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PERF: Introducing HashTables for datatypes with 8,16 and 32 bits
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CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Starts work on #33287
Proof of concept for 32bit/16bit hash tables. If works it should become blue print for UInt32/Int16/UInt16/Float32-HashTables.
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TYP: __getitem__ method of EA (2nd pass)
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MEMBER
| null |
follow-on from #37898
remove casts and ignores added in #37898 (needed to use typevar in DatetimeLikeArrayMixin in conjunction with overload for slice to return correct DatetimeArray/PeriodArray)
another pass required to
more typevars for return type of type(self)
type \_\_getitem__ in concrete classes (Categorical, IntervalArray and SparseArray)
overloads with Any report overlapping overloads
NAType resolves to `Any` since implemented in cython without stub file
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BUG: idxmax/min (and argmax/min) for Series with underlying ExtensionArray
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| 2020-11-17T23:44:09Z
| 2021-01-02T00:26:01Z
| 2021-01-01T21:38:40Z
|
CONTRIBUTOR
| null |
- [x] closes #32749, closes #33719, closes #36566
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Currently `pd.Series([1, 2, 3]).astype('Int8').idxmax()` raises an error.
This request proposes a fix.
My first time using Github and opening a pull request. Apologise if it's no good.
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MDU6SXNzdWU3NDUyMTA4ODM=
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CI: mypy error in pandas/core/indexes/datetimelike.py::_simple_new
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MEMBER
| null |
Seeing this in a bunch of unrelated PRs (my own and others'):
```
pandas/core/indexes/datetimelike.py:776: error: Argument 1 to "_simple_new" of "DatetimeIndexOpsMixin" has incompatible type "Union[ExtensionArray, Any]"; expected "Union[DatetimeArray, TimedeltaArray, PeriodArray]" [arg-type]
Found 1 error in 1 file (checked 1119 source files)
Performing static analysis using mypy DONE
```
I wonder if it's a bug on master?
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CLN: tests/window/*
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| 2
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|
MEMBER
| null |
* Renamed `test_window.py` to `test_win_type.py`
* Added `'count'` to `arithmetic_win_operators`
* Reused fixtures
* Used more pytest idioms in `test_moments_consistency_expanding.py`
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QST: Does pandas merge guarantee that row order preserved from both tables, even if duplicate keys exist?
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| 2021-08-14T05:16:30Z
| null |
NONE
| null |
- [x] I have searched the [[pandas] tag](https://stackoverflow.com/questions/tagged/pandas) on StackOverflow for similar questions.
---
Suppose I left join a DataFrame `B` onto `A` (with `sort=False`) such that some keys are duplicated in both A and B. Is the resulting DataFrame guaranteed to be lex-sorted based on the occurrence of rows in A and then B? For example...
If I make two DataFrames `A` and `B` like this
```python
N = 1000
np.random.seed(2020)
A = pd.DataFrame({'x': np.random.choice(100, size=N), 'A_row':np.arange(N)})
B = pd.DataFrame({'x': np.random.choice(100, size=N), 'B_row':np.arange(N)})
print(A)
x A_row
0 96 0
1 8 1
2 67 2
.. .. ...
998 52 998
999 55 999
print(B)
x B_row
0 62 0
1 16 1
.. .. ...
998 71 998
999 39 999
```
and then I left-join `B` onto `A`
```python
d1 = pd.merge(left=A, right=B, how='left', on='x')
print(d1)
x A_row B_row
0 96 0 7
1 96 0 94
.. ... ...
10070 55 999 960
10071 55 999 988
```
I can confirm that the result is in lex-sorted via
```python
np.all(d1.index == d1.sort_values(['A_row', 'B_row']).index) # True
```
My question is, *is this guaranteed*? [The docs say](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)
> left: use only keys from left frame, similar to a SQL left outer join; preserve key order.
but this doesn't sounds like a guarantee for lex-sort.
Note that if I do the same thing with a right-join, the result is **not** lex-sorted
```python
d2 = pd.merge(left=A, right=B, how='right', on='x')
np.all(d2.index == d2.sort_values(['A_row', 'B_row']).index) # False
```
but I think this is just a bug related to #35382. Thanks
---
```
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : 2a7d3326dee660824a8433ffd01065f8ac37f7d6
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 1.1.2
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
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 : 7.19.0
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 : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
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MDU6SXNzdWU3NDUyNTkwMDE=
| 37,928
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BUG: SingleBlockManager' object has no attribute 'external_vaues' while using drop_duplicate
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| null |
[] | null | 1
| 2020-11-18T02:14:57Z
| 2020-11-18T11:50:15Z
| 2020-11-18T11:50:15Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
In [1]: import pandas as pd
In [2]: pd.__version
Out[2]: '1.1.3'
In [3]: df = pd.DataFrame({'a': [1,2,3,4,5], 'b': [3,3,3,3,3]})
In [4]: df.drop_duplicates()
```
#### Problem description
The following error occurred when executing the code above
```shell
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-c128d71b3023> in <module>
----> 1 df.drop_duplicates()
~/.conda/envs/test/lib/python3.6/site-packages/pandas/core/frame.py in drop_duplicates(self, subset, keep, inplace, ignore_index)
5106
5107 inplace = validate_bool_kwarg(inplace, "inplace")
-> 5108 duplicated = self.duplicated(subset, keep=keep)
5109
5110 result = self[-duplicated]
~/.conda/envs/test/lib/python3.6/site-packages/pandas/core/frame.py in duplicated(self, subset, keep)
5245
5246 vals = (col.values for name, col in self.items() if name in subset)
-> 5247 labels, shape = map(list, zip(*map(f, vals)))
5248
5249 ids = get_group_index(labels, shape, sort=False, xnull=False)
~/.conda/envs/test/lib/python3.6/site-packages/pandas/core/frame.py in <genexpr>(.0)
5244 raise KeyError(diff)
5245
-> 5246 vals = (col.values for name, col in self.items() if name in subset)
5247 labels, shape = map(list, zip(*map(f, vals)))
5248
~/.conda/envs/test/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)
5137 if self._info_axis._can_hold_identifiers_and_holds_name(name):
5138 return self[name]
-> 5139 return object.__getattribute__(self, name)
5140
5141 def __setattr__(self, name: str, value) -> None:
~/.conda/envs/test/lib/python3.6/site-packages/pandas/core/series.py in values(self)
536 '2013-01-03T05:00:00.000000000'], dtype='datetime64[ns]')
537 """
--> 538 return self._mgr.external_vaues()
539
540 @property
AttributeError: 'SingleBlockManager' object has no attribute 'external_vaues'
```
#### Expected Output
No error occured
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.6.12.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : zh_CN.UTF-8
LOCALE : zh_CN.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : 6.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.10.1
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 : 1.5.2
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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API: make CategoricalDtype.__eq__ stricter
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[] | null | 3
| 2020-11-18T03:14:41Z
| 2021-08-14T05:17:14Z
| null |
MEMBER
| null |
We allow for partially-initialized CategoricalDtypes to compare as equal to fully-initialized ones, leading to some surprising behaviors. The one that I stumbled over was this:
```
cat = pd.Categorical(["a", "b", "c"], ordered=True)
dtype = pd.CategoricalDtype()
>>> cat.dtype == dtype
True
cat2 = cat.astype(dtype)
>>> cat2.dtype == cat.dtype
False
```
We should stop special-casing categories=None for this purpose.
