupby.py", line 1083, in _python_agg_general
result, counts = self.grouper.agg_series(obj, f)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\ops.py", line 644, in agg_series
return self._aggregate_series_fast(obj, func)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\ops.py", line 669, in _aggregate_series_fast
result, counts = grouper.get_result()
File "pandas\_libs\reduction.pyx", line 256, in pandas._libs.reduction.SeriesGrouper.get_result
File "pandas\_libs\reduction.pyx", line 74, in pandas._libs.reduction._BaseGrouper._apply_to_group
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\groupby.py", line 1060, in <lambda>
f = lambda x: func(x, *args, **kwargs)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\groupby\groupby.py", line 1015, in <lambda>
result = self.aggregate(lambda x: npfunc(x, axis=self.axis))
File "<__array_function__ internals>", line 6, in amax
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\numpy\core\fromnumeric.py", line 2706, in amax
keepdims=keepdims, initial=initial, where=where)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\numpy\core\fromnumeric.py", line 85, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\generic.py", line 11460, in stat_func
func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\series.py", line 4220, in _reduce
delegate = self._values
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\series.py", line 572, in _values
return self._mgr.internal_values()
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\managers.py", line 1615, in internal_values
return self._block.internal_values()
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\blocks.py", line 2019, in internal_values
return self.array_values()
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\internals\blocks.py", line 2022, in array_values
return self._holder._simple_new(self.values)
File "C:\Users\My.Name\AppData\Local\Continuum\miniconda3\envs\main\lib\site-packages\pandas\core\arrays\datetimes.py", line 290, in _simple_new
assert values.dtype == "i8"
AssertionError
```
From testing with mixing `pd.Timestamp` and `datetime.datetime` types I presume pandas is converting applicable dates (first line in the example) to `pd.Timestamp` while leaving the others as `datetime.datetime` leading to a mixed-type result column and the assertion error.
#### Expected Output
Since I'm explicitely operating with datatype `datetime.datetime` there should be no implicit conversion to `pd.Timestamp` if it's not assured that all values are within the range that `pd.Timestamp` allows.
#### Output of ``pd.show_versions()``
<details>
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.7.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 1.1.1
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.2
setuptools : 50.0.0.post20200830
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.3
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 : None
fsspec : 0.8.0
fastparquet : 0.4.1
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.19
tables : None
tabulate : 0.8.7
xarray : None
xlrd : 1.2.0
xlwt : None
numba : 0.51.1
</details>
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MDExOlB1bGxSZXF1ZXN0NDc2MTQ4Nzcx
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BUG: Can't restore index from parquet with offset-specified timezone #35997
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| 13
| 2020-08-31T08:30:23Z
| 2020-10-07T09:23:21Z
| 2020-10-07T01:44:44Z
|
CONTRIBUTOR
| null |
- [x] closes #35997
- [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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|
MEMBER
| null |
pandas\core\computation\expressions.py:78: error: Need type annotation for 'dtypes' (hint: "dtypes: Set[<type>] = ...") [var-annotated]
pandas\core\computation\expressions.py:258: error: Need type annotation for '_TEST_RESULT' (hint: "_TEST_RESULT: List[<type>] = ...") [var-annotated]
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DOC: use single back-ticks for variable, module, function, and class names
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| 4
| 2020-08-31T11:22:13Z
| 2020-09-03T16:15:23Z
| 2020-09-03T16:15:23Z
|
MEMBER
| null |
EDIT I've rewritten this issue after Joris' comment.
----
Pandas' docstrings are somewhat inconsistent in using single or double back-ticks, e.g. [isin](https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.isin.html?highlight=isin#pandas.DataFrame.isin) has both
```
`values`
```
and
```
``values``
```
In keeping with the [numpydoc docstring guide](https://numpydoc.readthedocs.io/en/latest/format.html), which reads
> Use *italics*, **bold** and ``monospace`` if needed in any explanations (but not for variable names and doctest code or multi-line code). Variable, module, function, and class names should be written between single back-ticks (`numpy`).
, it might be good update docstrings which don't follow this convention, as well as to mention this convention in the pandas docstring guide.
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TYP: misc typing cleanup in core/indexes/multi.py
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| 2020-09-01T16:33:31Z
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|
MEMBER
| null |
pandas\core\indexes\multi.py:496: error: Need type annotation for 'arrays' [var-annotated]
pandas\core\indexes\multi.py:722: error: Incompatible types in assignment (expression has type "List[Any]", variable has type "FrozenList") [assignment]
pandas\core\indexes\multi.py:893: error: Incompatible types in assignment (expression has type "List[Any]", variable has type "FrozenList") [assignment]
pandas\core\indexes\multi.py:2438: error: List item 0 has incompatible type "slice"; expected "str" [list-item]
pandas\core\indexes\multi.py:3095: error: Unsupported left operand type for | ("None") [operator]
|
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MDExOlB1bGxSZXF1ZXN0NDc2MjY5MDI3
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TYP: check_untyped_defs core.internals.concat
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| 2020-09-01T09:16:11Z
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MEMBER
| null |
pandas\core\internals\concat.py:106: error: Incompatible types in assignment (expression has type "Tuple[Any, ...]", variable has type "List[Any]") [assignment]
pandas\core\internals\concat.py:131: error: Incompatible types in assignment (expression has type "Tuple[Any, ...]", variable has type "List[Any]") [assignment]
pandas\core\internals\concat.py:377: error: Need type annotation for 'upcast_classes' [var-annotated]
pandas\core\internals\concat.py:378: error: Need type annotation for 'null_upcast_classes' [var-annotated]
|
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Backport PR #35936: (REGR: Fix inplace updates on column to set correct values)
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| 0
| 2020-08-31T13:08:37Z
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MEMBER
| null |
xref #35936
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POC: ArrayManager -- array-based data manager for columnar store
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| 39
| 2020-08-31T14:17:54Z
| 2021-01-13T14:10:37Z
| 2021-01-13T13:23:14Z
|
MEMBER
| null |
Related to the discussion in https://github.com/pandas-dev/pandas/issues/10556, and following up on the mailing list discussion *"A case for a simplified (non-consolidating) BlockManager with 1D blocks"* ([archive](https://mail.python.org/pipermail/pandas-dev/2020-May/001219.html)).
This branch experiments with an *"array manager"*, storing a list of 1D arrays instead of blocks.
The idea is that this `ArrayManager` could optionally be used instead of `BlockManager`. If we ensure the "DataManager" has a clear interface for the rest of pandas (and thus parts outside of the internals don't rely on details like block layout, xref https://github.com/pandas-dev/pandas/issues/34669), this should be possible without much changes outside of /core/internals.
Some notes on this experiment:
- This is not a complete POC, not every aspect and behaviour of the BlockManager has already been replicated, and there are still places in pandas that rely on the blocks being present, so lots of tests are still failing (although changes in behaviour are also desired). That said, a *lot* of the basic operations do work. Two illustrations of this:
- An updated version of the notebook I showed in the mailing list discussion as well: with a certain setup, comparing a set of operations between block vs array manager: https://nbviewer.jupyter.org/gist/jorisvandenbossche/f917d4301d21069e2be2e3b7c7aa4d07
- I ran the arithmetic.py benchmark file, comparing against master, see below for the results.
- For now, I focused on an ArrayManager storing a list of *numpy arrays*. Of course we need to expand that to support ExtensionArrays as well (or ExtensionArrays only?), but the reason I limited to numpy arrays for now: besides making it a bit simpler to experiment with, this also gives a fairer comparison with the consolidated BlockManager (because it focuses on the numpy array being 1D vs 2D, and doesn't mix in performance/implementation differences of numpy array vs ExtensionArray).
- Personally, I think this looks promising. Many of the methods are a *lot* simpler than the BlockManager equivalent (although not every aspect is implemented yet, that's correct). And for the case I showed in the notebook, performance looks also good. For the benchmark suite I ran, there are obviously slowdowns for the "wide dataframe" benchmarks.