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INT: include NA values in Categorical.categories
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| 2020-11-18T03:19:11Z
| 2020-11-18T16:18:00Z
| null |
MEMBER
| null |
There are a bunch of places in CategoricalIndex where we check something like:
```
codes = self.categories.get_indexer(target)
if (codes == -1).any():
do_something()
```
This leads to ambiguity, as a -1 code can indicate an NA value that is present, or a non-NA value that is not among self.categories. Having to sort out which we're looking at is a hassle which we should try to avoid.
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| 1
| 2020-11-18T04:03:03Z
| 2020-11-26T22:26:54Z
| 2020-11-26T22:26:12Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @phofl if you've got any thoughts on how to handle this case
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BUG: loc.setitem with expansion expanding rows
|
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| 2020-11-18T04:14:47Z
| 2020-11-27T19:52:26Z
| 2020-11-27T19:49:44Z
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MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
discovered on branch that has dataframe always go through split_path
|
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| 745,325,097
|
MDExOlB1bGxSZXF1ZXN0NTIyOTA3ODA0
| 37,933
|
ENH: make closed part of IntervalDtype
|
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[] | null | 2
| 2020-11-18T04:45:09Z
| 2021-01-15T18:09:57Z
| 2020-11-24T21:07:27Z
|
MEMBER
| null |
- [x] closes #19371
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @jschendel
Still need to figure out what to do for backwards compat for unpickled IntervalDtype objects; ATM this just sets closed=None.
This causes IntervalIndex.append to cast to object dtype instead of raising if we try to append a non-matching closed. I think thats the right thing to do, but we haven't fully discussed that xref #37774
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MDU6SXNzdWU3NDU0NTM4NDQ=
| 37,934
|
BUG: json_normalize() does not parse floats in scientific notation
|
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[] | null | 5
| 2020-11-18T08:39:37Z
| 2020-11-18T12:13:12Z
| 2020-11-18T11:56:54Z
|
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import json
import pandas as pd
body = '''[
{ "c1": 1, "c2": "1.01" },
{ "c1": 2, "c2": "1E-3" },
{ "c1": 3, "c2": "3.03" }
]'''
df1 = pd.read_json(body)
df2 = pd.json_normalize(json.loads(body))
print(df1, "\n\n", df1.dtypes)
print("\n\n", df2, "\n\n", df2.dtypes)
```
#### Problem description
When creating a dataframe from json string input via `json.loads()` and `pd.json_normalize()` float values in scientific notation (e.g. "1E-3") are not parsed as floats. When using `pd.read_json()` they are parsed correctly.
Output:
```
c1 c2
0 1 1.010
1 2 0.001
2 3 3.030
c1 int64
c2 float64
dtype: object
c1 c2
0 1 1.01
1 2 1E-3
2 3 3.03
c1 int64
c2 object
dtype: object
```
#### Expected Output
```
c1 c2
0 1 1.010
1 2 0.001
2 3 3.030
c1 int64
c2 float64
dtype: object
c1 c2
0 1 1.010
1 2 0.001
2 3 3.030
c1 int64
c2 float64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1019-gcp
Version : #19-Ubuntu SMP Tue Jun 23 15:46:40 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 : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
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 : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3NDU0ODU3ODg=
| 37,935
|
BUG: json_normalize() upcasts column with missing values
|
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open
| false
| null |
[] | null | 0
| 2020-11-18T09:25:13Z
| 2021-08-14T05:18:39Z
| null |
NONE
| null |
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
data = [
{ "c1": 1, "c2": 1, "c3": 1.01 },
{ "c1": 2, "c3": 2.02 },
{ "c1": 3, "c2": 3, "c3": 3.03 }
]
df = pd.json_normalize(data)
print(df, "\n\n", df.dtypes)
```
#### Problem description
When creating a dataframe via `pd.json_normalize()` integer columns are upcast to float if column values are missing for some records even when all other values are consistently integer.
Output:
```
c1 c2 c3
0 1 1.0 1.01
1 2 NaN 2.02
2 3 3.0 3.03
c1 int64
c2 float64
c3 float64
```
#### Expected Output
```
c1 c2 c3
0 1 1 1.01
1 2 NaN 2.02
2 3 3 3.03
c1 int64
c2 int64
c3 float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
commit : 67a3d42
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-1019-gcp
Version : #19-Ubuntu SMP Tue Jun 23 15:46:40 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 : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.1.1
setuptools : 47.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : None
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 : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.19
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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MDU6SXNzdWU3NDU1MTQ5MDI=
| 37,936
|
BUG: style.background_gradient works in jupyter but not in Github
|
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| 2020-11-18T12:29:53Z
|
NONE
| null |
The bakground color gradient works fine in jupyter but when I upload it to Github the color disappears. Is there anyway I can fix this?
From jupyter
After uploading to Github
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BUG: Concat automatically sorts index when axis=1
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| 3
| 2020-11-18T15:09:48Z
| 2021-07-01T21:08:44Z
| null |
NONE
| null |
---
#### Problem description
Hi! I've noticed that when concat'ing DataFrames, along `axis=1`, integer indexes are automatically sorted even when `sort=False`. This is not the case when `axis = 1` and we have a stringIndex. This also diverges from when `axis=0`. This behaviour is a bit confusing and so I thought it might be a bug.
In the [_unique_indices](https://github.com/pandas-dev/pandas/blob/1f6710035a5295b2d3171ab811ae7ffca2344389/pandas/core/indexes/api.py#L187-L207) API, if the indexes are integers they are brought together using a union, `result = result.union(other)` that sorts the numbers by default. So the resulting index is sorted whether or not sort=True.
I think this can be fixed by changing the code to
`result = result.union(other, sort=sort)` or something along those lines.
I've added examples below. Thank you :D
#### Example of difference between stringIndex and intIndex behaviour
```python
#This is what happens with string index (union of indexes ordered)
>>> p1 = pd.DataFrame(
... {"a": [1, 2, 3], "b": [4, 5, 6]}, index=["p", "q", "r"]
... )
>>> p2 = pd.DataFrame(
... {"c": [7, 8, 9], "d": [10, 11, 12]}, index=["r", "p", "z"]
... )
>>> pd.concat([p1,p2], axis=1)
a b c d
p 1.0 4.0 8.0 11.0
q 2.0 5.0 NaN NaN
r 3.0 6.0 7.0 10.0
z NaN NaN 9.0 12.0
>>> pd.concat([p2,p1], axis=1)
c d a b
r 7.0 10.0 3.0 6.0
p 8.0 11.0 1.0 4.0
z 9.0 12.0 NaN NaN
q NaN NaN 2.0 5.0
#This is what happens with an int index (sorted, regardless of order of input)
>>> p_int1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[1,2,3])
>>> p_int2 = pd.DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}, index=[3,1,6])
>>> pd.concat([p_int1,p_int2], axis=1)
a b c d
1 1.0 4.0 8.0 11.0
2 2.0 5.0 NaN NaN
3 3.0 6.0 7.0 10.0
6 NaN NaN 9.0 12.0
>>> pd.concat([p_int2,p_int1], axis=1)
c d a b
1 8.0 11.0 1.0 4.0
2 NaN NaN 2.0 5.0
3 7.0 10.0 3.0 6.0
6 9.0 12.0 NaN NaN
```
#### Example of expected output
```
>>> p_int1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=[1,2,3])
>>> p_int2 = pd.DataFrame({"c": [7, 8, 9], "d": [10, 11, 12]}, index=[3,1,6])
>>> pd.concat([p_int2,p_int1], axis=1, sort=False)
c d a b
3 7.0 10.0 3.0 6.0
1 8.0 11.0 1.0 4.0
6 9.0 12.0 NaN NaN
2 NaN NaN 2.0 5.0
#### Output of ``pd.show_versions(1.1.4)``
<details>
>>> pd.concat([p_int2,p_int1], axis=1)
c d a b
1 8.0 11.0 1.0 4.0
2 NaN NaN 2.0 5.0
3 7.0 10.0 3.0 6.0
6 9.0 12.0 NaN NaN
</details>
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CI: Fix ci flake error
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| 10
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| 2020-11-18T16:50:36Z
| 2020-11-18T16:47:46Z
|
MEMBER
| null |
- [ ] xref #36305
came across it in #36305
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QST: how to run Pandas tests involving Numba on installed package
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| 2020-11-18T16:08:07Z
| 2021-08-14T05:20:44Z
| 2021-08-14T05:20:44Z
|
CONTRIBUTOR
| 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
I am trying to run the Pandas tests involving Numba like so:
```shell
pytest --pyargs pandas.tests.window.test_numba
```
But unfortunately all 49 tests fail with the same error:
```
______________________________________________________________ ERROR at setup of test_use_global_config _______________________________________________________________
self = <_pytest.config.Config object at 0x7fdf2e856190>, name = 'only_slow', default = <NOTSET>, skip = False
def getoption(self, name: str, default=notset, skip: bool = False):
"""Return command line option value.
:param name: Name of the option. You may also specify
the literal ``--OPT`` option instead of the "dest" option name.
:param default: Default value if no option of that name exists.
:param skip: If True, raise pytest.skip if option does not exists
or has a None value.
"""
name = self._opt2dest.get(name, name)
try:
> val = getattr(self.option, name)
E AttributeError: 'Namespace' object has no attribute 'only_slow'
miniconda3/envs/pandas-dev/lib/python3.8/site-packages/_pytest/config/__init__.py:1460: AttributeError
The above exception was the direct cause of the following exception:
item = <Function test_use_global_config>
def pytest_runtest_setup(item):
if "slow" in item.keywords and item.config.getoption("--skip-slow"):
pytest.skip("skipping due to --skip-slow")
> if "slow" not in item.keywords and item.config.getoption("--only-slow"):
E ValueError: no option named 'only_slow'
miniconda3/envs/pandas-dev/lib/python3.8/site-packages/pandas/conftest.py:79: ValueError
```
Perhaps I am not running the tests correctly?
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CI: flake8 check does not work properly
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| 2020-11-18T16:18:27Z
| 2020-11-18T16:30:41Z
| 2020-11-18T16:30:41Z
|
MEMBER
| null |
xref #37938
Have seen it in #37923 and in #37703 , so ci checks ran successfully in those feature PRs, but actually they should have detected this unused import.
Basically flake8 check in CI should detect cases such as `unused imports`, however, right now, observed from those two PRs, it didn't detect them in feature PRs and then resulted in CI failure on master
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MDU6SXNzdWU3NDU4NTUzMzY=
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|
ENH: An argument for .query() that returns all rows
|
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[] | null | 4
| 2020-11-18T17:21:47Z
| 2021-08-14T05:21:12Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
Pandas query strings are very useful, however when using a variable to pass in the query, e.g.
```df.query(my_query)```
There is no null or empty `my_query` that will return all rows. An empty string returns a ValueError. Any non-string argument returns an error.
#### Describe the solution you'd like
An empty string or None or some keyword argument (even with an empty `expr` to .query() should return all rows.
#### Additional context
Without the ability to pass something to `.query` that returns all rows you end up with something like this, when `_query` may or may not set. It'd be nice to be able to avoid this conditional by having something to pass to query that returns all rows.
```python
if _query:
dfs = [f(x).query(_query) for x in the_list]
else:
dfs = [f(x) for x in list]
```
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MDU6SXNzdWU3NDU4ODUzODM=
| 37,942
|
BUG: Area plot overwrites matplotlib's default share y axes behaviour
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| 0
| 2020-11-18T17:59:26Z
| 2020-11-20T09:38:14Z
| 2020-11-20T09:38:14Z
|
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
import pandas as pd
import matplotlib.pyplot as plt
df0 = pd.DataFrame([{'a': 10, 'b': 12}, {'a': 13, 'b': 9}])
df1 = pd.DataFrame([{'a': 100, 'b': 120}, {'a': 130, 'b': 90}])
fig, (axes_0, axes_1) = plt.subplots(1, 2, figsize=(12, 6), sharey='row')
df0.plot(ax=axes_0, kind='area')
df1.plot(ax=axes_1, kind='area')
```
#### Problem description
The area plot overwrites matplotlib's share y axes behaviour if `sharey` parameter is not passed to `.plot` and as a result the second plot get's cut off.
<img width="776" alt="Screenshot 2020-11-18 at 18 55 02" src="https://user-images.githubusercontent.com/1005173/99568355-9afa5c00-29cf-11eb-8267-e3f358c4d6f8.png">
#### Expected Output
same plot made directly with matplotlib, expected behaviour:
```python
fig, (axes_0, axes_1) = plt.subplots(1, 2, figsize=(12, 6), sharey='row')
axes_0.stackplot(df0.index, df0['a'], df0['b'])
axes_1.stackplot(df1.index, df1['a'], df1['b'])
```
<img width="773" alt="Screenshot 2020-11-18 at 18 56 54" src="https://user-images.githubusercontent.com/1005173/99568558-db59da00-29cf-11eb-916b-c967c33ec6be.png">
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MDExOlB1bGxSZXF1ZXN0NTIzMzczNDQ3
| 37,943
|
BUG: fix sharey overwrite on area plots
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| 9
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| 2020-12-02T16:32:06Z
| 2020-11-20T09:38:15Z
|
CONTRIBUTOR
| null |
- [x] closes #37942
- [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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CLN: test_moments_expanding_consistency.py
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| 2020-11-18T18:39:04Z
| 2020-11-18T20:07:45Z
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|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Breaking up the predefined `moments_consistency_*` functions into more scoped tests with more pytest idioms.
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PERF: use np.putmask instead of ndarray.__setitem__
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| 2020-11-19T02:16:36Z
| 2020-11-19T02:07:37Z
|
MEMBER
| null |
- [x] closes #37833
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: test_moments_rolling_consistency.py
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| 1
| 2020-11-18T21:26:53Z
| 2020-11-19T01:10:48Z
| 2020-11-18T23:48:16Z
|
MEMBER
| null |
Same changes as https://github.com/pandas-dev/pandas/pull/37944
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|
Slow autocompletion in python/ipython console for large DataFrame containing strings
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| 2021-08-14T05:25:50Z
| 2021-08-14T05:25:50Z
|
NONE
| null |
I'm not sure if this is the right place to ask, but it seems the autocompletion in python or ipython console is especially slow for large DataFrame with strings (object) in it.
For example, consider the following two DataFrames:
```python
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.random.rand(400000,60))
df2 = pd.DataFrame([["asdfasdf"]*60]*400000)
```
By typing `df1.<TAB>` in the interactive python/ipython session, the autocompletion is quick, but it takes a very long time for `df2.<TAB>` to finish (it gets stucked for many seconds).