There is still a lot of work needed to make this fully working with the rest of pandas, though ;)
- Given the early proof of concept stage, detailed code feedback is not yet needed, but I would find it very useful to discuss the following aspects:
- High-level feedback on the approach: does the approach of the two subclasses look interesting? The approach of the ArrayManager itself storing a list of arrays? ...
- What to do with Series, which now is a SingleBlockManager inheriting from BlockManager (should we also have a "SingleArrayManager"?)
- *If* we find this interesting, how can we go from here? How do we decide on this? (what aspects already need to work, how fast does it need to be?) I don't think getting a fully complete implementation passing all tests is is possible in a single PR. Are we fine with merging something partial in master and continue from there? Or a shared feature branch in upstream? ...
<details>
<summary>Benchmark results for asv_bench/arithmetic.py</summary>
As an example, I ran `asv continuous -f 1.1 upstream/master HEAD -b arithmetic`.
The benchmarks with a slowdown bigger than a factor 2 can basically be brought back to two cases:
- Benchmarks for "wide" dataframes (eg `FrameWithFrameWide` using a case with n_cols > n_rows)
- Benchmarks from the `IntFrameWithScalar` class: from a quick profile, it seems that the usage of numexpr is the cause, and disabling this seems to reduce the slowdown to a factor 2. The numexpr code (and checking if it should be used etc) apparently has a high overhead per call, which I assume is something that can be solved (moving those checks a level higher up, so we don't need to repeat it for each column)
```
before after ratio
[b45327f5] [047f9091]
<master>
! 40.6±6ms failed n/a arithmetic.Ops.time_frame_multi_and(False, 'default')
! 32.7±2ms failed n/a arithmetic.Ops.time_frame_multi_and(False, 1)
! 26.5±1ms failed n/a arithmetic.Ops.time_frame_multi_and(True, 'default')
! 37.7±2ms failed n/a arithmetic.Ops.time_frame_multi_and(True, 1)
+ 1.06±0.3ms 93.5±7ms 88.57 arithmetic.FrameWithFrameWide.time_op_same_blocks(<built-in function gt>)
+ 1.51±0.2ms 80.6±3ms 53.34 arithmetic.FrameWithFrameWide.time_op_same_blocks(<built-in function add>)
+ 1.22±0.08ms 55.1±5ms 45.19 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le')
+ 1.30±0.07ms 55.6±20ms 42.83 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ne')
+ 2.12±0.4ms 90.1±4ms 42.47 arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function gt>)
+ 1.17±0.04ms 49.4±4ms 42.38 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('gt')
+ 1.28±0.07ms 52.9±3ms 41.28 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('lt')
+ 1.29±0.2ms 52.5±0.6ms 40.63 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ge')
+ 1.44±0.02ms 56.8±7ms 39.56 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('eq')
+ 2.08±0.3ms 78.9±10ms 37.90 arithmetic.Ops2.time_frame_float_mod
+ 2.34±0.1ms 78.3±4ms 33.51 arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function add>)
+ 1.66±0.2ms 46.6±1ms 28.00 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('mul')
+ 1.78±0.2ms 48.2±5ms 27.02 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('truediv')
+ 1.14±0.04ms 26.8±4ms 23.49 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function le>)
+ 1.83±0.2ms 42.9±1ms 23.39 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('add')
+ 1.94±0.3ms 45.1±4ms 23.29 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('sub')
+ 1.23±0.07ms 23.0±3ms 18.65 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function ge>)
+ 1.33±0.08ms 22.8±1ms 17.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function eq>)
+ 1.03±0.05ms 17.6±2ms 17.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function ge>)
+ 1.65±0.5ms 28.1±7ms 17.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function eq>)
+ 1.21±0.05ms 20.1±3ms 16.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function gt>)
+ 1.18±0.03ms 19.4±0.9ms 16.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function eq>)
+ 1.08±0.07ms 17.8±1ms 16.53 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function lt>)
+ 1.22±0.05ms 20.0±2ms 16.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function gt>)