I'm not sure if this is due to different implementation of pandas for DataFrames containing numbers vs. strings (object), or this is due to issues in the interactive python/ipython session.
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DOC: Change warning for sort behavior in concat
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| 1
| 2020-11-18T23:08:14Z
| 2020-11-19T19:30:23Z
| 2020-11-19T18:11:18Z
|
MEMBER
| null |
We should either add a ``versionchanged`` attribute, for when this behavior changed or remove the warning completly. Additionally I removed the ``sort=False`` input for the function, because this is no longer necessary.
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ENH: IntervalIndex as groups in groupby
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| 2020-11-19T00:02:13Z
| 2021-07-01T21:10:04Z
| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
I often have a list of (variable-size) intervals and I want to aggregate multiple statistics for these intervals.
```py
import numpy as np
import pandas as pd
data = pd.DataFrame({'a': [1]*100, 'b': [2]*100}, index=np.arange(0.0, 10, 0.1))
starts = [0.0, 2.5, 5, 7]
ends = [0.7, 3.8, 6.1, 9.5]
means = []
for start, end in zip(starts, ends):
means.append(data.loc[start:end, :].mean())
```
#### Describe the solution you'd like
It would be cool (and probably faster) to do:
```py
groups = pd.IntervalIndex.from_arrays(starts, ends)
data.groupby(groups).mean() # *** ValueError: Grouper and axis must be same length
# or for multiple statistics
data.groupby(groups).aggregate(['mean', 'sum', lambda x: x.quantile(0.75) - x.quantile(0.25)])
```
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MDExOlB1bGxSZXF1ZXN0NTIzNjE4MTk5
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ENH: 2D compat for DTA tz_localize, to_period
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| 9
| 2020-11-19T01:43:19Z
| 2020-12-17T17:47:48Z
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|
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- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NTIzNjc2NzY1
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CLN: test_ewm_rolling_consistency.py
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| 1
| 2020-11-19T04:58:41Z
| 2020-11-19T17:56:57Z
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|
MEMBER
| null |
Same changes as https://github.com/pandas-dev/pandas/pull/37944 and https://github.com/pandas-dev/pandas/pull/37946
Additionally allows removal of `moments/conftest.py` and `tests/window/common.py`
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MDExOlB1bGxSZXF1ZXN0NTIzNjk0Mjcw
| 37,952
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TST: add error message match for raise in test_datetimelike.py GH30999
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| 2
| 2020-11-19T05:49:50Z
| 2020-11-20T19:38:53Z
| 2020-11-20T09:15:44Z
|
CONTRIBUTOR
| null |
Reference #30999
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0NTIzNjk5MTU1
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DOC: re-use storage_options
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| 1
| 2020-11-19T06:03:21Z
| 2020-11-19T21:17:00Z
| 2020-11-19T21:16:54Z
|
CONTRIBUTOR
| null |
The `{{ }}` are necessary so that `format` doesn't complain about missing keys.
TODO:
- `compression` can be re-used
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BUG: df.__setitem__ can be 10x slower than pd.concat(..., axis=1)
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| 14
| 2020-11-19T10:48:50Z
| 2020-12-02T12:10:26Z
| 2020-12-02T12:10:26Z
|
CONTRIBUTOR
| null |
- [x] I have checked that this issue has not already been reported.
* I think so, it's a bit hard to search for though. Nothing under concat and __setindex__
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd, numpy as np
from string import ascii_lowercase
def setitem(x, x_cols, df):
new = pd.DataFrame(index=df.index)
new[x_cols] = x
new[df.columns] = df
return new
def concat(x, x_cols, df):
return pd.concat(
[
pd.DataFrame(x, columns=x_cols, index=df.index),
df,
],
axis=1,
)
x = np.ones((1000, 10))
x_col = list(ascii_lowercase[:10])
df = pd.DataFrame(
{
"str": np.random.choice(np.array(list(ascii_lowercase)), size=1000),
"int": np.arange(1000, dtype=int),
}
)
%timeit setitem(x, x_col, df)
# 3.78 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit concat(x, x_col, df)
# 306 µs ± 9.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
#### Problem description
This seems unintuitive from a performance perspective. I would assume that these would be close to equivalent. The `setitem` implementation might even be expected to have better performance due to less allocation.
While this is a stripped down example, the use case is building a dataframe to return from a function. Making a dataframe, then adding columns seemed like the natural idiom here.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : 0.29.21
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
```
</details>
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MDU6SXNzdWU3NDY0OTU1Mzk=
| 37,955
|
ENH: Add argument "multiprocessing" to pd.read_csv() method
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| 2020-11-19T11:27:49Z
| 2021-08-14T05:33:03Z
| 2021-08-14T05:33:03Z
|
NONE
| null |
#### Is your feature request related to a problem?
When the method pd.read_csv() is called, unfortunately this doesn't take advantage of the multiprocessing module, making it inefficient to read multiple datasets, especially when more cores are available to work.
Other modules like modin or dask, already implement this, but I think that pandas should implement by itself, if called for.
#### Describe the solution you'd like
It should be able to work with the multiprocessing module out of the box, as an initial enhancement, and then in the future support other possible backends like joblib.
A list of filenames should be passed:
An example of application would be:
`pd.read_csv(list_of_filenames, multiprocessing=True)`
`pd.read_csv(glob.glob('table_*.csv'), multiprocessing=True)`
#### API breaking implications
This should not change established behavior, considering that the default value for the "multiprocessing" argument should be "None" by default.
The memory consumption should be the same, it just consumes the memory much faster.
For this method option, the indices will be the same from each file, but likely to be in different order, but the user can reset_index() afterwards if needed.
#### Describe alternatives you've considered
[this should provide a description of any alternative solutions or features you've considered]
#### Additional context
I have also considered extra backend options for future enhancements of this implementation, like joblib, ray, dask.
#NOTE: I have already a proof-of-concept for the solution, so I can work a bit further on it, commit and make a pull request.
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ENH: Add argument "multiprocessing" to DataFrame.apply() method (any axis)
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| 2020-11-19T11:31:35Z
| 2021-08-14T05:37:41Z
| null |
NONE
| null |
#### Is your feature request related to a problem?
When a function is passed to DataFrame.apply (with any axis) unfortunately, this doesn't take advantage of the multiprocessing module, making it inefficient for large datasets, especially when more cores are available to work.
Other modules like modin or dask, already implement this, but I think that pandas should implement by itself, if called for.
#### Describe the solution you'd like
It should be able to work with the multiprocessing module out of the box, as an initial enhancement, and then in the future support other possible backends like joblib.
An example of application would be:
`df.apply(lambda x: x['A'] * x['B'], axis=1, multiprocessing=True)`
#### API breaking implications
This should not change established behavior, considering that the default value for the "multiprocessing" argument should be "None" by default.
The only concern would be to return the DataFrame without changing indices, and the result be the same as without multiprocessing.
#### Describe alternatives you've considered
I have also considered extra backend options for future enhancements of this implementation, like joblib, ray, dask.
#### Additional context
NOTE: I have already a proof-of-concept for the solution, so I can work a bit further on it.
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STYLE: fail on pd.testing direct usage
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| 2020-11-19T15:44:36Z
| 2020-12-27T12:19:33Z
| 2020-12-27T12:19:33Z
|
CONTRIBUTOR
| null |
`pd.testing.assert_series_equal(result, expected)`
should fail style checks, rather we want `tm.assert_series_equal`
we need to update any usages in the codebase as well as add this to the pre-commit checks.
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BUG: DataFrame.to_html ignores formatters
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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CLN: More tests/window/*
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MEMBER
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- Utilize more `frame_or_series` fixtures
- Cleaned `conftest.py`
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- Reorg `test_groupby` into `TestRolling` and `TestExpanding` classes
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CLN: Add comment and clarify if condition in indexing
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MEMBER
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Implemented comments
cc @jbrockmendel
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CLN: Remove duplicate from MultiIndex.equals
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| 2020-11-19T20:11:17Z
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| null |
- [x] tests passed
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really small clean up
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„case-when“ function is missing?
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| 2020-11-25T01:17:20Z
| 2020-11-25T01:17:20Z
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NONE
| null |
I only started working with pandas a few weeks ago. What I often miss is a case-when-function. Here I always use the numpy equivalent np.where().
Is there a case-when-function in pandas not? If there is, I would like to add this functionality.
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BUG: Some string methods treat "." as regex, others don't
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| 1
| 2020-11-19T23:31:38Z
| 2020-11-20T02:42:55Z
| 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.
---
#### Code Sample, a copy-pastable example
```python
import re
import pandas as pd
texts = ['aa.aa', 'bbb']
# str.replace vs re.sub: '.' is not treated as regex by pandas
print(*pd.Series(texts).str.replace('.', '*'), sep=', ')
print(*pd.Series(texts).str.replace('..', '**'), sep=', ')
print(*[re.sub('.', '*', t) for t in texts], sep=', ')
print(*[re.sub('..', '**', t) for t in texts], sep=', ')
print()
# str.split vs re.split: '.' is not treated as regex by pandas
print(*pd.Series(texts).str.split('.'), sep=', ')
print(*pd.Series(texts).str.split('..'), sep=', ')
print(*[re.split('.', t) for t in texts], sep=', ')
print(*[re.split('..', t) for t in texts], sep=', ')
print()
# str.contains vs re.search: '.' is treated as regex by both
print(*pd.Series(texts).str.contains('.'), sep=', ')
print(*pd.Series(texts).str.contains('..'), sep=', ')
print(*[re.search('.', t).start() for t in texts], sep=', ')
print(*[re.search('..', t).start() for t in texts], sep=', ')
```
#### Problem description
Currently some pandas string functions (eg str.replace, str.split) treat `"."` as a regex matching any character, while some others (eg str.contains) treat it like `"[.]"`, the regex matching the "dot" character. The `".."` string is instead always correctly handled as the regex matching any two characters.
#### Expected Output
pandas string functions (when in regex mode) should treat `"."` consistently as the regex matching any single character. If there is a reason to treat as an exception, or to do so only for some string functions, should be documented.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Jun 18 20:49:00 PDT 2020; root:xnu-6153.141.1~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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BUG: Bug in setitem raising ValueError when setting more than one column via array
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| 5
| 2020-11-19T23:35:59Z
| 2020-11-21T23:42:56Z
| 2020-11-21T21:09:20Z
|
MEMBER
| null |
- [x] xref #37954
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Did not add whatsnew, cause works on 1.1.4
Array to set has to have more or less rows than columns, otherwise bug won't occur. Wrong values would be set silently here.
This is only a regression on master, not related to any release
cc @jbrockmendel
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| 3
| 2020-11-19T23:43:03Z
| 2020-12-17T15:43:39Z
| 2020-12-17T13:50:58Z
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MEMBER
| null |
- [x] closes #37863
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
The fix to keep resample working is a kludge I'd like to make unnecessary before merging; cc @mroeschke
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Read csv headers
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- [x] tests added / passed
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- [x] whatsnew entry
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| 2
| 2020-11-20T01:28:12Z
| 2020-12-12T23:31:26Z
| 2020-12-12T23:31:26Z
|
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
# Your code here
import pandas as pd
df = pd.DataFrame( {'A': [1, 2, 3],
'B': [4, 5, 6],})
# /1 -- It Fails ---
df_out = (df.style.
highlight_max(color='orangered').
highlight_min(color='dodgerblue')
)
df_out.to_excel('pandas_to_excel_color_issue.xlsx')
# /2 -- It works ----
df_out = (df.style.
highlight_max(color='#FF4500'). # orangered
highlight_min(color='#1E90FF') # dodgerblue)
)
df_out.to_excel('pandas_to_excel_color_issue.xlsx')
```
#### Problem description
When using style, the `to_excel` function is not able to handle some common color name that otherwise works with an interactive python console.
See with code block /1 returns :
```python
c:\program files\python38\lib\site-packages\pandas\io\formats\excel.py:331: CSSWarning: Unhandled color format: 'dodgerblue'
warnings.warn(f"Unhandled color format: {repr(val)}", CSSWarning)
c:\program files\python38\lib\site-packages\pandas\io\formats\excel.py:331: CSSWarning: Unhandled color format: 'orangered'
warnings.warn(f"Unhandled color format: {repr(val)}", CSSWarning)
```
And in the Excel file :
While the /2 block is fine:
#### Expected Output
I understand that the to_excel function only accepts a minimal list of color names (based on the export engine I presume).
It would hence be great if pandas could manage the color Hex conversion under the hood for the user, and hence allow him to use a more large color name list (the one from matplotlib for example with `matplotlib.colors.cnames`), preventing the usage of unreadable color code.
In addition, I don't know if the current Excel output in case of CSS warning is really appropriate (the 'black render'), maybe simply ignoring the formatting would be better? (one could argue that it's more noticeable in the Excel file like that, that's right).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19041
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : fr_FR.cp1252
pandas : 1.1.4
numpy : 1.19.3
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.2
Cython : None
pytest : 6.1.2
hypothesis : None
sphinx : 3.2.0
blosc : None
feather : None
xlsxwriter : 1.3.2
lxml.etree : 4.5.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.16.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0NTI0MzUyNTMz
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BUG: IntervalIndex.putmask with datetimelike dtypes
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| 0
| 2020-11-20T01:42:56Z
| 2020-11-20T15:40:53Z
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|
MEMBER
| null |
- [ ] closes #xxxx
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- [x] whatsnew entry
Implements `NDArrayBackedExtensionArray.putmask` which will hopefully be rolled into `__array_function__` to simplify a bunch of code.
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ENH: bfloat16 when available?
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| 2020-11-20T02:50:43Z
| 2020-11-20T02:30:07Z
|
NONE
| null |
Same numpy bfloat16 feature request for pandas:
https://github.com/numpy/numpy/issues/17809
Hi,
I saw NEP 41 Status: | Accepted
https://github.com/numpy/numpy/blob/master/doc/neps/nep-0041-improved-dtype-support.rst
I'm just wondering when will bfloat16 be available in numpy?
For some ML problems, this will reduce the memory requirement by half (or process 2x more data), can we give this high priority?
Thanks.
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REF: share more methods in ExtensionIndex
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| 747,107,825
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MDExOlB1bGxSZXF1ZXN0NTI0MzgxNjg1
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PERF: IntervalArray.argsort
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| 2
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| 2020-11-21T22:00:25Z
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|
MEMBER
| null |
IntervalIndex.argsort has a more performant implementation (at least for default kwargs). This just moves that up to IntervalArray.