+ 1.30±0.06ms 21.2±3ms 16.28 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function ne>)
+ 1.15±0.06ms 18.6±3ms 16.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function lt>)
+ 1.42±0.1ms 22.6±1ms 15.96 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function lt>)
+ 1.11±0.01ms 17.6±0.4ms 15.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function ne>)
+ 5.30±0.8ms 81.7±20ms 15.40 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt')
+ 1.37±0.2ms 20.7±3ms 15.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function gt>)
+ 1.22±0.05ms 18.0±6ms 14.72 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function ge>)
+ 1.28±0.1ms 18.6±3ms 14.55 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function gt>)
+ 1.17±0.08ms 17.0±3ms 14.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function gt>)
+ 1.22±0.1ms 17.6±0.8ms 14.44 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function eq>)
+ 1.35±0.1ms 19.4±2ms 14.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function le>)
+ 1.35±0.1ms 19.2±4ms 14.21 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function ge>)
+ 4.36±0.3ms 61.8±8ms 14.17 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le')
+ 1.31±0.1ms 18.5±2ms 14.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function lt>)
+ 4.48±0.5ms 62.9±5ms 14.06 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge')
+ 1.15±0.1ms 16.1±1ms 14.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function ne>)
+ 1.33±0.1ms 18.6±2ms 14.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function ge>)
+ 4.37±0.4ms 58.9±2ms 13.48 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne')
+ 1.22±0.2ms 16.2±3ms 13.25 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function le>)
+ 1.25±0.1ms 16.5±1ms 13.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function le>)
+ 1.44±0.2ms 18.6±4ms 12.90 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function ge>)
+ 1.75±0.3ms 22.3±2ms 12.74 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function eq>)
+ 1.42±0.3ms 18.0±7ms 12.68 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function gt>)
+ 1.36±0.1ms 17.2±1ms 12.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function ne>)
+ 440±30μs 5.57±0.1ms 12.65 arithmetic.Ops2.time_frame_series_dot
+ 1.63±0.2ms 20.6±2ms 12.65 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function lt>)
+ 1.35±0.07ms 17.0±3ms 12.58 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function le>)
+ 1.34±0.2ms 16.7±1ms 12.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function eq>)
+ 1.50±0.1ms 18.6±5ms 12.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function ge>)
+ 1.35±0.07ms 16.8±1ms 12.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function ge>)
+ 1.35±0.1ms 16.7±2ms 12.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function le>)
+ 1.55±0.3ms 18.9±2ms 12.20 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function le>)
+ 1.67±0.3ms 20.3±5ms 12.17 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function ne>)
+ 1.55±0.2ms 18.5±0.7ms 11.94 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function le>)
+ 5.05±0.5ms 59.1±3ms 11.70 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt')
+ 1.51±0.2ms 17.6±2ms 11.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function lt>)
+ 1.33±0.08ms 15.3±1ms 11.50 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function ne>)
+ 4.47±0.1ms 51.2±1ms 11.45 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq')
+ 1.35±0.1ms 15.4±2ms 11.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function lt>)
+ 1.76±0.5ms 19.8±2ms 11.28 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function lt>)
+ 1.55±0.09ms 16.8±0.3ms 10.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function ne>)
+ 1.71±0.1ms 18.2±2ms 10.58 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function eq>)
+ 1.51±0.2ms 15.9±3ms 10.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function eq>)
+ 1.53±0.2ms 15.6±0.3ms 10.19 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function ne>)
+ 1.95±0.2ms 19.7±5ms 10.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function gt>)
+ 2.22±0.08ms 21.6±4ms 9.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function add>)