```
In [2]: idx = pd.IntervalIndex.from_breaks(range(10**4))
In [3]: arr = idx._data
In [4]: %timeit arr.argsort()
24.5 ms ± 458 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) # <-- master
79 µs ± 1.64 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
```
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TST: add nullable array frame constructor dtype tests
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| 1
| 2020-11-20T04:04:52Z
| 2020-11-21T22:44:09Z
| 2020-11-21T22:21:47Z
|
MEMBER
| null |
- [x] closes #28424
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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BLD: set inplace in setup.cfg
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MEMBER
| null |
specify inplace for build_ext in setup.cfg file
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MDExOlB1bGxSZXF1ZXN0NTI0NTA1NjYx
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BUG: fix astype conversion string -> float
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|
CONTRIBUTOR
| null |
- [x] Is a step towards closing #37626
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixes the `string` -> `float` conversion as discussed in #37626 in presence of `pd.NA`. I used the same approach as used in `string` -> `Int64`. Note that differently to the `Int64` case, we return a `numpy.ndarray`, not a `pd.array`.
I added a test that compares `pd.Series`. Replacing this with `pd.array` the `tm.assert_numpy_array_equal` assertion will raise, as `expected` is of type `FloatingArray`. IIUC this is a new feature of pandas 1.2.0. Should we return this in `.astype("float")` by default?
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TST: add messages to bare pytest raises in pandas/tests/io/pytables/test_timezones.py
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CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This PR adds messages to the bare pytest raises in pandas/tests/io/pytables/test_timezones.py. This PR references https://github.com/pandas-dev/pandas/issues/30999.
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PERF: Severe performance hit on DataFrame.sum with multi index and 'skipna=False'
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CONTRIBUTOR
| null |
`Series.sum(..., skipna=False)` and `DataFrame.sum(..., skipna=False)` are *very* slow on a `MultiIndex`,
if compared to the same method called with `(..., skipna=True)`
---
- [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
dims = (1000, 100, 10)
# make random data
rg = np.random.default_rng(0)
x = rg.random(dims)
# construct dataframe with multindex
idx = pd.MultiIndex.from_product(map(range, dims), names=["i0", "i1", "i2"])
df = pd.Series(data=x.ravel(), index=idx, name="x")
# comupute sum over "i2" level in pandas
s1 = df.sum(level=["i0", "i1"], skipna=False)
s2 = df.sum(level=["i0", "i1"], skipna=True)
# compute sum over "axis=2" in numpy
s_ref = x.sum(axis=2)
# check results
s_ref_norm = np.linalg.norm(s_ref)
for s in (s1, s2):
print(np.linalg.norm(s.to_numpy().reshape(s_ref.shape) - s_ref) / s_ref_norm)
```
#### Problem description
The computation of `s1 = df.sum(level=["i0", "i1"], skipna=False)` takes forever in the sample above:
```python
In [2]: %timeit df.sum(level=["i0", "i1"], skipna=False)
8.44 s ± 47.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [3]: %timeit df.sum(level=["i0", "i1"], skipna=True)
52.1 ms ± 266 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [4]: %timeit x.sum(axis=2)
1.09 ms ± 13.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
Here we see a severe performance hit (8.61 s vs 0.05 s) for the very same `DataFrame.sum` if `skipna=False`.
Maybe this is related to #16788 and #37622
The performance of `df.sum(level=["i0", "i1"], skipna=True)` with respect to baseline numpy is not great, but I think this is not an issue here.
Results are correct, fortunately. Curiously the slow result (`skipna=False`) is identical with the reference numpy sum, while the faster result (`skipna=True`) is within ±2 ULP, but of course this is not a concern.
Almost the same results hold if we use a `DataFrame` instead of a `Series`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 49.6.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fsspec : 0.8.0
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 1.0.1
pytables : None
pyxlsb : None
s3fs : 0.5.0
scipy : 1.5.3
sqlalchemy : None
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.16.0
xlrd : 1.2.0
xlwt : None
numba : 0.51.1
</details>
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BUG: CategoricalIndex.where nulling out non-categories
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| 2
| 2020-11-20T15:57:12Z
| 2020-11-22T01:45:54Z
| 2020-11-22T01:19:52Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
ATM CategoricalIndex.where has some idiosyncratic behavior:
```
ci = CategoricalIndex(["a", "b", "c", "d"])
mask = np.array([True, False, True, False])
>>> ci.where(mask, 2)
CategoricalIndex(['a', nan, 'c', nan], categories=['a', 'b', 'c', 'd'], ordered=False, dtype='category')
```
This makes that call raise instead.
Index.where is only used in one place in reshape.merge. Might be worth deprecating+privatizing.
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MDU6SXNzdWU3NDc2NTM2ODI=
| 37,978
|
BUG: ImportError: cannot import name 'EmptyDataError' from 'pandas.io.common'
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| 1
| 2020-11-20T17:09:34Z
| 2020-11-20T17:32:35Z
| 2020-11-20T17:32:11Z
|
NONE
| null |
I upgraded pandas and python version
python 3.8.6
numpy 1.19.4
pandas 1.1.4
pip 20.2.4
When I try:
`import pandas as pd
from pandas.io.common import EmptyDataError
`
I have error:
`ImportError: cannot import name 'EmptyDataError' from 'pandas.io.common' (/xxx/xxx/enviroments/py3_8_env/lib/python3.8/site-packages/pandas/io/common.py)`
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| 747,692,661
|
MDU6SXNzdWU3NDc2OTI2NjE=
| 37,979
|
Should FilePathorBuffer use os.PathLike instead of pathlib.Path?
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| 4
| 2020-11-20T18:01:26Z
| 2020-12-02T02:02:32Z
| 2020-12-02T02:02:32Z
|
NONE
| null |
The [documentation for `pandas.read_csv(...)`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) states that it can support anything that adheres to the `os.PathLike` protocol, but the definition of `FilePathOrBuffer` is too strict.
https://github.com/pandas-dev/pandas/blob/08e4baf13005d33136608bd4e6e15f6b38f50887/pandas/_typing.py#L138
where `Path` is a `pathlib.Path` object.
Could this be changed to use `os.PathLike[str]` or just `os.PathLike`?
```py3
from os import PathLike
FilePathOrBuffer = Union["PathLike[str]", FileOrBuffer]
```
`PathLike` is in quotes because `PathLike` only supports `__class_getitem__` for Python 3.9 and later and `PathLike` alone might be too broad, assuming you don't want to support byte-encoded paths.
Thanks!
|
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MDExOlB1bGxSZXF1ZXN0NTI0ODkzNzA5
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|
DOC: Fix typo
|
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| 1
| 2020-11-20T18:57:30Z
| 2020-11-21T00:24:45Z
| 2020-11-20T22:46:02Z
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CONTRIBUTOR
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| 747,818,312
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MDExOlB1bGxSZXF1ZXN0NTI0OTcyMjA0
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|
Deprecate inplace in Categorical.remove_categories
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CLN: remove panel compat shim
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MEMBER
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MDExOlB1bGxSZXF1ZXN0NTI1MDE2MTYx
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BUG: IntervalArray.astype(categorical_dtype) losing ordered
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CONTRIBUTOR
| null |
- [x] closes #16360
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (not applicable)
Added a test to verify the behavior described in issue #16360. However, I'm not 100% sure about the location of the test.
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Found along the way that we can incorrectly put an ndarray[object] into an ExtensionBlock if we're not careful. This will fix some of those, will make another branch that fixes the rest (with tests)
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| 2020-11-22T01:01:47Z
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|
MEMBER
| null |
- [x] closes #34603
- [x] closes #31330
- [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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CONTRIBUTOR
| null |
- [x] xref #37875, #27977
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: Use new hashtables in libindex to avoid casting
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|
MEMBER
| null |
Small, hard-to-measure perf improvement, since it only makes a difference on the first lookup:
```
In [2]: rng = pd.Index(range(50)).repeat(20).astype("category")
In [14]: %timeit rng._engine.clear_mapping(); rng.get_loc(40)
24.8 µs ± 624 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
21.3 µs ± 296 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
```
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Removed period to make it more Consistent
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CONTRIBUTOR
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Removed period as above lines don't have it to make it more consistent
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ENH: raise column number limit for user-completion or add warning
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| 2
| 2020-11-22T04:55:33Z
| 2021-11-13T17:27:47Z
| null |
MEMBER
| null |
xref PR #35207
> Currently the user-completion (ipython "<tab>") on a dataframe with more than 100 columns will silently ignore some columns, letting an unaware user confused on whether data disapeared.
>
> This is actually documented, and due to an arbitrary limit set to workaround a performance issue ( See #18587 )
>
> Dataframe with more than 100 columns are quite common, so this can potentially affect and suprise many users. Therefore, I suggest to increase that limit to, say, 1000. In any case, it would probably be good to warn the user hitting that limit.
>
> The attached quickfix raises the limit to 1000 and adds a warning beyond.
xref https://github.com/pandas-dev/pandas/pull/35207#issuecomment-663506204
>
> This is still an arbitrary limit. adding a warning is maybe a good idea. Maybe this could be a pandas option to allow users to change this if required.
>
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BUG: crosstab with duplicate column or index labels
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|
MEMBER
| null |
- [x] closes #22529
- [x] tests added / passed
- [x] passes black pandas
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
- [x] whatsnew entry
Picking up from #28474
cc @jreback in case this can go in in time for 1.2
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DOC: add a link to new styler method
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| 1
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| 2021-01-26T08:08:42Z
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|
CONTRIBUTOR
| null |
Adds a documentation link for the new `Styler` method from #36159
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DOC: Deprecate null_counts parameter of DataFrame.info
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|
CONTRIBUTOR
| null |
- [x] closes #36805
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
### Background.
> Might be worth it actually - I _think_ people are only likely to use `.info` in an interactive session, rather than as part of production code, so perhaps it is worth going through the deprecation process to make this argument clearer
* xref https://github.com/pandas-dev/pandas/issues/36805#issuecomment-702754290
* Deprecate `null_counts` parameter of DataFrame.info in favor of `show_counts` and add warning.
* Add whatsnew and add tests to test for FutureWarning. Move usages of null_counts in tests to show_counts.
Doc preview:
<img width="755" alt="Screenshot 2020-11-22 at 6 19 05 PM" src="https://user-images.githubusercontent.com/19281800/99901143-63512400-2cef-11eb-846a-447050de6d8f.png">
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DOC: DataFrame.last() needs the index to be sorted to deliver the expected results
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| 2020-12-17T16:54:23Z
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NONE
| null |
#### Location of the documentation
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.last.html
#### Documentation problem
For the method to work it is needed that the **_DatetimeIndex_ of the dataframe is sorted**, otherwise it will notwork.
Example:
```
import pandas as pd
df = pd.DataFrame({
'Date': ['2020-01-03', '2020-01-02', '2020-01-05', '2020-01-01', '2020-01-04'],
'Value': [1, 2, 3, 4, 5]
})
df['Date'] = pd.to_datetime(df.Date)
df.set_index('Date', inplace=True)
#df.sort_index(inplace=True) This line is needed to make it work
df.last('3D')
```
returns all the rows in the DataFrame
```
Value
Date
2020-01-03 1
2020-01-02 2
2020-01-05 3
2020-01-01 4
2020-01-04 5
```
#### Suggested fix for documentation
It should be mentioned that for the _last()_ method to work, the **_DatetimeIndex_ must be sorted**, or the user may think that there is a bug in the function.
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MDU6SXNzdWU3NDgyODkwNjQ=
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|
ENH: add default value to str.extract
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| 5
| 2020-11-22T17:15:55Z
| 2021-08-14T05:40:07Z
| null |
CONTRIBUTOR
| null |
#### Is your feature request related to a problem?
In some cases we can set a default value for non matches of `str.extract` with `Series/Frame.fillna`.
But there are cases when the data prior to applying `str.extract` already has NaN values. So running fillna on the results would fill both, and there we cannot distinguish between what the actual NaN values were and what the NaN values are as a result of str.extract.
#### Describe the solution you'd like
Set a default value which has to be a string, to indicate the non matches of the regex pattern.
#### API breaking implications
None I think
```python
from pandas import DataFrame
import numpy as np
df = DataFrame({'A': ['a84', 'abcd', '99string', np.nan]})
result = df['A'].str.extract(r'(\d+)', expand=False, default='missing')
print(df, '\n')
print(result)
A
0 a84
1 abcd
2 99string
3 NaN
0 84
1 missing # <--- the value which did not match
2 99
3 NaN # <--- the NaN already present in the data prior str.extract
Name: A, dtype: object
```
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| 7
| 2020-11-22T17:26:09Z
| 2020-12-06T14:04:00Z
| 2020-11-25T22:27:33Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Cleanup of spelling, grammar, and links.
----
cc @jorisvandenbossche, @dsaxton, @phofl:
Using ``:meth:`.DataFrameGroupBy.quantile` `` (note the `.`) displays the text `DataFrameGroupBy.quantile` and sphinx finds the link to the proper page. Similar remarks apply to `Rolling` and others. From the [sphinx docs](https://www.sphinx-doc.org/en/master/usage/restructuredtext/domains.html):
> Also, if the name is prefixed with a dot, and no exact match is found, the target is taken as a suffix and all object names with that suffix are searched.
I don't believe this was known about (at least to me!) and is different from what was aligned on in #37145, so I wanted to get thoughts here on which is preferred. While I've added many cross-links using this, `DataFrameGroupBy.quantile` is the sole example where I have replaced a preexisting link.
----
Other notable changes that are worth a mention to make sure they are correct:
* Numpy is always referred to as NumPy in plain-text (no backticks)
* When a method exists on only Series and DataFrame, replaced unlinked reference with a reference to each. For example, ``:meth:`read_csv` `` becomes ``:meth:`DataFrame.to_csv` and :meth:`Series.to_csv` ``
* dtypes and argument values are always double-backticked
* Moved the following note from Other to Missing
> Bug in :meth:`Series.nunique` with ``dropna=True`` was returning incorrect results when both ``NA`` and ``None`` missing values were present (:issue:`37566`)
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ENH: str extract with default value
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| 2020-11-22T17:28:42Z
| 2021-07-03T10:48:21Z
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|
CONTRIBUTOR
| null |
- [x] closes #38001
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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REF: ensure_arraylike in algos.isin
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MEMBER
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This is one of two places where we do not explicitly call ensure_arraylike before ensure_data. Once I get the other one sorted out, we can annotate _ensure_data and others.