+ 1.77±0.08ms 16.7±1ms 9.48 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function gt>)
+ 2.19±0.1ms 19.9±2ms 9.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function mul>)
+ 1.91±0.04ms 17.0±2ms 8.88 arithmetic.Ops.time_frame_comparison(True, 'default')
+ 2.18±0.1ms 19.0±1ms 8.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function add>)
+ 2.23±0.08ms 19.1±1ms 8.59 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function sub>)
+ 2.24±0.07ms 19.0±3ms 8.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function mul>)
+ 2.34±0.06ms 19.5±2ms 8.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function truediv>)
+ 2.52±0.2ms 20.3±6ms 8.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function truediv>)
+ 2.39±0.2ms 19.2±2ms 8.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function truediv>)
+ 3.07±0.4ms 24.4±5ms 7.94 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function mod>)
+ 2.24±0.1ms 17.5±2ms 7.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function add>)
+ 2.24±0.2ms 17.4±0.7ms 7.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function sub>)
+ 2.33±0.1ms 18.0±2ms 7.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function mul>)
+ 2.15±0.1ms 16.4±4ms 7.60 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function sub>)
+ 2.10±0.05ms 15.9±2ms 7.57 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function add>)
+ 2.27±0.1ms 16.8±1ms 7.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function add>)
+ 3.59±0.1ms 26.1±5ms 7.27 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function mod>)
+ 2.32±0.1ms 16.8±3ms 7.25 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function sub>)
+ 2.36±0.08ms 17.1±0.7ms 7.23 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function truediv>)
+ 2.42±0.2ms 17.4±2ms 7.17 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function sub>)
+ 2.31±0.09ms 16.4±0.9ms 7.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function add>)
+ 7.34±0.9ms 52.2±2ms 7.10 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('add')
+ 2.32±0.1ms 16.4±0.9ms 7.07 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function add>)
+ 2.25±0.2ms 15.8±2ms 7.03 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function sub>)
+ 2.51±0.5ms 17.3±2ms 6.91 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function add>)
+ 2.43±0.1ms 16.7±0.8ms 6.84 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function mul>)
+ 2.24±0.1ms 15.2±2ms 6.81 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function mul>)
+ 7.81±1ms 52.9±4ms 6.78 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('sub')
+ 2.48±0.2ms 16.4±2ms 6.62 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function mul>)
+ 6.82±1ms 44.4±0.7ms 6.51 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('mul')
+ 2.25±0.05ms 14.6±0.8ms 6.48 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function sub>)
+ 3.14±0.7ms 19.8±2ms 6.30 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function mod>)
+ 2.57±0.2ms 15.9±2ms 6.19 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function sub>)
+ 2.57±0.1ms 15.8±2ms 6.16 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function truediv>)
+ 7.70±1ms 47.2±3ms 6.13 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('truediv')
+ 3.02±0.1ms 18.4±3ms 6.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function mod>)
+ 2.79±0.2ms 16.8±0.8ms 6.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function truediv>)
+ 3.16±0.3ms 19.1±0.7ms 6.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function mod>)
+ 2.51±0.2ms 14.9±0.5ms 5.92 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function mul>)
+ 2.71±0.1ms 15.9±0.8ms 5.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function mul>)
+ 2.72±0.3ms 15.9±1ms 5.83 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function truediv>)
+ 11.9±1ms 64.0±5ms 5.39 arithmetic.Ops2.time_frame_int_mod
+ 3.59±0.4ms 19.1±5ms 5.33 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function mod>)
+ 6.23±0.4ms 32.7±6ms 5.25 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function mod>)
+ 3.28±0.2ms 17.2±2ms 5.23 arithmetic.Ops.time_frame_add(True, 'default')
+ 23.7±6ms 112±7ms 4.70 arithmetic.FrameWithFrameWide.time_op_same_blocks(<built-in function floordiv>)
+ 3.51±0.4ms 16.5±0.6ms 4.70 arithmetic.Ops.time_frame_mult(True, 'default')
+ 3.61±2ms 16.3±1ms 4.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function truediv>)
+ 45.8±4ms 194±20ms 4.25 arithmetic.FrameWithFrameWide.time_op_different_blocks(<built-in function floordiv>)
+ 5.64±0.6ms 21.9±1ms 3.89 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function mod>)
+ 3.13±0.1ms 11.4±0.5ms 3.63 arithmetic.Ops.time_frame_comparison(True, 1)
+ 12.2±0.8ms 42.5±4ms 3.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function pow>)
+ 4.03±0.7ms 11.2±0.3ms 2.79 arithmetic.Ops.time_frame_add(True, 1)
+ 53.0±6ms 143±10ms 2.69 arithmetic.Ops2.time_frame_float_floor_by_zero
+ 4.11±0.2ms 11.1±1ms 2.69 arithmetic.Ops.time_frame_mult(True, 1)
+ 54.9±4ms 125±9ms 2.28 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('floordiv')
+ 25.0±0.6ms 55.9±5ms 2.24 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function pow>)
+ 2.42±0.2ms 5.21±0.6ms 2.16 arithmetic.Ops.time_frame_comparison(False, 'default')
+ 16.2±1ms 31.9±3ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function pow>)
+ 30.9±3ms 58.1±10ms 1.88 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function pow>)
+ 3.36±0.3ms 5.76±0.4ms 1.71 arithmetic.Ops.time_frame_add(False, 'default')
+ 3.10±0.3ms 5.03±0.3ms 1.62 arithmetic.Ops.time_frame_comparison(False, 1)
+ 30.5±3ms 49.2±9ms 1.61 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function pow>)
+ 3.42±0.3ms 5.51±0.4ms 1.61 arithmetic.Ops.time_frame_mult(False, 1)
+ 3.52±0.2ms 5.63±0.1ms 1.60 arithmetic.Ops.time_frame_add(False, 1)
+ 3.60±0.2ms 5.74±0.5ms 1.59 arithmetic.Ops.time_frame_mult(False, 'default')
+ 57.9±1ms 89.7±6ms 1.55 arithmetic.Ops2.time_frame_float_div
+ 32.1±0.5ms 48.7±2ms 1.52 arithmetic.Ops2.time_frame_dot
+ 2.96±0.06ms 4.32±0.4ms 1.46 arithmetic.DateInferOps.time_add_timedeltas
+ 65.9±2ms 93.8±1ms 1.42 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('pow')
+ 106±2ms 132±3ms 1.25 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('pow')
+ 1.33±0.01ms 1.64±0.2ms 1.24 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<YearEnd: month=12>)
+ 7.09±0.2ms 8.49±0.5ms 1.20 arithmetic.DateInferOps.time_subtract_datetimes
+ 1.13±0ms 1.33±0.09ms 1.18 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<YearBegin: month=1>)
+ 1.25±0.02ms 1.47±0.1ms 1.18 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<SemiMonthEnd: day_of_month=15>)
+ 2.52±0.04ms 2.97±0.2ms 1.18 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<BusinessDay>)
+ 1.16±0.01ms 1.32±0.06ms 1.13 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<QuarterBegin: startingMonth=3>)
- 1.67±0.2ms 1.42±0.02ms 0.85 arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<MonthEnd>)
- 282±20μs 230±5μs 0.81 arithmetic.NumericInferOps.time_subtract(<class 'numpy.int8'>)
- 4.36±0.2ms 3.54±0.3ms 0.81 arithmetic.NumericInferOps.time_modulo(<class 'numpy.uint16'>)
- 1.29±0.1ms 1.03±0.06ms 0.80 arithmetic.NumericInferOps.time_multiply(<class 'numpy.int64'>)
- 1.77±0.09ms 1.39±0.03ms 0.79 arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<SemiMonthBegin: day_of_month=15>)
- 1.54±0.2ms 1.13±0.02ms 0.74 arithmetic.NumericInferOps.time_divide(<class 'numpy.int8'>)
- 301±40μs 221±4μs 0.73 arithmetic.OffsetArrayArithmetic.time_add_series_offset(<Day>)
- 3.85±0.5ms 2.58±0.2ms 0.67 arithmetic.OffsetArrayArithmetic.time_add_dti_offset(<BusinessDay>)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```
</details>
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MEMBER
| null |
Seeing this on some new PRs. xref #35252
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NONE
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Backport PR #36011: CI: suppress another setuptools warning
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BUG: groupby and agg on read-only array gives ValueError: buffer source array is read-only
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| 8
| 2020-08-31T18:23:31Z
| 2020-09-04T14:28:16Z
| 2020-09-04T14:28:16Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
Two variants of this bug have been reported - #35436 and #34857
EDIT: I read into those two issues a bit more. They don't seem similar. But I'll keep it there.