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| 2021-03-02T17:09:39Z
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MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/35169#issuecomment-727217462, follow-on to #35259
This is moreless copy/paste from https://github.com/xhochy/fletcher with a slight tidy for initial review feedback
still to do
- [x] benchmarking
- [x] return type for chunked array with more than 1 chunk
- [x] maybe update tests.
we don't have failing tests, but the return type should be `Tuple[np.ndarray, ExtensionArray]` while we have `return factorize(np_array, na_sentinel=na_sentinel)` if more than 1 chunk and the return type of pd.factorize is `Tuple[np.ndarray, Union[np.ndarray, ABCIndex]]` (mypy doesn't report this as an error since np.ndarray resolves to `Any`)
since we don't have failing tests, we probably should paramatrize data for the base extension tests with an array with one chunk and a array with multiple chunks.
cc @jorisvandenbossche @xhochy
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CLN: fix E741 ambiguous variable #34150
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xref #34150
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BUG: loc returning wrong elements for non-monotonic DatetimeIndex
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- [x] closes #33146
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
That one was tricky. When only one existing key was given or both string parts exist, ``Index.slice_indexer`` is not raising KeyError but selecting a lof of erroneous values
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TST/REF: collect Index.equals tests
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REF: Implement isin on DTA instead of DTI
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BUG: pandas truncate too large data without any warning of any kind, causing data loss
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[] | null | 3
| 2020-11-23T06:27:07Z
| 2021-08-14T05:41:13Z
| null |
NONE
| null |
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import io
indata = io.StringIO("c\n10000000000")
df = pd.read_csv(indata, header=0)
print(df)
indata.seek(0)
df = pd.read_csv(indata, header=0, dtype={"c":int})
print(df)
```
#### Problem description
The data gets truncated without pandas issuing any warning of any kind.
This causes data loss.
This is the actual ouput:
```
c
0 10000000000
c
0 1410065408
```
#### Expected Output
```
c
0 10000000000
c
0 10000000000
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : db08276bc116c438d3fdee492026f8223584c477
python : 3.8.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.18362
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Italian_Italy.1252
pandas : 1.1.3
numpy : 1.19.2
pytz : 2020.1
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : 0.29.21
pytest : 6.1.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.3
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : 1.3.20
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.51.2
</details>
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BUG: Fixes plotting with nullable integers (#32073)
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|
CONTRIBUTOR
| null |
- [x] closes #32073
- [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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CONTRIBUTOR
| null |
```python
>>> idx = pd.Index([('A','cat'),('B','cat'),('B','cat')])
>>> df = pd.DataFrame(index=idx)
>>> df.rename(index={'A':'Apple'}, level=0)
ValueError: non-broadcastable output operand with shape (1,1) doesn't match the broadcast shape (1,2)
```
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| 2020-11-23T13:01:03Z
| 2020-12-15T18:40:37Z
| 2020-12-15T18:40:36Z
|
NONE
| null |
Hi i have a huge problem and I suppose it could be a bug but not sure.
```
def volat(prices, n):
n_years = prices.last_valid_index()-pd.DateOffset(years=n)
return 100*np.sqrt(52)*prices[n_years:].asfreq('W-FRI', method='ffill').pct_change().std()
################### METHOD 1 ###################
table = (base_corr
.query("classification_produit=='Sans classification'")
[['date', 'gestionnaire', 'nom_de_produit', 'code_isin_part',
'classification_produit', 'vl', 'collecte', 'nbre_part', 'actif_net_part']]
.pivot(columns='date',
index=['gestionnaire', 'nom_de_produit', 'code_isin_part', 'classification_produit'],
values=['vl', 'collecte', 'nbre_part', 'actif_net_part'])
.apply([
lambda x: volat(x.vl, 3),
], axis=1)
.set_axis([
'volat 3 ans'
],
axis='columns')
)
table.loc[(slice(None),slice(None),'FR0011026905')] # part of Result from method 1
################### METHOD 2 ###################
volat(base.query("code_isin_part=='FR0011026905'").set_index('date')['vl'], 3) # Result from method 2
################### METHOD 3 ###################
(base_corr
.query("code_isin_part=='FR0011026905'")
[['date', 'gestionnaire', 'nom_de_produit', 'code_isin_part',
'classification_produit', 'vl', 'collecte', 'nbre_part', 'actif_net_part']]
.pivot(columns='date',
index=['gestionnaire', 'nom_de_produit', 'code_isin_part', 'classification_produit'],
values=['vl', 'collecte', 'nbre_part', 'actif_net_part'])
.apply([
lambda x: volat(x.vl, 3),
], axis=1)
.set_axis([
'volat 3 ans'
],
axis='columns')
)
```
and the output is :
```
RESULT FROM METHOD 1 (the wrong one) :
volat 3 ans
FIDEAS CAPITAL SAS SICAV DES OLIVIERS Sans classification 0.05
RESULT FROM METHOD 2 :
7.922765211671436
RESULT FROM METHOD 3 :
volat 3 ans
FIDEAS CAPITAL SAS SICAV DES OLIVIERS FR0011026905 Sans classification 7.92
```
From my point of view I should get exactly the same answer (method 2 & 3 are alright but not method 1).
I don't know why this is not the case.
I need maybe to add that base_corr is a huge database (10_892_693 rows × 12 columns) with dailies and weekly data that why I convert each series to weekly data to make it possible to annualize the volatility figures (with np.sqrt(52) multiplication).
INSTALLED VERSIONS
------------------
commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3
python : 3.8.6.final.0
python-bits : 64
OS : Darwin
OS-release : 20.1.0
Version : Darwin Kernel Version 20.1.0: Sat Oct 31 00:07:11 PDT 2020; root:xnu-7195.50.7~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.4
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 20.2.4
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: 0.9.0
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.4
fastparquet : None
gcsfs : None
matplotlib : 3.3.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.5
pandas_gbq : None
pyarrow : 2.0.0
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.5.3
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.16.1
xlrd : 1.2.0
xlwt : None
numba : 0.51.2
</details>
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MDU6SXNzdWU3NDg4MjgwOTc=
| 38,017
|
TST: any_* fixtures are confusingly named
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| 12
| 2020-11-23T14:01:16Z
| 2021-08-18T17:22:25Z
| 2021-08-18T17:22:25Z
|
CONTRIBUTOR
| null |
the naming on the any_* fixtures in `pandas/conftest.py` is pretty confusing
- `any_int_dtype` (numpy only)
- `any_int_nullable_dtype` (EA only)
- `any_numeric_dtype` both numpy & EA
- `any_real_dtype` both numpy int & float
- `sint_dtype` signed numpy ints
for example
so we need dtypes that are numpy_only and EA only and numpy + EA both for ints & floats
would propose something like
`any_int_numpy_dtype`
`any_int_ea_dtype`
`any_int_dtype` -> both numpy + ea
same for float
then
`any_numeric_nump_dtype`
`any_numeric_ea_dtype`
for float + int
which means we rename
`sint_dtype` -> `any_signed_int_numpy_dtype`
I think this would require a PR to organize, a PR to create new names & fix everything.
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Using os.PathLike instead of pathlib.Path (#37979)
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|
CONTRIBUTOR
| null |
- [x] closes #37979
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Replaces `pathlib.Path` with `os.PathLike` to bring behaviour further in-line with the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)
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