- [x] I have confirmed this bug exists on the latest version of pandas.
Bug exists in pandas 1.1.1
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import pyarrow as pa
df = pd.DataFrame(
{
"sepal_length": [5.1, 4.9, 4.7, 4.6, 5.0],
"species": ["setosa", "setosa", "setosa", "setosa", "setosa"],
}
)
context = pa.default_serialization_context()
data = context.serialize(df).to_buffer().to_pybytes()
df_new = context.deserialize(data)
# this fails
df_new.groupby(["species"]).agg({"sepal_length": "sum"})
# this works
# df_new.copy().groupby(["species"]).agg({"sepal_length": "sum"})
```
#### Problem description
This is the traceback.
```python-traceback
Traceback (most recent call last):
File "demo.py", line 16, in <module>
df_new.groupby(["species"]).agg({"sepal_length": "sum"})
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 949, in aggregate
result, how = self._aggregate(func, *args, **kwargs)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/base.py", line 416, in _aggregate
result = _agg(arg, _agg_1dim)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/base.py", line 383, in _agg
result[fname] = func(fname, agg_how)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/base.py", line 367, in _agg_1dim
return colg.aggregate(how)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/generic.py", line 240, in aggregate
return getattr(self, func)(*args, **kwargs)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 1539, in sum
return self._agg_general(
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 999, in _agg_general
return self._cython_agg_general(
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/groupby.py", line 1033, in _cython_agg_general
result, agg_names = self.grouper.aggregate(
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/ops.py", line 584, in aggregate
return self._cython_operation(
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/ops.py", line 537, in _cython_operation
result = self._aggregate(result, counts, values, codes, func, min_count)
File "/home/jeet/miniconda3/envs/rnd/lib/python3.8/site-packages/pandas/core/groupby/ops.py", line 599, in _aggregate
agg_func(result, counts, values, comp_ids, min_count)
File "pandas/_libs/groupby.pyx", line 475, in pandas._libs.groupby._group_add
File "stringsource", line 658, in View.MemoryView.memoryview_cwrapper
File "stringsource", line 349, in View.MemoryView.memoryview.__cinit__
ValueError: buffer source array is read-only
```
In the `.agg` line that fails, if you do a min, max, median, or count aggregation, then it's going to work.
But if you do a sum or mean, then it fails.
#### Expected Output
I expected the aggregation to succeed without any error.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2ca0a2665b2d169c97de87b8e778dbed86aea07
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-7642-generic
Version : #46~1597422484~20.04~e78f762-Ubuntu SMP Wed Aug 19 14:35:06 UTC
machine : x86_64
processor : x86_64
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 : 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 : 2.8.5 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
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 : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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| 689,529,826
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MDExOlB1bGxSZXF1ZXN0NDc2NTYwMjQ4
| 36,015
|
BUG: PeriodIndex.get_loc incorrectly raising ValueError instead of KeyError
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| 0
| 2020-08-31T21:51:19Z
| 2020-09-02T01:02:52Z
| 2020-09-01T23:32:01Z
|
MEMBER
| null |
- [x] closes #34240
- [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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TYP: Annotate plotting stacker
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| 4
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|
MEMBER
| null |
In AreaPlot._plot we call `ax.fill_between`, which means `ax` must be an `Axes` object (and in particular, not an `Axis` object). This chases down all the other places we can infer `Axes` from that.
Then some edits in an `__init__` to get mypy passing, and revert annotations of `_plot` in a few places because mypy complained about signature mismatch.
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